A. A. Aljabali, A, A. Bakshi, H, L. Hakkim, F, Haggag, YA, M. Al-Batanyeh, K, S. Al Zoubi, M, Al-Trad, B, M. Nasef, M, Satija, S, Mehta, M, Pabreja, K, Mishra, V, Khan, M, Abobaker, S, M. Azzouz, I, Dureja, H, M. Pabari, R, Ali K. Dardouri, A, Kesharwani, P, Gupta, G, Dhar Shukla, S, Prasher, P, B. Charbe, N, Negi, P, N. Kapoor, D, Chellappan, DK, Webba da Silva, M, Thompson, P, Dua, K, McCarron, P & M. Tambuwala, M 2020, 'Albumin Nano-Encapsulation of Piceatannol Enhances Its Anticancer Potential in Colon Cancer Via Downregulation of Nuclear p65 and HIF-1α', Cancers, vol. 12, no. 1, pp. 113-113.
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Piceatannol (PIC) is known to have anticancer activity, which has been attributed to its ability to block the proliferation of cancer cells via suppression of the NF-kB signaling pathway. However, its effect on hypoxia-inducible factor (HIF) is not well known in cancer. In this study, PIC was loaded into bovine serum albumin (BSA) by desolvation method as PIC–BSA nanoparticles (NPs). These PIC–BSA nanoparticles were assessed for in vitro cytotoxicity, migration, invasion, and colony formation studies and levels of p65 and HIF-1α. Our results indicate that PIC–BSA NPs were more effective in downregulating the expression of nuclear p65 and HIF-1α in colon cancer cells as compared to free PIC. We also observed a significant reduction in inflammation induced by chemical colitis in mice by PIC–BSA NPs. Furthermore, a significant reduction in tumor size and number of colon tumors was also observed in the murine model of colitis-associated colorectal cancer, when treated with PIC–BSA NPs as compared to free PIC. The overall results indicate that PIC, when formulated as PIC–BSA NPs, enhances its therapeutic potential. Our work could prompt further research in using natural anticancer agents as nanoparticels with possible human clinical trails. This could lead to the development of a new line of safe and effective therapeutics for cancer patients.
Abbasi, M, Abbasi, E, Tousi, B & Gharehpetian, GB 2020, 'New family of expandable step‐up/‐down DC‐DC converters with increased voltage gain and decreased voltage stress on capacitors', International Transactions on Electrical Energy Systems, vol. 30, no. 3.
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© 2020 John Wiley & Sons, Ltd. This paper presents a new family of extendible hybrid and nonhybrid step-up/-down switched capacitor DC-DC converter structures benefiting from numerous advantages like lower voltage stress on switched capacitors, fewer power components like switches, and higher voltage gain compared with other converters. In the proposed family, it is aimed to use diodes rather than switches, since they are simpler, cheaper, and smaller than switches, which in turn makes the proposed converters cost-, weight-, and size-effective structures. Also, because of the existence of multiple switched capacitors, more power can be transferred from the input source to the load (output) in the proposed topologies. In general, the proposed hybrid and nonhybrid structures are more suitable for a vast variety of industrial applications like regulating output voltage of renewable energy sources, specifically in high power ratings and high voltage gains. For validating the proposed ideas, thorough comparisons and experiments are presented.
Abbasi, M, Sharifi Miyab, M, Tousi, B & Gharehpetian, GB 2020, 'Using Dynamic Thermal Rating and Energy Storage Systems Technologies Simultaneously for Optimal Integration and Utilization of Renewable Energy Sources', International Journal of Engineering, vol. 33, no. 2, pp. 92-104.
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Abdar, M, Zomorodi-Moghadam, M, Zhou, X, Gururajan, R, Tao, X, Barua, PD & Gururajan, R 2020, 'A new nested ensemble technique for automated diagnosis of breast cancer', Pattern Recognition Letters, vol. 132, pp. 123-131.
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Abdin, Z, Zafaranloo, A, Rafiee, A, Mérida, W, Lipiński, W & Khalilpour, KR 2020, 'Hydrogen as an energy vector', Renewable and Sustainable Energy Reviews, vol. 120, pp. 109620-109620.
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© 2019 Elsevier Ltd Hydrogen is known as a technically viable and benign energy vector for applications ranging from the small-scale power supply in off-grid modes to large-scale chemical energy exports. However, with hydrogen being naturally unavailable in its pure form, traditionally reliant industries such as oil refining and fertilisers have sourced it through emission-intensive gasification and reforming of fossil fuels. Although the deployment of hydrogen as an alternative energy vector has long been discussed, it has not been realised because of the lack of low-cost hydrogen generation and conversion technologies. The recent tipping point in the cost of some renewable energy technologies such as wind and photovoltaics (PV) has mobilised continuing sustained interest in renewable hydrogen through water splitting. This paper presents a critical review of the current state of the arts of hydrogen supply chain as a forwarding energy vector, comprising its resources, generation and storage technologies, demand market, and economics.
Abdo, P, Huynh, BP, Braytee, A & Taghipour, R 2020, 'An experimental investigation of the thermal effect due to discharging of phase change material in a room fitted with a windcatcher', Sustainable Cities and Society, vol. 61, pp. 102277-102277.
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© 2020 Elsevier Ltd This paper investigates experimentally the effect of the Phase Change Material (PCM) discharging process as a passive cooling technique on the performance of a two sided windcatcher fitted on an acrylic chamber with dimensions 1250 × 1000 × 750 mm3. Four different models with different locations of PCM are studied, and the results are compared with each other and with a fifth model with No PCM. PCM is integrated respectively at the walls of the chamber, its floor and ceiling and also within the windcatcher's inlet channel. Humidity, temperature and air velocity are monitored for each of the models studied. It is noted that with all the models containing PCM, the average humidity inside the chamber changed only slightly compared to the model with No PCM. The difference in humidity ranged between 0 and 3.88 % which indicates that the humidity variations are not significant. The model with the PCM located on the floor, ceiling and walls as well as in the windcatcher's inlet channel has shown the best performance, with a significant minimum reduction of average temperature in the chamber of about 2.75 °C (approximately 9.33 %) compared with the model with No PCM.
Abdo, P, Taghipour, R & Huynh, BP 2020, 'Three-Dimensional Simulation of Wind-Driven Ventilation Through a Windcatcher With Different Inlet Designs', Journal of Thermal Science and Engineering Applications, vol. 12, no. 4, pp. 1-34.
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Abstract Windcatcher is an effective natural ventilation system, and its performance depends on several factors including wind speed and wind direction. It provides a comfortable and healthy indoor environment since the introduced fresh air decreases the moisture content and reduces the pollutant concentration. Since the wind speed and its direction are generally unpredictable, it is important to use special inlet forms and exits to increase the efficiency of a windcatcher. In this study, computational fluid dynamics (CFD) modeling is implemented using ansys fluent to investigate the airflow entering a three-dimensional room through a windcatcher with different inlet designs. Three designs are studied which are a uniform inlet, a divergent inlet, and a bulging-convergent inlet. The airflow pattern with all inlets provided adequate ventilation through the room. With all the applied wind velocities (1, 2, 3, and 6 m/s) at the domain's inlet, the divergent inlet shape has captured the highest airflow through the room and provided higher average velocity at 1.2 m high enhancing the thermal comfort where most of the human occupancy occurs. With 6 m/s wind velocity, the divergent inlet has captured 2.55% more flow rate compared to the uniform inlet and 4.70% compared to the bulging-convergent inlet, and it has also provided an average velocity at 1.2 m high in the room of 7.16% higher than the uniform inlet and 8.44% higher than the bulging-convergent inlet.
Abdollahi, A, Pradhan, B & Alamri, A 2020, 'VNet: An End-to-End Fully Convolutional Neural Network for Road Extraction From High-Resolution Remote Sensing Data', IEEE Access, vol. 8, pp. 179424-179436.
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Abdollahi, A, Pradhan, B, Shukla, N, Chakraborty, S & Alamri, A 2020, 'Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review', Remote Sensing, vol. 12, no. 9, pp. 1444-1444.
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One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.
Abdulkareem, SA, Augustijn, E-W, Filatova, T, Musial, K & Mustafa, YT 2020, 'Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning', PLOS ONE, vol. 15, no. 1, pp. e0226483-e0226483.
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Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agent-based modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socio-environmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.
Abedin, B, Milne, D & Erfani, E 2020, 'Attraction, selection, and attrition in online health communities: Initial conversations and their association with subsequent activity levels', International Journal of Medical Informatics, vol. 141, pp. 104216-104216.
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BACKGROUND:The effectiveness of online health communities (OHCs) for improving outcomes for health care consumers, health professionals, and health services has already been well investigated. However, research on determinants of OHC users' activity levels, what is associated with attrition or attraction to these communities, and the impacts of initial posts is limited. OBJECTIVES:We sought to explore topic exchanges in OHCs and determine how users' initial posts and community reactions to them are associated with their subsequent activity levels. We also aimed to extend the theory of Attraction-Selection-Attrition for Online Communities (OCASA) to this area. METHODS:We examined exchanges in a major Australian OHC for cancer patients, analyzing about 2500 messages posted over 2009-18. We developed a novel annotation scheme to examine new members' initial posts and the community's reactions to them. RESULTS:The annotation scheme includes five themes: informational support provision, emotional support provision, requests for help, self-reflection & disclosures, and conversational cues. Initial conversations were associated with future activity levels in terms of active posting versus non-active engagement in the community. We found that most OHC members disclosed personal reflections to bond with the community, and many actively posted to the community solely to provide informational and emotional support to others. CONCLUSION:Our work extends OCASA theory to bond-based contexts, presents a new annotation scheme for OHC support topics, and makes an important contribution to knowledge about the relationship between users' activity levels and their initial posts. The findings help managers and owners understand how members use OHCs and how to encourage active participation. They also suggest how to attract new members and minimize attrition among existing members.
Abidin, C, Van der Nagel, E, Johns, A, Bailo, F, Rodriguez, A, Valdovinos-Kaye, B, Wikstrom, P, Gerrard, Y & Leaver, T 2020, '‘PLEASE READ THE COMMENTS’: COMMENTING CULTURES ACROSS PLATFORMS', AoIR Selected Papers of Internet Research.
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An old adage about the internet goes “Don’t Read The Comments”. It is a cynical word of caution from supposedly more experienced and savvy internet users, against a slew of negative, abusive, and unhelpful comments that are usually rampant online, stemming from trolling behaviour (Phillips 2015). “Don’t Read The Comments” has become an internet meme. Alongside parody websites (i.e. @AvoidComments n.d.), trawling through the comments section in search of ludicrosity has become an internet genre in and of itself. This comprises the likes of meme factory ‘The Straits Times Comment Section’ which collates absurd comments from users on a specific newspaper’s Facebook page (STcomments n.d.), as well as internet celebrity troll commentators like ‘American Ken’ M (Know Your Meme n.d.) and Singaporean ‘Peter Tan’ (Yeoh 2018), who post comments on a network of social media and fora in stealthily satirical ways that have even been co-opted for advertorials (Vox 2016). Such vernacular practice has in turn provoked a counter-genre of memes known as “I’m just Here For The Comments” (Tenor n.d.), in which users closely follow social media posts mainly for the resulting discussion and engagement in the comments section rather than the actual post itself. It is on this point of departure that this panel turns its focus to commenting cultures across platforms.
Aboulkheyr Es, H, Zhand, S, Thiery, JP & Warkiani, ME 2020, 'Pirfenidone reduces immune-suppressive capacity of cancer-associated fibroblasts through targeting CCL17 and TNF-beta', Integrative Biology, vol. 12, no. 7, pp. 188-197.
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Abstract Various factors in the tumor microenvironment (TME) regulate the expression of PD-L1 in carcinoma cells. The cancer-associated fibroblasts (CAFs) play a crucial role in regulating and rewiring TME to enhance their immune suppressive function and to favor the invasion of the malignant cells. Tumor progression may be retarded by targeting CAFs in the TME. Various studies highlighted the ability of targeting CAF with pirfenidone (PFD), leading to increased efficacy of chemotherapy. However, its potential for the reduction of immune-suppression capacity of CAFs remains to be elusive. Here, we assessed the effect of PFD on the expression of PD-L1 on CAF cells. Besides migration inhibitory effects of PFD on CAFs, the expression level of PD-L1 reduced in CAFs after treatment with PFD. The downstream analysis of released cytokines from CAFs showed that PFD significantly dropped the secretion of CCL17 and TNF-β, where a positive association between PFD-targeted proteins and PD-L1 was observed. These data suggest that the treatment of CAF within TME through the PFD may reduce the acquisition of CAF-mediated invasive and immune-suppressive capacity of breast carcinoma cells.
Abraham, MT, Satyam, N, Bulzinetti, MA, Pradhan, B, Pham, BT & Segoni, S 2020, 'Using Field-Based Monitoring to Enhance the Performance of Rainfall Thresholds for Landslide Warning', Water, vol. 12, no. 12, pp. 3453-3453.
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Landslides are natural disasters which can create major setbacks to the socioeconomic of a region. Destructive landslides may happen in a quick time, resulting in severe loss of lives and properties. Landslide Early Warning Systems (LEWS) can reduce the risk associated with landslides by providing enough time for the authorities and the public to take necessary decisions and actions. LEWS are usually based on statistical rainfall thresholds, but this approach is often associated to high false alarms rates. This manuscript discusses the development of an integrated approach, considering both rainfall thresholds and field monitoring data. The method was implemented in Kalimpong, a town in the Darjeeling Himalayas, India. In this work, a decisional algorithm is proposed using rainfall and real-time field monitoring data as inputs. The tilting angles measured using MicroElectroMechanical Systems (MEMS) tilt sensors were used to reduce the false alarms issued by the empirical rainfall thresholds. When critical conditions are exceeded for both components of the systems (rainfall thresholds and tiltmeters), authorities can issue an alert to the public regarding a possible slope failure. This approach was found effective in improving the performance of the conventional rainfall thresholds. We improved the efficiency of the model from 84% (model based solely on rainfall thresholds) to 92% (model with the integration of field monitoring data). This conceptual improvement in the rainfall thresholds enhances the performance of the system significantly and makes it a potential tool that can be used in LEWS for the study area.
Abraham, MT, Satyam, N, Kushal, S, Rosi, A, Pradhan, B & Segoni, S 2020, 'Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model', Water, vol. 12, no. 4, pp. 1195-1195.
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Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a l...
Abraham, MT, Satyam, N, Pradhan, B & Alamri, AM 2020, 'Forecasting of Landslides Using Rainfall Severity and Soil Wetness: A Probabilistic Approach for Darjeeling Himalayas', Water, vol. 12, no. 3, pp. 804-804.
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Rainfall induced landslides are creating havoc in hilly areas and have become an important concern for the stakeholders and public. Many approaches have been proposed to derive rainfall thresholds to identify the critical conditions that can initiate landslides. Most of the empirical methods are defined in such a way that it does not depend upon any of the in situ conditions. Soil moisture plays a key role in the initiation of landslides as the pore pressure increase and loss in shear strength of soil result in sliding of soil mass, which in turn are termed as landslides. Hence this study focuses on a Bayesian analysis, to calculate the probability of occurrence of landslides, based on different combinations of severity of rainfall and antecedent soil moisture content. A hydrological model, called Système Hydrologique Européen Transport (SHETRAN) is used for the simulation of soil moisture during the study period and event rainfall-duration (ED) thresholds of various exceedance probabilities were used to characterize the severity of a rainfall event. The approach was used to define two-dimensional Bayesian probabilities for occurrence of landslides in Kalimpong (India), which is a highly landslide susceptible zone in the Darjeeling Himalayas. The study proves the applicability of SHETRAN model for simulating moisture conditions for the study area and delivers an effective approach to enhance the prediction capability of empirical thresholds defined for the region.
Abraham, MT, Satyam, N, Pradhan, B & Alamri, AM 2020, 'IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas', Sensors, vol. 20, no. 9, pp. 2611-2611.
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In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seas...
Abraham, MT, Satyam, N, Rosi, A, Pradhan, B & Segoni, S 2020, 'The Selection of Rain Gauges and Rainfall Parameters in Estimating Intensity-Duration Thresholds for Landslide Occurrence: Case Study from Wayanad (India)', Water, vol. 12, no. 4, pp. 1000-1000.
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Recurring landslides in the Western Ghats have become an important concern for authorities, considering the recent disasters that occurred during the 2018 and 2019 monsoons. Wayanad is one of the highly affected districts in Kerala State (India), where landslides have become a threat to lives and properties. Rainfall is the major factor which triggers landslides in this region, and hence, an early warning system could be developed based on empirical rainfall thresholds considering the relationship between rainfall events and their potential to initiate landslides. As an initial step in achieving this goal, a detailed study was conducted to develop a regional scale rainfall threshold for the area using intensity and duration conditions, using the landslides that occurred during the years from 2010 to 2018. Detailed analyses were conducted in order to select the most effective method for choosing a reference rain gauge and rainfall event associated with the occurrence of landslides. The study ponders the effect of the selection of rainfall parameters for this data-sparse region by considering four different approaches. First, a regional scale threshold was defined using the nearest rain gauge. The second approach was achieved by selecting the most extreme rainfall event recorded in the area, irrespective of the location of landslide and rain gauge. Third, the classical definition of intensity was modified from average intensity to peak daily intensity measured by the nearest rain gauge. In the last approach, four different local scale thresholds were defined, exploring the possibility of developing a threshold for a uniform meteo-hydro-geological condition instead of merging the data and developing a regional scale threshold. All developed thresholds were then validated and empirically compared to find the best suited approach for the study area. From the analysis, it was observed that the approach selecting the rain gauge based on the most extrem...
Abu Bakar, MS, Ahmed, A, Jeffery, DM, Hidayat, S, Sukri, RS, Mahlia, TMI, Jamil, F, Khurrum, MS, Inayat, A, Moogi, S & Park, Y-K 2020, 'Pyrolysis of solid waste residues from Lemon Myrtle essential oils extraction for bio-oil production', Bioresource Technology, vol. 318, pp. 123913-123913.
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Solid waste residues from the extraction of essential oils are projected to increase and need to be treated appropriately. Valorization of waste via pyrolysis can generate value-added products, such as chemicals and energy. The characterization of lemon myrtle residues (LMR) highlights their suitability for pyrolysis, with high volatile matter and low ash content. Thermogravimetric analysis/derivative thermogravimetric revealed the maximum pyrolytic degradation of LMR at 335 °C. The pyrolysis of LMR for bio-oil production was conducted in a fixed-bed reactor within a temperature range of 350-550 °C. Gas chromatography-mass spectrometry showed that the bio-oil contained abundant amounts of acetic acid, phenol, 3-methyl-1,2-cyclopentanedione, 1,2-benzenediol, guaiacol, 2-furanmethanol, and methyl dodecanoate. An increase in pyrolysis temperature led to a decrease in organic acid and ketones from 18.09% to 8.95% and 11.99% to 8.75%, respectively. In contrast, guaiacols and anhydrosugars increased from 24.23% to 30.05% and from 3.57% to 7.98%, respectively.
Abu ul Fazal, M, Ferguson, S & Johnston, A 2020, 'Investigating efficient speech-based information communication: a comparison between the high-rate and the concurrent playback designs', Multimedia Systems, vol. 26, no. 5, pp. 621-630.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. This research aims to assist users to seek information efficiently while interacting with speech-based information, particularly in multimedia delivery, and reports on an experiment that tested two speech-based designs for communicating multiple speech-based information streams efficiently. In this experiment, a high-rate playback design and a concurrent playback design are investigated. In the high-rate playback design, two speech-based information streams were communicated by doubling the normal playback-rate, and in the concurrent playback design, two speech-based information streams were played concurrently. Comprehension of content in both the designs was also compared with the benchmark set from regular baseline condition. The results showed that the users’ comprehension regarding the main information dropped significantly in the high-rate playback and the concurrent playback designs compared to the baseline condition. However, in answering the questions set from the detailed information, the comprehension was not significantly different in all three designs. It is expected that such equeryfficient communication methods may increase productivity by providing information efficiently while interacting with an interactive multimedia system.
Abualigah, L, Gandomi, AH, Elaziz, MA, Hussien, AG, Khasawneh, AM, Alshinwan, M & Houssein, EH 2020, 'Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis', Algorithms, vol. 13, no. 12, pp. 345-345.
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Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems.
Acharya, P, Nguyen, KD, La, HM, Liu, D & Chen, I-M 2020, 'Nonprehensile Manipulation: a Trajectory-Planning Perspective', IEEE/ASME Transactions on Mechatronics, vol. PP, no. 99, pp. 1-1.
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IEEE This paper discusses nonprehensile manipulation of an asymmetric object using a robotic manipulator from a motion planning point of view. Four different aspects of the problem will be analyzed: object stability, motion planning, manipulator control, and experimental validation. Specifically, via an analysis of marginal stability of an object resting on a moving tray, the work establishes the critical accelerations of the manipulator's end-effector, below which the object's stability is guaranteed. These critical accelerations guide the design of the end-effector's motion for successful nonprehensile manipulation of the object. In particular, we propose two methods to formulate polynomial asymmetric s-curve trajectories such that the end- effector completes its motion in minimum time. In one method, the trajectory is divided into segments whose time intervals are then computed via a recursive algorithm. In the other method, we formulate an optimization problem and design the minimum-time trajectory by balancing the trade-off between the travel time and actuator effort. A series of experiments with a robotic arm is designed to validate and compare these motion planning methods in the context of nonprehensile manipulation. In addition, the experimental results demonstrate the advantages of the asymmetric s-curve motion profiles over the traditional symmetric s-curves.
Adanta, D, Fattah, IMR & Muhammad, NM 2020, 'COMPARISON OF STANDARD k-epsilon AND SST k-omega TURBULENCE MODEL FOR BREASTSHOT WATERWHEEL SIMULATION', Journal of Mechanical Science and Engineering, vol. 7, no. 2, pp. 039-044.
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Currently, Computational Fluid Dynamics (CFD) was utilized to predict the performance, geometry optimization or physical phenomena of a breastshot waterwheel. The CFD method requires the turbulent model to predict the turbulent flow. However, until now there is special attention on the effective turbulent model used in the analysis of breastshot waterwheel. This study is to identify the suitable turbulence model for a breatshot waterwheel. The two turbulence models investigated are: standard k-epsilon model and shear stress transport (SST) k-omega. Pressure based and one degrees of freedom (one-DoF) feature was used in this case with 75 Nm, 150 Nm, 225 Nm and 300 Nm as preloads. Based on the results, the standard k-epsilon model gave similar result with the SST k-omega model. Therefore, the simulation for breastshot waterwheel will be efficient if using the standard k-epsilon model because it requires lower computational power than the SST k-omega model. However, to study about physical phenomenon, the SST k-omega model is recommend.
Afroz, F & Braun, R 2020, 'Energy-efficient MAC protocols for wireless sensor networks: a survey', International Journal of Sensor Networks, vol. 32, no. 3, pp. 150-150.
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Afshar, A, Jahandari, S, Rasekh, H, Shariati, M, Afshar, A & Shokrgozar, A 2020, 'Corrosion resistance evaluation of rebars with various primers and coatings in concrete modified with different additives', Construction and Building Materials, vol. 262, pp. 120034-120034.
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Corrosion of steel rebars in concrete can reduce the durability of concrete structures in coastal environments. The corrosion rate of these concrete structures can be reduced by using suitable concrete additives and coating on rebars. This paper investigates the corrosion resistance of steel rebars by the addition of pozzolanic materials including fly ash, silica fume, polypropylene fibers, and industrial 2-dimethylaminoethanol (FerroGard 901) inhibitors to the concrete mixture. Three different types of rebars including mild steel rebar st37, and two stainless steel reinforcements, AISI 304 and AISI 316, were used. Various types of primer and coating including alkyd based primer, hot-dip galvanized coatings, alkyd top coating, zinc-rich epoxy primer, polyamide epoxy primer, polyamide epoxy top coating, polyurethane coatings, double layer of epoxy primer and alkyd top coating, and double layer of alkyd primer and alkyd top coating were applied on steel rebars to investigate the effect of coating type on the corrosion resistance of steel rebars in concrete. Polarization tests, electrochemical impedance spectroscopy, compressive strength and color adhesion tests were conducted. The best reinforced concrete mix design for corrosion resistance was the one including the rebar with zinc-rich epoxy primer and 25% fly ash, 10% silica fume, and 3% FerroGard 901 inhibitors by cementitious material weight. Polyurethane was the best coating due to the highest strength and the lowest corrosion rate. Alkyd primer was the weakest coating, although it was the most economical coating system.
Afshar, S, Hamilton, TJ, Davis, L, Van Schaik, A & Delic, D 2020, 'Event-Based Processing of Single Photon Avalanche Diode Sensors', IEEE Sensors Journal, vol. 20, no. 14, pp. 7677-7691.
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© 2001-2012 IEEE. Single Photon Avalanche Diode sensor arrays operating in direct time of flight mode can perform 3D imaging using pulsed lasers. Operating at high frame rates, SPAD imagers typically generate large volumes of noisy and largely redundant spatio-temporal data. This results in communication bottlenecks and unnecessary data processing. In this work, we propose a neuromorphic processing solution to this problem. By processing the spatio-temporal patterns generated by the SPADs in a local, event-based manner, the proposed 128\times 128 pixel sensor-processor system reduces the size of output data from the sensor by orders of magnitude while increasing the utility of the output data in the context of challenging recognition tasks. To test the proposed system, the first large scale complex SPAD imaging dataset is captured using an existing 32\times 32 pixel sensor. The generated dataset consists of 24000 recordings and involves high-speed view-invariant recognition of airplanes with background clutter. The frame-based SPAD imaging dataset is converted via several alternative methods into event-based data streams and processed using the proposed 125\times 125 receptive field neuromorphic processor as well as a range of feature extractor networks and pooling methods. The output of the proposed event generation methods are then processed by an event-based feature extraction and classification system implemented in FPGA hardware. The event-based processing methods are compared to processing the original frame-based dataset via frame-based but otherwise identical architectures. The results show the event-based methods are superior to the frame-based approach both in terms of classification accuracy and output data-rate.
Afzal, MU, Matekovits, L, Esselle, KP & Lalbakhsh, A 2020, 'Beam-Scanning Antenna Based on Near-Electric Field Phase Transformation and Refraction of Electromagnetic Wave Through Dielectric Structures', IEEE Access, vol. 8, pp. 199242-199253.
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Aggarwal, T, Wadhwa, R, Gupta, R, Paudel, KR, Collet, T, Chellappan, DK, Gupta, G, Perumalsamy, H, Mehta, M, Satija, S, Hansbro, PM, Dua, K & Maurya, PK 2020, 'MicroRNAs as Biomarker for Breast Cancer', Endocrine, Metabolic & Immune Disorders - Drug Targets, vol. 20, no. 10, pp. 1597-1610.
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Regardless of advances in detection and treatment, breast cancer affects about 1.5 millionwomen all over the world. Since the last decade, genome-wide association studies (GWAS) have beenextensively conducted for breast cancer to define the role of miRNA as a tool for diagnosis, prognosisand therapeutics. MicroRNAs are small, non-coding RNAs that are associated with the regulation ofkey cellular processes such as cell multiplication, differentiation, and death. They cause a disturbancein the cell physiology by interfering directly with the translation and stability of a targeted gene transcript.MicroRNAs (miRNAs) constitute a large family of non-coding RNAs, which regulate targetgene expression and protein levels that affect several human diseases and are suggested as the novelmarkers or therapeutic targets, including breast cancer. MicroRNA (miRNA) alterations are not onlyassociated with metastasis, tumor genesis but also used as biomarkers for breast cancer diagnosis orprognosis. These are explained in detail in the following review. This review will also provide an impetusto study the role of microRNAs in breast cancer.
Aghaabbasi, M, Shekari, ZA, Shah, MZ, Olakunle, O, Armaghani, DJ & Moeinaddini, M 2020, 'Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques', Transportation Research Part A: Policy and Practice, vol. 136, pp. 262-281.
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Aghayarzadeh, M, Khabbaz, H, Fatahi, B & Terzaghi, S 2020, 'Interpretation of Dynamic Pile Load Testing for Open-Ended Tubular Piles Using Finite-Element Method', International Journal of Geomechanics, vol. 20, no. 2, pp. 04019169-04019169.
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© 2019 American Society of Civil Engineers. For a foundation to perform safely, the ultimate strength of each pile must satisfy the structural and geotechnical requirements. Pile load testing is considered to be a direct method for determining the ultimate geotechnical capacity of piles. In this paper the dynamic and static response of a driven steel pipe pile monitored as part of a highway bridge construction project in New South Wales, Australia, has been simulated and then numerically analyzed using the finite-element method. A continuum numerical model has been established to simulate the dynamic load testing of steel pipe piles with unplugged behavior in which adopting measured soil properties resulted in a reasonable match between the measured and predicted results and without needing random signal matching in an iterative process. Settlement at the head and toe of the pile was then calculated when a static load represented by a dead load plus a heavy platform load of a bridge was applied over the pile head. During the dynamic and static load testing simulation, a hardening soil model with small strain stiffness was used to obtain the best correlation between the large and small strains, which occurred while the pile was under static load and being driven. The numerical predictions obtained using continuum finite-element simulations were then compared with the corresponding predictions obtained from the Case Western Reserve University (CASE) method and CASE Pile Wave Analysis Program (CAPWAP) to evaluate the predictions. The results show that the hardening soil model with small strain stiffness exhibits a reasonable correlation with the field measurements during static and dynamic loading. Moreover, parametric studies have been carried out in the established continuum numerical model to evaluate how the interface properties between the pile and soil and the reference shear strain define the backbone on the velocity at the head of the pile and trac...
Aghdam, MM, Li, L & Zhu, J 2020, 'Comprehensive study of finite control set model predictive control algorithms for power converter control in microgrids', IET Smart Grid, vol. 3, no. 1, pp. 1-10.
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Advances in power electronics and digital control open a new horizon in the control of power converters. Particularly, model predictive control has been developed for control applications in industrial electronics and power systems. This study presents a comprehensive study on recent achievements of model predictive control algorithms to overcome the challenges in the real‐time implementation of power converter control, which is the lowest level control of hierarchical control in microgrids. The study shows that most of these alternate solutions can enhance system reliability, stability, and efficiency. The control platform devices for the real‐time implementation of these algorithms are compared. The related issues are discussed and classified, respectively. Finally, a summary is provided, leading to some further research questions and future work.
Ahmad, HA, Ni, S-Q, Ahmad, S, Zhang, J, Ali, M, Ngo, HH, Guo, W, Tan, Z & Wang, Q 2020, 'Gel immobilization: A strategy to improve the performance of anaerobic ammonium oxidation (anammox) bacteria for nitrogen-rich wastewater treatment', Bioresource Technology, vol. 313, pp. 123642-123642.
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Anaerobic ammonium oxidation (anammox) process appears a suitable substitute to nitrification-denitrification at a lower C/N ratios. Anammox is a chemolithoautotrophic process, belong to phylum Planctomycetes, and they are slow growing bacteria. Different strategies, e.g., biofilm formation, granulation and gel immobilization, have been applied to maintain a critical mass of bacterial cells in the system by avoiding washout from the bioreactor. Gel immobilization of anammox appears the best alternative to the natural process of biofilm formation and granulation. Polyvinyl alcohol-sodium alginate, polyethylene glycol, and waterborne polyurethane are the most reported materials used for the entrapment of anammox bacteria. However, dissolution of the gel beads refrains its application for long term bioprocess. Magnetic powder could coat on the surface of the beads which may increase the mechanical strength and durability of pellets. Application and problem of immobilization technology for the commercialization of this technology also addressed.
Ahmad, N, Aghdam, RF, Butt, I & Naveed, A 2020, 'Citation-based systematic literature review of energy-growth nexus: An overview of the field and content analysis of the top 50 influential papers', Energy Economics, vol. 86, pp. 104642-104642.
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Ahmed, AM, Chacon, A, Rutherford, H, Akamatsu, G, Mohammadi, A, Nishikido, F, Tashima, H, Yoshida, E, Yamaya, T, Franklin, DR, Rosenfeld, A, Guatelli, S & Safavi-Naeini, M 2020, 'A validated Geant4 model of a whole-body PET scanner with four-layer DOI detectors', Physics in Medicine & Biology, vol. 65, no. 23, pp. 235051-235051.
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Abstract The purpose of this work is to develop a validated Geant4 simulation model of a whole-body prototype PET scanner constructed from the four-layer depth-of-interaction detectors developed at the National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Japan. The simulation model emulates the behaviour of the unique depth of interaction sensing capability of the scanner without needing to directly simulate optical photon transport in the scintillator and photodetector modules. The model was validated by evaluating and comparing performance metrics from the NEMA NU 2-2012 protocol on both the simulated and physical scanner, including spatial resolution, sensitivity, scatter fraction, noise equivalent count rates and image quality. The results show that the average sensitivities of the scanner in the field-of-view were 5.9 cps kBq−1 and 6.0 cps kBq−1 for experiment and simulation, respectively. The average spatial resolutions measured for point sources placed at several radial offsets were 5.2± 0.7 mm and 5.0± 0.8 mm FWHM for experiment and simulation, respectively. The peak NECR was 22.9 kcps at 7.4 kBq ml−1 for the experiment, while the NECR obtained via simulation was 23.3 kcps at the same activity. The scatter fractions were 44% and 41.3% for the experiment and simulation, respectively. Contrast recovery estimates performed in different regions of a simulated image quality phantom matched the experimental results with an average error of -8.7% and +3.4% for hot and cold lesions, respectively. The results demonstrate that the developed Geant4 model reliably reproduces the key NEMA NU 2-2012 performance metrics evaluated on the prototype PET scanner. A simplified version of the model is included as an advanced example in Geant4 version 10.5.
Ahmed, JB, Salisu, A, Pradhan, B & Alamri, AM 2020, 'Do Termitaria Indicate the Presence of Groundwater? A Case Study of Hydrogeophysical Investigation on a Land Parcel with Termite Activity', Insects, vol. 11, no. 11, pp. 728-728.
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Termite nests have long been suggested to be good indicators of groundwater but only a few studies are available to demonstrate the relationship between the two. This study therefore aims at investigating the most favourable spots for locating groundwater structures on a small parcel of land with conspicuous termite activity. To achieve this, geophysical soundings using the renowned vertical electrical sounding (VES) technique was carried out on the gridded study area. A total of nine VESs with one at the foot of a termitarium were conducted. The VES results were interpreted and assessed via two different techniques: (1) physical evaluation as performed by drillers in the field and (2) integration of primary and secondary geoelectrical parameters in a geographic information system (GIS). The result of the physical evaluation indicated a clear case of subjectivity in the interpretation but was consistent with the choice of VES points 1 and 6 (termitarium location) as being the most prospective points to be considered for drilling. Similarly, the integration of the geoelectrical parameters led to the mapping of the most prospective groundwater portion of the study area with the termitarium chiefly in the center of the most suitable region. This shows that termitaria are valuable landscape features that can be employed as biomarkers in the search of groundwater.
Ahmed, MB, Alam, MM, Zhou, JL, Xu, B, Johir, MAH, Karmakar, AK, Rahman, MS, Hossen, J, Hasan, ATMK & Moni, MA 2020, 'Advanced treatment technologies efficacies and mechanism of per- and poly-fluoroalkyl substances removal from water', Process Safety and Environmental Protection, vol. 136, pp. 1-14.
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© 2020 Institution of Chemical Engineers The increasing occurrence of chemically resistant per- and poly-fluoroalkyl substances (PFASs) in the natural environment, animal tissues and even the human body poses a significant health risk. Temporal trend studies on water, sediments, bird, fish, marine mammal and the human show that the exposure of PFAS has significantly increased over the last 20–30 years. Different physical, biological and chemical treatment processes have been investigated for PFAS removal from water. However, there is a lack of detailed understating of the mechanism of removal by different methods, especially by different advanced chemical treatment processes. This article reviews PFASs removal efficacy and mechanism by the advanced chemical treatment methods from aqueous solution. Review shows that several advanced oxidation processes (e.g., electrochemical oxidation, activated persulfate oxidation, photocatalysis, UV-induced oxidation) are successful in degrading PFASs. Moreover, defluorination treatment, some thermal and non-thermal degradation processes are also found to be prominent for the degradation of PFASs with some limitations including process costs over physical treatment (e.g., sorption), production of toxic by-products and greenhouse gases. Finally, knowledge gaps concerning the advanced chemical treatment of PFASs are discussed.
Ahmed, MB, Johir, MAH, McLaughlan, R, Nguyen, LN, Xu, B & Nghiem, LD 2020, 'Per- and polyfluoroalkyl substances in soil and sediments: Occurrence, fate, remediation and future outlook', Science of The Total Environment, vol. 748, pp. 141251-141251.
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Ahmed, R, Yafi, E, Su�ud, MM, Alam, MM & Faizan, M 2020, 'A Framework for Real-Time Healthcare System Performance in Developing Countries', Journal of Computer Science, vol. 16, no. 9, pp. 1250-1257.
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Ajeng, AA, Abdullah, R, Ling, TC, Ismail, S, Lau, BF, Ong, HC, Chew, KW, Show, PL & Chang, J-S 2020, 'Bioformulation of biochar as a potential inoculant carrier for sustainable agriculture', Environmental Technology & Innovation, vol. 20, pp. 101168-101168.
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© 2020 Elsevier B.V. The dependence on chemical fertilizers and pesticides to increase agricultural outputs owing to the demands of a growing human population creates the need for a sustainable fertilizer. Biochar is presently a promising candidate as an inoculant carrier, given its highly porous structure, with nutrients naturally derived from the biomass, high water, and nutrient retention properties, which favor microbial growth. Biochar can be produced through pyrolysis, hydrothermal carbonization, gasification, and torrefaction. The porosity and adsorption ability of biochar allows it to be effectively used as a carrier to immobilize plant growth-promoting rhizobacteria (PGPR) for enhanced crop growth. Furthermore, the physicochemical properties of biochar like surface area, pore properties, and surface functional groups can be further modified via several activation methods, such as chemical oxidation and reduction, and physical activation to optimize the PGPR immobilization. The understanding of the agronomic impacts of biochar and the possible scaling up of cell immobilization will provide insights on the mechanism of biochar as an efficient inoculant carrier. This will contribute to fewer environmental hazards with the utilization of biochar for promoting plant growth. The complex interplay of physicochemical properties of biochar as a carrier to immobilize PGPR and the potential mechanisms of biochar-based inoculants are significant to achieve agricultural sustainability.
Akbari, F, Saberi, M & Hussain, OK 2020, 'Social network structure-based framework for innovation evaluation and propagation for new product development', Service Oriented Computing and Applications, vol. 14, no. 3, pp. 189-201.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. Evaluating the innovation of a new idea before its implementation is a complicated but important phenomenon as it plays a critical role in the success of a product. The literature widely uses sentiment analysis as a technique for product designers to ascertain users’ opinion toward an idea before its implementation. However, that technique focuses only on determining the opinion of users studied. It does not assist designers in providing insights in terms of what needs to be done to propagate the popularity of the idea further to ensure its success. One framework by which this can be done is by considering social network structure and representing users as nodes of that network. In this paper, we investigate how a social network structure can be used to influence a user’s opinion among the society. Our proposed framework consists of four main components, namely data collection, sentiment extraction, budget approximation and presentation. After gathering customers’ comments in the data collection phase, the opinion of users who have expressed it is analyzed in the sentiment analysis phase. The budget approximation component then determines the cost of spreading positive opinion among the network of users, including those who have not given it. For that, influence maximization is used to compare the cost of convergence of the general opinion of society in the direction of innovation. In presentation component, the comparative information will be used by product designers to assist them in determining the viability of selecting an idea for implementation. The simulation results show that the network structure and the individuals’ positions are important factors in the acceptance of an innovation by society. This framework can be used to compare different innovative ideas and provide decision makers in organizations with informative reports as decision support materials.
Aksoy, YA, Deng, W, Stoddart, J, Chung, R, Guillemin, G, Cole, NJ, Neely, GG & Hesselson, D 2020, '“STRESSED OUT”: The role of FUS and TDP-43 in amyotrophic lateral sclerosis', The International Journal of Biochemistry & Cell Biology, vol. 126, pp. 105821-105821.
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Aksoy, YA, Yang, B, Chen, W, Hung, T, Kuchel, RP, Zammit, NW, Grey, ST, Goldys, EM & Deng, W 2020, 'Spatial and Temporal Control of CRISPR-Cas9-Mediated Gene Editing Delivered via a Light-Triggered Liposome System', ACS Applied Materials & Interfaces, vol. 12, no. 47, pp. 52433-52444.
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Akther, N, Ali, SM, Phuntsho, S & Shon, H 2020, 'Surface modification of thin-film composite forward osmosis membranes with polyvinyl alcohol–graphene oxide composite hydrogels for antifouling properties', Desalination, vol. 491, pp. 114591-114591.
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© 2020 Elsevier B.V. In this study, the polyamide (PA) layers of commercial thin-film composite (TFC) forward osmosis (FO) membranes were coated with glutaraldehyde cross-linked polyvinyl alcohol (PVA) hydrogel comprising of graphene oxide (GO) at various loadings to enhance their fouling resistance. The optimal GO concentration of 0.02 wt% in hydrogel solution was confirmed from the FO membrane performance, and its influence on membrane antifouling properties was studied. The properties of the modified membranes, such as surface morphology, surface charge and wettability, were also investigated. PVA/GO coating was observed to increase the smoothness and hydrophilicity of the membrane surface. The foulant resistances of the pristine, PVA-coated and PVA/GO-coated membranes were also reported. PVA hydrogel-coated TFC membrane with a GO loading of 0.02 wt% showed a 55% reduction in specific reverse solute flux, only a marginal reduction in the water flux, and the best antifouling property with a 58% higher flux recovery than the pristine TFC membrane. The significant improvement in the selectivity of the modified membranes meant that the hydrogel coating could be used to seal PA defects. The biocidal GO flakes in PVA hydrogel coating also improved the biofouling resistance of the modified membranes, which could be attributed to their morphologies and superior surface properties.
Akther, N, Yuan, Z, Chen, Y, Lim, S, Phuntsho, S, Ghaffour, N, Matsuyama, H & Shon, H 2020, 'Influence of graphene oxide lateral size on the properties and performances of forward osmosis membrane', Desalination, vol. 484, pp. 114421-114421.
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Al zahrani, S, Islam, MS & Saha, SC 2020, 'Heat transfer augmentation in retrofitted corrugated plate heat exchanger', International Journal of Heat and Mass Transfer, vol. 161, pp. 120226-120226.
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Al zahrani, S, Islam, MS, Xu, F & Saha, SC 2020, 'Thermal performance investigation in a novel corrugated plate heat exchanger', International Journal of Heat and Mass Transfer, vol. 148, pp. 119095-119095.
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© 2019 Compact heat exchangers have become an essential necessity for power production and multi other purposes on a daily basis. The corrugated plate heat exchangers (CPHEs) are well-known for their high thermal performance. This study proposes a unique CPHE with a simple modification that can boost its thermal performance significantly. The overall tests have been conducted on four CPHEs for two symmetric chevron angles (β) of 30°/30° and 60°/60° Two CPHEs belong to the newly CPHEs, and the other two belong to the well-known basic CPHE. Data are obtained for steady-state, single-phase (water-water), counter-current arrangements, and for Reynolds number (Re) ranges from 500 to 2500. Sophisticated mesh techniques have been adopted to develop the mesh for the plates and the fluids between the plates. An appropriate grid refinement test has been carried out for the accuracy of the numerical results. The results have been validated with benchmark experimental and numerical data. A realizable k−ε turbulence model with scalable wall treatment found to provide the most consistent and accurate prediction of the thermal performance of CPHE. The numerical results showed that the Nusselt number (Nu) and the effectiveness (ϵ) of the newly developed CPHEs are much higher than that of the basic one, which can be very useful when a heavy heat duty is required. The enhancement for Nu is up to 75% and for ϵ is up to 42%, and generally both exhibit a direct proportional relationship with Re. Based on the numerical result, a new correlation to predict Nu has been developed.
Al-Abadi, AM & Pradhan, B 2020, 'In flood susceptibility assessment, is it scientifically correct to represent flood events as a point vector format and create flood inventory map?', Journal of Hydrology, vol. 590, pp. 125475-125475.
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© 2020 Elsevier B.V. In this discussion article, we try to highlight and discuss the wrong way for representing an areal phenomenon “flood” as a point vector format in GIS-based flood susceptibility studies and creating what is called “flood inventory map”. Two examples from the literature were taken to show that a flood event cannot be represented by point except with very small map scales (1: 10000000) and this flood event should be with other flood events to form the “flood inventory map”. With the help of the other two examples from the previous studies, this article showed the wrong used way for representing flood worldwide and suggested an appropriate method for mapping flood susceptibility.
Alajlouni, D, Bliuc, D, Tran, T, Eisman, JA, Nguyen, TV & Center, JR 2020, 'Decline in Muscle Strength and Performance Predicts Fracture Risk in Elderly Women and Men', The Journal of Clinical Endocrinology & Metabolism, vol. 105, no. 9, pp. e3363-e3373.
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Abstract Context Muscle strength and performance are associated with fractures. However, the contribution of their rate of decline is unclear. Objective To assess the independent contribution of the rate of decline in muscle strength and performance to fracture risk. Design, Setting, and Participants Community-dwelling women (n = 811) and men (n = 440) aged 60 years or older from the prospective Dubbo Osteoporosis Epidemiology Study followed from 2000 to 2018 for incident fracture. Clinical data, appendicular lean mass/height2 (ht)2, bone mineral density, quadricep strength/ht (QS), timed get-up-and-go (TGUG), 5 times repeated sit-to-stand (5xSTS), and gait speed (GS) measured biennially. Rates of decline in muscle parameters were calculated using ordinary least squares regression and fracture risk was assessed using Cox’s models. Main Outcome Incident low-trauma fracture ascertained by x-ray report. Results Apart from lean mass in women, all muscle parameters declined over time. Greater rates of decline in physical performance were associated with increased fracture risk in women (Hazard ratios [HRs] ranging from 2.1 (95% CI: 1.5–2.9) for GS to 2.7 (95% CI: 1.9–3.6) for 5xSTS, while in men only the decline in GS was associated with fracture risk (HR: 3.4 [95% CI: 1.8–6.3]). Baseline performance and strength were also ass...
Alajlouni, D, Tran, T, Bliuc, D, Blank, RD, Cawthon, PM, Orwoll, ES & Center, JR 2020, 'Muscle Strength and Physical Performance Improve Fracture Risk Prediction Beyond Garvan and FRAX: The Osteoporotic Fractures in Men (MrOS) Study', Journal of Bone and Mineral Research, vol. 37, no. 3, pp. 411-419.
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ABSTRACT Muscle strength and physical performance are associated with fracture risk in men. However, it is not known whether these measurements enhance fracture prediction beyond Garvan and FRAX tools. A total of 5665 community-dwelling men, aged ≥65 years, from the Osteoporotic Fractures in Men (MrOS) Study, who had data on muscle strength (grip strength) and physical performance (gait speed and chair stand tests), were followed from 2000 to 2019 for any fracture, major osteoporotic fracture (MOF), initial hip, and any hip fracture. The contributions to different fracture outcomes were assessed using Cox's proportional hazard models. Tool-specific analysis approaches and outcome definitions were used. The added predictive values of muscle strength and physical performance beyond Garvan and FRAX were assessed using categorical net reclassification improvement (NRI) and relative importance analyses. During a median follow-up of 13 (interquartile range 7–17) years, there were 1014 fractures, 536 MOFs, 215 initial hip, and 274 any hip fractures. Grip strength and chair stand improved prediction of any fracture (NRI for grip strength 3.9% and for chair stand 3.2%) and MOF (5.2% and 6.1%). Gait speed improved prediction of initial hip (5.7%) and any hip (7.0%) fracture. Combining grip strength and the relevant performance test further improved the models (5.7%, 8.9%, 9.4%, and 7.0% for any, MOF, initial, and any hip fractures, respectively). The improvements were predominantly driven by reclassification of those with fracture to higher risk categories. Apart from age and femoral neck bone mineral density, muscle strength and performance were ranked equal to or better than the other risk factors included in fracture models, including prior fractures, falls, smoking, alcohol, and glucocorticoid use. Muscle strength and performance measurements improved fracture risk prediction in men beyond Garvan and FRAX. They ...
Alajlouni, DA, Bliuc, D, Tran, TS, Blank, RD, Cawthon, PM, Ensrud, KE, Lane, NE, Orwoll, ES, Cauley, JA & Center, JR 2020, 'Muscle Strength and Physical Performance Are Associated With Risk of Postfracture Mortality But Not Subsequent Fracture in Men', Journal of Bone and Mineral Research, vol. 37, no. 8, pp. 1571-1579.
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ABSTRACT Muscle strength and physical performance are associated with incident fractures and mortality. However, their role in the risk of subsequent fracture and postfracture mortality is not clear. We assessed the association between muscle strength (grip strength) and performance (gait speed and chair stands time) and the risk of subsequent fracture and mortality in 830 men with low-trauma index fracture, who participated in the Osteoporotic Fractures in Men (MrOS) USA Study and had their index measurements assessed within 5 years prior to the index fracture. The annual decline in muscle strength and performance following index fracture, estimated using linear mixed-effects regression, was also examined in relation to mortality. The associations were assessed using Cox proportional hazards models adjusted for age, femoral neck bone mineral density (FN BMD), prior fractures, falls, body mass index (BMI), index fracture site, lifestyle factors, and comorbidities. Over a median follow-up of 3.7 (interquartile range [IQR], 1.3–8.1) years from index fracture to subsequent fracture, 201 (24%) men had a subsequent fracture and over 5.1 (IQR, 1.8–9.6) years to death, and 536 (65%) men died. Index measurements were not associated with subsequent fracture (hazard ratios [HRs] ranging from 0.97 to 1.07). However, they were associated with postfracture mortality. HR (95% confidence interval [CI]) per 1 standard deviation (1-SD) decrement in grip strength: HR 1.12 (95% CI, 1.01–1.25) and gait speed: HR 1.14 (95% CI, 1.02–1.27), and 1-SD increment in chair stands time: HR 1.08 (95% CI, 0.97–1.21). Greater annual declines in these measurements were associated with higher mortality risk, independent of the index values and other covariates. HR (95% CI) per 1-SD annual decrement in change in grip strength: HR 1.15 (95% CI, 1.01–1.33) and in gait speed: HR 1.38 (95% CI, 1.13–1.68), and 1-SD annual increment in chair stan...
Alam, MM, Hossain, MA, Hossain, MD, Johir, MAH, Hossen, J, Rahman, MS, Zhou, JL, Hasan, ATMK, Karmakar, AK & Ahmed, MB 2020, 'The Potentiality of Rice Husk-Derived Activated Carbon: From Synthesis to Application', Processes, vol. 8, no. 2, pp. 203-203.
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Activated carbon (AC) has been extensively utilized as an adsorbent over the past few decades. AC has widespread applications, including the removal of different contaminants from water and wastewater, and it is also being used in capacitors, battery electrodes, catalytic supports, and gas storage materials because of its specific characteristics e.g., high surface area with electrical properties. The production of AC from naturally occurring precursors (e.g., coal, biomass, coconut shell, sugarcane bagasse, and so on) is highly interesting in terms of the material applications in chemistry; however, recently much focus has been placed on the use of agricultural wastes (e.g., rice husk) to produce AC. Rice husk (RH) is an abundant as well as cheap material which can be converted into AC for various applications. Various pollutants such as textile dyes, organic contaminants, inorganic anions, pesticides, and heavy metals can be effectively removed by RH-derived AC. In addition, RH-derived AC has been applied in supercapacitors, electrodes for Li-ion batteries, catalytic support, and energy storage, among other uses. Cost-effective synthesis of AC can be an alternative for AC production. Therefore, this review mainly covers different synthetic routes and applications of AC produced from RH precursors. Different environmental, catalytic, and energy applications have been pinpointed. Furthermore, AC regeneration, desorption, and relevant environmental concerns have also been covered. Future scopes for further research and development activities are also discussed. Overall, it was found that RH-derived AC has great potential for different applications which can be further explored at real scales, i.e., for industrial applications in the future.
Alam, MM, Lu, DC & Siwakoti, Y 2020, 'Small signal analysis of dual input buck converter', International Journal of Smart Grid and Clean Energy, vol. 9, no. 1, pp. 8-16.
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© 2020 by the authors. This paper presents a small signal modelling and voltage-mode control of a pulse-width modulated (PWM) dual-input DC-DC buck converter. The control of multiple switches in a power converter is the main challenge for multipleinput converters addressed in this paper. Using the concept of linearization and perturbation depicted in circuit averaging technique, the closed-loop small signal model for multi-input DC-DC buck converter is derived. The closed loop control to output voltage transfer function is derived. A brief compensator design is introduced for a multi-input buck converter. In order to control the duty cycles of multiple switches and control the output voltage, a new variable is introduced to relate the duty cycles in the closed loop control to output voltage transfer function. The analysis and controller design are simulated in LTSpice.
Alambeigi, P, Burry, J, Zhao, S & Cheng, E 2020, 'A study of human vocal effort in response to the architectural auditory environment', Architectural Science Review, vol. 63, no. 3-4, pp. 262-274.
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© 2020 Informa UK Limited, trading as Taylor & Francis Group. This paper examines human auditory interaction with an architectural design hypothesized to decrease users’ vocal effort and thus enhance their speech privacy. This detailed design increased sound scattering in semi-enclosed meeting rooms within open plan offices. To achieve desirable speech intelligibility, a live sound environment is strongly recommended for meeting rooms. The research explores the hypothesis that by adding early reflections to the direct sound energy with an integrated design, the speaker as a self-listener might benefit from perceiving their own voice with more clarity. This can cause adaptive changes to subconscious vocal effort and increase the corresponding speech privacy of the space. An architecture-driven talker-quality experiment in a natural situation has been conducted in two rounds and in two different acoustic environments with 20 participants. The results implied the importance of human visual and spatial perception of privacy over auditory interaction with the environment on decreasing vocal effort. Such factors could thus be considered within the architectural design process.
Alanezi, AA, Alanezi, YA, Alazmi, R, Altaee, A, Alsalhy, QF & Sharif, AO 2020, 'Enhancing performance of the membrane distillation process using air injection zigzag system for water desalination', Desalination and Water Treatment, vol. 207, pp. 43-50.
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A novel design of an air injection zigzag system was developed to enhance the tubular membrane distillation module’s performance for desalination of water, unlike the basic design that works without an air injection system. Designed in a zigzag mode, the membrane distillation module is set to yield a high turbulence flow. Operating parameter effects, e.g. the feed temperature (40, 50, 60, and 70°C), feed concentration (1, 3, and 5 g/L), and airflow rate (30 - 90 L/h), on process performance were investigated. The system proved its capability to enhance the heat and mass transfer coefficients. The basic and developed modules’ performances were compared in terms of permeate flux (Jm) and thermal efficiency (η). The Reynolds Number increased threefold, which consequently, increased the mass transfer coefficient by 25% and the heat transfer coefficient twofold compared to the basic module at air flow rate of 90 (L/h). Moreover, the thermal efficiency and permeate flux were higher than the basic module’s by roughly 1.4 and 1.5-fold, respectively, for a 5 g/L feed concentration.
Alarkawi, D, Bliuc, D, Tran, T, Ahmed, LA, Emaus, N, Bjørnerem, A, Jørgensen, L, Christoffersen, T, Eisman, JA & Center, JR 2020, 'Impact of osteoporotic fracture type and subsequent fracture on mortality: the Tromsø Study', Osteoporosis International, vol. 31, no. 1, pp. 119-130.
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UNLABELLED: Less is known about the impact of non-hip non-vertebral fractures (NHNV) on early death. This study demonstrated increased risk of dying following hip and NHNV fractures which was further increased by a subsequent fracture. This highlights the importance of early intervention to prevent both initial and subsequent fractures and improve survival. INTRODUCTION: Osteoporotic fractures are a major health concern. Limited evidence exists on their impact on mortality in ageing populations. This study examined the contribution of initial fracture type and subsequent fracture on mortality in a Norwegian population that has one of the highest rates of fractures. METHODS: The Tromsø Study is a prospective population-based cohort in Norway. Women and men aged 50+ years were followed from 1994 to 2010. All incident hip and non-hip non-vertebral (NHNV) fractures were registered. NHNV fractures were classified as either proximal or distal. Information on self-reported co-morbidities, lifestyle factors, general health and education level was collected. Multivariable Cox models were used to quantify mortality risk with incident and subsequent fractures analysed as time-dependent variables. RESULTS: Of 5214 women and 4620 men, 1549 (30%) and 504 (11%) sustained a fracture, followed by 589 (38%) and 254 (51%) deaths over 10,523 and 2821 person-years, respectively. There were 403 (26%) subsequent fractures in women and 68 (13%) in men. Hip fracture was associated with a two-fold increase in mortality risk (HR 2.05, 95% CI 1.73-2.42 in women and 2.49, 95% CI 2.00-3.11 in men). Proximal NHNV fractures were associated with 49% and 81% increased mortality risk in women and men (HR 1.49, 95% CI 1.21-1.84 and 1.81, 95% CI 1.37-2.41), respectively. Distal NHNV fractures were not associated with mortality. Subsequent fracture was associated with 89% and 77% increased mortality risk in women and men (HR 1.89, 95% CI 1.52-2.35 and 1.77, 95% CI 1.16-2.71), respectively. ...
Alexander-Floyd, J, Haroon, S, Ying, M, Entezari, AA, Jaeger, C, Vermulst, M & Gidalevitz, T 2020, 'Unexpected cell type-dependent effects of autophagy on polyglutamine aggregation revealed by natural genetic variation in C. elegans', BMC Biology, vol. 18, no. 1.
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Abstract Background Monogenic protein aggregation diseases, in addition to cell selectivity, exhibit clinical variation in the age of onset and progression, driven in part by inter-individual genetic variation. While natural genetic variants may pinpoint plastic networks amenable to intervention, the mechanisms by which they impact individual susceptibility to proteotoxicity are still largely unknown. Results We have previously shown that natural variation modifies polyglutamine (polyQ) aggregation phenotypes in C. elegans muscle cells. Here, we find that a genomic locus from C. elegans wild isolate DR1350 causes two genetically separable aggregation phenotypes, without changing the basal activity of muscle proteostasis pathways known to affect polyQ aggregation. We find that the increased aggregation phenotype was due to regulatory variants in the gene encoding a conserved autophagy protein ATG-5. The atg-5 gene itself conferred dosage-dependent enhancement of aggregation, with the DR1350-derived allele behaving as hypermorph. Surprisingly, increased aggregation in animals carrying the modifier locus was accompanied by enhanced autophagy activation in response to activating treatment. Because autophagy is expected to clear, not increase, protein aggregates, we activated autophagy in three different polyQ models and found a striking tissue-dependent effect: activation of autophagy decreased polyQ aggregation in neurons and intestine, but increased it in the muscle cells. Conclusions Our data show that cryptic natural variants in genes encoding proteostasis com...
Alfaro-García, VG, Merigó, JM, Alfaro Calderón, GG, Plata-Pérez, L, Gil-Lafuente, AM & Herrera-Viedma, E 2020, 'A citation analysis of fuzzy research by universities and countries', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5355-5367.
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Alfaro-García, VG, Merigó, JM, Pedrycz, W & Gómez Monge, R 2020, 'Citation Analysis of Fuzzy Set Theory Journals: Bibliometric Insights About Authors and Research Areas', International Journal of Fuzzy Systems, vol. 22, no. 8, pp. 2414-2448.
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Alfouneh, M, Ji, J & Luo, Q 2020, 'Optimal design of multi-cellular cores for sandwich panels under harmonic excitation', Composite Structures, vol. 248, pp. 112507-112507.
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© 2020 Elsevier Ltd Sandwich panels with cellular cores are increasingly used in engineering due to their superb dynamic performance. In this type of structure, core design significantly affects its mechanical property. This article is to study optimal design of a multi-cellular core to minimize dynamic response of the sandwich panel under harmonic excitation by use of topology optimization. In this study, structural dynamic responses to harmonic excitation are discussed and formulations of the dynamic response in terms of strain and kinetic energy densities are derived. Topology optimization with multi-fractional volume constraint is conducted for multi-cellular core design to minimize the dynamic response of the sandwich panel under harmonic excitation. The optimization to minimize or maximize the dynamic responses are discussed in optimal core designs of sandwich panels. A multi-objective optimisation problem is also considered to optimally suppress harmonic vibrations with a range of several frequencies. Numerical examples are presented to validate the derived formulations and to optimally design multi-cellular cores for sandwich panels to achieve better dynamic performance.
Alghamdi, K & Braun, R 2020, 'Software Defined Network (SDN) and OpenFlow Protocol in 5G Network', Communications and Network, vol. 12, no. 01, pp. 28-40.
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The world is moving at a high speed in the implementation and innovations of new systems and gadgets. 3G and 4G networks support currently wireless network communications. However, the networks are deemed to be slow and fail to receive signals or data transmission to various regions as a result of solving the problem. This paper will analyze the use of Software Defined Network (SDN) in a 5G (fifth generation) network that can be faster and reliable. Further, in Mobile IP, there exist triangulation problems between the sending and receiving nodes along with latency issues during handoff for the mobile nodes causing huge burden in the network. With Cloud Computing and ecosystem for Virtualization developed for the Core and Radio Networks SDN OpenFlow seems to be a seamless solution for determining signal flow between mobiles. There have been a lot of researches going on for deploying SDN OpenFlow with the 5G Cellular Network. The current paper performs benchmarks as a feasibility need for implementing SDN OpenFlow for 5G Cellular Network. The Handoff mechanism impacts the scalability required for a cellular network and simulation results can be further used to be deployed the 5G Network.
Al-Hadhrami, Y & Hussain, FK 2020, 'Real time dataset generation framework for intrusion detection systems in IoT', Future Generation Computer Systems, vol. 108, pp. 414-423.
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© 2020 The Internet of Things (IoT) has evolved in the last few years to become one of the hottest topics in the area of computer science research. This drastic increase in IoT applications across different disciplines, such as in health-care and smart industries, comes with a considerable security risk. This is not limited only to attacks on privacy; it can also extend to attacks on network availability and performance. Therefore, an intrusion detection system is essential to act as the first line of defense for the network. IDS systems and algorithms depend heavily on the quality of the dataset provided. Sadly, there has been a lack of work in evaluating and collecting intrusion detection system related datasets that are designed specifically for an IoT ecosystem. Most of the studies published focus on outdated and non-compatible datasets such as the KDD98 dataset. Therefore, in this paper, we aim to investigate the existing datasets and their applications for IoT environments. Then we introduce a real-time data collection framework for building a dataset for intrusion detection system evaluation and testing. The main advantages of the proposed dataset are that it contains features that are explicitly designed for the 6LoWPAN/RPL network, the most widely used protocol in the IoT environment.
Alhathal Alanezi, A, Altaee, A & Sharif, AO 2020, 'The effect of energy recovery device and feed flow rate on the energy efficiency of reverse osmosis process', Chemical Engineering Research and Design, vol. 158, pp. 12-23.
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© 2020 Institution of Chemical Engineers The energy requirements for reverse osmosis (RO) seawater desalination continue to be a major matter of debate. Previous studies have shown the dependence of optimum RO desalination energy on the RO recovery rate. However, they overlooked including the effect of Energy Recovery Device (ERD) and pretreatment on the power consumption. In this work, a computer model was used to analyze the energy requirements for RO desalination, taking into account the effect of ERD efficiencies and pretreatment. The specific power consumption (SPC) of the RO was found to increase with the increase of RO recovery rate when the ERD system was included. The optimum SPC became more dependent on the RO recovery rate when the pretreatment energy was added. The recovery for optimum desalination energy was 46%, 44%, and 40% for the RO system coupled with an ERD of 65%, 80%, and 95% efficiency, respectively. The results showed that RO process could be operated at lower recovery rate and still meet the projected desalination capacity by increasing the feed flow rate and coupling with high-efficiency ERD. A trivial decrease of the total desalination energy was achieved when the feed flow rate increased from 7 m3/h to 8 m3/h and recovery rate decreased from 46% to 44% by coupling the RO with an ERD of 95% efficiency. This suggests that the RO–ERD system can be operated at a high feed flow rate and low recovery rate without affecting the plant capacity.
Ali, A & Hawryszkiewycz, I 2020, 'A model for dynamic alliance networks', Knowledge and Process Management, vol. 27, no. 4, pp. 280-293.
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Business networks are now becoming increasingly important in the business environment. Their nature is also changing because of the increasing complexity of the business environment, which now not only calls for exchange of resources but also to create new knowledge to address emerging issues and improve the products and services of network partners. Developing new knowledge often calls for changes in network membership, and in changes the way the network operates. In many cases, specialized knowledge is transient in nature; it is often required quickly and hence needs to be quickly developed. The result is that business networks are becoming increasingly dynamic in nature as they require to continually adapt to changes in the business environment often by change of membership and what members do. There are, however, many ways to organize a business network. This paper describes a study of a large number of business network arrangements found in practice. The paper then identifies common characteristics of networks and develops a model that can be used to classify business networks in terms of these characteristics. The model defines levels that distinguish between organizational and operational levels now increasingly used in creative organizations. The levels are defined in terms of common characteristics that include governance, collaboration, knowledge, and privacy. The paper then describes ways to model networks in terms of the common characteristics and verified implementation of the model. Businesses wishing to set up a business network can describe it in these characteristics and convert it to a computer support system. It shows how to organize networks where strategic planning, team management, information and knowledge systems, and CoDesign can be combined in flexible ways to meet emerging needs.
Ali, A, Syed, SM, Jamaluddin, MFB, Colino-Sanguino, Y, Gallego-Ortega, D & Tanwar, PS 2020, 'Cell Lineage Tracing Identifies Hormone-Regulated and Wnt-Responsive Vaginal Epithelial Stem Cells', Cell Reports, vol. 30, no. 5, pp. 1463-1477.e7.
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Ali, H, Afzal, MU, Esselle, KP & Hashmi, RM 2020, 'Integration of Geometrically Different Elements to Design Thin Near-Field Metasurfaces', IEEE Access, vol. 8, pp. 225336-225346.
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Phase-gradient metasurfaces, also known as phase-shifting surfaces, are used to steer the beam of medium-to-high gain antennas. Almost all such surfaces are made of cell elements that are similar in shape and only differ in dimensional parameters to achieve the required spatial phase gradient. A limitation of using the same geometry for the cell elements is that only limited phase shift range can be achieved while maintaining high transmission through each cell. A new strategy of integrating geometrically different cell elements, having different transmission phase and amplitude characteristics, is presented in this article. To demonstrate the concept, four different cell geometries are considered. The results indicate that the hybrid approach allows the designer to achieve the required phase shift range together with a high transmission with thinner metasurfaces having fewer dielectric and metal layers. When used to steer the beam of a microstrip patch array, the hybrid metasurface produced more accurate beam steering with 1.6° less steering error compared to a reference single-geometry metasurface
Ali, SM, Qamar, A, Phuntsho, S, Ghaffour, N, Vrouwenvelder, JS & Shon, HK 2020, 'Conceptual design of a dynamic turbospacer for efficient low pressure membrane filtration', Desalination, vol. 496, pp. 114712-114712.
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Ali, SMN, Hossain, MJ, Wang, D, Lu, K, Rasmussen, PO, Sharma, V & Kashif, M 2020, 'Robust Sensorless Control Against Thermally Degraded Speed Performance in an IM Drive Based Electric Vehicle', IEEE Transactions on Energy Conversion, vol. 35, no. 2, pp. 896-907.
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© 1986-2012 IEEE. This article investigates and proposes an efficient control design to address the degradation in the mechanical speed of a traction machine drive (TMD) in an electric vehicle (EV) caused by thermal effects during its operation. Variations in the operating as well as ambient temperature cause unexpected uncertainties in TMD parameters such as stator and rotor resistances, which results in significant degradation in EV's speed performance capability. To mitigate this problem, an output feedback robust linear parameter varying (LPV) controller-observer set is designed using H$ control theory that enhances the EV's speed performance in field-oriented control (FOC) frame. The internal stability of the closed-loop control and the $L_{2}$ gain bound are ensured by linear matrix inequalities. The performance of the proposed control technique is compared with that of conventional FOC, sliding mode control (SMC) and higher order sliding mode control (HOSMC) to validate its efficacy and advantages. The robustness of the proposed control technique is tested for an EV operation against the Worldwide Harmonised Light Vehicles Test Procedure (WLTP) Class 3 driving cycle. The nonlinear MATLAB simulation results guarantee the effectiveness of the proposed controller-observer set. These results are verified experimentally on an induction machine drive setup.
ALIBEIKLOO, M, ISFAHANI, HS & KHABBAZ, H 2020, 'EFFECT OF SURCHARGE HEIGHT AND PRELOADING TIME ON LONG-TERM SETTLEMENT OF CLOSED LANDFILLS: A NUMERICAL ANALYSIS', WIT Transactions on Ecology and the Environment, vol. 1, pp. 81-92.
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In recent years, by developing cities and increasing population, reconstructing on closed landfill sites is unavoidable in some regions. Long-term settlement is one of the major concerns associated with reconstruction on landfills after closure. The purpose of this research is evaluating the effect of preloading in various patterns of height and time on long-term settlements of closed landfills. In this regard, five scenarios of surcharge from 1 to 3 m high within 3, 4.5 and 6 months of preloading time have been modelled using PLAXIS 2D software. Moreover, the numerical results have been compared to those obtained from analytical methods, and a good agreement has been achieved. The findings indicate that there is a linear relationship between settlement and surcharge height. Although, long-term settlement decreased by applying a longer and higher preloading, the time of preloading was found to be a more effective factor compared to preloading height.
Aljabali, AAA, Bakshi, HA, Hakkim, FL, Haggag, YA, Al-Batanyeh, KM, Zoubi, MSA, Al-Trad, B, Nasef, MM, Satija, S, Mehta, M, Pabreja, K, Mishra, V, Khan, M, Abobaker, S, Azzouz, IM, Dureja, H, Pabari, RM, Dardouri, AAK, Kesharwani, P, Gupta, G, Dhar Shukla, S, Prasher, P, Charbe, NB, Negi, P, Kapoor, DN, Chellappan, DK, Webba da Silva, M, Thompson, P, Dua, K, McCarron, P & Tambuwala, MM 2020, 'Correction: Aljabali, A.A.A.; et al. Albumin Nano-Encapsulation of Piceatannol Enhances Its Anticancer Potential in Colon Cancer via down Regulation of Nuclear p65 and HIF-1α. Cancers 2020, 12, 113', Cancers, vol. 12, no. 12, pp. 3587-3587.
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The authors wish to make the following corrections to this paper [...]
Aljabali, AAA, Bakshi, HA, Satija, S, Metha, M, Prasher, P, Ennab, RM, Chellappan, DK, Gupta, G, Negi, P, Goyal, R, Sharma, A, Mishra, V, Dureja, H, Dua, K & Tambuwala, MM 2020, 'COVID-19: Underpinning Research for Detection, Therapeutics, and Vaccines Development', Pharmaceutical Nanotechnology, vol. 8, no. 4, pp. 323-353.
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Objectives:The newly emerged coronavirus SARS-CoV-2, first reported in December 2019, has infected about five and a half million people globally and resulted in nearly 9063264 deaths until the 24th of June 2020. Nevertheless, the highly contagious virus has instigated an unimaginably rapid response from scientific and medical communities.Methods:Pioneering research on molecular mechanisms underlying the viral transmission, molecular pathogenicity, and potential treatments will be highlighted in this review. The development of antiviral drugs specific to SARS-CoV-2 is a complicated and tedious process. To accelerate scientific discoveries and advancement, researchers are consolidating available data from associated coronaviruses into a single pipeline, which can be readily made available to vaccine developers.Results:In order to find studies evaluating the COVID-19 virus epidemiology, repurposed drugs and potential vaccines, web searches and bibliographical bases have been used with keywords that matches the content of this review.Lay Summary:An innovative analysis is evaluating the nature of the COVID-19 pandemic. The aim is to increase knowledge of possible viral detection methods, which highlights several new technology limitations and advantages. We have assessed some drugs currently for patients (Lopinavir, Ritonavir, Anakinra and Interferon beta 1a), as the feasibility of COVID-19 specific antivirals is not presently known. The study explores the race toward vaccine development and highlights some significant trials and candidates in various clinical phases. This research addresses critical knowledge gaps by identifying repurposed drugs currently under clinical trials. Findings will be fed back rapidly to the researchers...
Al-Jubainawi, A, Ma, Z, Guo, Y & Nghiem, LD 2020, 'Effect of regulating main governing factors on the selectivity membranes of electrodialysis used for LiCl liquid desiccant regeneration', Journal of Building Engineering, vol. 28, pp. 101022-101022.
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Allioux, F-M, Merhebi, S, Ghasemian, MB, Tang, J, Merenda, A, Abbasi, R, Mayyas, M, Daeneke, T, O’Mullane, AP, Daiyan, R, Amal, R & Kalantar-Zadeh, K 2020, 'Bi–Sn Catalytic Foam Governed by Nanometallurgy of Liquid Metals', Nano Letters, vol. 20, no. 6, pp. 4403-4409.
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Metallic foams, with intrinsic catalytic properties, are critical for heterogeneous catalysis reactions and reactor designs. Market ready catalytic foams are costly and made of multimaterial coatings with large sub-millimeter open cells providing insufficient active surface area. Here we use the principle of nanometallurgy within liquid metals to prepare nanostructured catalytic metal foams using a low-cost alloy of bismuth and tin with sub-micrometer open cells. The eutectic bismuth and tin liquid metal alloy was processed into nanoparticles and blown into a tin and bismuth nanophase separated heterostructure in aqueous media at room temperature and using an indium brazing agent. The CO2 electroconversion efficiency of the catalytic foam is presented with an impressive 82% conversion efficiency toward formates at high current density of -25 mA cm-2 (-1.2 V vs RHE). Nanometallurgical process applied to liquid metals will lead to exciting possibilities for expanding industrial and research accessibility of catalytic foams.
Allison‐Logan, S, Fu, Q, Sun, Y, Liu, M, Xie, J, Tang, J & Qiao, GG 2020, 'From UV to NIR: A Full‐Spectrum Metal‐Free Photocatalyst for Efficient Polymer Synthesis in Aqueous Conditions', Angewandte Chemie, vol. 132, no. 48, pp. 21576-21580.
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AbstractPhoto‐mediation offers unparalleled spatiotemporal control over controlled radical polymerizations (CRP). Photo‐induced electron/energy transfer reversible addition–fragmentation chain transfer (PET‐RAFT) polymerization is particularly versatile owing to its oxygen tolerance and wide range of compatible photocatalysts. In recent years, broadband‐ and near‐infrared (NIR)‐mediated polymerizations have been of particular interest owing to their potential for solar‐driven chemistry and biomedical applications. In this work, we present the first example of a novel photocatalyst for both full broadband‐ and NIR‐mediated CRP in aqueous conditions. Well‐defined polymers were synthesized in water under blue, green, red, and NIR light irradiation. Exploiting the oxygen tolerant and aqueous nature of our system, we also report PET‐RAFT polymerization at the microliter scale in a mammalian cell culture medium.
Allison‐Logan, S, Fu, Q, Sun, Y, Liu, M, Xie, J, Tang, J & Qiao, GG 2020, 'From UV to NIR: A Full‐Spectrum Metal‐Free Photocatalyst for Efficient Polymer Synthesis in Aqueous Conditions', Angewandte Chemie International Edition, vol. 59, no. 48, pp. 21392-21396.
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AbstractPhoto‐mediation offers unparalleled spatiotemporal control over controlled radical polymerizations (CRP). Photo‐induced electron/energy transfer reversible addition–fragmentation chain transfer (PET‐RAFT) polymerization is particularly versatile owing to its oxygen tolerance and wide range of compatible photocatalysts. In recent years, broadband‐ and near‐infrared (NIR)‐mediated polymerizations have been of particular interest owing to their potential for solar‐driven chemistry and biomedical applications. In this work, we present the first example of a novel photocatalyst for both full broadband‐ and NIR‐mediated CRP in aqueous conditions. Well‐defined polymers were synthesized in water under blue, green, red, and NIR light irradiation. Exploiting the oxygen tolerant and aqueous nature of our system, we also report PET‐RAFT polymerization at the microliter scale in a mammalian cell culture medium.
Almasoud, AS, Hussain, FK & Hussain, OK 2020, 'Smart contracts for blockchain-based reputation systems: A systematic literature review', Journal of Network and Computer Applications, vol. 170, pp. 102814-102814.
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© 2020 Elsevier Ltd Reputation systems offer a medium where users can quantify the trustworthiness or reliability of individuals providing online services or products. In the past, researchers have used blockchain technology for reputation systems. Smart contracts are computer protocols which have the primary objective to supervise, implement, or validate performances or negotiations of contracts. However, through a systematic literature review, in this paper, we find that the existing literature has not proposed a framework that facilitates the interchangeable use of smart contracts for blockchain-based reputation systems. We adopt a systematic literature review from 30 relevant studies and the data from them were extracted before identifying the research gaps. As a solution to the research gaps, we propose the FarMed framework for creating an intelligent framework that will execute Ethereum smart contact-based reputation systems and develop reliable blockchain-based protocols for transferring reputation values from one provider to another. We briefly explain our proposed framework before concluding with our future work.
Almotairy, SM, Boostani, AF, Hassani, M, Wei, D & Jiang, ZY 2020, 'Effect of hot isostatic pressing on the mechanical properties of aluminium metal matrix nanocomposites produced by dual speed ball milling', Journal of Materials Research and Technology, vol. 9, no. 2, pp. 1151-1161.
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© 2019 The Authors. In this study a suggested model for flake powder metallurgy were implemented and its mechanism was explained. The suggested model includes dual-speed ball milling (DSBM) to take the advantage of the low-speed and high-speed ball milling (LSBM and HSBM). The modelled process was utilised to uniformly disperse SiC nanoparticles into aluminium metal matrix to produce nanocomposites. The produced mixed powder was hot isostatically pressed. The effects of processing parameters such as stearic acid content, SiC volume content, ball milling speed and time on the microstructure and consequently tensile properties of the manufactured composites have been investigated experimentally to optimise the processing parameters bringing about the enhanced tensile properties of the fabricated composites. The results showed that the implementation of LSBM and HSBM processes can be considered as a unique strategy, i.e. the dual-speed ball milling (DSBM), for uniform dispersion of SiC nanoparticles associated with perfect bonding.
Alosime, EM, Alshahrani, AA, Nghiem, LD & in het Panhuis, M 2020, 'The preparation and characterization of buckypaper made from carbon nanotubes impregnated with chitosan', Polymer Composites, vol. 41, no. 4, pp. 1393-1404.
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AbstractBiopolymer chitosan was incorporated into a thin multiwalled carbon nanotube membrane (MWNT buckypaper) via filtration and soaking in 0.1% (w/v) of low‐molecular‐weight (MW) chitosan. The properties of the buckypaper membrane before and after annealing and after soaking were characterized by measurement of their electrical conductivities (19 ± 2 to 42 ± 2 S/cm), contact angles (31 ± 4° to 71 ± 4°), and mechanical properties (tensile strength, small‐ranging between 1.4 ± 0.1 and 4.2 ± 0.7 MPa; Young's modulus: 85 ± 4 to 443 ± 20 MPa). Moreover, the morphological properties, surface area, and permeability toward water of these buckypaper membranes were characterized and compared with corresponding carbon nanotube membranes prepared with Triton X‐100 (Trix) as the surfactant. Scanning electron microscopic (SEM) images and Brunauer, Emmett, and Teller (BET) data of MWNT‐annealing buckypaper membranes revealed that the diameters of their surface pores were significantly higher than that of the corresponding buckypaper membranes soaked in chitosan solution. The solution of chitosan incorporated inside the porous structure of the annealed MWNT membrane led to a significantly reduced surface area and pore size distribution of the composite membrane, revealing that this could be a useful method for desalination.
Alshahrani, AA, Algamdi, MS, Alsohaimi, IH, Nghiem, LD, Tu, KL, Al-Rawajfeh, AE & in het Panhuis, M 2020, 'The rejection of mono- and di-valent ions from aquatic environment by MWNT/chitosan buckypaper composite membranes: Influences of chitosan concentrations', Separation and Purification Technology, vol. 234, pp. 116088-116088.
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© 2019 Elsevier B.V. Owing to the scarcity of proper drinking water is an urgent problem, MWNT/Chitosan membrane is greeting to reject mono- and di-valent ions from water. MWNT/Chitosan membrane was fabricated through the dispersion of Multi-walled carbon nanotubes (MWNTs) in an aqueous solution containing different concentrations of chitosan. The influence of solution concentration on membrane salt rejection properties, as well as contact angle, electrical conductivity, water permeability, mechanical properties, zeta potential, surface area and internal pores morphologies has been investigated. The resulting buckypaper demonstrate that the contact angle (91° ± 4° to 124° ± 3°), electrical conductivity (17 ± 1 to 83 ± 3 S/cm), water permeability (0.59 ± 0.04 to 5.73 ± 0.3 L/m2 h bar), surface area and internal pores morphologies of the buckypaper membranes were decreased by increasing the concentration of chitosan. While, the mechanical properties (tensile strengths varied between 35 ± 2 and 75 ± 3 MPa) and zeta potential of these buckypaper membranes were found to increase with increasing the amounts of chitosan. A buckypaper fabricated from MWNTs and a high concentration of chitosan (0.4% w/v) showed a higher rejection efficiency for these salts, possibly due to their smaller internal pore volumes and lower specific surface area.
Alshahrani, AA, Alsohaimi, IH, Alshehri, S, Alawady, AR, El-Aassar, MR, Nghiem, LD & Panhuis, MIH 2020, 'Nanofiltration membranes prepared from pristine and functionalised multiwall carbon nanotubes/biopolymer composites for water treatment applications', Journal of Materials Research and Technology, vol. 9, no. 4, pp. 9080-9092.
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Al-Shatari, MOA, Hussin, FA, Aziz, AA, Witjaksono, G & Tran, X-T 2020, 'FPGA-Based Lightweight Hardware Architecture of the PHOTON Hash Function for IoT Edge Devices', IEEE Access, vol. 8, pp. 207610-207618.
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Alsheikh, MA, Hoang, DT, Niyato, D, Leong, D, Wang, P & Han, Z 2020, 'Optimal Pricing of Internet of Things: A Machine Learning Approach', IEEE Journal on Selected Areas in Communications, vol. 38, no. 4, pp. 669-684.
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© 1983-2012 IEEE. Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition.
Al-Shetwi, AQ, Hannan, MA, Jern, KP, Mansur, M & Mahlia, TMI 2020, 'Grid-connected renewable energy sources: Review of the recent integration requirements and control methods', Journal of Cleaner Production, vol. 253, pp. 119831-119831.
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© 2020 Elsevier Ltd The growing of renewable power generation and integration into the utility grid has started to touch on the security and stability of the power system operation. Hence, the grid integration requirements have become the major concern as renewable energy sources (RESs) such as wind and solar photovoltaic (PV) started to replace the conventional power plant slowly. In line with this, some of the new requirements and technical regulations have been established to ensure grid stability. This study aims to fill the gap and conduct an updating review of the recent integration requirements and compliance control methods regarding the penetration of renewable power plants to the power grid. The review is conducted by a comparing of the key requirements related to voltage stability, frequency stability, voltage ride-through (VRT), power quality, active and reactive power regulations towards grid stability. In order to fulfill these requirements, different control methods have been recently proposed. Accordingly, this paper compares and reviews the state-of-the-art solutions for compliance technology and control methods. Furthermore, a broad discussion on the global harmonization of the integration requirements, challenges, advantages and disadvantages is also highlighted. The rigorous review indicates that although the recent integration requirements can improve the grid operation, stability, security, and reliability, further improvements are still required with respect to protective regulations, global harmonization, and control optimization. Various recommendations for future research related to the integration and technical regulations of RESs are then presented. In sum, the insights provided by this review may aid the development of smooth and stable grid integration of RESs, help developers and researchers to develop the design and control strategies in the sense of current requirements. Additionally, assist power system operators in est...
Al-Soeidat, MR, Aljarajreh, H, Khawaldeh, HA, Lu, DD-C & Zhu, J 2020, 'A Reconfigurable Three-Port DC–DC Converter for Integrated PV-Battery System', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 8, no. 4, pp. 3423-3433.
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In this article, a new nonisolated three-port dc-dc converter to integrate a battery storage with a photovoltaic (PV) module is proposed for off-grid solar-power applications. The proposed converter can be used to integrate the PV module with a backup battery to minimize the impacts of renewable-energy intermittency and unpredictable load demand. The proposed converter is reconfigurable and able to operate as a conventional boost converter, a buck-boost converter, or a forward converter in different modes to support several power flow combinations and achieve power conditioning and regulation among the PV module, battery, and output port, simultaneously. Nevertheless, the power stage only consists of two switches, one coupled inductor, one diode, and two capacitors. A high-voltage conversion ratio is achieved by using a coupled inductor and by combining the PV module and the battery in series. Experimental results of the proposed converter operating in the steady state and during transitions between different modes are reported.
Altaee, A & AlZainati, N 2020, 'Novel Thermal Desalination Brine Reject-Sewage Effluent Salinity Gradient for Power Generation and Dilution of Brine Reject', Energies, vol. 13, no. 7, pp. 1756-1756.
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Salinity gradient resource presents an essential role for power generated in the process of pressure-retarded osmosis (PRO). Researchers proposed several designs for coupling the PRO process with the desalination plants, particularly reverse osmosis technology for low-cost desalination but there is no study available yet on the utilization of the concentrated brine reject from a thermal desalination plant. This study evaluates the feasibility of power generation in the PRO process using thermal plant brine reject-tertiary sewage effluent (TSE) salinity gradient resource. Power generation in the PRO process was determined for several commercially available FO membranes. Water flux in Oasys Forward Osmosis membrane was more than 31 L/m2h while the average water flux in the Oasys module was 17 L/m2h. The specific power generation was higher in the thin film composite (TFC) membranes compared to the cellulose triacetate (CTA) membranes. The specific power generation for the Oasys membrane was 0.194 kWh/m3, which is 41% of the maximum Gibbs energy of the brine reject-TSE salinity gradient. However, the Hydration Technology Innovation CTA membrane extracted only 0.133 kWh/m3 or 28% of Gibbs free energy of mixing for brine reject-TSE salinity gradient. The study reveals the potential of the brine reject-TSE salinity gradient resource for power generation and the dilution of brine reject.
Altaee, A, Khlaifat, N & Zhou, JL 2020, 'Assessment of wind energy potential at Yanco, New South Wales, Australia', International Journal of Industrial Electronics and Electrical Engineering, vol. 8, no. 1, pp. 26-30.
Altamish, M, Dahiya, R, Singh, AK, Mishra, A, Aljabali, AAA, Satija, S, Mehta, M, Dureja, H, Prasher, P, Negi, P, Kapoor, DN, Goyal, R, Tambuwala, MM, Chellappan, DK, Dua, K & Gupta, G 2020, 'Role of the Serine/Threonine Kinase 11 (STK11) or Liver Kinase B1 (LKB1) Gene in Peutz-Jeghers Syndrome', Critical Reviews in Eukaryotic Gene Expression, vol. 30, no. 3, pp. 245-252.
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Peutz-Jeghers syndrome (PJS) is a well-described inherited syndrome, characterized by the development of gastrointestinal polyps and characteristic mucocutaneous freckling. PJS is an autosomal prevailing disease, due to genetic mutation on chromosome 19p, manifested by restricted mucocutaneous melanosis in association with gastrointestinal (GI) polyposis. The gene for PJS has recently been shown to be a serine/threonine kinase, known as LKB1 or STK11, which maps to chromosome subband 19p13.3. This gene has a putative coding region of 1302 bp, divided into nine exons, and acts as a tumor suppressor in the hamartomatous polyps of PJS patients and in the other neoplasms that develop in PJS patients. It is probable that these neoplasms develop from hamartomas, but it remains possible that the LKB1 or STK11 locus plays a role in a different genetic pathway of tumor growth in the cancers of PJS patients. This article focuses on the role of LKB1 or STK11 gene expression in PJS and related cancers.
Altulyan, M, Yao, L, Kanhere, SS, Wang, X & Huang, C 2020, 'A unified framework for data integrity protection in people-centric smart cities', Multimedia Tools and Applications, vol. 79, no. 7-8, pp. 4989-5002.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. With the rapid increase in urbanisation, the concept of smart cities has attracted considerable attention. By leveraging emerging technologies such as the Internet of Things (IoT), artificial intelligence and cloud computing, smart cities have the potential to improve various indicators of residents’ quality of life. However, threats to data integrity may affect the delivery of such benefits, especially in the IoT environment where most devices are inherently dynamic and have limited resources. Prior work has focused on ensuring integrity of data in a piecemeal manner and covering only some parts of the smart city ecosystem. In this paper, we address integrity of data from an end-to-end perspective, i.e., from the data source to the data consumer. We propose a holistic framework for ensuring integrity of data in smart cities that covers the entire data lifecycle. Our framework is founded on three fundamental concepts, namely, secret sharing, fog computing and blockchain. We provide a detailed description of various components of the framework and also utilize smart healthcare as use case.
Alzoubi, YI & Gill, AQ 2020, 'An Empirical Investigation of Geographically Distributed Agile Development: The Agile Enterprise Architecture is a Communication Enabler.', IEEE Access, vol. 8, pp. 80269-80289.
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Amin, BMR, Taghizadeh, S, Rahman, MS, Hossain, MJ, Varadharajan, V & Chen, Z 2020, 'Cyber attacks in smart grid – dynamic impacts, analyses and recommendations', IET Cyber-Physical Systems: Theory & Applications, vol. 5, no. 4, pp. 321-329.
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Cyber attacks can cause cascading failures and blackouts in smart grids. Therefore, it is highly necessary to identify the types, impacts and solutions of cyber attacks to ensure the secure operation of power systems. As a well‐known practice, steady‐state analysis is commonly used to identify cyber attacks and provide effective solutions. However, it cannot fully cover non‐linear behaviours and cascaded blackouts of the system caused by dynamic perturbations, as well as provide a post‐disturbance operating point. This study presents a novel approach based on dynamic analysis that excludes the limitations of the steady‐state analysis and can be used in the events of various cyber attacks. Four types of common attacks are reviewed, and their dynamic impacts are shown on the IEEE benchmark model of the Western System Coordinating Council system implemented in MATLAB Simulink. Then, recommendations are provided to enhance the security of the future smart power grids from the possible cyber attacks.
Amin, DB, Tavakoli, J, Freeman, BJC & Costi, JJ 2020, 'Mechanisms of Failure Following Simulated Repetitive Lifting', Spine, vol. 45, no. 6, pp. 357-367.
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Study Design. A biomechanical analysis correlating internal disc strains and tissue damage during simulated repetitive lifting. Objective. To understand the failure modes during simulated safe and unsafe repetitive lifting. Summary of Background Data. Repetitive lifting has been shown to lead to lumbar disc herniation (LDH). In vitro studies have developed a qualitative understanding of the effect of repetitive loading on LDH. However, no studies have measured internal disc strains and subsequently correlated these with disc damage. Methods. Thirty human cadaver lumbar functional spinal units were subjected to an equivalent of 1 year of simulated repetitive lifting under safe and unsafe levels of compression, in combination with flexion (13–15°), and right axial rotation (2°) for 20,000 cycles or until failure. Safe or unsafe lifting were applied as a compressive load to mimic holding a 20 kg weight either close to, or at arm's length, from the body, respectively. Maximum shear strains (MSS) were measured, and disc damage scores were determined in nine regions from axial post-test magnetic resonance imaging (MRI) and macroscopic images. Results. Twenty percent of specimens in the safe lifting group failed before 20,000 cycles due to endplate failure, compared with 67% in the unsafe group. Over half of the specimens in the safe lifting group failed via either disc protrusion or LDH, compared with only 20% via protrusion ...
Amin, U, Hossain, MJ & Fernandez, E 2020, 'Optimal price based control of HVAC systems in multizone office buildings for demand response', Journal of Cleaner Production, vol. 270, pp. 122059-122059.
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Optimizing the scheduling of heating, ventilation, and air-conditioning (HVAC) systems in multizone buildings is a challenging task, as occupants in various zones have different thermal preferences dependent on time-varying indoor and outdoor environmental conditions and price signals. Price-based demand response (PBDR) is a powerful technique that can be used to handle the aggregated peak demand, energy consumption, and cost by controlling HVAC thermostat settings based on time-varying price signals. This paper proposes an intelligent and new PBDR control strategy for multizone office buildings fed from renewable energy sources (RESs) and/or utility grid to optimize the HVAC operation considering the varying thermal preferences of occupants in various zones as a response of real-time pricing (RTP) signals. A detailed mathematical model of a commercial building is presented to evaluate the thermal response of a multizone office building to the operation of an HVAC system. The developed thermal model considers all architectural and geographical effects to provide an accurate calculation of the HVAC load demand for analyses. Further, Occupants’ varying thermal preferences represented as a coefficient of a bidding price (chosen by the occupants) in response to price signals are modeled using an artificial neural network (ANN) and integrated into the optimal HVAC scheduling. Furthermore, a control mechanism is developed to determine the varying HVAC thermostat settings in various zones based on the ANN prediction model results. The effect of the proposed strategy on aggregator utility with wider implementation of the developed mechanism is also considered. The optimization problem for the proposed PBDR control strategy is formulated using a building's thermal model and an occupant's thermal preferences model, and simulation results are obtained using MATLAB/Simulink tool. The results indicate that the proposed strategy with realistic parameter settings show...
Amirbagheri, K, Merigó, JM, Guitart-Tarrés, L & Nuñez-Carballosa, A 2020, 'OWA operators in the calculation of the average green-house gases emissions', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5427-5439.
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Amiri, M, Tofigh, F, Shariati, N, Lipman, J & Abolhasan, M 2020, 'Wide-angle metamaterial absorber with highly insensitive absorption for TE and TM modes', Scientific Reports, vol. 10, no. 1.
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AbstractBeing incident and polarization angle insensitive are crucial characteristics of metamaterial perfect absorbers due to the variety of incident signals. In the case of incident angles insensitivity, facing transverse electric (TE) and transverse magnetic (TM) waves affect the absorption ratio significantly. In this scientific report, a crescent shape resonator has been introduced that provides over 99% absorption ratio for all polarization angles, as well as 70% and 93% efficiencies for different incident angles up to $$\theta =80^{\circ }$$θ=80∘ for TE and TM polarized waves, respectively. Moreover, the insensitivity for TE and TM modes can be adjusted due to the semi-symmetric structure. By adjusting the structure parameters, the absorption ratio for TE and TM waves at $$\theta =80^{\circ }$$θ=80∘ has been increased to 83% and 97%, respectively. This structure has been designed to operate at 5 GHz spectrum to absorb undesired signals generated due to the growing adoption of Wi-Fi networks. Finally, the proposed absorber has been fabricated in a $$20 \times 20$$20×20 arr...
Amjadipour, M, MacLeod, J, Motta, N & Iacopi, F 2020, 'Fabrication of free-standing silicon carbide on silicon microstructures via massive silicon sublimation', Journal of Vacuum Science & Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phenomena, vol. 38, no. 6, pp. 062202-062202.
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Heteroepitaxial thin films of cubic silicon carbide (3C-SiC) on silicon offer a promising platform for leveraging the properties of SiC, such as wide bandgap, high mechanical strength, and chemical stability on a silicon substrate. Such heteroepitaxial films also attract considerable interest as pseudosubstrates for the growth of GaN as well as graphene on silicon wafers. However, due to a substantial lattice mismatch, the growth of 3C-SiC on silicon leads to a considerable amount of stresses, defects, and diffusion phenomena at the heterointerface. We show here that the extent of such interface phenomena and stresses is so large that, after patterning of the SiC, a massive sublimation of the silicon underneath the SiC/Si interface is promoted via a high-temperature anneal, either in high or medium vacuum ambient. A micrometer-thick air gap can be formed below the SiC structures, making them suspended. Hence, the described approach can be used as a straightforward methodology to form free-standing silicon carbide structures without the need for wet or anisotropic etching and could be of great interest for devices where suspended moving parts are needed, such as micro- and nanoelectromechanical systems.
Amjadipour, M, Su, D & Iacopi, F 2020, 'Cover Picture: Graphitic‐Based Solid‐State Supercapacitors: Enabling Redox Reaction by In Situ Electrochemical Treatment (Batteries & Supercaps 7/2020)', Batteries & Supercaps, vol. 3, no. 7, pp. 566-566.
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Amjadipour, M, Su, D & Iacopi, F 2020, 'Graphitic‐Based Solid‐State Supercapacitors: Enabling Redox Reaction by In Situ Electrochemical Treatment', Batteries & Supercaps, vol. 3, no. 7, pp. 587-595.
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AbstractThe quest for supercapacitors that can hold both high energy and power density is of increasing significance as the need for green and reliable energy storage devices grows, for both large‐scale and integrated systems. While supercapacitors for integrated technologies require a solid‐state approach, gel‐based electrolytes are generally not as efficient as their aqueous counterparts. Here, we demonstrate a strategy to enhance the performance of quasi‐solid‐state supercapacitors made by graphitized silicon carbide on silicon electrodes and polyvinyl alcohol (PVA)+H2SO4 gel electrolyte. The electrochemical characterization shows an increase of the specific capacitance of the cell up to 3‐fold resulting from a simple agent‐free, in situ, electrochemical treatment leading to functionalization of the graphitic electrodes. The functionalization of the electrodes simultaneously enables redox reactions, without adding any redox agent, and increases the double layer contribution to the overall capacitance. The strategy and insights offered by this work hold great promise for improving quasi‐solid‐state, miniaturized on‐chip energy storage systems, which are compatible with silicon electronics.
Ang, L, Hellmann, A, Kanbaty, M & Sood, S 2020, 'Emotional and attentional influences of photographs on impression management and financial decision making', Journal of Behavioral and Experimental Finance, vol. 27, pp. 100348-100348.
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The use of photographs has become a key feature of corporate reporting in the last decades. As a form of impression management, photographs may be designed to influence investors’ judgments. Where a paucity of research on the use of photographs in corporate reports exists, this short communication discusses two important photographic features that can frame judgments — its ability to attract attention and convey emotions with simplicity. Usually, the non-creative content of photographs, such as size, is responsible for capturing our attention, while its creative content influences cognition and judgments through the elicitation of specific emotional responses. The use of photographs is now a norm and this letter will help open new avenues of behavioral research and methodologies.
Apostolopoulou, M, Asteris, PG, Armaghani, DJ, Douvika, MG, Lourenço, PB, Cavaleri, L, Bakolas, A & Moropoulou, A 2020, 'Mapping and holistic design of natural hydraulic lime mortars', Cement and Concrete Research, vol. 136, pp. 106167-106167.
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Arabameri, A, Asadi Nalivan, O, Chandra Pal, S, Chakrabortty, R, Saha, A, Lee, S, Pradhan, B & Tien Bui, D 2020, 'Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility', Remote Sensing, vol. 12, no. 17, pp. 2833-2833.
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The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help futu...
Arabameri, A, Asadi Nalivan, O, Saha, S, Roy, J, Pradhan, B, Tiefenbacher, JP & Thi Ngo, PT 2020, 'Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility', Remote Sensing, vol. 12, no. 11, pp. 1890-1890.
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Gully erosion has become one of the major environmental issues, due to the severity of its impact in many parts of the world. Gully erosion directly and indirectly affects agriculture and infrastructural development. The Golestan Dam basin, where soil erosion and degradation are very severe problems, was selected as the study area. This research maps gully erosion susceptibility (GES) by integrating four models: maximum entropy (MaxEnt), artificial neural network (ANN), support vector machine (SVM), and general linear model (GLM). Of 1042 gully locations, 729 (70%) and 313 (30%) gully locations were used for modeling and validation purposes, respectively. Fourteen effective gully erosion conditioning factors (GECFs) were selected for spatial gully erosion modeling. Tolerance and variance inflation factors (VIFs) were used to examine the collinearity among the GECFs. The random forest (RF) model was used to assess factors’ effectiveness and significance in gully erosion modeling. An ensemble of techniques can provide more accurate results than can single, standalone models. Therefore, we compared two-, three-, and four-model ensembles (ANN-SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, ANN-SVM-GLM, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM, MaxEnt-ANN-SVM and GLM-ANN-SVM-MaxEnt) for GES modeling. The susceptibility zones of the GESMs were classified as very-low, low, medium, high, and very-high using Jenks’ natural break classification method (NBM). Subsequently, the receiver operating characteristics (ROC) curve and the seed cell area index (SCAI) methods measured the reliability of the models. The success rate curve (SRC) and predication rate curve (PRC) and their area under the curve (AUC) values were obtained from the GES maps. The results show that the ANN model combined with two and three models are more accurate than the other combinations, but the ANN-SVM model had the highest accuracy. The rank of the others from best to worst accuracy ...
Arabameri, A, Blaschke, T, Pradhan, B, Pourghasemi, HR, Tiefenbacher, JP & Bui, DT 2020, 'Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study', Sensors, vol. 20, no. 2, pp. 335-335.
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Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.
Arabameri, A, Cerda, A, Pradhan, B, Tiefenbacher, JP, Lombardo, L & Bui, DT 2020, 'A methodological comparison of head-cut based gully erosion susceptibility models: Combined use of statistical and artificial intelligence', Geomorphology, vol. 359, pp. 107136-107136.
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Arabameri, A, Chen, W, Loche, M, Zhao, X, Li, Y, Lombardo, L, Cerda, A, Pradhan, B & Bui, DT 2020, 'Comparison of machine learning models for gully erosion susceptibility mapping', Geoscience Frontiers, vol. 11, no. 5, pp. 1609-1620.
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© 2019 China University of Geosciences (Beijing) and Peking University Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory, especially in the Northern provinces. A number of studies have been recently undertaken to study this process and to predict it over space and ultimately, in a broader national effort, to limit its negative effects on local communities. We focused on the Bastam watershed where 9.3% of its surface is currently affected by gullying. Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability. However, unlike the bivariate statistical models, their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors. To cope with such weakness, we interpret preconditioning causes on the basis of a bivariate approach namely, Index of Entropy. And, we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely, Alternating Decision Tree (ADTree), Naïve-Bayes tree (NBTree), and Logistic Model Tree (LMT). We dichotomized the gully information over space into gully presence/absence conditions, which we further explored in their calibration and validation stages. Being the presence/absence information and associated factors identical, the resulting differences are only due to the algorithmic structures of the three models we chose. Such differences are not significant in terms of performances; in fact, the three models produce outstanding predictive AUC measures (ADTree = 0.922; NBTree = 0.939; LMT = 0.944). However, the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns. This is a strong indication of what model combines best performance and mapping for any natural hazard – oriented application.
Arabameri, A, Pradhan, B & Bui, DT 2020, 'Spatial modelling of gully erosion in the Ardib River Watershed using three statistical-based techniques', CATENA, vol. 190, pp. 104545-104545.
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© 2020 Elsevier B.V. Gully erosion threatens land sustainability. Gullies trigger considerable erosion, damaging agricultural land, infrastructure and urban areas; thus, predicting and modelling gully susceptibility is of utmost concern. In particular, such a model is urgently required in semiarid areas where soil loss from gullies is high. Three predictive models are evaluated to assess gully erosion susceptibility mapping (GESM) in Semnan Province, Iran. The index of entropy (IOE), frequency ratio (FR) and certainty factor (CF) models are combined with remote sensing and geographic information system techniques to predict gully erosion. The collation of data from geographic resources identified 287 gullies in the study area. These areas were then randomly divided into 2 groups for calibration (70% or 201 gullies) and validation (30% or 86 gullies). Pairwise linear dependency amongst geoenvironmental factors was also assessed. A total of 16 factors were screened for modelling. Four performance metrics, namely, true skill statistic (TSS), area under the receiver operating characteristic (AUROC) curve, seed cell area index (SCAI) and modified SCAI (mSCAI), were used to evaluate the prediction accuracy and robustness of each model using validation datasets. Bootstrapped replicates were considered in estimating the accuracy and robustness of each model by varying gully/no-gully samples. The IOE results indicated that elevation, lithology and slope angle promoted favourable conditions for gully erosion in the study area. The results showed that the IOE model performed better than the FR and CF models for all three validation datasets (AUROCmean = 0.874 and TSSmean = 0.855). This finding was also confirmed in terms of stability and robustness (RTSS = 0.024 and RAUROC = 0.023). The SCAI and mSCAI results showed that all the models exhibited acceptable accuracy, but IOE demonstrated superior performance. Accordingly, IOE was used as the reference model for the study are...
Arabameri, A, Pradhan, B, Rezaei, K, Lee, S & Sohrabi, M 2020, 'An ensemble model for landslide susceptibility mapping in a forested area', Geocarto International, vol. 35, no. 15, pp. 1680-1705.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This article proposes a new methodological approach using a combination of expert knowledge-based (analytic hierarchy process, AHP), bivariate (statistical index, SI) and multivariate (linear discriminant analysis, LDA) models for landslide susceptibility mapping (LSM) in Mazandran Province, Iran. Tolerance and variance inflation factor indicators were used for assessing multi-collinearity among parameters, and three (i.e. profile curvature, soil type and topography wetness index) of 18 factors were eliminated because of multi-collinearity issues. Fifteen geo-environmental conditioning factors including elevation, slope degree, slope aspect, plan curvature, slope length, convergence index, stream power index, distance from river, drainage density, distance from road, distance from fault, lithology, rainfall, land use/landcover and normalized difference vegetation index and 321 landslide locations (testing data set, 70% of total landslides) were used for modeling. The importance of factors showed that distance to road (AHP = 0.201, LDA = 0.301) was the most important factor in landslide occurrence. The validation results using validation data set (138 landslide locations, 30% of total landslides) and area under the receiver operating characteristic curve (AUROC) showed that the ensemble models AHP-LDR (83%), AHP-SI (95%) and SI-LDR (83%) had higher prediction accuracies than the individual AHP (82%), SI (82%) and LDA (79%) models and combination of AHP and SI models along with ALOS-PALSAR remote sensing data and geographic information system (GIS) technique provide powerful tool in LSM in the study area. The results of proposed novel methodological framework can be used by decision-makers and forest engineers for forest management spatially forest roads conservation that have key importance in sustainable development in local and regional scales.
Arabameri, A, Saha, S, Chen, W, Roy, J, Pradhan, B & Bui, DT 2020, 'Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques', Journal of Hydrology, vol. 587, pp. 125007-125007.
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© 2020 Elsevier B.V. The present research aims to assess and judge the capability of flash flood susceptibility (FFS) models considering hybrid machine learning ensemble techniques for the FFS assessment in the Gorgan Basin in Iran. Three novel intelligence approaches, namely, bagging–functional tree (BFT), dagging–functional tree, and rotational forest–functional tree are used for modelling, with consideration to 15 flood conditioning factors (FCFs) as independent variables and 426 flood locations as dependent variables. Three threshold-dependent and -independent approaches are used to evaluate the goodness-of-fit and prediction capability of the ensemble models with a single functional tree (FT). These approaches include the area under the receiver operating characteristic curve of the success rate curve (SRC) and prediction rate curve (PRC), efficiency (E) and true skill statistics (TSS). The random forest model is used to determine the relative importance of FCFs. Elevation, stream distance and normalized difference vegetation index (NDVI) have crucial roles in the study area during flash flood occurrences. According to the results of all threshold-dependent and -independent approaches (AUC of SRC = 0.933, AUC of PRC = 0.959, E = 0.76 and TSS = 0.72), the BFT ensemble model has the greatest accuracy in terms of modelling FFS. Results also show that the performance of the FT model is enhanced by three meta-classifiers. The seed cell area index technique is also used to check model classification accuracy and reliability. Results of this technique show that all the models demonstrate good performance and reliability. However, the FFS maps prepared by machine learning ensemble techniques have excellent accuracy and reliability, as per the results of validation methods. Thus, these FFS maps can be used as a convenient tool to reduce the effect of flood in flash flood-prone areas.
Arabameri, A, Saha, S, Roy, J, Tiefenbacher, JP, Cerda, A, Biggs, T, Pradhan, B, Thi Ngo, PT & Collins, AL 2020, 'A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility', Science of The Total Environment, vol. 726, pp. 138595-138595.
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Arabameri, A, Tiefenbacher, JP, Blaschke, T, Pradhan, B & Tien Bui, D 2020, 'Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model', Remote Sensing, vol. 12, no. 5, pp. 874-874.
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The morphometric characteristics of the Kalvārī basin were analyzed to prioritize sub-basins based on their susceptibility to erosion by water using a remote sensing-based data and a GIS. The morphometric parameters (MPs)—linear, relief, and shape—of the drainage network were calculated using data from the Advanced Land-observing Satellite (ALOS) phased-array L-type synthetic-aperture radar (PALSAR) digital elevation model (DEM) with a spatial resolution of 12.5 m. Interferometric synthetic aperture radar (InSAR) was used to generate the DEM. These parameters revealed the network’s texture, morpho-tectonics, geometry, and relief characteristics. A complex proportional assessment of alternatives (COPRAS)-analytical hierarchy process (AHP) novel-ensemble multiple-criteria decision-making (MCDM) model was used to rank sub-basins and to identify the major MPs that significantly influence erosion landforms of the Kalvārī drainage basin. The results show that in evolutionary terms this is a youthful landscape. Rejuvenation has influenced the erosional development of the basin, but lithology and relief, structure, and tectonics have determined the drainage patterns of the catchment. Results of the AHP model indicate that slope and drainage density influence erosion in the study area. The COPRAS-AHP ensemble model results reveal that sub-basin 1 is the most susceptible to soil erosion (SE) and that sub-basin 5 is least susceptible. The ensemble model was compared to the two individual models using the Spearman correlation coefficient test (SCCT) and the Kendall Tau correlation coefficient test (KTCCT). To evaluate the prediction accuracy of the ensemble model, its results were compared to results generated by the modified Pacific Southwest Inter-Agency Committee (MPSIAC) model in each sub-basin. Based on SCCT and KTCCT, the ensemble model was better at ranking sub-basins than the MPSIAC model, which indicated that sub-basins 1 and 4, with mean sediment ...
Ardalan, RB, Emamzadeh, ZN, Rasekh, H, Joshaghani, A & Samali, B 2020, 'Physical and mechanical properties of polymer modified self-compacting concrete (SCC) using natural and recycled aggregates', Journal of Sustainable Cement-Based Materials, vol. 9, no. 1, pp. 1-16.
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The study researched the effectiveness of four polymer admixtures (3%, 5%, 10%, and 15% of water weight) on the fresh and hardened properties of self-compacting concrete (SCC) cast using recycled and natural aggregates. Results show that polymer additives had positive effects on the fresh properties of SCC using recycled aggregates. Incorporating polymer additives increased the filling ability of concrete by more than four times. All polymer modified SCCs had a 100% passing ability compared to the 80% passing ability of the control samples. The compressive strength of materials at similar polymer ratios decreased by about 50% when natural aggregates were replaced with recycled aggregates. The flexural strength of SCC including recycled aggregates with 15% polymer was maintained compared to the control SCC including natural aggregates. The addition of 15% polymer to recycled aggregates concrete could improve workability and maintain flexural strength.
Arjmandi, A, Peyravi, M, Arjmandi, M & Altaee, A 2020, 'Exploring the use of cheap natural raw materials to reduce the internal concentration polarization in thin-film composite forward osmosis membranes', Chemical Engineering Journal, vol. 398, pp. 125483-125483.
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© 2020 Elsevier B.V. Internal concentration polarization (ICP) is a significant problem in Forward osmosis (FO) membranes, which reduces the water flux. In order to mitigate the ICP phenomenon, rice bran (RB) and wood sawdust (WSD) particles were selected as natural green pore formers and incorporated into the polyethersulfone (PES) matrix to fabricate mixed matrix membranes (MMMs). Fabricated MMMs were used as the porous support layer (SL) to make thin-film composite (TFC) FO membranes. Firstly, the water uptake experiment was performed to evaluate the water adsorption capacity of the RB and WSD particles. Furthermore, all samples were characterized by FTIR, FESEM, AFM, XPS, DLS, static contact angle (CA), and tensile strength. Also, performance tests in reverse osmosis (RO) and the FO units were performed to evaluate the fabricated membranes. The results showed that the use of RB and WSD particles dramatically reduced the structural parameter in all MMMs, resulting in lower ICP effects and high water flux. Due to the softer structure, smaller size, and more water uptake, the RB-based TFC membranes recorded better results. The TFC-RB-5 (with 5% of RB in the SL) was the best membrane with a water flux of about 65.71 L/m2.h for Caspian seawater desalination, while the FO water flux for DI water as the feed solution (FS) was 83.65 L/m2.h. The present study showed the membranes made in this study are competitive with the existing FO membranes and very cost-effective for broad applications.
Arjmandi, M, Altaee, A, Arjmandi, A, Pourafshari Chenar, M, Peyravi, M & Jahanshahi, M 2020, 'A facile and efficient approach to increase the magnetic property of MOF-5', Solid State Sciences, vol. 106, pp. 106292-106292.
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© 2020 Elsevier Masson SAS In this study, a facile and efficient approach to increase the magnetic property of metal-organic framework-5 (MOF-5) has been investigated. The basis of this approach is the encapsulation of cluster-oxygen composition (i.e. ZnO in MOF-5) during the synthesis process of MOF-5 to form ZnO@MOF-5 nanocrystals. Both MOF-5 and ZnO@MOF-5 were synthesized for comparison purposes, considering their magnetic property. The physicochemical properties of MOF-5 and ZnO@MOF-5 were characterized by XRD, FTIR, TGA, DLS, FESEM, and Magnetization measurements. The FTIR spectra confirmed the presence of additional ZnO molecules in the ZnO@MOF-5 structure. Results from the XRD showed that the presence of additional ZnO molecules in the ZnO@MOF-5 altered the structure of MOF-5. The TGA analysis also confirmed the presence of additional ZnO molecules in the ZnO@MOF-5 structure, indicating that the ZnO@MOF-5 contains 15.23 wt% ZnO more than MOF-5. The FESEM and DLS results showed that the average sizes of MOF-5 and ZnO@MOF-5 nanocrystals are below 100 nm, with no defined morphology. Finally, the magnetization measurements showed that the MOF-5 nanocrystals have diamagnetic properties. For ZnO@MOF-5 nanocrystals, a ferromagnetic-like character was observed in the scanned field range and the saturation value of about 2.59 × 10−3 emu/g was obtained. The success of this facile and hassle-free approach can be an important step towards enhancing the magnetic properties of MOFs.
Arjmandi, M, Peyravi, M, Altaee, A, Arjmandi, A, Pourafshari Chenar, M, Jahanshahi, M & Binaeian, E 2020, 'A state-of-the-art protocol to minimize the internal concentration polarization in forward osmosis membranes', Desalination, vol. 480, pp. 114355-114355.
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© 2020 Elsevier B.V. The main reason for the lower water flux, than expected, in the forward osmosis (FO) process, is the internal concentration polarization (DICP). Usually, the structural parameter (S) is used as an indicator of the intensity of DICP. Small S value is desirable for the FO membrane due to the low DICP. However, due to design and construction problems, structural parameter reduction has some drawbacks. In this work, DICP reduction in FO membranes will be investigated using an approach other than structural parameter reduction. Accordingly, during the FO process, the feed solution (FS) valve is opened and closed at a constant period of time (feed valve timing (FVT)). Four types of FO membranes with different S parameters were used. The effects of the implementation of the proposed protocol on the water flux (Jw), reverse salt flux (Js), specific reverse solute flux (Js/Jw) and effective driving force were investigated. The effects of the S parameter and draw solution (DS) concentration also investigated separately. The results showed that the proposed protocol significantly increased Jw. Also, the values of Js/Jw decreased with increasing the FVT values and reached the lowest level in the practical recovery time (PRT).
Arjmandi, M, Pourafshari Chenar, M, Altaee, A, Arjmandi, A, Peyravi, M, Jahanshahi, M & Binaeian, E 2020, 'Caspian seawater desalination and whey concentration through forward osmosis (FO)-reverse osmosis (RO) and FO-FO-RO hybrid systems: Experimental and theoretical study', Journal of Water Process Engineering, vol. 37, pp. 101492-101492.
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Armaghani, DJ, Koopialipoor, M, Bahri, M, Hasanipanah, M & Tahir, MM 2020, 'A SVR-GWO technique to minimize flyrock distance resulting from blasting', Bulletin of Engineering Geology and the Environment, vol. 79, no. 8, pp. 4369-4385.
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Armaghani, DJ, Mirzaei, F, Shariati, M, Trung, NT, Shariati, M & Trnavac, D 2020, 'Hybrid ann-based techniques in predicting cohesion of sandy-soil combined with fiber', Geomechanics and Engineering, vol. 20, no. 3, pp. 191-205.
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Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices’ values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.
Armaghani, DJ, Mirzaei, F, Toghroli, A & Shariati, A 2020, 'Indirect measure of shear strength parameters of fiber-reinforced sandy soil using laboratory tests and intelligent systems', Geomechanics and Engineering, vol. 22, no. 5, pp. 397-414.
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In this paper, practical predictive models for soil shear strength parameters are proposed. As cohesion and internal friction angle are of essential shear strength parameters in any geotechnical studies, we try to predict them via artificial neural network (ANN) and neuro-imperialism approaches. The proposed models was based on the result of a series of consolidated undrained triaxial tests were conducted on reinforced sandy soil. The experimental program surveys the increase in internal friction angle of sandy soil due to addition of polypropylene fibers with different lengths and percentages. According to the result of the experimental study, the most important parameters impact on internal friction angle i.e., fiber percentage, fiber length, deviator stress, and pore water pressure were selected as predictive model inputs. The inputs were used to construct several ANN and neuro-imperialism models and a series of statistical indices were calculated to evaluate the prediction accuracy of the developed models. Both simulation results and the values of computed indices confirm that the newly-proposed neuro-imperialism model performs noticeably better comparing to the proposed ANN model. While neuro-imperialism model has training and test error values of 0.068 and 0.094, respectively, ANN model give error values of 0.083 for training sets and 0.26 for testing sets. Therefore, the neuro-imperialism can provide a new applicable model to effectively predict the internal friction angle of fiber-reinforced sandy soil.
Arodudu, O, Holmatov, B & Voinov, A 2020, 'Ecological impacts and limits of biomass use: a critical review', Clean Technologies and Environmental Policy, vol. 22, no. 8, pp. 1591-1611.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Conventional biomass sources have been widely exploited for several end uses (mostly food, feed, fuel and chemicals). More unconventional sources are continually being sought for meeting the growing planetary demands for biomass materials. Biofuels are already commercially produced in many countries and are becoming mainstream. The role of biorefineries for production of chemicals is also on the rise. Plant biomass is the primary source of food for all multicellular living organisms. Primary production remains a key link in the chain of life support on planet Earth. Is there enough for all? What new strategies (or technologies) are available or promising for providing plant biomass in a safe and sustainable way? What are the potential impacts (footprints and efficiencies) of such strategies? What can be the limiting factors—land, water, energy and nutrients? What might be the limits for specific regions (OECD vs. non-OECD, advanced vs. developing, dry and warm vs. wet and cool, etc.). In this paper, we provided answers to these questions by critically reviewing the pros and cons associated with current and future production and use pathways for biomass. We conclude that in many cases, the jury is still out, and we cannot come to a solid verdict about the future of biomass production and use.
Asadabadi, MR, Chang, E, Zwikael, O, Saberi, M & Sharpe, K 2020, 'Hidden fuzzy information: Requirement specification and measurement of project provider performance using the best worst method', Fuzzy Sets and Systems, vol. 383, pp. 127-145.
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© 2019 Elsevier B.V. The requirement specification process is an important part of a project and has the potential to prevent problems that may last for years after a project is delivered. Previous studies on the requirement specification process have focused on clarifying stated fuzzy terms in software requirement engineering. However, in many projects there is information that is not stated, but it is implied and can be inferred. This hidden information is usually ignored due to the assumption that ‘the provider understands what they mean/need’. This assumption is not always true. Such information, if extracted, may include fuzzy terms, namely hidden fuzzy terms (HFTs), which need specification. Therefore, these fuzzy terms have to be identified and then specified to avoid potential future consequences. This study proposes an algorithm to extract the hidden fuzzy terms, utilises a fuzzy inference system (FIS) to specify them, and applies the best worst multi-criteria decision making method (BWM) to evaluate the delivered product and measure the performance of the provider. The model is then used to examine a case from Defence Housing Australia. Such evaluation and measurement enable the project owner/manager to have a transparent basis to support decisions later in different phases of the project, and to ultimately reduce the likelihood of conflict and the receipt of an unsatisfactory product.
Asadabadi, MR, Saberi, M, Zwikael, O & Chang, E 2020, 'Ambiguous requirements: A semi-automated approach to identify and clarify ambiguity in large-scale projects', Computers & Industrial Engineering, vol. 149, pp. 106828-106828.
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Asadian, S, Mirzaei, H, Kalantari, BA, Davarpanah, MR, Mohamadi, M, Shpichka, A, Nasehi, L, Es, HA, Timashev, P, Najimi, M, Gheibi, N, Hassan, M & Vosough, M 2020, 'β-radiating radionuclides in cancer treatment, novel insight into promising approach', Pharmacological Research, vol. 160, pp. 105070-105070.
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Asadniaye Fardjahromi, M, Razmjou, A, Vesey, G, Ejeian, F, Banerjee, B, Chandra Mukhopadhyay, S & Ebrahimi Warkiani, M 2020, 'Mussel inspired ZIF8 microcarriers: a new approach for large-scale production of stem cells', RSC Advances, vol. 10, no. 34, pp. 20118-20128.
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Mussel inspired ZIF8 microcarriers with high surface area, biocompatibility, and nanoscale surface roughness are applied to enhance mesenchymal stem cell attachment and proliferation in 3D cell culture.
Ashcroft, L, Cobb, M, Bailey, L, Martin, J & Daniel, S 2020, 'The Australian Science Communicators conference 2020', Journal of Science Communication, vol. 19, no. 03, pp. C01-C01.
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This special issue of JCOM features six commentary articles from the research stream of the Australian Science Communicators conference, held in February 2020. These opportunistic assessments and deliberate analyses explore important themes of trust, engagement, and communication strategy across a diverse range of scientific contexts. Together, they demonstrate the importance of opportunities to come together and share the research that underpins our practice. The conference and these commentaries enable us to engage in professional development during these exceptional times when successful evidence-based science communication is of critical significance.
Ashok, B, Nanthagopal, K, Chyuan, OH, Le, PTK, khanolkar, K, Raje, N, Raj, A, Karthickeyan, V & Tamilvanan, A 2020, 'Multi-functional fuel additive as a combustion catalyst for diesel and biodiesel in CI engine characteristics', Fuel, vol. 278, pp. 118250-118250.
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© 2020 Elsevier Ltd The present research work aims at investigating the effect of newly developed multifunctional additive with diesel and Calophyllum Inophyllum biodiesel on compression ignition engine characteristics. A newly developed hydrocarbon based multifunctional fuel additive named as “Thermol-D” which comprises of various ingredients at suitable composition like surfactant, demulsifier, lubricity enhancer, dispersant, cetane improver, antioxidant and combustion catalyst. In this present study, the Thermol-D has been doped with conventional diesel and Calophyllum Inophyllum biodiesel at 0.5 ml, 1 ml and 2 ml concentrations. Moreover, the Thermol-D addition with diesel and biodiesel has shown remarkable stability at all concentrations without any phase separation issues. All the fuel comparative analysis is carried out using all the fuel samples at same operating conditions under load variation from No load to full load at constant engine speed. It has been noticed that the doping of Thermol-D with diesel and biodiesel has increased the brake thermal efficiency by 21% and 43% at 100% loading conditions due to the presence of combustion catalyst and cetane improver in the additive. The multifunctional additive presence in the fuel blends is reduced the carbon monoxide and unburnt hydrocarbon emissions by 32–36% and 20% respectively. Furthermore, the oxides of nitrogen emission has also reduced at significant rate in the range of 18–20.5% for 2% Thermol-D addition with diesel and biodiesel. The Thermol-D contains slight fraction of antioxidant and cetane improvers which has resulted in combustion temperature. All the combustion characteristics are improved by the addition of Thermol-D with diesel and biodiesel.
Ashrafian, A, Gandomi, AH, Rezaie-Balf, M & Emadi, M 2020, 'An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement', Measurement, vol. 152, pp. 107309-107309.
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© 2019 Elsevier Ltd The construction and maintenance of roads pavement was a critical problem in the last years. Therefore, the use of roller-compacted concrete pavement (RCCP) in road problems is widespread. The compressive strength (fc) is the key characteristic of the RCCP caused to significant impact on the cost of production. In this study, an evolutionary-based algorithm named gene expression programming (GEP) is implemented to propose novel predictive formulas for the fc of RCCP. The fc is formulated based on important factor used in mixture proportion in three different combinations of dimensional form (coarse aggregate, fine aggregate, cement, pulverized fly ash, water, and binder), non-dimensional form (water to cement ratio, water to binder ratio, coarse to fine aggregate ratio and pulverized fly ash to binder ratio) and percentage form of input variables. A comprehensive and reliable database incorporating 235 experimental cases collected from several studies. Furthermore, mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), average absolute error (AAE), performance index (PI), and objective function (OBJ) as the internal standard statistical measures and external validation evaluated proposed GEP-based models. Uncertainty and parametric studies were carried out to verify the results. Moreover, sensitivity analysis to determine the importance of each predictor on fc of RCCP revealed that fine aggregate content and water to binder ratio is the most useful predictor in dimensional, non-dimensional and percentage forms, respectively. The proposed equation-based models are found to be simple, robustness and straightforward to utilize, and provide consequently new formulations for fc of RCCP.
Atif, A, Richards, D, Liu, D & Bilgin, AA 2020, 'Perceived benefits and barriers of a prototype early alert system to detect engagement and support ‘at-risk’ students: The teacher perspective', Computers & Education, vol. 156, pp. 103954-103954.
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Awan, I, Younas, M & Hussain, F 2020, 'Emerging challenges and frontiers in cloud computing', Concurrency and Computation: Practice and Experience, vol. 32, no. 1.
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Ayanian, N, Robuffo Giordano, P, Fitch, R, Franchi, A & Sabattini, L 2020, 'Guest editorial: special issue on multi-robot and multi-agent systems', Autonomous Robots, vol. 44, no. 3-4, pp. 297-298.
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Azadi, M, Izadikhah, M, Ramezani, F & Hussain, FK 2020, 'A mixed ideal and anti-ideal DEA model: an application to evaluate cloud service providers', IMA Journal of Management Mathematics, vol. 31, no. 2, pp. 233-256.
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Abstract The rapid development of cloud computing and the sharp increase in the number of cloud service providers (CSPs) have resulted in many challenges in the suitability and selection of the best CSPs according to quality of service requirements. The main objective of this study is to propose three novel models based on the enhanced Russell model to increase the discrimination power in the evaluation and selection of CSPs. The proposed models are designed based on the distances to two special decision-making units (DMUs), namely the ideal DMU and the anti-ideal DMU. There are two advantages to the proposed ranking methods. First, they consider both pessimistic and optimistic scenarios of data envelopment analysis, so they are more equitable than methods that are based on only one of these scenarios. The second strength of this approach is its discrimination power, enabling it to provide a complete ranking for all CSPs. The proposed method can help customers to choose the most appropriate CSP while at the same time, it helps software developers to identify inefficient CSPs in order to improve their performance in the marketplace.
Azadi, S, Aboulkheyr Es, H, Kulasinghe, A, Bordhan, P & Ebrahimi Warkiani, M 2020, 'Application of microfluidic technology in cancer research and therapy', Advances in Clinical Chemistry, vol. 99, pp. 193-235.
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Cancer is a heterogeneous disease that requires a multimodal approach to diagnose, manage and treat. A better understanding of the disease biology can lead to identification of novel diagnostic/prognostic biomarkers and the discovery of the novel therapeutics with the goal of improving patient outcomes. Employing advanced technologies can facilitate this, enabling better diagnostic and treatment for cancer patients. In this regard, microfluidic technology has emerged as a promising tool in the studies of cancer, including single cancer cell analysis, modeling angiogenesis and metastasis, drug screening and liquid biopsy. Microfluidic technologies have opened new ways to study tumors in the preclinical and clinical settings. In this chapter, we highlight novel application of this technology in area of fundamental, translational and clinical cancer research.
Aziminezhad, M, Mardi, S, Hajikarimi, P, Moghadas Nejad, F & Gandomi, AH 2020, 'Loading rate effect on fracture behavior of fiber reinforced high strength concrete using a semi-circular bending test', Construction and Building Materials, vol. 240, pp. 117681-117681.
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© 2019 Elsevier Ltd Adding different types of fiber is one of the most common ways to enhance high strength concrete's mechanical behavior. In this paper, the effect of the loading rate and different type of fibers including glass, polypropylene, and steel were studied using the semi-circular bending (SCB) test method. It was evaluated that the SCB test can be used as a rapid and simple method to measure fracture properties of fiber reinforced high strength concrete (HSC) including ductility, energy absorption, and loading capacity by considering the effect of the loading rate on the parameters mentioned above. Specimens with glass fibers showed the most ductile behavior among all specimens with different types of fiber. On the other hand, steel fibers provided higher strength and higher energy absorption among the specimens. While specimens with steel fibers are highly sensitive to the loading rate in terms of peak load, this effect is not significant for specimens with glass and polypropylene fibers.
Azizivahed, A, Arefi, A, Ghavidel, S, Shafie-khah, M, Li, L, Zhang, J & Catalao, JPS 2020, 'Energy Management Strategy in Dynamic Distribution Network Reconfiguration Considering Renewable Energy Resources and Storage', IEEE Transactions on Sustainable Energy, vol. 11, no. 2, pp. 662-673.
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© 2010-2012 IEEE. Penetration of renewable energy sources (RESs) and electrical energy storage (EES) systems in distribution systems is increasing, and it is crucial to investigate their impact on systems' operation scheme, reliability, and security. In this paper, expected energy not supplied (EENS) and voltage stability index (VSI) of distribution networks are investigated in dynamic balanced and unbalanced distribution network reconfiguration, including RESs and EES systems. Furthermore, due to the high investment cost of the EES systems, the number of charge and discharge is limited, and the state-of-health constraint is included in the underlying problem to prolong the lifetime of these facilities. The optimal charging/discharging scheme for EES systems and optimal distribution network topology are presented in order to optimize the operational costs, and reliability and security indices simultaneously. The proposed strategy is applied to a large-scale 119-bus distribution test network in order to show the economic justification of the proposed approach.
Azizivahed, A, Razavi, S-E, Arefi, A, Ghadi, MJ, Li, L, Zhang, J, Shafie-khah, M & Catalao, JPS 2020, 'Risk-Oriented Multi-Area Economic Dispatch Solution With High Penetration of Wind Power Generation and Compressed Air Energy Storage System', IEEE Transactions on Sustainable Energy, vol. 11, no. 3, pp. 1569-1578.
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© 2010-2012 IEEE. This paper investigates the risk-oriented multi-area economic dispatch (MAED) problem with high penetration of wind farms (WFs) combined with compressed air energy storage (CAES). The main objective is to help system operators to minimize the operational cost of thermal units and CAES units with an appropriate level of security through optimized WF power generation curtailment strategy and CAES charging/discharging control. In the obtained MAED model, several WFs integrated with CAES units are considered in different generation zones, and the probability to meet demand by available spinning reserve during N - 1 security contingency is characterized as a risk function. Furthermore, the contribution of CAES units in providing the system spinning reserve is taken into account in the MAED model. The proposed framework is demonstrated by a case study using the modified IEEE 40-generator system. The numerical results reveal that the proposed method brings a significant advantage to the efficient scheduling of thermal units' power generation, WF power curtailment, and CAES charging/discharging control in the power system.
Azzam, R, Taha, T, Huang, S & Zweiri, Y 2020, 'Feature-based visual simultaneous localization and mapping: a survey', SN Applied Sciences, vol. 2, no. 2, p. 224.
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Baba, AA, Hashmi, RM, Esselle, KP, Attygalle, M & Borg, D 2020, 'A Millimeter-Wave Antenna System for Wideband 2-D Beam Steering', IEEE Transactions on Antennas and Propagation, vol. 68, no. 5, pp. 3453-3464.
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© 1963-2012 IEEE. This article presents a wideband beam-steering antenna system for high-mobility millimeter-wave (mm-wave) systems. It can provide both continuous and discrete beam steering in two dimensions (elevation and azimuth) at a speed that is sufficient for various applications including some in defense. The antenna is completely passive and beam steering is achieved using near-field phase transformation by employing a pair of distinct rotatable stepped-dielectric phase transformers (SPTs) placed in the near-field region of a fixed radiating source. The antenna system has a steering and impedance-matching bandwidth of 40.6% from 26.5 to 40 GHz. A prototype of the beam-steering antenna system including a mechanical system to rotate each of the SPTs around the antenna axis has been fabricated and tested. The rotating SPT pair introduces a predetermined phase gradient to the input near-field and creates an output near-field that will radiate in an arbitrarily selected direction, which can be varied within a large conical region with a maximum apex angle of 104°. The system exhibits predicted and measured peak gains of 21.5 and 21.25 dBi, respectively, and the measured gain variation over 2-D beam steering is less than 3 dB except at 36 and 39 GHz, where it rises to 3.6 and 3.1 dB, respectively. This beam steering method obviates the need for expensive phase shifters and distribution networks, which are also lossy at mm-wave frequencies. The measured results validate the predicted wideband matching and steering performance of the system with close agreement.
BAHARVAND, S, PARDHAN, B & SOORI, S 2020, 'Evaluation of active tectonics using geomorphic indices in a mountainous basin of Iran', Earth and Environmental Science Transactions of the Royal Society of Edinburgh, vol. 111, no. 2, pp. 109-117.
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ABSTRACTThis study aims to evaluate the tectonic activities of the Vark basin, located in the great basin of Dez River in northwestern Iran, using geomorphologic indices combined with the geographical information system technique. Some geomorphic indices were used to achieve this aim. In this regard, the indices of stream length (SL), drainage asymmetry (Af), hypsometric integral (Hi), valley floor ratio (Vf), basin shape (Bs), and mountain sinuosity (Smf) were estimated to reach an average index of relative tectonics (Iat), indicating the intensity classes of tectonic activity. The mean SL,Hi,Vf, andBsvalues were estimated as 2273, 0.55, 0.45, and 1.75, respectively, regarding the active class of tectonic activity. Therefore, considering theAfandSmfindices with values of 27 and 1.14, the basin was categorised as having semi-active conditions. The overallIat, with a value of 1.33, represented the very high class (1.0 <Iat< 1.5) of tectonic activity. Hence, by calculating the index of relative active tectonics, the study area is observed as the intensive class concerning tectonic movements. Overall, the mean values of theIatfor all sub-basins were calculated as 1.50, 1.17, and 1.83, revealing the very high and high classes of active tectonics in the basin. ...
Bai, L, Yao, L, Wang, X, Kanhere, SS, Guo, B & Yu, Z 2020, 'Adversarial Multi-view Networks for Activity Recognition', Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 4, no. 2, pp. 1-22.
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Human activity recognition (HAR) plays an irreplaceable role in various applications and has been a prosperous research topic for years. Recent studies show significant progress in feature extraction (i.e., data representation) using deep learning techniques. However, they face significant challenges in capturing multi-modal spatial-temporal patterns from the sensory data, and they commonly overlook the variants between subjects. We propose a Discriminative Adversarial MUlti-view Network (DAMUN) to address the above issues in sensor-based HAR. We first design a multi-view feature extractor to obtain representations of sensory data streams from temporal, spatial, and spatio-temporal views using convolutional networks. Then, we fuse the multi-view representations into a robust joint representation through a trainable Hadamard fusion module, and finally employ a Siamese adversarial network architecture to decrease the variants between the representations of different subjects. We have conducted extensive experiments under an iterative left-one-subject-out setting on three real-world datasets and demonstrated both the effectiveness and robustness of our approach.
Bai, X, Sun, B, Wang, X, Zhang, T, Hao, Q, Ni, B-J, Zong, R, Zhang, Z, Zhang, X & Li, H 2020, 'Defective crystal plane-oriented induced lattice polarization for the photocatalytic enhancement of ZnO', CrystEngComm, vol. 22, no. 16, pp. 2709-2717.
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The mechanism of the photocatalytic reaction of defective ZnO systems was determined.
Bai, X, Wang, X, Lu, X, Liang, Y, Li, J, Wu, L, Li, H, Hao, Q, Ni, B-J & Wang, C 2020, 'Surface defective g-C3N4−Cl with unique spongy structure by polarization effect for enhanced photocatalytic removal of organic pollutants', Journal of Hazardous Materials, vol. 398, pp. 122897-122897.
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Natural sponge is an ancient marine organism with a single lamellar structure, on which there are abundant porous channels to compose full-fledged spatial veins. Illumined by the natural spongy system, herein, the Cl doped surface defective graphite carbon nitride (g-C3N4-xClx) was constructed through microwave etching. In this process, microwave with HCl was employed to produce surface defects and peel bulk g-C3N4 to form natural spongy structured g-C3N4-xClx with three-dimensional networks. The spongy structure of the photocatalyst could provide abundant and unobstructed pathways for the transfer and separation of electron-hole pairs, and it was beneficial for photocatalytic reaction. The spongy defective g-C3N4-xClx achieved excellent degradation of diclofenac sodium (100%), bisphenol A (88.2%), phenol (85.7%) and methylene blue (97%) solution under simulated solar irradiation, respectively. The chlorine atoms were introduced into the g-C3N4 skeleton in microwave field with HCl, forming C-Cl bonds and surface polarization field, which could significantly accelerate the separation of photogenerated electrons and holes. As an efficient and universal approach, microwave etching can be generally used to create surface defects for most photocatalysts, which may have potential applications in environmental purification, energy conversion and photodynamic therapy.
Bai, X, Zhu, L, Liang, C, Li, J, Nie, X & Chang, X 2020, 'Multi-view feature selection via Nonnegative Structured Graph Learning', Neurocomputing, vol. 387, pp. 110-122.
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Graph-based solutions have achieved state-of-the-art performance on unsupervised multi-view feature selection. However, existing methods generally characterize the sample similarities first by constructing multiple fixed graphs with manually determined parameters, and then perform the feature selection on a composite one. They will suffer from two severe problems: (1) The fixed graphs may be unreliable as the raw multi-view features usually contain adverse noises and cannot accurately capture the intrinsic sample relations. (2) The graph construction and feature selection are separate and independent, the two-step learning may lead to sub-optimal performance. To tackle these problems, in this paper, we propose an effective unsupervised multi-view feature selection method, dubbed as Nonnegative Structured Graph Learning (NSGL). Specifically, we develop a unified learning framework, which directly learns the structured graph from the raw features by imposing a rank constraint, and simultaneously performs adaptive feature selection with exploiting the complementarity of multi-view features. Besides, we introduce the pseudo label learning to extract the discriminative semantic information in unsupervised scenarios and steer the graph learning process. The informative features are finally selected by forcing the feature selection matrix to be sparse in rows with sparse regression. To solve the challenging optimization problem, we first transform the formulated problem into an equivalent one that can be tackled more easily, and then develop an efficient alternate optimization algorithm guaranteed with convergence to calculate the solution iteratively. Extensive experiments on several widely tested benchmarks demonstrate the superiority of NSGL compared with several state-of-the-art approaches.
Baidya, R, Aguilera, RP, Acuna, P, Geyer, T, Delgado, RA, Quevedo, DE & Mouton, HDT 2020, 'Enabling Multistep Model Predictive Control for Transient Operation of Power Converters', IEEE Open Journal of the Industrial Electronics Society, vol. 1, pp. 284-297.
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Bajan, S & Hutvagner, G 2020, 'RNA-Based Therapeutics: From Antisense Oligonucleotides to miRNAs', Cells, vol. 9, no. 1, pp. 137-137.
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The first therapeutic nucleic acid, a DNA oligonucleotide, was approved for clinical use in 1998. Twenty years later, in 2018, the first therapeutic RNA-based oligonucleotide was United States Food and Drug Administration (FDA) approved. This promises to be a rapidly expanding market, as many emerging biopharmaceutical companies are developing RNA interference (RNAi)-based, and RNA-based antisense oligonucleotide therapies. However, miRNA therapeutics are noticeably absent. miRNAs are regulatory RNAs that regulate gene expression. In disease states, the expression of many miRNAs is measurably altered. The potential of miRNAs as therapies and therapeutic targets has long been discussed and in the context of a wide variety of infections and diseases. Despite the great number of studies identifying miRNAs as potential therapeutic targets, only a handful of miRNA-targeting drugs (mimics or inhibitors) have entered clinical trials. In this review, we will discuss whether the investment in finding potential miRNA therapeutic targets has yielded feasible and practicable results, the benefits and obstacles of miRNAs as therapeutic targets, and the potential future of the field.
Bakhanova, E, Garcia, JA, Raffe, WL & Voinov, A 2020, 'Targeting social learning and engagement: What serious games and gamification can offer to participatory modeling', Environmental Modelling & Software, vol. 134, pp. 104846-104846.
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© 2020 Elsevier Ltd Serious games and gamification are useful tools for learning and sustaining long-term engagement in the activities that are not meant to be entertaining. However, the application of game design in the participatory modeling context remains fragmented and mostly limited to user-friendly interfaces, storytelling, and visualization for better representation of the simulation models. This paper suggests possible extensions of game design use for each stage of the participatory modeling process, aiming at better learning, communication among stakeholders, and overall engagement. The proposed extensions are based on the effects that different types of game-like applications bring to the aspects of social learning and the contribution of gamification to engagement, motivation, and enjoyment of some activities. We conclude that serious games and gamification have a high potential for improving the quality of the participatory modeling process, while also highlighting additional research that is needed for designing particular practical gamified applications in this context.
Bakshi, H, Zoubi, M, Faruck, H, Aljabali, A, Rabi, F, Hafiz, A, Al-Batanyeh, K, Al-Trad, B, Ansari, P, Nasef, M, Charbe, N, Satija, S, Mehta, M, Mishra, V, Gupta, G, Abobaker, S, Negi, P, Azzouz, I, Dardouri, A, Dureja, H, Prasher, P, Chellappan, D, Dua, K, Webba da Silva, M, Tanani, M, McCarron, P & Tambuwala, M 2020, 'Dietary Crocin is Protective in Pancreatic Cancer while Reducing Radiation-Induced Hepatic Oxidative Damage', Nutrients, vol. 12, no. 6, pp. 1901-1901.
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Pancreatic cancer is one of the fatal causes of global cancer-related deaths. Although surgery and chemotherapy are standard treatment options, post-treatment outcomes often end in a poor prognosis. In the present study, we investigated anti-pancreatic cancer and amelioration of radiation-induced oxidative damage by crocin. Crocin is a carotenoid isolated from the dietary herb saffron, a prospect for novel leads as an anti-cancer agent. Crocin significantly reduced cell viability of BXPC3 and Capan-2 by triggering caspase signaling via the downregulation of Bcl-2. It modulated the expression of cell cycle signaling proteins P53, P21, P27, CDK2, c-MYC, Cyt-c and P38. Concomitantly, crocin treatment-induced apoptosis by inducing the release of cytochrome c from mitochondria to cytosol. Microarray analysis of the expression signature of genes induced by crocin showed a substantial number of genes involved in cell signaling pathways and checkpoints (723) are significantly affected by crocin. In mice bearing pancreatic tumors, crocin significantly reduced tumor burden without a change in body weight. Additionally, it showed significant protection against radiation-induced hepatic oxidative damage, reduced the levels of hepatic toxicity and preserved liver morphology. These findings indicate that crocin has a potential role in the treatment, prevention and management of pancreatic cancer.
Baliarsingh, SK, Vipsita, S, Gandomi, AH, Panda, A, Bakshi, S & Ramasubbareddy, S 2020, 'Analysis of high-dimensional genomic data using MapReduce based probabilistic neural network', Computer Methods and Programs in Biomedicine, vol. 195, pp. 105625-105625.
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BACKGROUND:The size of genomics data has been growing rapidly over the last decade. However, the conventional data analysis techniques are incapable of processing this huge amount of data. For the efficient processing of high dimensional datasets, it is essential to develop some new parallel methods. METHODS:In this work, a novel distributed method is presented using Map-Reduce (MR)-based approach. The proposed algorithm consists of MR-based Fisher score (mrFScore), MR-based ReliefF (mrRefiefF), and MR-based probabilistic neural network (mrPNN) using a weighted chaotic grey wolf optimization technique (WCGWO). Here, mrFScore, and mrRefiefF methods are introduced for feature selection (FS), and mrPNN is implemented as an effective method for microarray classification. The proper choice of smoothing parameter (σ) plays a major role in the prediction ability of the PNN which is addressed using a novel technique namely, WCGWO. The WCGWO algorithm is used to select the optimal value of σ in PNN. RESULTS:These algorithms have been successfully implemented using the Hadoop framework. The proposed model is tested by using three large and one small microarray datasets, and a comparative analysis is carried out with the existing FS and classification techniques. The results suggest that WCGWO-mrPNN can outperform other methods for high dimensional microarray classification. CONCLUSION:The effectiveness of the proposed methods are compared with other existing schemes. Experimental results reveal that the proposed scheme is accurate and robust. Hence, the suggested scheme is considered to be a reliable framework for microarray data analysis. SIGNIFICANCE:Such a method promotes the application of parallel programming using Hadoop cluster for the analysis of large-scale genomics data, particularly when the dataset is of high dimension.
Ball, J, Qiuhua, L, Jingming, H, Bingyao, L, Yu, T, Hui, L & Liping, M 2020, 'Cause Analysis for a New Type of Devastating Flash Flood', Hydrology Research: an international journal, vol. 51, no. 1, pp. 1-16.
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This work introduces an unprecedented flash flood that resulted in nine casualties in Shimen Valley, China, 2015. Through field survey and numerical simulation, the causes of the disaster are systematically analyzed, finding that the intense storm, terrain features, and the large woody debris(LWD) played important roles. The intense storm induced fast runoff and, in turn, high discharges as a result of the steep catchment surfaces and channels. The flood flushed LWD and boulders downstream until blockage occurred in a contraction section, forming a debris lake. When the debris dam broke, a dam break wave rapidly propagated to the valley mouth, washing people away. After considering the disaster-inducing factors, measures for preventing similar floods are proposed. The analysis presented herein should help others manage flash floods in mountain areas.
Ball, JE 2020, 'An Assessment of Continuous Modeling for Robust Design Flood Estimation in Urban Environments', Frontiers in Earth Science, vol. 8, p. 124.
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© Copyright © 2020 Ball. Catchment management is a complex task that, over the past decade, has become increasingly important to urban communities. While there are many water related management issues, estimation of the magnitude and likelihood of flood events is one that remains a concern to many mangers of urban drainage systems. Data is an essential component of any approach for estimation of the magnitude and likelihood of design flood characteristics. This data can be obtained from catchment monitoring or catchment modeling with these data sources being complementary rather than competitive. However, the absence of monitored data in urban environments has resulted in the data being obtained predominantly from the use of catchment modeling. Numerous alternative approaches for catchment modeling have been developed; these approaches can be categorized as either single event or continuous models. The philosophical basis behind the use of a continuous modeling approach is the concept that the model predictions will replicate the data that would have been recorded if catchment monitoring were to be undertaken at that location and for the modeled catchment conditions. When using this philosophy, a modeler must determine when the predicted data suitably replicates the true data. Presented herein is an analysis of continuous and event modeling undertaken for design flood estimation in an urban catchment located in Sydney, Australia where monitored data is available to assess the utility of the catchment model. It will be shown that frequency analysis of the predicted flows from the continuous model more closely resemble the frequency analysis of the recorded data.
Balogun, A-L, Yekeen, ST, Pradhan, B & Althuwaynee, OF 2020, 'Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models', Remote Sensing, vol. 12, no. 7, pp. 1225-1225.
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Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and environmental dimensions of the coastal ecosystem. However, comprehensive assessment of oil spill impacts and selection of appropriate remediation approaches have been restricted due to reliance on laboratory experiments which offer limited area coverage and classification accuracy. Thus, this study utilizes multispectral Landsat 8-OLI remote sensing imagery and machine learning models to assess the impacts of oil spills on coastal vegetation and wetland and monitor the recovery pattern of polluted vegetation and wetland in a coastal city. The spatial extent of polluted areas was also precisely quantified for effective management of the coastal ecosystem. Using Johor, a coastal city in Malaysia as a case study, a total of 49 oil spill (ground truth) locations, 54 non-oil-spill locations and Landsat 8-OLI data were utilized for the study. The ground truth points were divided into 70% training and 30% validation parts for the classification of polluted vegetation and wetland. Sixteen different indices that have been used to monitor vegetation and wetland stress in literature were adopted for impact and recovery analysis. To eliminate similarities in spectral appearance of oil-spill-affected vegetation, wetland and other elements like burnt and dead vegetation, Support Vector Machine (SVM) and Random Forest (RF) machine learning models were used for the classification of polluted and nonpolluted vegetation and wetlands. Model optimization was performed using a random search method to improve the models’ performance, and accuracy assessments confirmed the effectiveness of the two machine learning models to identify, classify and quantify the area extent of oil pollution on coastal vegetation and wetland. Considering the harmonic mean (F1), overall accuracy (OA), User’s accuracy (UA), and producers’ accuracy (PA), both models have high accuracies. However, the...
Bandara, M & Rabhi, FA 2020, 'Semantic modeling for engineering data analytics solutions', Semantic Web, vol. 11, no. 3, pp. 525-547.
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Bao, T, Damtie, MM, Hosseinzadeh, A, Frost, RL, Yu, ZM, Jin, J & Wu, K 2020, 'Catalytic degradation of P-chlorophenol by muscovite-supported nano zero valent iron composite: Synthesis, characterization, and mechanism studies', Applied Clay Science, vol. 195, pp. 105735-105735.
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Bao, T, Damtie, MM, Hosseinzadeh, A, Wei, W, Jin, J, Phong Vo, HN, Ye, JS, Liu, Y, Wang, XF, Yu, ZM, Chen, ZJ, Wu, K, Frost, RL & Ni, B-J 2020, 'Bentonite-supported nano zero-valent iron composite as a green catalyst for bisphenol A degradation: Preparation, performance, and mechanism of action', Journal of Environmental Management, vol. 260, pp. 110105-110105.
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Bisphenol A (BPA) is a toxic environmental pollutant commonly found in wastewater. Using non-toxic materials and eco-friendly technology to remove this pollutant from wastewater presents multiple advantages. Treatment of wastewater with clay minerals has received growing interest because of the environment friendliness of these materials. Bentonite is a 2:1 layered phyllosilicate clay mineral that can support nano-metal catalysts. It can prevent the agglomeration of nano-metal catalysts and improve their activity. In this article, a green catalytic nano zero-valent iron/bentonite composite material (NZVI@bentonite) was synthesized via liquid-phase reduction. The average size of NZVI was approximately 40-50 nm. Good dispersion and low aggregation were observed when NZVI was loaded on the surface or embedded into the nanosheets of bentonite. Degradation of BPA, a harmful contaminant widely found in wastewater at relatively high levels, by NZVI@bentonite was then investigated and compared with that by pristine NZVI through batch Fenton-like reaction experiments. Compared with pristine NZVI and bentonite alone, the NZVI@bentonite showed a higher BPA degradation ratio and offered highly effective BPA degradation up to 450 mg/g in wastewater under optimum operating conditions. Adsorption coupled with the Fenton-like reaction was responsible for BPA degradation by NZVI@bentonite. This work extends the application of NZVI@bentonite as an effective green catalyst for BPA degradation in aqueous environments.
Barthe, G, Hsu, J, Ying, M, Yu, N & Zhou, L 2020, 'Relational proofs for quantum programs.', Proc. ACM Program. Lang., vol. 4, no. POPL, pp. 21:1-21:1.
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© 2020 Copyright held by the owner/author(s). Relational verification of quantum programs has many potential applications in quantum and post-quantum security and other domains. We propose a relational program logic for quantum programs. The interpretation of our logic is based on a quantum analogue of probabilistic couplings. We use our logic to verify non-trivial relational properties of quantum programs, including uniformity for samples generated by the quantum Bernoulli factory, reliability of quantum teleportation against noise (bit and phase flip), security of quantum one-time pad and equivalence of quantum walks.
Barzegarkhoo, R, Siwakoti, YP & Blaabjerg, F 2020, 'A New Switched-Capacitor Five-Level Inverter Suitable for Transformerless Grid-Connected Applications', IEEE Transactions on Power Electronics, vol. 35, no. 8, pp. 8140-8153.
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Bashir, MR, Gill, AQ, Beydoun, G & Mccusker, B 2020, 'Big Data Management and Analytics Metamodel for IoT-Enabled Smart Buildings.', IEEE Access, vol. 8, pp. 169740-169758.
Bashir, MR, Gill, AQ, Beydoun, G & McCusker, B 2020, 'Big Data Management and Analytics Metamodel for IoT-Enabled Smart Buildings.', IEEE Access, vol. 8, pp. 169740-169758.
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Big data management and analytics, in the context of IoT (Internet of Things)-enabled smart buildings, is a challenging task. It is a diffused and complex area of knowledge due to the diversity of IoT devices and the nature of data generated by the IoT devices. Many international bodies have developed metamodels for IoT-enabled ecosystems to allow knowledge sharing. However, these are often narrow in focus and deal with only the IoT aspects without taking into account the management and analytics of big data generated by the IoT devices. Hence, in this article we propose a metamodel for the Integrated Big Data Management and Analytics (IBDMA) framework for IoT-enabled smart buildings. The IBDMA Metamodel can be used to facilitate interoperability between existing big data management and analytics ecosystems deployed in smart buildings or other smart environments. We import the metamodel into a knowledge graph management tool and by considering a case study we validate the metamodel using this tool. The evaluation results demonstrate that IBDMA Metamodel is indeed suitable for its intended purpose.
Bayat, P, Monjezi, M, Rezakhah, M & Armaghani, DJ 2020, 'Artificial Neural Network and Firefly Algorithm for Estimation and Minimization of Ground Vibration Induced by Blasting in a Mine', Natural Resources Research, vol. 29, no. 6, pp. 4121-4132.
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Bednarik, R, Busjahn, T, Gibaldi, A, Ahadi, A, Bielikova, M, Crosby, M, Essig, K, Fagerholm, F, Jbara, A, Lister, R, Orlov, P, Paterson, J, Sharif, B, Sirkiä, T, Stelovsky, J, Tvarozek, J, Vrzakova, H & van der Linde, I 2020, 'EMIP: The eye movements in programming dataset', Science of Computer Programming, vol. 198, pp. 102520-102520.
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© 2020 A large dataset that contains the eye movements of N=216 programmers of different experience levels captured during two code comprehension tasks is presented. Data are grouped in terms of programming expertise (from none to high) and other demographic descriptors. Data were collected through an international collaborative effort that involved eleven research teams across eight countries on four continents. The same eye tracking apparatus and software was used for the data collection. The Eye Movements in Programming (EMIP) dataset is freely available for download. The varied metadata in the EMIP dataset provides fertile ground for the analysis of gaze behavior and may be used to make novel insights about code comprehension.
Behera, TM, Mohapatra, SK, Samal, UC, Khan, MS, Daneshmand, M & Gandomi, AH 2020, 'I-SEP: An Improved Routing Protocol for Heterogeneous WSN for IoT-Based Environmental Monitoring', IEEE Internet of Things Journal, vol. 7, no. 1, pp. 710-717.
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© 2014 IEEE. Wireless sensor networks (WSNs) is a virtual layer in the paradigm of the Internet of Things (IoT). It inter-relates information associated with the physical domain to the IoT drove computational systems. WSN provides an ubiquitous access to location, the status of different entities of the environment, and data acquisition for long-term IoT monitoring. Since energy is a major constraint in the design process of a WSN, recent advances have led to project various energy-efficient protocols. Routing of data involves energy expenditure in considerable amount. In recent times, various heuristic clustering protocols have been discussed to solve the purpose. This article is an improvement of the existing stable election protocol (SEP) that implements a threshold-based cluster head (CH) selection for a heterogeneous network. The threshold maintains uniform energy distribution between member and CH nodes. The sensor nodes are also categorized into three different types called normal, intermediate, and advanced depending on the initial energy supply to distribute the network load evenly. The simulation result shows that the proposed scheme outperforms SEP and DEEC protocols with an improvement of 300% in network lifetime and 56% in throughput.
Bejarbaneh, EY, Masoumnezhad, M, Armaghani, DJ & Pham, BT 2020, 'Design of robust control based on linear matrix inequality and a novel hybrid PSO search technique for autonomous underwater vehicle', Applied Ocean Research, vol. 101, pp. 102231-102231.
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Bellamy, J, Szemes, M, Melegh, Z, Dallosso, A, Kollareddy, M, Catchpoole, D & Malik, K 2020, 'Increased Efficacy of Histone Methyltransferase G9a Inhibitors Against MYCN-Amplified Neuroblastoma', Frontiers in Oncology, vol. 10, p. 818.
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Targeted inhibition of proteins modulating epigenetic changes is an increasingly important priority in cancer therapeutics, and many small molecule inhibitors are currently being developed. In the case of neuroblastoma (NB), a pediatric solid tumor with a paucity of intragenic mutations, epigenetic deregulation may be especially important. In this study we validate the histone methyltransferase G9a/EHMT2 as being associated with indicators of poor prognosis in NB. Immunological analysis of G9a protein shows it to be more highly expressed in NB cell-lines with MYCN amplification, which is a primary determinant of dismal outcome in NB patients. Furthermore, G9a protein in primary tumors is expressed at higher levels in poorly differentiated/undifferentiated NB, and correlates with high EZH2 expression, a known co-operative oncoprotein in NB. Our functional analyses demonstrate that siRNA-mediated G9a depletion inhibits cell growth in all NB cell lines, but, strikingly, only triggers apoptosis in NB cells with MYCN amplification, suggesting a synthetic lethal relationship between G9a and MYCN. This pattern of sensitivity is also evident when using small molecule inhibitors of G9a, UNC0638, and UNC0642. The increased efficacy of G9a inhibition in the presence of MYCN-overexpression is also demonstrated in the SHEP-21N isogenic model with tet-regulatable MYCN. Finally, using RNA sequencing, we identify several potential tumor suppressor genes that are reactivated by G9a inhibition in NB, including the CLU, FLCN, AMHR2, and AKR1C1-3. Together, our study underlines the under-appreciated role of G9a in NB, especially in MYCN-amplified tumors.
Belotti, Y, McGloin, D & Weijer, CJ 2020, 'Analysis of barotactic and chemotactic guidance cues on directional decision-making of Dictyostelium discoideum cells in confined environments', Proceedings of the National Academy of Sciences, vol. 117, no. 41, pp. 25553-25559.
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Significance Cells confined in complex environments use a combination of chemical and mechanical cues for robust pathfinding and effective migration. Analysis of directional “decision-making” of Dictyostelium discoideum cells migrating within microchannels harboring asymmetric bifurcations shows that unlike neutrophils and immature dendritic cells Dictyostelium cells use chemical rather than barotactic guidance cues. Cells in steeper adenosine 3′,5′-cyclic monophosphate gradients migrating at higher speeds split their leading edges more readily when confronted with a bifurcation in the channel. The point at which one of the competing pseudopods starts to retract appears to be dependent on a relative force imbalance between two competing pseudopods, showing that cellular mechanics plays a major role in leading-edge dynamics, including front splitting, polarization, and retraction in D. discoideum .
Bernhardt, N, Koshelev, K, White, SJU, Meng, KWC, Fröch, JE, Kim, S, Tran, TT, Choi, D-Y, Kivshar, Y & Solntsev, AS 2020, 'Quasi-BIC Resonant Enhancement of Second-Harmonic Generation in WS2 Monolayers', Nano Letters, vol. 20, no. 7, pp. 5309-5314.
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Atomically thin monolayers of transition metal dichalcogenides (TMDs) have emerged as a promising class of novel materials for optoelectronics and nonlinear optics. However, the intrinsic nonlinearity of TMD monolayers is weak, limiting their functionalities for nonlinear optical processes such as frequency conversion. Here we boost the effective nonlinear susceptibility of a TMD monolayer by integrating it with a resonant dielectric metasurface that supports pronounced optical resonances with high quality factors: bound states in the continuum (BICs). We demonstrate that a WS2 monolayer combined with a silicon metasurface hosting BICs exhibits enhanced second-harmonic intensity by more than 3 orders of magnitude relative to a WS2 monolayer on top of a flat silicon film of the same thickness. Our work suggests a pathway to employ high-index dielectric metasurfaces as hybrid structures for enhancement of TMD nonlinearities with applications in nonlinear microscopy, optoelectronics, and signal processing.
Beydoun, G, Hoffmann, A, Garcia, RV, Shen, J & Gill, A 2020, 'Towards an assessment framework of reuse: a knowledge-level analysis approach', Complex & Intelligent Systems, vol. 6, no. 1, pp. 87-95.
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Bharill, N, Tiwari, A, Malviya, A, Patel, OP, Gupta, A, Puthal, D, Saxena, A & Prasad, M 2020, 'Fuzzy knowledge based performance analysis on big data', Neurocomputing, vol. 389, pp. 218-228.
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© 2019 Elsevier B.V. Due to the various emerging technologies, an enormous amount of data, termed as Big Data, gets collected every day and can be of great use in various domains. Clustering algorithms that store the entire data into memory for analysis become unfeasible when the dataset is too large. Many clustering algorithms present in the literature deal with the analysis of huge amount of data. The paper discusses a new clustering approach called an Incremental Random Sampling with Iterative Optimization Fuzzy c-Means (IRSIO-FCM) algorithm. It is implemented on Apache Spark, a framework for Big Data processing. Sparks works really well for iterative algorithms by supporting in-memory computations, scalability, etc. IRSIO-FCM not only facilitates effective clustering of Big Data but also performs storage space optimization during clustering. To establish a fair comparison of IRSIO-FCM, we propose an incremental version of the Literal Fuzzy c-Means (LFCM) called ILFCM implemented in Apache Spark framework. The experimental results are analyzed in terms of time and space complexity, NMI, ARI, speedup, sizeup, and scaleup measures. The reported results show that IRSIO-FCM achieves a significant reduction in run-time in comparison with ILFCM.
Bian, Y, Wang, D, Liu, X, Yang, Q, Liu, Y, Wang, Q, Ni, B-J, Li, H & Zhang, Y 2020, 'The fate and impact of TCC in nitrifying cultures', Water Research, vol. 178, pp. 115851-115851.
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Triclocarban (TCC) is a highly effective antibacterial agent, which is widely used in a variety of applications and present at significant levels (e.g., 760 μg/L) in wastewater worldwide. However, the interaction between TCC and nitrifiers, important microbial cultures in wastewater treatment plants, has not been documented. This work therefore aimed to evaluate the fate of TCC in a nitrifying culture and its impact on nitrifiers in four long-term nitrifiers-rich reactors, which received synthetic wastewater containing 0, 0.1, 1, or 5 mg/L TCC. Experimental results showed that 36.7%-50.7% of wastewater TCC was removed by nitrifying cultures in stable operation. Mass balance analysis revealed that the removal of TCC was mainly achieved through adsorption rather than biodegradation. Adsorption kinetic analysis indicated that inhomogeneous multilayer adsorption was responsible for the removal while fourier transform infrared spectroscopy indicated that several functional groups such as hydroxyl, amide and polysaccharide seemed to be the main adsorption sites. The adsorbed TCC significantly deteriorated settleability and performance of nitrifying cultures. With an increase of influent TCC from 0 to 5 mg/L, reactor volatile suspended solids and effluent nitrate decreased from 1200 ± 90 mg/L and 300.81 ± 7.52 mg/L to 880 ± 80 and 7.35 ± 4.62 mg/L while effluent ammonium and nitrite increased from 0.41 ± 0.03 and 0.45 ± 0.23 mg/L to104.65 ± 3.46 and 182.06 ± 7.54 mg/L, respectively. TCC increased the extracellular polymeric substances of nitrifying cultures, inhibited the specific activities of nitrifiers, and altered the abundance of nitrifiers especially Nitrospira sp.. In particular, TCC at environmentally relevant concentration (i.e., 0.1 mg/L) significantly inhibited NOB activity and reduced NOB population.
Biloria, N, Reddy, P, Fatimah, YA & Mehta, D 2020, 'Urban Wellbeing in the Contemporary City', HealthManagement, vol. 21, no. 6, pp. 317-335.
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This paper explores and debates the intricate connection between our built environment and an increasingly technocentric approach to distinguish health and wellbeing from a multidisciplinary perspective. The authors profess the dire need for rethinking the ‘smart’ within the city by reconsidering models of urban development and focusing on the democratisation of technology for the purpose of enhancing our lived urban experience and psychophysiological wellbeing.
Binh, NTM, Binh, HTT, Van Linh, N & Yu, S 2020, 'Efficient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks in internet of things', Applied Intelligence, vol. 50, no. 6, pp. 1889-1907.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. One of the well-known methods for evaluating Heterogeneous wireless multimedia sensor networks (HWMSNs) in Internet of Things have drawn attention of the research community because this type of networks possesses great advantages of both coverage and performance. One of the most fundamental issues in HWMSNs is the barrier coverage problem which evaluates the surveillance capability of the network systems, especially those designed for security purposes. Among multiple approaches to solve this issue, finding the minimal exposure path (MEP), which corresponds to the worst-case coverage of the network is the most popular and efficient way. However, the MEP problem in HWMSNs (hereinafter heterogeneous multimedia MEP or HM-MEP) is specifically complex and challenging with the unique features of the HWMSNs. Thus, the problem is then converted into numerical functional extreme with high dimension, non-differential and non-linearity. Adapting to these features, two efficient meta-heuristic algorithms, Hybrid Evolutionary Algorithm (HEA) and Gravitation Particle Swarm Optimization (GPSO) are proposed for solving the problem. The HEA is a hybrid evolutionary algorithm in combination with local search while the GPSO is a novel particle swarm optimization based on the gravity force theory. Experimental results on extensive instances indicate that the proposed algorithms are suitable for the HM-MEP problem and perform well in term of both solution accuracy and computation time compared to existing approaches.
Bird, TS 2020, 'Celebration and Dedication of an IEEE Milestone at Parkes, Australia [Historical Corner]', IEEE Antennas and Propagation Magazine, vol. 62, no. 6, pp. 85-90.
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Blamires, SJ 2020, 'Biomechanical costs and benefits of sit-and-wait foraging traps', Israel Journal of Ecology and Evolution, vol. 66, no. 1-2, pp. 5-14.
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AbstractTraps are rarely used by animals, despite the plausible benefits of broadening the number and diversity of prey that sit-and-wait foragers might be able to capture. The most well-known trap building sit-and-wait foragers are among the invertebrates, i.e. antlions, wormlions, glow worms, caddisflies, and spiders. A plausible hypothesis for the paucity of trap building by other animals is that biomechanical limitations render them inefficient or ineffective at catching sufficient prey. Here I examined the literature to make a valued judgement about the validity of this hypothesis. It appears that antlion and wormlion pit traps cannot catch and retain the largest prey they might expect to encounter. Arachnacampa glowworm traps are functionally efficient, facilitated by the animal’s bioluminescence. Nevertheless they only function in very moist or humid conditions. Caddisfly traps rely on flowing water to be able to capture their prey. Spiders are exceptional in developing a wide range of prey trapping strategies, from webs with dry adhesives, to sticky orb webs, to modified orb webs, e.g. elongated “ladder” webs, to webs with additional structures, and web aggregations. Some spiders have even redesigned their webs to minimize the high prey escape rates associated with web two dimensionality. These webs nevertheless are constructed and used at specific costs. While hard data across all of the invertebrate predators is lacking, there seems to be credence in the hypothesis that the biomechanical limitations placed on trap functionality can explain their limited use among animals.
Blamires, SJ, Little, DJ, White, TE & Kane, DM 2020, 'Photoreflectance/scattering measurements of spider silks informed by standard optics', Royal Society Open Science, vol. 7, no. 4, pp. 192174-192174.
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The silks of certain orb weaving spiders are emerging as high-quality optical materials. This motivates study of the optical properties of such silk and particularly the comparative optical properties of the silks of different species. Any differences in optical properties may impart biological advantage for a spider species and make the silks interesting for biomimetic prospecting as optical materials. A prior study of the reflectance of spider silks from 18 species reported results for three species of modern orb weaving spiders ( Nephila clavipes, Argiope argentata and Micrathena Schreibersi ) as having reduced reflectance in the UV range. (Modern in the context used here means more recently derived.) The reduced UV reflectance was interpreted as an adaptive advantage in making the silks less visible to insects. Herein, a standard, experimental technique for measuring the reflectance spectrum of diffuse surfaces, using commercially available equipment, has been applied to samples of the silks of four modern species of orb weaving spiders: Phonognatha graeffei , Eriophora transmarina , Nephila plumipes and Argiope keyserlingi . This is a different technique than used in the previous study. Three of the four silks measured have a reduced signal in the UV. By taking the form of the silks as optical elements into account, it is shown that this is attributable to a combination of wavelength-dependent absorption and scattering by the silks rather than differences in reflectance for the different silks. Phonognatha graeffei dragline silk emerges as a very interesting spider silk with a ...
Blanco-Mesa, F & Merigó, JM 2020, 'Retracted Article: Bonferroni Distances and Their Application in Group Decision Making', Cybernetics and Systems, vol. 51, no. 1, pp. 27-58.
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© 2019, © 2019 Taylor & Francis Group, LLC. The aim of the paper is to develop new aggregation operators using Bonferroni means, ordered weighted averaging (OWA) operators and some distance measures. We introduce the Bonferroni-Hamming weighted distance (BON-HWD), Bonferroni OWA distance (BON-OWAD), Bonferroni OWA adequacy coefficient (BON-OWAAC) and Bonferroni distances with OWA operators and weighted averages (BON-IWOWAD). The main advantages of using these operators are that they allow the consideration of different aggregations contexts to be considered and multiple comparison between each argument and distance measures in the same formulation. An application is developed using these new algorithms in combination with Pichat algorithm to solve a group decision-making problem. Creative personality is taken as an example for forming creative groups. The results show fuzzy dissimilarity relations in order to establish the maximum similarity subrelations and find groups according to each individual’s creative personality similarities.
Blanco-Mesa, F, León-Castro, E & Merigó, JM 2020, 'Covariances with OWA operators and Bonferroni means', Soft Computing, vol. 24, no. 19, pp. 14999-15014.
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Blazevski, A, Scheltema, MJ, Yuen, B, Masand, N, Nguyen, TV, Delprado, W, Shnier, R, Haynes, A-M, Cusick, T, Thompson, J & Stricker, P 2020, 'Oncological and Quality-of-life Outcomes Following Focal Irreversible Electroporation as Primary Treatment for Localised Prostate Cancer: A Biopsy-monitored Prospective Cohort', European Urology Oncology, vol. 3, no. 3, pp. 283-290.
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BACKGROUND:Focal irreversible electroporation (IRE) can be used to treat men with localised prostate cancer (PCa) with reduced impact on quality of life (QoL). OBJECTIVE:To assess oncological and functional outcomes. DESIGN, SETTING, AND PARTICIPANTS:To report on a prospective database of patients undergoing primary IRE between February 2013 and August 2018. A minimum of 12-mo follow-up was available for 123 patients. Median follow-up was 36 mo (interquartile range [IQR] 24-52 mo). A total of 112 (91%) patients had National Comprehensive Cancer Network intermediate risk and 11 (9%) had low risk. A total of 12 (9.8%) had International Society of Urological Pathology (ISUP) grade 1, 88 (71.5%) had ISUP 2, and 23 (18.7%) had ISUP 3. INTERVENTION:Focal IRE ablation of PCa lesions. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:Follow-up involved serial prostate-specific antigen (PSA), multiparametric magnetic resonance imaging (mpMRI), and transperineal template mapping biopsy (TTMB) at 12 mo. Failure-free survival (FFS) was defined as progression to whole-gland or systemic treatment or metastasis/death. Functional outcomes were assessed. RESULTS AND LIMITATIONS:Median age was 68yr (IQR 62-73yr). Median preoperative PSA was 5.7ng/ml (IQR 3.8-8.0ng/ml). On post-treatment TTMB, in-field recurrence was present in 2.7-9.8% of patients. FFS at 3yr was 96.75%, metastasis-free survival 99%, and overall survival 100%. A total of 18 patients required salvage treatment (12 had repeat IRE; six had whole-gland treatment). The negative predictive value of mpMRI was 94% and sensitivity 40% for detecting in-field residual disease 6 mo after treatment. Among patients who returned questionnaires, 80/81 (98.8%) remained pad free and 40/53 (76%) had no change in erectile function. CONCLUSIONS:Focal IRE in select patients with localised clinically significant PCa has satisfactory short-term oncological outcomes with a minimal impact on patient QoL. PATIENT SUMMARY:In this study...
Bobba, SS, Hamdouni, N, Pande, K, Namassivayane, K, Agrawal, A & Grattan, KTV 2020, 'Design and optimization of perovskite plasmonic nano-laser for operation at room temperature', Journal of Laser Applications, vol. 32, no. 2, pp. 022017-022017.
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This work presents the design and optimization of a cascade nano-laser using CH3NH3PbI3 perovskite. Due to increasing threshold gain with decreasing device size and high Auger losses, the use of perovskite as the active medium in the cascade nano-laser was proposed, as the material possesses a high emission rate in the visible wavelength region, with relative ease of device fabrication. By optimizing the thickness of the perovskite, its width, and the thickness of the silica used, photonic and plasmonic modes were created, which were further considered to permit the generation of lasing, using their respective Purcell factors. The pump wavelength considered was 400 nm, with the laser emission then at 537 nm. For suitability of plasmonic lasing, a Purcell factor FP of 1.22 is reported here, with no possibility for photonic lasing due to its FP value being less than 1 in this design. However, mode-crossing effects were observed in the plasmonic mode at λ = 400 nm for two designs: at a silica thickness of 27.5 nm with perovskite thickness and width of 100 and 300 nm, respectively, and at a silica thickness of 30 nm with perovskite thickness and width of 95 and 300 nm, respectively. These mode-crossing effects can be further analyzed to use these devices in the design of potential new sensor systems, mainly for gas and chemical sensing, exploiting the refractive index sensing capability as a means to determine the concentration of the gases, or other chemicals, under study.
Bommes, D, Pietroni, N & Hu, R 2020, 'Foreword to the Special Section on Shape Modeling International 2020.', Comput. Graph., vol. 90, pp. 4-4.
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Image, graphical abstract.
Booth, E & Narayan, B 2020, '“The Expectations That We Be Educators”: The Views of Australian Authors of Young Adult Fiction on Their OwnVoices Novels as “Windows” for Learning about Marginalized Experiences', The Journal of Research on Libraries and Young Adults, vol. 11, no. 1.
Bordbar, M, Neshat, A, Javadi, S, Pradhan, B & Aghamohammadi, H 2020, 'Meta-heuristic algorithms in optimizing GALDIT framework: A comparative study for coastal aquifer vulnerability assessment', Journal of Hydrology, vol. 585, pp. 124768-124768.
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Boyd-Weetman, B & Thomas, P 2020, 'Assessment of the ground aggregate paste (GAP) test for aggregate alkali–silica reactivity screening', Journal of Thermal Analysis and Calorimetry, vol. 142, no. 5, pp. 1635-1641.
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This paper investigates the potential of a laboratory test for the screening of aggregate reactivity to alkali–silica reaction (ASR) through phase analysis of the phases developed in ground aggregate paste (GAP) specimens subjected to accelerated ageing. GAPs were prepared using two aggregates categorised as non-reactive and potentially reactive by standard expansion test methods and were aged at 40, 60 and 80 °C in 1 M NaOH solution over periods up to 84 days. Phase development was monitored using TG, XRD and FTIR, and the reactivity was correlated with quartz and calcium hydroxide consumption. The data demonstrate that this test has the potential to be developed as a screening test, based on the correlation of phase consumption with Australian standard expansion test reactivity categorisation.
Brambley, G & Kim, J 2020, 'Unit dual quaternion‐based pose optimisation for visual runway observations', IET Cyber-Systems and Robotics, vol. 2, no. 4, pp. 181-189.
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This study addresses the pose estimation problem of an aircraft runway using visual observations in a landing approach scenario. The authors utilised the fact that the geodetic coordinates of most runways are known precisely with highly visible markers. Thus, the runway observations can increase the level of situational awareness during the landing approach, providing additional redundancy of navigation and less reliance on global positioning system. A novel pose optimisation algorithm is proposed utilising unit dual quaternion for the runway corner observations obtained from a monocular camera. The estimated runway pose is further fused with an inertial navigation system in an extended Kalman filter. An open‐source flight simulator is used to collect and process the visual and flight dataset during the landing approach, demonstrating reliable runway pose estimates and the improved inertial navigation solution.
Bramerdorfer, G, Cavagnino, A, Choi, S, Lei, G, Lowther, D, Stipetic, S, Sykulski, J, Zhang, Y & Zhu, JG 2020, 'Guest Editorial: Robust Design and Analysis of Electric Machines and Drives', IEEE Transactions on Energy Conversion, vol. 35, no. 4, pp. 1995-1996.
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Bramerdorfer, G, Lei, G, Cavagnino, A, Zhang, Y, Sykulski, J & Lowther, DA 2020, 'More Robust and Reliable Optimized Energy Conversion Facilitated through Electric Machines, Power Electronics and Drives, and Their Control: State-of-the-Art and Trends', IEEE Transactions on Energy Conversion, vol. 35, no. 4, pp. 1997-2012.
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According to the special section entitled 'Robust design and analysis of electric machines and drives', to be published in IEEE Transactions on Energy Conversion, the authors present an introduction to tolerance analysis, robust optimization, and measures to improve the reliability of electric machines, power electronics and drives, and their robust control in general. A comprehensive review of modeling uncertainties and evaluating robustness and reliability based measures is presented. In addition, techniques facilitating solving dedicated optimization scenarios are introduced. The most recent research activities will be illustrated. The article thus enables to easily catch up with the state-of-the-art in these fields and to take notice of ongoing and future work.
Braun, R & Afroz, F 2020, 'Energy-efficient MAC protocols for wireless sensor networks: a survey', International Journal of Sensor Networks, vol. 32, no. 3, pp. 150-150.
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Bray, K, Fedyanin, DY, Khramtsov, IA, Bilokur, MO, Regan, B, Toth, M & Aharonovich, I 2020, 'Electrical excitation and charge-state conversion of silicon vacancy color centers in single-crystal diamond membranes', Applied Physics Letters, vol. 116, no. 10, pp. 101103-101103.
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The silicon-vacancy (SiV) color center in diamond has recently emerged as a promising qubit for quantum photonics. However, the electrical control and excitation of the SiV centers are challenging due to the low density of free carriers in doped diamond. Here, we realize electrical excitation of SiV centers in a single-crystal diamond membrane, which is promising for scalable photonic architectures. We further demonstrate electrical switching of the charge states of the SiV centers by applying a forward bias voltage to the fabricated diamond-membrane devices and identify the position of the SiV−/SiV0 charge transition level. Our findings provide a perspective toward electrical triggering of color centers in diamond and accelerate the development of scalable quantum nanophotonic technologies.
Broadbent, A, Ji, Z, Song, F & Watrous, J 2020, 'Zero-Knowledge Proof Systems for QMA', SIAM Journal on Computing, vol. 49, no. 2, pp. 245-283.
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© 2020 Society for Industrial and Applied Mathematics. Prior work has established that all problems in NP admit classical zero-knowledge proof systems, and under reasonable hardness assumptions for quantum computations, these proof systems can be made secure against quantum attacks. We prove a result representing a further quantum generalization of this fact, which is that every problem in the complexity class QMA has a quantum zero-knowledge proof system. More specifically, assuming the existence of an unconditionally binding and quantum computationally concealing commitment scheme, we prove that every problem in the complexity class QMA has a quantum interactive proof system that is zero-knowledge with respect to efficient quantum computations. Our QMA proof system is sound against arbitrary quantum provers, but only requires an honest prover to perform polynomial-time quantum computations, provided that it holds a quantum witness for a given instance of the QMA problem under consideration. The proof system relies on a new variant of the QMA-complete local Hamiltonian problem in which the local terms are described by Clifford operations and standard basis measurements. We believe that the QMA-completeness of this problem may have other uses in quantum complexity.
Brodka, P, Musial, K & Jankowski, J 2020, 'Interacting Spreading Processes in Multilayer Networks: A Systematic Review', IEEE Access, vol. 8, pp. 10316-10341.
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Buchlak, QD, Esmaili, N, Leveque, J-C, Bennett, C, Piccardi, M & Farrokhi, F 2020, 'Ethical thinking machines in surgery and the requirement for clinical leadership', The American Journal of Surgery, vol. 220, no. 5, pp. 1372-1374.
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Buchlak, QD, Esmaili, N, Leveque, J-C, Farrokhi, F, Bennett, C, Piccardi, M & Sethi, RK 2020, 'Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review', Neurosurgical Review, vol. 43, no. 5, pp. 1235-1253.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied. Our systematic search strategy yielded 6866 results, 70 of which met inclusion criteria. Performance statistics analyzed included area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Natural language processing (NLP) was used to model topics across the corpus and to identify keywords within surgical subspecialties. ML applications were heterogeneous. The densest cluster of studies focused on preoperative evaluation, planning, and outcome prediction in spine surgery. The main algorithms applied were NN, LR, and SVM. Input and output features varied widely and were listed to facilitate future research. The accuracy (F(2,19) = 6.56, p < 0.01) and specificity (F(2,16) = 5.57, p < 0.01) of NN, LR, and SVM differed significantly. NN algorithms demonstrated significantly higher accuracy than LR. SVM demonstrated significantly higher specificity than LR. We found no significant difference between NN, LR, and SVM AUC and sensitivity. NLP topic modeling reached maximum coherence at seven topics, which were defined by modeling approach, surgery type, and pathology themes. Keywords captured research foci within surgical domains. ML technology accurately predicts outcomes and facilitates clinical decision-making in neurosurgery. NNs frequently outperformed other algorithms on supervised learning tasks. This study identified gaps in the literature and opportunities for future neurosurgical ML research.
Burton, GJ, Sheng, D & Airey, DW 2020, 'Critical state behaviour of an unsaturated high-plasticity clay', Géotechnique, vol. 70, no. 2, pp. 161-172.
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This paper describes a series of tests carried out to examine triaxial compression and shearing of a high-plasticity compacted clay. Reconstituted and compacted samples which were saturated are used as a basis for interpreting the unsaturated test results within a critical state soil mechanics (CSSM) framework. The shear strength behaviour of unsaturated soils have previously been found to be reasonably well captured in a CSSM framework, whereas the volume change behaviour has been more difficult to rationalise. Based on test results presented using the Bishop effective stress, the volume change behaviour during shear suggests that a unique critical state line is approached, independent of the applied suction. The normalised shearing behaviour of the compacted unsaturated soil is interpreted to be analogous to that of saturated-structured soils.
Cai, G, He, X, Dong, L, Liu, S, Xu, Z, Zhao, C & Sheng, D 2020, 'The shear and tensile strength of unsaturated soils by a grain-scale investigation', Granular Matter, vol. 22, no. 1.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents a study of the tensile strength of unsaturated soil by a DEM model in a novel uniaxial tensile test device. For validation and comparison, traditional triaxial shear test of unsaturated soil are also conducted. In the DEM model, the capillary effects and some other cementation effects are modelled by a bond, whose strength is a function of the moisture content and void ratio in uniaxial tensile tests and also the confining pressure in triaxial tests. To compare the DEM simulations with experiments, the bond strength function is calibrated through a quantity measurable in both laboratory and DEM simulations such as the shear strength in triaxial tests or the uniaxial tensile strength in uniaxial tensile tests. The comparison shows that the proposed model is able to capture the phenomena observed in experiments. Most importantly, through investigation of the grain-scale data such as the motion, force chains and development of fractures, it is possible to explain some macroscopic observations such as the form of shear bands in the sample, the influence of the moisture content on the shear and tensile strength, etc.
Cai, G, Li, J, Xu, Z, He, X & Zhao, C 2020, 'Three-dimensional Distinct Element Analysis of Shear Properties of Unsaturated Soils', Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering, vol. 28, no. 6, pp. 1447-1459.
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Based on the discrete element theory and the existing laboratory experimental results, a method to determine the corresponding relationship between macro parameters and grain-scale parameters of unsaturated soil is proposed, that is, taking the structural yield stress as the intermediate variable, the corresponding relationship between the bond strength and water content between particles is constructed, and a flexible boundary program is compiled and added to PFC3D(particle flow code in three dimensions).Three dimensional discrete element model of unsaturated soil is established in the program of dimensions.The numerical simulation of triaxial consolidation drained shear test of unsaturated soil with different water content is carried out.The internal grain-scale evolution mechanism of macro mechanical properties such as strength, deformation and failure of unsaturated soil is deeply studied.The feasibility of using discrete element method to study unsaturated materials is also discussed.The results show that: with the increase of water content, the smaller the contact force between particles in the sample is, the less the number of soil particles under stress will be, and the earlier the bond failure point will appear.In addition, the change of bond failure number in the shear process can be divided into three stages: slow growth stage, rapid development stage and residual stage. Compared with the laboratory test results of unsaturated soil, the established DEM model and analysis program show good applicability in the aspects of deviatoric stress-strain relationship and strength characteristics.
Cai, Z, Yang, Y, Tang, X, Li, Z, Zhang, T & Zhu, H 2020, 'Ultra‐low phase noise oscillator employing mixed electric and magnetic coupling resonator', Microwave and Optical Technology Letters, vol. 62, no. 5, pp. 1914-1919.
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AbstractIn this article, a pair of stub‐loaded interdigital hairpin resonators‐based filter with high group delay and improved stopband suppression is used to design an S‐band ultra‐low phase noise single‐ended oscillator. A transmission zero closed to the edge of passband in the filter is generated by introducing strong electric and magnetic coupling, which can improve the phase noise performance of the proposed oscillator. A low phase noise figure‐of‐merit (FOM) that considers both insertion loss and group delay of the feedback loop is used to evaluate the overall phase noise performance of the proposed oscillator. In order to verify the concept, the proposed oscillator is designed and fabricated. The measured results show that the oscillation frequency is 2.037 GHz with the output power of 8.92 dBm while the second harmonic suppression level is about 35.66 dB. The phase noise and the FOM of the proposed oscillator at 100 kHz frequency offset are −127.95 and −199.31 dBc/Hz, respectively. According to the open literature, this is one of the best phase noise performances of the single‐ended hybrid integrated oscillators oscillating at the similar frequency range.
Calma, A & Dickson-Deane, C 2020, 'The student as customer and quality in higher education', International Journal of Educational Management, vol. 34, no. 8, pp. 1221-1235.
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PurposeThis paper explores some management concepts and how applying these concepts from business to higher education can be problematic, let alone incompatible, particularly in relation to measuring quality in higher education.Design/methodology/approachIt provides a conceptual understanding of the literature on quality in the higher education context. It does so by examining the literature on students as customers, customer expectations, customer satisfaction and other management theories that have been applied to higher education.FindingsIt argues that the current bases for perceiving quality such as meeting customer expectations, satisfying the customer, ensuring quality control, meeting standards and assessing the cost associated with poor quality are in disagreement with the principal aims and measures of quality in higher education.Research limitations/implicationsThis paper can certainly benefit from many other concepts in business that have been applied in higher education, which it lacks. It only focussed on a number of key and popular ideas in management theory that have been used in higher education more broadly.Practical implicationsStudent-focussed quality initiatives can be devoid of the student as customer concept. How programs, subjects and experiences are curated can be solely for the purpose of continuous improvement. Second, universities that choose to treat the student as a customer may find it beneficial to apply a relationship marketing approach to higher education. Las...
Camphausen, R, Marini, L, Tawfik, SA, Tran, TT, Ford, MJ & Palomba, S 2020, 'Observation of near-infrared sub-Poissonian photon emission in hexagonal boron nitride at room temperature', APL Photonics, vol. 5, no. 7, pp. 076103-076103.
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The generation of non-classical light states in the near-infrared (NIR) is important for a number of photonic quantum technologies. Here, we report the first experimental observation of sub-Poissonian NIR (1.24 eV) light emission from defects in a 2D hexagonal boron nitride (hBN) sheet at room temperature. Photoluminescence statistics shows g(2)(0) = 0.6, which is a signature of the quantum nature of the emission. Density functional-theory calculations, at the level of the generalized gradient approximation, for the negatively charged nitrogen anti-site lattice defects are consistent with the observed emission energy. This work demonstrates that the defects in hBN could be a promising platform for single-photon generation in the NIR.
Cao, L 2020, 'Coupling Learning of Complex Interactions', Journal of Information Processing and Management, vol. 51, no. 2, pp. 167-186.
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Complex applications such as big data analytics involve different forms ofcoupling relationships that reflect interactions between factors related totechnical, business (domain-specific) and environmental (includingsocio-cultural and economic) aspects. There are diverse forms of couplingsembedded in poor-structured and ill-structured data. Such couplings areubiquitous, implicit and/or explicit, objective and/or subjective,heterogeneous and/or homogeneous, presenting complexities to existing learningsystems in statistics, mathematics and computer sciences, such as typicaldependency, association and correlation relationships. Modeling and learningsuch couplings thus is fundamental but challenging. This paper discusses theconcept of coupling learning, focusing on the involvement of couplingrelationships in learning systems. Coupling learning has great potential forbuilding a deep understanding of the essence of business problems and handlingchallenges that have not been addressed well by existing learning theories andtools. This argument is verified by several case studies on coupling learning,including handling coupling in recommender systems, incorporating couplingsinto coupled clustering, coupling document clustering, coupled recommenderalgorithms and coupled behavior analysis for groups.
Cao, L 2020, 'Data Science: A Comprehensive Overview', ACM Computing Surveys, 50(3), 43:1-42, 2017, vol. 50, no. 3.
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The twenty-first century has ushered in the age of big data and data economy,in which data DNA, which carries important knowledge, insights and potential,has become an intrinsic constituent of all data-based organisms. An appropriateunderstanding of data DNA and its organisms relies on the new field of datascience and its keystone, analytics. Although it is widely debated whether bigdata is only hype and buzz, and data science is still in a very early phase,significant challenges and opportunities are emerging or have been inspired bythe research, innovation, business, profession, and education of data science.This paper provides a comprehensive survey and tutorial of the fundamentalaspects of data science: the evolution from data analysis to data science, thedata science concepts, a big picture of the era of data science, the majorchallenges and directions in data innovation, the nature of data analytics, newindustrialization and service opportunities in the data economy, the professionand competency of data education, and the future of data science. This articleis the first in the field to draw a comprehensive big picture, in addition tooffering rich observations, lessons and thinking about data science andanalytics.
Cao, L 2020, 'Data Science: Challenges and Directions', Communications of the ACM, vol. 60, no. 8, pp. 8-68.
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While data science has emerged as a contentious new scientific field,enormous debates and discussions have been made on it why we need data scienceand what makes it as a science. In reviewing hundreds of pieces of literaturewhich include data science in their titles, we find that the majority of thediscussions essentially concern statistics, data mining, machine learning, bigdata, or broadly data analytics, and only a limited number of new data-drivenchallenges and directions have been explored. In this paper, we explore theintrinsic challenges and directions inspired by comprehensively exploring thecomplexities and intelligence embedded in data science problems. We focus onthe research and innovation challenges inspired by the nature of data scienceproblems as complex systems, and the methodologies for handling such systems.
Cao, L 2020, 'Data Science: Nature and Pitfalls', IEEE Intelligent Systems, vol. 31, no. 5, pp. 5-75.
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Data science is creating very exciting trends as well as significantcontroversy. A critical matter for the healthy development of data science inits early stages is to deeply understand the nature of data and data science,and to discuss the various pitfalls. These important issues motivate thediscussions in this article.
Cao, L 2020, 'In-Depth Behavior Understanding and Use: The Behavior Informatics Approach', Information Science, 180(17); 3067-3085, 2010, vol. 180, no. 17, pp. 3067-3085.
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The in-depth analysis of human behavior has been increasingly recognized as acrucial means for disclosing interior driving forces, causes and impact onbusinesses in handling many challenging issues. The modeling and analysis ofbehaviors in virtual organizations is an open area. Traditional behaviormodeling mainly relies on qualitative methods from behavioral science andsocial science perspectives. The so-called behavior analysis is actually basedon human demographic and business usage data, where behavior-oriented elementsare hidden in routinely collected transactional data. As a result, it isineffective or even impossible to deeply scrutinize native behavior intention,lifecycle and impact on complex problems and business issues. We propose theapproach of Behavior Informatics (BI), in order to support explicit andquantitative behavior involvement through a conversion from source data tobehavioral data, and further conduct genuine analysis of behavior patterns andimpacts. BI consists of key components including behavior representation,behavioral data construction, behavior impact analysis, behavior patternanalysis, behavior simulation, and behavior presentation and behavior use. Wediscuss the concepts of behavior and an abstract behavioral model, as well asthe research tasks, process and theoretical underpinnings of BI. Substantialexperiments have shown that BI has the potential to greatly complement theexisting empirical and specific means by finding deeper and more informativepatterns leading to greater in-depth behavior understanding. BI creates newdirections and means to enhance the quantitative, formal and systematicmodeling and analysis of behaviors in both physical and virtual organizations.
Cao, L 2020, 'Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting', Engineering, 2: 212-224, 2016, vol. 2, no. 2, pp. 212-224.
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While recommendation plays an increasingly critical role in our living,study, work, and entertainment, the recommendations we receive are often forirrelevant, duplicate, or uninteresting products and services. A criticalreason for such bad recommendations lies in the intrinsic assumption thatrecommended users and items are independent and identically distributed (IID)in existing theories and systems. Another phenomenon is that, while tremendousefforts have been made to model specific aspects of users or items, the overalluser and item characteristics and their non-IIDness have been overlooked. Inthis paper, the non-IID nature and characteristics of recommendation arediscussed, followed by the non-IID theoretical framework in order to build adeep and comprehensive understanding of the intrinsic nature of recommendationproblems, from the perspective of both couplings and heterogeneity. Thisnon-IID recommendation research triggers the paradigm shift from IID to non-IIDrecommendation research and can hopefully deliver informed, relevant,personalized, and actionable recommendations. It creates exciting newdirections and fundamental solutions to address various complexities includingcold-start, sparse data-based, cross-domain, group-based, and shillingattack-related issues.
Cao, L, Yang, Q & Yu, PS 2020, 'Data science and AI in FinTech: An overview', International Journal of Data Science and Analytics, vol. 12, no. 2, pp. 81-99.
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Financial technology (FinTech) has been playing an increasingly critical rolein driving modern economies, society, technology, and many other areas. SmartFinTech is the new-generation FinTech, largely inspired and empowered by datascience and new-generation AI and (DSAI) techniques. Smart FinTech synthesizesbroad DSAI and transforms finance and economies to drive intelligent,automated, whole-of-business and personalized economic and financialbusinesses, services and systems. The research on data science and AI inFinTech involves many latest progress made in smart FinTech for BankingTech,TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech,cryptocurrencies, and blockchain, and the DSAI techniques including complexsystem methods, quantitative methods, intelligent interactions, recognition andresponses, data analytics, deep learning, federated learning,privacy-preserving processing, augmentation, optimization, and systemintelligence enhancement. Here, we present a highly dense research overview ofsmart financial businesses and their challenges, the smart FinTech ecosystem,the DSAI techniques to enable smart FinTech, and some research directions ofsmart FinTech futures to the DSAI communities.
Cao, L, Yuan, G, Leung, T & Zhang, W 2020, 'Special Issue on AI and FinTech: The Challenge Ahead', IEEE Intelligent Systems, vol. 35, no. 3, pp. 3-6.
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Cao, S, Wu, C & Wang, W 2020, 'Behavior of FRP confined UHPFRC-filled steel tube columns under axial compressive loading', Journal of Building Engineering, vol. 32, pp. 101511-101511.
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© 2020 Elsevier Ltd Ultra-high performance fiber-reinforced concrete (UHPFRC) has been widely investigated in recent years. This study focusses on the experimental results of FRP confined UHPFRC filled steel tube (UHPFRCFST) specimens under axial compression. In total 37 specimens, both square and circular, were prepared and tested to investigate the axial compressive behaviors of FRP confined UHPFRCFST specimens. The main investigated parameters were the FRP layers, the concrete type and the steel fiber addition. The experimental results indicate that axial load capacity of concrete filled steel tube (CFST) specimens can be effectively enhanced by the FRP confinement. However, the performance enhancement was less significant for square UHPFRCFST specimens as compared to circular UHPFRCFST specimens. Comparisons of these results demonstrate that FRP confined CFST specimens exhibit a higher load-bearing capacity in the post-peak stage than the non-wrapped CFST specimens. Moreover, prediction equations were proposed to predict the ultimate axial load capacity of FRP confined UHPFRCFST specimens, and the predicted results matched well with the experimental results.
Cao, Y & Veitch, D 2020, 'Toward Trusted Time: Remote Server Vetting and the Misfiring Heart of Internet Timing', IEEE/ACM Transactions on Networking, vol. 28, no. 2, pp. 944-956.
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© 1993-2012 IEEE. The core of the Internet's timekeeping system are the Stratum-1 timeservers, those connected to reference hardware, that anchor the server hierarchy. It is essential that these root servers are accurate and reliable, and this it is typically taken as a given. We examine this premise through an examination of 102 prominent Stratum-1 servers, using 3 datasets spanning 6 years, collected in reference testbeds with authoritative timestamping. We describe a methodology capable of rigorously removing congestion related variability, allowing server errors to be unambiguously revealed. We use the data and methodology to assess the health of public network timing, and how it varies over time, by reporting on the type, severity, duration, and prevalence of server errors, and how they relate to protocol level information. We present conclusive evidence that the system has problems. We find that errors are widespread, significant, often endemic, consistent over time, and typically come with no warning at the protocol level. Our results highlight the lack of oversight in the current system, and provides the foundation of a server health monitoring capability, necessary to restore and maintain trust in network timing. We describe three specific applications where our results can have an impact. Our data, detailed results and software are publically available.
Cao, Y, Cao, Y, Guo, Z, Huang, T & Wen, S 2020, 'Global exponential synchronization of delayed memristive neural networks with reaction–diffusion terms', Neural Networks, vol. 123, pp. 70-81.
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This paper investigates the global exponential synchronization problem of delayed memristive neural networks (MNNs) with reaction-diffusion terms. First, by utilizing the pinning control technique, two novel kinds of control methods are introduced to achieve synchronization of delayed MNNs with reaction-diffusion terms. Then, with the help of inequality techniques, pinning control technique, the drive-response concept and Lyapunov functional method, two sufficient conditions are obtained in the form of algebraic inequalities, which can be used for ensuring the exponential synchronization of the proposed delayed MNNs with reaction-diffusion terms. Moreover, the obtained results based on algebraic inequality complement and improve the previously known results. Finally, two illustrative examples are given to support the effectiveness and validity of the obtained theoretical results.
Cao, Y, Sun, B, Guo, Z, Huang, T, Yan, Z & Wen, S 2020, 'Global Stabilization of Memristive Neural Networks with Leakage and Time-Varying Delays Via Quantized Sliding-Mode Controller', Neural Processing Letters, vol. 52, no. 3, pp. 2451-2468.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. This pape investigates the global stabilization of memristive neural networks (MNNs) with leakage and time-varying delays via quantized sliding-mode controller. The leakage delay is considered in the MNNs. Sliding mode controller is imported to ensure global stabilization of delayed MNNs. We also introduce two quantization schemes with uniform quantizer and logarithmic quantizer. Our goal is to deal with errors before and after quantization. We give some simulations and comparisons between two quantizers in the end of this paper.
Cao, Y, Wang, S & Wen, S 2020, 'Exponential Synchronization of Switched Neural Networks With Mixed Time-Varying Delays via Static/Dynamic Event-Triggering Rules', IEEE Access, vol. 8, pp. 338-347.
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© 2013 IEEE. This paper is devoted to the exponential synchronization of switched neural networks with mixed time-varying delays via static/dynamic event-based rules. At first, by introducing an indicator function, the switched neural networks are transformed into neural networks with general form. Then, sufficient conditions are deduced to achieve exponential synchronization for drive-response systems by two different types of event-Triggering rules, i.e., static and dynamic event-Triggering rules. Meanwhile, we can ensure that the Zeno phenomenon does not occur by proving that the time interval between two successive trigger events has a positive lower bound. Finally, two illustrative examples are elaborated to substantiate the theoretical results.
Cao, Y, Wang, S, Guo, Z, Huang, T & Wen, S 2020, 'Stabilization of memristive neural networks with mixed time-varying delays via continuous/periodic event-based control', Journal of the Franklin Institute, vol. 357, no. 11, pp. 7122-7138.
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This paper addresses the asymptotic stabilization of memristive neural networks with mixed time-varying delays. With two different sampling schemes, sufficient conditions for asymptotic stability of the delayed memristive neural networks system can be obtained by designing appropriate event-based controllers. It is worth mentioning that the state-dependent memristive neural network model in this paper includes time-varying discrete and distributed delays, which is a generalization of the traditional neural network model. Furthermore, based on the continuous sampling event trigger control scheme, a method for designing more economical periodic sampling event trigger control scheme is proposed. Finally, to verify the validity of our conclusions, two numerical simulation examples are given.
Cao, Y, Zheng, X, De Camillis, S, Shi, B, Piper, JA, Packer, NH & Lu, Y 2020, 'Light-Emitting Diode Excitation for Upconversion Microscopy: A Quantitative Assessment', Nano Letters, vol. 20, no. 12, pp. 8487-8492.
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Cao, Z, Ding, W, Wang, Y-K, Hussain, FK, Al-Jumaily, A & Lin, C-T 2020, 'Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy', Neurocomputing, vol. 389, pp. 198-206.
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© 2019 Elsevier B.V. Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generally believed to reflect the ability of the brain to adapt to a visual stimulus environment. In this study, we explored repetitive steady-state visual evoked potential (SSVEP)-based EEG complexity by assessing multiscale inherent fuzzy entropy with relative measurements. We used a wearable EEG device with Oz and Fpz electrodes to collect EEG signals from 40 participants under the following three conditions: a resting state (closed-eyes (CE) and open-eyes (OE) stimulation with five 15-Hz CE SSVEPs and stimulation with five 20-Hz OE SSVEPs. We noted monotonic enhancement of occipital EEG relative complexity with increasing stimulus times in CE and OE conditions. The occipital EEG relative complexity was significantly higher for the fifth SSVEP than for the first SSEVP (FDR-adjusted p < 0.05). Similarly, the prefrontal EEG relative complexity tended to be significantly higher in the OE condition compared to that in the CE condition (FDR-adjusted p < 0.05). The results also indicate that multiscale inherent fuzzy entropy is superior to other competing multiscale-based entropy methods. In conclusion, EEG relative complexity increases with stimulus times, a finding that reflects the strong habituation of brain systems. These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.
Cao, Z, Lin, C-T, Lai, K-L, Ko, L-W, King, J-T, Liao, K-K, Fuh, J-L & Wang, S-J 2020, 'Extraction of SSVEPs-Based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients', IEEE Transactions on Fuzzy Systems, vol. 28, no. 1, pp. 14-27.
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© 1993-2012 IEEE. Inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity reflecting the robustness of brain systems. In this study, we present a novel application of multiscale relative inherent fuzzy entropy using repetitive steady-state visual evoked potentials (SSVEPs) to investigate EEG complexity change between two migraine phases, i.e., interictal (baseline) and preictal (before migraine attacks) phases. We used a wearable headband EEG device with O1, Oz, O2, and Fpz electrodes to collect EEG signals from 80 participants [40 migraine patients and 40 healthy controls (HCs)] under the following two conditions: During resting state and SSVEPs with five 15-Hz photic stimuli. We found a significant enhancement in occipital EEG entropy with increasing stimulus times in both HCs and patients in the interictal phase, but a reverse trend in patients in the preictal phase. In the 1st SSVEP, occipital EEG entropy of the HCs was significantly lower than that of patents in the preictal phase (FDR-adjusted p < 0.05). Regarding the transitional variance of EEG entropy between the 1st and 5th SSVEPs, patients in the preictal phase exhibited significantly lower values than patients in the interictal phase (FDR-adjusted p < 0.05). Furthermore, in the classification model, the AdaBoost ensemble learning showed an accuracy of 81 pm 6%and area under the curve of 0.87 for classifying interictal and preictal phases. In contrast, there were no differences in EEG entropy among groups or sessions by using other competing entropy models, including approximate entropy, sample entropy, and fuzzy entropy on the same dataset. In conclusion, inherent fuzzy entropy offers novel applications in visual stimulus environments and may have the potential to provide a preictal alert to migraine patients.
Cao, Z, Xu, P, Zhang, Z, Wang, G, Taulu, S & Beltrachini, L 2020, 'IEEE Access Special Section Editorial: Neural Engineering Informatics', IEEE Access, vol. 8, pp. 201696-201699.
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Carmichael, CL, Wang, J, Nguyen, T, Kolawole, O, Benyoucef, A, De Mazière, C, Milne, AR, Samuel, S, Gillinder, K, Hediyeh-zadeh, S, Vo, ANQ, Huang, Y, Knezevic, K, McInnes, WRL, Shields, BJ, Mitchell, H, Ritchie, ME, Lammens, T, Lintermans, B, Van Vlierberghe, P, Wong, NC, Haigh, K, Thoms, JAI, Toulmin, E, Curtis, DJ, Oxley, EP, Dickins, RA, Beck, D, Perkins, A, McCormack, MP, Davis, MJ, Berx, G, Zuber, J, Pimanda, JE, Kile, BT, Goossens, S & Haigh, JJ 2020, 'The EMT modulator SNAI1 contributes to AML pathogenesis via its interaction with LSD1', Blood, vol. 136, no. 8, pp. 957-973.
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Abstract Modulators of epithelial-to-mesenchymal transition (EMT) have recently emerged as novel players in the field of leukemia biology. The mechanisms by which EMT modulators contribute to leukemia pathogenesis, however, remain to be elucidated. Here we show that overexpression of SNAI1, a key modulator of EMT, is a pathologically relevant event in human acute myeloid leukemia (AML) that contributes to impaired differentiation, enhanced self-renewal, and proliferation of immature myeloid cells. We demonstrate that ectopic expression of Snai1 in hematopoietic cells predisposes mice to AML development. This effect is mediated by interaction with the histone demethylase KDM1A/LSD1. Our data shed new light on the role of SNAI1 in leukemia development and identify a novel mechanism of LSD1 corruption in cancer. This is particularly pertinent given the current interest surrounding the use of LSD1 inhibitors in the treatment of multiple different malignancies, including AML.
Casanovas, M, Torres-Martínez, A & Merigó, JM 2020, 'Multi-person and multi-criteria decision making with the induced probabilistic ordered weighted average distance', Soft Computing, vol. 24, no. 2, pp. 1435-1446.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents a new approach for selecting suppliers of products or services, specifically with respect to complex decisions that require evaluating different business characteristics to ensure their suitability and to meet the conditions defined in the recruitment process. To address this type of problem, this study presents the multi-person multi-criteria induced ordered weighted average distance (MP-MC-IOWAD) operator, which is an extension of the OWA operators that includes the notion of distances to multiple criteria and expert valuations. Thus, this work introduces new distance measures that can aggregate the information with probabilistic information and consider the attitudinal character of the decision maker. Further extensions are developed using probabilities to form the induced probabilistic ordered weighted average distance (IPOWAD) operator. An example in the management of insurance policies is presented, where the selection of insurance companies is very complex and requires the consideration of subjective criteria by experts in decision making.
Casares-Arias, J, González, MU, San Paulo, A, Ventimiglia, LN, Sadler, JBA, Miguez, DG, Labat-de-Hoz, L, Rubio-Ramos, A, Rangel, L, Bernabé-Rubio, M, Fernández-Barrera, J, Correas, I, Martín-Serrano, J & Alonso, MA 2020, 'Midbody Remnant Inheritance Is Regulated by the ESCRT Subunit CHMP4C', iScience, vol. 23, no. 6, pp. 101244-101244.
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Castillo, L, Young, AIJ, Mawson, A, Schafranek, P, Steinmann, AM, Nessem, D, Parkin, A, Johns, A, Chou, A, Law, AMK, Lucas, MC, Murphy, KJ, Deng, N, Gallego-Ortega, D, Caldon, CE, Timpson, P, Pajic, M, Ormandy, CJ & Oakes, SR 2020, 'MCL-1 antagonism enhances the anti-invasive effects of dasatinib in pancreatic adenocarcinoma', Oncogene, vol. 39, no. 8, pp. 1821-1829.
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AbstractPancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies. It is phenotypically heterogeneous with a highly unstable genome and provides few common therapeutic targets. We found that MCL1, Cofilin1 (CFL1) and SRC mRNA were highly expressed by a wide range of these cancers, suggesting that a strategy of dual MCL-1 and SRC inhibition might be efficacious for many patients. Immunohistochemistry revealed that MCL-1 protein was present at high levels in 94.7% of patients in a cohort of PDACs from Australian Pancreatic Genome Initiative (APGI). High MCL1 and Cofilin1 mRNA expression was also strongly predictive of poor outcome in the TCGA dataset and in the APGI cohort. In culture, MCL-1 antagonism reduced the level of the cytoskeletal remodeling protein Cofilin1 and phosphorylated SRC on the active Y416 residue, suggestive of reduced invasive capacity. The MCL-1 antagonist S63845 synergized with the SRC kinase inhibitor dasatinib to reduce cell viability and invasiveness through 3D-organotypic matrices. In preclinical murine models, this combination reduced primary tumor growth and liver metastasis of pancreatic cancer xenografts. These data suggest that MCL-1 antagonism, while reducing cell viability, may have an additional benefit in increasing the antimetastatic efficacy of dasatinib for the treatment of PDAC.
Catchpoole, D 2020, 'Moving ISBER into the Future: Looking Beyond Our Horizons', Biopreservation and Biobanking, vol. 18, no. 3, pp. 254-255.
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Catchpoole, DR, Carpentieri, D, Vercauteren, S, Wadhwa, L, Schleif, W, Zhou, L, Zhou, J, Labib, RM, Smits, E & Conradie, EH 2020, 'Pediatric Biobanking: Kids Are Not Just Little Adults', Biopreservation and Biobanking, vol. 18, no. 4, pp. 258-265.
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Catchpoole, DR, Florindi, F, Ahern, C, Garcia, DL, Mullins, P, Van Enckevort, E, Zaayenga, A, Mayrhofer, MT & Holub, P 2020, 'Expanding the BBMRI-ERIC Directory into a Global Catalogue of COVID-19–Ready Collections: A Joint Initiative of BBMRI-ERIC and ISBER', Biopreservation and Biobanking, vol. 18, no. 5, pp. 479-480.
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Cetindamar, D, Lammers, T & Zhang, Y 2020, 'Exploring the knowledge spillovers of a technology in an entrepreneurial ecosystem—The case of artificial intelligence in Sydney', Thunderbird International Business Review, vol. 62, no. 5, pp. 457-474.
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AbstractNew knowledge presents opportunities for commercial value and can hence be a critical asset for entrepreneurial ecosystems (EEs). In particular, general purpose technologies are major drivers of entrepreneurship. Thus, a nuanced understanding on technological knowledge and its spillovers among actors within an EE is warranted. Using knowledge‐spillover‐based strategic entrepreneurship theory, we propose to observe knowledge spillovers through the assessment of the knowledge bases of a technology in an EE. To do so, this article proposes to use three key sources of knowledge: publications reflecting the emerging knowledge base, patents representing the realized knowledge base, and startups showing the experimental knowledge base. This article uses secondary data sources such as Web of Science and applies the method of bibliometrics to illustrate how an assessment is carried out in practice by evaluating the artificial intelligence (AI) knowledge bases in Sydney from 2000 to 2018. The findings are summarized with an illustration of the evolution of the key actors and their activities over time in order to indicate the key strengths and weaknesses in Sydney's AI knowledge among the different bases. Contrary to expectations from the high potential of knowledge spillovers from a general purpose digital technology such as AI, the article shows that apparent knowledge spillovers are yet highly limited in Sydney. Even though Sydney has a strong emerging knowledge base, the realized knowledge base seems weak and the experimental knowledge base is slowly improving. That observation itself verifies the need to take strategic actions to facilitate knowledge spillovers within EEs. After the implications for theory and policy makers are discussed, suggestions for further studies are proposed.
Chacon, A, James, B, Tran, L, Guatelli, S, Chartier, L, Prokopvich, D, Franklin, DR, Mohammadi, A, Nishikido, F, Iwao, Y, Akamatsu, G, Takyu, S, Tashima, H, Yamaya, T, Parodi, K, Rosenfeld, A & Safavi‐Naeini, M 2020, 'Experimental investigation of the characteristics of radioactive beams for heavy ion therapy', Medical Physics, vol. 47, no. 7, pp. 3123-3132.
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PurposeThis work has two related objectives. The first is to estimate the relative biological effectiveness of two radioactive heavy ion beams based on experimental measurements, and compare these to the relative biological effectiveness of corresponding stable isotopes to determine whether they are therapeutically equivalent. The second aim is to quantitatively compare the quality of images acquired postirradiation using an in‐beam whole‐body positron emission tomography scanner for range verification quality assurance.MethodsThe energy deposited by monoenergetic beams of C at 350 MeV/u, O at 250 MeV/u, C at 350 MeV/u, and O at 430 MeV/u was measured using a cruciform transmission ionization chamber in a water phantom at the Heavy Ion Medical Accelerator in Chiba (HIMAC), Japan. Dose‐mean lineal energy was measured at various depths along the path of each beam in a water phantom using a silicon‐on‐insulator mushroom microdosimeter. Using the modified microdosimetric kinetic model, the relative biological effectiveness at 10% survival fraction of the radioactive ion beams was evaluated and compared to that of the corresponding stable ions along the path of th...
Chaczko, Z, Klempous, R, Rozenblit, J, Adegbija, T, Chiu, C, Kluwak, K & Smutnick, C 2020, 'Biomimetic Middleware Design Principles for IoT Infrastructures', Acta Polytechnica Hungarica, vol. 17, no. 5, pp. 135-150.
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Chai, J, Tsang, IW & Chen, W 2020, 'Large Margin Partial Label Machine', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 7, pp. 2594-2608.
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Partial label learning (PLL) is a multi-class weakly supervised learning problem where each training instance is associated with a set of candidate labels but only one label is the ground truth. The main challenge of PLL is how to deal with the label ambiguities. Among various disambiguation techniques, large margin (LM)-based algorithms attract much attention due to their powerful discriminative performance. However, existing LM-based algorithms either neglect some potential candidate labels in constructing the margin or introduce auxiliary estimation of class capacities which is generally inaccurate. As a result, their generalization performances are deteriorated. To address the above-mentioned drawbacks, motivated by the optimistic superset loss, we propose an LM Partial LAbel machiNE (LM-PLANE) by extending multi-class support vector machines (SVM) to PLL. Compared with existing LM-based disambiguation algorithms, LM-PLANE considers the margin of all potential candidate labels without auxiliary estimation of class capacities. Furthermore, an efficient cutting plane (CP) method is developed to train LM-PLANE in the dual space. Theoretical insights into the effectiveness and convergence of our CP method are also presented. Extensive experiments on various PLL tasks demonstrate the superiority of LM-PLANE over existing LM based and other representative PLL algorithms in terms of classification accuracy.
Chakrabortty, R, Pradhan, B, Mondal, P & Pal, SC 2020, 'The use of RUSLE and GCMs to predict potential soil erosion associated with climate change in a monsoon-dominated region of eastern India', Arabian Journal of Geosciences, vol. 13, no. 20.
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© 2020, Saudi Society for Geosciences. Soil is one of the most important natural resources; therefore, there is an urgent need to estimate soil erosion. The subtropical monsoon-dominated region also faces a comparatively greater problem due to heavy rainfall with high intensity in a very short time and the presence of longer dry seasons and shorter wet seasons. The Arkosa watershed faces the problem of extreme land degradation in the form of soil erosion; therefore, the rate of soil erosion needs to be estimated according to appropriate models. GCM (general circulation model) data such as MIROC5 (Model for Interdisciplinary Climate Research) of CMIP5 (Coupled Model Intercomparison Project Phase 5) have been used to project future storm rainfall and soil erosion rates following the revised universal soil loss equation (RUSLE) in various influential time frames. Apart from that, different satellite data and relevant primary field-based data for future prediction were considered. The average annual soil erosion of Arkosa watershed ranges from < 1 to > 6 t/ha/year. The very high (> 6 t/ha/year) and high (5–6 t/ha/year) soil loss areas are found in the southern, south-eastern, and eastern part of the watershed. Apart from this, low (1–2 t/ha/year) and very low (< 1 t/ha/year) soil loss areas are associated with the western, northern, southern, and major portion of the watershed. Extreme precipitation rates with high kinetic energy due to climate change are favorable to soil erosion susceptibility. The results of this research will help to implement management strategies to minimize soil erosion by keeping authorities and researchers at risk for future erosion and vulnerability.
Chalmers, T, Maharaj, S, Lees, T, Lin, CT, Newton, P, Clifton-Bligh, R, S McLachlan, C, M Gustin, S & Lal, S 2020, 'Impact of acute stress on cortical electrical activity and cardiac autonomic coupling', Journal of Integrative Neuroscience, vol. 19, no. 2, pp. 239-239.
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Assessment of heart rate variability (reflective of the cardiac autonomic nervous system) has shown some predictive power for stress. Further, the predictive power of the distinct patterns of cortical brain activity and - cardiac autonomic interactions are yet to be explored in the context of acute stress, as assessed by an electrocardiogram and electroencephalogram. The present study identified distinct patterns of neural-cardiac autonomic coupling during both resting and acute stress states. In particular, during the stress task, frontal delta waves activity was positively associated with low-frequency heart rate variability and negatively associated with high-frequency heart rate variability. Low high-frequency power is associated with stress and anxiety and reduced vagal control. A positive association between resting high-frequency heart rate variability and frontocentral gamma activity was found, with a direct inverse relationship of low-frequency heart rate variability and gamma wave coupling at rest. During the stress task, low-frequency heart rate variability was positively associated with frontal delta activity. That is, the parasympathetic nervous system is reduced during a stress task, whereas frontal delta wave activity is increased. Our findings suggest an association between cardiac parasympathetic nervous system activity and frontocentral gamma and delta activity at rest and during acute stress. This suggests that parasympathetic activity is decreased during acute stress, and this is coupled with neuronal cortical prefrontal activity. The distinct patterns of neural-cardiac coupling identified in this study provide a unique insight into the dynamic associations between brain and heart function during both resting and acute stress states.
Chan, NJ-A, Gu, D, Tan, S, Fu, Q, Pattison, TG, O’Connor, AJ & Qiao, GG 2020, 'Spider-silk inspired polymeric networks by harnessing the mechanical potential of β-sheets through network guided assembly', Nature Communications, vol. 11, no. 1.
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AbstractThe high toughness of natural spider-silk is attributed to their unique β-sheet secondary structures. However, the preparation of mechanically strong β-sheet rich materials remains a significant challenge due to challenges involved in processing the polymers/proteins, and managing the assembly of the hydrophobic residues. Inspired by spider-silk, our approach effectively utilizes the superior mechanical toughness and stability afforded by localised β-sheet domains within an amorphous network. Using a grafting-from polymerisation approach within an amorphous hydrophilic network allows for spatially controlled growth of poly(valine) and poly(valine-r-glycine) as β-sheet forming polypeptides via N-carboxyanhydride ring opening polymerisation. The resulting continuous β-sheet nanocrystal network exhibits improved compressive strength and stiffness over the initial network lacking β-sheets of up to 30 MPa (300 times greater than the initial network) and 6 MPa (100 times greater than the initial network) respectively. The network demonstrates improved resistance to strong acid, base and protein denaturants over 28 days.
Chandran, M, Ebeling, PR, Mitchell, PJ & Nguyen, TV 2020, 'Harmonization of Osteoporosis Guidelines: Paving the Way for Disrupting the Status Quo in Osteoporosis Management in the Asia Pacific', Journal of Bone and Mineral Research, vol. 37, no. 4, pp. 608-615.
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ABSTRACT In the Asia Pacific (AP) region, osteoporosis and its consequence of fragility fractures are not widely recognized as a major public health problem. Several challenges including underdiagnosis and undertreatment exist. The Asia Pacific Consortium on Osteoporosis (APCO) is a nonpartisan and apolitical organization comprising musculoskeletal experts and stakeholders from both private and public sectors who have united to develop tangible solutions for these substantive challenges. APCO's vision is to reduce the burden of osteoporosis and fragility fractures in the AP region. Heterogeneity in both scope and recommendations among the available clinical practice guidelines (CPGs) contribute to the large osteoporosis treatment gap in the Asia Pacific. APCO has therefore developed a pan Asia-Oceania harmonized set of standards of care (The Framework), for the screening, diagnosis, and management of osteoporosis. First, a structured analysis of the 18 extant AP CPGs was completed. Subsequently, a prioritization of themes and agreement on fundamental principles in osteoporosis management were made through a Delphi process of consensus building. This approach, ensuring the opinions of all participating members were equally considered, was especially useful for a geographically diverse group such as APCO. It is hoped that the Framework will serve as a platform upon which new AP national CPGs can be developed and existing ones be revised. APCO is currently embarking on country-specific engagement plans to embed the Framework in clinical practice in the AP region. This is through partnering with regulatory bodies and national guidelines development authorities, through peer-to-peer health care professional education and by conducting path finder audits to benchmark current osteoporosis services against the Framework standards. The principles underpinning the harmonization of guidelines in the AP region can als...
Chang, LC, Pare, S, Meena, MS, Jain, D, Li, DL, Saxena, A, Prasad, M & Lin, CT 2020, 'An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering', Big Data and Cognitive Computing, vol. 4, no. 4, pp. 27-27.
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At present, traditional visual-based surveillance systems are becoming impractical, inefficient, and time-consuming. Automation-based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted resampling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.
Chang, X, Liang, X, Yan, Y & Nie, L 2020, 'Guest editorial: Image/video understanding and analysis', Pattern Recognition Letters, vol. 130, pp. 1-3.
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Chang, Y-C, Dostovalova, A, Lin, C-T & Kim, J 2020, 'Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller', Frontiers in Artificial Intelligence, vol. 3, p. 50.
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Chang, Z, Long, G, Zhou, JL & Ma, C 2020, 'Valorization of sewage sludge in the fabrication of construction and building materials: A review', Resources, Conservation and Recycling, vol. 154, pp. 104606-104606.
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© 2019 Elsevier B.V. With increasing amount of sewage sludge becoming an urgent and inevitable issue for every country, its applications in the production of construction and building materials provide an alternative solution for sludge disposal and resource recovery. Similar to clay and Portland cement, the main oxides in sewage sludge are SiO2 (10–25 %), Al2O3 (5–10 %) and CaO (10–30 %) which are increased in sludge ash after incineration to 25–50 %, 10–20 % and 15–30 %. Therefore, this solid waste can be utilized not only as raw material for the production of eco-cement, bricks, ceramic materials and lightweight aggregates through sintering process, but also as supplementary admixtures in cementitious materials such as pozzolanic component, fine aggregate or filling material. By critically reviewing current utilizations of sewage sludge, it is feasible to replace up to 15 % natural raw materials with sewage sludge in cement production and the manufactured eco-cement clinkers show comparable performance to traditional Portland cement. Whilst as raw feed in the fabrication of bricks, ceramic materials and lightweight aggregates, 20 % of sewage sludge substitution is acceptable to produce good quality products (within 8 % firing shrinkage and 15 % water absorption). Though high content of organic matter in raw sludge causes a decrease in mechanical strength and delay in hydration process, controlled low-strength materials offer an innovative reuse with large amount of sludge. The immobilization of heavy metals in products prevents sewage sludge causing secondary environmental pollution. Furthermore, suggestions for future research are proposed in order to strengthen the high value-added applications of sewage sludge.
Changani, Z, Razmjou, A, Taheri-Kafrani, A, Warkiani, ME & Asadnia, M 2020, 'Surface modification of polypropylene membrane for the removal of iodine using polydopamine chemistry', Chemosphere, vol. 249, pp. 126079-126079.
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The development of stable and effective iodine removal systems would be highly desirable in addressing environmental issues relevant to water contamination. In the present research, a novel iodine adsorbent was synthesized by self-polymerization of dopamine (PDA) onto inert polypropylene (PP) membrane. This PP/PDA membrane was thoroughly characterized and its susrface propeties was analyzed by various analytical techniques indcluding field emission scanning electron microscopy (FESEM), atomic force microscopy (AFM), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH), contact angle, and surface free energy measurement. The PP/PDA membranes were subsequently used for batchwise removal of iodine at different temperatures (25-70 °C), pH (2-7), and surface areas (1-10 cm2) to understand the underlying adsorption phenomena and to estimate the membrane capacity for iodine uptake. The increase in temperature and pH both led to higher adsorption of iodine. The present approach showed a removal efficiency of over 75% for iodine using 10 cm2 PP/PDA membrane (18.87 m2 g-1) within 2 h at moderate temperatures (∼50 °C) and pH > 4, about 15 fold compared to the PP control membrane. The adsorption kinetics and isotherms were well fitted to the pseudo-second-order kinetic and Langmuir isotherm models (R2 > 0.99). This adsorbent can be recycled and reused at least six times with stable iodine adsorption. These findings were attributed to the homogenous monolayer adsorption of the iodide on the surface due to the presence of catechol and amine groups in the PP/PDA membrane. This study proposes an efficient adsorbent for iodine removal.
Charbe, NB, Amnerkar, ND, Ramesh, B, Tambuwala, MM, Bakshi, HA, Aljabali, AAA, Khadse, SC, Satheeshkumar, R, Satija, S, Metha, M, Chellappan, DK, Shrivastava, G, Gupta, G, Negi, P, Dua, K & Zacconi, FC 2020, 'Small interfering RNA for cancer treatment: overcoming hurdles in delivery', Acta Pharmaceutica Sinica B, vol. 10, no. 11, pp. 2075-2109.
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© 2020 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences In many ways, cancer cells are different from healthy cells. A lot of tactical nano-based drug delivery systems are based on the difference between cancer and healthy cells. Currently, nanotechnology-based delivery systems are the most promising tool to deliver DNA-based products to cancer cells. This review aims to highlight the latest development in the lipids and polymeric nanocarrier for siRNA delivery to the cancer cells. It also provides the necessary information about siRNA development and its mechanism of action. Overall, this review gives us a clear picture of lipid and polymer-based drug delivery systems, which in the future could form the base to translate the basic siRNA biology into siRNA-based cancer therapies.
Chauviré, B & Thomas, PS 2020, 'DSC of natural opal: insights into the incorporation of crystallisable water in the opal microstructure', Journal of Thermal Analysis and Calorimetry, vol. 140, no. 5, pp. 2077-2085.
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© 2019, Akadémiai Kiadó, Budapest, Hungary. Low-temperature DSC on a wide range of opal-A and opal-CT samples was carried out to estimate the proportion of crystallisable water and to determine the size of water-filled cavities. A wide range of crystallisable water contents in the range 4.9 to 41.9% of the water contained in opals were observed, although the proportion of crystallisable water did not correlate with structure. Pore size and pore size distribution were estimated from the melt temperature depression and heat flow data, respectively. Opal-CT was observed to have smaller water-filled pores (radii < 2 nm) than opal-A (radii from 2.5 to 4.9 nm), suggesting that molecular water may be contained between nanograins in the microstructural units (spheres or lepispheres). A narrower pore size distribution was calculated for opal-CT, and no melting of the crystallisable water was observed where bulk water would be expected to melt, suggesting the absence of larger voids. The melting peaks for opal-A, on the other hand, transitioned into the melting of bulk water suggesting the presence of significantly larger water-filled pores, an observation consistent with the microstructure observed in SEM micrographs.
Che, L, Jin, W, Zhou, X, Cao, C, Han, W, Qin, C, Tu, R, Chen, Y, Feng, X & Wang, Q 2020, 'Biological Reduction of Organic Matter in Buji River Sediment (Shenzhen, China) with Artificial Oxygenation', Water, vol. 12, no. 12, pp. 3592-3592.
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In this work, artificial oxygenation treatment (pure oxygen aeration or oxygen enriched water injection) combined with the introduction of exogenous microorganisms was employed to purify urban river sediment for the first time. Results showed that the developed in situ remediation strategy could increase the dissolved oxygen (DO) concentration and oxidation-reduction potential (ORP) value of the sediments. Benefiting from the increase of DO concentration, the bacterial diversity was enhanced. The highest removal efficiencies of organic matter were 18.4% and 22.3% through pure oxygen aeration and oxygen enriched water injection, respectively. More importantly, overlying water quality was not affected. By comparison, oxygen enriched water injection treatment could achieve better performance on sediment purification. Introducing exogenous microorganisms further reduced the organic matter content of the sediment. In short, the current work not only proposed a promising strategy for controlling urban river sediment pollution, but also provided novel insight for the understanding of river sediment containing highly concentrated organic matter.
Cheah, MY, Ong, HC, Zulkifli, NWM, Masjuki, HH & Salleh, A 2020, 'Physicochemical and tribological properties of microalgae oil as biolubricant for hydrogen-powered engine', International Journal of Hydrogen Energy, vol. 45, no. 42, pp. 22364-22381.
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Chebil, W, Wedyan, MO, Lu, H & Elshaweesh, OG 2020, 'Context-Aware Personalized Web Search Using Navigation History', International Journal on Semantic Web and Information Systems, vol. 16, no. 2, pp. 91-107.
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It is highly desirable that web search engines know users well and provide just what the user needs. Although great effort has been devoted to achieve this dream, the commonly used web search engines still provide a “one-fit-all” results. One of the barriers is lack of an accurate representation of user search context that supports personalised web search. This article presents a method to represent user search context and incorporate this representation to produce personalised web search results based on Google search results. The key contributions are twofold: a method to build contextual user profiles using their browsing behaviour and the semantic knowledge represented in a domain ontology; and an algorithm to re-rank the original search results using these contextual user profiles. The effectiveness of proposed new techniques were evaluated through comparisons of cases with and without these techniques respectively and a promising result of 35% precision improvement is achieved.
Chehade, M, Bullock, M, Glover, A, Hutvagner, G & Sidhu, S 2020, 'Key MicroRNA’s and Their Targetome in Adrenocortical Cancer', Cancers, vol. 12, no. 8, pp. 2198-2198.
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Adrenocortical Carcinoma (ACC) is a rare but aggressive malignancy with poor prognosis and limited response to available systemic therapies. Although complete surgical resection gives the best chance for long-term survival, ACC has a two-year recurrence rate of 50%, which poses a therapeutic challenge. High throughput analyses focused on characterizing the molecular signature of ACC have revealed specific micro-RNAs (miRNAs) that are associated with aggressive tumor phenotypes. MiRNAs are small non-coding RNA molecules that regulate gene expression by inhibiting mRNA translation or degrading mRNA transcripts and have been generally implicated in carcinogenesis. This review summarizes the current insights into dysregulated miRNAs in ACC tumorigenesis, their known functions, and specific targetomes. In addition, we explore the possibility of particular miRNAs to be exploited as clinical biomarkers in ACC and as potential therapeutics.
Chen, B, Peng, F, Wang, H & Yu, Y 2020, 'Compound fault identification of rolling element bearing based on adaptive resonant frequency band extraction', Mechanism and Machine Theory, vol. 154, pp. 104051-104051.
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© 2020 The high frequency resonance (HFR) technique is regarded as a powerful tool for fault diagnosis of rolling element bearings. Different from the usage of the HFR in single fault, the determination of multiple resonant frequency bands under the compound faults and extraneous random impulses is still a challenging task. This paper develops a novel compound fault identification method based on adaptive resonant frequency band extraction. The improved redundant second generation wavelet packet transform is first presented to decompose vibration signal into various narrow bands for providing a fine separation of fault signatures. Then the squared envelope spectrum sparsity criteria is designed to quantify fault characteristics buried in narrow frequency bands. Consequently, the squared envelope spectrum sparsogram is constructed to highlight optimal resonant bands, and the compound faults can be well detected by band-pass filtering and envelope analysis. The numerical and experimental results confirm effectiveness and superiority of the proposed method, which is more sensitive to fault-related impulses and robust to extraneous interferences.
Chen, C-Y, Quan, W, Cheng, N, Yu, S, Lee, J-H, Perez, GM, Zhang, H & Shieh, S 2020, 'IEEE Access Special Section Editorial: Artificial Intelligence in Cybersecurity', IEEE Access, vol. 8, pp. 163329-163333.
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Chen, H, Fang, Y, Zhang, Y, Zhang, W & Wang, L 2020, 'ESPM: Efficient Spatial Pattern Matching', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 6, pp. 1227-1233.
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Chen, H, Heidari, AA, Chen, H, Wang, M, Pan, Z & Gandomi, AH 2020, 'Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies', Future Generation Computer Systems, vol. 111, pp. 175-198.
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© 2020 Elsevier B.V. The first powerful variant of the Harris hawks optimization (HHO) is proposed in this work. HHO is a recently developed swarm-based stochastic algorithm that has previously shown excellent performance. In fact, the original HHO has features that can still be improved as it may experience convergence problems or may easily become trapped in local optima. To overcome these shortcomings of the original HHO, the first powerful variant of HHO integrates chaos strategy, topological multi-population strategy, and differential evolution (DE) strategy. For this, chaos mechanism is first introduced into the original algorithm to improve the exploitation propensities of HHO. The multi-population strategy with three mechanisms is embedded to augment the global search ability of the method. Finally, the DE mechanism is introduced into the HHO to enhance the quality of the solutions. Based on these well-regarded evolutionary mechanisms, we propose an enhanced DE-driven multi-population HHO (CMDHHO) algorithm. In this work, the proposed CMDHHO is compared with a range of other methods, including four original meta-heuristic algorithms, conventional HHO, twelve advanced algorithms based on IEEE CEC2017 benchmark functions, and IEEE CEC2011 real-world problems. Furthermore, the Friedman test and the non-parametric statistical Wilcoxon sign rank test are used to verify the significance of the results. The results of the experiments show that the three embedded mechanisms can effectively enhance the exploratory and exploitative traits of HHO. The time required for HHO to converge was substantially shortened. We suggest using the proposed CMDHHO as an effective tool to solve complex optimization problems.
Chen, H, Qi, C, Shen, L, Fu, Q, Wang, Z, Xiong, Z, Sun, Y & Liu, Y 2020, 'Tunable d-spacing of dry reduced graphene oxide nanosheets for enhancing re-dispersibility in organic solvents', Applied Surface Science, vol. 531, pp. 147375-147375.
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© 2020 Elsevier B.V. The reduced graphene oxide (rGO) nanosheets were modified by using polymeric nanospheres to adjust the d-spacings between dry rGO nanosheets. The experimental and computational studies show that the incorporation of polymeric nanospheres can effectively increase the d-spacing between dry rGO nanosheets, and meanwhile provide good compatibility of rGO with organic solvents. Specifically, when the d-spacing between nanosheets is greater than 14.0 nm, the dried rGO nanosheets can be well redispersed and stabilized in various organic solvents. This study thus provides a new technology that can produce dry rGO nanosheets with good re-dispersibility and stability in various organic solvents on a large-scale in a more environmental friendly manner.
Chen, K, Yao, L, Zhang, D, Wang, X, Chang, X & Nie, F 2020, 'A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 5, pp. 1747-1756.
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Recent years have witnessed the success of deep learning methods in human activity recognition (HAR). The longstanding shortage of labeled activity data inherently calls for a plethora of semisupervised learning methods, and one of the most challenging and common issues with semisupervised learning is the imbalanced distribution of labeled data over classes. Although the problem has long existed in broad real-world HAR applications, it is rarely explored in the literature. In this paper, we propose a semisupervised deep model for imbalanced activity recognition from multimodal wearable sensory data. We aim to address not only the challenges of multimodal sensor data (e.g., interperson variability and interclass similarity) but also the limited labeled data and class-imbalance issues simultaneously. In particular, we propose a pattern-balanced semisupervised framework to extract and preserve diverse latent patterns of activities. Furthermore, we exploit the independence of multi-modalities of sensory data and attentively identify salient regions that are indicative of human activities from inputs by our recurrent convolutional attention networks. Our experimental results demonstrate that the proposed model achieves a competitive performance compared to a multitude of state-of-the-art methods, both semisupervised and supervised ones, with 10% labeled training data. The results also show the robustness of our method over imbalanced, small training data sets.
Chen, L, He, Z, Li, C, Wen, S & Chen, Y 2020, 'Revisiting Memristor Properties', International Journal of Bifurcation and Chaos, vol. 30, no. 12, pp. 2050172-2050172.
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Memristor is a natural synapse because of its nanoscale and memory property, which influences the performance of memristive artificial neural networks. A three-variable memristor model is simplified with 15 kinds of properties, including the learning experience, the forgetting curve, the spiking time-dependent plasticity (STDP), the spiking rate dependent plasticity (SRDP), and the integration property. Through the analysis of the model, one more unobserved property called pseudo-polarity reversibility property is predicted by assuming the long-term memory is independent of memductance.
Chen, L, Zhang, N, Sun, H-M, Chang, C-C, Yu, S & Choo, K-KR 2020, 'Secure search for encrypted personal health records from big data NoSQL databases in cloud', Computing, vol. 102, no. 6, pp. 1521-1545.
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© 2019, Springer-Verlag GmbH Austria, part of Springer Nature. As the healthcare industry adopts the use of cloud to store personal health record (PHR), there is a need to ensure that we maintain the ability to perform efficient search on encrypted data (stored in the cloud). In this paper, we propose a secure searchable encryption scheme, which is designed to search on encrypted personal health records from a NoSQL database in semi-trusted cloud servers. The proposed scheme supports almost all query operations available in plaintext database environments, especially multi-dimensional, multi-keyword searches with range query. Specifically, in the proposed scheme, an Adelson-Velsky Landis (AVL) tree is utilized to construct the index, and an order-revealing encryption (ORE) algorithm is used to encrypt the AVL tree and realize range query. As document-based databases are probably the most popular NoSQL database, due to their flexibility, high efficiency, and ease of use, MongoDB, a document-based NoSQL database, is chosen to store the encrypted PHR data in our scheme. Experimental results show that the scheme can achieve secure and practical searchable encryption for PHRs. A comparison of the range query demonstrates that the time overhead of our ORE-based scheme is 25.5% shorter than that of the mOPE-based Arx (an encrypted database system) scheme.
Chen, M, Ji, J, Liu, H & Yan, F 2020, 'Periodic Oscillations in the Quorum-Sensing System with Time Delay', International Journal of Bifurcation and Chaos, vol. 30, no. 09, pp. 2050127-2050127.
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The main aim of this paper is to study the oscillatory behaviors of gene expression networks in quorum-sensing system with time delay. The stability of the unique positive equilibrium and the existence of Hopf bifurcation are investigated by choosing the time delay as the bifurcation parameter and by applying the bifurcation theory. The explicit criteria determining the direction of Hopf bifurcation and the stability of bifurcating periodic solutions are developed based on the normal form theory and the center manifold theorem. Numerical simulations demonstrate good agreements with the theoretical results. Results of this paper indicate that the time delay plays a crucial role in the regulation of the dynamic behaviors of quorum-sensing system.
Chen, M, Voinov, A, Ames, DP, Kettner, AJ, Goodall, JL, Jakeman, AJ, Barton, MC, Harpham, Q, Cuddy, SM, DeLuca, C, Yue, S, Wang, J, Zhang, F, Wen, Y & Lü, G 2020, 'Position paper: Open web-distributed integrated geographic modelling and simulation to enable broader participation and applications', Earth-Science Reviews, vol. 207, pp. 103223-103223.
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© 2020 The Authors Integrated geographic modelling and simulation is a computational means to improve understanding of the environment. With the development of Service Oriented Architecture (SOA) and web technologies, it is possible to conduct open, extensible integrated geographic modelling across a network in which resources can be accessed and integrated, and further distributed geographic simulations can be performed. This open web-distributed modelling and simulation approach is likely to enhance the use of existing resources and can attract diverse participants. With this approach, participants from different physical locations or domains of expertise can perform comprehensive modelling and simulation tasks collaboratively. This paper reviews past integrated modelling and simulation systems, highlighting the associated development challenges when moving to an open web-distributed system. A conceptual framework is proposed to introduce a roadmap from a system design perspective, with potential use cases provided. The four components of this conceptual framework - a set of standards, a resource sharing environment, a collaborative integrated modelling environment, and a distributed simulation environment - are also discussed in detail with the goal of advancing this emerging field.
Chen, R-H, Ong, HC & Wang, W-C 2020, 'The optimal blendings of diesel, biodiesel and gasoline with various exhaust gas recirculations for reducing NOx and smoke emissions from a diesel engine', International Journal of Environmental Science and Technology, vol. 17, no. 11, pp. 4623-4654.
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Chen, R-S, Zhu, L, Lin, J-Y, Wong, S-W, Li, Y, Yang, Y & He, Y 2020, 'Miniaturized Full-Metal Dual-Band Filter Using Dual-Mode Circular Spiral Resonators', IEEE Microwave and Wireless Components Letters, vol. 30, no. 6, pp. 573-576.
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© 2001-2012 IEEE. A miniaturized full-metal dual-band bandpass filter using a dual-mode circular spiral resonator (CSR) in a single metal cavity is proposed. Two spirals are combined together to form a dual-mode resonator, and the transmission zero (TZ) produced by the source-load coupling can separate the two modes and achieve the desired dual-band property. Then, a second-order dual-band filter is designed based on the dual-mode resonator, and each band can be individually synthesized, designed, and tuned to achieve the desired filtering performances. In addition, each band has TZs on both sides of the passband to achieve high selectivity. The size of the second-order dual-band filter is only 0.073\lambda _{0} \times 0.055\lambda _{0} \times 0.015\lambda _{0} calculated at the center frequency of the lower band. Finally, the filter is fabricated and measured, and a good agreement is achieved between the measurement and simulation.
Chen, R-S, Zhu, L, Lin, J-Y, Wong, S-W, Yang, Y, Li, Y, Zhang, L & He, Y 2020, 'High-Efficiency and Wideband Dual-Resonance Full-Metal Cavity-Backed Slot Antenna Array', IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 8, pp. 1360-1364.
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© 2002-2011 IEEE. A dual-resonance wideband full-metal cavity-backed slot antenna array (CBSA) with high efficiency is proposed. A cavity mode and a resonant-iris mode are used to produce a wide impedance bandwidth. Radiation slots (elements) placed on the top wall are directly fed by electric fields of the modes using a single feeding slot without extra structures. This simplified feeding structure can reduce the antenna complexity, and can also improve the radiation efficiency by reducing power loss along with the conventional feeding power dividers. Thereafter, two wideband and high-efficiency slot arrays with 2 × 2 and 4 × 4 elements are designed based on these two cavity modes and proposed a simplified feeding structure. The measurement of 2 × 2 slot array shows that the proposed 2 × 2 slot antenna array can achieve 15% bandwidth, 13.4 dBi peak gain, and 95% total efficiency.
Chen, S, Zhou, F, Xu, K, Zhao, P, Yang, Y, Zhu, X & Wang, G 2020, 'A Portable Microwave Interferometry Sensor for Permittivity Detection Based on CCMRC', IEEE Access, vol. 8, pp. 140323-140332.
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© 2013 IEEE. A portable microwave complex permittivity sensor based on the interferometry configuration is proposed. A complementary compact microstrip resonant cell (CCMRC) is applied as the sensitive element, which converts the dielectric information of the material under test (MUT) into the phase variations of its transmission coefficient. A miniaturized interferometry platform based on a down-converting mixer further translates the phase change into DC output voltage variation, which can be readily recorded with a direct readout circuit. In this context, expensive and bulky vector network analyzer is no longer needed, thereby leading to a low hardware cost. With comprehensive theoretical analysis, the material permittivity is simply extracted using a specific extrapolation algorithm. As a proof of concept, several different solid material samples with known permittivity values are used to verify the devised detection system.
Chen, S-L, Karmokar, DK, Qin, P-Y, Ziolkowski, RW & Guo, YJ 2020, 'Polarization-Reconfigurable Leaky-Wave Antenna With Continuous Beam Scanning Through Broadside', IEEE Transactions on Antennas and Propagation, vol. 68, no. 1, pp. 121-133.
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© 2019 IEEE. A simple single-layer reconfigurable leaky-wave antenna (LWA) is presented that has polarization agility and beam-scanning functionality. This LWA system realizes a scanned beam that can be switched between all of its linear polarization (LP) and circular polarization (CP) states using only one dc biasing source. A slot-loaded substrate-integrated waveguide (SIW)-based LWA is first explored to attain CP performance with continuous beam scanning through broadside. This CP LWA realizes a measured CP performance with a 3 dB gain variance within 2.75-3.35 GHz for scan angles ranging from -28.6° to +31.5°. A row of shorted stubs is then incorporated into the CP LWA to obtain similar LP performance. Finally, by introducing p-i-n diodes into this LP LWA configuration to facilitate reconfigurable connections between the main patch and the shorted stubs, the radiated fields can be switched between all of its CP and LP states. The measured results of all three antennas confirm their simulated performance. It is demonstrated that the main beam of the polarization-reconfigurable LWA can be scanned from -31.5° to +17.1° with gain variations between 9.5 and 12.8 dBic in its CP state and from -34.3° to +20° with them between 7.8 and 11.7 dBi in its LP state.
Chen, W, Goldys, EM & Deng, W 2020, 'Light-induced liposomes for cancer therapeutics', Progress in Lipid Research, vol. 79, pp. 101052-101052.
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Chen, W, Jia, S, Zhang, X, Zhang, S, Liu, H, Yang, X, Zhang, C & Wu, W 2020, 'Dimeric Thymosin β4 Loaded Nanofibrous Interface Enhanced Regeneration of Muscular Artery in Aging Body through Modulating Perivascular Adipose Stem Cell–Macrophage Interaction', Advanced Science, vol. 7, no. 8.
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AbstractRegenerating nonthrombotic and compliant artery, especially in the aging body, remains a major surgical challenge, mainly owing to the inadequate knowledge of the major cell sources contributing to arterial regeneration and insufficient bioactivity of delivered peptides in grafts. Ultrathin nanofibrous sheaths stented with biodegrading elastomer present opening channels and reduced material residue, enabling fast cell recruitment and host remodeling, while incorporating peptides offering developmental cues are challenging. In this study, a recombinant human thymosin β4 dimer (DTβ4) that contains two complete Tβ4 molecules is produced. The adult perivascular adipose is found as the dominant source of vascular progenitors which, when stimulated by the DTβ4‐loaded nanofibrous sheath, enables 100% patency rates, near‐complete structural as well as adequate functional regeneration of artery, and effectively ameliorates aging‐induced defective regeneration. As compared with Tβ4, DTβ4 exhibits durable regenerative activity including recruiting more progenitors for endothelial cells and smooth muscle cells, when incorporated into the ultrathin polycaprolactone sheath. Moreover, the DTβ4‐loaded interface promotes smooth muscle cells differentiation, mainly through promoting M2 macrophage polarization and chemokines. Incorporating artificial DTβ4 into ultrathin sheaths of fast degrading vascular grafts creates an effective interface for sufficient muscular remodeling thus offering a robust tool for vessel replacement.
Chen, W, Long, G, Yao, L & Sheng, QZ 2020, 'AMRNN: attended multi-task recurrent neural networks for dynamic illness severity prediction', World Wide Web, vol. 23, no. 5, pp. 2753-2770.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Illness severity prediction (ISP) is crucial for caregivers in the intensive care unit (ICU) while saving the life of patients. Existing ISP methods fail to provide sufficient evidence for the time-critical decision making in the dynamic changing environment. Moreover, the correlated temporal features in multivariate time-series are rarely be considered in existing machine learning-based ISP models. Therefore, in this paper, we propose a novel interpretable analysis framework which simultaneously analyses organ systems differentiated based on the pathological and physiological evidence to predict illness severity of patients in ICU. It not only timely but also intuitively reflects the critical conditions of patients for caregivers. In particular, we develop a deep interpretable learning model, namely AMRNN, which is based on the Multi-task RNNs and Attention Mechanism. Physiological features of each organ system in multivariate time series are learned by a single Long-Short Term Memory unit as a dedicated task. To utilize the functional and temporal relationships among organ systems, we use a shared LSTM task to exploit correlations between different learning tasks for further performance improvement. Real-world clinical datasets (MIMIC-III) are used for conducting extensive experiments, and our method is compared with the existing state-of-the-art methods. The experimental results demonstrated that our proposed approach outperforms those methods and suggests a promising way of evidence-based decision support.
Chen, W, Sarir, P, Bui, X-N, Nguyen, H, Tahir, MM & Jahed Armaghani, D 2020, 'Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile', Engineering with Computers, vol. 36, no. 3, pp. 1101-1115.
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Chen, X, Chamoli, U, Vargas Castillo, J, Ramakrishna, VAS & Diwan, AD 2020, 'Complication rates of different discectomy techniques for symptomatic lumbar disc herniation: a systematic review and meta-analysis', European Spine Journal, vol. 29, no. 7, pp. 1752-1770.
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Chen, X, Cui, J, Ni, W, Wang, X, Zhu, Y, Zhang, J & Xu, S 2020, 'DFT-s-OFDM: Enabling Flexibility in Frequency Selectivity and Multiuser Diversity for 5G', IEEE Consumer Electronics Magazine, vol. 9, no. 6, pp. 15-22.
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IEEE The upcoming fifth-generation (5G) wireless cellular communication systems are expected to provide efficiency and productivity by ushering in flexibility of waveforms and resource allocation. Clustered discrete Fourier transform-spread-orthogonal frequency division multiplexing (DFT-s-OFDM) has been specified as the waveform for 5G, due to its flexibility in the exploitation of frequency selectivity and multiuser diversity. This article discusses the flexibility that clustered DFT-s-OFDM is able to achieve at the physical layer, and the requirements that it needs to comply with at the media access control layer. This article emphasizes on the resource allocation of clustered DFT-s-OFDM that can leverage between the flexibility and compliance requirements, and reveals that clustered DFT-s-OFDM is suited for cells with UEs closely distributed around the base stations. A new enhanced riding peak method which operates on the basis of multiple resolutions is identified to be able to balance the data rate of clustered DFT-s-OFDM and computational complexity.
Chen, X, Feng, Z, Wei, Z, Gao, F & Yuan, X 2020, 'Performance of Joint Sensing-Communication Cooperative Sensing UAV Network', IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 15545-15556.
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Chen, X, Rodríguez, Y, López, JC, Muñoz, R, Ni, B-J & Sin, G 2020, 'Modeling of Polyhydroxyalkanoate Synthesis from Biogas by Methylocystis hirsuta', ACS Sustainable Chemistry & Engineering, vol. 8, no. 9, pp. 3906-3912.
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Copyright © 2020 American Chemical Society. Methylocystis hirsuta, a type II methanotroph, has been experimentally demonstrated to be able to efficiently synthesize polyhydroxyalkanoates (PHA) from biogas under nutrient-limited conditions. A mechanistic model capable of describing the relevant processes of M. hirsuta, which is currently not available, would therefore lay a solid foundation for future practical demonstration and optimization of the PHA synthesis technology using biogas. To this end, dedicated batch tests were designed and conducted to obtain experimental data for different mechanistic processes of M. hirsuta. Through utilizing the experimental data of well-designed batch tests and following a step-wise model calibration/validation protocol, the stoichiometrics and kinetics of M. hirsuta are reported for the first time, including the yields of growth and PHA synthesis on CH4 (0.14 ± 0.01 g COD g-1 COD and 0.25 ± 0.02 g COD g-1 COD), the CH4 and O2 affinity constants (5.1 ± 2.1 g COD m-3 and 4.1 ± 1.7 g O2 m-3), the maximum PHA consumption rate (0.019 ± 0.001 g COD g-1 COD d-1), and the maximum PHA synthesis rate on CH4 (0.39 ± 0.05 g COD g-1 COD d-1). Through applying the developed model, an optimal O2:CH4 molar ratio of 1.6 mol O2 mol-1 CH4 was found to maximize the PHA synthesis by M. hirsuta. Practically, the model and parameters obtained would not only benefit the design and operation of bioreactors performing PHA synthesis from biogas, but also enable specific research on selection for type II methanotrophs in diverse environments.
Chen, X, Yang, L, Sun, J, Wei, W, Liu, Y & Ni, B-J 2020, 'Influences of Longitudinal Heterogeneity on Nitrous Oxide Production from Membrane-Aerated Biofilm Reactor: A Modeling Perspective', Environmental Science & Technology, vol. 54, no. 17, pp. 10964-10973.
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As a promising technology for sustainable nitrogen removal from wastewater, the membrane-aerated biofilm reactors (MABRs) performing autotrophic deammonification are faced with the problem of unwanted production of nitrous oxide (N2O, a potent greenhouse gas). As a common tool to study N2O production from such an MABR, the traditional one-dimensional modeling approach fails to simulate the existence of longitudinal gradients in the reactor and therefore might render N2O production significantly deviated from reality. To this end, this work aims to study the influences of key longitudinal gradients (i.e., in oxygen, liquid-phase components, and biofilm thickness) on the N2O production from a typical MABR performing autotrophic deammonification by applying a modified version of a newly developed compartmental model. Through comparing the modeling results of different reactor configurations, this work reveals that the single impact of the longitudinal gradients studied on the N2O production from the MABR follows the order: oxygen (significant) > liquid-phase components (slight) > biofilm thickness (almost none). When multiple longitudinal gradients are present, they become correlated and would jointly influence the N2O production and nitrogen removal of the MABR. The results also show the need for multispot measurements to get an accurate representation of spatial biofilm features of the MABR configuration with the membrane lumen designed/operated as a plug flow reactor. While the traditional modeling approach is acceptable to evaluate the nitrogen removal in most cases, it might overestimate or underestimate the N2O production from the MABR with at least one of the longitudinal gradients in oxygen and liquid-phase components. For such an MABR, the longitudinal heterogeneity in biofilm thickness and the number of biofilm thickness classes to be included in the model would also make a difference to the simulation results, especially the N2O production. The ...
Chen, Y, An, P, Huang, X, Yang, C, Liu, D & Wu, Q 2020, 'Light Field Compression Using Global Multiplane Representation and Two-Step Prediction', IEEE Signal Processing Letters, vol. 27, pp. 1135-1139.
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© 1994-2012 IEEE. Due to its spatio-angular structure, light field image allows for a wealth of post-processing techniques like digital refocusing and depth estimation. In order to compress the data of the two domains, the current proposal intends to embed the disparity-based view synthesis method into the decoder. However, predicting each view separately or in local groups means bringing more computational burden to the decoder and destroying the light field structure. Since disparity contains the relationship between all light rays in the light field, the proposed solution is to predict a disparity-based global representation as the first step. In the second step, all the views can be predicted easily based on this representation. In this letter, we use the recently proposed multiplane as the form of this global representation. The experimental results show the effectiveness of the proposed solution, and the better RD performance compared to other schemes especially under low bitrates.
Chen, Y, Huang, S & Fitch, R 2020, 'Active SLAM for Mobile Robots With Area Coverage and Obstacle Avoidance', IEEE/ASME Transactions on Mechatronics, vol. 25, no. 3, pp. 1182-1192.
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© 1996-2012 IEEE. In this article, we present an active simultaneous localization and mapping (SLAM) framework for a mobile robot to obtain a collision-free trajectory with good performance in SLAM uncertainty reduction and in an area coverage task. Based on a model predictive control framework, these two tasks are solved by the introduction of a control switching mechanism. For SLAM uncertainty reduction, graph topology is used to approximate the original problem as a constrained nonlinear least squares problem. A convex half-space representation is applied to relax nonconvex spatial constraints that represent obstacle avoidance. Using convex relaxation, the problem is solved by a convex optimization method and a rounding procedure based on singular value decomposition. The area coverage task is addressed with a sequential quadratic programming method. A submap joining approach, called linear SLAM, is used to address the associated challenges of avoiding local minima, minimizing control switching, and potentially high computational cost. Finally, various simulations and experiments using an aerial robot are presented that verify the effectiveness of the proposed method, showing that our method produces a more accurate SLAM result and is more computationally efficient compared with multiple existing methods.
Chen, Y, Leighton, B, Zhu, H, Ke, X, Liu, S & Zhao, L 2020, 'Submap-Based Indoor Navigation System for the Fetch Robot', IEEE Access, vol. 8, pp. 81479-81491.
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Chen, Y, Mao, Y, Liang, H, Yu, S, Wei, Y & Leng, S 2020, 'Data Poison Detection Schemes for Distributed Machine Learning', IEEE Access, vol. 8, pp. 7442-7454.
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© 2013 IEEE. Distributed machine learning (DML) can realize massive dataset training when no single node can work out the accurate results within an acceptable time. However, this will inevitably expose more potential targets to attackers compared with the non-distributed environment. In this paper, we classify DML into basic-DML and semi-DML. In basic-DML, the center server dispatches learning tasks to distributed machines and aggregates their learning results. While in semi-DML, the center server further devotes resources into dataset learning in addition to its duty in basic-DML. We firstly put forward a novel data poison detection scheme for basic-DML, which utilizes a cross-learning mechanism to find out the poisoned data. We prove that the proposed cross-learning mechanism would generate training loops, based on which a mathematical model is established to find the optimal number of training loops. Then, for semi-DML, we present an improved data poison detection scheme to provide better learning protection with the aid of the central resource. To efficiently utilize the system resources, an optimal resource allocation approach is developed. Simulation results show that the proposed scheme can significantly improve the accuracy of the final model by up to 20% for support vector machine and 60% for logistic regression in the basic-DML scenario. Moreover, in the semi-DML scenario, the improved data poison detection scheme with optimal resource allocation can decrease the wasted resources for 20-100%.
Chen, Y, Sobczak, F, Pais-Roldán, P, Schwarz, C, Koretsky, AP & Yu, X 2020, 'Mapping the Brain-Wide Network Effects by Optogenetic Activation of the Corpus Callosum', Cerebral Cortex, vol. 30, no. 11, pp. 5885-5898.
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Abstract Optogenetically driven manipulation of circuit-specific activity enables causality studies, but its global brain-wide effect is rarely reported. Here, we applied simultaneous functional magnetic resonance imaging (fMRI) and calcium recording with optogenetic activation of the corpus callosum (CC) connecting barrel cortices (BC). Robust positive BOLD was detected in the ipsilateral BC due to antidromic activity, spreading to the ipsilateral motor cortex (MC), and posterior thalamus (PO). In the orthodromic target, positive BOLD was reliably evoked by 2 Hz light pulses, whereas 40 Hz light pulses led to reduced calcium, indicative of CC-mediated inhibition. This presumed optogenetic CC-mediated inhibition was further elucidated by pairing light pulses with whisker stimulation at varied interstimulus intervals. Whisker-induced positive BOLD and calcium signals were reduced at intervals of 50/100 ms. The calcium-amplitude-modulation-based correlation with whole-brain fMRI signal revealed that the inhibitory effects spread to contralateral BC, ipsilateral MC, and PO. This work raises the need for fMRI to elucidate the brain-wide network activation in response to optogenetic stimulation.
Chen, Y, Tran, TN, Duong, NMH, Li, C, Toth, M, Bradac, C, Aharonovich, I, Solntsev, A & Tran, TT 2020, 'Optical Thermometry with Quantum Emitters in Hexagonal Boron Nitride', ACS Applied Materials & Interfaces, vol. 12, no. 22, pp. 25464-25470.
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Nanoscale optical thermometry is a promising noncontact route for measuring local temperature with both high sensitivity and spatial resolution. In this work, we present a deterministic optical thermometry technique based on quantum emitters in nanoscale hexagonal boron nitride. We show that these nanothermometers show better performance than homologous, all-optical nanothermometers in both sensitivity and the range of working temperature. We demonstrate their effectiveness as nanothermometers by monitoring the local temperature at specific locations in a variety of custom-built microcircuits. This work opens new avenues for nanoscale temperature measurements and heat flow studies in miniaturized, integrated devices.
Chen, Y, Zhao, L, Lee, KMB, Yoo, C, Huang, S & Fitch, R 2020, 'Broadcast Your Weaknesses: Cooperative Active Pose-Graph SLAM for Multiple Robots', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2200-2207.
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© 2016 IEEE. In this letter, we propose a low-cost, high-efficiency framework for cooperative active pose-graph simultaneous localization and mapping (SLAM) for multiple robots in three-dimensional (3D) environments based on graph topology. Based on the selection of weak connections in pose graphs, this method aims to find the best trajectories for optimal information exchange to repair these weaknesses opportunistically when robots move near them. Based on tree-connectivity, which is greatly related to the D-optimality metric of the Fisher information matrix (FIM), we explore the relationship between measurement (edge) selection and pose-measurement (node-edge) selection, which often occurs in active SLAM, in terms of information increment. The measurement selection problem is formulated as a submodular optimization problem and solved by an exhaustive method using rank-1 updates. We decide which robot takes the selected measurements through a bidding framework where each robot computes its predicted cost. Finally, based on a novel continuous trajectory optimization method, these additional measurements collected by the winning robot are sent to the requesting robot to strengthen its pose graph. In simulations and experiments, we validate our approach by comparing against existing methods. Further, we demonstrate online communication based on offline planning results using two unmanned aerial vehicles (UAVs).
Chen, Y, Zhao, L, Zhang, Y & Huang, S 2020, 'Dense Isometric Non-Rigid Shape-From-Motion Based on Graph Optimization and Edge Selection', IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5889-5896.
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In this letter, we propose a novel framework for dense isometric non-rigid shape-from-motion (Iso-NRSfM) based on graph topology and edge selection. A weighted undirected graph, of which nodes, edges, and weighted values are respectively the images, the image warps, and the number of the common features, is built. An edge selection algorithm based on maximum spanning tree and sub-modular optimization is presented to pick out the well-connected sub-graph for the warps with multiple images. Using the infinitesimal planarity assumption, the Iso-NRSfM problem is formulated as a graph optimization problem with the virtual measurements, which are based on metric tensor and Christoffel Symbol, and the variables related to the derivatives of the constructed points along the surface. The solution of this graph optimization problem directly leads to the normal field of the shape. Then, using a separable iterative optimization method, we obtain the dense point cloud with texture corresponding to the deformable shape robustly. In the experiments, the proposed method outperforms existing work in terms of constructed accuracy, especially when there exists missing/appearing (changing) data, noisy data, and outliers.
Chen, Z, Duan, X, Wei, W, Wang, S & Ni, B-J 2020, 'Electrocatalysts for acidic oxygen evolution reaction: Achievements and perspectives', Nano Energy, vol. 78, pp. 105392-105392.
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© 2020 Elsevier Ltd Developing efficient electrocatalysts toward acidic oxygen evolution reaction (AOER) is of vital significance in proton exchange membrane (PEM) water electrolysis, which is a promising technique to tackle the approaching energy crisis by supplying high-purity hydrogen. In this work, we first present a general introduction to the AOER mechanism as well as the most important parameters in evaluation of the catalytic performances of catalysts. Fruitful achievements of noble metal-based catalysts (e.g., metals, alloys, and oxides) and noble metal-free catalysts (e.g., transition metal oxides, chalcogenides, and metal-free materials) are fully described, with an emphasis on advanced strategies of catalyst modification/engineering, structure-catalysis correlations, and evolution of catalyst structures and surface chemistry under operational conditions. The representative electrocatalysts are benchmarked based on their catalytic performances. Finally, the challenges are summarized and future opportunities are directed for the rational design of AOER catalysts toward sustainable fuel production.
Chen, Z, Duan, X, Wei, W, Wang, S & Ni, B-J 2020, 'Iridium-based nanomaterials for electrochemical water splitting', Nano Energy, vol. 78, pp. 105270-105270.
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© 2020 Elsevier Ltd Electrochemical water splitting is an appealing technology to produce high-purity hydrogen as a clean and sustainable energy carrier. The efficiency of water splitting largely depends on the intrinsic activity, selectivity, and stability of the electrocatalysts. Hence, soaring scientific endeavors have been made to develop high-performance electrocatalysts and uncover the underling reaction mechanisms. Iridium (Ir)-based nanomaterials are most promising for water splitting due to their favorable intrinsic activity, wide pH window, and high stability. In this review, we first discussed the mechanisms of various Ir-based catalysts in hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), including metal, alloys, and oxides. Important criteria and methods for precise evaluation of water splitting catalysts are discussed. Then, the applications of Ir-based nanomaterials in the HER, OER and the overall water splitting are comprehensively reviewed, with an emphasis on correlating the structure-function relationships and the advanced strategies for rational design of reaction-oriented Ir catalysts. Lastly, the current challenges in fundamental studies and future directions in this field are presented.
Chen, Z, Duan, X, Wei, W, Wang, S, Zhang, Z & Ni, B-J 2020, 'Boride-based electrocatalysts: Emerging candidates for water splitting', Nano Research, vol. 13, no. 2, pp. 293-314.
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© 2020, Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature. Electrocatalytic water splitting (EWS) is a promising route to produce hydrogen in a sustainable and environment-benign manner. To realize the large-scale hydrogen production, it is paramount to develop desirable electrocatalysts with engineered structure, high catalytic activity, facile accessibility, low cost, and good durability. Of late, boride-based materials, especially transition-metal borides (TMBs), are emerging as promising candidates for the EWS process. However, so far, little attempt has been made to provide a comprehensive summary on these findings. Herein, this review provides the up-to-date status on upgrading the catalytic performance of TMB-based nanomaterials by regulating the internal and external characteristics. The conventional synthetic techniques are first presented for the preparation of TMB-based catalysts. Afterwards, the advanced strategies are summarized to enhance the catalytic performance of TMBs, including morphology control, component regulation, phase engineering, surface oxidation and hybridization. Then, the design principles of TMB-based electrocatalysts for high-performance EWS are outlined. Lastly, the current challenges and future directions in the development of TMB-based materials are proposed. This review article is expected to envisage insights into the TMBs-based water splitting and to provide strategies for design of the next-generation TMB-based electrocatalysts. [Figure not available: see fulltext.].
Chen, Z, Ibrahim, I, Hao, D, Liu, X, Wu, L, Wei, W, Su, D & Ni, B-J 2020, 'Controllable design of nanoworm-like nickel sulfides for efficient electrochemical water splitting in alkaline media', Materials Today Energy, vol. 18, pp. 100573-100573.
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© 2020 Elsevier Ltd Developing cost-effective electrocatalysts for electrochemical water splitting (EWS) is appealing and challenging for sustainable water electrolysis. Currently, nickel sulfides are considered as promising candidates for EWS due to their low cost and high catalytic activity. However, the facile design of nickel sulfides with high catalytic performance is still highly demanded. In this study, we have developed a one-step solvothermal strategy to construct nickel sulfides as efficient water splitting catalysts. By taking advantage of the small size, abundant active sites, large electrochemical surface area, and good conductivity, the nanoworm-like nickel sulfides (NiS-NW/Ni foam [NF]) exhibit better oxygen evolution reaction performance (a low overpotential of 279 mV to achieve 100 mA cm−2, Tafel slope of 38.44 mV dce−1) than the nanoplate-like analogs, as well as most of reported nickel sulfide–based electrocatalysts. In addition, the NiS-NW/NF directly used as bifunctional electrodes for overall water splitting requires a low voltage of 1.563 V to attain a current density of 10 mA cm−2 with good long-term durability. This work provides a facile strategy for the design of efficient nickel sulfide-based electrocatalysts for energy conversion applications.
Chen, Z, Wu, G, Wu, Y, Wu, Q, Shi, Q, Ngo, HH, Vargas Saucedo, OA & Hu, H-Y 2020, 'Water Eco-Nexus Cycle System (WaterEcoNet) as a key solution for water shortage and water environment problems in urban areas', Water Cycle, vol. 1, pp. 71-77.
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Cheng, C, Cao, Z & Xiao, F 2020, 'A generalized belief interval-valued soft set with applications in decision making', Soft Computing, vol. 24, no. 13, pp. 9339-9350.
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Cheng, D, Hao Ngo, H, Guo, W, Wang Chang, S, Duc Nguyen, D, Liu, Y, Zhang, X, Shan, X & Liu, Y 2020, 'Contribution of antibiotics to the fate of antibiotic resistance genes in anaerobic treatment processes of swine wastewater: A review', Bioresource Technology, vol. 299, pp. 122654-122654.
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Antibiotic resistance genes (ARGs) in water environment have become a global health concern. Swine wastewater is widely considered to be one of the major contributors for promoting the proliferation of ARGs in water environments. This paper comprehensively reviews and discusses the occurrence and removal of ARGs in anaerobic treatment of swine wastewater, and contributions of antibiotics to the fate of ARGs. The results reveal that ARGs' removal is unstable during anaerobic processes, which negatively associated with the presence of antibiotics. The abundance of bacteria carrying ARGs increases with the addition of antibiotics and results in the spread of ARGs. The positive relationship was found between antibiotics and the abundance and transfer of ARGs in this review. However, it is necessary to understand the correlation among antibiotics, ARGs and microbial communities, and obtain more knowledge about controlling the dissemination of ARGs in the environment.
Cheng, D, Liu, Y, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, S, Luo, G & Liu, Y 2020, 'A review on application of enzymatic bioprocesses in animal wastewater and manure treatment', Bioresource Technology, vol. 313, pp. 123683-123683.
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Enzymatic processing has been considered an interesting technology as enzymes play important roles in the process of waste bioconversion, whilst heling to develop valuable products from animal wastes. In this paper, the application of enzymes in animal waste management were critically reviewed in short with respect to utilization in: (i) animal wastewater treatment and (ii) animal manure management. The results indicate that the application of enzymes could increase both chemical oxygen demand (COD) removal efficiency and production of biogas. The enzymatic bioprocesses were found to be affected by the type, source and dosage of enzymes and the operating conditions. Further studies on optimizing the operating conditions and developing cost-effective enzymes for the future large-scale application are therefore necessary.
Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Li, J, Ly, QV, Nguyen, TAH & Tran, VS 2020, 'Applying a new pomelo peel derived biochar in microbial fell cell for enhancing sulfonamide antibiotics removal in swine wastewater', Bioresource Technology, vol. 318, pp. 123886-123886.
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A sequential anode-cathode double-chamber microbial fuel cell (MFC) is a promising system for simultaneously removing contaminants, recovering nutrients and producing energy from swine wastewater. To improve sulfonamide antibiotics (SMs)'s removal in the continuous operating of MFC, one new pomelo peel-derived biochar was applied in the anode chamber in this study. Results demonstrated that SMs can be absorbed onto the heterogeneous surfaces of biochar through pore-filling and π-π EDA interaction. Adding biochar to a certain concentration (500 mg/L) could enhance the efficiency in removing sulfamethoxazole, sulfadiazine and sulfamethazine to 82.44-88.15%, 53.40-77.53% and 61.12-80.68%, respectively. Moreover, electricity production, COD and nutrients removal were improved by increasing the concentration of biochar. Hence, it is proved that adding biochar in MFC could effectively improve the performance of MFC in treating swine wastewater containing SMs.
Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Shan, X, Nghiem, LD & Nguyen, LN 2020, 'Removal process of antibiotics during anaerobic treatment of swine wastewater', Bioresource Technology, vol. 300, pp. 122707-122707.
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High concentrations of antibiotics in swine wastewater pose potentially serious risks to the environment, human and animal health. Identifying the mechanism for removing antibiotics during the anaerobic treatment of swine wastewater is essential for reducing the serious damage they do to the environment. In this study, batch experiments were conducted to investigate the biosorption and biodegradation of tetracycline and sulfonamide antibiotics (TCs and SMs) in anaerobic processes. Results indicated that the removal of TCs in the anaerobic reactor contributed to biosorption, while biodegradation was responsible for the SMs' removal. The adsorption of TCs fitted well with the pseudo-second kinetic mode and the Freundlich isotherm, which suggested a heterogeneous chemisorption process. Cometabolism was the main mechanism for the biodegradation of SMs and the process fitted well with the first-order kinetic model. Microbial activity in the anaerobic sludge might be curtailed due to the presence of high concentrations of SMs.
Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Wei, Q & Wei, D 2020, 'A critical review on antibiotics and hormones in swine wastewater: Water pollution problems and control approaches', Journal of Hazardous Materials, vol. 387, pp. 121682-121682.
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Swine wastewater (SW) is an important source of antibiotics and hormones (A&H) in the environment due to their large-scale application in swine industry. A&H in SW can be released into the water environment through the direct discharge of SW, effluent from SW treatment plants, and runoff and leaching from farmland polluted by swine wastes. The presence of A&H in the water environment has become an increasing global concern considering their adverse effects to the aquatic organism and human. This review critically discusses: (i) the occurrence of A&H in global water environment and their potential risks to water organisms and human; (ii) the management and technical approaches for reducing the emission of A&H in SW to the water environment. The development of antibiotic alternatives and the enhanced implementation of vaccination and biosecurity are promising management approaches to cut down the consumption of antibiotics during swine production. Through the comparison of different biological treatment technologies for removing A&H in SW, membrane-based bioprocesses have relatively higher and more stable removal efficiencies. Whereas, the combined system of bioprocesses and AOPs is expected to be a promising technology for elimination and mineralization of A&H in swine wastewater. Further study on this system is therefore necessary.
Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, X, Varjani, S & Liu, Y 2020, 'Feasibility study on a new pomelo peel derived biochar for tetracycline antibiotics removal in swine wastewater', Science of The Total Environment, vol. 720, pp. 137662-137662.
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Cheng, D, Ngo, HH, Guo, W, Lee, D, Nghiem, DL, Zhang, J, Liang, S, Varjani, S & Wang, J 2020, 'Performance of microbial fuel cell for treating swine wastewater containing sulfonamide antibiotics', Bioresource Technology, vol. 311, pp. 123588-123588.
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Cheng, EJ, Prasad, M, Yang, J, Khanna, P, Chen, B-H, Tao, X, Young, K-Y & Lin, C-T 2020, 'A fast fused part-based model with new deep feature for pedestrian detection and security monitoring', Measurement, vol. 151, pp. 107081-107081.
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© 2019 Elsevier Ltd In recent years, pedestrian detection based on computer vision has been widely used in intelligent transportation, security monitoring, assistance driving and other related applications. However, one of the remaining open challenges is that pedestrians are partially obscured and their posture changes. To address this problem, deformable part model (DPM) uses a mixture of part filters to capture variation in view point and appearance and achieves success for challenging datasets. Nevertheless, the expensive computation cost of DPM limits its ability in the real-time application. This study propose a fast fused part-based model (FFPM) for pedestrian detection to detect the pedestrians efficiently and accurately in the crowded environment. The first step of the proposed method trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. These six response feature maps are combined with full-body model to produce spatial deep features. The second step of the proposed method uses the deep features as an input to support vector machine (SVM) to detect pedestrian. A variety of strategies is introduced in the proposed model, including part-based to full-body method, spatial filtering, and multi-ratios combination. Experiment results show that the proposed FFPM method improves the computation speed of DPM and maintains the performance in detection.
Cheng, H, Liu, Y, Huang, D, Pan, Y & Wang, Q 2020, 'Adaptive Transfer Learning of Cross-Spatiotemporal Canonical Correlation Analysis for Plant-Wide Process Monitoring', Industrial & Engineering Chemistry Research, vol. 59, no. 49, pp. 21602-21614.
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© Multivariate statistical methods have gained significant popularity in past decades. However, process dynamics and insufficient training data usually result in degradation or even failure of a trained model. To deal with these problems, this paper proposes a novel process monitoring method, called cross-spatiotemporal adaptive boosting transfer learning (CS-AdBoostTrLM). Different from the standard methods, CS-AdBoostTrLM has the following advantages: first, source domain (SD) data, which are discarded by the factory, can be re-enabled to alleviate the issue of insufficient training data. Second, cross-spatiotemporal canonical correlation analysis is proposed to achieve the domain adaptation between the SD data and target domain data, so as to overcome the negative transfer. Third, the particle swarm optimization algorithm is used to optimize the local detection model, in such a way that the integrated detection model can converge to the optimality globally. Finally, the data from the wastewater treatment plant and chemical plant are analyzed to demonstrate the effectiveness of the proposed method.
Cheng, Q, Nguyen, DN, Dutkiewicz, E & Mueck, M 2020, 'Preserving Honest/Dishonest Users’ Operational Privacy with Blind Interference Calculation in Spectrum Sharing System', IEEE Transactions on Mobile Computing, vol. 19, no. 12, pp. 2874-2890.
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In this paper, we investigate the operational privacy issue of Incumbent Users (IUs) and honest/dishonest Secondary Users (SUs). For the case of IUs and honest SUs, we propose a privacy-preserving scheme for DSA by leveraging encryption and obfuscation methods (PSEO). To implement PSEO, we introduce an interference calculation scheme that allows users to calculate an interference budget without revealing operational information, referred to as the blind interference calculation scheme (BICS). BICS also reduces the computing overhead of PSEO, compared with FCC's SAS by moving interference budgeting tasks to local users and calculating it in an offline manner. To further save the overhead in calculating the interference map, we introduce a quantization method and optimize the grid sizes of the terrestrial area of interest. Additionally, for the case of IUs and dishonest SUs, we propose a "punishment and forgiveness" (PF) mechanism, which draws support from SUs' reputation scores (RSs) and reputation histories (RHs), to encourage SUs to provide truthful information. Theoretical analysis and extensive simulations show that our proposed PSEO and PF-PSEO schemes can better protect all users' operational privacy under various privacy attacks, yielding higher spectrum utilization with less online overhead, compared with state of the art approaches
Cheng, Z, Zhao, R, Yuan, Y, Li, F, Castel, A & Xu, T 2020, 'Ageing coefficient for early age tensile creep of blended slag and low calcium fly ash geopolymer concrete', Construction and Building Materials, vol. 262, pp. 119855-119855.
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© 2020 Elsevier Ltd The ageing coefficient is required in the age-adjusted effective modulus method to assess the effect of creep in concrete structures subjected to varied stress history. In this paper, experiments were carried out to calibrate the ageing coefficient for early age tensile creep of blended slag and low calcium fly ash geopolymer concrete. The development of total strains of geopolymer concrete under sustained tension, including instantaneous strain, creep strain and shrinkage strain was monitored by using the dog-bone shaped specimens. The specimens were loaded at the age of 2, 3, 4, 7, 14, 21 days, respectively. The strains of the unloaded companion specimens were monitored as well. The development of the creep coefficient φ(t,τ0) for geopolymer concrete was calculated based on the experimental results. By using the step-by-step numerical analysis, the ageing coefficient for early age tensile creep of geopolymer concrete was assessed. An ageing coefficient of 0.8 is recommended for structural design. The comparison between calculated and measured tensile strains from the restrained concrete ring test shows the validity of the proposed value of the ageing coefficient.
Cheshomi, A, Bakhtiyari, E & Khabbaz, H 2020, 'A comparison between undrained shear strength of clayey soils acquired by “PMT” and laboratory tests', Arabian Journal of Geosciences, vol. 13, no. 14, p. 640.
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© 2020, Saudi Society for Geosciences. A pressuremeter test (PMT) is one of the in situ tests, which is used to evaluate deformation and strength parameters of soils for various projects, including subway projects. The limit pressure (PL) and undrained shear strength (Su) are the key parameters that are obtained directly and indirectly from the pressuremeter testing results. This research was carried out using geotechnical information obtained from a subway project in Qom city, Iran. Based on 44 PMT and uniaxial tests on very stiff to hard saturated clayey soils, a linear empirical equation between Su − PL and Su − PL* = (PL − σH) with R2 = 0.68 was proposed and it was found that σH had an insignificant effect on the proposed relationship. The effect of physical properties of soil, including plastic index (PI), liquid limit (LL), and water content (ω), was evaluated, and a multivariate equation was proposed between them. A comparison between the equations obtained in this research and those proposed by other researchers reveals that the empirical relationships between Su and PL are associated with the consistency of soils; the stiffer the soil is, the slope of relationship between Su and PL is less.
Chia, R, Zhong, H, Vissel, B, Edgerton, VR & Gad, P 2020, 'Novel Activity Detection Algorithm to Characterize Spontaneous Stepping During Multimodal Spinal Neuromodulation After Mid-Thoracic Spinal Cord Injury in Rats', Frontiers in Systems Neuroscience, vol. 13.
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© Copyright © 2020 Chia, Zhong, Vissel, Edgerton and Gad. A mid-thoracic spinal cord injury (SCI) severely impairs activation of the lower limb sensorimotor spinal networks, leading to paralysis. Various neuromodulatory techniques including electrical and pharmacological activation of the spinal networks have been successful in restoring locomotor function after SCI. We hypothesized that the combination of self-training in a natural environment with epidural stimulation (ES), quipazine (Quip), and strychnine (Strych) would result in greater activity in a cage environment after paralysis compared to either intervention alone. To assess this, we developed a method measuring and characterizing the chronic EMG recordings from tibialis anterior (TA) and soleus (Sol) muscles while rats were freely moving in their home cages. We then assessed the relationship between the change in recorded activity over time and motor-evoked potentials (MEPs) in animals receiving treatments. We found that the combination of ES, Quip, and Strych (sqES) generated the greatest level of recovery followed by ES + Quip (qES) while ES + Strych (sES) and ES alone showed least improvement in recorded activity. Further, we observed an exponential relationship between late response (LR) component of the MEPs and spontaneously generated step-like activity. Our data demonstrate the feasibility and potential importance of quantitatively monitoring mechanistic factors linked to activity-dependence in response to combinatorial interventions compared to individual therapies after SCI.
Chiang, YK, Oberst, S, Melnikov, A, Quan, L, Marburg, S, Alù, A & Powell, DA 2020, 'Reconfigurable Acoustic Metagrating for High-Efficiency Anomalous Reflection', Physical Review Applied, vol. 13, no. 6, pp. 064067-064067.
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Recent study revealed that the scattering behaviors of bianisotropic scatterers can be controlled by an additional degree of freedom, represented as Willis coupling, which can be endowed with asymmetric wave scattering to form an acoustic metagrating for wavefront manipulation. Here, we introduce a flexible acoustic metagrating, formed by periodic arrays of properly design Willis scatterers, for anomalous reflection with nearly unitary efficiency and significantly less necessity of fine discretization. Numericalapproaches to predict the wave steering efficiency of the proposed acoustic metagratings with infinite and finite length are developed, which are utilized to demonstrate the strength and flexible features of the metagratings. Results reveal that the proposed acoustic metagrating can reroute incident wave into desired direction at a large angle with nearly unitary efficiency in reflection. The numerical predictions also show that the proposed designs offer a high efficient tunable platform in controlling the steering angles and operating frequencies. To practically realize the ability of extreme angle steering and tunable characteristics of the metagratings, designed structures are fabricated and examined experimentally. The acoustic wave is successfully rerouted to the targeted reflection angles by the finite metagrating. The flexibility regarding different steering angles and operating frequencies of the proposed metagratings are also demonstrated experimentally.
Chin, LH, Hon, CM, Chellappan, DK, Chellian, J, Madheswaran, T, Zeeshan, F, Awasthi, R, Aljabali, AAA, Tambuwala, MM, Dureja, H, Negi, P, Kapoor, DN, Goyal, R, Paudel, KR, Satija, S, Gupta, G, Hsu, A, Wark, P, Mehta, M, Wadhwa, R, Hansbro, PM & Dua, K 2020, 'Molecular mechanisms of action of naringenin in chronic airway diseases', European Journal of Pharmacology, vol. 879, pp. 173139-173139.
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Chronic airway inflammatory diseases are characterized by persistent proinflammatory responses in the respiratory tract. Although, several treatment strategies are currently available, lifelong therapy is necessary for most of these diseases. In recent years, phytophenols, namely, flavonoids, derived from fruits and vegetables have been gaining tremendous interest and have been extensively studied due to their low toxicological profile. Naringenin is a bioflavonoid abundantly found in citrus fruits. This substance has shown notable therapeutic potential in various diseases due to its promising diverse biological activities. In this review, we have attempted to review the published studies from the available literature, discussing the molecular level mechanisms of naringenin in different experimental models of airway inflammatory diseases including asthma, chronic obstructive pulmonary disease (COPD), lung cancer, pulmonary fibrosis and cystic fibrosis. Current evidences have proposed that the anti-inflammatory properties of naringenin play a major role in ameliorating inflammatory disease states. In addition, naringenin also possesses several other biological properties. Despite the proposed mechanisms suggesting remarkable therapeutic benefits, the clinical use of naringenin is, however, hampered by its low solubility and bioavailability. Furthermore, this review also discusses on the studies that utilise nanocarriers as a drug delivery system to address the issue of poor solubility.
Choi, Y, Naidu, G, Lee, S & Vigneswaran, S 2020, 'Recovery of sodium sulfate from seawater brine using fractional submerged membrane distillation crystallizer', Chemosphere, vol. 238, pp. 124641-124641.
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Seawater reverse osmosis (SWRO) brine contain many valuable resources. In this study, fractional-submerged membrane distillation crystallizer (F-SMDC) was used to recover sodium sulfate (Na2SO4) from SWRO brine. The concentration/temperature gradient (CG/TG) in the reactor enhanced water recovery utilizing MD and Na2SO4 crystallization via a crystallizer. Crystals were not obtained at the bottom section of the F-SMDC due to: firstly, calcium sulfate crystallization occurring on the membrane surface; and secondly, low temperature-sensitivity solubility component such as NaCl exerting a negative influence. In order to obtain supersaturation, a sulfate-rich scenario was created in the reactor through the addition of the following three components: Na2SO4, MgSO4 and (NH4)2SO4. When Na2SO4 and MgSO4 were added, a larger concentration was observed at the top section, resulting in a low concentration gradient (CG) ratio, i.e. around 1.7. Conversely, the addition of (NH4)2SO4 achieved faster Na2SO4 crystallization (VCF 1.42) at the bottom section with a greater CG ratio of more than 2.0. Total water recovery ratio of 72% and 223.73 g Na2SO4 crystals were successfully extracted from simulated SWRO brine using laboratory scale F-SMDC.
Chomsiri, T, He, X, Nanda, P & Tan, Z 2020, 'Hybrid Tree-Rule Firewall for High Speed Data Transmission', IEEE Transactions on Cloud Computing, vol. 8, no. 4, pp. 1237-1249.
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Traditional firewalls employ listed rules in both configuration and process phases to regulate network traffic. However, configuring a firewall with listed rules may create rule conflicts, and slows down the firewall. To overcome this problem, we have proposed a Tree-rule firewall in our previous study. Although the Tree-rule firewall guarantees no conflicts within its rule set and operates faster than traditional firewalls, keeping track of the state of network connections using hashing functions incurs extra computational overhead. In order to reduce this overhead, we propose a hybrid Tree-rule firewall in this paper. This hybrid scheme takes advantages of both Tree-rule firewalls and traditional listed-rule firewalls. The GUIs of our Tree-rule firewalls are utilized to provide a means for users to create conflict-free firewall rules, which are organized in a tree structure and called 'tree rules'. These tree rules are later converted into listed rules that share the merit of being conflict-free. Finally, in decision making, the listed rules are used to verify against packet header information. The rules which have matched with most packets are moved up to the top positions by the core firewall. The mechanism applied in this hybrid scheme can significantly improve the functional speed of a firewall.
Choo, Y, Halat, DM, Villaluenga, I, Timachova, K & Balsara, NP 2020, 'Diffusion and migration in polymer electrolytes', Progress in Polymer Science, vol. 103, pp. 101220-101220.
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Mixtures of neutral polymers and lithium salts have the potential to serve as electrolytes in next-generation rechargeable Li-ion batteries. The purpose of this review is to expose the delicate interplay between polymer-salt interactions at the segmental level and macroscopic ion transport at the battery level. Since complete characterization of this interplay has only been completed in one system: mixtures of poly(ethylene oxide) and lithium bis(trifluoromethanesulfonyl)imide (PEO/LiTFSI), we focus on data obtained from this system. We begin with a discussion of the activity coefficient, followed by a discussion of six different diffusion coefficients: the Rouse motion of polymer segments is quantified by Dseg, the self-diffusion of cations and anions is quantified by Dself,+ and Dself,−, and the build-up of concentration gradients in electrolytes under an applied potential is quantified by Stefan-Maxwell diffusion coefficients, D0+, D0-, and D+-. The Stefan-Maxwell diffusion coefficients can be used to predict the velocities of the ions at very early times after an electric field is applied across the electrolyte. The surprising result is that D0- is negative in certain concentration windows. A consequence of this finding is that at these concentrations, both cations and anions are predicted to migrate toward the positive electrode at early times. We describe the controversies that surround this result. Knowledge of the Stefan-Maxwell diffusion coefficients enable prediction of the limiting current. We argue that the limiting current is the most important characteristic of an electrolyte. Excellent agreement between theoretical and experimental limiting current is seen in PEO/LiTFSI mixtures. What sequence of monomers that, when polymerized, will lead to the highest limiting current remains an important unanswered question. It is our hope that the approach presented in this review will guide the development of such polymers.
Chowdhuri, I, Pal, SC, Arabameri, A, Saha, A, Chakrabortty, R, Blaschke, T, Pradhan, B & Band, SS 2020, 'Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment', Remote Sensing, vol. 12, no. 21, pp. 3620-3620.
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The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study.
Chowdhury, FR, Hoque, A, Chowdhury, FUH, Amin, MR, Rahim, A, Rahman, MM, Yasmin, R, Amin, MR, Miah, MT, Kalam, MA & Rahman, MS 2020, 'Convalescent plasma transfusion therapy in severe COVID-19 patients- a safety, efficacy and dose response study: A structured summary of a study protocol of a phase II randomized controlled trial', Trials, vol. 21, no. 1.
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AbstractObjectivesGeneral: To assess the safety, efficacy and dose response of convalescent plasma (CP) transfusion in severe COVID-19 patientsSpecific:a. To identify the appropriate effective dose of CP therapy in severe patientsb. To identify the efficacy of the therapy with their end point based on clinical improvement within seven days of treatment or until discharge whichever is later and in-hospital mortalityc. To assess the clinical improvement after CP transfusion in severe COVID-19 patientsd. To assess the laboratory improvement after CP transfusion in severe COVID-19 patientsTrial DesignThis is a multicentre, multi-arm phase II Randomised Controlled Trial.ParticipantsAge and sex matched COVID-19 positive (by RT-PCR) severe cases will be enrolled in this trial. Severe case is defined by the World Health Organization (W.H.O) clinical case definition. The inclusion criteria are1. Respiratory rate > 30 breaths/min; PLUS2. Severe respiratory distress; or SpO2 ≤ 88% on room air or PaO2/FiO2≤ 300 mm of Hg, PLUS3. Radiological (X-ray or CT scan) evidence of bilateral lung infiltrate, AND OR4. Systolic BP < 90 mm of Hg or diastolic BP <60 mm of Hg.AND/OR5. Criteria 1 to 4 AND or patient in ventilator supportPatients’ below18 years, pregnant and lactating women, previous history of allergic reaction to plasma, patients who have already received plasma from a different source will be excluded. Patients will be enrolled at Bangabandhu Sheikh Mujib Medical University (BSMMU) hospital, Dhaka medical college hospital (DMCH) and Mugda medic...
Chowdhury, PN, Shivakumara, P, Pal, U, Lu, T & Blumenstein, M 2020, 'A new augmentation-based method for text detection in night and day license plate images', Multimedia Tools and Applications, vol. 79, no. 43-44, pp. 33303-33330.
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Despite a number of methods that have been developed for License Plate Detection (LPD), most of these focus on day images for license plate detection. As a result, license plate detection in night images is still an elusive goal for researchers. This paper presents a new method for LPD based on augmentation and Gradient Vector Flow (GVF) in night and day images. The augmentation involves expanding windows for each pixel in R, G and B color spaces of the input image until the process finds dominant pixels in both night and day license plate images of the respective color spaces. We propose to fuse the dominant pixels in R, G and B color spaces to restore missing pixels. For the results of fusing night and day images, the proposed method explores Gradient Vector Flow (GVF) patterns to eliminate false dominant pixels, which results in candidate pixels. The proposed method explores further GVF arrow patterns to define a unique loop pattern that represents hole in the characters, which gives candidate components. Furthermore, the proposed approach uses a recognition concept to fix the bounding boxes, merging the bounding boxes and eliminating false positives, resulting in text/license plate detection in both night and day images. Experimental results on night images of our dataset and day images of standard license plate datasets, demonstrate that the proposed approach is robust compared to the state-of-the-art methods. To show the effectiveness of the proposed method, we also tested our approach on standard natural scene datasets, namely, ICDAR 2015, MSRA-TD-500, ICDAR 2017-MLT, Total-Text, CTW1500 and MS-COCO datasets, and their results are discussed.
Chu, P, Zhang, JA, Wang, X, Fang, G & Wang, D 2020, 'Semi-Persistent Resource Allocation Based on Traffic Prediction for Vehicular Communications', IEEE Transactions on Intelligent Vehicles, vol. 5, no. 2, pp. 345-355.
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Chu, P, Zhang, JA, Wang, X, Fei, Z, Fang, G & Wang, D 2020, 'Interference Characterization and Power Optimization for Automotive Radar With Directional Antenna', IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 3703-3716.
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© 1967-2012 IEEE. Wide deployment of radar sensors on automotive vehicles can potentially lead to a severe interference problem. Such interference has been characterized without considering directional antenna patterns, which could lead to results significantly larger than the actual ones. In this paper, we study the mean power of effective echo signals and interference, by considering both front- and side- mounted radars equipped with directional antennas. We employ the stochastic geometry method to characterize the randomness of vehicles and hence radars in both two-lane and multi-lane scenarios, and derive closed-form expressions for the mean interference by approximating the radiation pattern by Gaussian waveforms. Simulation results are shown to match the analytical results very well, and insights are obtained for the impact of radar parameters on interference. Based on the interference analysis, we aim to minimize the total transmission power of each vehicle with constraints on the required signal to interference and noise ratio. An optimal solution is obtained based on linear programming techniques and corroborated by simulation results.
Chua, BB & Zhang, Y 2020, 'Applying a Systematic Literature Review and Content Analysis Method to Analyse Open Source Developers’ Forking Motivation Interpretation, Categories and Consequences', Australasian Journal of Information Systems, vol. 24.
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In open source (OS) environments, forking is a powerful social collaborative technique that creates a social coding community and increases code visibility but it has not been adopted by OS software (OSS) developers. This paper investigates OS forking divergence using contextual frameworks (systematic literature review and content analysis) to analyse OSS developer forking motivation, interpretation, categorisation and consequences. We identified five theoretical forking patterns: 1) forking can revive original project health; 2) few effective frameworks exist to describe project-to-project developer migration; 3) there is a literature on social forking community behaviour; 4) poor guidance is a threat to forking; and 5) most research uses mixed methods. We introduce guidelines for OSS communities to reduce organisational barriers to developer motivation and highlight the important of understanding developer forking. The challenge remains to analyse forking and sustainability from a social community perspective, particularly how programming language, file repositories and developer interest can predict forking motivation and behaviour for both novice OSS developers or experienced developers who want to improve forking performance.
Chung, H-J, Islam, MS, Rahman, MM & Hong, S-T 2020, 'Neuroprotective function of Omi to α-synuclein-induced neurotoxicity', Neurobiology of Disease, vol. 136, pp. 104706-104706.
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Chu-Van, T, Surawski, N, Ristovski, Z, Yuan, C-S, Stevanovic, S, Ashrafur Rahman, SM, Hossain, FM, Guo, Y, Rainey, T & Brown, RJ 2020, 'The effect of diesel fuel sulphur and vanadium on engine performance and emissions', Fuel, vol. 261, pp. 116437-116437.
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© 2019 Elsevier Ltd Metallic composition of diesel particulate matter, even though a relatively small proportion of total mass, can reveal important information regarding engine conditions, fuel/lubricating oil characteristics and for health impacts. In this study, a detailed investigation into the metallic elemental composition at different particle diameter sizes has been undertaken. A bivariate statistical analysis was performed in order to investigate the correlation between the metallic element, measured engine performance and engine emission variables. Major sources of metallic elements in the emitted particles are considered in this study, including the fuel and lubricating oil compositions, engine wear emissions and metal-containing dust in the ambient air. Metallic solid ultrafine-particles (Dp < 100 nm) are strongly associated with metallic compounds derived from lubricating oil (Ca, Zn, Mg and K), while the fuel related metallic compounds and engine wear emissions are represented in the accumulation mode particle fraction (>100 nm). Calculated correlation matrices show a clear effect of engine load conditions and fuel S contents on particle number and mass emissions.
Clement, S, Campbell, JM, Deng, W, Guller, A, Nisar, S, Liu, G, Wilson, BC & Goldys, EM 2020, 'Mechanisms for Tuning Engineered Nanomaterials to Enhance Radiation Therapy of Cancer', Advanced Science, vol. 7, no. 24, pp. 2003584-2003584.
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AbstractEngineered nanomaterials that produce reactive oxygen species on exposure to X‐ and gamma‐rays used in radiation therapy offer promise of novel cancer treatment strategies. Similar to photodynamic therapy but suitable for large and deep tumors, this new approach where nanomaterials acting as sensitizing agents are combined with clinical radiation can be effective at well‐tolerated low radiation doses. Suitably engineered nanomaterials can enhance cancer radiotherapy by increasing the tumor selectivity and decreasing side effects. Additionally, the nanomaterial platform offers therapeutically valuable functionalities, including molecular targeting, drug/gene delivery, and adaptive responses to trigger drug release. The potential of such nanomaterials to be combined with radiotherapy is widely recognized. In order for further breakthroughs to be made, and to facilitate clinical translation, the applicable principles and fundamentals should be articulated. This review focuses on mechanisms underpinning rational nanomaterial design to enhance radiation therapy, the understanding of which will enable novel ways to optimize its therapeutic efficacy. A roadmap for designing nanomaterials with optimized anticancer performance is also shown and the potential clinical significance and future translation are discussed.
Cong Nguyen, N, Cong Duong, H, Chen, S-S, Thi Nguyen, H, Hao Ngo, H, Guo, W, Quang Le, H, Cong Duong, C, Thuy Trang, L, Hoang Le, A, Thanh Bui, X & Dan Nguyen, P 2020, 'Water and nutrient recovery by a novel moving sponge – Anaerobic osmotic membrane bioreactor – Membrane distillation (AnOMBR-MD) closed-loop system', Bioresource Technology, vol. 312, pp. 123573-123573.
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For the first time, a novel sponge-based moving bed-anaerobic osmosis membrane bioreactor/membrane distillation (AnOMBR/MD) system using mixed Na3PO4/EDTA-2Na as the draw solution was employed to treat wastewater for enhanced water flux and reduced membrane fouling. Results indicated that the moving sponge-AnOMBR/MD system obtained a stable water flux of 4.01 L/m2 h and less membrane fouling for a period lasting 45 days. Continuous moving sponge around the FO module is the main mechanism for minimizing membrane fouling during the 45-day AnOMBR operation. The proposed system's nutrient removal was almost 100%, thus showing the superiority of simultaneous FO and MD membranes. Nutrient recovery from the MF permeate was best when solution pH was controlled to 9.5, whereby 17.4% (wt/wt) of phosphorus was contained in precipitated components. Moreover, diluted draw solute following AnOMBR was effectively regenerated using the MD process with water flux above 2.48 L/m2 h and salt rejection > 99.99%.
Cook, RHW, Seymour, N, Spekkens, K, Hurley-Walker, N, Hancock, PJ, Bell, ME, Callingham, JR, For, B-Q, Franzen, TMO, Gaensler, BM, Hindson, L, Johnston-Hollitt, M, Kapińska, AD, Morgan, J, Offringa, AR, Procopio, P, Staveley-Smith, L, Wayth, RB, Wu, C & Zheng, Q 2020, 'Searching for dark matter signals from local dwarf spheroidal galaxies at low radio frequencies in the GLEAM survey', Monthly Notices of the Royal Astronomical Society, vol. 494, no. 1, pp. 135-145.
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ABSTRACT The search for emission from weakly interacting massive particle (WIMP) dark matter annihilation and decay has become a multipronged area of research not only targeting a diverse selection of astrophysical objects, but also taking advantage of the entire electromagnetic spectrum. The decay of WIMP particles into standard model particles has been suggested as a possible channel for synchrotron emission to be detected at low radio frequencies. Here, we present the stacking analysis of a sample of 33 dwarf spheroidal (dSph) galaxies with low-frequency (72–231 MHz) radio images from the GaLactic and Extragalactic All-sky Murchison Widefield Array (GLEAM) survey. We produce radial surface brightness profiles of images centred upon each dSph galaxy with background radio sources masked. We remove 10 fields from the stacking due to contamination from either poorly subtracted, bright radio sources or strong background gradients across the field. The remaining 23 dSph galaxies are stacked in an attempt to obtain a statistical detection of any WIMP-induced synchrotron emission in these systems. We find that the stacked radial brightness profile does not exhibit a statistically significant detection above the 95 per cent confidence level of ∼1.5 mJy beam−1. This novel technique shows the potential of using low-frequency radio images to constrain fundamental properties of particle dark matter.
Craig, HC, Piorkowski, D, Nakagawa, S, Kasumovic, MM & Blamires, SJ 2020, 'Meta-analysis reveals materiomic relationships in major ampullate silk across the spider phylogeny', Journal of The Royal Society Interface, vol. 17, no. 170, pp. 20200471-20200471.
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Spider major ampullate (MA) silk, with its combination of strength and extensibility, outperforms any synthetic equivalents. There is thus much interest in understanding its underlying materiome. While the expression of the different silk proteins (spidroins) appears an integral component of silk performance, our understanding of the nature of the relationship between the spidroins, their constituent amino acids and MA silk mechanics is ambiguous. To provide clarity on these relationships across spider species, we performed a meta-analysis using phylogenetic comparative methods. These showed that glycine and proline, both of which are indicators of differential spidroin expression, had effects on MA silk mechanics across the phylogeny. We also found serine to correlate with silk mechanics, probably via its presence within the carboxyl and amino-terminal domains of the spidroins. From our analyses, we concluded that the spidroin expression shifts across the phylogeny from predominantly MaSp1 in the MA silks of ancestral spiders to predominantly MaSp2 in the more derived spiders' silks. This trend was accompanied by an enhanced ultimate strain and decreased Young's modulus in the silks. Our meta-analysis enabled us to decipher between real and apparent influences on MA silk properties, providing significant insights into spider silk and web coevolution and enhancing our capacity to create spider silk-like materials.
Crowther, CA, Ashwood, P, Andersen, CC, Middleton, PF, Tran, T, Doyle, LW, Robinson, JS & Harding, JE 2020, 'Maternal dexamethasone before preterm births: implications for lower middle-income countries – Authors' reply', The Lancet Child & Adolescent Health, vol. 4, no. 1, pp. e2-e2.
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Cui, L, Qu, Y, Gao, L, Xie, G & Yu, S 2020, 'Detecting false data attacks using machine learning techniques in smart grid: A survey', Journal of Network and Computer Applications, vol. 170, pp. 102808-102808.
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© 2020 The big data sources in smart grid (SG) enable utilities to monitor, control, and manage the energy system effectively, which is also promising to advance the efficiency, reliability, and sustainability of energy usage. However, false data attacks, as a major threat with wide targets and severe impacts, have exposed the SG systems to a large variety of security issues. To detect this threat effectively, several machine learning (ML)-based methods have been developed in the past few years. In this paper, we provide a comprehensive survey of these advances. The paper starts by providing a brief overview of SG architecture and its data sources. Moreover, the categories of false data attacks followed by data security requirements are introduced. Then, the recent ML-based detection techniques are summarized by grouping them into three major detection scenarios: non-technical losses, state estimation, and load forecasting. At last, we further investigate the potential research directions at the end of the paper, considering the deficiencies of current ML-based mechanisms. Specifically, we discuss intrusion detection against adversarial attacks, collaborative and decentralized detection framework, detection with privacy preservation, and some potential advanced ML techniques.
Cui, L, Wu, J, Pi, D, Zhang, P & Kennedy, P 2020, 'Dual Implicit Mining-Based Latent Friend Recommendation', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 5, pp. 1663-1678.
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IEEE The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.
Cui, L, Xie, G, Yu, S, Zhai, X & Gao, L 2020, 'An Inherent Property-Based Rumor Dissemination Model in Online Social Networks', IEEE Networking Letters, vol. 2, no. 1, pp. 43-46.
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Cui, Q, Ni, W, Li, S, Zhao, B, Liu, RP & Zhang, P 2020, 'Learning-Assisted Clustered Access of 5G/B5G Networks to Unlicensed Spectrum', IEEE Wireless Communications, vol. 27, no. 1, pp. 31-37.
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Cui, Z, Hao Ngo, H, Cheng, Z, Zhang, H, Guo, W, Meng, X, Jia, H & Wang, J 2020, 'Hysteresis effect on backwashing process in a submerged hollow fiber membrane bioreactor (MBR) applied to membrane fouling mitigation', Bioresource Technology, vol. 300, pp. 122710-122710.
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Hysteresis effect on backwashing in a submerged MBR was investigated with dead-end hollow fiber membranes. The out-of-step changes in TMP and flux is the real hysteresis effect which is common but easily overlooked. Methods of visualization and ultrasonic spectrum analysis were implemented. The results showed that fouling layer is just the culprit of hysteresis effect. Fouling level and fiber length were determined as two key factors that affect hysteresis effect by data and model derivation. Moreover, a hysteresis evaluation index "τbw" is proposed to quantify the result of TMP vs time. The relationship between influence factors and "τbw" is interactive. A linear relationship between fouling level and "τbw" was found as well as an extreme value between fiber length and "τbw". A lower fouling level (lower backwashing flow) and optimal backwashing duration will be helpful for an effective backwashing no matter for membrane fouling control or energy cost reduce.
Curiskis, SA, Drake, B, Osborn, TR & Kennedy, PJ 2020, 'An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit', Information Processing & Management, vol. 57, no. 2, pp. 102034-102034.
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© 2019 Elsevier Ltd Methods for document clustering and topic modelling in online social networks (OSNs) offer a means of categorising, annotating and making sense of large volumes of user generated content. Many techniques have been developed over the years, ranging from text mining and clustering methods to latent topic models and neural embedding approaches. However, many of these methods deliver poor results when applied to OSN data as such text is notoriously short and noisy, and often results are not comparable across studies. In this study we evaluate several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit. We benchmark four different feature representations derived from term-frequency inverse-document-frequency (tf-idf) matrices and word embedding models combined with four clustering methods, and we include a Latent Dirichlet Allocation topic model for comparison. Several different evaluation measures are used in the literature, so we provide a discussion and recommendation for the most appropriate extrinsic measures for this task. We also demonstrate the performance of the methods over data sets with different document lengths. Our results show that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures. We also demonstrate a method for interpreting the clusters with a top-words based approach using tf-idf weights combined with embedding distance measures.
Dadol, GC, Kilic, A, Tijing, LD, Lim, KJA, Cabatingan, LK, Tan, NPB, Stojanovska, E & Polat, Y 2020, 'Solution blow spinning (SBS) and SBS-spun nanofibers: Materials, methods, and applications', Materials Today Communications, vol. 25, pp. 101656-101656.
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© 2020 Elsevier Ltd Solution blow spinning (SBS) is a maturing nanofiber fabrication technology. Over the past decade, there has been a growing interest in employing and developing this facile method of fabricating nanofibers, sourced from different materials to suit varied applications. For the first time, this review will provide a comprehensive overview of solution blow spinning, including the principles, materials, methods, and applications. We start with the principles of the SBS method, followed by a detailed account of the different precursor polymers (i.e., synthetic, biocompatible, and bio-based materials) and composites that have been used in the SBS of nanofibers. The proceeding section presents the known applications of nanofibers obtained through SBS which are discussed primarily in the areas of energy and electronics, biomedical, environmental, membrane separation, and, textile and smart material applications. We highlight the most important and recent advances related to SBS over the last ten years. Lastly, we give perspectives, challenges, opportunities, and new directions of the SBS technology.
Daer, S, Akther, N, Wei, Q, Shon, HK & Hasan, SW 2020, 'Influence of silica nanoparticles on the desalination performance of forward osmosis polybenzimidazole membranes', Desalination, vol. 491, pp. 114441-114441.
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© 2020 Elsevier B.V. Polybenzimidazole (PBI) is a chemically and thermally stable polymer, which is being considered for forward osmosis (FO) seawater desalination in regions with high seawater temperatures and salinities. In this work, FO flat sheet membranes were fabricated using non-solvent induced phase separation (NIPS) method from PBI dope solution incorporated with silica nanoparticles (SNPs) at different concentrations (0, 0.5, 1 and 2 wt%). The influence of draw solution concentration, cross-flow velocity and membrane cell orientation on the performance of pristine PBI and PBI/SNP membranes was examined. Results showed that the performance of the PBI/SNP membranes improved compared to the pristine PBI membrane. Addition of 0.5 wt% of SNPs to PBI membranes (S0.5) reduced the membrane's structural parameter (809.4 μm vs. 1193.2 μm), augmented the tensile strength (31.9 MPa vs. 27.3 MPa), and increased water flux by two folds (16.9 Lm−2 h−1 vs. 7.4 Lm−2 h−1) compared to the pristine PBI membrane (S0). Given the thermal stability of the PBI/SNP membrane along with its improved water permeation performance, the modified membrane offers a promising option for the FO process in hot and arid zones.
Dai, P, Lu, W, Le, K & Liu, D 2020, 'Sliding Mode Impedance Control for contact intervention of an I-AUV: Simulation and experimental validation', Ocean Engineering, vol. 196, pp. 106855-106855.
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© 2019 Elsevier Ltd Position/force control of an Intervention Autonomous Underwater Vehicle (I-AUV) is essential to many underwater intervention tasks (e.g., marine equipment maintenance, underwater welding and so on), and is challenging due to unknown fluid disturbances and model uncertainties. This paper applies the Sliding Mode Impedance Control (SMIC) to the full contact intervention of an I-AUV, from non-contact phase to contact phase. Both computational simulations and practical experiments are conducted to investigate the performance of SMIC. In simulations, considering model uncertainties and detailed fluid disturbances, accurate position and force tracking can be achieved, with the position tracking errors within ±3 × 10−3m and a Root Mean Square Error (RMSE) of 4 × 10−2N in tracking the desired contact force of 10N. For the purpose of experimental validation, the SMIC is implemented on an I-AUV developed in the University of Technology Sydney (UTS). The experimental results demonstrate the SMIC's good performance in the contact intervention of the I-AUV, with the position tracking errors within ±1.6×10−2 m and a RMSE of 0.97N in maintaining the desired contact force of 10N.
Dai, P, Lu, W, Le, K & Liu, D 2020, 'Sliding Mode Impedance Control for contact intervention of an I-AUV: Simulation and experimental validation', Ocean Engineering, vol. 196.
Dang, DNM, Ngo, QT, Trung, QL & Le, LB 2020, 'An adaptive and cooperative MAC protocol in vehicular ad hoc network: design and performance analysis', International Journal of Ad Hoc and Ubiquitous Computing, vol. 35, no. 4, pp. 191-191.
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Dang, KN, Ahmed, AB, Abdallah, AB & Tran, X-T 2020, 'A Thermal-Aware On-Line Fault Tolerance Method for TSV Lifetime Reliability in 3D-NoC Systems', IEEE Access, vol. 8, pp. 166642-166657.
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Dang, KN, Ahmed, AB, Abdallah, AB & Tran, X-T 2020, 'TSV-OCT: A Scalable Online Multiple-TSV Defects Localization for Real-Time 3-D-IC Systems', IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 28, no. 3, pp. 672-685.
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Dang, LC, Dang, CC & Khabbaz, H 2020, 'Modelling of columns and fibre-reinforced load-transfer platform-supported embankments', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 173, no. 4, pp. 197-215.
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A novel ground modification technique is proposed utilising a fibre-reinforced load-transfer platform (FRLTP) and deep cement mixing column-supported (CS) embankment constructed over soft soils. An equivalent two-dimensional finite-element model was developed to simulate the full geometry of a CS embankment reinforced without or with an FRLTP. A series of numerical analyses was first conducted on the proposed model for different improvement depths to assess the effectiveness of the introduction of FRLTP into the CS embankment system in terms of total and differential settlements, the stress-transfer mechanism and lateral displacement with depth. Subsequently, another extensive parametric study was conducted to further investigate the influence of the FRLTP key parameters, including elastic deformation modulus, shear strength properties and tensile strength, on the embankment performance during construction and consolidation time. The numerical results showed that the FRLTP effectively diminished the total settlement and the lateral deformation of the embankment, while improving the stress concentration ratio and the embankment stability to a great extent. The findings of the extensive parametric study indicate that the FRLTP's shear strength properties appear to be the most influential factors to be considered in the design procedure of a target CS–FRLTP–embankment system.
Daniel, S & Mazzurco, A 2020, 'Development of a scenario-based instrument to assess co-design expertise in humanitarian engineering', European Journal of Engineering Education, vol. 45, no. 5, pp. 654-674.
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Co-design is fundamental to humanitarian engineering and increasingly recognised as such in engineering curricula. However, it is challenging to teach, learn, and assess. In this paper, we describe the development and validation of a scenario-based instrument to distinguish novice and expert approaches to co-design in the context of humanitarian engineering. The instrument assesses the extent to which respondents describe stakeholder participation in each of the scope, design, and deliver phases of the design process, with co-design experts taking a collaborative approach throughout. We analyse and compare responses to the instrument from first-year undergraduate engineering students and experienced humanitarian engineering practitioners. Implications for educators, to use this scenario-based assessment in their own research, teaching, and curriculum development, are discussed in detail.
Darwish, MA, Yafi, E, Al Ghamdi, MA & Almasri, A 2020, 'Decentralizing Privacy Implementation at Cloud Storage Using Blockchain-Based Hybrid Algorithm', Arabian Journal for Science and Engineering, vol. 45, no. 4, pp. 3369-3378.
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Das, S, Pradhan, B, Shit, PK & Alamri, AM 2020, 'Assessment of Wetland Ecosystem Health Using the Pressure–State–Response (PSR) Model: A Case Study of Mursidabad District of West Bengal (India)', Sustainability, vol. 12, no. 15, pp. 5932-5932.
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Wetlands are essential for protein production, water sanctification, groundwater recharge, climate purification, nutrient cycling, decreasing floods and biodiversity preservation. The Mursidabad district in West Bengal (India) is situated in the floodplain of the Ganga–Padma and Bhagirathi rivers. The region is characterized by diverse types of wetlands; however, the wetlands are getting depredated day-by-day due to hydro-ecological changes, uncontrolled human activities and rapid urbanization. This study attempted to explore the health status of the wetland ecosystem in 2013 and 2020 at the block level in the Mursidabad district, using the pressure–state–response model. Based on wetland ecosystem health values, we categorized the health conditions and identified the blocks where the health conditions are poor. A total of seven Landsat ETM+ spaceborne satellite images in 2001, 2013 and 2020 were selected as the data sources. The statistical data included the population density and urbanization increase rate, for all administrative units, and were collected from the census data of India for 2001 and 2011. We picked nine ecosystem indicators for the incorporated assessment of wetland ecosystem health. The indicators were selected considering every block in the Mursidabad district and for the computation of the wetland ecosystem health index by using the analytical hierarchy processes method. This study determined that 26.92% of the blocks fell under the sick category in 2013, but increased to 30.77% in 2020, while the percentage of blocks in the very healthy category has decreased markedly from 11.54% to 3.85%. These blocks were affected by higher human pressure, such as population density, urbanization growth rate and road density, which resulted in the degradation of wetland health. The scientific protection and restoration techniques of these wetlands should be emphasized in these areas.
Dawson, N, Molitorisz, S, Rizoiu, M-A & Fray, P 2020, 'Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to 2020', Journalism, p. 146488492199628. 2021, pp. 146488492199628-146488492199628.
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In Australia and beyond, journalism is reportedly an industry in crisis, acrisis exacerbated by COVID-19. However, the evidence revealing the crisis isoften anecdotal or limited in scope. In this unprecedented longitudinalresearch, we draw on data from the Australian journalism jobs market fromJanuary 2012 until March 2020. Using Data Science and Machine Learningtechniques, we analyse two distinct data sets: job advertisements (ads) datacomprising 3,698 journalist job ads from a corpus of over 8 million Australianjob ads; and official employment data from the Australian Bureau of Statistics.Having matched and analysed both sources, we address both the demand for andsupply of journalists in Australia over this critical period. The data showthat the crisis is real, but there are also surprises. Counter-intuitively, thenumber of journalism job ads in Australia rose from 2012 until 2016, beforefalling into decline. Less surprisingly, for the entire period studied thefigures reveal extreme volatility, characterised by large and erraticfluctuations. The data also clearly show that COVID-19 has significantlyworsened the crisis. We then tease out more granular findings, including: thatthere are now more women than men journalists in Australia, but that genderinequity is worsening, with women journalists getting younger and worse-paidjust as men journalists are, on average, getting older and better-paid; that,despite the crisis besetting the industry, the demand for journalism skills hasincreased; and that, perhaps concerningly, the skills sought by journalism jobads increasingly include social media and generalist communications.
Dawson, N, Williams, M-A & Rizoiu, M-A 2020, 'Skill-driven Recommendations for Job Transition Pathways', PLOS ONE 16(8): e0254722, 2021.
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Job security can never be taken for granted, especially in times of rapid,widespread and unexpected social and economic change. These changes can forceworkers to transition to new jobs. This may be because new technologies emergeor production is moved abroad. Perhaps it is a global crisis, such as COVID-19,which shutters industries and displaces labor en masse. Regardless of theimpetus, people are faced with the challenge of moving between jobs to find newwork. Successful transitions typically occur when workers leverage theirexisting skills in the new occupation. Here, we propose a novel method tomeasure the similarity between occupations using their underlying skills. Wethen build a recommender system for identifying optimal transition pathwaysbetween occupations using job advertisements (ads) data and a longitudinalhousehold survey. Our results show that not only can we accurately predictoccupational transitions (Accuracy = 76%), but we account for the asymmetricdifficulties of moving between jobs (it is easier to move in one direction thanthe other). We also build an early warning indicator for new technologyadoption (showcasing Artificial Intelligence), a major driver of rising jobtransitions. By using real-time data, our systems can respond to labor demandshifts as they occur (such as those caused by COVID-19). They can be leveragedby policy-makers, educators, and job seekers who are forced to confront theoften distressing challenges of finding new jobs.
Dehdashti, S, Fell, L, Karim Obeid, A, Moreira, C & Bruza, P 2020, 'Bistable probabilities: a unified framework for studying rationality and irrationality in classical and quantum games', Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 476, no. 2237.
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This article presents a unified probabilistic framework that allows both rational and irrational decision-making to be theoretically investigated and simulated in classical and quantum games. Rational choice theory is a basic component of game-theoretic models, which assumes that a decision-maker chooses the best action according to their preferences. In this article, we define irrationality as a deviation from a rational choice. Bistable probabilities are proposed as a principled and straightforward means for modelling (ir)rational decision-making in games. Bistable variants of classical and quantum Prisoner’s Dilemma, Stag Hunt and Chicken are analysed in order to assess the effect of (ir)rationality on agent utility and Nash equilibria. It was found that up to three Nash equilibria exist for all three classical bistable games and maximal utility was attained when agents were rational. Up to three Nash equilibria exist for all three quantum bistable games; however, utility was shown to increase according to higher levels of agent irrationality.
Dehkordi, MR, Seifzadeh, H, Beydoun, G & Nadimi-Shahraki, MH 2020, 'Success prediction of android applications in a novel repository using neural networks', Complex & Intelligent Systems, vol. 6, no. 3, pp. 573-590.
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AbstractNowadays, Android applications play a major role in software industry. Therefore, having a system that can help companies predict the success probability of such applications would be useful. Thus far, numerous research works have been conducted to predict the success probability of desktop applications using a variety of machine learning techniques. However, since features of desktop programs are different from those of mobile applications, they are not applicable to mobile applications. To our knowledge, there has not been a repository or even a method to predict the success probability of Android applications so far. In this research, we introduce a repository composed of 100 successful and 100 unsuccessful apps of Android operating system in Google PlayStoreTM including 34 features per application. Then, we use the repository to a neural network and other classification algorithms to predict the success probability. Finally, we compare the proposed method with the previous approaches based on the accuracy criterion. Experimental results show that the best accuracy which we achieved is 99.99%, which obtained when we used MLP and PCA, while the best accuracy achieved by the previous work in desktop platforms was 96%. However, the time complexity of the proposed approach is higher than previous methods, since the time complexities of NPR and MLP are O$$( n^3$$ ( n 3 ) and O$$( nph^koi$$
Delhomme, F, Hajimohammadi, A, Almeida, A, Jiang, C, Moreau, D, Gan, Y, Wang, X & Castel, A 2020, 'Physical properties of Australian hurd used as aggregate for hemp concrete', Materials Today Communications, vol. 24, pp. 100986-100986.
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The purpose of this study is to determine the key properties of Australian hemp particles which are used for manufacturing hempcrete. Hemp characteristics have a wide variability due to the influence of the environment conditions in various farmed areas. This study focuses on the measurements of the mechanical, thermal and acoustic performances of three Australian hemp: unretted hemp hurd, retted hemp hurd, and hemp fines. Hemp hurd is usually used in non-load bearing building walls, and hemp fine, which is the by-product of hemp manufacturing industry, is usually incorporated into a render. The experimental results show that the main impact of the retting process is a decrease in bulk density and leading to an improvement in thermal and acoustic properties. Without compaction, the bulk density is ranged from 97 and 118.8 kg.m−3, the max sound absorption coefficient from 0.88 and 0.99, and the thermal conductivity from 64 to 97 mW.m-1. K-1. Hemp fines have excellent thermal and acoustic properties and appear to be an efficient aggregate to produce an insulating render. Australian hemps investigated in this study have shown very similar characteristics to European hemps.
Deng, L, Guo, W, Ngo, HH, Wang, XC, Hu, Y, Chen, R, Cheng, D, Guo, S & Cao, Y 2020, 'Application of a specific membrane fouling control enhancer in membrane bioreactor for real municipal wastewater treatment: Sludge characteristics and microbial community', Bioresource Technology, vol. 312, pp. 123612-123612.
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Deng, S, Ji, J, Wen, G & Xu, H 2020, 'Delay-induced novel dynamics in a hexagonal centrifugal governor system', International Journal of Non-Linear Mechanics, vol. 121, pp. 103465-103465.
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© 2020 Elsevier Ltd Inherent time delays are often neglected in the modeling and dynamic analysis of centrifugal governor systems for the sake of simplicity, yet they can have a significant effect on the dynamic behavior of the governor systems. This paper investigates the effect of time-delay on the dynamics of a hexagonal centrifugal governor system through a comparative study on the stability and bifurcation of the equilibrium for the system with and without delay considered. It is found that the presence of time-delay can decrease the stability region of the equilibrium and generate many fine structures on the stability boundaries. New dynamic phenomena can be induced by the time-delay, including the 1:4 resonant and non-resonant double Hopf bifurcations. In addition, generic Hopf and Bautin bifurcations can be observed in the system for both the non-delay and delay cases. The unfolding of bifurcations which exhibits all possible behavior at the points of such complicated bifurcations is given by studying the normal form of the response amplitude obtained using the method of multiple scales. Numerical simulations are performed to validate the proposed theoretical analyses.
Deng, W, McKelvey, KJ, Guller, A, Fayzullin, A, Campbell, JM, Clement, S, Habibalahi, A, Wargocka, Z, Liang, L, Shen, C, Howell, VM, Engel, AF & Goldys, EM 2020, 'Application of Mitochondrially Targeted Nanoconstructs to Neoadjuvant X-ray-Induced Photodynamic Therapy for Rectal Cancer', ACS Central Science, vol. 6, no. 5, pp. 715-726.
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In this work, we brought together two existing clinical techniques used in cancer treatment-X-ray radiation and photodynamic therapy (PDT), whose combination termed X-PDT uniquely allows PDT to be therapeutically effective in deep tissue. To this end, we developed mitochondrially targeted biodegradable polymer poly(lactic-co-glycolic acid) nanocarriers incorporating a photosensitizer verteporfin, ultrasmall (2-5 nm) gold nanoparticles as radiation enhancers, and triphenylphosphonium acting as the mitochondrial targeting moiety. The average size of the nanocarriers was about 160 nm. Upon X-ray radiation our nanocarriers generated cytotoxic amounts of singlet oxygen within the mitochondria, triggering the loss of membrane potential and mitochondria-related apoptosis of cancer cells. Our X-PDT strategy effectively controlled tumor growth with only a fraction of radiotherapy dose (4 Gy) and improved the survival rate of a mouse model bearing colorectal cancer cells. In vivo data indicate that our X-PDT treatment is cytoreductive, antiproliferative, and profibrotic. The nanocarriers induce radiosensitization effectively, which makes it possible to amplify the effects of radiation. A radiation dose of 4 Gy combined with our nanocarriers allows equivalent control of tumor growth as 12 Gy of radiation, but with greatly reduced radiation side effects (significant weight loss and resultant death).
Deng, ZX, Tao, JW, Zhao, LJ, Zhang, W, Wang, YB, Mu, HJ, Wu, HJ, Xu, XX & Zheng, W 2020, 'Effect of protein adsorption on bioelectrochemistry of electrospun core-shell MWCNTs/gelatin-Hb nanobelts on electrode surface', Process Biochemistry, vol. 96, pp. 73-79.
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© 2020 Elsevier Ltd Implantable electrochemical biosensor is one powerful tool for the accurate and reliable measurements of small molecules in vivo. However, the electrode is inevitably subjected to the protein adsorption when implanted into the living animals, affecting the sensitivity and stability of biosensor. Herein, we designed the multi-walled carbon nanotubes/gelatin-hemoglobin (MWCNTs/gelatin-Hb) core-shell nanobelts constructed on glassy carbon electrode (GC) using the one-step electrospinning technique for studying the effect of protein adsorption on the electrode surface properties. The results of the water contact angle and the scanning electron microscopy (SEM) showed that the electrospun core-shell MWCNTs/gelatin-Hb nanobelts present hydrophilic and certain anti-protein adsorption properties. Direct electron transfer between the Hb molecules in the electrospun core-shell nanobelts and electrode and catalysis of hydrogen peroxide (H2O2) can be still achieved after the electrospun core-shell MWCNTs/gelatin-Hb nanobelts adsorbed protein. Moreover, compared with before protein adsorption (Kmapp =0.0155 mmol/L), the electrospun core-shell MWCNTs/gelatin-Hb nanobelts after protein adsorption still displayed high biological affinity to H2O2 (Kmapp =0.0382 mmol/L). The constructed H2O2 biosensor by using the electrospun core-shell MWCNTs/gelatin-Hb nanobelts showed high sensitivity, great reproducibility and stability after protein adsorption. This study provides a novel design and an effective platform for the development of implantable electrochemical biosensors.
Depczynski, B, Liew, PY & White, C 2020, 'Association of glycaemic variables with trabecular bone score in post‐menopausal women with type 2 diabetes mellitus', Diabetic Medicine, vol. 37, no. 9, pp. 1545-1552.
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AbstractAimTo determine the relationship between bone microarchitecture, as measured by trabecular bone score, and advanced glycation end‐product accumulation, as assessed by skin autofluorescence.MethodsThis was a cross‐sectional study. Participants were 64 post‐menopausal women with type 2 diabetes and 175 post‐menopausal women without diabetes. Trabecular bone score and skin autofluorescence data were obtained at time of bone density measurement.ResultsTrabecular bone score and skin autofluorescence were inversely correlated in women with type 2 diabetes (r = –0.34, P = 0.006); no correlation was seen in post‐menopausal women without diabetes (r = –0.029, P = 0.707). After adjustment, neither skin autofluorescence nor a diagnosis of diabetes were associated with trabecular bone score, but HbA1c and waist circumference were independently associated with trabecular bone score.ConclusionSkin autofluorescence did not predict trabecular bone score. In contrast, glycaemia, as reflected by HbA1c, and visceral adiposity, as reflected by waist circumference, were independently associated with trabecular bone score.
Deuse, J, Dombrowski, U, Nöhring, F, Mazarov, J & Dix, Y 2020, 'Systematic combination of Lean Management with digitalization to improve production systems on the example of Jidoka 4.0', International Journal of Engineering Business Management, vol. 12, pp. 184797902095135-184797902095135.
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Lean Management builds the basis for efficient production systems for many industrial companies. However, lots of potentials of Lean Management have been lifted and information and communication technologies in the context of digitalization and cyber-physical production systems (CPPS) offer new possibilities to enhance the performance of companies. Even though surveys indicate that companies recognize these potentials, especially small and medium-sized companies still face challenges in selection and implementation of suitable solutions. Thus, the research project GaProSys 4.0 aims at supporting companies with a systematic approach to combine existing structures of Lean Management with potentials of digitalization in development of a new set of methods to enhance production systems. This paper presents the approach of the research project to develop a structured set of methods and provides an example to illustrate the potentials.
Deutsch, FT, Khoury, SJ, Sunwoo, JB, Elliott, MS & Tran, NT 2020, 'Application of salivary noncoding microRNAs for the diagnosis of oral cancers', Head & Neck, vol. 42, no. 10, pp. 3072-3083.
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AbstractOral cancer is on the rise globally and survival rates, despite improvements in clinical care, have not significantly improved. Early detection followed by immediate intervention is key to improving patient outcomes. The use of biomarkers has changed the diagnostic landscape for many cancers. For oral cancers, visual inspection followed by a tissue biopsy is standard practice. The discovery of microRNAs as potential biomarkers has attracted clinical interest but several challenges remain. These microRNAs can be found in bodily fluids such as blood and saliva which have been investigated as potential sources of biomarker discovery. As oral cancer is localized within the oral cavity, saliva may contain clinically relevant molecular markers for disease detection. Our review provides an outline of the current advances for the application of salivary microRNAs in oral cancer. We also provide a technical guide for the processing of salivary RNAs to ensure accurate clinical measurement and validation.
Deveci, Ö & Shannon, AG 2020, 'A note on balanced incomplete block designs and projective geometry', International Journal of Mathematical Education in Science and Technology.
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© 2020 Informa UK Limited, trading as Taylor & Francis Group. This note outlines some connections between projective geometry and some designs used in clinical trials in the health sciences. The connections are not immediately obvious but they widen the scope for enrichment work at both the senior high school level and for capstone subjects at the undergraduate level.
Di, X, Wang, D, Zhou, J, Zhang, L, Stenzel, M, Su, QP & Jin, D 2020, 'Quantitatively Monitoringin situMitochondrial Thermal Dynamics by Upconversion Nanoparticles', p. 2020.11.29.402818.
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AbstractTemperature dynamics reflect the physiological conditions of cells and organisms. Mitochondria regulates temperature dynamics in living cells, as they oxidize the respiratory substrates and synthesize ATP, with heat being released as a by-product of active metabolism. Here, we report an upconversion nanoparticles based thermometer that allowsin situthermal dynamics monitoring of mitochondria in living cells. We demonstrate that the upconversion nanothermometers can efficiently target mitochondria and the temperature responsive feature is independent of probe concentration and medium conditions. The relative sensing sensitivity of 3.2% K−1in HeLa cells allows us to measure the mitochondrial temperature difference through the stimulations of high glucose, lipid, Ca2+shock and the inhibitor of oxidative phosphorylation. Moreover, cells display distinct response time and thermal dynamic profiles under different stimulations, which highlights the potential applications of this thermometer to studyin situvital processes related to mitochondrial metabolism pathways and interactions between organelles.
Diao, K, Sun, X, Lei, G, Guo, Y & Zhu, J 2020, 'Multiobjective System Level Optimization Method for Switched Reluctance Motor Drive Systems Using Finite-Element Model', IEEE Transactions on Industrial Electronics, vol. 67, no. 12, pp. 10055-10064.
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© 1982-2012 IEEE. This article presents a novel multiobjective system level optimization method to achieve the best performance of switched reluctance motor (SRM) drive systems. First, the multiobjective optimization problem for the SRM drive systems is defined. Then, all parameters of the drive systems, including the motor level and control level, are divided into three subspaces according to their influences on the objectives. Finally, the optimization of each subspace is performed sequentially until a convergence criterion is met. Then, the optimal solution can be chosen from the Pareto solutions according to a selection criterion. Meanwhile, the sensitivity analysis, the approximate models, and the genetic algorithm are employed to reduce the computation cost. To verify the effectiveness of the proposed method, an SRM drive system with a segmented-rotor SRM (SSRM) and the angle position control method is investigated. This is a high-dimensional system level optimization problem with ten parameters. The finite-element model (FEM) results are verified by the experiment results. The optimal solution has been listed and verified by the FEM. From the discussion, it can be found that the proposed optimization method is efficient and optimized SSRM drive system has high efficiency and low torque ripple.
Dibaei, M, Zheng, X, Jiang, K, Abbas, R, Liu, S, Zhang, Y, Xiang, Y & Yu, S 2020, 'Attacks and defences on intelligent connected vehicles: a survey', Digital Communications and Networks, vol. 6, no. 4, pp. 399-421.
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© 2020 Chongqing University of Posts and Telecommunications Intelligent vehicles are advancing at a fast speed with the improvement of automation and connectivity, which opens up new possibilities for different cyber-attacks, including in-vehicle attacks (e.g., hijacking attacks) and vehicle-to-everything communicationattacks (e.g., data theft). These problems are becoming increasingly serious with the development of 4G LTE and 5G communication technologies. Although many efforts are made to improve the resilience to cyber attacks, there are still many unsolved challenges. This paper first identifies some major security attacks on intelligent connected vehicles. Then, we investigate and summarize the available defences against these attacks and classify them into four categories: cryptography, network security, software vulnerability detection, and malware detection. Remaining challenges and future directions for preventing attacks on intelligent vehicle systems have been discussed as well.
Dickson-Deane, C 2020, 'Where Do we Go from Here …', TechTrends, vol. 64, no. 6, pp. 812-813.
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Dikshit, A, Pradhan, B & Alamri, AM 2020, 'Short-Term Spatio-Temporal Drought Forecasting Using Random Forests Model at New South Wales, Australia', Applied Sciences, vol. 10, no. 12, pp. 4254-4254.
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Droughts can cause significant damage to agriculture and water resources, leading to severe economic losses and loss of life. One of the most important aspect is to develop effective tools to forecast drought events that could be helpful in mitigation strategies. The understanding of droughts has become more challenging because of the effect of climate change, urbanization and water management; therefore, the present study aims to forecast droughts by determining an appropriate index and analyzing its changes, using climate variables. The work was conducted in three different phases, first being the determination of Standard Precipitation Evaporation Index (SPEI), using global climatic dataset of Climate Research Unit (CRU) from 1901–2018. The indices are calculated at different monthly intervals which could depict short-term or long-term changes, and the index value represents different drought classes, ranging from extremely dry to extremely wet. However, the present study was focused only on forecasting at short-term scales for New South Wales (NSW) region of Australia and was conducted at two different time scales, one month and three months. The second phase involved dividing the data into three sample sizes, training (1901–2010), testing (2011–2015) and validation (2016–2018). Finally, a machine learning approach, Random Forest (RF), was used to train and test the data, using various climatic variables, e.g., rainfall, potential evapotranspiration, cloud cover, vapor pressure and temperature (maximum, minimum and mean). The final phase was to analyze the performance of the model based on statistical metrics and drought classes. Regarding this, the performance of the testing period was conducted by using statistical metrics, Coefficient of Determination (R2) and Root-Mean-Square-Error (RMSE) method. The performance of the model showed a considerably higher value of R2 for both the time scales. However, statistical metrics analyzes the varia...
Dikshit, A, Pradhan, B & Alamri, AM 2020, 'Temporal Hydrological Drought Index Forecasting for New South Wales, Australia Using Machine Learning Approaches', Atmosphere, vol. 11, no. 6, pp. 585-585.
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Droughts can cause significant damage to agriculture and water resources leading to severe economic losses. One of the most important aspects of drought management is to develop useful tools to forecast drought events, which could be helpful in mitigation strategies. The recent global trends in drought events reveal that climate change would be a dominant factor in influencing such events. The present study aims to understand this effect for the New South Wales (NSW) region of Australia, which has suffered from several droughts in recent decades. The understanding of the drought is usually carried out using a drought index, therefore the Standard Precipitation Evaporation Index (SPEI) was chosen as it uses both rainfall and temperature parameters in its calculation and has proven to better reflect drought. The drought index was calculated at various time scales (1, 3, 6, and 12 months) using a Climate Research Unit (CRU) dataset. The study focused on predicting the temporal aspect of the drought index using 13 different variables, of which eight were climatic drivers and sea surface temperature indices, and the remainder were various meteorological variables. The models used for forecasting were an artificial neural network (ANN) and support vector regression (SVR). The model was trained from 1901–2010 and tested for nine years (2011–2018), using three different performance metric scores (coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results indicate that ANN was better than SVR in predicting temporal drought trends, with the highest R2 value of 0.86 for the former compared to 0.75 for the latter. The study also reveals that sea surface temperatures and the climatic index (Pacific Decadal Oscillation) do not have a significant effect on the temporal drought aspect. The present work can be considered as a first step, wherein we only study the temporal trends, towards the use of climatological...
Dikshit, A, Sarkar, R, Pradhan, B, Acharya, S & Alamri, AM 2020, 'Spatial Landslide Risk Assessment at Phuentsholing, Bhutan', Geosciences, vol. 10, no. 4, pp. 131-131.
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Landslides are one of the most destructive and most recurring natural calamities in the Himalayan region. Their occurrence leads to immense damage to infrastructure and loss of land, human lives, and livestock. One of the most affected regions is the Bhutan Himalayas, where the majority of the landslides are rainfall-induced. The present study aims to determine the hazard and risk associated with rainfall-induced landslides for the Phuentsholing region located in the southwestern part of the Bhutan Himalayas. The work involves developing a landslide risk map using hazard and vulnerability maps utilizing landslide records from 2004 to 2014. The landslide hazard map was generated by determining spatial and temporal probabilities for the study region. The spatial probability was computed by analyzing the landslide contributing factors like geology, slope, elevation, rainfall, and vegetation based on comprehensive field study and expertise about the area. The contributing factors were divided into various classes and the percentage of landslide occurrence under each class was calculated to understand its contributing significance. Thereafter, a weighted linear combination approach was used in a GIS environment to develop the spatial probability map which was multiplied with temporal probabilities based on regional rainfall thresholds already determined for the region. Consequently, vulnerability assessment was conducted using key elements at risk (population, land use/land cover, proximity to road, proximity to stream) and the weights were provided based on expert judgment and comprehensive field study. Finally, risk was determined and the various regions in the study area were categorized as high, medium, and low risk. Such a study is necessary for low-economic countries like Bhutan which suffers from unavailability of extensive data and research. The study is conducted for a specific region but can be extended to other areas around the investigate...
Dikshit, A, Sarkar, R, Pradhan, B, Jena, R, Drukpa, D & Alamri, AM 2020, 'Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan', Water, vol. 12, no. 1, pp. 267-267.
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Landslides are one of the major natural disasters that Bhutan faces every year. The monsoon season in Bhutan is usually marked by heavy rainfall, which leads to multiple landslides, especially across the highways, and affects the entire transportation network of the nation. The determinations of rainfall thresholds are often used to predict the possible occurrence of landslides. A rainfall threshold was defined along Samdrup Jongkhar–Trashigang highway in eastern Bhutan using cumulated event rainfall and antecedent rainfall conditions. Threshold values were determined using the available daily rainfall and landslide data from 2014 to 2017, and validated using the 2018 dataset. The threshold determined was used to estimate temporal probability using a Poisson probability model. Finally, a landslide susceptibility map using the analytic hierarchy process was developed for the highway to identify the sections of the highway that are more susceptible to landslides. The accuracy of the model was validated using the area under the receiver operating characteristic curves. The results presented here may be regarded as a first step towards understanding of landslide hazards and development of an early warning system for a region where such studies have not previously been conducted.
Dikshit, A, Sarkar, R, Pradhan, B, Segoni, S & Alamri, AM 2020, 'Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review', Applied Sciences, vol. 10, no. 7, pp. 2466-2466.
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Landslides are one of the most devastating and recurring natural disasters and have affected several mountainous regions across the globe. The Indian Himalayan region is no exception to landslide incidences affecting key economic sectors such as transportation and agriculture and often leading to loss of lives. As reflected in the global landslide dataset, most of the landslides in this region are rainfall triggered. The region is prone to 15% of the global rainfall-induced landslides, and thereby a review of the studies in the region is inevitable. The high exposure to landslide risk has made the Indian Himalayas receive growing attention by the landslides community. A review of landslides studies conducted in this region is therefore important to provide a general picture of the state-of-the-art, a reference point for researchers and practitioners working in this region for the first time, and a summary of the improvements most urgently needed to better address landslide hazard research and management. This article focuses on various studies ranging from forecasting and monitoring to hazard and susceptibility analysis. The various factors used to analyze landslide are also studied for various landslide zones in the region. The analysis reveals that there are several avenues where significant research work is needed such as the inclusion of climate change factors or the acquisition of basic data of highest quality to be used as input data for computational models. In addition, the review reveals that, despite the entire region being highly landslide prone, most of the studies have focused on few regions and large areas have been neglected. The aim of the review is to provide a reference for stakeholders and researchers who are currently or looking to work in the Indian Himalayas, to highlight the shortcomings and the points of strength of the research being conducted, and to provide a contribution in addressing the future developments most urge...
Dikshit, A, Satyam, N, Pradhan, B & Kushal, S 2020, 'Estimating rainfall threshold and temporal probability for landslide occurrences in Darjeeling Himalayas', Geosciences Journal, vol. 24, no. 2, pp. 225-233.
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© 2020, The Association of Korean Geoscience Societies and Springer. The Indian Himalayan region has been severely affected by landslides causing an immense loss in terms of human lives and economic loss. The landslides are usually induced by rainfall which can be slow and continuous or heavy downpour. The incidences of landslide events in Indian Himalayas have been further aggravated due to the rapid increase in urbanization and thus its increasing impact on socio-economic aspects. There is a dire need for understanding landslide phenomena, estimating its occurrence potential and formulating strategies to minimize the damage caused by them. One of the most affected area is Kalimpong of Darjeeling Himalayas where significant studies have been conducted on zonation, threshold estimation and other related aspects. However, a comprehensive study in terms of temporal prediction for this region remains unattended. The paper deals with assessing landslide hazard using a rainfall threshold model involving daily and cumulative antecedent rainfall values for landslide events. The threshold values were determined using daily rainfall and antecedent rainfall using precipitation and landslide records for 2010–2016. The results show that 20-day antecedent rainfall provides the best fit for landslide occurrences in the region. The rainfall thresholds were further validated using rainfall and landslide data of 2017, which was not considered for threshold estimation. Finally, the results were used to determine the temporal probability for landslide incidence using a Poisson probability model. The validated results suggest that the model has the potential to be used as a preliminary early warning system.
Dimuro, GP, Lucca, G, Bedregal, B, Mesiar, R, Sanz, JA, Lin, C-T & Bustince, H 2020, 'Generalized CF1F2-integrals: From Choquet-like aggregation to ordered directionally monotone functions', Fuzzy Sets and Systems, vol. 378, pp. 44-67.
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© 2019 Elsevier B.V. This paper introduces the theoretical framework for a generalization of CF1F2-integrals, a family of Choquet-like integrals used successfully in the aggregation process of the fuzzy reasoning mechanisms of fuzzy rule based classification systems. The proposed generalization, called by gCF1F2-integrals, is based on the so-called pseudo pre-aggregation function pairs (F1,F2), which are pairs of fusion functions satisfying a minimal set of requirements in order to guarantee that the gCF1F2-integrals to be either an aggregation function or just an ordered directionally increasing function satisfying the appropriate boundary conditions. We propose a dimension reduction of the input space, in order to deal with repeated elements in the input, avoiding ambiguities in the definition of gCF1F2-integrals. We study several properties of gCF1F2-integrals, considering different constraints for the functions F1 and F2, and state under which conditions gCF1F2-integrals present or not averaging behaviors. Several examples of gCF1F2-integrals are presented, considering different pseudo pre-aggregation function pairs, defined on, e.g., t-norms, overlap functions, copulas that are neither t-norms nor overlap functions and other functions that are not even pre-aggregation functions.
Ding, A, Zhao, Y, Ngo, HH, Bai, L, Li, G, Liang, H, Ren, N & Nan, J 2020, 'Metabolic uncoupler, 3,3′,4′,5-tetrachlorosalicylanilide addition for sludge reduction and fouling control in a gravity-driven membrane bioreactor', Frontiers of Environmental Science & Engineering, vol. 14, no. 6.
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© 2020, Higher Education Press. The gravity-driven membrane bioreactor (MBR) system is promising for decentralized sewage treatment because of its low energy consumption and maintenance requirements. However, the growing sludge not only increases membrane fouling, but also augments operational complexities (sludge discharge). We added the metabolic uncoupler 3,3′,4′,5-tetrachlorosalicylanilide (TCS) to the system to deal with the mentioned issues. Based on the results, TCS addition effectively decreased sludge ATP and sludge yield (reduced by 50%). Extracellular polymeric substances (EPS; proteins and polysaccharides) decreased with the addition of TCS and were transformed into dissolved soluble microbial products (SMPs) in the bulk solution, leading to the break of sludge flocs into small fragments. Permeability was increased by more than two times, reaching 60–70 L/m2/h bar when 10–30 mg/L TCS were added, because of the reduced suspended sludge and the formation of a thin cake layer with low EPS levels. Resistance analyses confirmed that appropriate dosages of TCS primarily decreased the cake layer and hydraulically reversible resistances. Permeability decreased at high dosage (50 mg/L) due to the release of excess sludge fragments and SMP into the supernatant, with a thin but more compact fouling layer with low bioactivity developing on the membrane surface, causing higher cake layer and pore blocking resistances. Our study provides a fundamental understanding of how a metabolic uncoupler affects the sludge and bio-fouling layers at different dosages, with practical relevance for in situ sludge reduction and membrane fouling alleviation in MBR systems. [Figure not available: see fulltext.].
Ding, C, Sun, H-H, Zhu, H & Jay Guo, Y 2020, 'Achieving Wider Bandwidth With Full-Wavelength Dipoles for 5G Base Stations', IEEE Transactions on Antennas and Propagation, vol. 68, no. 2, pp. 1119-1127.
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© 1963-2012 IEEE. A new method of designing full-wavelength dipoles (FWDs) is presented. A dual-polarized antenna is built based on FWDs for base station applications as an example. The antenna has four FWDs arranged in a square loop array form. The employed FWDs are bent upward to maintain a small aperture size, so that the realized element still fits in traditional base station antenna (BSA) array. The antenna is first matched across the band from 1.63 to 3.71 GHz, which can cover both the LTE band from 1.7 to 2.7 GHz and the 5G (sub-6 GHz) band from 3.3 to 3.6 GHz simultaneously. Then, band-stop filters are inserted in the feed networks of the antenna to suppress the radiation between 2.7 to 3.3 GHz. The antenna is fabricated and tested. Experimental results validate the simulation results. Comparing with the previously available FWD that has a bandwidth of 32%, the FWD proposed in this article exhibits a much wider bandwidth of 78%. Moreover, this bandwidth is also comparable to and wider than those of the state-of-the-art BSAs based on half-wavelength dipoles (HWDs). The bandwidth enhancement and footprint reduction of the FWD in this article demonstrate a high potential of FWDs to be used in other applications.
Ding, S, Zhang, G, Gao, Y, Chen, S & Cao, C 2020, 'Circular RNA hsa_circ_0005909 modulates osteosarcoma progression via the miR-936/HMGB1 axis', Cancer Cell International, vol. 20, no. 1.
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AbstractBackgroundOsteosarcoma (OS) is the most common bone malignant tumor in children, youth, and adolescents. Circular RNA hsa_circ_0005909 (circ_0005909) is involved in the progression of OS. Nevertheless, there are few reports on the role and mechanism of circ_0005909 in OS.MethodsQuantitative real-time polymerase chain reaction (qRT-PCR) was executed to examine the expression of circ_0005909, miR-936, and High Mobility Group Box 1 (HMGB1) mRNA in OS tissues and cells. Cell viability, colony formation, migration, and invasion were evaluated by Cell Counting Kit-8 (CCK-8), cell colony formation, or transwell assays. Cell epithelial-mesenchymal transition (EMT) and HMGB1 protein levels were assessed through western blot analysis. The role of circ_0005909 on tumor growth in vivo was verified by xenograft assay. The relationship between circ_0005909 or HMGB1 and miR-936 was confirmed with the dual-luciferase reporter or RNA pull-down assays.ResultsCirc_0005909 level was upregulated in OS tissues and cells. OS patients with high circ_0005909 expression had a lower survival rate. Circ_0005909 inhibition reduced tumor growth in vivo and constrained cell viability, colony formation, migration, invasion, and EMT of OS cells in vitro. Furthermore, circ_0005909 served as a sponge for miR-936 and the repressive impacts of circ_0005909 silencing on malignant behaviors of OS cells were abolished by miR-936 inhibitors. Also, HMGB1 acted as a target for miR-936 and was modulated by circ_0005909 via miR-936. Additionally, HMGB1 overexpression restored the inhibitory influence on the malignant behaviors of OS cells mediated by circ_0005909 inhibition.ConclusionsCirc_0005909 inhibition impeded the progre...
Ding, W, Lin, C-T & Pedrycz, W 2020, 'Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce', IEEE Transactions on Cybernetics, vol. 50, no. 2, pp. 425-439.
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IEEE Although feature selection for large data has been intensively investigated in data mining, machine learning, and pattern recognition, the challenges are not just to invent new algorithms to handle noisy and uncertain large data in applications, but rather to link the multiple relevant feature sources, structured, or unstructured, to develop an effective feature reduction method. In this paper, we propose a multiple relevant feature ensemble selection (MRFES) algorithm based on multilayer co-evolutionary consensus MapReduce (MCCM). We construct an effective MCCM model to handle feature ensemble selection of large-scale datasets with multiple relevant feature sources, and explore the unified consistency aggregation between the local solutions and global dominance solutions achieved by the co-evolutionary memeplexes, which participate in the cooperative feature ensemble selection process. This model attempts to reach a mutual decision agreement among co-evolutionary memeplexes, which calls for the need for mechanisms to detect some noncooperative co-evolutionary behaviors and achieve better Nash equilibrium resolutions. Extensive experimental comparative studies substantiate the effectiveness of MRFES to solve large-scale dataset problems with the complex noise and multiple relevant feature sources on some well-known benchmark datasets. The algorithm can greatly facilitate the selection of relevant feature subsets coming from the original feature space with better accuracy, efficiency, and interpretability. Moreover, we apply MRFES to human cerebral cortex-based classification prediction. Such successful applications are expected to significantly scale up classification prediction for large-scale and complex brain data in terms of efficiency and feasibility.
Ding, W, Lin, C-T, Liew, AW-C, Triguero, I & Luo, W 2020, 'Current trends of granular data mining for biomedical data analysis', Information Sciences, vol. 510, pp. 341-343.
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Ding, W, Pedrycz, W & Lin, C-T 2020, 'Guest Editorial for the Special Issue on Fuzzy Rough Sets for Big Data', IEEE Transactions on Fuzzy Systems, vol. 28, no. 5, pp. 803-805.
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Ding, W, Yen, GG, Cai, X & Cao, Z 2020, 'Foreword: Evolutionary data mining for big data', Swarm and Evolutionary Computation, vol. 57, pp. 100738-100738.
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Ding, X, Wang, Y, Xiong, R, Li, D, Tang, L, Yin, H & Zhao, L 2020, 'Persistent Stereo Visual Localization on Cross-Modal Invariant Map', IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 11, pp. 4646-4658.
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Autonomous mobile vehicles are expected to perform persistent and accurate localization with low-cost equipment. To achieve this goal, we propose a stereo camera based visual localization method using a modified laser map, which takes the advantage of both the low cost of camera, and high geometric precision of laser data to achieve long-term performance. Considering that LiDAR and camera give measurements of the same environment in different modalities, the cross-modal invariance is investigated to modify the laser map for visual localization. Specifically, a map learning algorithm is introduced to sample the robust subsets in laser maps that are useful for visual localization using multi-session visual and laser data. Further, a generative map model is derived to describe this cross-modal invariance, based on which two types of measurements are defined to model the laser map points as appropriate visual observations. Tightly coupling these measurements within the local bundle adjustment during online sliding-window based visual odometry, the vehicle can achieve robust localization even one year after the map was built. The effectiveness of the proposed method is evaluated on both the public KITTI datasets and self-collected datasets in our campus, which include seasonal, illumination and object variations. On all experimental localization sessions, our method provides satisfactory results, even when the direction is opposite to that in the mapping session, verifying the superior performance of the laser map based visual localization method.
Dinh, TH, Phung, MD & Ha, QP 2020, 'Summit Navigator: A Novel Approach for Local Maxima Extraction', IEEE Transactions on Image Processing, vol. 29, pp. 551-564.
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© 1992-2012 IEEE. This paper presents a novel method, called the Summit Navigator, to effectively extract local maxima of an image histogram for multi-object segmentation of images. After smoothing with a moving average filter, the obtained histogram is analyzed, based on the data density and distribution to find the best observing location. An observability index for each initial peak is proposed to evaluate if it can be considered as dominant by using the calculated observing location. Recursive algorithms are then developed for peak searching and merging to remove any false detection of peaks that are located on one side of each mode. Experimental results demonstrated the advantages of the proposed approach in terms of accuracy and consistency in different reputable datasets.
Do, MH, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Deng, L, Chen, Z & Nguyen, TV 2020, 'Performance of mediator-less double chamber microbial fuel cell-based biosensor for measuring biological chemical oxygen', Journal of Environmental Management, vol. 276, pp. 111279-111279.
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Recently, the microbial fuel cell-based biosensor has been considered as an attractive technology for measuring wastewater quality such as biochemical oxygen demand (BOD). In this study, a mediator-less double compartment MFC based biosensor utilizing carbon felt as an anode electrode and inoculated with mixed culture was developed to improve the real application of a rapid BOD detection. This study aims to: (i) establish the effect of the operating conditions (i.e., pH, external resistance, fuel feeding rate) on MFC performance; (ii) investigate the correlation between biochemical oxygen demand (BOD) and signal output, and (iii) evaluate the operational stability of the biosensor. The presented result reveals that the maximum current and power production was obtained while 100 mM NaCl and 50 mM Phosphate buffer saline was used as a catholyte solution, neutral pH condition of media and fuel feeding rate at 0.3 mL min-1. Notably, a wider range of BOD concentration up to 300 mg L -1 can be obtained with the voltage output (R2 > 0.9901). Stable and steady power was produced by running MFC in 30 days when cells operated at 1000 Ω external resistance. Our research has some competition with the previous double chamber MFC in the upper limit of BOD detection. This results might help to increase the real application of MFC based BOD biosensor in real-time measurement.
Do, MH, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Varjani, S & Kumar, M 2020, 'Microbial fuel cell-based biosensor for online monitoring wastewater quality: A critical review', Science of The Total Environment, vol. 712, pp. 135612-135612.
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© 2019 Elsevier B.V. Recently, the application of the microbial fuel cell (MFC)-based biosensor for rapid and real-time monitoring wastewater quality is very innovative due to its simple compact design, disposability, and cost-effectiveness. This review represents recent advances in this emerging technology for the management of wastewater quality, where the emphasis is on biochemical oxygen demand, toxicity, and other environmental applications. In addition, the main challenges of this technology are discussed, followed by proposing possible solutions to those challenges based on the existing knowledge of detection principles and signal processing. Potential future research of MFC-based biosensor has been demonstrated in this review.
Doan, S & Fatahi, B 2020, 'Analytical solution for free strain consolidation of stone column-reinforced soft ground considering spatial variation of total stress and drain resistance', Computers and Geotechnics, vol. 118, pp. 103291-103291.
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© 2019 Elsevier Ltd This paper provides an analytical solution for consolidation problem of a stone column-improved soft soil layer subjected to an instantly applied loading under free strain condition. The radial and vertical consolidation equations are solved in a coupled fashion for both the stone column and its surrounding soil. A general solution of excess pore water pressure at any point of a unit cell model in terms of a Fourier-Bessel series was achieved using the combination of separation of variables method and orthogonal expansion technique. The obtained solution can capture the drain (well) resistance effect and the space-dependent distribution of total vertical stress induced by the external loading. Indeed, since the permeability and size of the stone column are directly utilised in the governing equations and the analytical solution, the drain resistance is directly captured. The capabilities of the proposed solution are exhibited through a comprehensive worked example, while the accuracy of the solution is verified against a finite element simulation and field measurements of a case history with good agreements. To examine the effect of various factors on consolidation behaviour of the composite ground, a parametric study involving column spacing, modulus and permeability of soft soil along with distribution pattern of total stress and thickness of soil layer is also conducted. A decrease in the column spacing or an increase in the modulus or permeability of soft soil led to the acceleration of the consolidation process of the soil region, while the variation of the total stress with depth and the thickness of soil deposit primarily affected the consolidation rate of stone column. Under the free strain condition, the average differential settlement between the stone column and encircling soil was indeed considerable during the consolidation process.
Dodangeh, E, Panahi, M, Rezaie, F, Lee, S, Tien Bui, D, Lee, C-W & Pradhan, B 2020, 'Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search', Journal of Hydrology, vol. 590, pp. 125423-125423.
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© 2020 Elsevier B.V. Floods are among the deadliest natural hazards for humans and the environment. Identifying the most flood-susceptible areas is a fundamental step in the development of flood mitigation strategies and for reducing flood damage. There is an ongoing global debate regarding the most suitable model for flood-susceptibility modeling and predictions. There is also a growing interest in the development of parsimonious and precise models for flood-susceptibility prediction. This study proposed several novel hybrid intelligence models based on the meta-optimization of the support vector regression (SVR) and group method of data handling (GMDH) using different meta-heuristic algorithms, i.e., the genetic algorithm (GA) and harmony search (HS). In contrast to the traditional models, in the SVR model computational complexity does not depend on the dimensionality of the input space. GMDH model has also advantage of being appropriate to analyze multi-parametric data sets. The methodology was developed for the Haraz-Neka watershed, one of the most flood-prone areas in the coastal margins of the Caspian Sea. A total of nine geospatial parameters (slope degree, aspect, elevation, plan curvature, profile curvature, distance to the river, land use, lithology, and rainfall) were identified as the main flood-conditioning factors using information gain ratio (IGR) analyses. Based on existing reports, 132 flood locations were identified in the study area, 92 points (70%) were used together with geospatial data for flood-susceptibility modeling, and the remaining 40 points (30%) were used to validate the models. An initial flood-susceptibility model was constructed based on the SVR and GMDH models. The model parameters were optimized using the GA and HS to reproduce the flood-susceptibility maps. The prediction accuracy of the resultant maps was evaluated in terms of various statistical measures, i.e., mean square error (MSE), root mean square error (RMSE),...
Dong, D, Xing, X, Ma, H, Chen, C, Liu, Z & Rabitz, H 2020, 'Learning-Based Quantum Robust Control: Algorithm, Applications, and Experiments', IEEE Transactions on Cybernetics, vol. 50, no. 8, pp. 3581-3593.
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Dong, M, Yao, L, Wang, X, Benatallah, B, Huang, C & Ning, X 2020, 'Opinion fraud detection via neural autoencoder decision forest', Pattern Recognition Letters, vol. 132, no. 10, pp. 21-29.
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Online reviews play an important role in influencing buyers’ daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users’ genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.
Dong, W, Li, W, Guo, Y, He, X & Sheng, D 2020, 'Effects of silica fume on physicochemical properties and piezoresistivity of intelligent carbon black-cementitious composites', Construction and Building Materials, vol. 259, pp. 120399-120399.
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© 2020 Elsevier Ltd Carbon black (CB) filled cementitious composites as cement-based sensors with intrinsic piezoresistivity have the potential applications for structural health monitoring (SHM). Effect of silica fume (SF) replacement ratio on the physicochemical, mechanical and piezoresistive properties, and microstructure of CB-cementitious composite were experimentally investigated in this study. The results show that 5% or 10% replacement ratio of SF can improve the water impermeability, setting time and electrical conductivity, but decrease the fresh flowability. Cementitious composite with 10% SF exhibiteds excellent compressive and flexural strengths. Moreover, cement hydration in the acceleration stage decreased with the increase of SF content in the early stage, but the phase analysis after 28 days curing demonstrates that with the addition of SF, there are more hydrated products and less ettringite. In addition, the microstructures of cementitious composites without SF present more porous structures and CB agglomerations. In contrast, the amount of micropores or voids was significantly reduced by the addition of SF due to the physical filling effect and less CB agglomerations. In terms of piezoresistivity, SF can obviously improve the fractional changes of resistivity (FCR) under cyclic compression. With 10% SF, CB-cementitious composites as cement-based sensors exhibited excellent FCR and electrical stability, which will promote their development and application in the SHM for smart infrastructures.
Dong, W, Li, W, Luo, Z, Guo, Y & Wang, K 2020, 'Effect of layer-distributed carbon nanotube (CNT) on mechanical and piezoresistive performance of intelligent cement-based sensor', Nanotechnology, vol. 31, no. 50, pp. 505503-505503.
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Abstract Agglomerated carbon nanotube (CNT) powder was scattered into a cement paste layer-by-layer to form layer-distributed CNT composite (LDCC) as intelligent cement-based sensor. The characteristic of the CNT agglomerations and its effect on the mechanical and piezoresistive properties of cement paste were investigated in this study, and the results were compared with those of uniformly-dispersed CNT composites (UDCC). Based on the statistics of CNT agglomerations, it was found that the sizes of agglomerations varied from several to dozens of micrometres. The larger sized agglomerations with poorer roundness exhibited a higher possibility to cause the pores or voids accompanied with stress concentration when subjected to external forces. Hence, it is necessary to control the agglomeration sizes to reduce the porosity with edges and corners. The UDCC reached the highest compressive strength, followed by the plain cement paste and then LDCC. The mechanical strength of LDCC decreased with the increase of CNT layers. The piezoresistivity occurred in both the UDCC and LDCC, with the former possessing stable and repeatable performance. In addition, the strain-sensing ability of LDCC with moderate CNT layers presented similar sensing efficiency and repeatability to that of UDCC. The related results provide insight into the intelligent cement-based sensors with layer-distributed CNT and agglomerations, which can improve the efficiency and effectively reduce the cost for practical application.
Dong, W, Li, W, Luo, Z, Long, G, Vessalas, K & Sheng, D 2020, 'Structural response monitoring of concrete beam under flexural loading using smart carbon black/cement-based sensors', Smart Materials and Structures, vol. 29, no. 6, pp. 065001-065001.
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© 2020 IOP Publishing Ltd. The fractional changes of resistivity (FCR) of cement-based sensors with various carbon black (CB) contents were firstly investigated under uniaxial compression in this study. Then the piezoresistive behaviours of embedded cement-based sensors in unreinforced small-scale concrete beams were investigated under flexural bending load. As for the embedded cement-based sensors in the compression zones of the beam, the stress magnitude and crack failure initiation of the beams can be detected and monitored by a gradual decrease and then a sharp increase in the FRC. On the other hand, as for the counterpart sensors in the tension zones of the beam, the stress magnitude and crack failure initiation can be recognized by the gradual increase in resistivity and then a rapid jump in the FRC. During the stress monitoring of the concrete beam, the FCR values of cement-based sensors in both the compression and tension zones were consistent with the flexural stress changes, which exhibit acceptable sensitivity and reversibility. Moreover, very firm and dense interfaces in the boundaries indicate the excellent cohesion between embedded CB/cement-based sensors and beams. The related results demonstrate that the CB/cement-based sensors embedded in concrete exhibit excellent piezoresistive behaviours to potentially monitor the stress magnitude and failure process of concrete structures and pavements.
Dong, W, Li, W, Shen, L, Sun, Z & Sheng, D 2020, 'Piezoresistivity of smart carbon nanotubes (CNTs) reinforced cementitious composite under integrated cyclic compression and impact', Composite Structures, vol. 241, pp. 112106-112106.
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© 2020 Elsevier Ltd The cyclic compression and four series of fixed magnitude impact loads with an increment of 50 times were conducted alternatively on the smart carbon nanotubes (CNTs) reinforced cementitious composites, to evaluate the piezoresistive sensitivity and repeatability of composites after exposed to different drop impact energies. The results show that the impacts procedure suddenly increased in electrical resistivity due to the emerged micro-cracks and pores, and higher impact energy led to faster resistivity increase. On the other hand, when the impact is repeatedly applied, a high impact resistance of the cementitious composites could be observed, which was attributed to the dense microstructures. Moreover, instead of instable and uneven output of electrical resistivity during cyclical compression, more stable and uniform fractional changes of resistivity were achieved after exposed to impact load. However, severe nonlinearity with swift resistivity reduction of cementitious composites under low loads was observed at the beginning and the end of cyclic compression after subjected to many impacts with impact energy of 18.72 × 10−4 J/cm3. The related outcomes of smart conductive cementitious composites subjected to cyclic compression and impact will provide an insight into the stable electrical signal output and promote the applications of cement-based sensors for structural health monitoring under various loading conditions.
Dong, W, Li, W, Vessalas, K & Wang, K 2020, 'Mechanical and Conductive Properties of Smart Cementitious Composites with Conductive Rubber Crumbs', ES Materials & Manufacturing, vol. 7, pp. 51-63.
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Dong, W, Li, W, Wang, K, Guo, Y, Sheng, D & Shah, SP 2020, 'Piezoresistivity enhancement of functional carbon black filled cement-based sensor using polypropylene fibre', Powder Technology, vol. 373, pp. 184-194.
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In this study, different dosages of carbon black (CB) and polypropylene (PP) were added to develop functional cementitious composites as cement-based sensors. The results show that electrical conductivity increased with the amount of PP fibres, due to the enclosed CB nanoparticles and more conductive passages. The compressive strength slightly decreased, while the flexural strength was significantly increased with the increased amount of PP fibres. The improvement is mainly achieved by the reduced CB concentration in cement matrix and the excellent tensile strength of PP fibres. Under the cyclic compression, the piezoresistivity increased by three times for 0.4 wt% PP fibres filled CB/cementitious composite, regardless of the loading rates. The flexural stress sensing efficiency was considerably lower than that of compressive stress sensing, but it increased with the amount of PP fibres. Moreover, fitting formulas were proposed and used to evaluate the self-sensing capacity, with the attempts to apply cement-based sensors for structural health monitoring.
Dong, W, Li, W, Wang, K, Han, B, Sheng, D & Shah, SP 2020, 'Investigation on physicochemical and piezoresistive properties of smart MWCNT/cementitious composite exposed to elevated temperatures', Cement and Concrete Composites, vol. 112, pp. 103675-103675.
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© 2020 Elsevier Ltd Piezoresistivity of smart carbon nanotube/cementitious composite has been experimentally investigated, but the piezoresistive performance had been rarely studied when exposed to elevated temperatures. In this study, the physicochemical and mechanical properties, and piezoresistive behaviours of multi-walled carbon nanotube (MWCNT) reinforced smart cementitious composite were investigated under heat treatments of elevated temperatures of 300 °C and 600 °C. The microstructures, crystal deterioration and thermal gravity relationships were characterized by scanning electron microscope (SEM), X-ray diffraction (XRD) and thermos-gravimetric (TG) analysis. The results show that the compressive strength and elastic modulus of MWCNT/cementitious composite after heat treatments gradually decreased, especially under the high temperature of 600 °C. There was a sudden growth of fractional changes of resistivity (FCR) after heat treatment. The higher temperature treatments led to more extensive sudden increase in the piezoresistivity. In the linear part of the relationship curves of FCR to the strain, the gauge factor even increased at the temperature of 300 °C. Moreover, the mechanism for the altered piezoresistivity was fundamentally explained and discussed by the MWCNT purification and destructions of MWCNT, cement matrix and agglomerations after heat treatments. Therefore, the related outcomes will promote the understanding and application of smart MWCNT/cementitious composite for structural health monitoring (SHM) under extreme environments.
Dong, W, Li, W, Wang, K, Luo, Z & Sheng, D 2020, 'Self-sensing capabilities of cement-based sensor with layer-distributed conductive rubber fibres', Sensors and Actuators A: Physical, vol. 301, pp. 111763-111763.
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Dong, W, Li, W, Wang, K, Vessalas, K & Zhang, S 2020, 'Mechanical strength and self-sensing capacity of smart cementitious composite containing conductive rubber crumbs', Journal of Intelligent Material Systems and Structures, vol. 31, no. 10, pp. 1325-1340.
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The effects of conductive rubber crumbs on the mechanical properties and self-sensing capacities of cementitious composites are investigated in this study. The rubberized cementitious composites with five different contents of conductive rubber crumbs are incorporated, ranging from 0%, 10%, 20%, 30% and 40% by mass of fine aggregate. Under the uniaxial cyclic compression, all the conductive rubber crumbs–filled cement composites exhibit excellent repeatability of piezoresistivity. The mortar with 20% conductive rubber crumbs at a water-to-binder ratio of 0.42 displayed the best piezoresistive sensitivity. Based on the relative positions of conductive rubber crumbs in the rubberized cement mortar, three conductive mechanisms were proposed for the conductive rubber crumbs, including complete isolation state, neighbouring state and the contact state. The isolation state plays a dominant role when the content of the conductive rubber crumbs is low, in which the piezoresistive behaviour is mainly controlled by the resistivity changes in cement matrix. In the neighbouring state, pores or voids in the gaps between nearby conductive rubber crumbs make the conductive rubber crumbs easier to connect, thus decreasing the resistivity under uniaxial compression. As for the contact state, the decreased contact resistance and the absence of sand between conductive rubber crumbs lead to higher resistivity changes under cyclic compression. The related results indicate that conductive rubber crumbs in cement mortar have application potentials for structural health monitoring.
Dong, X, Gong, Y & Cao, L 2020, 'e-RNSP: An Efficient Method for Mining Repetition Negative Sequential Patterns', IEEE Transactions on Cybernetics, vol. 50, no. 5, pp. 2084-2096.
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Negative sequential patterns (NSPs), which capture both frequent occurring and nonoccurring behaviors, become increasingly important and sometimes play a role irreplaceable by analyzing occurring behaviors only. Repetition sequential patterns capture repetitions of patterns in different sequences as well as within a sequence and are very important to understand the repetition relations between behaviors. Though some methods are available for mining NSP and repetition positive sequential patterns (RPSPs), we have not found any methods for mining repetition NSP (RNSP). RNSP can help the analysts to further understand the repetition relationships between items and capture more comprehensive information with repetition properties. However, mining RNSP is much more difficult than mining NSP due to the intrinsic challenges of nonoccurring items. To address the above issues, we first propose a formal definition of repetition negative containment. Then, we propose a method to convert repetition negative containment to repetition positive containment, which fast calculates the repetition supports by only using the corresponding RPSP's information without rescanning databases. Finally, we propose an efficient algorithm, called e-RNSP, to mine RNSP efficiently. To the best of our knowledge, e-RNSP is the first algorithm to efficiently mine RNSP. Intensive experimental results on the first four real and synthetic datasets clearly show that e-RNSP can efficiently discover the repetition negative patterns; results on the fifth dataset prove the effectiveness of RNSP which are captured by the proposed method; and the results on the rest 16 datasets analyze the impacts of data characteristics on mining process.
Dong, X, Liu, L, Musial, K & Gabrys, B 2020, 'NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size', IEEE transactions on pattern analysis and machine intelligence, vol. PP, pp. 1-1.
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Neural architecture search (NAS) has attracted a lot of attention and hasbeen illustrated to bring tangible benefits in a large number of applicationsin the past few years. Architecture topology and architecture size have beenregarded as two of the most important aspects for the performance of deeplearning models and the community has spawned lots of searching algorithms forboth aspects of the neural architectures. However, the performance gain fromthese searching algorithms is achieved under different search spaces andtraining setups. This makes the overall performance of the algorithms to someextent incomparable and the improvement from a sub-module of the searchingmodel unclear. In this paper, we propose NATS-Bench, a unified benchmark onsearching for both topology and size, for (almost) any up-to-date NASalgorithm. NATS-Bench includes the search space of 15,625 neural cellcandidates for architecture topology and 32,768 for architecture size on threedatasets. We analyze the validity of our benchmark in terms of various criteriaand performance comparison of all candidates in the search space. We also showthe versatility of NATS-Bench by benchmarking 13 recent state-of-the-art NASalgorithms on it. All logs and diagnostic information trained using the samesetup for each candidate are provided. This facilitates a much larger communityof researchers to focus on developing better NAS algorithms in a morecomparable and computationally cost friendly environment. All codes arepublicly available at: https://xuanyidong.com/assets/projects/NATS-Bench.
Dong, Y & Fatahi, B 2020, 'Discrete element simulation of cavity expansion in lightly cemented sands considering cementation degradation', Computers and Geotechnics, vol. 124, pp. 103628-103628.
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© 2020 Elsevier Ltd This study aims to investigate the influence of cementation on the stress-strain and strength characteristics of soil during cavity expansion in lightly cemented sand deposit using three-dimensional discrete element simulations. Contact models, simulating the cementation effects of bonded clumps and capturing the interlocking effects between discrete sand particles, are incorporated to mimic the cemented sands with various cement contents. The microscopic parameters are calibrated and validated against existing experimental results. Real scale cylindrical cavity expansion models starting from zero initial cavity radius with different levels of cementation are developed, and each proposed model consists of 150,000 particles with boundary conditions carefully selected to reproduce the realistic scenario. The embedded scripting is utilised to precisely measure both the local and global stress–strain variations, and record and analyse the cementation bond breakage during the cavity expansion process. The results confirm that the cementation enhances the material strength through the increase in cohesion and tensile strength at the contacting interfaces, whereas the friction angle is not altered notably. Hence, the failure envelope of the cemented sand gradually merges with the critical state line due to the cementation degradation, particularly at a high confining pressure. It was found that the failure mode of the lightly cemented sand adopted in this study, was mainly controlled by the shear rather than tensile strength at the contacting interfaces. Referring to the numerical predictions it is evident that the zone with significant cementation degradation due to the cavity expansion extends as far as 4af for all cemented specimens (af being the final cavity radius). In addition, specimens with higher cement content experience a more pronounced dilation at the internal cavity wall, while an inverse trend is captured at a greater radial ...
Dong, Y, Fatahi, B & Khabbaz, H 2020, 'Three dimensional discrete element simulation of cylindrical cavity expansion from zero initial radius in sand', Computers and Geotechnics, vol. 117, pp. 103230-103230.
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© 2019 Elsevier Ltd This study seeks to assess the influence of choice of initial cavity radius on the soil response during cavity expansion in sandy soil adopting three-dimensional discrete element simulations and obtaining the size of the influence zone when the expansion starts from zero initial radius. Sandy soil is modelled adopting rolling resistance contact model to capture the effects of particle interlocking, and the microscopic parameters are calibrated utilising linear model deformability method for both loose and dense sands against experimental results. Four cylindrical cavity expansions that commenced from different initial radii are simulated in dense and loose sand specimens. The large-scale three-dimensional model is proposed with more than 500,000 particles, enabling precise volumetric dilation and contraction predictions using strain rate tensors. During the cavity expansion process, cavity pressure is constantly recorded by appropriate subroutines, while the stress-strain and void ratio variations are continuously monitored using an array of prediction spheres situated close to the internal cavities. The results confirm that the initial cavity radius chosen has conspicuous effects on the cavity pressure, the stress path, the volumetric strain and the deviatoric stress, especially at the initial stage of expansion; however, these effects become less pronounced and are ultimately minor as the cavity reaches full expansion. The results confirmed that given the same expansion volume, the pressure required to create a cavity is significantly larger than expanding an existing cavity in the same soil medium, whereas the pressure needed to maintain an already expanded cavity is not sensitive to the choice of initial cavity radius. The results obtained were further validated adopting the variations of stress path, deviatoric stress and volumetric strain in the vicinity of the cavity wall. The findings from this study may provide practicing en...
Dorji, P, Kim, DI, Hong, S, Phuntsho, S & Shon, HK 2020, 'Pilot-scale membrane capacitive deionisation for effective bromide removal and high water recovery in seawater desalination', Desalination, vol. 479, pp. 114309-114309.
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© 2020 Although seawater desalination is becoming an important technology for freshwater production, the presence of a high concentration of bromide in the seawater presents a major challenge. Bromide is one of the major inorganic precursors for the formation of disinfection by-products such as bromate, which is highly regulated due to its toxicity and carcinogenicity. Hence, a significant reduction of bromide ions is required prior to water disinfection. In Australia, all the desalination plants have to operate a two-stage reverse osmosis system to ensure effective bromide removal, which adds significant cost to the desalination system. In this study, a pilot-scale membrane capacitive deionisation (MCDI) was investigated as a potential alternative to the 2nd stage RO in seawater desalination. Moreover, strategies to enhance water recovery in MCDI was also carried out by using lower flow rates and shorter duration during the desorption stage. In order to reduce energy consumption in MCDI, a combined short-circuit and reverse polarity desorption is introduced. The results showed that MCDI can effectively remove bromide and dissolved salt at a much lower energy consumption compared with membrane process and that MCDI can be operated to achieve high water recovery without increasing the total energy consumption.
Downie, AS, Hancock, M, Abdel Shaheed, C, McLachlan, AJ, Kocaballi, AB, Williams, CM, Michaleff, ZA & Maher, CG 2020, 'An Electronic Clinical Decision Support System for the Management of Low Back Pain in Community Pharmacy: Development and Mixed Methods Feasibility Study', JMIR Medical Informatics, vol. 8, no. 5, pp. e17203-e17203.
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Background People with low back pain (LBP) in the community often do not receive evidence-based advice and management. Community pharmacists can play an important role in supporting people with LBP as pharmacists are easily accessible to provide first-line care. However, previous research suggests that pharmacists may not consistently deliver advice that is concordant with guideline recommendations and may demonstrate difficulty determining which patients require prompt medical review. A clinical decision support system (CDSS) may enhance first-line care of LBP, but none exists to support the community pharmacist–client consultation. Objective This study aimed to develop a CDSS to guide first-line care of LBP in the community pharmacy setting and to evaluate the pharmacist-reported usability and acceptance of the prototype system. Methods A cross-platform Web app for the Apple iPad was developed in conjunction with academic and clinical experts using an iterative user-centered design process during interface design, clinical reasoning, program development, and evaluation. The CDSS was evaluated via one-to-one user-testing with 5 community pharmacists (5 case vignettes each). Data were collected via video recording, screen capture, survey instrument (system usability scale), and direct observation. Results Pharmacists’ agreement with CDSS-generated self-care recommendations was 90% (18/20), with medicines recommendations was 100% (25/25), and with referral advice was 88% (22/25; total 70 recommendations). Pharmacists expressed uncertainty when screening for serious p...
Drumond, PDP, Ball, JE, Moura, P & Pinto Coelho, MML 2020, 'Are the current On-site Stormwater Detention (OSD) policies the best solution for source control stormwater management? A case study of Australian and Brazilian cities', Urban Water Journal, vol. 17, no. 3, pp. 273-281.
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Du, G, Huang, N, He, H, Lei, G & Zhu, J 2020, 'Parameter Design for a High-Speed Permanent Magnet Machine Under Multiphysics Constraints', IEEE Transactions on Energy Conversion, vol. 35, no. 4, pp. 2025-2035.
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Du, J, Dong, P & Sugumaran, V 2020, 'Dynamic Production Scheduling for Prefabricated Components Considering the Demand Fluctuation', Intelligent Automation & Soft Computing, vol. 26, no. 4, pp. 715-723.
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Du, J, Jing, H, Choo, K-KR, Sugumaran, V & Castro-Lacouture, D 2020, 'An Ontology and Multi-Agent Based Decision Support Framework for Prefabricated Component Supply Chain', Information Systems Frontiers, vol. 22, no. 6, pp. 1467-1485.
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Du, X, Yin, H, Chen, L, Wang, Y, Yang, Y & Zhou, X 2020, 'Personalized Video Recommendation Using Rich Contents from Videos', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 3, pp. 492-505.
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IEEE Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods.
Du, Y, Hsieh, M-H, Liu, T & Tao, D 2020, 'Quantum-inspired algorithm for general minimum conical hull problems', Physical Review Research, vol. 2, no. 3, p. 033199.
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Du, Y, Hsieh, M-H, Liu, T, You, S & Tao, D 2020, 'On the learnability of quantum neural networks'.
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We consider the learnability of the quantum neural network (QNN) built on thevariational hybrid quantum-classical scheme, which remains largely unknown dueto the non-convex optimization landscape, the measurement error, and theunavoidable gate errors introduced by noisy intermediate-scale quantum (NISQ)machines. Our contributions in this paper are multi-fold. First, we derive theutility bounds of QNN towards empirical risk minimization, and show that largegate noise, few quantum measurements, and deep circuit depth will lead to thepoor utility bounds. This result also applies to the variational quantumcircuits with gradient-based classical optimization, and can be of independentinterest. We then prove that QNN can be treated as a differentially private(DP) model. Thirdly, we show that if a concept class can be efficiently learnedby QNN, then it can also be effectively learned by QNN even with gate noise.This result implies the same learnability of QNN whether it is implemented onnoiseless or noisy quantum machines. We last exhibit that the quantumstatistical query (QSQ) model can be effectively simulated by noisy QNN. Sincethe QSQ model can tackle certain tasks with runtime speedup, our resultsuggests that the modified QNN implemented on NISQ devices will retain thequantum advantage. Numerical simulations support the theoretical results.
Du, Y, Hsieh, M-H, Liu, T, You, S & Tao, D 2020, 'Quantum Differentially Private Sparse Regression Learning'.
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The eligibility of various advanced quantum algorithms will be questioned ifthey can not guarantee privacy. To fill this knowledge gap, here we devise anefficient quantum differentially private (QDP) Lasso estimator to solve sparseregression tasks. Concretely, given $N$ $d$-dimensional data points with $N\lld$, we first prove that the optimal classical and quantum non-private Lassorequires $\Omega(N+d)$ and $\Omega(\sqrt{N}+\sqrt{d})$ runtime, respectively.We next prove that the runtime cost of QDP Lasso is \textit{dimensionindependent}, i.e., $O(N^{5/2})$, which implies that the QDP Lasso can befaster than both the optimal classical and quantum non-private Lasso. Last, weexhibit that the QDP Lasso attains a near-optimal utility bound$\tilde{O}(N^{-2/3})$ with privacy guarantees and discuss the chance to realizeit on near-term quantum chips with advantages.
Du, Z, Gupta, A, Clarke, C, Cappadona, M, Clases, D, Liu, D, Yang, Z, Karan, S, Price, WS & Xu, X 2020, 'Porous Upconversion Nanostructures as Bimodal Biomedical Imaging Contrast Agents', The Journal of Physical Chemistry C, vol. 124, no. 22, pp. 12168-12174.
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Copyright © 2020 American Chemical Society. Lanthanide ion doped upconversion nanoparticles (UCNPs) hold great promise as multimodal contrast agents for a range of medical imaging techniques, including optical bioimaging (OBI), magnetic resonance imaging (MRI), and computed tomography (CT). However, it is challenging to obtain UCNPs with both maximal contrast enhancement effects for both OBI and MRI simultaneously owing to the dilemma in the size of UCNPs. UCNPs in large dimensions contain more photonic Ln ions and less surface defects, which is favored for high luminescent emissions, while small UCNPs with high specific surface areas allow a higher proportion of paramagnetic Ln ions to be more accessible to water molecules, which offers enhanced contrast in MRI. In this work, we report the novel design of core-porous shell UCNPs with both high luminescent emissions and magnetic relaxivities as potential dual-modal contrast agents. The core-porous shell UCNPs were fabricated via the selective etching of the inert shell of NaYF4: 30%Gd at the active core of NaYF4: 20%Yb, 1%Er. Their morphology and composition were carefully characterized using transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy, X-ray diffraction, and high resolution TEM. Their photoluminescent and magnetic resonance properties were experimentally determined and compared for the core, core-dense shell, and core-porous shell UCNPs. Core-porous shell UCNPs were found to display bright luminescence and superior MRI contrast enhancement, thus showing great potential as bimodal OBI and MRI contrast agents.
Duan, H, Gao, S, Li, X, Ab Hamid, NH, Jiang, G, Zheng, M, Bai, X, Bond, PL, Lu, X, Chislett, MM, Hu, S, Ye, L & Yuan, Z 2020, 'Improving wastewater management using free nitrous acid (FNA)', Water Research, vol. 171, pp. 115382-115382.
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Free nitrous acid (FNA), the protonated form of nitrite, has historically been an unwanted substance in wastewater systems due to its inhibition on a wide range of microorganisms. However, in recent years, advanced understanding of FNA inhibitory and biocidal effects on microorganisms has led to the development of a series of FNA-based applications that improve wastewater management practices. FNA has been used in sewer systems to control sewer corrosion and odor; in wastewater treatment to achieve carbon and energy efficient nitrogen removal; in sludge management to improve the sludge reduction and energy recovery; in membrane systems to address membrane fouling; and in wastewater algae systems to facilitate algae harvesting. This paper aims to comprehensively and critically review the current status of FNA-based applications in improving wastewater management. The underlying mechanisms of FNA inhibitory and biocidal effects are also reviewed and discussed. Knowledge gaps and current limitations of the FNA-based applications are identified; and perspectives on the development of FNA-based applications are discussed. We conclude that the FNA-based technologies have great potential for enhancing the performance of wastewater systems; however, further development and demonstration at larger scales are still required for their wider applications.
Duan, H, van den Akker, B, Thwaites, BJ, Peng, L, Herman, C, Pan, Y, Ni, B-J, Watt, S, Yuan, Z & Ye, L 2020, 'Mitigating nitrous oxide emissions at a full-scale wastewater treatment plant', Water Research, vol. 185, pp. 116196-116196.
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Mitigation of nitrous oxide (N2O) emissions is of primary importance to meet the targets of reducing carbon footprints of wastewater treatment plants (WWTPs). Despite of a large amount of N2O mitigation studies conducted in laboratories, full-scale implementation of N2O mitigation is scarce, mainly due to uncertainties of mitigation effectiveness, validation of N2O mathematical model, risks to nutrient removal performance and additional costs. This study aims to address the uncertainties by investigating the quantification, development and implementation of N2O mitigation strategies at a full-scale sequencing batch reactor (SBR). To achieve this, N2O emission dynamics, nutrient removal performance and operation of the SBR were monitored to quantify N2O emissions, and identify the N2O generation mechanisms. N2O mitigation strategies centered on reducing dissolved oxygen (DO) levels were consequently proposed and evaluated using a multi-pathway N2O production mathematical model before implementation. The implemented mitigation strategy resulted in a 35% reduction in N2O emissions (from the emission factor of 0.89 ± 0.05 to 0.58 ± 0.06%), which was equivalent to annual reduction of 2.35 tonne of N2O from the studied WWTP. This could be mainly attributed to reductions in N2O generated via the NH2OH oxidation pathway due to the lowering of DO level. As the first reported mitigation strategy permanently implemented at a full scale WWTP, it showcased that the mitigation of N2O emissions at full-scale is feasible and that widely accepted N2O mitigation strategies developed in laboratory studies are also likely effective in full-scale plants. Furthermore, the close agreement between the validated and predicted N2O emission factors (0.58% vs 0.55%, respectively), showed that the ...
Dun, MD, Mannan, A, Rigby, CJ, Butler, S, Toop, HD, Beck, D, Connerty, P, Sillar, J, Kahl, RGS, Duchatel, RJ, Germon, Z, Faulkner, S, Chi, M, Skerrett-Byrne, D, Murray, HC, Flanagan, H, Almazi, JG, Hondermarck, H, Nixon, B, De Iuliis, G, Chamberlain, J, Alvaro, F, de Bock, CE, Morris, JC, Enjeti, AK & Verrills, NM 2020, 'Shwachman–Bodian–Diamond syndrome (SBDS) protein is a direct inhibitor of protein phosphatase 2A (PP2A) activity and overexpressed in acute myeloid leukaemia', Leukemia, vol. 34, no. 12, pp. 3393-3397.
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Duong, HC, Ansari, AJ, Cao, HT, Nguyen, NC, Do, K-U & Nghiem, LD 2020, 'Membrane distillation regeneration of liquid desiccant solution for air-conditioning: Insights into polarisation effects and mass transfer', Environmental Technology & Innovation, vol. 19, pp. 100941-100941.
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© 2020 Membrane distillation (MD) embodies ideal attributes for the regeneration of liquid desiccant solutions used in air-conditioning systems. The MD process has been experimentally proven technically viable for the regeneration of liquid desiccant solutions; however, it suffers severely from temperature and concentration polarisation effects. In this study, for the first time a descriptive mass and heat transfer (DMHT) model is developed to quantitatively describe the mass transfer and the negative impacts of temperature and concentration polarisation during the MD regeneration of the LiCl desiccant solution. The simulation results demonstrate significant reduction in water flux along the membrane due to decreasing mass transfer coefficient (Cm) and transmembrane water vapour pressure gradient (ΔPm). Over the length of the membrane leaf of 0.145 m, water flux reduces by 31% from 11.0 to 7.6 L/m2⋅h. The temperature and concentration polarisation effects cause a substantial decline in the process driving force - ΔPm is only two thirds of the water vapour pressure difference between the bulk feed and distillate (ΔPb). Temperature polarisation is the predominant cause of the reduction in ΔPm compared with ΔPb; however, the negative impact of concentration polarisation is also notable. Finally, amongst the key operating conditions, the inlet feed temperature and concentration exert the most profound influence on the temperature and concentration polarisation during the DCMD regeneration of the hyper saline LiCl solution.
Duong, HC, Ansari, AJ, Hailemariam, RH, Woo, YC, Pham, TM, Ngo, LT, Dao, DT & Nghiem, LD 2020, 'Membrane Distillation for Strategic Water Treatment Applications: Opportunities, Challenges, and Current Status', Current Pollution Reports, vol. 6, no. 3, pp. 173-187.
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© 2020, Springer Nature Switzerland AG. Purpose of Review: Membrane distillation (MD) has been known as a promising water treatment process for many years. However, despite its advantages, MD has never been able to compete with other processes for industrial water treatment and supply. Instead, it has been orientated towards several unique strategic water treatment applications. This review aims to uncover the opportunities and technical challenges pertinent to the MD process and the current status of its strategic water treatment applications most notably including decentralised small-scale desalination for fresh water provision in remote areas, hybridisation with forward osmosis (FO) for treatment of challenging polluted waters, regeneration of liquid desiccant solutions for air conditioning, and treatment of acid effluents for beneficial reuse. Recent Findings: Pilot and small-scale MD systems have been demonstrated for decentralised desalination using various renewable energy sources to supply fresh water in remote, rural areas and on ships where other desalination processes are inefficient or unfeasible. For this strategic desalination application, MD is technically viable, but more works on configuration modification and process optimisation are required to reduce the process energy consumption and water production costs. For the three other strategic applications, the technical viability of the MD process has been proved by extensive lab-scale researches, but its economic feasibility is still questionable due to the lack of large-scale evaluation and the uncertain costs of MD systems. Summary: The orientation of MD towards strategic water treatment applications is clear. However, huge efforts are required to facilitate these applications at commercial and full scale.
Dutta, S & Gandomi, AH 2020, 'Bilevel Data-Driven Modeling Framework for High-Dimensional Structural Optimization under Uncertainty Problems', Journal of Structural Engineering, vol. 146, no. 11, pp. 04020245-04020245.
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Optimization under uncertainty (OUU) is a robust framework to obtain optimal designs for real engineering problems considering uncertainties. The numerical solution for large-scale problems involving millions of degrees-of-freedom is typically computation-intensive in nature. Also, OUU problems constitutes an uncertainty analysis, involving a computation-intensive numerical solver for large-scale systems. Hence, the solution of OUU problems are computationally demanding in nature. In this study, a bilevel data-driven modeling framework is proposed using proper orthogonal decomposition (POD) and polynomial chaos expansion (PCE) metamodels. A heuristic particle swarm optimization (PSO) technique is used for optimization. The effectiveness of the POD-PCE metamodel combined with PSO is demonstrated for two practical large-scale structural optimizations under uncertainty problems. From the case studies, it has been observed that the proposed method gives solutions that are almost hundreds and thousands of times faster as compared to the crude Monte Carlo simulation.
Dwi Prasetyo, W, Putra, ZA, Bilad, MR, Mahlia, TMI, Wibisono, Y, Nordin, NAH & Wirzal, MDH 2020, 'Insight into the Sustainable Integration of Bio- and Petroleum Refineries for the Production of Fuels and Chemicals', Polymers, vol. 12, no. 5, pp. 1091-1091.
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A petroleum refinery heavily depends on crude oil as its main feedstock to produce liquid fuels and chemicals. In the long term, this unyielding dependency is threatened by the depletion of the crude oil reserve. However, in the short term, its price highly fluctuates due to various factors, such as regional and global security instability causing additional complexity on refinery production planning. The petroleum refining industries are also drawing criticism and pressure due to their direct and indirect impacts on the environment. The exhaust gas emission of automobiles apart from the industrial and power plant emission has been viewed as the cause of global warming. In this sense, there is a need for a feasible, sustainable, and environmentally friendly generation process of fuels and chemicals. The attention turns to the utilization of biomass as a potential feedstock to produce substitutes for petroleum-derived fuels and building blocks for biochemicals. Biomass is abundant and currently is still low in utilization. The biorefinery, a facility to convert biomass into biofuels and biochemicals, is still lacking in competitiveness to a petroleum refinery. An attractive solution that addresses both is by the integration of bio- and petroleum refineries. In this context, the right decision making in the process selection and technologies can lower the investment and operational costs and assure optimum yield. Process optimization based on mathematical programming has been extensively used to conduct techno-economic and sustainability analysis for bio-, petroleum, and the integration of both refineries. This paper provides insights into the context of crude oil and biomass as potential refinery feedstocks. The current optimization status of either bio- or petroleum refineries and their integration is reviewed with the focus on the methods to solve the multi-objective optimization problems. Internal and external uncertain parameters are importan...
Ejeian, F, Razmjou, A, Nasr-Esfahani, MH, Mohammad, M, Karamali, F, Ebrahimi Warkiani, M, Asadnia, M & Chen, V 2020, '<p>ZIF-8 Modified Polypropylene Membrane: A Biomimetic Cell Culture Platform with a View to the Improvement of Guided Bone Regeneration</p>', International Journal of Nanomedicine, vol. Volume 15, pp. 10029-10043.
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Purpose
Despite the significant advances in modeling of biomechanical aspects of cell microenvironment, it remains a major challenge to precisely mimic the physiological condition of the particular cell niche. Here, the metal-organic frameworks (MOFs) have been introduced as a feasible platform for multifactorial control of cell-substrate interaction, given the wide range of physical and mechanical properties of MOF materials and their structural flexibility.Results
In situ crystallization of zeolitic imidazolate framework-8 (ZIF-8) on the polydopamine (PDA)-modified membrane significantly raised surface energy, wettability, roughness, and stiffness of the substrate. This modulation led to an almost twofold increment in the primary attachment of dental pulp stem cells (DPSCs) compare to conventional plastic culture dishes. The findings indicate that polypropylene (PP) membrane modified by PDA/ZIF-8 coating effectively supports the growth and proliferation of DPSCs at a substantial rate. Further analysis also displayed the exaggerated multilineage differentiation of DPSCs with amplified level of autocrine cell fate determination signals, like BSP1, BMP2, PPARG, FABP4, ACAN, and COL2A. Notably, osteogenic markers were dramatically overexpressed (more than 100-folds rather than tissue culture plate) in response to biomechanical characteristics of the ZIF-8 layer.Conclusion
Hence, surface modification of cell culture platforms with MOF nanostructures proposed as a powerful nanomedical approach for selectively guiding stem cells for tissue regeneration. In particular, PP/PDA/ZIF-8 membrane presented ideal characteristics for using as a barrier membrane for guided bone regeneration (GBR) in periodontal tissue engineering.
Ekanayake, UGM, Seo, DH, Faershteyn, K, O'Mullane, AP, Shon, H, MacLeod, J, Golberg, D & Ostrikov, KK 2020, 'Atmospheric-pressure plasma seawater desalination: Clean energy, agriculture, and resource recovery nexus for a blue planet', Sustainable Materials and Technologies, vol. 25, pp. e00181-e00181.
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Eldosouky, AM, Pham, LT, Mohmed, H & Pradhan, B 2020, 'A comparative study of THG, AS, TA, Theta, TDX and LTHG techniques for improving source boundaries detection of magnetic data using synthetic models: A case study from G. Um Monqul, North Eastern Desert, Egypt', Journal of African Earth Sciences, vol. 170, pp. 103940-103940.
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© 2020 Elsevier Ltd The boundaries detection techniques have a great role in enhancing and interpreting the geologic features of magnetic data. In the literature, several filters (THG, AS, TA, NTilt, Theta, TDX, TAHG, LTHG) for identifying the boundaries of the magnetic sources have been suggested. These methods are generally performed based on gradients (vertical and horizontal) of the potential field. This paper presents a comparative investigation of different boundary detection filters including THG (total horizontal gradient), AS (analytical signal), TA (tilt angle), Theta (Cos θ), TDX (horizontal tilt angle), and LTHG (Logistic function of the THG). The effect of each filter was examined on two synthetic magnetic data sets. Moreover, the filters are also applied to a real magnetic data set from the Gabal (G) Um Monqul, North Eastern Desert (NED) of Egypt. The obtained results were correlated with known geologic structures of the study area. From the comparison between several applied methods, the horizontal boundaries of geologic sources obtained by the LTHG were found to be sharper and clearer than other ones. The results confirm that the LTHG method is an effective filter for interpreting aeromagnetic data qualitatively and can be applied for enhancing the source edges of different potential field datasets.
Entezari, A, Liu, NC, Roohani, I, Zhang, Z, Chen, J, Sarrafpour, B, Zoellner, H, Behi, M, Zreiqat, H & Li, Q 2020, 'On design for additive manufacturing (DAM) parameter and its effects on biomechanical properties of 3D printed ceramic scaffolds', Materials Today Communications, vol. 23, pp. 101065-101065.
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Entezari, A, Swain, MV, Gooding, JJ, Roohani, I & Li, Q 2020, 'A modular design strategy to integrate mechanotransduction concepts in scaffold-based bone tissue engineering', Acta Biomaterialia, vol. 118, pp. 100-112.
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Erdağ, Ö, Deveci, Ö & Shannon, AG 2020, 'Matrix Manipulations for Properties of Pell p-Numbers and their Generalizations', Analele Universitatii 'Ovidius' Constanta - Seria Matematica, vol. 28, no. 3, pp. 89-102.
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Abstract In this paper, we define the Pell-Pell p-sequence and then we discuss the connection of the Pell-Pell p-sequence with Pell and Pell p-sequences. Also, we provide a new Binet formula and a new combinatorial representation of the Pell-Pell p-numbers by the aid of the nth power of the generating matrix the Pell-Pell p-sequence. Furthermore, we obtain an exponential representation of the Pell-Pell p-numbers and we develop relationships between the Pell-Pell p-numbers and their permanent, determinant and sums of certain matrices.
Eskandari, M, Blaabjerg, F, Li, L, Moradi, MH & Siano, P 2020, 'Optimal Voltage Regulator for Inverter Interfaced Distributed Generation Units Part II: Application', IEEE Transactions on Sustainable Energy, vol. 11, no. 4, pp. 2825-2835.
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The inverter-interfaced distributed generation (IIDG) units are operated either in grid-forming or grid-feeding modes. To this end, the inner control loops are embedded into the inverters' control system to achieve the control objectives. However, the dynamic performance of IIDG units are greatly affected by their control system and also by the grid's impedance characteristics. Optimal voltage regulator (OVR) previously has been proposed where the conventional inner loops have been replaced by the state feedback loop to compensate for the LC filter dynamics in order to achieve the desired dynamic performance. Utilizing the OVR, a universal model is proposed in this article which is useful for both grid-feeding and grid-forming modes. Each mode of operation is achieved through impedance shaping as a feedback gain adjustment. To this end, the optimal impedance shaping for the universal model is determined based on the desired dynamic performance, control objectives and grid's impedance characteristics. Eigenvalue-analysis and simulation results prove the effectiveness of the universal model in the grid-feeding and grid-forming modes, in unbalanced and harmonic conditions as well as being able to suppress circulating, transient and fault currents in autonomous networked MGs.
Eslahi, H, Hamilton, TJ & Khandelwal, S 2020, 'Energy-Efficient Ferroelectric Field-Effect Transistor-Based Oscillators for Neuromorphic System Design', IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, vol. 6, no. 2, pp. 122-129.
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Espinoza-Audelo, LF, León-Castro, E, Olazabal-Lugo, M, Merigó, JM & Gil-Lafuente, AM 2020, 'Using Ordered Weighted Average for Weighted Averages Inflation', International Journal of Information Technology & Decision Making, vol. 19, no. 02, pp. 601-628.
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This paper presents the ordered weighted average weighted average inflation (OWAWAI) and some extensions using induced and heavy aggregation operators and presents the generalized operators and some of their families. The main advantage of these new formulations is that they can use two different sets of weighting vectors and generate new scenarios based on the reordering of the arguments with the weights. With this idea, it is possible to generate new approaches that under- or overestimate the results according to the knowledge and expertise of the decision-maker. The work presents an application of these new approaches in the analysis of the inflation in Chile, Colombia, and Argentina during 2017.
Esselle, KP 2020, 'Call for IEEE AP-S Distinguished Lecturer Nominations', IEEE Antennas and Propagation Magazine, vol. 62, no. 3, pp. 127-127.
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Esselle, KP 2020, 'CALL FOR IEEE AP-S DISTINGUISHED LECTURER NOMINATIONS NOMINATION DEADLINE: JULY 31,2020', IEEE ANTENNAS AND PROPAGATION MAGAZINE, vol. 62, no. 3, pp. 127-127.
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Esselle, KP 2020, 'Distinguished Lectures on Radio Astronomy and Training the Next Generation [Distinguished Lecturers]', IEEE Antennas and Propagation Magazine, vol. 62, no. 4, pp. 140-145.
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Esselle, KP 2020, 'IEEE AP-S Holds First Distinguished Lecture in Entrepreneurship Category [Distinguished Lecturers]', IEEE Antennas and Propagation Magazine, vol. 62, no. 6, pp. 104-112.
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Esselle, KP 2020, 'Meet the New Distinguished Lecturers for 2020?2022 [Distinguished Lecturers]', IEEE Antennas and Propagation Magazine, vol. 62, no. 3, pp. 120-123.
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Esselle, KP 2020, 'Virtual Distinguished Lectures During COVID-19 [Distinguished Lecturers]', IEEE Antennas and Propagation Magazine, vol. 62, no. 5, pp. 150-151.
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Every, JP, Li, L & Dorrell, DG 2020, 'Köppen-Geiger climate classification adjustment of the BRL diffuse irradiation model for Australian locations', Renewable Energy, vol. 147, pp. 2453-2469.
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© 2019 Elsevier Ltd Numerous mathematical models have been developed to estimate diffuse and direct irradiance components based on global irradiation measurements. The Boland–Ridley–Lauret (BRL) model consists of a single set of parameters for all global locations. There is scope to improve the BRL model to better match local climatic conditions. In this research, the Köppen-Geiger climate classification system is considered to develop a set of adjusted BRL models for Australian conditions. Ground-based and satellite-based irradiation data derived from the Australian Bureau of Meteorology are used to tune and test new BRL models developed at a national level and for each climate zone. Irradiation data are processed through a rigorous quality control procedure before parameter tuning. For ground-based data, a new national model results in an improvement in 96% of statistical indicators over the original BRL model while Köppen-Geiger zone adjusted models show improvement over the new national model in 72% of the statistics. For satellite-based global irradiation estimates, a new national BRL model also results in observed improvements, however, no discernible improvement is observed for Köppen-Geiger zone models.
Faber, MH, Miraglia, S, Qin, J & Stewart, MG 2020, 'Bridging resilience and sustainability - decision analysis for design and management of infrastructure systems', Sustainable and Resilient Infrastructure, vol. 5, no. 1-2, pp. 102-124.
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The paper proposes a novel decision analysis framework and corresponding probabilistic systems representations allowing for the consistent and integral quantification of systems resilience and sustainability. This facilitates–to the knowledge of the authors, for the first time–that decisions relating to the governance of socio-ecologic-technical systems may be optimized with due consideration of their impacts at both local and short-term time scales as well as on global and long-term time scales. The resilience performance of the interlinked system is modeled through the formulation of resilience failure events which occur if one or more of the capacities of the interlinked system are exhausted. Sustainability failure is analogously introduced as the event that one or more of the Planetary Boundaries are exceeded. A principal example shows there is a trade-off between resilience, generation of benefits, consumption of materials, and emissions to the environment. Resilience provides benefits to society but at the same time imposes material consumption and emissions to the environment. Systems can, however, be designed such that resource consumption and associated environmental impacts are reduced and the resilience performance is increased simultaneously. The example further illustrates that social governance system failure may follow from inadequate design and governance of infrastructure.
Fachrunnisa, O & Hussain, FK 2020, 'A methodology for creating sustainable communities based on dynamic factors in virtual environments', International Journal of Electronic Business, vol. 15, no. 2, pp. 133-133.
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Copyright © 2020 Inderscience Enterprises Ltd. A virtual community is one of communities that exist in an internet economy; however, little research has been conducted on how to make it sustainable. We propose a methodology for creating sustainable virtual communities which depends on the community’s respond to the dynamic factors in its environment such as number of members, shared contents and interaction rules. The methodology proposes the use of iterative negotiation and a panel of expert agents to assess the quality of service (QoS) delivered. This QoS assessment is based on an interaction agreement between the community members and expert agent as the administrator’s representative. The administrators use this QoS assessment to determine whether an individual’s membership will be renewed or terminated after a certain period of time. We present a metric to measure the sustainability index and demonstrate the validity of the methodology by engineering a prototype setup and running simulations under various operational conditions.
Fachrunnisa, O & Hussain, FK 2020, 'Blockchain-based human resource management practices for mitigating skills and competencies gap in workforce', International Journal of Engineering Business Management, vol. 12, pp. 184797902096640-184797902096640.
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Skills gap between company needs and competencies occupied by the workforce can be the source of inefficiencies. The purpose of this research is to develop a blockchain-based human resource (HR) framework to match the needs from the company and workforce competencies This framework will help Corporate Training Centre to standardized the competencies which then used by HR Department to develop the training material. In order to get valid information regarding skills that are needed from the company, we develop a prototype based on Blockchain. Hence, blockchain-based HRM is built to improve the quality of workforce competency in an organization. The current organizations are struggling to fulfil the needs of the workforce in accordance with industry quality standards. Therefore, this will help all parties to create a consensus between the needs of the industry with the labour market. Corporate Training Centre through the competent institution will be the mediator or intermediary to unite the information from companies, training institutions, and Professional Certification Institutions. As a result, in the long term, the needs of the workforce with the qualification required by the company in such industries will always fit the current situation. Blockchain helps to process the information and data needed by each party so that the connection between parties will be assisted efficiently and effectively.
Fahmideh, M & Zowghi, D 2020, 'An exploration of IoT platform development', Information Systems, vol. 87, pp. 101409-101409.
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© 2019 Elsevier Ltd IoT (Internet of Things) platforms are key enablers for smart city initiatives, targeting the improvement of citizens’ quality of life and economic growth. As IoT platforms are dynamic, proactive, and heterogeneous socio-technical artefacts, systematic approaches are required for their development. Limited surveys have exclusively explored how IoT platforms are developed and maintained from the perspective of information system development process lifecycle. In this paper, we present a detailed analysis of 63 approaches. This is accomplished by proposing an evaluation framework as a cornerstone to highlight the characteristics, strengths, and weaknesses of these approaches. The survey results not only provide insights of empirical findings, recommendations, and mechanisms for the development of quality aware IoT platforms, but also identify important issues and gaps that need to be addressed.
Fahmideh, M, Daneshgar, F, Rabhi, FA & Beydoun, G 2020, 'A generic cloud migration process model.', CoRR, vol. abs/2004.10857.
Faisal, M, Hannan, MA, Ker, PJ, Rahman, MSA, Begum, RA & Mahlia, TMI 2020, 'Particle swarm optimised fuzzy controller for charging–discharging and scheduling of battery energy storage system in MG applications', Energy Reports, vol. 6, pp. 215-228.
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© 2020 The Authors Aiming at reducing the power consumption and costs of grids, this paper deals with the development of particle swarm optimisation (PSO) based fuzzy logic controller (FLC) for charging–discharging and scheduling of the battery energy storage systems (ESSs) in microgrid (MG) applications. Initially, FLC was developed to control the charging–discharging of the storage system to avoid mathematical calculation of the conventional system. However, to improve the charging–discharging control, the membership function of the FLC is optimised using PSO technique considering the available power, load demand, battery temperature and state of charge (SOC). The scheduling controller is the optimal solution to achieve low-cost uninterrupted reliable power according to the loads. To reduce the grid power demand and consumption costs, an optimal binary PSO is also introduced to schedule the ESS, grid and distributed sources under various load conditions at different times of the day. The obtained results proved that the robustness of the developed PSO based fuzzy control can effectively manage the battery charging–discharging with reducing the significant grid power consumption of 42.26% and the costs of the energy usage by 45.11% which also demonstrates the contribution of the research.
Fan, H, Zhu, L, Yang, Y & Wu, F 2020, 'Recurrent Attention Network with Reinforced Generator for Visual Dialog', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 3, pp. 1-16.
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In Visual Dialog, an agent has to parse temporal context in the dialog history and spatial context in the image to hold a meaningful dialog with humans. For example, to answer “what is the man on her left wearing?” the agent needs to (1) analyze the temporal context in the dialog history to infer who is being referred to as “her,” (2) parse the image to attend “her,” and (3) uncover the spatial context to shift the attention to “her left” and check the apparel of the man. In this article, we use a dialog network to memorize the temporal context and an attention processor to parse the spatial context. Since the question and the image are usually very complex, which makes it difficult for the question to be grounded with a single glimpse, the attention processor attends to the image multiple times to better collect visual information. In the Visual Dialog task, the generative decoder (G) is trained under the word-by-word paradigm, which suffers from the lack of sentence-level training. We propose to reinforce G at the sentence level using the discriminative model (D), which aims to select the right answer from a few candidates, to ameliorate the problem. Experimental results on the VisDial dataset demonstrate the effectiveness of our approach.
Fan, X, Xiang, C, Gong, L, He, X, Qu, Y, Amirgholipour, S, Xi, Y, Nanda, P & He, X 2020, 'Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges', CCF Transactions on Pervasive Computing and Interaction, vol. 2, no. 4, pp. 240-260.
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With the emerging concepts of smart cities and intelligent transportation systems, accurate traffic sensing and prediction have become critically important to support urban management and traffic control. In recent years, the rapid uptake of the Internet of Vehicles and the rising pervasiveness of mobile services have produced unprecedented amounts of data to serve traffic sensing and prediction applications. However, it is significantly challenging to fulfill the computation demands by the big traffic data with ever-increasing complexity and diversity. Deep learning, with its powerful capabilities in representation learning and multi-level abstractions, has recently become the most effective approach in many intelligent sensing systems. In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.
Fan, Y, Ma, S & Wu, T 2020, 'Individual wheat kernels vigor assessment based on NIR spectroscopy coupled with machine learning methodologies', Infrared Physics & Technology, vol. 105, pp. 103213-103213.
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Fang, C, Rajabzadeh, S, Zhang, P, Liu, W, Kato, N, Shon, HK & Matsuyama, H 2020, 'Controlling spherulitic structures at surface and sub-layer of hollow fiber membranes prepared using nucleation agents via triple-orifice spinneret in TIPS process', Journal of Membrane Science, vol. 609, pp. 118229-118229.
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Fang, J, Wu, C, Rabczuk, T, Wu, C, Sun, G & Li, Q 2020, 'Phase field fracture in elasto-plastic solids: a length-scale insensitive model for quasi-brittle materials', Computational Mechanics, vol. 66, no. 4, pp. 931-961.
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Phase-field methods for fracture have been integrated with plasticity for better describing constitutive behaviours. In most of the previous phase-field models, however, the length-scale parameter must be interpreted as a material property in order to match the material strength in experiments. This study presents a phase-field model for fracture coupled with plasticity for quasi-brittle materials with emphasis on insensitivity of the length-scale parameter. The proposed model is formulated using variational principles and implemented numerically in the finite element framework. The effective yield stress is calibrated to vary with the length-scale parameter such that the tensile strength remains the same. Moreover, semi-analytical solutions are derived to demonstrate that the length-scale parameter has a negligible effect on the stress–displacement curve. Five representative examples are considered here to validate the phase-field model for fracture in quasi-brittle materials. The simulated force–displacement curves and crack paths agree well with the corresponding experimental results. Importantly, it is found that the global structural response is insensitive to the length scale though it may influence the size of the failure zone. In most cases, a large length-scale parameter can be used for saving the computational cost by allowing the use of a coarse mesh. On the other hand, a sufficiently small length-scale parameter can be selected to prevent overly diffusive damage, making it possible for the proposed phase-field model to simulate the fracture behaviour with Γ-convergence.
Fang, K, Wang, X, Tomamichel, M & Berta, M 2020, 'Quantum Channel Simulation and the Channel’s Smooth Max-Information', IEEE Transactions on Information Theory, vol. 66, no. 4, pp. 2129-2140.
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Fang, L, Li, Y, Yun, X, Wen, Z, Ji, S, Meng, W, Cao, Z & Tanveer, M 2020, 'THP: A Novel Authentication Scheme to Prevent Multiple Attacks in SDN-Based IoT Network', IEEE Internet of Things Journal, vol. 7, no. 7, pp. 5745-5759.
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© 2014 IEEE. SDN has provided significant convenience for network providers and operators in cloud computing. Such a great advantage is extending to the Internet of Things network. However, it also increases the risk if the security of an SDN network is compromised. For example, if the network operator's permission is illegally obtained by a hacker, he/she can control the entry of the SDN network. Therefore, an effective authentication scheme is needed to fit various application scenarios with high-security requirements. In this article, we design, implement, and evaluate a new authentication scheme called the hidden pattern (THP), which combines graphics password and digital challenge value to prevent multiple types of authentication attacks at the same time. We examined THP in the perspectives of both security and usability, with a total number of 694 participants in 63 days. Our evaluation shows that THP can provide better performance than the existing schemes in terms of security and usability.
Fang, L, Zhang, X, Sood, K, Wang, Y & Yu, S 2020, 'Reliability-aware virtual network function placement in carrier networks', Journal of Network and Computer Applications, vol. 154, pp. 102536-102536.
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© 2020 Network Function Virtualization (NFV) is a promising technology that implements Virtual Network Function (VNF) with software on general servers. Traffic needs to go through a set of ordered VNFs, which is called a Service Function Chain (SFC). Rational deployment of VNFs can reduce costs and increase profits for network operators. However, during the deployment of the VNFs, how to guarantee the reliability of SFC requirements while optimizing network resource cost is still an open problem. To this end, we study the problem of reliability-aware VNF placement in carrier networks. In this paper, we firstly redefine the reliability of SFC, which is the product of the reliability of all nodes and physical links in SFC. On this basis, we propose two reliability protection mechanisms: the All-Nodes Protection Mechanism (ANPM) and the Single-Node Protection Mechanism (SNPM). Following this, for each protection mechanism, we formulate the problem as an Integer Linear Programming (ILP) model. Due to the problem complexity, we propose a heuristic algorithm based on Dynamic Programming and Lagrangian Relaxation for each protection mechanism. With extensive simulations using real world topologies, our results show that compared with the benchmark algorithm and ANPM, SNPM can save up to 33.34% and 26.76% network resource cost on average respectively while guaranteeing the reliability requirement of SFC requests, indicating that SNPM performs better than ANPM and has better application potential in carrier networks.
Fang, L, Zhu, H, Lv, B, Liu, Z, Meng, W, Yu, Y, Ji, S & Cao, Z 2020, 'HandiText', ACM/IMS Transactions on Data Science, vol. 1, no. 4, pp. 1-18.
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The Internet of Things (IoT) is a new manifestation of data science. To ensure the credibility of data about IoT devices, authentication has gradually become an important research topic in the IoT ecosystem. However, traditional graphical passwords and text passwords can cause user’s serious memory burdens. Therefore, a convenient method for determining user identity is needed. In this article, we propose a handwriting recognition authentication scheme named HandiText based on behavior and biometrics features. When people write a word by hand, HandiText captures their static biological features and dynamic behavior features during the writing process (writing speed, pressure, etc.). The features are related to habits, which make it difficult for attackers to imitate. We also carry out algorithms comparisons and experiments evaluation to prove the reliability of our scheme. The experiment results show that the Long Short-Term Memory has the best classification accuracy, reaching 99% while keeping relatively low false-positive rate and false-negative rate. We also test other datasets, the average accuracy of HandiText reach 98%, with strong generalization ability. Besides, the 324 users we investigated indicated that they are willing to use this scheme on IoT devices.
Fang, XS, Sheng, QZ, Wang, X, Zhang, WE, Ngu, AHH & Yang, J 2020, 'From Appearance to Essence', ACM Transactions on Intelligent Systems and Technology, vol. 11, no. 6, pp. 1-24.
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Truth discovery has been widely studied in recent years as a fundamental means for resolving the conflicts in multi-source data. Although many truth discovery methods have been proposed based on different considerations and intuitions, investigations show that no single method consistently outperforms the others. To select the right truth discovery method for a specific application scenario, it becomes essential to evaluate and compare the performance of different methods. A drawback of current research efforts is that they commonly assume the availability of certain ground truth for the evaluation of methods. However, the ground truth may be very limited or even impossible to obtain, rendering the evaluation biased. In this article, we present CompTruthHyp , a generic approach for comparing the performance of truth discovery methods without using ground truth. In particular, our approach calculates the probability of observations in a dataset based on the output of different methods. The probability is then ranked to reflect the performance of these methods. We review and compare 12 representative truth discovery methods and consider both single-valued and multi-valued objects. The empirical studies on both real-world and synthetic datasets demonstrate the effectiveness of our approach for comparing truth discovery methods.
Fang, Z, Hu, J, Lu, Y & Ni, W 2020, 'Three-User Cooperative NOMA Transmission', IEEE Wireless Communications Letters, vol. 9, no. 4, pp. 465-469.
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© 2012 IEEE. This letter presents a new downlink cooperative non-orthogonal multiple access (NOMA) transmission scheme to serve three users within only two time slots. The scheme involves a base station (BS), two direct-link users and one indirect-link user. The BS sends superposed signals to the two direct-link users which decode and forward the signals to the indirect-link user in an alternating fashion. Closed-form expression is derived for the sum-rate of the proposed scheme and a simple expression of sum-rate is also derived in the high signal-to-noise ratio (SNR) region. In the presence of strong inter-user interference (IUI), the proposed scheme can be applied by having each direct-link user to detect the IUI first and then cancel the IUI to decode the signals destined for the indirect-link user and itself. Simulation results show that the scheme with configurable decoding orders at the users is able to achieve a higher sum-rate than existing orthogonal multiple access (OMA) based alternatives.
Fang, Z, Shen, S, Liu, J, Ni, W & Jamalipour, A 2020, 'New NOMA-Based Two-Way Relay Networks', IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 15314-15324.
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Fanos, AM, Pradhan, B, Alamri, A & Lee, C-W 2020, 'Machine Learning-Based and 3D Kinematic Models for Rockfall Hazard Assessment Using LiDAR Data and GIS', Remote Sensing, vol. 12, no. 11, pp. 1755-1755.
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Rockfall is one of the most hazardous phenomena in mountainous and hilly regions with high and steep terrain. Such incidents can cause massive damage to people, properties, and infrastructure. Therefore, proper rockfall hazard assessment methods are required to save lives and provide a guide for the development of an area. The aim of this research is to develop a method for rockfall hazard assessment at two different scales (regional and local). A high-resolution airborne laser scanning (ALS) technique was utilized to derive an accurate digital terrain model (DTM); next, a terrestrial laser scanner (TLS) was used to capture the topography of the two most critical areas within the study area. A staking machine-learning model based on different classifiers, namely logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (KNN), was optimized and employed to determine rockfall probability by utilizing various rockfall conditioning factors. A developed 3D rockfall kinematic model was used to obtain rockfall trajectories, velocity, frequency, bouncing height, kinetic energy, and impact location. Next, a spatial model combined with a fuzzy analytical hierarchy process (fuzzy-AHP) integrated in the Geographic Information System (GIS) was developed to assess rockfall hazard in two different areas in Ipoh, Malaysia. Additionally, mitigation processes were suggested and assessed to provide a comprehensive information for urban planning management. The results show that, the stacking random forest–k-nearest neighbor (RF-KNN) model is the best hybrid model compared to other tested models with an accuracy of 89%, 86%, and 87% based on training, validation, and cross-validation datasets, respectively. The three-dimension rockfall kinematic model was calibrated with an accuracy of 93% and 95% for the two study areas and subsequently the rockfall trajectories and their characteristics wer...
Faramarzi, A, Heidarinejad, M, Mirjalili, S & Gandomi, AH 2020, 'Marine Predators Algorithm: A nature-inspired metaheuristic', Expert Systems with Applications, vol. 152, pp. 113377-113377.
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© 2020 This paper presents a nature-inspired metaheuristic called Marine Predators Algorithm (MPA) and its application in engineering. The main inspiration of MPA is the widespread foraging strategy namely Lévy and Brownian movements in ocean predators along with optimal encounter rate policy in biological interaction between predator and prey. MPA follows the rules that naturally govern in optimal foraging strategy and encounters rate policy between predator and prey in marine ecosystems. This paper evaluates the MPA's performance on twenty-nine test functions, test suite of CEC-BC-2017, randomly generated landscape, three engineering benchmarks, and two real-world engineering design problems in the areas of ventilation and building energy performance. MPA is compared with three classes of existing optimization methods, including (1) GA and PSO as the most well-studied metaheuristics, (2) GSA, CS and SSA as almost recently developed algorithms and (3) CMA-ES, SHADE and LSHADE-cnEpSin as high performance optimizers and winners of IEEE CEC competition. Among all methods, MPA gained the second rank and demonstrated very competitive results compared to LSHADE-cnEpSin as the best performing method and one of the winners of CEC 2017 competition. The statistical post hoc analysis revealed that MPA can be nominated as a high-performance optimizer and is a significantly superior algorithm than GA, PSO, GSA, CS, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-cnEpSin. The source code is publicly available at: https://github.com/afshinfaramarzi/Marine-Predators-Algorithm, http://built-envi.com/portfolio/marine-predators-algorithm/, https://www.mathworks.com/matlabcentral/fileexchange/74578-marine-predators-algorithm-mpa, and http://www.alimirjalili.com/MPA.html.
Farhood, H, Perry, S, Cheng, E & Kim, J 2020, 'Enhanced 3D Point Cloud from a Light Field Image', Remote Sensing, vol. 12, no. 7, pp. 1125-1125.
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The importance of three-dimensional (3D) point cloud technologies in the field of agriculture environmental research has increased in recent years. Obtaining dense and accurate 3D reconstructions of plants and urban areas provide useful information for remote sensing. In this paper, we propose a novel strategy for the enhancement of 3D point clouds from a single 4D light field (LF) image. Using a light field camera in this way creates an easy way for obtaining 3D point clouds from one snapshot and enabling diversity in monitoring and modelling applications for remote sensing. Considering an LF image and associated depth map as an input, we first apply histogram equalization and histogram stretching to enhance the separation between depth planes. We then apply multi-modal edge detection by using feature matching and fuzzy logic from the central sub-aperture LF image and the depth map. These two steps of depth map enhancement are significant parts of our novelty for this work. After combing the two previous steps and transforming the point–plane correspondence, we can obtain the 3D point cloud. We tested our method with synthetic and real world image databases. To verify the accuracy of our method, we compared our results with two different state-of-the-art algorithms. The results showed that our method can reliably mitigate noise and had the highest level of detail compared to other existing methods.
Fatahi, B, Huang, B, Yeganeh, N, Terzaghi, S & Banerjee, S 2020, 'Three-Dimensional Simulation of Seismic Slope–Foundation–Structure Interaction for Buildings Near Shallow Slopes', International Journal of Geomechanics, vol. 20, no. 1, pp. 04019140-04019140.
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© 2019 American Society of Civil Engineers. Buildings constructed adjacent to the slope crest in seismically active areas might be exposed to serious danger when they are subjected to strong earthquake excitations. The ground conditions can influence the seismic response of structures through a phenomenon known as the slope-foundation-structure interaction. Indeed, the presence of the slope in the vicinity of a building foundation can significantly affect the seismic response of the superstructure. In this study, the impact of shallow slopes on the seismic performance of nearby buildings was numerically assessed. In the adopted three-dimensional finite-element simulation, the nonlinear variations of the soil stiffness and damping with the cyclic shear strain plus varying distances between the edge of the foundation and crest of the slope were employed. A 15-story moment-resisting structure, a 30-m-thick clayey deposit, and a 2-m-high shallow slope were considered as the benchmark model, being simulated using the direct method in the time domain. According to the results of the analyses, the seismic response of a building could be highly sensitive to the distance between the slope crest and foundation. Particularly, the building closer to the slope crest experienced more severe foundation rocking, lateral deformation, and interstory drifts owing to the amplified effect of the slope-foundation-structure interaction. Moreover, the results highlighted the importance of the slope-foundation-structure interaction in altering the natural period and damping of the system. Hence, it is critical for practicing engineers to assess the impact of nearby slopes on the seismic performance of structures with extreme care to ensure the reliability and safety of the design.
Fathollahipour, S, Koosha, M, Tavakoli, J, Maziarfar, S & Fallah Mehrabadi, J 2020, 'Erythromycin Releasing PVA/sucrose and PVA/honey Hydrogels as Wound Dressings with Antibacterial Activity and Enhanced Bio-adhesion.', Iran J Pharm Res, vol. 19, no. 1, pp. 448-464.
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The present study deals with preparation and characterization of thermally crosslinked PVA-based hydrogels containing honey and sucrose for the purpose of erythromycin delivery. The hydrogels have been characterized and compared by scanning electron microscopy, Fourier transform infrared spectroscopy, and bio-adhesion tests. Swelling measurements showed that addition of sucrose and honey decreased the equilibrium swelling of the hydrogels. Results of release studies showed that the amount of erythromycin, released at the early hours was higher for PVA/sucrose and PVA/honey hydrogels compared to PVA hydrogel while the drug released at later times was highly reduced for PVA/honey hydrogel. Both Peppas-Sahlin and Korsmeyer-Peppas models fitted well to the release data. Fitting Peppas-Sahlin model to the release data showed that at the initial times, release of drug from the hydrogel network was mainly governed by Fickian mechanism; however, at later times the drug is dominantly released by relaxational mechanism due to swelling of the network,. Addition of honey improved the bio-adhesion of PVA/honey hydrogel as compared to PVA/sucrose and pure PVA hydrogel. Results of antibacterial tests showed growth inhibitory action of erythromycin-loaded PVA hydrogels against Pseudomonas aeruginosa and Staphylococcus aureus bacteria. This study indicates that these hybrid hydrogels are capable of being used as functional wound dressings aiming to control the rate of antibiotic delivery to the wound site and prevent the wounds from infection.
Fattah, IMR, Noraini, MY, Mofijur, M, Silitonga, AS, Badruddin, IA, Khan, TMY, Ong, HC & Mahlia, TMI 2020, 'Lipid Extraction Maximization and Enzymatic Synthesis of Biodiesel from Microalgae', Applied Sciences, vol. 10, no. 17, pp. 6103-6103.
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Microalgae has received overwhelming attention worldwide as a sustainable source for energy generation. However, the production of biofuel from microalgae biomass consists of several steps, of which lipid extraction is the most important one. Because of the nature of feedstock, extraction needs special attention. Three different methods were studied to extract algal oil from two different algae variant, Chlorella sp. and Spirulina sp. The highest percentage oil yield was obtained by ultrasonication (9.4% for Chlorella sp., 6.6% for Spirulina sp.) followed by the Soxhlet and solvent extraction processes. Ultrasonication and Soxhlet extraction processes were further optimized to maximize oil extraction as solvent extraction was not effective in extracting lipid. For ultrasonication, an amplitude of 90% recorded the highest percentage yield of oil for Spirulina sp. and a 70% amplitude recorded the highest percentage yield of oil for Chlorella sp. On the other hand, for Soxhlet extraction, a combination of chloroform, hexane, and methanol at a 1:1:1 ratio resulted in the highest yield of algal oil. Afterward, the crude algae oil from the ultrasonication process was transesterified for 5 h using an immobilized lipase (Novozyme 435) at 40 °C to convert triglycerides into fatty acid methyl ester and glycerol. Thus, ultrasonic-assisted lipid extraction was successful in producing biodiesel from both the species.
Fazal, MAU, Ferguson, S & Johnston, A 2020, 'Evaluation of Information Comprehension in Concurrent Speech-based Designs', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 4, pp. 1-19.
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In human-computer interaction, particularly in multimedia delivery, information is communicated to users sequentially, whereas users are capable of receiving information from multiple sources concurrently. This mismatch indicates that a sequential mode of communication does not utilise human perception capabilities as efficiently as possible. This article reports an experiment that investigated various speech-based (audio) concurrent designs and evaluated the comprehension depth of information by comparing comprehension performance across several different formats of questions (main/detailed, implied/stated). The results showed that users, besides answering the main questions, were also successful in answering the implied questions, as well as the questions that required detailed information, and that the pattern of comprehension depth remained similar to that seen to a baseline condition, where only one speech source was presented. However, the participants answered more questions correctly that were drawn from the main information, and performance remained low where the questions were drawn from detailed information. The results are encouraging to explore the concurrent methods further for communicating multiple information streams efficiently in human-computer interaction, including multimedia.
Feng, B, Cui, Z, Huang, Y, Zhou, H & Yu, S 2020, 'Elastic Resilience for Software-Defined Satellite Networking: Challenges, Solutions, and Open Issues', IT Professional, vol. 22, no. 6, pp. 39-45.
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© 1999-2012 IEEE. Satellite networks have long been regarded as a key enabler for ubiquitous Internet access and global data distribution. However, since they are highly dynamic and much more vulnerable to various failures, how to detour traffic around fault satellites and interrupted links becomes an important but challenging issue. Thanks to the emerging software-defined networking, great controllability can be introduced to the satellite networks for agile management and automation. Hence, in this article, we focus on elastic resilience for software-defined satellite networking, and propose a preliminary solution to cope with the related fundamental challenges in guarantees of controller reachability, collections of network status, and failure detection and recovery. We also discuss several key open issues to be urgently addressed, hoping to shed some light on this promising land.
Feng, Y, Wang, Q, Wu, D, Gao, W & Tin-Loi, F 2020, 'Stochastic nonlocal damage analysis by a machine learning approach', Computer Methods in Applied Mechanics and Engineering, vol. 372, pp. 113371-113371.
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© 2020 Elsevier B.V. A machine learning aided stochastic nonlocal damage analysis framework is proposed for quasi-brittle materials. The uncertain system parameters, including the material properties and loading actions, have been incorporated and analysed within a unified safety assessment framework against various working conditions. A three-dimensional integral-type nonlocal damage model through finite element method (FEM) has been adopted. For the purpose of investigating the probabilistic damage analysis problems, a freshly established machine learning approach, namely the capped-extended-support vector regression method (C-X-SVR), is proposed to eliminate the influences of random outliers in the first step, then establish the relationship between the uncertain systemic inputs and structural responses. Such that the training robustness and computational adaptability of the proposed regression model can be reinforced. Moreover, the proposed approach is competent of efficiently predicting the statistical information (i.e., means, standard deviations, probability density functions and cumulative density functions) of structural behaviours under continuous information update of the uncertain working condition from mercurial environment. One real-life experimental validation and two numerical investigations are implemented to further verify the effectiveness and efficiency of the uncertainty quantification framework against probabilistic damage analysis.
Ferrari, A, Spoletini, P, Bano, M & Zowghi, D 2020, 'SaPeer and ReverseSaPeer: teaching requirements elicitation interviews with role-playing and role reversal.', Requir. Eng., vol. 25, no. 4, pp. 417-438.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. Among the variety of the available requirements elicitation techniques, interviews are the most commonly used. Performing effective interviews is challenging, especially for students and novice analysts, since interviews’ success depends largely on soft skills and experience. Despite their diffusion and their challenging nature, when it comes to requirements engineering education and training (REET), limited resources and few well-founded pedagogical approaches are available to allow students to acquire and improve their skills as interviewers. To overcome this limitation, this paper presents two pedagogical approaches, namely SaPeer and ReverseSaPeer. SaPeer uses role-playing, peer review and self-assessment to enable students to experience first-hand the difficulties related to the interviewing process, reflect on their mistakes, and improve their interview skills by practice and analysis. ReverseSaPeer builds on the first approach and includes a role reversal activity in which participants play the role of a customer interviewed by a competent interviewer. We evaluate the effectiveness of SaPeer through a controlled quasi-experiment, which shows that the proposed approach significantly reduces the amount of mistakes made by the participants and that it is perceived as useful and easy by the participants. ReverseSaPeer and the impact of role reversal are analyzed through a thematic analysis of the participant’s reflections. The analysis shows that not only the students perceive the analysis as beneficial, but also that they have emotional involvement in learning. This work contributes to the body of knowledge of REET with two methods, quantitative and qualitative evaluated, respectively. Furthermore, we share the pedagogical material used, to enable other educators to apply and possibly tailor the approach.
Ferro, V, Chuai, M, McGloin, D & Weijer, CJ 2020, 'Measurement of junctional tension in epithelial cells at the onset of primitive streak formation in the chick embryo via non-destructive optical manipulation', Development, vol. 147, no. 3, pp. 1-1-.
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ABSTRACT Directional cell intercalations of epithelial cells during gastrulation has, in several organisms, been shown to be associated with a planar cell polarity in the organisation of the actin-myosin cytoskeleton and is postulated to reflect directional tension that drives oriented cell intercalations. We have characterised and applied a recently introduced non-destructive optical manipulation technique to measure the tension in individual epithelial cell junctions of cells in various locations and orientations in the epiblast of chick embryos in the early stages of primitive streak formation. Junctional tension of mesendoderm precursors in the epiblast is higher in junctions oriented in the direction of intercalation than in junctions oriented perpendicular to the direction of intercalation and higher than in junctions of other cells in the epiblast. The kinetic data fit best with a simple viscoelastic Maxwell model, and we find that junctional tension, and to a lesser extent viscoelastic relaxation time, are dependent on myosin activity.
Fleck, R, Gill, RL, Pettit, T, Irga, PJ, Williams, NLR, Seymour, JR & Torpy, FR 2020, 'Characterisation of fungal and bacterial dynamics in an active green wall used for indoor air pollutant removal', Building and Environment, vol. 179, pp. 106987-106987.
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© 2020 Elsevier Ltd Indoor air quality (IAQ) is of growing public health concern which has prompted the use of plants to phytoremediate air pollution in interior spaces. Active green walls are emerging as a means of reducing indoor contaminants and have demonstrated efficacy comparable to conventional air filtering technologies. However, the use of active airflow through organic substrates has the potential to emit bioaerosols into the surrounding environment, where the potential risk to human health is largely unknown. In this study, we demonstrate that two indoor green walls (with and without active airflow) contribute significantly to the ambient fungal load, however concentrations remained well below WHO safety guidelines. Bacterial dynamics within the rhizosphere/substrate of the operational botanical biofilters displayed variability across plant species. Phyla-wide distribution generally aligned with previous literature; however, differences from those previously reported were observed at the genus level, possibly due to geographic location, substrate composition, or plant species selection. Targeted assessment of Legionella aerosol contamination, an under-addressed potential pathogen for these active systems, yielded no positive identification during the sampling period. We conclude that active green walls host a unique bacterial profile and do not emit harmful levels of fungal propagules or pose significant risk of aerosolised Legionella species, provided systems are well monitored and maintained.
Flores-Sosa, M, Avilés-Ochoa, E & Merigó, JM 2020, 'Induced OWA operators in linear regression', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5509-5520.
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Fox, K, Mani, N, Rifai, A, Reineck, P, Jones, A, Tran, PA, Ramezannejad, A, Brandt, M, Gibson, BC, Greentree, AD & Tran, N 2020, '3D-Printed Diamond–Titanium Composite: A Hybrid Material for Implant Engineering', ACS Applied Bio Materials, vol. 3, no. 1, pp. 29-36.
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Fröch, JE, Kim, S, Stewart, C, Xu, X, Du, Z, Lockrey, M, Toth, M & Aharonovich, I 2020, 'Photonic Nanobeam Cavities with Nanopockets for Efficient Integration of Fluorescent Nanoparticles', Nano Letters, vol. 20, no. 4, pp. 2784-2790.
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Integrating fluorescent nanoparticles with high-Q, small mode volume cavities is indispensable for nanophotonics and quantum technologies. To date, nanoparticles have largely been coupled to evanescent fields of cavity modes, which limits the strength of the interaction. Here, we developed both a cavity design and a fabrication method that enable efficient coupling between a fluorescent nanoparticle and a cavity optical mode. The design consists of a fishbone-shaped, one-dimensional photonic crystal cavity with a nanopocket located at the electric field maximum of the fundamental optical mode. Furthermore, the presence of a nanoparticle inside the pocket reduces the mode volume substantially and induces subwavelength light confinement. Our approach opens exciting pathways to achieve tight light confinement around fluorescent nanoparticles for applications in energy, sensing, lasing, and quantum technologies.
Fronzi, M, Bishop, J, Martin, AA, Assadi, MHN, Regan, B, Stampfl, C, Aharonovich, I, Ford, MJ & Toth, M 2020, 'Role of knock-on in electron beam induced etching of diamond', Carbon, vol. 164, pp. 51-58.
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© 2020 Elsevier Ltd Electron beam induced etching (EBIE) has recently emerged as a promising direct-write nanofabrication technique. EBIE is typically assumed to proceed entirely through chemical pathways driven by electron-electron interactions. Here we show that knock-on (i.e., momentum transfer from electrons to nuclei) can play a significant role in EBIE, even at electron beam energies as low as 1.5 keV. Specifically, we calculate knock-on cross-sections for H, D, O and CO on the surface of diamond and show experimentally that they affect the kinetics of EBIE performed using oxygen, hydrogen and deuterium etch precursors. Our results advance basic understanding of electron-adsorbate interactions, particularly in relation to EBIE and the related techniques of electron beam-induced deposition and surface functionalisation.
Frost, SA, Kelly, A, Gaudin, J, Evoy, LM, Wilson, C, Marov, L, El Haddad, C, Center, J, Eisman, JA, Nguyen, TV & Hassett, G 2020, 'Establishing baseline absolute risk of subsequent fracture among adults presenting to hospital with a minimal-trauma-fracture', BMC Musculoskeletal Disorders, vol. 21, no. 1.
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AbstractBackgroundOne in three women and one in five men are expected to experience a minimal-trauma-fracture after the age of 50-years, which increases the risk of subsequent fracture. Importantly, timely diagnosis and optimal treatment in the form of a fracture liaison service (FLS), has been shown to reduce this risk of a subsequent fracture. However, baseline risk of subsequent fracture among this group of FLS patients has not been well described. Therefore, this study aims to estimate absolute risk of subsequent fracture, among women and men aged 50-years or more, presenting to hospital with a minimal-trauma-fracture.MethodsWomen and men aged 50-years or more with a minimal-trauma-fracture, presenting to hospitals across the South Western Sydney Local Health District between January 2003 and December 2017 were followed to identify subsequent fracture presentations to hospital. Absolute risk of subsequent fracture was estimated, by taking into account the competing risk of death.ResultsBetween January 2003 and December 2017–15,088 patients presented to the emergency departments of the five hospitals in the SWSLHD (11,149, women [74%]), with minimal-trauma-fractures. Subsequent fractures identified during the follow-up period (median = 4.5 years [IQR, 1.6–8.2]), occurred in 2024 (13%) patients. Death during the initial hospital stay, or during a subsequent hospital visit was recorded among 1646 patients (11%). Women were observed to have 7.1% risk of subsequent fracture after 1-year, following an initial fracture; and, the risk of subsequent fracture after 1-year was 6.2% for men. After 5-years the rate among women was 13.7, and 11.3% for men, respectively. Cumulative risk of subsequent fracture when initial fractures were classified as being at proxim...
Fry, CV, Cai, X, Zhang, Y & Wagner, C 2020, 'Consolidation in a Crisis: Patterns of International Collaboration in COVID-19 Research', PloS one, vol. 15, no. 7, p. e0236307.
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This paper seeks to understand whether a catastrophic and urgent event, such as the first months of the COVID-19 pandemic, accelerates or reverses trends in international collaboration, especially in and between China and the United States. A review of research articles produced in the first months of the COVID-19 pandemic shows that COVID-19 research had smaller teams and involved fewer nations than pre-COVID-19 coronavirus research. The United States and China were, and continue to be in the pandemic era, at the center of the global network in coronavirus related research, while developing countries are relatively absent from early research activities in the COVID-19 period. Not only are China and the United States at the center of the global network of coronavirus research, but they strengthen their bilateral research relationship during COVID-19, producing more than 4.9% of all global articles together, in contrast to 3.6% before the pandemic. In addition, in the COVID-19 period, joined by the United Kingdom, China and the United States continued their roles as the largest contributors to, and home to the main funders of, coronavirus related research. These findings suggest that the global COVID-19 pandemic shifted the geographic loci of coronavirus research, as well as the structure of scientific teams, narrowing team membership and favoring elite structures. These findings raise further questions over the decisions that scientists face in the formation of teams to maximize a speed, skill trade-off. Policy implications are discussed.
G. Asteris, P, G. Douvika, M, A. Karamani, C, D. Skentou, A, Chlichlia, K, Cavaleri, L, Daras, T, J. Armaghani, D & E. Zaoutis, T 2020, 'A Novel Heuristic Algorithm for the Modeling and RiskAssessment of the COVID-19 Pandemic Phenomenon', Computer Modeling in Engineering & Sciences, vol. 125, no. 2, pp. 815-828.
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Gan, YY, Chen, W-H, Ong, HC, Sheen, H-K, Chang, J-S, Hsieh, T-H & Ling, TC 2020, 'Effects of dry and wet torrefaction pretreatment on microalgae pyrolysis analyzed by TG-FTIR and double-shot Py-GC/MS', Energy, vol. 210, pp. 118579-118579.
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© 2020 Elsevier Ltd TG-FTIR and double-shot Py-GC/MS were executed to investigate the effects of torrefaction pretreatment on microalga (Chlorella vulgaris ESP-31) pyrolysis. TG-FTIR was performed to analyze the pyrolysis and combustion gas of raw and wet torrefied microalgae, whereas double-shot Py-GC/MS was applied to investigate the product distributions of single and two-stage thermal degradation of the microalgae. From the result, wet torrefaction successfully eliminated the release of CO in the pyrolysis gas. The highest generation of C–H during pyrolysis was achieved by the microalgae pretreated with dilute sulfuric acid. In the combustion gas, the intensity of O–H absorption band was removed in the first stage after wet torrefaction. The Py-GC/MS analysis revealed that the fatty acids (48.22%) were the dominant component in the bio-oil derived from the microalgae pretreated by the dilute sulfuric acid in wet torrefaction. Meanwhile, the productivity of carbohydrate-derived products (anhydrous sugars) decreased from 18.58 to 0.39% in the pyrolytic bio-oil after the wet torrefaction pretreatment. In contrast, carbohydrate- and lipid-derived products were decreased in the bio-oil after the dry torrefaction pretreatment. Similarly, microwave-assisted wet torrefaction in sulfuric acid is an effective pretreatment technique to produce high-quality pyrolytic bio-oil for biofuel production.
Gan, YY, Ong, HC, Chen, W-H, Sheen, H-K, Chang, J-S, Chong, CT & Ling, TC 2020, 'Microwave-assisted wet torrefaction of microalgae under various acids for coproduction of biochar and sugar', Journal of Cleaner Production, vol. 253, pp. 119944-119944.
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Gandomi, AH & Atefi, E 2020, 'Software review: the GPTIPS platform', Genetic Programming and Evolvable Machines, vol. 21, no. 1-2, pp. 273-280.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. GPTIPS is a widely used genetic programming software that was developed in Matlab. The most recent version of this software, GPTIPS 2.0, provides a symbolic multi-gene regression for data analysis, in addition to traditional evolutionary algorithms. We briefly explain the GPTIPS methodology and describe its main features, including its weaknesses and strengths, and give examples of GPTIPS applications.
Gandomi, AH & Deb, K 2020, 'Implicit constraints handling for efficient search of feasible solutions', Computer Methods in Applied Mechanics and Engineering, vol. 363, pp. 112917-112917.
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© 2020 Elsevier B.V. Real-world optimization problems usually involve constraints and sometimes even finding a single feasible solution is a challenging task. This study introduces a new approach for implicitly handling constraints. The proposed approach reduces the consideration of infeasible solutions by directly updating variable bounds with constraints, which is called the boundary update (BU) method. Two illustrative examples are used to explain the proposed approach, followed by applying it to mathematical and engineering constrained optimization problems. Finally, a surrogate-based problem and a large-dimensional and highly constrained problem are used to evaluate the BU method on these types of problems. The BU method is coupled with seven well-known evolutionary and mathematical optimization algorithms and the results show that the proposed BU method is a practical and effective approach and leads to better solutions with fewer function evaluations in nearly all cases, particularly for population-based optimization algorithms. This study should motivate optimization researchers and practitioners to pay more attention to the direct handling of constraints, rather than constraint handling by penalty or other fix-ups.
Gao, H, Yin, Y & Hussain, W 2020, 'Editorial: The ubiquitous internet of things in electricity (IOTE): Computational-intelligence-based optimization, security control, and fault diagnosis', IAENG International Journal of Computer Science, vol. 47, no. 3, pp. 565-566.
Gao, H, Yin, Y & Hussain, W 2020, 'The Ubiquitous Internet of Things in Electricity (IOTE): Computational-Intelligence-based Optimization, Security Control, and Fault Diagnosis', IAENG International Journal of Computer Science, vol. 47, no. 3, pp. 565-566.
Gao, J, Koopialipoor, M, Armaghani, DJ, Ghabussi, A, Baharom, S, Morasaei, A, Shariati, A, Khorami, M & Zhou, J 2020, 'Evaluating the bond strength of FRP in concrete samples using machine learning methods', Smart Structures and Systems, vol. 26, no. 4, pp. 403-418.
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In recent years, the use of Fiber Reinforced Polymers (FRPs) as one of the most common ways to increase the strength of concrete samples, has been introduced. Evaluation of the final strength of these specimens is performed with different experimental methods. In this research, due to the variety of models, the low accuracy and impact of different parameters, the use of new intelligence methods is considered. Therefore, using artificial intelligent-based models, a new solution for evaluating the bond strength of FRP is presented in this paper. 150 experimental samples were collected from previous studies, and then two new hybrid models of Imperialist Competitive Algorithm (ICA)-Artificial Neural Network (ANN) and Artificial Bee Colony (ABC)-ANN were developed. These models were evaluated using different performance indices and then, a comparison was made between the developed models. The results showed that the ICA-ANN model's ability to predict the bond strength of FRP is higher than the ABC-ANN model. Finally, to demonstrate the capabilities of this new model, a comparison was made between the five experimental models and the results were presented for all data. This comparison showed that the new model could offer better performance. It is concluded that the proposed hybrid models can be utilized in the field of this study as a suitable substitute for empirical models.
Gao, J, Luo, Z, Xiao, M, Gao, L & Li, P 2020, 'A NURBS-based Multi-Material Interpolation (N-MMI) for isogeometric topology optimization of structures', Applied Mathematical Modelling, vol. 81, pp. 818-843.
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© 2020 In this paper, the main intention is to propose a new Multi-material Isogeometric Topology Optimization (M-ITO) method for the optimization of multiple materials distribution, where an improved Multi-Material Interpolation model is developed using Non-Uniform Rational B-splines (NURBS), namely the “NURBS-based Multi-Material Interpolation (N-MMI)”. In the N-MMI model, three key components are involved: (1) multiple Fields of Design Variables (DVFs): NURBS basis functions with control design variables are applied to construct DVFs with the sufficient smoothness and continuity; (2) multiple Fields of Topology Variables (TVFs): each TVF is expressed by a combination of all DVFs to present the layout of a distinct material in the design domain; (3) Multi-material interpolation: the material property at each point is equal to the summation of all TVFs interpolated with constitutive elastic properties. DVFs and TVFs are in the decoupled expression and optimized in a serial evolving mechanism. This feature can ensure the constraint functions are separate and linear with respect to TVFs, which can be beneficial to lower the complexity of numerical computations and eliminate numerical troubles in the multi-material optimization. Two kinds of multi-material topology optimization problems are discussed, i.e., one with multiple volume constraints and the other with the total mass constraint. Finally, several numerical examples in 2D and 3D are provided to demonstrate the effectiveness of the M-ITO method.
Gao, KW, Loo, WS, Snyder, RL, Abel, BA, Choo, Y, Lee, A, Teixeira, SCM, Garetz, BA, Coates, GW & Balsara, NP 2020, 'Miscible Polyether/Poly(ether–acetal) Electrolyte Blends', Macromolecules, vol. 53, no. 14, pp. 5728-5739.
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Gao, L, Shan, X, Xu, X, Liu, Y, Liu, B, Li, S, Wen, S, Ma, C, Jin, D & Wang, F 2020, 'Video-rate upconversion display from optimized lanthanide ion doped upconversion nanoparticles', Nanoscale, vol. 12, no. 36, pp. 18595-18599.
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A method for video-rate display with optimized single UCNP brightness by integrating the full emission intensity over excitation time and lifetime.
Gao, P, Huang, Z & Yu, H 2020, 'Exploration of the Dehydrogenation Pathways of Ammonia Diborane and Diammoniate of Diborane by Molecular Dynamics Simulations Using Reactive Force Fields', The Journal of Physical Chemistry A, vol. 124, no. 9, pp. 1698-1704.
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Ammonium aminodiboranate (AADB) and diammoniate of diborane (DADB) are two isomers of ammonia borane (AB), which have been intensively studied for hydrogen storage. Their high hydrogen contents give them the high potential to serve as hydrogen storage materials. To explore their dehydrogenation pathways, molecular dynamics (MD) simulations with a reactive force field (ReaxFF) were applied. Temperature ramping simulations of their thermolysis were carried out. For AADB, at low temperatures, its hydrogen release can be realized mainly via intermolecular dehydrogenations. As the temperature of the simulated system increases, the formations of B-N bonds begin to occur. In the case of DADB, we found that this molecule could release hydrogen at a lower temperature with the cleavage of the B-N bond. The compositional analysis of the simulated systems was also conducted to monitor the potential intermediates along their dehydrogenation pathways. Our current work provides a detailed picture of the initial dehydrogenation steps of AADB and DADB and highlights the difference in their respective dehydrogenation processes.
Gao, X, Zhang, T, Du, J & Guo, YJ 2020, '340 GHz Double-Sideband Mixer Based on Antenna-Coupled High-Temperature Superconducting Josephson Junction', IEEE Transactions on Terahertz Science and Technology, vol. 10, no. 1, pp. 21-31.
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© 2011-2012 IEEE. Wireless communication and sensing are moving from microwave, millimeter-wave into the terahertz (THz) frequency regime to meet the fast growing demand of ultrahigh data-rate communications and super resolution imaging. Faced with severe atmospheric absorption attenuation and the lack of power efficient transmitting source at the higher band, ultrasensitive and cost-effective receiver frontend technology is required for advanced THz wireless systems. To date, the most sensitive heterodyne mixers, the key components of frontend receiver systems, are based on low-temperature superconducting materials that operate at liquid helium (4.2 K) temperature range, requiring expensive and bulky cryogenic cooling systems thus hindering them from commercial applications such as wireless communications and sensing. In this article, we present a 340 GHz double-sideband fundamental mixer based on thin-film antenna-coupled high-temperature superconducting (HTS) Josephson junction that operates at a much higher temperature range attainable with smaller and cheaper cryocoolers. Based on our innovative work in terms of advanced device circuit and on-chip antenna designs, accurate parametric simulation analyses, and Josephson junction parameter optimizations, the reported mixer exhibits a measured noise temperature of 470 and 780 K at operating temperatures of 20 and 40 K respectively at 340 GHz, a performance significantly higher than any HTS THz mixers reported to date.
Garbrecht, M, McCarroll, I, Yang, L, Bhatia, V, Biswas, B, Rao, D, Cairney, JM & Saha, B 2020, 'Thermally stable epitaxial ZrN/carrier-compensated Sc0.99Mg0.01N metal/semiconductor multilayers for thermionic energy conversion', Journal of Materials Science, vol. 55, no. 4, pp. 1592-1602.
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Gautam, S, Dah‐Chuan Lu, D, Xiao, W & Lu, Y 2020, 'Realisation of RPS from electrical home appliances in a smart home energy management system', IET Smart Grid, vol. 3, no. 1, pp. 11-21.
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With the increasing integration of photovoltaic power generation in the low‐voltage distribution network, the grid voltage regulation becomes critical, which demands support from different resources. This study presents the feasibility study of home appliance to be applied for appliance to grid mode of operation. The analysis includes the amendments in topology and control to support the concept of supportive platform provided by smart home and smart grid. Home appliances are then proposed as new distributed reactive sources, which are utilised to resolve the issue of voltage regulation, as well as produce reactive power locally for voltage stability. This study discusses the technical transitions in current home appliance to accommodate auxiliary functionality of grid reactive power support (RPS) and how it can fit in the home energy management system architecture to provide the required RPS.
Ge, Z, Mahapatra, D, Chang, X, Chen, Z, Chi, L & Lu, H 2020, 'Improving multi-label chest X-ray disease diagnosis by exploiting disease and health labels dependencies', Multimedia Tools and Applications, vol. 79, no. 21-22, pp. 14889-14902.
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The widely used ChestX-ray14 dataset addresses an important medical image classification problem and has the following caveats: 1) many lung pathologies are visually similar, 2) a variant of multiple diseases including lung cancer, tuberculosis, and pneumonia are present in a single scan at the same time, i.e. multiple labels. Existing literature uses state-of-the-art deep learning models being transfer learned where output neurons of the networks are trained for individual diseases to cater for multiple disease labels in each image. However, most of them don’t consider the label relationship explicitly between present and absent classes. In this work we have proposed a pair of novel error functions that can be employed for any deep learning model, Multi-label Softmax Loss (MSML) and Correlation Loss (CorLoss), to specifically address the properties of multiple labels and visually similar data. Moreover, we provide a fine-grained perspective into this problem and use bilinear pooling as an encoding scheme to increase discrimination of the model. The experiments are conducted on the ChestX-ray14 dataset. We first report improvements using our proposed loss with various backbone networks. After that, we extend our experiments to prove the rich disparity being learned by the model with our proposed losses, which can be fused with other models to improve the overall performances.
Ghaedi, S, Tousi, B, Abbasi, M & Alilou, M 2020, 'Optimal Placement and Sizing of TCSC for Improving the Voltage and Economic Indices of System with Stochastic Load Model', Journal of Circuits, Systems and Computers, vol. 29, no. 13, pp. 2050217-2050217.
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In this paper, an efficient method is proposed for optimal allocation and sizing of Thyristor Controlled Series Compensator (TCSC) to improve the technical and economic indices of a power network with deterministic and stochastic load models. First, the compensator allocation is done in the transmission system with the deterministic load model. After calculating the technical and economic indices of the network in the presence of a deterministic load model, the proposed method is applied to the system with a stochastic load model. The two-point estimation method is used for simulating the stochastic conditions. The indices of voltage deviation and economics of the system are optimized for selecting the optimal location and size of TCSCs. The economic index comprises loss cost, cost of the produced active power of generators and also the costs of installation, operation and maintenance of TCSCs. The multi-objective particle swarm optimization (MOPSO) is utilized to optimize the objective functions. After the multi-objective optimization, the fuzzy decision method is employed to extract one of the Pareto-optimal solutions as the best compromise one. For evaluating the proposed method, comprehensive simulations have been performed on the IEEE 39-bus network by using MATLAB/Matpower software. The simulation results clearly prove the remarkable performance of the proposed method in improving the technical and economic indices of the system.
Ghasemi, M, Davoudkhani, IF, Akbari, E, Rahimnejad, A, Ghavidel, S & Li, L 2020, 'A novel and effective optimization algorithm for global optimization and its engineering applications: Turbulent Flow of Water-based Optimization (TFWO)', Engineering Applications of Artificial Intelligence, vol. 92, pp. 103666-103666.
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© 2020 Elsevier Ltd In this study we present a new and effective grouping optimization algorithm (namely, the Turbulent Flow of Water-based Optimization (TFWO)), inspired from a nature search phenomenon, i.e. whirlpools created in turbulent flow of water, for global real-world optimization problems. In the proposed algorithm, the problem of selecting control parameters is eliminated, the convergence power is increased and the algorithm have a fixed structure. The proposed algorithm is used to find the global solutions of real-parameter benchmark functions with different dimensions. Besides, in order to further investigate the effectiveness of TFWO, it was used to solve various types of nonlinear Economic Load Dispatch (ELD) optimization problems in power systems and Reliability–RedundancyAllocation Optimization (RRAO) for the overspeed protection system of a gas turbine, as two real-world engineering optimization problems. The results of TFWO are compared with other algorithms, which provide evidence for efficient performance with superior solution quality of the proposed TFWO algorithm in solving a great range of real-parameter benchmark and real-world engineering problems. Also, the results prove the competitive performance and robustness of TFWO algorithm compared to other state-of-the-art optimization algorithms in this study. The source codes of the TFWO algorithm are publicly available at https://github.com/ebrahimakbary/TFWO.
Ghasemkhani, N, Vayghan, S, Abdollahi, A, Pradhan, B & Alamri, A 2020, 'Urban Development Modeling Using Integrated Fuzzy Systems, Ordered Weighted Averaging (OWA), and Geospatial Techniques', Sustainability, vol. 12, no. 3, pp. 809-809.
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This paper proposes a model to identify the changing of bare grounds into built-up or developed areas. The model is based on the fuzzy system and the Ordered Weighted Averaging (OWA) methods. The proposed model consists of four main sections, which include physical suitability, accessibility, the neighborhood effect, and a calculation of the overall suitability. In the first two parts, physical suitability and accessibility were obtained by defining fuzzy inference systems and applying the required map data associated with each section. However, in order to calculate the neighborhood effect, we used an enrichment factor method and a hybrid method consisting of the enrichment factor with the Few, Half, Most, and Majority quantifiers of the ordered weighted averaging (OWA) method. Finally, the three maps of physical suitability, accessibility, and the neighborhood effect were integrated by the fuzzy system method and the quantifiers of OWA to obtain the overall suitability maps. Then, the areas with high suitability were selected from the overall suitability map to be changed from bare ground into built-up areas. For this purpose, the proposed model was implemented and calibrated in the first period (2004–2010) and was evaluated by being applied to the second period (2010–2016). By comparing the estimated map of changes to the reference data and after the formation of the error matrix, it was determined that the OWA-Majority method has the best estimation compared to those of the other methods. Finally, the total accuracy and the Kappa coefficient for the OWA-Majority method in the second period were 98.98% and 98.98%, respectively, indicating this method’s high accuracy in predicting changes. In addition, the results were compared with those of other studies, which showed the effectiveness of the suggested method for urban development modeling.
Ghavidel, S, Ghadi, MJ, Azizivahed, A, Aghaei, J, Li, L & Zhang, J 2020, 'Risk-Constrained Bidding Strategy for a Joint Operation of Wind Power and CAES Aggregators', IEEE Transactions on Sustainable Energy, vol. 11, no. 1, pp. 457-466.
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© 2010-2012 IEEE. This paper proposes a coordinated strategy of a hybrid power plant (HPP), which includes a wind power aggregator and a commercial compressed air energy storage (CAES) aggregator to participate in three electricity markets (day-ahead, intraday, and balancing markets). The CAES aggregator has an extra ability which is called a simple-cycle mode operation that makes it works like a gas turbine when needed, which helps the HPP to economically handle the miscalculations of the wind power and electricity price predictions. The coordinated strategy of the HPP is formulated as a three-stage stochastic optimization problem. To control the financial risks, the conditional value-at-risk model is added to the optimization problem. Moreover, the proposed offering method is capable of submitting both bidding quantity and curves to the day-ahead market. A mixed integer linear programming formulation is written for the problem that can be easily solved by commercially available software such as GAMS. The results that were tested on a realistic-based case study located in Spain show the applicability of the suggested method to increase the joint operation profit and decrease the financial risks.
Ghobadi, R, Altaee, A, Zhou, JL, McLean, P & Yadav, S 2020, 'Copper removal from contaminated soil through electrokinetic process with reactive filter media', Chemosphere, vol. 252, pp. 126607-126607.
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Ghorbani, F, Fathi, F, Aghebati-Maleki, L, Abolhasan, R, Rikhtegar, R, Dolatabadi, JEN, Babaloo, Z, Khalilzadeh, B, Ebrahimi-Warkiani, M, Sharifzadeh, Z, Rashidi, M-R & Yousefi, M 2020, 'Kinetic and thermodynamic study of c-Met interaction with single chain fragment variable (scFv) antibodies using phage based surface plasmon resonance', European Journal of Pharmaceutical Sciences, vol. 150, pp. 105362-105362.
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Mesenchymal epithelial transition factor (c-Met) has been recently regarded as an attractive target for the treatment of cancer. Our previous study showed that c-Met-specific single chain fragment variables (scFvs) can be considered as a promising therapy for cancer, however, their molecular interaction with c-Met protein have not been assessed. Accordingly, in the current study we aim to evaluate the kinetic and thermodynamic properties of c-Met interaction with these scFvs as anticancer agents by means of surface plasmon resonance (SPR) technique. Phage-scFvs were immobilized on the 11-mercaptoundecanoic acid gold chips after carboxylic groups activation by N-ethyl-N-(3-diethylaminopropyl) carbodiimide/N-hydroxysuccinimide and, then the c-Met binding to each scFvs (ES1, ES2, and ES3) at different concentrations (ranging from 20 to 665 μM) was explored. Kinetic studies revealed that ES1 has the highest affinity (KD = 3.36 × 10-8) toward its target at 25°C. Calculation of thermodynamic parameters also showed positive values for enthalpy and entropy changes, which was representative of hydrophobic forces between c-Met and ES1. Furthermore, the positive value of Gibbs free energy indicated that c-Met binding to ES1 was enthalpy-driven. Taken together, we concluded that produced ES1 can be applied as promising scFv-based therapy for diagnosis or targeting of c-Met in various cancers.
Ghosh, A, Islam, MS & Saha, SC 2020, 'Targeted Drug Delivery of Magnetic Nano-Particle in the Specific Lung Region', Computation, vol. 8, no. 1, pp. 10-10.
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Aerosolized drug inhalation plays an important role in the treatment of respiratory diseases. All of the published in silico, in vivo, and in vitro studies have improved the knowledge of aerosol delivery in the human respiratory system. However, aerosolized magnetic nano-particle (MNP) transport and deposition (TD) for the specific position of the human lung are still unavailable in the literature. Therefore, this study is aimed to provide an understanding of the magnetic nano-particle TD in the targeted region by imposing an external magnetic field for the development of future therapeutics. Uniform aerosolized nano-particle TD in the specific position of the lung airways will be modelled by adopting turbulence k–ω low Reynolds number simulation. The Euler–Lagrange (E–L) approach and the magneto hydrodynamics (MHD) model are incorporated in the ANSYS fluent (18.0) solver to investigate the targeted nano-particle TD. The human physical activity conditions of sleeping, resting, light activity and fast breathing are considered in this study. The aerosolized drug particles are navigated to the targeted position under the influence of external magnetic force (EMF), which is applied in two different positions of the two-generation lung airways. A numerical particle tracing model is also developed to predict the magnetic drug targeting behavior in the lung. The numerical results reveal that nano-particle deposition efficiency (DE) in two different magnetic field position is different for various physical activities, which could be helpful for targeted drug delivery to a specific region of the lung after extensive clinical trials. This process will also be cost-effective and will minimize unwanted side effects due to systemic drug distribution in the lung.
Ghosh, S, Das, A, Hembram, TK, Saha, S, Pradhan, B & Alamri, AM 2020, 'Impact of COVID-19 Induced Lockdown on Environmental Quality in Four Indian Megacities Using Landsat 8 OLI and TIRS-Derived Data and Mamdani Fuzzy Logic Modelling Approach', Sustainability, vol. 12, no. 13, pp. 5464-5464.
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The deadly COVID-19 virus has caused a global pandemic health emergency. This COVID-19 has spread its arms to 200 countries globally and the megacities of the world were particularly affected with a large number of infections and deaths, which is still increasing day by day. On the other hand, the outbreak of COVID-19 has greatly impacted the global environment to regain its health. This study takes four megacities (Mumbai, Delhi, Kolkata, and Chennai) of India for a comprehensive assessment of the dynamicity of environmental quality resulting from the COVID-19 induced lockdown situation. An environmental quality index was formulated using remotely sensed biophysical parameters like Particulate Matters PM10 concentration, Land Surface Temperature (LST), Normalized Different Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). Fuzzy-AHP, which is a Multi-Criteria Decision-Making process, has been utilized to derive the weight of the indicators and aggregation. The results showing that COVID-19 induced lockdown in the form of restrictions on human and vehicular movements and decreasing economic activities has improved the overall quality of the environment in the selected Indian cities for a short time span. Overall, the results indicate that lockdown is not only capable of controlling COVID-19 spread, but also helpful in minimizing environmental degradation. The findings of this study can be utilized for assessing and analyzing the impacts of COVID-19 induced lockdown situation on the overall environmental quality of other megacities of the world.
Gluth, GJG, Arbi, K, Bernal, SA, Bondar, D, Castel, A, Chithiraputhiran, S, Dehghan, A, Dombrowski-Daube, K, Dubey, A, Ducman, V, Peterson, K, Pipilikaki, P, Valcke, SLA, Ye, G, Zuo, Y & Provis, JL 2020, 'RILEM TC 247-DTA round robin test: carbonation and chloride penetration testing of alkali-activated concretes', Materials and Structures, vol. 53, no. 1.
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AbstractMany standardised durability testing methods have been developed for Portland cement-based concretes, but require validation to determine whether they are also applicable to alkali-activated materials. To address this question, RILEM TC 247-DTA ‘Durability Testing of Alkali-Activated Materials’ carried out round robin testing of carbonation and chloride penetration test methods, applied to five different alkali-activated concretes based on fly ash, blast furnace slag or metakaolin. The methods appeared overall to demonstrate an intrinsic precision comparable to their precision when applied to conventional concretes. The ranking of test outcomes for pairs of concretes of similar binder chemistry was satisfactory, but rankings were not always reliable when comparing alkali-activated concretes based on different precursors. Accelerated carbonation testing gave similar results for fly ash-based and blast furnace slag-based alkali-activated concretes, whereas natural carbonation testing did not. Carbonation of concrete specimens was observed to have occurred already during curing, which has implications for extrapolation of carbonation testing results to longer service life periods. Accelerated chloride penetration testing according to NT BUILD 443 ranked the tested concretes consistently, while this was not the case for the rapid chloride migration test. Both of these chloride penetration testing methods exhibited comparatively low precision when applied to blast furnace slag-based concretes which are more resistant to chloride ingress than the other materials tested.
Goh, BHH, Chong, CT, Ge, Y, Ong, HC, Ng, J-H, Tian, B, Ashokkumar, V, Lim, S, Seljak, T & Józsa, V 2020, 'Progress in utilisation of waste cooking oil for sustainable biodiesel and biojet fuel production', Energy Conversion and Management, vol. 223, pp. 113296-113296.
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© 2020 Elsevier Ltd The increase in human consumption of plant and animal oils has led to the rise in waste cooking oil (WCO) production. Instead of disposing the used cooking oil as waste, recent technological advance has enabled the use of WCO as a sustainable feedstock for biofuels production, thereby maximising the value of biowastes via energy recovery while concomitantly solving the disposal issue. The current regulatory frameworks for WCO collection and recycling practices imposed by major WCO producing countries are reviewed, followed by the overview of the progress in biodiesel conversion techniques, along with novel methods to improve the feasibility for upscaling. The factors which influence the efficiency of the reactions such as properties of feedstock, heterogenous catalytic processes, cost effectiveness and selectivity of reaction product are discussed. Ultrasonic-assisted transesterification is found to be the least energy intensive method for producing biodiesel. The production of bio-jet fuels from WCO, while scarce, provide diversity in waste utilisation if problems such as carbon chain length, requirements of bio-jet fuel properties, extreme reaction conditions and effectiveness of selected catalyst-support system can be solved. Technoeconomic studies revealed that WCO biofuels is financially viable with benefit of mitigating carbon emissions, provided that the price gap between the produced fuel and commercial fuels, sufficient supply of WCO and variation in the oil properties are addressed. This review shows that WCO is a biowaste with high potential for advanced transportation fuel production for ground and aviation industries. The advancement in fuel production technology and relevant policies would accelerate the application of sustainable WCO biofuels.
Goh, BHH, Ong, HC, Chong, CT, Chen, W-H, Leong, KY, Tan, SX & Lee, XJ 2020, 'Ultrasonic assisted oil extraction and biodiesel synthesis of Spent Coffee Ground', Fuel, vol. 261, pp. 116121-116121.
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Gokuldas, S, Banerjee, S & Nimbalkar, SS 2020, 'Effects of Tunneling-Induced Ground Movements on Stability of Piled Raft Foundation: Three-Dimensional Finite-Element Approach', International Journal of Geomechanics, vol. 20, no. 8, pp. 04020104-04020104.
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Gomes, SDC, Zhou, JL, Li, W & Qu, F 2020, 'Recycling of raw water treatment sludge in cementitious composites: effects on heat evolution, compressive strength and microstructure', Resources, Conservation and Recycling, vol. 161, pp. 104970-104970.
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Water treatment sludge (WTS) is produced daily and represents a globally significant solid waste stream. The application of this sludge as construction materials has been studied although most studies have modified the sludge before its incorporation, hence involving significant energy consumption. This study aims to use raw sludge as a novel cementitious material, by determining the effects of sludge addition on the composition and performance of cementitious composites. Important aspects such as the physicochemical interaction of the raw sludge with the Portland cement, the heat evolution of the cement paste and the compressive strength of the composite cement were carefully studied. The results show that for 1-2% of WTS addition, the compressive strength and heat evolution of the cement paste was well maintained being close to the reference specimen after 28 days of curing. However, for sludge addition above 5%, a delay in the hydration reaction was observed, together with about 25% reduction in compressive strength at 28 days of curing. The mineralogical and thermal analysis showed decreasing portlandite content and increasing calcite in the WTS-amended composites. Scanning electron microscope analysis demonstrated that the addition of sludge induced more porous and weak surface structures compared to the reference specimen.
Gong, S, Lu, X, Hoang, DT, Niyato, D, Shu, L, Kim, DI & Liang, Y-C 2020, 'Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey', IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2283-2314.
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© 1998-2012 IEEE. This paper presents a literature review on recent applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks. Conventionally, the network optimization has been limited to transmission control at two endpoints, i.e., end users and network controller. The fading wireless channel is uncontrollable and becomes one of the main limiting factors for performance improvement. The IRS is composed of a large array of scattering elements, which can be individually configured to generate additional phase shifts to the signal reflections. Hence, it can actively control the signal propagation properties in favor of signal reception, and thus realize the notion of a smart radio environment. As such, the IRS's phase control, combined with the conventional transmission control, can potentially bring performance gain compared to wireless networks without IRS. In this survey, we first introduce basic concepts of the IRS and the realizations of its reconfigurability. Then, we focus on applications of the IRS in wireless communications. We overview different performance metrics and analytical approaches to characterize the performance improvement of IRS-assisted wireless networks. To exploit the performance gain, we discuss the joint optimization of the IRS's phase control and the transceivers' transmission control in different network design problems, e.g., rate maximization and power minimization problems. Furthermore, we extend the discussion of IRS-assisted wireless networks to some emerging use cases. Finally, we highlight important practical challenges and future research directions for realizing IRS-assisted wireless networks in beyond 5G communications.
Gong, S, Oberst, S & Wang, X 2020, 'An experimentally validated rubber shear spring model for vibrating flip-flow screens', Mechanical Systems and Signal Processing, vol. 139, pp. 106619-106619.
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© 2020 Elsevier Ltd Vibrating flip-flow screens (VFFS) provide an effective solution for screening highly moist and fine-grained minerals, and the dynamic response of the main and the floating screen frames largely accounts for a VFFS's screening performance and its processing capacity. An accurate dynamic model of the rubber shear springs inserted between the frames of the VFFS is critical for its dynamic analysis but has rarely been studied in detail. In this paper, a variance-based global sensitivity analysis is applied to actually illustrate that the rubber shear spring is the most important component for the dynamics of VFFS. Then a nonlinear rubber shear spring model is proposed to predict its amplitude and frequency dependency, which is described by a friction model and a fractional derivative viscoelastic model, respectively, and the elasticity is predicted by a nonlinear spring. The reasonability of the proposed model is verified by experimental cyclic tests of the rubber shear spring. Comparisons between the newly proposed model and other classic models, including the Generalized Maxwell model, adopted for the dynamic analysis of the VFFS are carried out, and experimental tests of an industrial VFFS's dynamic response show that dynamics of the VFFS can be better described using the proposed model than the existing models. Furthermore, the method of the global sensitivity analysis is also applied to the newly VFFS dynamic model to calculate the sensitivities of model outputs caused by the input parameters. The results reveal that the dynamic response of an operating VFFS is most sensitive to changes in the stiffness of the rubber shear spring, followed by the mass of the floating screen frames.
Gong, S, Zou, Y, Hoang, DT, Xu, J, Cheng, W & Niyato, D 2020, 'Capitalizing Backscatter-Aided Hybrid Relay Communications With Wireless Energy Harvesting', IEEE Internet of Things Journal, vol. 7, no. 9, pp. 8709-8721.
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Gong, Y, Zhang, L, Yu, K & Liu, R 2020, 'Exploring Uplink Achievable Rate for HPO MIMO Through Quasi-Monte Carlo and Variance Reduction Techniques', IEEE Access, vol. 8, pp. 75874-75883.
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Gong, Z, Singh, M & Wei, D 2020, 'An advanced technique for determining NC machining tool path to fabricate drawing die surface considering non-uniform thickness distribution in stamped blank', The International Journal of Advanced Manufacturing Technology, vol. 111, no. 5-6, pp. 1445-1455.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. Drawing process represents a significant area of production technology since it influences the feasibility of producing auto-body die panels. In the drawing process, the plastic deformation of blank is not uniform due to the intermittent deformation behavior of the material. This primes a non-uniform thickness distribution in the formed panels, which directly affects the die life and the quality of panels. In the presented study, a new algorithm was proposed for constructing the numerical control machining tool path for the new die surface obtained from FEM simulation and mesh mapping. The commercial package LS-DYNA was employed for the FEM simulation and to calculate the thickness distribution in the drawn workpiece. In order to construct the new numerical control machining tool path according to the new die surface, the positions of all cutter location points relative to the movement of new die mesh were determined. A set of forming die was machined using the proposed algorithm to fabricate a real workpiece of steel DC04. A comparison between the measured thicknesses in the fabricated workpiece and the FEM simulation results shows that they agree with each other very well, which directly validates the proposed algorithm. The developed method can improve product quality, increase production efficiency, and reduce labor intensity.
Gonzales, RR, Yang, Y, Park, MJ, Bae, T-H, Abdel-Wahab, A, Phuntsho, S & Shon, HK 2020, 'Enhanced water permeability and osmotic power generation with sulfonate-functionalized porous polymer-incorporated thin film nanocomposite membranes', Desalination, vol. 496, pp. 114756-114756.
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Gour, G & Tomamichel, M 2020, 'Entropy and relative entropy from information-theoretic principles', IEEE Trans. Inf. Theory, vol. 67, pp. 6313-6327.
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We introduce an axiomatic approach to entropies and relative entropies thatrelies only on minimal information-theoretic axioms, namely monotonicity undermixing and data-processing as well as additivity for product distributions. Wefind that these axioms induce sufficient structure to establish continuity inthe interior of the probability simplex and meaningful upper and lower bounds,e.g., we find that every relative entropy must lie between the R\'enyidivergences of order $0$ and $\infty$. We further show simple conditions forpositive definiteness of such relative entropies and a characterisation in termof a variant of relative trumping. Our main result is a one-to-onecorrespondence between entropies and relative entropies.
Gour, G & Tomamichel, M 2020, 'Optimal Extensions of Resource Measures and their Applications', Physical Review A, vol. 102, no. 6.
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We develop a framework to extend resource measures from one domain to alarger one. We find that all extensions of resource measures are boundedbetween two quantities that we call the minimal and maximal extensions. Wediscuss various applications of our framework. We show that any relativeentropy (i.e. an additive function on pairs of quantum states that satisfiesthe data processing inequality) must be bounded by the min and max relativeentropies. We prove that the generalized trace distance, the generalizedfidelity, and the purified distance are optimal extensions. And in entanglementtheory we introduce a new technique to extend pure state entanglement measuresto mixed bipartite states.
Govindarajan, P, Soundarapandian, RK, Gandomi, AH, Patan, R, Jayaraman, P & Manikandan, R 2020, 'RETRACTED ARTICLE: Classification of stroke disease using machine learning algorithms', Neural Computing and Applications, vol. 32, no. 3, pp. 817-828.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the tracking of information with respect to semantic as well as syntactic perspectives. The proposed idea is to mine patients’ symptoms from the case sheets and train the system with the acquired data. In the data collection phase, the case sheets of 507 patients were collected from Sugam Multispecialty Hospital, Kumbakonam, Tamil Nadu, India. Next, the case sheets were mined using tagging and maximum entropy methodologies, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes. Then, the processed data were fed into various machine learning algorithms such as artificial neural networks, support vector machine, boosting and bagging and random forests. Among these algorithms, artificial neural networks trained with a stochastic gradient descent algorithm outperformed the other algorithms with a higher classification accuracy of 95% and a smaller standard deviation of 14.69.
Gowri, AK, Karunakaran, MJ, Muthunarayanan, V, Ravindran, B, Nguyen-Tri, P, Ngo, HH, Bui, X-T, Nguyen, XH, Nguyen, DD, Chang, SW & Chandran, T 2020, 'Evaluation of bioremediation competence of indigenous bacterial strains isolated from fabric dyeing effluent', Bioresource Technology Reports, vol. 11, pp. 100536-100536.
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© 2020 Elsevier Ltd In this present assessment, fabric dyeing wastewater was subjected to the characterization of physical-chemical parameters in terms of colour, TDS, COD and chloride. The indigenous bacterial strains were isolated from the effluent and identified as Bacillus velezensis, Chryseomicrobium imtechense, Planococcus maritimus and Sphingobacterium daejeonense by 16S rRNA gene sequencing method. The bioremediation competency of the strains was evaluated by conducting treatment process with monoculture and bacterial consortium. The consortia removed about 98%, 71.5%, 79%, 69.65% of colour, TDS, COD and chloride, respectively. Among the four isolates, monoculture of B. velezensis showed effective diminution of pollutants from the effluent than other strains. The bacterial degradation of pollutants was determined by GC–MS based on the disappearance of certain peaks after bioremediation. The results suggested that the bioremediation efficiency of bacterial strains can be utilized as an eco-friendly and inexpensive method for dyeing effluent treatment.
Grymin, R, Bożejko, W, Chaczko, Z, Pempera, J & Wodecki, M 2020, 'Algorithm for solving the Discrete-Continuous Inspection Problem', Archives of Control Sciences, vol. 30, no. 4, pp. 653-666.
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The article introduces an innovative approch for the inspection challenge that represents a generalization of the classical Traveling Salesman Problem. Its priciple idea is to visit continuous areas (circles) in a way, that minimizes travelled distance. In practice, the problem can be defined as an issue of scheduling unmanned aerial vehicle which has discrete-continuous nature. In order to solve this problem the use of local search algorithms is proposed.
Gu, B, Gao, L, Wang, X, Qu, Y, Jin, J & Yu, S 2020, 'Privacy on the Edge: Customizable Privacy-Preserving Context Sharing in Hierarchical Edge Computing', IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 2298-2309.
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© 2013 IEEE. The booming of edge computing enables and reshapes this big data era. However, privacy issues arise because increasing volume of data are published per second while the edge devices can only provide limited computing and storage resources. In addition, this has been aggravated by new emerging features of edge computing, such as decentralized and hierarchical infrastructure, mobility, and content-Aware applications. Although some existing privacy preserving methods are extended to this domain, the privacy issues of data dissemination between multiple edge nodes and end users is barely studied. Motivated by this, we propose a dynamic customizable privacy-preserving model based on Markov decision process to obtain the optimized trade-off between customizable privacy protection and data utility. We start with establishing a game model between users and adversaries based on a QoS-based payoff function. A modified reinforcement learning algorithm is deployed to derive the exclusive Nash Equilibrium. Furthermore, the model can achieve fast convergence by the reduction of cardinality from n to 2. Extensive experimental results confirm the significance of the proposed model comparing to the existing work both in terms of effectiveness and feasibility.
Gu, F, Niu, J, Jin, X & Yu, S 2020, 'FDFA: A fog computing assisted distributed analytics and detecting system for family activities', Peer-to-Peer Networking and Applications, vol. 13, no. 1, pp. 38-52.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Researches have shown that taking parting in family activities could establish good relationships with family members. Fine-grained family activities detection is proven effective for increasing self-awareness and motivating people to modify their life styles for improved well being. Mobile health provides the possibility to solve this problem. However, with the increase of such applications, the requirements for computation, communication, and storage capability are becoming higher and higher. Fog computing, a new computing paradigm, utilizes a collaborative multitude of end-user clients or near-user edge devices to conduct a substantial amount of computing, communication, storage, and so on. In this paper, we propose FDFA, the first fog computing assisted distributed analytics and detecting system for family activities using smartphones and smart watches. Specifically, FDFA firstly uses the built-in sensors to obtain sensing data, such as the striding frequency and heart rate of the users, the sound of environment, and so forth. Then, a fog computing assisted resolution framework is proposed to efficiently detect family activities in an unobtrusive manner based on sensed data. Finally, considering the characteristics of different people, FDFA sets a personal plan for family members in doing some exercise and making continuous progress in the process of communicating. We have fully implemented FDFA on the Android platform and the extensive experimental results demonstrate that FDFA is easy to use, accurate, and appropriate for family activities with the accuracy of 79.1% and the user satisfaction degree of 82.4%. Moreover, the system can achieve more than 90% bandwidth efficiency and offer low-latency real time response with fog computing.
Gu, P, Wu, T, Zou, M, Pan, Y, Guo, J, Xiahou, J, Peng, X, Li, H, Ma, J & Zhang, L 2020, 'Multi-Head Self-Attention Model for Classification of Temporal Lobe Epilepsy Subtypes', Frontiers in Physiology, vol. 11.
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As a long-standing chronic disease, Temporal Lobe Epilepsy (TLE), resulting from abnormal discharges of neurons and characterized by recurrent episodic central nervous system dysfunctions, has affected more than 70% of drug-resistant epilepsy patients across the world. As the etiology and clinical symptoms are complicated, differential diagnosis of TLE mainly relies on experienced clinicians, and specific diagnostic biomarkers remain unclear. Though great effort has been made regarding the genetics, pathology, and neuroimaging of TLE, an accurate and effective diagnosis of TLE, especially the TLE subtypes, remains an open problem. It is of a great importance to explore the brain network of TLE, since it can provide the basis for diagnoses and treatments of TLE. To this end, in this paper, we proposed a multi-head self-attention model (MSAM). By integrating the self-attention mechanism and multilayer perceptron method, the MSAM offers a promising tool to enhance the classification of TLE subtypes. In comparison with other approaches, including convolutional neural network (CNN), support vector machine (SVM), and random forest (RF), experimental results on our collected MEG dataset show that the MSAM achieves a supreme performance of 83.6% on accuracy, 90.9% on recall, 90.7% on precision, and 83.4% on F1-score, which outperforms its counterparts. Furthermore, effectiveness of varying head numbers of multi-head self-attention is assessed, which helps select the optimal number of multi-head. The self-attention aspect learns the weights of different signal locations which can effectively improve classification accuracy. In addition, the robustness of MSAM is extensively assessed with various ablation tests, which demonstrates the effectiveness and generalizability of the proposed approach.
Gu, X, Li, J & Li, Y 2020, 'Experimental realisation of the real‐time controlled smart magnetorheological elastomer seismic isolation system with shake table', Structural Control and Health Monitoring, vol. 27, no. 1.
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© 2019 John Wiley & Sons, Ltd. Traditional base isolation protects structures against severe seismic events by providing a designated lateral flexibility at the base level of the structures. Due to its inherent passive nature, in the design process, compromises have to be made among performance of different design targets (displacements, interstorey drifts, accelerations, etc.). In addition, as the working principle, the effectiveness of a base isolation relies on the degree of “decoupling” between ground excitation and superstructure. However, a higher degree of decoupling compromises the stability of the structures. In other words, for a base solation system, it possesses inherent conflicts between the effectiveness of the isolation and the lateral stability of the structure. A concept of new smart base isolation system is proposed, in which real-time controllable decoupling for a base isolation structure is achieved by employing magnetorheological elastomer (MRE) base isolators. With controllable lateral stiffness, the smart base isolation system can achieve an optimal decoupling by instantly shifting the structure's natural frequencies to a nonresonant region. This paper aims at experimentally proving and validating this innovative concept, including designing a three-storey shear building model equipped with MRE base isolators, demonstrating the feasibility and evaluating the performance of the proposed system by a series of shake table testing. The comprehensive experimental design and results of shake table testing have concept-proved the proposed smart MRE base isolation system for future development in practical applications.
Gu, X, Shen, C, Li, H, Goldys, EM & Deng, W 2020, 'X-ray induced photodynamic therapy (PDT) with a mitochondria-targeted liposome delivery system', Journal of Nanobiotechnology, vol. 18, no. 1, p. 87.
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AbstractIn this study, we constructed multifunctional liposomes with preferentially mitochondria-targeted feature and gold nanoparticles-assisted synergistic photodynamic therapy. We systemically investigated the in vitro X-ray triggered PDT effect of these liposomes on HCT 116 cells including the levels of singlet oxygen, mitochondrial membrane potential, cell apoptosis/necrosis and the expression of apoptosis-related proteins. The results corroborated that synchronous action of PDT and X-ray radiation enhance the generation of cytotoxic reactive oxygen species produced from the engineered liposomes, causing mitochondrial dysfunction and increasing the levels of apoptosis.
Guan, L, Abbasi, A & Ryan, MJ 2020, 'Analyzing green building project risk interdependencies using Interpretive Structural Modeling', Journal of Cleaner Production, vol. 256, pp. 120372-120372.
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Guan, L, Liu, Q, Abbasi, A & Ryan, MJ 2020, 'DEVELOPING A COMPREHENSIVE RISK ASSESSMENT MODEL BASED ON FUZZY BAYESIAN BELIEF NETWORK (FBBN)', JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, vol. 26, no. 7, pp. 614-634.
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Reliable and efficient risk assessments are essential to deal effectively with potential risks in international construction projects. However, most conventional risk modeling methods are based on the hypothesis that risk factors are independent, which does not account adequately for the causal relationships among risk factors. In this study, a risk assessment model for international construction projects was developed to improve the efficacy of risk management by integrating fault tree analysis and fuzzy set theory with a Bayesian belief network. The risk rating of each risk factor, expressed as the product of risk occurrence probability and impact, was incorporated into the risk assessment model to evaluate degrees of risk. Therefore, risk factors were categorized into different risk levels taking into account their inherent causal relationships, which allowed the identification of critical risk factors. The applicability of the fuzzy Bayesian belief network-based risk assessment model was verified using a case study through a comparative analysis with the results from a fuzzy synthetic evaluation method. The comparison shows that the proposed risk assessment model is able to provide guidelines for an effective risk management process and ultimately to increase project performance in a complex environment such as international construction projects.
Guertler, MR, Kriz, A & Sick, N 2020, 'Encouraging and enabling action research in innovation management', R&D Management, vol. 50, no. 3, pp. 380-395.
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Although action research offers great advantages of connecting academia and practice, it is surprisingly underutilised in innovation management. This paper, therefore, focuses on how innovation management research and researchers can more effectively and efficiently apply action research to their domain. The analysis commences with the rationale for aligning action research and innovation management before assessing the strengths and limitations of existing interdisciplinary action research approaches from an innovation management perspective. Combining and enhancing the strengths of these approaches, a new Action Innovation Management Research (AIM‐R) framework is developed to assist in resolving the increasing demand for action‐orientation in innovation management. AIM‐R offers a structured research process for systematically applying action research as a way of encouraging rigorous research processes, while also importantly stimulating relevant practical outcomes. AIM‐R specifically considers different change levels (individual, team, organisational) and objects (e.g. outcome, process, capability) critical for the multi‐faceted character of innovation management. A real‐world example towards the end of the article illustrates how AIM‐R has been applied to a complex problem‐solution space. This example adds important insights for readers wanting to apply this more engaged, but currently underutilised, innovation management research technique.
Guirguis, A, Maina, JW, Zhang, X, Henderson, LC, Kong, L, Shon, H & Dumée, LF 2020, 'Applications of nano-porous graphene materials – critical review on performance and challenges', Materials Horizons, vol. 7, no. 5, pp. 1218-1245.
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A critical review on the potential of nano-porous graphene materials, their key structural and physicochemical properties for applications in the areas of separation and sensing and energy storage.
Guirguis, A, Polaki, SR, Sahoo, G, Ghosh, S, Kamruddin, M, Merenda, A, Chen, X, Maina, JW, Szekely, G & Dumee, L 2020, 'Engineering high-defect densities across vertically-aligned graphene nanosheets to induce photocatalytic reactivity', Carbon, vol. 168, pp. 32-41.
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The fabrication of graphene nanostructures, with a variety of morphologies and densities of defective sites, can be a promising tool to tune their characteristics towards photocatalytic applications, without the need for external dopants. In this study, the impact of morphological properties in terms of the orientation and defect concentrations of graphene nanostructures is demonstrated to support the development of active photocatalytic sites across graphitic structures. Vertically-aligned graphene nanosheets were grown across carbon fibres via electron cyclotron resonance microwave plasma chemical vapour deposition, to yield a range of different wall densities and edge functionalities. The variation of growth conditions was correlated to the photocatalytic activity for the degradation of methylene blue dye under ultra-violet and visible light. The chemical state of oxygen content hybridized with nanosheets was studied by X-ray photoelectron spectroscopy and correlated to the growth conditions and photocatalytic performance. The fastest degradation rate of dye was found on the graphene samples which were grown at 800 °C for 240 min, with a kinetic constant of 46.6 × 10−4 min−1. Such performance has not been observed to date for any graphitic materials and is shown to be on the same order of performance as the conventional photocatalytic materials.
Gul, M, Kalam, MA, Mujtaba, MA, Alam, S, Bashir, MN, Javed, I, Aziz, U, Farid, MR, Hassan, MT & Iqbal, S 2020, 'Multi-objective-optimization of process parameters of industrial-gas-turbine fueled with natural gas by using Grey-Taguchi and ANN methods for better performance', Energy Reports, vol. 6, pp. 2394-2402.
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Gul, M, Masjuki, HH, Kalam, MA, Zulkifli, NWM & Mujtaba, MA 2020, 'A Review: Role of Fatty Acids Composition in Characterizing Potential Feedstock for Sustainable Green Lubricants by Advance Transesterification Process and its Global as Well as Pakistani Prospective', BioEnergy Research, vol. 13, no. 1, pp. 1-22.
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High demand for crop oils is anticipated in the lubricant industry because of their renewable, non-toxic, environment-friendly nature. Crop oils typically offer high viscosities, viscosity indexes, and flashpoints. The unique structure of crop oils provides good lubrication, high flammability, and anti-corrosion ability. In contrast, petroleum-based lubricants face a difficult future because of declining petroleum reservoirs that will increase their prices. This paper reviews green-lubricant feedstock requirements, the effect of fatty acids composition to improve physicochemical properties, chemical modifications of green lubricants by applying transesterification to find suitable environmentally -friendly and cheaper feedstock to replace petroleum lubricants. Moreover, global and Pakistani indigenous crop oils are also analyzed for their potential use in green lubricants by comparing their fatty acid compositions, characteristics and reaction conditions according to applications and standards. This review discovers that cottonseed oil has great potential as a new sustainable and cheaper feedstock for the global and Pakistani green-lubricant markets. Green lubricant production rate can be enhanced significantly after upgrading the conventional production method. It is believed that this review paper will provide useful information to engineers, researchers, chemists, industrialists, and policymakers, who are interested in green-lubricants synthesis.
Gul, M, Zulkifli, NWM, Masjuki, HH, Kalam, MA, Mujtaba, MA, Harith, MH, Syahir, AZ, Ahmed, W & Bari Farooq, A 2020, 'Effect of TMP-based-cottonseed oil-biolubricant blends on tribological behavior of cylinder liner-piston ring combinations', Fuel, vol. 278, pp. 118242-118242.
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Cottonseed oil-based biolubricant was synthesized by the TMP-based transesterification process. 10–50% by volume blends of TMP-based cotton-biolubricant and SAE-40 were prepared and tested on the high-frequency-reciprocating-rig with engine cylinder-liner and piston-ring combination to investigate their tribology. While tribological characteristics were also evaluated by four-ball tribo-testers at high constant load of 785 N. 10% addition of cotton-biolubricant showed the lowest friction and wear as compared to SAE-40 but>10% volume of cotton biolubricant in blend increased the wear and friction considerably as tested by both HFRR and four-ball. Hence, 10% addition of TMP-cotton-biolubricant can be utilized as an energy-saving lubricant additive to partially reduce the dependency on petroleum-based lubricant for automotive engine application.
Guo, A, Wang, B, Lyu, C, Li, W, Wu, Y, Zhu, L, Bi, R, Huang, C, Li, JJ & Du, Y 2020, 'Consistent apparent Young’s modulus of human embryonic stem cells and derived cell types stabilized by substrate stiffness regulation promotes lineage specificity maintenance', Cell Regeneration, vol. 9, no. 1, p. 15.
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Abstract Background Apparent Young’s modulus (AYM), which reflects the fundamental mechanical property of live cells measured by atomic force microscopy and is determined by substrate stiffness regulated cytoskeletal organization, has been investigated as potential indicators of cell fate in specific cell types. However, applying biophysical cues, such as modulating the substrate stiffness, to regulate AYM and thereby reflect and/or control stem cell lineage specificity for downstream applications, remains a primary challenge during in vitro stem cell expansion. Moreover, substrate stiffness could modulate cell heterogeneity in the single-cell stage and contribute to cell fate regulation, yet the indicative link between AYM and cell fate determination during in vitro dynamic cell expansion (from single-cell stage to multi-cell stage) has not been established. Results Here, we show that the AYM of cells changed dynamically during passaging and proliferation on substrates with different stiffness. Moreover, the same change in substrate stiffness caused different patterns of AYM change in epithelial and mesenchymal cell types. Embryonic stem cells and their derived progenitor cells exhibited distinguishing AYM changes in response to different substrate stiffness that had significant effects on their maintenance of pluripotency and/or lineage-specific characteristics. On substrates that were too rigid or too soft, fluctuations in AYM occurred during cell passaging and proliferation that led to a loss in lineage specificity. On a substrate with ‘optimal’ stiffness (i.e., 3.5 kPa), the AYM was maintained at a constant level that was consistent with the parental cells during passaging and proliferation and led to preservation of lineage specificity. The ...
Guo, J & Ying, M 2020, 'Software Pipelining for Quantum Loop Programs.', CoRR, vol. abs/2012.12700.
Guo, Y, Karimi, F, Fu, Q, G. Qiao, G & Zhang, H 2020, 'Reduced administration frequency for the treatment of fungal keratitis: a sustained natamycin release from a micellar solution', Expert Opinion on Drug Delivery, vol. 17, no. 3, pp. 407-421.
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Background: Natamycin is the only topical ophthalmic antifungal drug approved by the Food and Drug Administration (FDA) of the United States, but has unsatisfactory factors such as high dosing frequency.Methods: We report the synthesis and preparation of self-assembled poly(ethylene glycol)-block-poly(glycidyl methacrylate) (PEG-b-PGMA) micelles. These nanoparticles exhibit sustained delivery of a hydrophobic natamycin by topical administration on eye due to the hydrolysable properties of PGMA segments of micelle. Hydrolysis of glycidyl groups within a physiologically relevant environment provides an additional driving force for drug release by generation of hydrophilic hydroxyl groups to 'push' the encapsulated hydrophobic drug away from the resultant hydrophilic domains and into surrounding environment.Results: In vitro and in vivo results revealed that the self-assembled micelles and the encapsulated natamycin were not cytotoxic and the released drug have strong antifungal ability to Candida albicans. Importantly, sustained natamycin release from micelles leads to the reduced administration frequency of natamycin from 8 times per day to 3 times per day in rabbits suffering from fungal keratitis (FK).Conclusion: This study demonstrates a facile method that can greatly reduce dosing frequency of natamycin administration and thus improve long-term patient compliance.
Guo, Z, Kang, Y, Hu, Z, Liang, S, Xie, H, Ngo, HH & Zhang, J 2020, 'Removal pathways of benzofluoranthene in a constructed wetland amended with metallic ions embedded carbon', Bioresource Technology, vol. 311, pp. 123481-123481.
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Guo, Z, Xiao, F, Sheng, B, Fei, H & Yu, S 2020, 'WiReader: Adaptive Air Handwriting Recognition Based on Commercial WiFi Signal', IEEE Internet of Things Journal, vol. 7, no. 10, pp. 10483-10494.
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© 2014 IEEE. In recent years, with the rapid development of the Internet-of-Things (IoT) technologies, many intelligent sensing applications have emerged, which realize contactless sensing and human-computer interaction (HCI). Handwriting recognition is the communication link between the human and computer. Previous handwriting recognition applications are usually founded on images and sensors, which require significant device overhead and are device dependent. Recently, the revolution of the wireless signal sensing technology has laid the foundation for the intelligent handwriting recognition technology without devices. In this article, we propose WiReader, an adaptive air handwriting recognition system based on wireless signals. WiReader utilizes ubiquitous commercial WiFi devices to process the collected channel state information (CSI), segments the data in combination with activity factors, and then transforms the original signal using the CSI-Ratio model. In order to address the problem of feature extraction caused by handwriting, we utilize the cumulative principal components and multilayer wavelet transform for the transformed signal. Finally, the energy feature matrix is generated and combines with long short-term memory (LSTM) to realize the recognition of different handwriting actions. Extensive real-world experiments show that WiReader achieves an average recognition accuracy of 90.64% leading other applications in three scenarios and has strong robustness to user location, user diversity, and different scenarios.
Gupta, A, Agrawal, RK, Kirar, JS, Kaur, B, Ding, W, Lin, C-T, Andreu-Perez, J & Prasad, M 2020, 'A hierarchical meta-model for multi-class mental task based brain-computer interfaces', Neurocomputing, vol. 389, pp. 207-217.
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© 2019 In the last few years, many research works have been suggested on Brain-Computer Interface (BCI), which assists severely physically disabled persons to communicate directly with the help of electroencephalogram (EEG)signal, generated by the thought process of the brain. Thought generation inside the brain is a dynamic process, and plenty thoughts occur within a small time window. Thus, there is a need for a BCI device that can distinguish these various ideas simultaneously. In this research work, our previous binary-class mental task classification has been extended to the multi-class mental task problem. The present work proposed a novel feature construction scheme for multi mental task classification. In the proposed method, features are extracted in two phases. In the first step, the wavelet transform is used to decompose EEG signal. In the second phase, each feature component obtained is represented compactly using eight parameters (statistical and uncertainty measures). After that, a set of relevant and non-redundant features is selected using linear regression, a multivariate feature selection approach. Finally, optimal decision tree based support vector machine (ODT-SVM)classifier is used for multi mental task classification. The performance of the proposed method is evaluated on the publicly available dataset for 3-class, 4-class, and 5-class mental task classification. Experimental results are compared with existing methods, and it is observed that the proposed plan provides better classification accuracy in comparison to the existing methods for 3-class, 4-class, and 5-class mental task classification. The efficacy of the proposed method encourages that the proposed method may be helpful in developing BCI devices for multi-class classification.
Gupta, A, Pradhan, B & Maulud, KNA 2020, 'Estimating the Impact of Daily Weather on the Temporal Pattern of COVID-19 Outbreak in India', Earth Systems and Environment, vol. 4, no. 3, pp. 523-534.
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AbstractThe COVID-19 pandemic has spread obstreperously in India. The increase in daily confirmed cases accelerated significantly from ~ 5 additional new cases (ANC)/day during early March up to ~ 249 ANC/day during early June. An abrupt change in this temporal pattern was noticed during mid-April, from which can be inferred a much reduced impact of the nationwide lockdown in India. Daily maximum (TMax), minimum (TMin), mean (TMean) and dew point temperature (TDew), wind speed (WS), relative humidity, and diurnal range in temperature and relative humidity during March 01 to June 04, 2020 over 9 major affected cities are analyzed to look into the impact of daily weather on COVID-19 infections on that day and 7, 10, 12, 14, 16 days before those cases were detected (i.e., on the likely transmission days). Spearman’s correlation exhibits significantly lower association with WS,TMax,TMin,TMean,TDew, but is comparatively better with a lag of 14 days. Support Vector regression successfully estimated the count of confirmed cases (R2 > 0.8) at a lag of 12–16 days, thus reflecting a probable incubation period of 14 ± 02 days in India. Approximately 75% of total cases were registered whenTMax,TMean,TMin,TDew, and WS at 12–16 days previously were varying within the range of 33.6–41.3 °C, 29.8–36.5 °C, 24.8–30.4 °C, 18...
Gupta, AK, Seal, A, Prasad, M & Khanna, P 2020, 'Salient Object Detection Techniques in Computer Vision—A Survey', Entropy, vol. 22, no. 10, pp. 1174-1174.
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Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.
Gupta, BB, Chang, X & Yamaguchi, S 2020, 'Editorial', International Journal of Information and Computer Security, vol. 12, no. 4, pp. 379-382.
Ha, QP, Metia, S & Phung, MD 2020, 'Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring', IEEE Sensors Journal, vol. 20, no. 8, pp. 4430-4441.
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© 2001-2012 IEEE. Multisensor fusion of air pollutant data in smart buildings remains an important input to address the well-being and comfort perceived by their inhabitants. An integrated sensing system is part of a smart building where real-time indoor air quality data are monitored round the clock using sensors and operating in the Internet-of-Things (IoT) environment. In this work, we propose an air quality management system merging indoor air quality index (IAQI) and humidex into an enhanced indoor air quality index (EIAQI) by using sensor data on a real-time basis. Here, indoor air pollutant levels are measured by a network of waspmote sensors while IAQI and humidex data are fused together using an extended fractional-order Kalman filter (EFKF). According to the obtained EIAQI, overall air quality alerts are provided in a timely fashion for accurate prediction with enhanced performance against measurement noise and nonlinearity. The estimation scheme is implemented by using the fractional-order modeling and control (FOMCON) toolbox. A case study is analysed to prove the effectiveness and validity of the proposed approach.
Ha, VKL, Chai, R & Nguyen, HT 2020, 'A Telepresence Wheelchair with 360-Degree Vision Using WebRTC', Applied Sciences, vol. 10, no. 1, pp. 369-369.
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This paper presents an innovative approach to develop an advanced 360-degree vision telepresence wheelchair for healthcare applications. The study aims at improving a wide field of view surrounding the wheelchair to provide safe wheelchair navigation and efficient assistance for wheelchair users. A dual-fisheye camera is mounted in front of the wheelchair to capture images which can be then streamed over the Internet. A web real-time communication (WebRTC) protocol was implemented to provide efficient video and data streaming. An estimation model based on artificial neural networks was developed to evaluate the quality of experience (QoE) of video streaming. Experimental results confirmed that the proposed telepresence wheelchair system was able to stream a 360-degree video surrounding the wheelchair smoothly in real-time. The average streaming rate of the entire 360-degree video was 25.83 frames per second (fps), and the average peak signal to noise ratio (PSNR) was 29.06 dB. Simulation results of the proposed QoE estimation scheme provided a prediction accuracy of 94%. Furthermore, the results showed that the designed system could be controlled remotely via the wireless Internet to follow the desired path with high accuracy. The overall results demonstrate the effectiveness of our proposed approach for the 360-degree vision telepresence wheelchair for assistive technology applications.
Hadgraft, RG & Kolmos, A 2020, 'Emerging learning environments in engineering education', Australasian Journal of Engineering Education, vol. 25, no. 1, pp. 3-16.
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© 2020 Engineers Australia. Three major challenges, sustainability, the fourth industrial revolution, and employability, will require new types of engineering programs, to help students develop skills in cross-disciplinarity, complexity, and contextual understanding. Future engineering students should be able to understand the needs for technological, sustainable solutions in context. The engineering graduates should be able to act in complex and chaotic situations. The question is how engineering institutions are responding now and how they should respond in the future. This article analyses the general responses from engineering education over the last 20 years. These responses are student-centred learning, integration of theory and practice, digital and online learning, and the definition of professional competencies. Examples are given of institutions that are already applying several of these components in the curriculum. On the long-term horizon, more personalised curriculum models are emerging based on students developing and documenting their own learning and career trajectories, as part of their lifelong learning strategy.
Haes Alhelou, H, Hamedani Golshan, ME & Hatziargyriou, ND 2020, 'Deterministic Dynamic State Estimation-Based Optimal LFC for Interconnected Power Systems Using Unknown Input Observer', IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1582-1592.
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Haes Alhelou, H, Hamedani Golshan, ME, Njenda, TC & Hatziargyriou, ND 2020, 'An Overview of UFLS in Conventional, Modern, and Future Smart Power Systems: Challenges and Opportunities', Electric Power Systems Research, vol. 179, pp. 106054-106054.
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Hafiz, MA, Hawari, AH, Das, P, Khan, S & Altaee, A 2020, 'Comparison of dual stage ultrafiltration and hybrid ultrafiltration-forward osmosis process for harvesting microalgae (Tetraselmis sp.) biomass', Chemical Engineering and Processing - Process Intensification, vol. 157, pp. 108112-108112.
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Hafiz, MA, Hawari, AH, Yasir, AT, Alfahel, R, Hassan, MK & Altaee, A 2020, 'Impact of high turbidity on reverse osmosis: evaluation of pretreatment processes', Desalination and Water Treatment, vol. 208, pp. 96-103.
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This study evaluates the performance of sand filtration (SF) and ultra-filtration (UF) as pretreatment processes for reverse osmosis (RO) for seawater with turbidities of 4.8, 23.2, and 99.7 NTU. For seawater with a turbidity of 4.8 and 23.2 NTU, the average membrane flux and the water recovery rate in the RO process did not improve significantly by pretreating the seawater using SF or UF. However, when the turbidity of seawater was 99.7 NTU, pretreating the seawater with UF improved the average membrane flux and the water recovery rate in the RO process by 5 LMH and 1.7%, respectively. Pretreatment of seawater with a turbidity of 99.7 NTU with UF reduces the specific energy demand and increases the average membrane flux and water recovery rate.
Hagihghi, R, Razmjou, A, Orooji, Y, Warkiani, ME & Asadnia, M 2020, 'A miniaturized piezoresistive flow sensor for real‐time monitoring of intravenous infusion', Journal of Biomedical Materials Research Part B: Applied Biomaterials, vol. 108, no. 2, pp. 568-576.
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AbstractDrug overdose (DO) is considered one of the current issues of intravenous (IV) infusion particularly resulting in serious injuries and deaths. Malfunction of infusion pumps is reported as the main cause of the drug overdose. Live monitoring and flow rate calculation by health professionals have been practicing to avoid DO. However, human errors and miscalculations are inevitable. A secondary measurement tool is required to avoid the risk of OD when infusion pump malfunctions cannot be detected immediately. Here, inspired by nature, we developed a real‐time monitoring device through which an administrator can review, evaluate, and modify the IV infusion process. Our flow sensor possesses an erected polymer hair cell on a multi‐layered silicon base forming from a patterned gold strained gauge layer on a piezoresistive liquid crystal polymer (LCP) membrane. Gold strain gauges on an LCP membrane have been used instead of a piezoresistive silicon membrane as the sensing element. The combination of gold strain gauges and LCP membrane provides better sensitivity than a piezoresistive silicon membrane of the same dimensions and thickness. We also miniaturized our biocompatible sensor such that it can be possible to install it inside the IV tube in contact with the liquid providing an in‐suite online flow monitoring. The proposed LCP membrane sensor is compared with two commercially available IV sensors to validate its flow sensing ability. The experimental results demonstrate that the proposed sensor provides a low threshold detection limit of 5 mL/hr, which betters the performance of other commercial sensors at low flow rates.
Hakdaoui, S, Emran, A, Pradhan, B, Qninba, A, Balla, TE, Mfondoum, AHN, Lee, C-W & Alamri, AM 2020, 'Assessing the Changes in the Moisture/Dryness of Water Cavity Surfaces in Imlili Sebkha in Southwestern Morocco by Using Machine Learning Classification in Google Earth Engine', Remote Sensing, vol. 12, no. 1, pp. 131-131.
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Imlili Sebkha is a stable and flat depression in southern Morocco that is more than 10 km long and almost 3 km wide. This region is mainly sandy, but its northern part holds permanent water pockets that contain fauna and flora despite their hypersaline water. Google Earth Engine (GEE) has revolutionized land monitoring analysis by allowing the use of satellite imagery and other datasets via cloud computing technology and server-side JavaScript programming. This work highlights the potential application of GEE in processing large amounts of satellite Earth Observation (EO) Big Data for the free, long-term, and wide spatio-temporal wet/dry permanent salt water cavities and moisture monitoring of Imlili Sebkha. Optical and radar images were used to understand the functions of Imlili Sebkha in discovering underground hydrological networks. The main objective of this work was to investigate and evaluate the complementarity of optical Landsat, Sentinel-2 data, and Sentinel-1 radar data in such a desert environment. Results show that radar images are not only well suited in studying desertic areas but also in mapping the water cavities in desert wetland zones. The sensitivity of these images to the variations in the slope of the topographic surface facilitated the geological and geomorphological analyses of desert zones and helped reveal the hydrological functions of Imlili Sebkha in discovering buried underground networks.
Halkon, BJ & Rothberg, SJ 2020, 'Establishing correction solutions for Scanning Laser Doppler Vibrometer measurements affected by sensor head vibration', Mechanical Systems and Signal Processing, vol. 150.
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Scanning Laser Doppler Vibrometer (SLDV) measurements are affected by sensorhead vibrations as if they are vibrations of the target surface itself. Thispaper presents practical correction schemes to solve this important problem.The study begins with a theoretical analysis, for arbitrary vibration and anyscanning configuration, which shows that the only measurement required is ofthe vibration velocity at the incident point on the final steering mirror inthe direction of the outgoing laser beam and this underpins the two correctionoptions investigated. Correction sensor location is critical; the first schemeuses an accelerometer pair located on the SLDV front panel, either side of theemitted laser beam, while the second uses a single accelerometer located alongthe optical axis behind the final steering mirror. Initial experiments with avibrating sensor head and stationary target confirmed the sensitivity to sensorhead vibration together with the effectiveness of the correction schemes whichreduced overall error by 17 dB (accelerometer pair) and 27 dB (singleaccelerometer). In extensive further tests with both sensor head and targetvibration, conducted across a range of scan angles, the correction schemesreduced error by typically 14 dB (accelerometer pair) and 20 dB (singleaccelerometer). RMS phase error was also up to 30% lower for the singleaccelerometer option, confirming it as the preferred option. The theorysuggests a geometrical weighting of the correction measurements and thisprovides a small additional improvement. Since the direction of the outgoinglaser beam and its incident point on the final steering mirror both change asthe mirrors scan the laser beam, the use of fixed axis correction transducersmounted in fixed locations makes the correction imperfect. The associatederrors are estimated and expected to be generally small, and the theoreticalbasis...
Hämäläinen, RP, Miliszewska, I & Voinov, A 2020, 'Leadership in participatory modelling – Is there a need for it?', Environmental Modelling & Software, vol. 133, pp. 104834-104834.
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Hambly, TW, Wong, NL & Yun, J 2020, 'Behçet disease‐associated rhabdomyolysis treated with infliximab', Internal Medicine Journal, vol. 50, no. 5, pp. 642-643.
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Han, DS, Zavahir, F, Gulied, M, ElMakki, T, Shon, HK & Park, H 2020, '(Invited) Integrated Green Engineering Process for Simultaneous Hydrogen Production and Fertigation', ECS Meeting Abstracts, vol. MA2020-02, no. 64, pp. 3272-3272.
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The constant utilization of hydrocarbon-based fuels such as petroleum, coal, and natural Gas has resulted in the detection of high concentration levels of sulfur containing gases in the atmosphere of many countries, including Qatar. Among those potential air pollutants, the rising concentrations of H2S and SO2 are of serious concern. In this work, sulfur-based seed solutions (SBSSs) such as sulfite or sulfide solutions are made by purging sulfur-containing gases released from industry into alkaline solutions. These SBSS solutions are simultaneously utilized towards the production of renewable hydrogen energy via a photoelectrochemical (PEC) process, and are used as draw solutions (DS) to produce diluted fertilizer water by a forward osmosis (FO) desalination process for agricultural irrigation purposes. The continuous bench scale of the integrated PEC-FDFO system was successfully demonstrated for simultaneous hydrogen production and dilution of SBSS DS. The experimental results showed that the reduction potential of SBSS DS in the PEC cell changes with variation of SBSS DS concentration and pH. This resulted in the continuous oxidation of sulfite into sulfate and led to more hydrogen production. Moreover, FDFO process exhibited high percentage of water recovery and DS dilution up to 80% and 68% at high SBSS DS concentration, respectively. In binary mixture of SBSS DS, increasing the concentration of ammonium sulfate (NH4)2SO4 led to high water flux to about 42%. The outcomes of this experimental study showed a successful practical continuous integrated system toward hydrogen production and fertigation.
Han, H, Jahed Armaghani, D, Tarinejad, R, Zhou, J & Tahir, MM 2020, 'Random Forest and Bayesian Network Techniques for Probabilistic Prediction of Flyrock Induced by Blasting in Quarry Sites', Natural Resources Research, vol. 29, no. 2, pp. 655-667.
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Han, R, Liu, F, Wang, X, Huang, M, Li, W, Yamauchi, Y, Sun, X & Huang, Z 2020, 'Functionalised hexagonal boron nitride for energy conversion and storage', Journal of Materials Chemistry A, vol. 8, no. 29, pp. 14384-14399.
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This review highlights recent research advances in functionalised hexagonal boron nitride for energy conversion and storage applications.
Han, Y, Deng, Y, Cao, Z & Lin, C-T 2020, 'An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making', Neural Computing and Applications, vol. 32, no. 12, pp. 7641-7659.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Multicriteria decision-making process explicitly evaluates multiple conflicting criteria in decision making. The conventional decision-making approaches assumed that each agent is independent, but the reality is that each agent aims to maximize personal benefit which causes a negative influence on other agents’ behaviors in a real-world competitive environment. In our study, we proposed an interval-valued Pythagorean prioritized operator-based game theoretical framework to mitigate the cross-influence problem. The proposed framework considers both prioritized levels among various criteria and decision makers within five stages. Notably, the interval-valued Pythagorean fuzzy sets are supposed to express the uncertainty of experts, and the game theories are applied to optimize the combination of strategies in interactive situations. Additionally, we also provided illustrative examples to address the application of our proposed framework. In summary, we provided a human-inspired framework to represent the behavior of group decision making in the interactive environment, which is potential to simulate the process of realistic humans thinking.
Handwerker, J, Pérez-Rodas, M, Beyerlein, M, Vincent, F, Beck, A, Freytag, N, Yu, X, Pohmann, R, Anders, J & Scheffler, K 2020, 'A CMOS NMR needle for probing brain physiology with high spatial and temporal resolution', Nature Methods, vol. 17, no. 1, pp. 64-67.
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Hannan, MA, Ali, JA, Hossain Lipu, MS, Mohamed, A, Ker, PJ, Indra Mahlia, TM, Mansor, M, Hussain, A, Muttaqi, KM & Dong, ZY 2020, 'Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement', Nature Communications, vol. 11, no. 1, p. 3792.
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AbstractThree-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results.
Hannan, MA, Begum, RA, Al-Shetwi, AQ, Ker, PJ, Al Mamun, MA, Hussain, A, Basri, H & Mahlia, TMI 2020, 'Waste collection route optimisation model for linking cost saving and emission reduction to achieve sustainable development goals', Sustainable Cities and Society, vol. 62, pp. 102393-102393.
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© 2020 Elsevier Ltd Developing an efficient, cost-effective and environmentally friendly solution for solid waste collection (SWC) and transportation system remains a major challenge for municipalities. Waste collection encompasses the largest part of the total budget in current waste management systems. SWC is a crosscutting issue that can be directly or indirectly linked to 10 of the 17 United Nations’ sustainable development goals (SDGs). This study aims to develop an SWC route optimisation model to improve collection efficiency, save collection costs and reduce emissions by considering fixed routing optimisation (FRO) with static data and variable routing optimisation (VRO) with real-time data. To realise the optimisation, a mixed-integer linear programming model utilising FRO and VRO was developed. Results show that VRO improved the collection efficiency by 26.08 % when the minimum filled-up level for collection was 70 %. Moreover, VRO achieved 44.44 % cost savings and 17.60 % carbon emission reduction at 70 % filled level. The proposed system achieved the targeted goals and demonstrated the feasibility of an optimisation model for the waste management sector to build a sustainable smart city. The findings of this study can be used to strengthen efforts towards the achievement of the SDGs related to solid waste collection and management.
Hannan, MA, Lipu, MSH, Hussain, A, Ker, PJ, Mahlia, TMI, Mansor, M, Ayob, A, Saad, MH & Dong, ZY 2020, 'Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques', Scientific Reports, vol. 10, no. 1, p. 4687.
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AbstractState of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.
Hao Ngo, H, Bui, X-T, Nghiem, LD & Guo, W 2020, 'Green technologies for sustainable water', Bioresource Technology, vol. 317, pp. 123978-123978.
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Hao, D, Liu, C, Xu, X, Kianinia, M, Aharonovich, I, Bai, X, Liu, X, Chen, Z, Wei, W, Jia, G & Ni, B-J 2020, 'Surface defect-abundant one-dimensional graphitic carbon nitride nanorods boost photocatalytic nitrogen fixation', New Journal of Chemistry, vol. 44, no. 47, pp. 20651-20658.
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Defective g-C3N4 nanorods enable to boots the adsorption and cleavage of N2 molecules to achieve higher photocatalytic nitrogen fixation performance.
Hao, H, Niu, J, Xue, B, Su, QP, Liu, M, Yang, J, Qin, J, Zhao, S, Wu, C & Sun, Y 2020, 'Golgi‐associated microtubules are fast cargo tracks and required for persistent cell migration', EMBO reports, vol. 21, no. 3.
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Microtubules derived from the Golgi (Golgi MTs) have been implicated to play critical roles in persistent cell migration, but the underlying mechanisms remain elusive, partially due to the lack of direct observation of Golgi MT-dependent vesicular trafficking. Here, using super-resolution stochastic optical reconstruction microscopy (STORM), we discovered that post-Golgi cargos are more enriched on Golgi MTs and also surprisingly move much faster than on non-Golgi MTs. We found that, compared to non-Golgi MTs, Golgi MTs are morphologically more polarized toward the cell leading edge with significantly fewer inter-MT intersections. In addition, Golgi MTs are more stable and contain fewer lattice repair sites than non-Golgi MTs. Our STORM/live-cell imaging demonstrates that cargos frequently pause at the sites of both MT intersections and MT defects. Furthermore, by optogenetic maneuvering of cell direction, we demonstrate that Golgi MTs are essential for persistent cell migration but not for cells to change direction. Together, our study unveils the role of Golgi MTs in serving as a group of 'fast tracks' for anterograde trafficking of post-Golgi cargos.
Hao, Q, Jia, G, Wei, W, Vinu, A, Wang, Y, Arandiyan, H & Ni, B-J 2020, 'Graphitic carbon nitride with different dimensionalities for energy and environmental applications', Nano Research, vol. 13, no. 1, pp. 18-37.
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© 2019, Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature. As a metal-free semiconductor, graphitic carbon nitride (g-C3N4) has received extensive attention due to its high stability, nontoxicity, facile and low-cost synthesis, appropriate band gap in the visible spectral range and wide availability of resources. The dimensions of g-C3N4 can influence the regime of the confinement of electrons, and consequently, g-C3N4 with various dimensionalities shows different properties, making them available for many stimulating applications. Although there are some reviews focusing on the synthesis strategy and applications of g-C3N4, there is still a lack of comprehensive review that systemically summarises the synthesis and application of different dimensions of g-C3N4, which can provide an important theoretical and practical basis for the development of g-C3N4 with different dimensionalities and maximises their potential in diverse applications. By reviewing the latest progress of g-C3N4 studies, we aim to summarise the preparation of g-C3N4 with different dimensionalities using various structural engineering strategies, discuss the fundamental bottlenecks of currently existing methods and their solution strategies, and explore their applications in energy and environmental applications. Furthermore, it also puts forward the views on the future research direction of these unique materials. [Figure not available: see fulltext.]
Hao, Q, Liu, C, Jia, G, Wang, Y, Arandiyan, H, Wei, W & Ni, B-J 2020, 'Catalytic reduction of nitrogen to produce ammonia by bismuth-based catalysts: state of the art and future prospects', Materials Horizons, vol. 7, no. 4, pp. 1014-1029.
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This review provides an up-to-date review on Bi-based nitrogen-fixation materials and future directions for the development of new Bi-based nitrogen-fixation materials under ambient conditions.
Hao, Q, Xie, C, Huang, Y, Chen, D, Liu, Y, Wei, W & Ni, B-J 2020, 'Accelerated separation of photogenerated charge carriers and enhanced photocatalytic performance of g-C3N4 by Bi2S3 nanoparticles', Chinese Journal of Catalysis, vol. 41, no. 2, pp. 249-258.
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© 2020 Dalian Institute of Chemical Physics, the Chinese Academy of Sciences Employing photothermal conversion to improve the photocatalytic activity of g-C3N4 is rarely reported previously. Herein, different ratios of g-C3N4/Bi2S3 heterojunction materials are synthesized by a facile ultrasonic method. Advanced characterizations such as X-ray diffraction, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy and high-resolution transmission electron microscopy are employed to analyze the morphology and structure of the prepared materials. Compared with sole counterparts, the heterojunction materials CN-BiS-2 exhibit significantly enhanced photocatalytic performance, which is 2.05-fold as g-C3N4 and 4.42-fold as Bi2S3. A possible degradation pathway of methylene blue (MB) was proposed. Based on the photoproduced high-energy electrons and photothermal effect of Bi2S3, the transfer and separation of electron-hole pairs are greatly enhanced and more active species are produced. In addition, the relatively high utilization efficiency of solar energy has synergistic effect for the better photocatalytic performance.
Harandizadeh, H, Armaghani, DJ & Mohamad, ET 2020, 'Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets', Neural Computing and Applications, vol. 32, no. 17, pp. 14047-14067.
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Harcombe, DM, Ruppert, MG & Fleming, AJ 2020, 'A review of demodulation techniques for multifrequency atomic force microscopy', Beilstein Journal of Nanotechnology, vol. 11.
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This article compares the performance of traditional and recently proposed demodulators for multifrequency atomic force microscopy. The compared methods include the lock-in amplifier, coherent demodulator, Kalman filter, Lyapunov filter, and direct-design demodulator. Each method is implemented on a field-programmable gate array (FPGA) with a sampling rate of 1.5 MHz. The metrics for comparison include the sensitivity to other frequency components and the magnitude of demodulation artifacts for a range of demodulator bandwidths. Performance differences are demonstrated through higher harmonic atomic force microscopy imaging.
Hasan, ASMM & Trianni, A 2020, 'A Review of Energy Management Assessment Models for Industrial Energy Efficiency', Energies, vol. 13, no. 21, pp. 5713-5713.
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The necessity to ensure energy efficiency in the industries is of significant importance to attain reduction of energy consumption and greenhouse gases emissions. Energy management is one of the effective features that ensure energy efficiency in the industries. Energy management models are the infancy in the industrial energy domain with practical guidelines towards implementation in the organizations. Despite the increased interest in energy efficiency, a gap exists concerning energy management literature and present application practices. This paper aims to methodologically review the energy management assessment models that facilitate the assessment of industrial energy management. In this context, the minimum requirements model, maturity model, energy management matrix model, and energy efficiency measures characterization framework are discussed with implications. The study concludes with interesting propositions for academia and industrial think tanks delineating few further research opportunities.
Hasan, SU, Hassan, HA, Scott, MJ, Siwakoti, YP, Town, G & Blaabjerg, F 2020, 'Common-Ground Transformerless Inverter with Virtual DC Bus Concept for Single-Phase PV Systems', IEEJ Journal of Industry Applications, vol. 9, no. 5, pp. 538-548.
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© 2020 The Institute of Electrical Engineers of Japan. This study investigates a single-phase common-ground transformerless inverter topology for grid-connected photovoltaic (PV) systems. The inverter shares a common ground with the grid and utilizes minimal components for power conversion, making it suitable for use as an integrated microinverter for solar PV modules. The peak of the ac output voltage is the same as the input DC voltage, and a virtual DC bus capacitor is used to provide power during the negative cycle of the inverter. A simple unipolar sinusoidal pulse-width modulation technique is used to modulate the inverter minimizing switching loss, output filter requirements, and output current ripple. Moreover, a double-charging process is employed to minimize the inrush charging current of the virtual DC bus capacitor. Various operating states along with the design guidelines for choosing the constituent components are presented. Finally, some simulation and experimental results are presented for a 1 kW prototype to validate the proposed topology.
Hasanipanah, M, Zhang, W, Armaghani, DJ & Nikafshan Rad, H 2020, 'The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock', IEEE Access, vol. 8, pp. 57148-57157.
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Hasanpour, S, Siwakoti, Y & Blaabjerg, F 2020, 'Hybrid cascaded high step‐up DC/DC converter with continuous input current for renewable energy applications', IET Power Electronics, vol. 13, no. 15, pp. 3487-3495.
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This study proposes a new structure of hybrid cascade coupled‐inductor high step‐up (HCCIHSU) DC/DC converter for renewable energy sources. The proposed topology can provide an ultra‐high voltage gain under continuous input current and low voltage stress on semiconductor devices. This converter is a hybrid cascade connection of the boost and buck–boost converters. The HCCIHSU utilises a coupled‐inductor (CI) and a voltage multiplier (VM) cell to enhance the voltage gain ratio as a semi‐quadratic function. The magnetic energy of the leakage inductor of the CI is recycled to the VM capacitors that reduce the component voltage stress and improve the converter voltage gain. Additionally, the voltage stress on the main power switch is clamped by two passive clamp capacitors. Due to the very high voltage conversion ratio at a reduced turn's ratio, the maximum voltage stresses on the switches and diodes are significantly alleviated, which further improve the efficiency. In this study, detailed steady‐state analysis and comparisons with other related converters are provided. Finally, a 160 W/200 V laboratory prototype is built with 24 V input voltage at a switching frequency of 50 kHz to verify the performance of the proposed converter.
Hasanpour, S, Siwakoti, Y & Blaabjerg, F 2020, 'New Single‐Switch quadratic boost DC/DC converter with Low voltage stress for renewable energy applications', IET Power Electronics, vol. 13, no. 19, pp. 4592-4600.
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In this paper, a novel non‐isolated Single‐Switch Quadratic Boost Coupled‐Inductor (SSQBCI) DC/DC converter with continuous input current and low voltage stress on the switching component is presented. The suggested structure is based on the traditional quadratic boost converter. In this new topology, to achieve an ultra‐high voltage gain without large duty cycle, a Coupled‐Inductor (CI) along with a Voltage Multiplier (VM) are employed. The magnetic energy stored in the leakage inductor of the CI is recycled by a regenerative passive clamp capacitor that is connected with the switch in parallel, which helps to limit the maximum voltage across the switch. Therefore, to reduce the switch conduction loss and improve the efficiency, a switch with the low static drain‐to‐source ON‐resistance can be used. Moreover, the low voltage stress on the output side diode alleviates the reverse recovery loss. The steady‐state operating principle, comparisons with other related topologies and also design considerations in Continuous Conduction Mode (CCM) will be analyzed in detail. Finally, the performance of the proposed SSQBCI is verified by experimental results using a prototype with 30V input and 200V ‐ 160 W output operation at a constant switching frequency 50 kHz.
Hashem Zadeh, SM, Mehryan, SAM, Islam, MS & Ghalambaz, M 2020, 'Irreversibility analysis of thermally driven flow of a water-based suspension with dispersed nano-sized capsules of phase change material', International Journal of Heat and Mass Transfer, vol. 155, pp. 119796-119796.
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© 2020 A precise understanding of the thermal behaviour and entropy generation of a suspension comprising nano-encapsulated phase change materials (NEPCM) is important for the thermal energy storage and heat transfer enhancement in various engineering applications. Studies to date, have improved the knowledge of the heat transfer of NCPCM. However, a suspension comprising NEPCM in the porous medium could enhance the overall heat transfer performance. Therefore, this study aims to investigate the thermal, hydrodynamic and entropy generation behaviour of the NEPCM-suspensions in a porous medium. Conjugate natural convection heat transfer and entropy generation in a square cavity composed of a porous matrix (glass balls), occupied by a suspension comprising nano-encapsulated phase change materials, and two solid blocks is numerically investigated. Galerkin Finite Element Method is employed to solve the nonlinear coupled equations for the porous flow and heat transfer. The phase transition and the released/absorbed latent heat of the nano-capsules are attributed in a temperature-dependent heat capacity field. The thermal conductivity ratio (1 ≤ Rk ≤ 100), the Darcy number (10−5 ≤ Da ≤ 10−1), the Stefan number (0.2 ≤ Ste ≤ 1), the porosity of porous medium (0.2 ≤ ε ≤ 0.9), the dimensionless fusion temperature (0.05 ≤ Tfu ≤ 0.95), the solid walls thickness (ds = 0.1 and 0.3), and the volume fraction of the nano-capsules (0.0 ≤ φ ≤ 5%) are considered for the numerical calculations. The numerical results illustrate that the rates of heat transfer and the average Bejan number are maximum and the generated entropy is minimum when the fusion temperature of the nano-capsules is Tfu = 0.5. Besides, adding the nano-sized particles of encapsulated phase change materials to the host fluid increases the heat transfer rate up to 45% (for the studied set of parameters) and also augments the average Bejan number. The total entropy generation elevates with the increment of the volume...
Hassan, M & Liu, D 2020, 'PPCPP: A Predator–Prey-Based Approach to Adaptive Coverage Path Planning', IEEE Transactions on Robotics, vol. 36, no. 1, pp. 284-301.
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© 2004-2012 IEEE. Most of the existing coverage path planning (CPP) algorithms do not have the capability of enabling a robot to handle unexpected changes in the coverage area of interest. Examples of unexpected changes include the sudden introduction of stationary or dynamic obstacles in the environment and change in the reachable area for coverage (e.g., due to imperfect base localization by an industrial robot). Thus, a novel adaptive CPP approach is developed that is efficient to respond to changes in real-time while aiming to achieve complete coverage with minimal cost. As part of the approach, a total reward function that incorporates three rewards is designed where the first reward is inspired by the predator-prey relation, the second reward is related to continuing motion in a straight direction, and the third reward is related to covering the boundary. The total reward function acts as a heuristic to guide the robot at each step. For a given map of an environment, model parameters are first tuned offline to minimize the path length while assuming no obstacles. It is shown that applying these learned parameters during real-time adaptive planning in the presence of obstacles will still result in a coverage path with a length close to the optimized path length. Many case studies with various scenarios are presented to validate the approach and to perform numerous comparisons.
Hassan, M, Shah, R & Hossain, J 2020, 'Frequency regulation of multiple asynchronous grids using adaptive droop in high‐voltage direct current system', IET Generation, Transmission & Distribution, vol. 14, no. 7, pp. 1389-1399.
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Frequency stability control in multiple asynchronous grids is a challenging and complex issue. An adaptive droop control strategy to improve the frequency regulation of asynchronous AC areas connected by a multi‐terminal DC grid is proposed here. The droop coefficients are adjusted to share the active power adaptively among multiple asynchronous AC grids based on the characteristics of frequency deviation and rate of change of frequency. This results in a cogent allocation of imbalance power in multiple asynchronous grids from the frequency variation perspective. The performance of the proposed scheme is evaluated in a modified multi‐machine power system using DIgSILENT Power Factory. Simulation results under significant frequency disturbances caused by credible contingencies are presented to demonstrate the effectiveness of the proposed approach. It is found that the proposed adaptive control ensures an excellent and robust frequency response under different operative conditions.
Hassan, W, Lu, Y, Farhangi, M, Lu, DD & Xiao, W 2020, 'Design, analysis and experimental verification of a high voltage gain and high‐efficiency DC–DC converter for photovoltaic applications', IET Renewable Power Generation, vol. 14, no. 10, pp. 1699-1709.
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With the rapid development of photovoltaic systems, high step‐up dc–dc converters draw significant attention, which shows the design challenges for simple topology, high efficiency, reduce voltage stress, and long lifespan. This study proposes a new high voltage gain converter that utilises the primary boost conversion cell and integrates with both switched‐capacitor and coupled‐inductor techniques. The proposed topology is modular and extendable for ultra‐high step‐up voltage gain. The leakage energy is recycled by a clamp circuit to minimise the switch voltage stress and power loss. One distinctive feature is that the voltage stress on the diodes and switch becomes low as well as constant against the variation of the duty cycle. Furthermore, the coupled inductor alleviates the diodes reverse recovery losses. The steady‐state analyses, operation principles, and design guidelines are presented comprehensively. A prototype circuit is constructed to test the maximum power point tracking operation with voltage conversion from 30 to 380 V at 300 W. Experimental results substantiate the theoretical analysis and claimed advantages. The proposed converter demonstrates maximum power point tracking capability and high conversion efficiency over a wide range of power. The prototype shows the weighted efficiency of 96.3% according to the EU standard.
Hassan, W, Soon, JL, Dah-Chuan Lu, D & Xiao, W 2020, 'A High Conversion Ratio and High-Efficiency Bidirectional DC–DC Converter With Reduced Voltage Stress', IEEE Transactions on Power Electronics, vol. 35, no. 11, pp. 11827-11842.
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© 1986-2012 IEEE. A dc-dc converter is proposed to achieve a high voltage conversion ratio for bidirectional power flow applications. The nonisolated topology is optimally designed to integrate both the switched capacitor and coupled inductor techniques for high efficiency. The windings of the coupled inductor are stacked at the low voltage source, which transfers any leakage energy of the coupled inductor directly into the output port and simplifies the clamping circuit. The optimal design keeps the voltage stress on the main switches low for the entire duty cycle operation. Thus, the converter demonstrates the advantage of wide-voltage gain based on common ground and low and steady voltage stress in both buck and boost modes of its operation. Furthermore, the converter can realize zero-voltage switching through the synchronous rectifiers without requiring extra hardware circuitry to enhance conversion efficiency. The operation principle, including the steady-state analysis, dynamic modeling, controller design, efficiency analysis, and optimization, are discussed in detail and verified by the experimental test. The prototype substantiates the theoretical analysis and soft-switching operation. The converter exhibits the capability for load and line regulation and demonstrates a peak efficiency of 96.38% in the boost mode and 95.61% in the buck mode of operation.
Hassanzadeh-Barforoushi, A, Warkiani, ME, Gallego-Ortega, D, Liu, G & Barber, T 2020, 'Capillary-assisted microfluidic biosensing platform captures single cell secretion dynamics in nanoliter compartments', Biosensors and Bioelectronics, vol. 155, pp. 112113-112113.
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Cancer cells continuously secrete inflammatory biomolecules which play significant roles in disease progression and tumor metastasis toward secondary sites. Despite recent efforts to capture cancer cells' intercellular secretion heterogeneity using microfluidics, the challenges in operation of these systems as well as the complexity of designing a biosensing assay for long-term and real-time measurement of single cell secretions have become grand research barriers. Here, we present a new capillary-based microfluidic biosensing approach to easily and reliably capture ~500 single cells inside isolated dead-end nanoliter compartments using simple pipette injection, and quantify their individual secretion dynamics at the single cell resolution over a long period of culture (~16 h). We first present a detailed investigation of the fluid mechanics underlying the formation of nanoliter compartments in the microfluidic system. Based on the measurement of single cell capture efficiency, we employ a one-step FRET-based biosensor which monitors the single cancer cells' protease activity. The sensor reports the fluorescent signal as a product of amino acid chain cleavage and reduction in its quenching capability. Using the single cell protease secretion data, we identified modes of cell secretion dynamics in our cell sample. While most of the cells had low secretion levels, two other smaller and more aggressive secretion dynamics were cells with secretion modes that include sharp spikes or slow but progressive trend. The method presented here overcomes the difficulties associated with performing single cell secretion assays, enabling a feasible and reliable technique for high throughput measurement of metabolic activities in cancer cells.
Hawari, AH, Hafiz, M, Yasir, AT, Alfahel, R & Altaee, A 2020, 'Evaluation of ultrafiltration and multimedia filtration as pretreatment process for forward osmosis', Desalination and Water Treatment, vol. 196, pp. 84-92.
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© 2020 Desalination Publications. All rights reserved. In order to reduce scaling in a multistage flash (MSF) desalination plant, the brine reject can be diluted using forward osmosis (FO) before recycling. In this FO process, the brine is used as the draw solution (DS) and seawater is used as the feed solution (FS). However, the FO process suffers from low water flux owing to membrane fouling. The water flux in FO can be enhanced by reduc-ing the foulant concentration in the FO feed solution (FS). Thus, in this paper seawater, multimedia sand filtered seawater, and ultrafiltrated seawater is being used as feed solution for the FO process. The flowrate of the feed solution was kept constant at 2.0 L/min. However, the flowrate of the draw solution (DS) were tested at 2.0 and 0.8 L/min. When the flowrate of the DS was 0.8 L/min, the highest initial flux of 44.1 L/m2 h were obtained using ultrafiltrated seawater as FS. After the initial run, the membrane was cleaned and during the second run, 83% of the initial flux was recovered using the ultrafiltrated seawater as FS. For ultrafiltrated seawater, the water recovery rate and specific energy consumption was 36.2% and 0.065 kWh/m3, respectively.
Hawchar, L, Naughton, O, Nolan, P, Stewart, MG & Ryan, PC 2020, 'A GIS-based framework for high-level climate change risk assessment of critical infrastructure', Climate Risk Management, vol. 29, pp. 100235-100235.
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The IPCC states that climate change unequivocally impacts on various aspects of the natural and built environment, including our vital critical infrastructure systems (transport, energy, water/wastewater and communications). It is thus essential for countries to gain an understanding of critical infrastructure vulnerability to current and future climate-related threats, in order to develop effective climate adaptation strategies. The first requisite step towards implementing these strategies, before any detailed analysis can commence, is high-level vulnerability or risk assessments. The work in this paper is concerned with such high-level assessments, however the framework presented is GIS-based, facilitating modelling of geographical variability in both climate and asset vulnerability within a country. This permits the identification of potential climate change risk hotspots across a range of critical infrastructure sectors. The framework involves a number of distinct steps. Sectoral information matrices are developed to highlight the key relationships between the infrastructure and climate threats. This information is complemented with sectoral maps showing, on an asset-level, the potential geospatial impacts of climate change, facilitating initial quantification of the vulnerable portions of the infrastructure systems. Finally, the approach allows for development of multi-sectoral semi-quantitative risk ranking maps that account for the geographical proximities of various assets from different critical infrastructure sectors which are vulnerable to a specific climate threat. The framework is presented in the paper and applied as a case study in the context of Irish critical infrastructure. The case-study identified for instance, potentially substantial increases in fluvial flooding risk for Irish critical infrastructure, while the multi-sectoral risk ranking maps highlighted a number of Ireland's urban and rural areas as climate change risk hotspots. These hig...
Hayat, T, Afzal, MU, Ahmed, F, Zhang, S, Esselle, KP & Vardaxoglou, Y 2020, 'Low-Cost Ultrawideband High-Gain Compact Resonant Cavity Antenna', IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 7, pp. 1271-1275.
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Hayati, H, Eager, D, Peham, C & Qi, Y 2020, 'Dynamic Behaviour of High Performance of Sand Surfaces Used in the Sports Industry', Vibration, vol. 3, no. 4, pp. 410-424.
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The sand surface is considered a critical injury and performance contributing factor in different sports, from beach volleyball to greyhound racing. However, there is still a significant gap in understanding the dynamic behaviour of sport sand surfaces, particularly their vibration behaviour under impact loads. The purpose of this research was to introduce different measurement techniques to the study of sports sand surface dynamic behaviour. This study utilised an experimental drop test, accelerometry, in-situ moisture content and firmness data, to investigate the possible correlation between the sand surface and injuries. The analysis is underpinned by data gathered from greyhound racing and discussed where relevant.
Hayati, H, Eager, D, Pendrill, A-M & Alberg, H 2020, 'Jerk within the Context of Science and Engineering—A Systematic Review', Vibration, vol. 3, no. 4, pp. 371-409.
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Rapid changes in forces and the resulting changes in acceleration, jerk and higher order derivatives can have undesired consequences beyond the effect of the forces themselves. Jerk can cause injuries in humans and racing animals and induce fatigue cracks in metals and other materials, which may ultimately lead to structure failures. This is a reason that it is used within standards for limits states. Examples of standards which use jerk include amusement rides and lifts. Despite its use in standards and many science and engineering applications, jerk is rarely discussed in university science and engineering textbooks and it remains a relatively unfamiliar concept even in engineering. This paper presents a literature review of the jerk and higher derivatives of displacement, from terminology and historical background to standards, measurements and current applications.
Hazrat, MA, Rasul, MG, Mofijur, M, Khan, MMK, Djavanroodi, F, Azad, AK, Bhuiya, MMK & Silitonga, AS 2020, 'A Mini Review on the Cold Flow Properties of Biodiesel and its Blends', Frontiers in Energy Research, vol. 8, p. 598651.
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Biodiesels are renewable fuel that may be produced from various feedstock using different techniques. It is endorsed in some countries of the world as a viable substitute to diesel fuel. While biodiesel possesses numerous benefits, the cold flow properties (CFP) of biodiesel in comparison with petro-diesel are significantly less satisfactory. This is due to the presence of saturated and unsaturated fatty acid esters. The poor CFP of biodiesel subsequently affects performance in cold weather and damages the engine fuel system, as well as chokes the fuel filter, fuel inlet lines, and injector nozzle. Previously, attempts were made to minimize the damaging impact of bad cold flow through the reduction of pour point, cloud point, and the cold filter plugging point of biodiesel. This study is focused on the biodiesel CFP-related mechanisms and highlights the factors that initialize and pace the crystallization process. This review indicates that the CFP of biodiesel fuel can be improved by utilizing different techniques. Winterisation of some biodiesel has been shown to improve CFP significantly. Additives such as polymethyl acrylate improved CFP by 3-9 ° C. However, it is recommended that improvement methods in terms of fuel properties and efficiency should be carefully studied and tested before being implemented in industrial applications as this might impact biodiesel yield, cetane number, etc.
He, B, He, N, Xu, BH, Cai, R, Shao, HL & Zhang, QL 2020, 'Tests on distributed monitoring of deflection of concrete faces of CFRDs', Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, vol. 42, no. 5, pp. 837-844.
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Ensuring the safety of concrete faces is the key to the safe operation of concrete face rockfill dams (CFRDs). The deflection is an important index to monitor the integrity of a concrete face. Based on the distributed optical fiber sensing technology, a new technology is proposed to monitor the deflection of concrete faces of CFRDs, and systematic tests are carried out to verify the measurement accuracy of this new technology as well as its feasibility. Based on the Matlab program and the quasi-distributed scatter strain test data, a method for calculating deflection is established. The research findings show that the calculated deflection at each measurement point on the concrete face is consistent with the measured one at the corresponding position (the absolute error is 5 mm, and the average relative error is 3%). It is validated that this new technology can monitor the deflection including irregular deflection with millimeter accuracy. It is also suitable for the distributed monitoring of the deflection of the full section of concrete faces and the measurement of large deflection. The proposed advanced technology is proved to be applicable to monitoring the deflection of the entire concrete face of a 300-meter level CFRD in a distributed manner.
He, S, Lyu, X, Ni, W, Tian, H, Liu, RP & Hossain, E 2020, 'Virtual Service Placement for Edge Computing Under Finite Memory and Bandwidth', IEEE Transactions on Communications, vol. 68, no. 12, pp. 7702-7718.
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© 1972-2012 IEEE. Edge computing allows an edge server to adaptively place virtual instances to serve different types of data. This article presents a new algorithm which jointly optimizes virtual service placement farsightedly and service data admission instantly to maximize the time-average service throughput of edge computing. The data admission is optimized, adapting to fast-changing data arrivals and wireless channels. The service placement is transformed into a two-dimensional knapsack problem by approximating future arrivals and channels with past observations, and solved over a slow timescale to allow services to be properly installed. Different from existing studies, our algorithm considers practical aspects of edge servers, such as finite memory size and bandwidth. We prove that the algorithm is asymptotically optimal and the optimality loss resulting from the approximation diminishes. Simulations show that our approach can improve the time-average throughput of existing alternatives by 16% for our considered simulation setup. The improvement becomes higher, as the memory size becomes increasingly tight. The number of services to be replaced is reduced without loss of throughput, after being placed farsightedly.
He, X, Wu, W, Cai, G, Qi, J, Kim, JR, Zhang, D & Jiang, M 2020, 'Work–energy analysis of granular assemblies validates and calibrates a constitutive model', Granular Matter, vol. 22, no. 1.
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He, X, Xu, H, Li, W & Sheng, D 2020, 'An improved VOF-DEM model for soil-water interaction with particle size scaling', Computers and Geotechnics, vol. 128, pp. 103818-103818.
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© 2020 This study presents an improved VOF-DEM model where the Darcian velocity and a compound variable are treated as unknowns in the pressure-velocity calculation procedure such that the use of interpolated porosity at cell faces is minimised and stability is ensured even if the porosity field is not smooth or even ragged. A higher-order porosity estimation method is also used such that the porosity and interaction force are evaluated correctly when the CFD cell size is of the same order as the DEM particle size. Additionally, a particle size scaling technique is proposed to let the DEM particle size different from the real soil particle size and soil-water interaction forces are the same as when the real soil particle size is used. This is achieved by modifying the calculation of drag force. The solution scheme is verified in two case where analytical solutions exist. Particle size scaling technique is also used and tested in permeability tests and wave interaction with porous structure. Subsequently, the settling and collision of particles in water, dambreak of soil-water mixture and submerged landslides are simulated. With the present improvements and the particle size scaling, the capability of the VOF-DEM is extended in soil-water interaction problems.
He, X, Xu, H, Sabetamal, H & Sheng, D 2020, 'Machine learning aided stochastic reliability analysis of spatially variable slopes', Computers and Geotechnics, vol. 126, pp. 103711-103711.
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© 2020 This paper presents machine learning aided stochastic reliability analysis of spatially variable slopes, which significantly reduces the computational efforts and gives a complete statistical description of the factor of safety with promising accuracy compared with traditional methods. Within this framework, a small number of traditional random finite-element simulations are conducted. The samples of the random fields and the calculated factor of safety are, respectively, treated as training input and output data, and are fed into machine learning algorithms to find mathematical models to replace finite-element simulations. Two powerful machine learning algorithms used are the neural networks and the support-vector regression with their associated learning strategies. Several slopes are examined including stratified slopes with 3 or 4 layers described by 4 or 6 random fields. It is found that with 200 to 300 finite-element simulations (finished in about 5 ~ 8 h), the machine-learning generated model can predict the factor of safety accurately, and a stochastic analysis of 105 samples takes several minutes. However, the same traditional analysis would require hundreds of days of computation.
He, Z, Teng, J, Yang, Z, Liang, L, Li, H & Zhang, S 2020, 'An analysis of vapour transfer in unsaturated freezing soils', Cold Regions Science and Technology, vol. 169, pp. 102914-102914.
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Heidary, A, Radmanesh, H, Bakhshi, A, Samandarpour, S, Rouzbehi, K & Shariati, N 2020, 'Compound ferroresonance overvoltage and fault current limiter for power system protection', IET Energy Systems Integration, vol. 2, no. 4, pp. 325-330.
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Power systems are subjected to various types of faults as well as ferroresonance overvoltages. These results in the interruption of the normal operation of the power grid, failure of equipment, electrical fires, etc. To tackle these issues, this study proposes a dual function limiter to control the fault current and ferroresonance phenomenon in power systems. This compound device is a solid-state series transformer-based limiter that includes IGBT switches, capacitors, rectifiers, and a DC reactor. During the grid normal operation, the proposed limiter is not active and therefore is invisible and it operates in the instant of fault inception or ferroresonance overvoltage occurrences. Analytical studies in all operation modes are presented and assessments on the performance of the proposed ferroresonance and fault current limiter (FFCL) are conducted in Matlab. Simulation results confirm the reported analytical studies and FFCL's performance.
Heidary, A, Radmanesh, H, Naghibi, SH, Samandarpour, S, Rouzbehi, K & Shariati, N 2020, 'Distribution system protection by coordinated fault current limiters', IET Energy Systems Integration, vol. 2, no. 1, pp. 59-65.
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The protection of distribution networks is one of the most substantial issues, which needs special attention. Using appropriate protective equipment enhances the safety of the power distribution network during the fault conditions. Fault current limiter (FCL) is a kind of modern preserving system being used for protecting power networks and equipment. One of the main concerns of power networks is the voltage restoration of buses during faulty conditions. In this study, a group of coordinated DC reactor type faults current limiters are designed and tested to protect the network and restore its buses voltage within the fault period. To coordinate FCLs and measurement devices during the fault sequences, a wireless communication system and decision‐making computer are used. The proposed FCLs coordination strategy is modelled and simulated in MATLAB platform and the results are validated by the developed laboratory test setup.
Hejazi, MA, Tong, W, Stacey, A, Soto-Breceda, A, Ibbotson, MR, Yunzab, M, Maturana, MI, Almasi, A, Jung, YJ, Sun, S, Meffin, H, Fang, J, Stamp, MEM, Ganesan, K, Fox, K, Rifai, A, Nadarajah, A, Falahatdoost, S, Prawer, S, Apollo, NV & Garrett, DJ 2020, 'Hybrid diamond/ carbon fiber microelectrodes enable multimodal electrical/chemical neural interfacing', Biomaterials, vol. 230, pp. 119648-119648.
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Hellmann, A, Ang, L & Sood, S 2020, 'Towards a conceptual framework for analysing impression management during face-to-face communication', Journal of Behavioral and Experimental Finance, vol. 25, pp. 100265-100265.
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Helwani, Z, Ramli, M, Rusyana, A, Marlina, M, Fatra, W, Idroes, GM, Suhendra, R, Ashwie, V, Mahlia, TMI & Idroes, R 2020, 'Alternative Briquette Material Made from Palm Stem Biomass Mediated by Glycerol Crude of Biodiesel Byproducts as a Natural Adhesive', Processes, vol. 8, no. 7, pp. 777-777.
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Recently, the global population has increased sharply, unfortunately, the availability of fossil fuel resources has significantly decreased. This phenomenon has become an attractive issue for many researchers in the world so that various studies in the context of finding renewable energy are developing continuously. Relating to this challenge, this research has been part of scientific work in the context of preparing an energy briquette employing palm oil stems and glycerol crude of biodiesel byproducts as inexpensive and green materials easily found in the Riau province, Indonesia. Technically, the palm oil stems are used for the production of charcoal particles and the glycerol crude as an adhesive compound in the production of energy briquettes. The heating value of palm oil stem is 17,180 kJ/kg, which can be increased to an even higher value through a carbonization process followed by a densification process so that it can be used as a potential matrix to produce energy briquettes. In detail, this study was designed to find out several parameters including the effect of sieve sizes consisting of 60, 80, and 100 mesh, respectively, which are used for the preparation of charcoal particles as the main matrix for the manufacture of the briquettes; the effect of charcoal-adhesive ratios (wt) of 60:40, 70:30, and 80:20; and the effect of varied pressures of 100, 110, and 120 kg/cm2 on the briquette quality. The quality of the obtained briquettes is analyzed through the observation of important properties which involve the heating value and the compressive strength using Response Surface Methodology (RSM). The results showed that the produced briquettes had an optimum heating value of 30,670 kJ/kg, while their loaded charcoal particles resulted from the mesh sieve of 80, in which there was a charcoal loading of 53 g and it pressed at 93.1821 bar, whereas, the compressive strength value of the briquette was 100,608 kg/cm2, which loaded charcoal part...
Helwani, Z, Ramli, M, Saputra, E, Bahruddin, B, Yolanda, D, Fatra, W, Idroes, GM, Muslem, M, Mahlia, TMI & Idroes, R 2020, 'Impregnation of CaO from Eggshell Waste with Magnetite as a Solid Catalyst (Fe3O4/CaO) for Transesterification of Palm Oil Off-Grade', Catalysts, vol. 10, no. 2, pp. 164-164.
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In this work, calcium oxide (CaO) extracted from eggshell impregnated with magnetite (Fe3O4) is prepared successfully and it had been applied on transesterification of palm oil off-grade. Prior experiment, the eggshells material are powdered and calcined at 900 °C then impregnated with Fe3O4 and recalcined. The obtained Fe3O4/CaO catalyst is characterized using X-ray diffraction and Braunaeur–Emmet–Teller (BET) surface area. The influence of various parameters including recalcined time and temperature are investigated. The prepared catalyst is tested for transesterification of palm oil off-grade to produce biodiesel in which the optimal conditions of a methanol/palm oil off-grade molar ratio of 10:1, the catalyst weight of 6%, the reaction temperature of 70 °C, and the reaction time of 2 h. The transesterification product was analyzed using GC-MS, which showed the biodiesel yield of 90% at the recalcined temperature of 600 °C and reaction time of 2 h. It has been noted that the catalyst activity is achieved when the moderate recalcination temperature is applied and the disordered structure of the catalyst is maintained. This study also confirms that CaO impregnated with Fe3O4 could be a solid catalyst for the biodiesel synthesis through transesterification reaction of palm oil off-grade.
Hendryx, M, Islam, MS, Dong, G-H & Paul, G 2020, 'Air Pollution Emissions 2008–2018 from Australian Coal Mining: Implications for Public and Occupational Health', International Journal of Environmental Research and Public Health, vol. 17, no. 5, pp. 1570-1570.
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Occupational exposure limits for respirable coal dust are based on exposure during working hours, but coal miners may experience additional community-based exposures during nonworking hours. We analyzed Australia National Pollutant Inventory (NPI) data for the years 2008–2018 to estimate air pollutants (metals, nitrogen oxides, particulate matter ≤ 10 micrometers (PM10) and ≤2.5 micrometers (PM2.5)) originating from coal mines. PM10 levels from community-based air monitors in Queensland and New South Wales were also compared between mining and nonmining communities. Results indicated that tons of coal mined increased over the study period, and that levels of particulate matter, metals, and nitrogen oxides increased significantly over time as well. Coal mines accounted for 42.1% of national PM10 air emissions from NPI sites. PM2.5 from coal mines accounted for 19.5% of the national total, metals for 12.1%, and nitrogen oxides for 10.1%. Coal mining occurred in 57 different post codes; the 20 coal-mining post codes with the highest PM10 emissions were home to 160,037 people. Emissions of all studied pollutants were significantly higher from coal mining sites than from other types of NPI sites. Results from community-based air monitoring stations indicated significantly higher population PM10 exposure in coal mining communities than in nonmining communities. The health of the public at large is impacted by coal mining, but to the extent that miners also live near coal mining operations, their total exposure is underestimated by consideration of exposure only during working hours.
Herath, S, Razavi Bazaz, S, Monkman, J, Ebrahimi Warkiani, M, Richard, D, O’Byrne, K & Kulasinghe, A 2020, 'Circulating tumor cell clusters: Insights into tumour dissemination and metastasis', Expert Review of Molecular Diagnostics, vol. 20, no. 11, pp. 1139-1147.
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INTRODUCTION:Metastasis results in more than 90% of cancer related deaths globally. The process is thought to be facilitated by metastatic precursor cells, commonly termed circulating tumour cells (CTCs). CTCs can exist as single cells or cell clusters and travel through the lymphovasculature to distant organs where they can form overt metastasis. Areas covered: Studies have highlighted that CTC clusters, which may be homotypic or heterotypic in composition, have a higher metastatic potential compared to single CTCs. The characterisation of CTC clusters is becoming important as heterotypic clusters can provide a mechanism for immune evasion. This review summarises the latest advances in CTC cluster mediated metastasis and clinical significance. Expert Opinion: Comprehensive characterisation of CTC clusters is needed to understand the cell types and interactions within clusters, in order to identify ways in which to reduce CTC cluster mediated metastasis. The role of CTC clusters in prognosticating disease progression needs to be determined by documenting CTC clusters from the time of diagnosis over the course of therapy.
Hesam-Shariati, N, Chang, W-J, McAuley, JH, Booth, A, Trost, Z, Lin, C-T, Newton-John, T & Gustin, SM 2020, 'The Analgesic Effect of Electroencephalographic Neurofeedback for People With Chronic Pain: Protocol for a Systematic Review and Meta-analysis', JMIR Research Protocols, vol. 9, no. 10, pp. e22821-e22821.
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Background Chronic pain is a global health problem, affecting around 1 in 5 individuals in the general population. The understanding of the key role of functional brain alterations in the generation of chronic pain has led researchers to focus on pain treatments that target brain activity. Electroencephalographic neurofeedback attempts to modulate the power of maladaptive electroencephalography frequency powers to decrease chronic pain. Although several studies have provided promising evidence, the effect of electroencephalographic neurofeedback on chronic pain is uncertain. Objective This systematic review aims to synthesize the evidence from randomized controlled trials to evaluate the analgesic effect of electroencephalographic neurofeedback. In addition, we will synthesize the findings of nonrandomized studies in a narrative review. Methods We will apply the search strategy in 5 electronic databases (Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, PsycInfo, and CINAHL) for published studies and in clinical trial registries for completed unpublished studies. We will include studies that used electroencephalographic neurofeedback as an intervention for people with chronic pain. Risk-of-bias tools will be used to assess methodological quality of the included studies. We will include randomized controlled trials if they have compared electroencephalographic neurofeedback with any other intervention or placebo control. The data from randomized controlled trials will be aggregated to perform a meta-analysis for quantitative synthesis. The primary outcome measure is pain intensity assessed by self-report scales. Secondary outcome measures include depressive s...
Hesam-Shariati, N, Newton-John, T, Singh, AK, Tirado Cortes, CA, Do, T-TN, Craig, A, Middleton, JW, Jensen, MP, Trost, Z, Lin, C-T & Gustin, SM 2020, 'Evaluation of the Effectiveness of a Novel Brain-Computer Interface Neuromodulative Intervention to Relieve Neuropathic Pain Following Spinal Cord Injury: Protocol for a Single-Case Experimental Design With Multiple Baselines', JMIR Research Protocols, vol. 9, no. 9, pp. e20979-e20979.
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Background Neuropathic pain is a debilitating secondary condition for many individuals with spinal cord injury. Spinal cord injury neuropathic pain often is poorly responsive to existing pharmacological and nonpharmacological treatments. A growing body of evidence supports the potential for brain-computer interface systems to reduce spinal cord injury neuropathic pain via electroencephalographic neurofeedback. However, further studies are needed to provide more definitive evidence regarding the effectiveness of this intervention. Objective The primary objective of this study is to evaluate the effectiveness of a multiday course of a brain-computer interface neuromodulative intervention in a gaming environment to provide pain relief for individuals with neuropathic pain following spinal cord injury. Methods We have developed a novel brain-computer interface-based neuromodulative intervention for spinal cord injury neuropathic pain. Our brain-computer interface neuromodulative treatment includes an interactive gaming interface, and a neuromodulation protocol targeted to suppress theta (4-8 Hz) and high beta (20-30 Hz) frequency powers, and enhance alpha (9-12 Hz) power. We will use a single-case experimental design with multiple baselines to examine the effectiveness of our self-developed brain-computer interface neuromodulative intervention for the treatment of spinal cord injury neuropathic pain. We will recruit 3 participants with spinal cord injury neuropathic pain. Each participant will be randomly allocated to a different baseline phase (ie, 7, 10, or 14 days), which will then be followed by 20 sessions of a 30-minute brain-computer interface neuromodulative interventi...
Hesam-Shariati, N, Newton-John, T, Singh, AK, Tirado Cortes, CA, Do, T-TN, Craig, A, Middleton, JW, Jensen, MP, Trost, Z, Lin, C-T & Gustin, SM 2020, 'Evaluation of the Effectiveness of a Novel Brain-Computer Interface Neuromodulative Intervention to Relieve Neuropathic Pain Following Spinal Cord Injury: Protocol for a Single-Case Experimental Design With Multiple Baselines (Preprint)', JMIR Research Protocols.
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BACKGROUND Neuropathic pain is a debilitating secondary condition for many individuals with spinal cord injury. Spinal cord injury neuropathic pain often is poorly responsive to existing pharmacological and nonpharmacological treatments. A growing body of evidence supports the potential for brain-computer interface systems to reduce spinal cord injury neuropathic pain via electroencephalographic neurofeedback. However, further studies are needed to provide more definitive evidence regarding the effectiveness of this intervention.
OBJECTIVE The primary objective of this study is to evaluate the effectiveness of a multiday course of a brain-computer interface neuromodulative intervention in a gaming environment to provide pain relief for individuals with neuropathic pain following spinal cord injury.
METHODS We have developed a novel brain-computer interface-based neuromodulative intervention for spinal cord injury neuropathic pain. Our brain-computer interface neuromodulative treatment includes an interactive gaming interface, and a neuromodulation protocol targeted to suppress theta (4-8 Hz) and high beta (20-30 Hz) frequency powers, and enhance alpha (9-12 Hz) power. We will use a single-case experimental design with multiple baselines to examine the effectiveness of our self-developed brain-computer interface neuromodulative intervention for the treatment of spinal cord injury neuropathic pain. We will recruit 3 participants with spinal cord injury neuropathic pain. Each participant will be randomly allocated to a different baseline phase (ie, 7, 10, or 14 days), which will then be followed by 20 sessions of a 30-minute brain-computer ...
Hien, NT, Nguyen, LH, Van, HT, Nguyen, TD, Nguyen, THV, Chu, THH, Nguyen, TV, Trinh, VT, Vu, XH & Aziz, KHH 2020, 'Heterogeneous catalyst ozonation of Direct Black 22 from aqueous solution in the presence of metal slags originating from industrial solid wastes', Separation and Purification Technology, vol. 233, pp. 115961-115961.
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© 2019 Elsevier B.V. This study developed a low cost catalyst, namely, zinc slag (Zn-S) for the ozonation process of Direct Black 22 (DB22) from aqueous solutions. Among five different kind of low cost metal slags including Fe-S, Cu-S, Cd-S, Pb-S and Zn-S, the Zn-S slag was selected as an efficient catalyst in this study. Zn-S contained mainly zinc (Zn) and calcium (Ca) discharged from zinc slag waste in Vietnam. It was found that Zn-S could effectively decolonize and mineralize DB22 through heterogeneous catalytic ozonation. The degradation kinetic of DB22 followed the pseudo-first order model. The best removal efficiency of DB22 (Zn-S/O3/H2O2 (76%) > Zn-S/O3 (69%) > O3/H2O2 (66%) > O3 (55% for COD) occurred at pH 11 for heterogeneous catalytic ozonation processes with Zn-S as the catalyst as well as ozone alone and perozone processes due to fast decomposition of O3 in alkaline solution to generate powerful and non-selective OH radicals. An increase in decolonization and mineralization rate was observed when increasing the Zn-S dosage from 0.125 g/L to 0.75 g/L for Zn-S/O3 and 0.125 g/L to 1.0 g/L for Zn-S/O3/H2O2. The K values of the pseudo-first order model followed the same sequence as mineralization rates of DB22 in term of COD removal. Ca and Zn constituents in the Zn-S catalyst contributed to the increase in O3 decomposition and improvement of reaction rate with H2O2. Subsequently, the degradation of DB22 by the ozonation process with Zn-S catalyst was enhanced through the enrichment mechanism of hydroxyl radicals (*OH) and surface adsorption. The degradation mechanism of DB22 by hydroxyl radicals was surely affirmed by tests with the decrease in degradation percentage of DB22 in case of the presence t-butanol, Cl− and CO32−.
Hieu, NQ, Hoang, DT, Luong, NC & Niyato, D 2020, 'iRDRC: An Intelligent Real-Time Dual-Functional Radar-Communication System for Automotive Vehicles', IEEE Wireless Communications Letters, vol. 9, no. 12, pp. 2140-2143.
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© 2012 IEEE. This letter introduces an intelligent Real-time Dual-functional Radar-Communication (iRDRC) system for autonomous vehicles (AVs). This system enables an AV to perform both radar and data communications functions to maximize bandwidth utilization as well as significantly enhance safety. In particular, the data communications function allows the AV to transmit data, e.g., of current traffic, to edge computing systems and the radar function is used to enhance the reliability and reduce the collision risks of the AV, e.g., under bad weather conditions. The problem of the iRDRC is to decide when to use the communication mode or the radar mode to maximize the data throughput while minimizing the miss detection probability of unexpected events given the uncertainty of surrounding environment. To solve the problem, we develop a deep reinforcement learning algorithm that allows the AV to quickly obtain the optimal policy without requiring any prior information about the environment. Simulation results show that the proposed scheme outperforms baseline schemes in terms of data throughput, miss detection probability, and convergence rate.
Hill, M, Sais, D, Monteiro Marques, T, Gama Carvalho, M & Tran, N 2020, 'Developing a virus-microRNA interactome using cytoscape', MethodsX, vol. 7, pp. 100700-100700.
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© 2019 It is currently difficult to determine the effect of oncogenic viruses on the global function and regulation of pathways within mammalian cells. A thorough understanding of the molecular pathways and individual genes altered by oncogenic viruses is needed for the identification of targets that can be utilised for early diagnosis, prevention, and treatment methods. We detail a logical step-by-step guide to uncover viral-protein-miRNA interactions using publically available datasets and the network building program, Cytoscape. This method may be applied to identify specific pathways that are altered in viral infection, and contribute to the oncogenic transformation of cells. To demonstrate this, we constructed a gene regulatory interactome encompassing Human Papillomavirus Type 16 (HPV16) and its control of specific miRNAs. This approach can be broadly applied to understand and map the regulatory functions of other oncogenic viruses, and determine their role in altering the cellular environment in cancer. Availability and Implementation Cytoscape (Shannon et al. (2003), Smoot et al. (2010)) is freely available at https://cytoscape.org/. • This method allows for the analysis and visualization of large datasets to generate an interactome that integrates key players of molecular biology • This approach may be applied to any oncogenic virus to map its regulatory functions, and its secondary impact on gene regulation via microRNAs.
Hirsimaki, C, Outram, JG, Millar, GJ & Altaee, A 2020, 'Process simulation of high pH reverse osmosis systems to facilitate reuse of coal seam gas associated water', Journal of Environmental Chemical Engineering, vol. 8, no. 5, pp. 104122-104122.
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Hoang, LP, Nguyen, TMP, Van, HT, Hoang, TKD, Vu, XH, Nguyen, TV & Ca, NX 2020, 'Cr(VI) Removal from Aqueous Solution Using a Magnetite Snail Shell', Water, Air, & Soil Pollution, vol. 231, no. 1.
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© 2020, Springer Nature Switzerland AG. In this study, magnetic snail shell (MSS) prepared by impregnating of iron oxide onto snail shell (SS) powder was used for removing Cr(VI) from aqueous solution. Among six different mass ratios of Fe/SS powder studied, the MSS25 produced at a ratio of 25% achieved the highest Cr(VI) adsorption capacity. Batch adsorption experiments were conducted to investigate the adsorption isotherm, kinetics, and mechanism of Cr(VI) onto MSS25. The results illustrated that adsorption of Cr(VI) onto MSS25 reached equilibrium after 150 min at pH 3. The adsorption kinetics could be well described by the pseudo-second order model (R2 = 0.986). The Langmuir model (R2 = 0.971) was the best-fitting model that described the adsorption isotherm of Cr(VI) onto MSS25. The maximum adsorption capacity was 46.08 mg Cr(VI) per gram of MSS25. Ion exchange, electrostatic attraction, and adsorption-coupled reduction were determined as the main adsorption mechanisms of Cr(VI) onto MSS25. The high percentages of CaCO3 and Fe3O4 found in the MSS25 structure made a significant contribution to the Cr(VI) adsorption process.
Hoang, TM, El Shafie, A, da Costa, DB, Duong, TQ, Tuan, HD & Marshall, A 2020, 'Security and Energy Harvesting for MIMO-OFDM Networks', IEEE Transactions on Communications, vol. 68, no. 4, pp. 2593-2606.
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IEEE We consider a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) network in which a source node, Alice, communicates with an energy-harvesting destination node, Bob, in the presence of a passive eavesdropper. To secure the wireless transmission, Alice generates a hybrid artificial noise (AN) in both frequency and time domains. Moreover, in order to collect more energy, Bob splits the received signal power of the cyclic prefix of each OFDM block. We then propose two non-convex optimization problems to balance both the need for security and the need for harvesting energy at Bob. While one considers maximizing the secrecy rate, the other approach aims at maximizing the harvested energy. Path-following algorithms of low computational complexity are developed and evaluated. Our numerical results show the gain of our proposed scheme and the effectiveness of our proposed algorithms.
Hoang, T-T, Duran, C, Nguyen, K-D, Dang, T-K, Nhu, QNQ, Than, PH, Tran, X-T, Le, D-H, Tsukamoto, A, Suzaki, K & Pham, C-K 2020, 'Low-power high-performance 32-bit RISC-V microcontroller on 65-nm silicon-on-thin-BOX (SOTB)', IEICE Electronics Express, vol. 17, no. 20, pp. 20200282-20200282.
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Hoang, VT, Phung, MD, Dinh, TH & Ha, QP 2020, 'System Architecture for Real-Time Surface Inspection Using Multiple UAVs', IEEE Systems Journal, vol. 14, no. 2, pp. 2925-2936.
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Ho-Le, TP & Nguyen, TV 2020, 'Hip Fracture and Mortality: A Loss of Life Expectancy Interpretation', Journal of Bone and Mineral Research, vol. 36, no. 12, pp. 2457-2458.
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Hong, L, Ju, S, Yang, Y, Zheng, J, Xia, G, Huang, Z, Liu, X & Yu, X 2020, 'Hollow-shell structured porous CoSe2 microspheres encapsulated by MXene nanosheets for advanced lithium storage', Sustainable Energy & Fuels, vol. 4, no. 5, pp. 2352-2362.
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Cobalt diselenide (CoSe2), a representative transition-metal chalcogenide (TMC), is attracting intensive interest as an anode material for lithium ion batteries (LIBs), in view of its high specific capacity based on the conversion reaction mechanism.
Hong, X, Zhou, X, Li, S, Feng, Y & Ying, M 2020, 'A Tensor Network based Decision Diagram for Representation of Quantum Circuits.', CoRR, vol. abs/2009.02618.
Ho-Pham, LT, Doan, MC, Van, LH & Nguyen, TV 2020, 'Development of a model for identification of individuals with high risk of osteoporosis', Archives of Osteoporosis, vol. 15, no. 1, p. 111.
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Many developing countries, including Vietnam, lack DXA resources for the diagnosis of osteoporosis, which poses difficulties in the treatment and prevention of osteoporosis at the individual level. We have developed and validated a prediction model for individualized assessment of osteoporosis based on age and body weight for men and women. PURPOSE:To estimate the prevalence of osteoporosis and to develop and validate a prediction model for estimating the absolute risk of osteoporosis in the Vietnamese population. METHODS:The study involved 1477 women and 669 men aged 50 years and older, who were recruited from the general population in Ho Chi Minh City (Vietnam). Bone mineral density (BMD) at the femoral neck, total hip, and lumbar spine was measured by DXA (Hologic Horizon). The diagnosis of osteoporosis was based on BMD T-score (T-score ≤ - 2.5) at the femoral neck or lumbar spine which was derived from a published reference range for the Vietnamese population. The logistic regression model was used to develop the prediction model for men and women separately. The bootstrap method was used to evaluate the model performance using 3 indices: the area under the receiver's operating characteristic curve (AUC), Brier score, and R-squared values. RESULTS:The prevalence of osteoporosis at any site was 28.3% in women and 15.5% in men. The best predictors of osteoporosis risk were age and body weight. Using these indices, a cut-off of 0.195 for women yielded an AUC of 0.825, Brier score = 0.112, and it explained 33.8% of total variance in risk of osteoporosis between individuals. Similarly, in men, the internal validation with a cut-off of 0.09 yielded good accuracy, with AUC = 0.858, Brier score = 0.040, and R-squared = 30.3%. CONCLUSION:We have developed and validated a prediction model for individualized assessment of osteoporosis. In settings without DXA, this model can serve as a useful screening tool to identify high-risk individuals for DXA scan.
Hoque, MA-A, Pradhan, B & Ahmed, N 2020, 'Assessing drought vulnerability using geospatial techniques in northwestern part of Bangladesh', Science of The Total Environment, vol. 705, pp. 135957-135957.
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Horry, MJ, Chakraborty, S, Paul, M, Ulhaq, A, Pradhan, B, Saha, M & Shukla, N 2020, 'COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data', IEEE Access, vol. 8, pp. 149808-149824.
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Hoskyn, K, Eady, MJ, Capocchiano, H, Lucas, P, Rae, S, Trede, F & Yuen, L 2020, 'GoodWIL placements: How COVID-19 shifts the conversation about unpaid placements', International Journal of Work-Integrated Learning, vol. 21, no. 4, pp. 439-450.
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This paper discusses how the COVID-19 pandemic can shift the conversation of paid and unpaid placements from an economic to a pedagogical and goodwill perspective. During the pandemic lockdown many placements were cancelled or postponed. Some continued as agreed but with students working from home, while other placements became unpaid. We build on the pertinent literature that raises legal, ethical, economic and pedagogical implications of paid versus unpaid placement models and what motivates placement organizations to offer placements. Four interdisciplinary trans-Tasman case studies are discussed to better understand the complex situations for placement organizations and universities to sustain WIL placements during this pandemic. Conclusions include recommendations to be vigilant and ensure goodwill is not used to mask the exploitation of students, but rather, positively influence the motivation behind offering placements during these trying times and beyond.
Hossain, MI, Eager, D & Walker, PD 2020, 'Greyhound racing ideal trajectory path generation for straight to bend based on jerk rate minimization', Scientific Reports, vol. 10, no. 1, p. 7088.
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AbstractThis paper presents methods for modelling and designing an ideal path trajectory between straight and bend track path segments for racing greyhounds. To do this, we numerically generate clothoid and algebraic curve segments for racing quadrupeds using a sequential vector transformation method as well as using a helper equation for approaching ideal clothoid segments that would respect greyhound kinematic parameters and boundary conditions of the track. Further, we look into the limitations of using a clothoid curve for racing dog track path design and propose a smooth composite curve for track transition design which roughly maintains G3 curvature continuity for smooth jerk to overcome limitations of a clothoid transition. Finally, we show results from race data modelling and past injury data, which provide a strong indication of clothoid curve segments improving the dynamics and safety of racing greyhounds while reducing injuries.
Hossain, N, Hasan, MH, Mahlia, TMI, Shamsuddin, AH & Silitonga, AS 2020, 'Feasibility of microalgae as feedstock for alternative fuel in Malaysia: A review', Energy Strategy Reviews, vol. 32, pp. 100536-100536.
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© 2020 The Authors Biodiesel is an attractive fuel replacement for diesel engine in Malaysia. The application of biodiesel as fuel-blend has been implemented commercially in transport sector in the country. Among various potential feedstock for biodiesel production, microalgae have been appeared as a promising source since a decade due to its' high biomass productivity, rapid growth rate, large amount of lipid content, capability of high CO2 capture and sequestration as well as suitable geographical location to be harvested. The main objective of this study was to determine the feasibility of microalgae harvesting in Malaysia to produce biodiesel and potential to implement microalgae-biodiesel as commercial transportation fuel. This study demonstrated the current scenario of overall biodiesel production and application in Malaysia. Since Malaysia is the world's second-largest oil palm producer, exploitation of edible palm oil for the making of biodiesel is to be blamed as the cause of soaring food price; therefore, the country is currently looking for 3rd generation biofuel sources and microalgae has been preferred for this purpose. Therefore, insight of the significance of microalgae cultivation for this purpose, suitable microalgae candidates and possible feasibility of microalgae biodiesel have been delineated in this review study. Prospects and challenges to implement microalgae biodiesel have also been emphasized in this study. Therefore, the advantages and limitations of this biodiesel can be transparent to government and non-government sectors. Thus, this study can re-direct both sectors in future. Consequently, it may contribute setting an appropriate government policy to encourage microalgae for biodiesel production to sustain the local biofuel and secure economic growth, energy security and improve environmental conditions in near future.
Hossain, N, Nizamuddin, S, Griffin, G, Selvakannan, P, Mubarak, NM & Mahlia, TMI 2020, 'Synthesis and characterization of rice husk biochar via hydrothermal carbonization for wastewater treatment and biofuel production', Scientific Reports, vol. 10, no. 1, p. 18851.
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AbstractThe recent implication of circular economy in Australia spurred the demand for waste material utilization for value-added product generations on a commercial scale. Therefore, this experimental study emphasized on agricultural waste biomass, rice husk (RH) as potential feedstock to produce valuable products. Rice husk biochar (RB) was obtained at temperature: 180 °C, pressure: 70 bar, reaction time: 20 min with water via hydrothermal carbonization (HTC), and the obtained biochar yield was 57.9%. Enhancement of zeta potential value from − 30.1 to − 10.6 mV in RB presented the higher suspension stability, and improvement of surface area and porosity in RB demonstrated the wastewater adsorption capacity. Along with that, an increase of crystallinity in RB, 60.5%, also indicates the enhancement of the catalytic performance of the material significantly more favorable to improve the adsorption efficiency of transitional compounds. In contrast, an increase of the atomic O/C ratio in RB, 0.51 delineated high breakdown of the cellulosic component, which is favorable for biofuel purpose. 13.98% SiO2 reduction in RB confirmed ash content minimization and better quality of fuel properties. Therefore, the rice husk biochar through HTC can be considered a suitable material for further application to treat wastewater and generate bioenergy.
Hossain, SM, Park, H, Kang, H-J, Kim, JB, Tijing, L, Rhee, I, Jun, Y-S, Shon, HK & Kim, J-H 2020, 'Preparation and Characterization of Photoactive Anatase TiO2 from Algae Bloomed Surface Water', Catalysts, vol. 10, no. 4, pp. 452-452.
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The purpose of the study was to effectively treat algae bloomed water while using a Ti-based coagulant (TiCl4) and recover photoactive novel anatase TiO2 from the flocculated sludge. Conventional jar tests were conducted in order to evaluate the coagulation efficiency, and TiCl4 was found superior compared to commercially available poly aluminum chloride (PAC). At a dose of 0.3 g Ti/L, the removal rate of turbidity, chemical oxygen demand (COD), and total phosphorus (TP) were measured as 99.8%, 66.7%, and 96.9%, respectively. Besides, TiO2 nanoparticles (NPs) were recovered from the flocculated sludge and scanning electron microscope (SEM), energy dispersive X-ray spectroscope (EDX), and X-ray diffraction (XRD) analysis confirmed the presence of only anatase phase. The recovered TiO2 was found to be effective in removing gaseous CH3CHO and NOx under UV-A lamp at a light intensity of 10 W/m2. Additionally, the TiO2 mixed mortar blocks that were prepared in this study successfully removed atmospheric nitrogen oxide (NOx) under UV irradiance. This study is one of the first to prepare anatase TiO2 from flocculated algal sludge and it showed promising results. Further research on this novel TiO2 concerning internal chemical bonds and shift in the absorbance spectrum could explore several practical implications.
Hossain, SM, Park, H, Kang, H-J, Mun, JS, Tijing, L, Rhee, I, Kim, J-H, Jun, Y-S & Shon, HK 2020, 'Modified Hydrothermal Route for Synthesis of Photoactive Anatase TiO2/g-CN Nanotubes from Sludge Generated TiO2', Catalysts, vol. 10, no. 11, pp. 1350-1350.
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Titania nanotube was prepared from sludge generated TiO2 (S-TNT) through a modified hydrothermal route and successfully composited with graphitic carbon nitride (g-CN) through a simple calcination step. Advanced characterization techniques such as X-ray diffraction, scanning and transmission electron microscopy, infrared spectroscopy, X-ray photoelectron spectroscopy, UV/visible diffuse reflectance spectroscopy, and photoluminescence analysis were utilized to characterize the prepared samples. A significant improvement in morphological and optical bandgap was observed. The effective surface area of the prepared composite increased threefold compared with sludge generated TiO2. The optical bandgap was narrowed to 3.00 eV from 3.18 in the pristine sludge generated TiO2 nanotubes. The extent of photoactivity of the prepared composites was investigated through photooxidation of NOx in a continuous flow reactor. Because of extended light absorption of the as-prepared composite, under visible light, 19.62% of NO removal was observed. On the other hand, under UV irradiation, owing to bandgap narrowing, although the light absorption was compromised, the impact on photoactivity was compensated by the increased effective surface area of 153.61 m2/g. Hence, under UV irradiance, the maximum NO removal was attained as 32.44% after 1 h of light irradiation. The proposed facile method in this study for the heterojunction of S-TNT and g-CN could significantly contribute to resource recovery from water treatment plants and photocatalytic atmospheric pollutant removal.
Hossain, SR, Ahmed, I, Azad, FS & Monjurul Hasan, ASM 2020, 'Empirical investigation of energy management practices in cement industries of Bangladesh', Energy, vol. 212, pp. 118741-118741.
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The aim of this study was to demonstrate and analyze the energy management practices in the cement industries of Bangladesh. The outcome of this study shows that there are some barriers in energy management and energy efficiency practices; Lack of staff consciousness, insufficient attention from government and bureaucratic intricacy are most significant among them. On the contrary, the most dominant drivers of energy management are risk of high energy prices in the future, highly motivated employee and high demand from consumer and Non-Government Organizations. According to the study, around 4–5% of energy efficiency can be enhanced with the assistance of energy management practices in cement industries. However, many industries are unaware of the idea of energy service companies as there is a lack of information about such company, and deficit of competent human resources in the energy management sector.
Hossein Abbasi, M, Taki, M, Rajabi, A, Li, L & Zhang, J 2020, 'Risk‐constrained offering strategies for a large‐scale price‐maker electric vehicle demand aggregator', IET Smart Grid, vol. 3, no. 6, pp. 860-869.
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In this study, the problem of an electric vehicle (EV) aggregator participating in a three‐settlement pool‐based market is presented. In addition to energy procurement, it is assumed that EVs can sell electricity back to the markets. In order to obtain optimised solutions, the aggregator is considered as a price‐maker agent who tries to minimise the cost of purchasing energy from the markets by offering price‐energy bids in the day‐ahead market and only energy bids in both adjustment and balancing markets. Since the problem is heavily constrained by equality constraints, the number of binary variables for a 24‐hour market horizon is too large which leads to intractability when solved by traditional mathematical algorithms like the interior point. Therefore, an evolutionary metaheuristic algorithm based on genetic algorithms (GAs) is proposed to deal with the intractability. In this regard, first, the stochastic problem is formulated as a mixed‐integer linear programming problem, and as a non‐linear programming problem to be solved by CPLEX and GA, respectively. The former is used to ensure that the GA is tuned properly, and helps to avoid converging to local extremums. Furthermore, the solutions of the two formulations are compared in simulations to demonstrate GA could be faster in obtaining better results.
Hosseinzadeh, A, Baziar, M, Alidadi, H, Zhou, JL, Altaee, A, Najafpoor, AA & Jafarpour, S 2020, 'Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions', Bioresource Technology, vol. 303, pp. 122926-122926.
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© 2020 Elsevier Ltd Vermicomposting is one of the best technologies for nutrient recovery from solid waste. This study aims to assess the efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models in predicting nutrient recovery from solid waste under different vermicompost treatments. Seven chemical and biological indices were studied as input variables to predict total nitrogen (TN) and total phosphorus (TP) recovery. The developed ANN and MLR models were compared by statistical analysis including R-squared (R2), Adjusted-R2, Root Mean Square Error and Absolute Average Deviation. The results showed that vermicomposting increased TN and TP proportions in final products by 1.5 and 16 times. The ANN models provided better prediction for TN and TP with R2 of 0.9983 and 0.9991 respectively, compared with MLR models with R2 of 0.834 and 0.729. TN and C/N ratio were key factors for TP and TN prediction by ANN with percentages of 17.76 and 18.33.
Hosseinzadeh, A, Zhou, JL, Altaee, A, Baziar, M & Li, D 2020, 'Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system', Bioresource Technology, vol. 316, pp. 123967-123967.
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Hosseinzadeh, A, Zhou, JL, Altaee, A, Baziar, M & Li, X 2020, 'Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network', Bioresource Technology, vol. 310, pp. 123391-123391.
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Osmotic Membrane Bioreactor (OMBR) is an emerging technology for wastewater treatment with membrane fouling as a major challenge. This study aims to develop Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models in simulating and predicting water flux in OMBR. Mixed liquor suspended solid (MLSS), electrical conductivity (EC) and dissolved oxygen (DO) were used as model inputs. Good prediction was demonstrated by both ANFIS models with R2 of 0.9755 and 0.9861, and ANN models with R2 of 0.9404 and 0.9817, for thin film composite (TFC) and cellulose triacetate (CTA) membranes, respectively. The root mean square error for TFC (0.2527) and CTA (0.1230) in ANFIS models was lower than in ANN models at 0.4049 and 0.1449. Sensitivity analysis showed that EC was the most important factor for both TFC and CTA membranes in ANN models, while EC (TFC) and MLSS (CTA) are key parameters in ANFIS models.
Hou, J, Li, B, Tong, Y, Ma, L, Ball, J, Luo, H, Liang, Q & Xia, J 2020, 'Cause analysis for a new type of devastating flash flood', Hydrology Research, vol. 51, no. 1, pp. 1-16.
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Abstract This work introduces an unprecedented flash flood that resulted in nine casualties in Shimen Valley, China, 2015. Through field survey and numerical simulation the causes of the disaster are systematically analyzed, finding that the intense storm, terrain features, and the large woody debris (LWD) played important roles. The intense storm induced fast runoff and, in turn, high discharges as a result of the steep catchment surfaces and channels. The flood flushed LWD and boulders downstream until blockage occurred in a contraction section, forming a debris lake. When the debris dam broke, a dam break wave rapidly propagated to the valley mouth, washing people away. After considering the disaster-inducing factors, measures for preventing similar floods are proposed. The analysis presented herein should help others manage flash floods in mountain areas.
Hou, S, Ni, W, Chen, S, Zhao, S, Cheng, B & Chen, J 2020, 'Real-Time Optimization of Dynamic Speed Scaling for Distributed Data Centers', IEEE Transactions on Network Science and Engineering, vol. 7, no. 3, pp. 2090-2103.
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© 2013 IEEE. This paper proposes a new distributed real-time optimization for MapReduce-style framework in emerging cloud platforms supporting dynamic speed scaling functions. Distinctively different from the existing MapReduce parallelism strategy with fixed specific data chuck sizes, the new approach is able to dynamically dispatch input data of adequate sizes and synthesize interim processing results according to applications and the state of data centers (DCs). The key idea is to decouple the optimizations of data dispatching, processing, and result aggregation without loss of optimality, by employing stochastic optimization techniques. Another important aspect is that we optimize the subproblem of data processing to leverage the energy- and speed-configurability of DCs, by optimally deciding the number of servers to be activated at every DC and the CPU speeds of the activated servers. Evident from extensive simulations, the proposed approach is able to increase the throughput-cost ratio by up to 94.3%, as compared to existing initiatives, and substantially improve the throughput in the case of high-rate data streams.
Hou, S, Ni, W, Zhao, S, Cheng, B, Chen, S & Chen, J 2020, 'Decentralized Real-Time Optimization of Voltage Reconfigurable Cloud Computing Data Center', IEEE Transactions on Green Communications and Networking, vol. 4, no. 2, pp. 577-592.
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© 2017 IEEE. Dynamic Voltage and Frequency Scaling, and Adaptive Body Biasing are increasingly adopted hardware techniques to improve energy efficiency of multi-core servers by adjusting reconfigurable supply and body bias voltages. Existing algorithms cannot fulfill the potential of the techniques because random variations of workload and background traffic can lead to coupling of voltage configurations over time and hinder effective real-time reconfigurations. This paper proposes a new approach which enables multi-core servers to optimize in real-time their configurations under random traffic variations. The approach asymptotically minimizes the time-averaged energy consumption of cloud computing while maintaining platform stability in a fully decentralized fashion. Lyapunov optimization is employed to decouple and separately optimize the voltage configuration, inter- and intra-server offloading schedules among servers and over time. The voltage configuration which is non-convex is proved to increasingly exhibit convexity with growing workloads. The optimality loss from the non-convexity asymptotically diminishes. Simulations show our approach dramatically reduces the power if the cloud is lightly loaded, or converts the power to processing capacity otherwise. Embraced by theoretical breakthroughs, the approach can potentially revolutionize cloud computing.
Hou, S, Ni, W, Zhao, S, Cheng, B, Chen, S & Chen, J 2020, 'Frequency-Reconfigurable Cloud Versus Fog Computing: An Energy-Efficiency Aspect', IEEE Transactions on Green Communications and Networking, vol. 4, no. 1, pp. 221-235.
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© 2017 IEEE. Cloud and fog computing are two emerging Internet-based collaborative technologies for big data analytics. An interesting question arising is whether the two technologies can resonate with significant gains of energy efficiency, especially in the case where advanced cloud platforms with Dynamic Voltage and Frequency Scaling (DVFS) are considered. This paper answers the question by formulating the optimization of a cloud with and without the assistance of fog, and deriving asymptotically optimal distributed solutions for the two cases. We also identify the critical condition under which fog computing helps the cloud to reduce the time-averaged queue lengths. The condition depends on the configurations of the fog, and the configurations of the connections between the fog and cloud. Extensive simulations exhibit good consistency with our analysis of the conditional benefits of fog computing. Evident from experimental datasets, the proposed fog-assisted cloud platform is able to increase the time-averaged energy efficiency by about 32.2%, and decrease the time-averaged queue length by around 37.0%, compared to a fog-coordinated counterpart where fog nodes only dispatch data and do not process the data.
Hou, W, Liu, Q & Cao, L 2020, 'Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge', Applied Sciences, vol. 10, no. 14, pp. 4893-4893.
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Short text is widely seen in applications including Internet of Things (IoT). The appropriate representation and classification of short text could be severely disrupted by the sparsity and shortness of short text. One important solution is to enrich short text representation by involving cognitive aspects of text, including semantic concept, knowledge, and category. In this paper, we propose a named Entity-based Concept Knowledge-Aware (ECKA) representation model which incorporates semantic information into short text representation. ECKA is a multi-level short text semantic representation model, which extracts the semantic features from the word, entity, concept and knowledge levels by CNN, respectively. Since word, entity, concept and knowledge entity in the same short text have different cognitive informativeness for short text classification, attention networks are formed to capture these category-related attentive representations from the multi-level textual features, respectively. The final multi-level semantic representations are formed by concatenating all of these individual-level representations, which are used for text classification. Experiments on three tasks demonstrate our method significantly outperforms the state-of-the-art methods.
Houshyar, S, Sarker, A, Jadhav, A, Kumar, GS, Bhattacharyya, A, Nayak, R, Shanks, RA, Saha, T, Rifai, A, Padhye, R & Fox, K 2020, 'Polypropylene-nanodiamond composite for hernia mesh', Materials Science and Engineering: C, vol. 111, pp. 110780-110780.
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Hsu, TW, Pare, S, Meena, MS, Jain, DK, Li, DL, Saxena, A, Prasad, M & Lin, CT 2020, 'An Early Flame Detection System Based on Image Block Threshold Selection Using Knowledge of Local and Global Feature Analysis', Sustainability, vol. 12, no. 21, pp. 8899-8899.
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Fire is one of the mutable hazards that damage properties and destroy forests. Many researchers are involved in early warning systems, which considerably minimize the consequences of fire damage. However, many existing image-based fire detection systems can perform well in a particular field. A general framework is proposed in this paper which works on realistic conditions. This approach filters out image blocks based on thresholds of different temporal and spatial features, starting with dividing the image into blocks and extraction of flames blocks from image foreground and background, and candidates blocks are analyzed to identify local features of color, source immobility, and flame flickering. Each local feature filter resolves different false-positive fire cases. Filtered blocks are further analyzed by global analysis to extract flame texture and flame reflection in surrounding blocks. Sequences of successful detections are buffered by a decision alarm system to reduce errors due to external camera influences. Research algorithms have low computation time. Through a sequence of experiments, the result is consistent with the empirical evidence and shows that the detection rate of the proposed system exceeds previous studies and reduces false alarm rates under various environments.
Hu, C, Liu, X, Lu, J & Wang, C-H 2020, 'Distributionally robust optimization for power trading of waste-to-energy plants under uncertainty', Applied Energy, vol. 276, pp. 115509-115509.
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© 2020 Waste-to-energy (WTE) plants are operated worldwide to address the management of municipal solid waste. Against this background, an increasing number of WTE plants serve as combined heat and power (CHP) producers that supply heat to the heating systems in local districts and trade electricity in the regional power markets. This paper studies a short-term operation planning problem of determining effective power trading strategies for WTE CHP plants that participate in day-ahead markets. A two-stage distributionally robust optimization (DRO) model is developed with the consideration of uncertain electricity prices, waste supply, and district heating demand. These different kinds of uncertainty are captured by an ambiguity set that contains a collection of possible probability distributions of the uncertain parameters. The two-stage DRO model seeks to ascertain a power trading strategy that maximizes the expected profit of a WTE CHP plant on a regular operating day under the worst-case distribution in the ambiguity set. As the DRO model is intractable, a solution method based on linear decision rule techniques is designed to reformulate the model as a tractable robust linear program. To test the applicability of the DRO model, a case study with real-world data is conducted. The computational results show that the two-stage DRO model can facilitate a WTE CHP plant in obtaining economical and robust power trading strategies for regular operating days in a day-ahead market. Furthermore, the impacts of the parameters in the ambiguity set on deriving robust power trading strategies for WTE CHP plants are investigated.
Hu, C, Liu, X, Lu, J & Wang, C-H 2020, 'Operations scheduling of waste-to-energy plants under uncertainty', Journal of Cleaner Production, vol. 253, pp. 119953-119953.
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© 2020 Elsevier Ltd Waste-to-energy (WTE) technologies provide effective solutions to the compelling challenges of waste management and the energy crisis globally. Many WTE plants utilize the combined heat and power (CHP) operation mode where both electricity and heat can be generated simultaneously. Thus, these WTE CHP plants can supply heat to the local district heating systems and trade power in the electricity markets. As such plants have the responsibilities of treating waste and of fulfilling the allocated district heating demand, necessary operational tasks such as preventive maintenance actions for the production units should be scheduled and performed periodically to ensure their continuous and reliable operations. This paper studies the scheduling of operational tasks in WTE CHP plants that participate in electricity markets and are connected to district heating networks. Firstly, we formulate a two-stage robust optimization model considering the uncertainty of electricity market prices, heat demand, and waste supply. The objective is to derive the robust optimal schedule that maximizes the worst-case operating profit of a WTE CHP plant under uncertainty. Subsequently, we design a constraint generation algorithm for the two-stage robust optimization model. Finally, a case study of scheduling preventive maintenance tasks is conducted for the production units of a WTE CHP plant over a 30-day horizon. The robust schedule thus derived is evaluated by Monte Carlo simulation tests and further compared to the deterministic schedule generated without the consideration of uncertainty. The simulation results show that the robust schedule enables an average profit of 877021.21€ to be attained for the plant over the scheduling horizon. Moreover, it improves the robustness of its deterministic counterpart from 68.4% to 98.8% with an increase of only 0.3% of the operating profit of the plant. In addition, a comprehensive sensitivity analysis is performed to...
Hu, M & Liu, Y 2020, 'E‐maintenance platform design for public infrastructure maintenance based on IFC ontology and Semantic Web services', Concurrency and Computation: Practice and Experience, vol. 32, no. 6.
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SummaryAs an important kind of infrastructure, a tunnel's life is estimated to be 100 years or more. During its service life, an effective maintenance strategy plays an essential role in keeping its availability and safety. Maintenance work involves a set of participants, activities, and resources, and in this case, tunnel‐related data are distributed to heterogeneous information management systems in varying formats, bringing difficulties to implement effective maintenance. This paper proposes an E‐maintenance Framework for Public Infrastructure (EFPI), combining Building Information Modeling (BIM), Industry Foundation Classes (IFC), and Semantic Web technologies to help integrate heterogeneous data and expert knowledge, enable information sharing through the whole life cycle, and support maintenance managers to make effective maintenance decisions. A cost estimation case is provided in this paper to illustrate the implementation mechanism and validate the proposed approach.
Hu, S, Chen, X, Ni, W, Wang, X & Hossain, E 2020, 'Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey', IEEE Transactions on Green Communications and Networking, vol. 4, no. 2, pp. 461-496.
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© 2017 IEEE. The advancements in smart power grid and the advocation of 'green communications' have inspired the wireless communication networks to harness energy from ambient environments and operate in an energy-efficient manner for economic and ecological benefits. This article presents a contemporary review of recent breakthroughs on the utilization, redistribution, trading and planning of energy harvested in future wireless networks interoperating with smart grids. This article starts with classical models of renewable energy harvesting technologies. We embark on constrained operation and optimization of different energy harvesting wireless systems, such as point-to-point, multipoint-to-point, multipoint-to-multipoint, multi-hop, and multi-cell systems. We also review wireless power and information transfer technologies which provide a special implementation of energy harvesting wireless communications. A significant part of the article is devoted to the redistribution of redundant (unused) energy harvested within cellular networks, the energy planning under dynamic pricing when smart grids are in place, and two-way energy trading between cellular networks and smart grids. Applications of different optimization tools, such as convex optimization, Lagrangian dual-based method, subgradient method, and Lyapunov-based online optimization, are compared. This article also collates the potential applications of energy harvesting techniques in emerging (or upcoming) 5G/B5G communication systems. It is revealed that an effective redistribution and two-way trading of energy can significantly reduce the electricity bills of wireless service providers and decrease the consumption of brown energy. A list of interesting research directions are provided, requiring further investigation.
Hu, T, Yang, H, Ni, W, Lei, Y, Jiang, Z, Shi, K, Yu, J, Gu, Y & Wang, Y 2020, 'Automatic detection of intracranial aneurysms in 3D-DSA based on a Bayesian optimized filter', BioMedical Engineering OnLine, vol. 19, no. 1.
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AbstractBackgroundIntracranial aneurysm is a common type of cerebrovascular disease with a risk of devastating subarachnoid hemorrhage if it is ruptured. Accurate computer-aided detection of aneurysms can help doctors improve the diagnostic accuracy, and it is very helpful in reducing the risk of subarachnoid hemorrhage. Aneurysms are detected in 2D or 3D images from different modalities. 3D images can provide more vascular information than 2D images, and it is more difficult to detect. The detection performance of 2D images is related to the angle of view; it may take several angles to determine the aneurysm. As the gold standard for the diagnosis of vascular diseases, the detection on digital subtraction angiography (DSA) has more clinical value than other modalities. In this study, we proposed an adaptive multiscale filter to detect intracranial aneurysms on 3D-DSA.MethodsAdaptive aneurysm detection consists of three parts. The first part is a filter based on Hessian matrix eigenvalues, whose parameters are automatically obtained by Bayesian optimization. The second part is aneurysm extraction based on region growth and adaptive thresholding. The third part is the iterative detection strategy for multiple aneurysms.ResultsThe proposed method was quantitatively evaluated on data sets of 145 patients. The results showed a detection precision of 94.6%, and a sensitivity of 96.4% with a false-positive rate of 6.2%. Among aneurysms smaller than 5 mm, 93.9% were found. Compared with aneurysm detection on 2D-DSA, automatic detection on 3D-DSA can effectively reduce the misdiagnosis rate and obtain more accurate detection results. Compared with other modalities detection, we also get similar or better detection performance.
Hu, W, Huang, J, Zhang, X, Zhao, S, Pei, L, Li, H, Liu, Y & Wang, Z 2020, 'UV and thermal dual responsive coatings with high adhesion and mechanical robust properties', Progress in Organic Coatings, vol. 147, pp. 105771-105771.
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Hu, W, Huang, J, Zhang, X, Zhao, S, Pei, L, Zhang, C, Liu, Y & Wang, Z 2020, 'A mechanically robust and reversibly wettable benzoxazine/epoxy/mesoporous TiO2 coating for oil/water separation', Applied Surface Science, vol. 507, pp. 145168-145168.
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Hu, W, Wang, H, Shi, X, Song, Y, Zhang, G, Xing, S, Zhang, K & Gao, Y 2020, 'Effect of Preoperative Zoledronic Acid Administration on Pain Intensity after Percutaneous Vertebroplasty for Osteoporotic Vertebral Compression Fractures', Pain Research and Management, vol. 2020, pp. 1-8.
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Introduction. This study aimed to compare and analyze the effect of preoperative zoledronic acid (ZOL) administration on pain intensity after percutaneous vertebroplasty (PVP) for osteoporotic vertebral compression fracture (OVCF). Methods. The study included 242 patients with OVCFs who underwent PVP in our hospital between January 2015 and June 2018. The patients were randomly assigned to either a ZOL group (n = 121) or a control group (n = 121). The patients in the ZOL group were treated preoperatively with intravenous infusion of 5 mg ZOL. Those in the control group were treated without ZOL. All the patients were followed up for 1 year. Results. No statistically significant differences in age, sex, weight, and body mass index (BMI) were found between the two groups. During the follow-up period, the visual analog scale score and Oswestry dysfunction index score in the ZOL group were lower than those in the control group. The bone mineral density at 6 or 12 months after treatment was significantly higher and the levels of the bone metabolism markers were significantly lower in the ZOL group than in the control group (P<0.05 for both). Two patients in the treatment group had new vertebral fractures, whereas 13 patients in the control group had new vertebral fractures, which translate to recompression vertebral fracture incidence rates of 1.7% and 10.7%, respectively. The incidence rate of mild adverse reactions was significantly higher in the ZOL group than in the control group, but all the cases were endurable. Conclusion. Intravenous infusion of ZOL before PVP can effectively reduce postoperative pain intensity, reduce bone loss, increase bone density, redu...
Hu, X, Zhang, X, Ngo, HH, Guo, W, Wen, H, Li, C, Zhang, Y & Ma, C 2020, 'Comparison study on the ammonium adsorption of the biochars derived from different kinds of fruit peel', Science of The Total Environment, vol. 707, pp. 135544-135544.
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Application of biochars to remove inorganic nitrogen (NH4+, NO2-, NH3, NO, NO2, N2O) from wastewater and agricultural fields has gained a significant interest. This study aims to investigate the relationship between ammonium sorption and physicochemical properties of biochars derived from different kinds of fruit peel. Biochars from three species of fruit peel (orange, pineapple and pitaya) were prepared at 300, 400, 500 and 600 °C with the residence time of 2 h and 4 h. Their characteristics and sorption for ammonium was evaluated. The results show a clear effect of pyrolysis conditions on physicochemical properties of biochars, including elemental composition, functional groups and pH. The maximum NH4+ adsorption capacities were associated with biochars of orange peel (4.71 mg/g) and pineapple peel (5.60 mg/g) produced at 300 °C for 2 h. The maximum NH4+ adsorption capacity of the pitaya peel biochar produced at 400 °C for 2 h was 2.65 mg/g. For all feedstocks, biochars produced at low temperatures showed better NH4+ adsorption capacity. It was found that biochars had better adsorption efficiency on ammonium at a pH of 9. Adsorption kinetics of ammonium on biochars followed the pseudo-second-order kinetic model while Langmuir isotherm model could well simulate the adsorption behavior of ammonium on biochars. The adsorption mechanism of ammonium on biochars predominantly involved surface complexation, cation exchange and electrostatic attraction. Conclusively, the fruit peel-derived biochars can be used as an alternative to conventional sorbents in water treatment.
Hu, Y, Liu, Y, Wang, Z, Wen, J, Li, J & Lu, J 2020, 'A two-stage dynamic capacity planning approach for agricultural machinery maintenance service with demand uncertainty', Biosystems Engineering, vol. 190, pp. 201-217.
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© 2019 IAgrE Reasonable capacity planning is important to improve the efficiency of agricultural operations and reduce the operating cost for maintenance service providers during the harvesting season. Many studies involve staffing and scheduling approaches that account for nonstationary demand. However, these methods are not applicable in the field of agricultural operations because of the explosive growth of the failure rate during the harvesting season. In addition, few studies have involved allocation methods and related models between different planning levels, especially for the uncertain demand in agricultural machinery maintenance service, which has a strong reliance on results between the different management levels. Motivated by this observed gap, this paper proposes a two-stage analytical methodology that connects the data between different planning levels and aims to develop a dynamic capacity planning method of maintenance service for agricultural machinery fleets. At the first stage, we develop a scheduling model for agricultural machinery fleets based on the time window of harvesting. At the second stage, we propose a following-service mode and a dynamic covering model based on the scheduling results, in which queuing theory is used to solve the service parameters. This study satisfies the needs of service providers to find the optimum balance between high service quality and reasonable costs. A real-life case study is presented to illustrate the applicability of the proposed model as well as the effectiveness of the designed approach.
Hu, Y, Zang, Y, Yang, Y, Duan, A, Wang, XC, Ngo, HH, Li, Y-Y & Du, R 2020, 'Zero-valent iron addition in anaerobic dynamic membrane bioreactors for preconcentrated wastewater treatment: Performance and impact', Science of The Total Environment, vol. 742, pp. 140687-140687.
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Wastewater preconcentration to capture abundant organics is promising for facilitating subsequent anaerobic digestion (AD) to recover bioenergy, however research efforts are still needed to verify the effectiveness of such an emerging strategy as carbon capture plus AD. Therefore, lab-scale anaerobic dynamic membrane bioreactors (AnDMBRs) without and with the addition of zero-valent iron (ZVI) (i.e., AnDMBR1 versus AnDMBR2) were developed for preconcentrated domestic wastewater (PDW) treatment, and the impact of ZVI addition on process performance and associated mechanisms were investigated. The stepwise addition of ZVI from 2 to 4 to 6 g/L improved the treatment performance as COD removal slightly increased and TP removal and methane production were enhanced by 53.3%-62.9% and 22.6%-31.3%, respectively, in consecutive operational phases. However, the average increasing rate of the transmembrane pressure (TMP) in AnDMBR2 (0.18 kPa/d) was obviously higher than that in AnDMBR1 (0.05 kPa/d), indicating an unfavorable impact of dosing ZVI on the dynamic membrane (DM) filtration performance. ZVI that has transformed to iron ions (mainly Fe2+) can behave as a coagulant, electron donor or inorganic foulant, thus enabling the excellent removal of dissolved phosphorous, enhancing the enrichment and activities of specific methanogens and causing the formation of a compact DM layer. Morphological, componential, and microbial community analyses provided new insights into the functional mechanisms of ZVI added to membrane-assisted anaerobic digesters, indicating that ZVI has the potential to improve bioenergy production and resource recovery, while optimizing the ZVI dosage should be considered to alleviate membrane fouling.
Hu, Z, Liu, RP, Ni, W, Wen, X, Lu, Z & Dutkiewicz, E 2020, 'Analysis of Clustered Licensed-Assisted Access in Unlicensed Spectrum', IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 349-360.
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© 1967-2012 IEEE. Faced with explosive growth of data traffic and shortage of licensed spectrum, licensed-assisted access (LAA) to unlicensed spectrum has been proposed to boost system capacity. To ensure fair coexistence with WiFi systems, listen-before-talk mechanism has been standardized under LAA framework. However, in densely deployed urban networks, the system performance could severely deteriorate due to high collision probability. In this paper, we propose cooperative LAA (CLAA), where multiple LAA small base stations form a cluster and construct a virtual multiuser multiple-input single-output (MISO)/multiple-input and multiple-output (MIMO) system to transmit data cooperatively. CLAA can effectively reduce the number of contending nodes, thereby alleviating transmission collisions. A closed-form expression for the upper bound sum rate of the cluster is derived. Markov analysis is employed to derive the system collision probability and throughput for WiFi and LTE. Our analytical results point to an adequate cluster sizes, where the highest system throughput can be achieved. Extensive simulations confirm the validity of the proposed approach, and demonstrate that CLAA can increase by up to 27% the overall system throughput, and improve by 30% in fairness.
Huang, C, Yao, L, Wang, X, Benatallah, B & Zhang, X 2020, 'Software expert discovery via knowledge domain embeddings in a collaborative network', Pattern Recognition Letters, vol. 130, pp. 46-53.
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© 2018 Elsevier B.V. Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considering that massive amount of users are participating online and generating huge amount data, management of knowledge here systematically can be challenging. Expert recommendation is one of the major challenges, as it highlights users in CQA with potential expertise, which may help match unresolved questions with existing high quality answers while at the same time may help external services like human resource systems as another reference to evaluate their candidates. In this paper, we in this work we propose to exploring experts in CQA websites. We take advantage of recent distributed word representation technology to help summarize text chunks, and in a semantic view exploiting the relationships between natural language phrases to extract latent knowledge domains. By domains, the users’ expertise is determined on their historical performance, and a rank can be compute to given recommendation accordingly. In particular, Stack Overflow is chosen as our dataset to test and evaluate our work, where inclusive experiment shows our competence.
Huang, G, Lin, G, Zhu, Y, Duan, W & Jin, D 2020, 'Emerging technologies for profiling extracellular vesicle heterogeneity', Lab on a Chip, vol. 20, no. 14, pp. 2423-2437.
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Approaches, challenges and promising opportunities towards decoding the complexity of extracellular vesicle heterogeneity are discussed.
Huang, H, Savkin, AV & Ni, W 2020, 'Energy-Efficient 3D Navigation of a Solar-Powered UAV for Secure Communication in the Presence of Eavesdroppers and No-Fly Zones', Energies, vol. 13, no. 6, pp. 1445-1445.
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Unmanned Aerial Vehicles (UAVs) have been regarded as a promising means to reshape future wireless communication systems. In this paper, we consider how to plan the trajectory of a solar-powered UAV under a cloudy condition to secure the communication between the UAV and a target ground node against multiple eavesdroppers. We propose a new 3D UAV trajectory optimization model by taking into account the UAV energy consumption, solar power harvesting, eavesdropping and no-fly zone avoidance. A Rapidly-exploring Random Tree (RRT) method is developed to construct the UAV trajectory. Computer simulations and comparisons with a baseline method demonstrate that the proposed method is able to produce trajectories to ensure the valid wireless communication link with the ground node and prevent eavesdropping.
Huang, L, Yang, Q, Wu, J, Huang, Y, Wu, Q & Xu, J 2020, 'Generated Data With Sparse Regularized Multi-Pseudo Label for Person Re-Identification', IEEE Signal Processing Letters, vol. 27, pp. 391-395.
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© 1994-2012 IEEE. Recently, Generative Adversarial Network (GAN) has been adopted to improve person re-identification (person re-ID) performance through data augmentation. However, directly leveraging generated data to train a re-ID model may easily lead to over-fitting issue on these extra data and decrease the generalisability of model to learn true ID-related features from real data. Inspired by the previous approach which assigns multi-pseudo labels on the generated data to reduce the risk of over-fitting, we propose to take sparse regularization into consideration. We attempt to further improve the performance of current re-ID models by using the unlabeled generated data. The proposed Sparse Regularized Multi-Pseudo Label (SRMpL) can effectively prevent the over-fitting issue when some larger weights are assigned to the generated data. Our experiments are carried out on two publicly available person re-ID datasets (e.g., Market-1501 and DukeMTMC-reID). Compared with existing unlabeled generated data re-ID solutions, our approach achieves competitive performance. Two classical re-ID models are used to verify our sparse regularization label on generated data, i.e., an ID-embedding network and a two-stream network.
Huang, L, Zhang, G & Yu, S 2020, 'A Data Storage and Sharing Scheme for Cyber-Physical-Social Systems', IEEE Access, vol. 8, pp. 31471-31480.
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© 2013 IEEE. Cyber-Physical-Social System (CPSS) provides users secure and high-quality mobile service applications to share and exchange data in the cyberspace and physical world. With the explosive growth of data, it is necessary to introduce cloud storage service, which allows devices frequently resort to the cloud for data storage and sharing, into CPSS. In this paper, we propose a data storage and sharing scheme for CPSS with the help of cloud storage service. Since data integrity assurance is an inevitable problem in cloud storage, we first design a secure and efficient data storage scheme based on the technology of public auditing and bilinear map, which also ensures the security of the verification. In order to meet the real-time and reliability requirements of the CPSS, the rewards of timeliness incentive and effectiveness incentive are considered in the scheme. Secondly, based on the proposed storage scheme and ElGamal encryption, we propose a lightweight access model for users to access the final data processed by cloud server. We formally prove the security of the proposed scheme, and conduct performance evaluation to validate its high efficiency. The experimental results show that the proposed scheme has lower overheads in communication and access as compared to the technique CDS.
Huang, L, Zhou, J, Zhang, G, Sun, J, Wei, T, Yu, S & Hu, S 2020, 'IPANM: Incentive Public Auditing Scheme for Non-Manager Groups in Clouds', IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 2, pp. 1-1.
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Cloud storage services give users a great facility in data management such as data collection, storage and sharing, but also bring some potential security hazards. An utmost importance is how to ensure the integrity of data files stored in the cloud, particular for user groups without trusted managers. Existing literature focuses on integrity checking for groups with managers who have lots of permissions. To overcome the shortage of public auditing for non-manager user groups in clouds, we develop a novel framework IPANM that integrates (t,n)(t,n) threshold technology, blinding technology, and incentive mechanism to realize an incentive privacy-preserving public auditing scheme. In IPANM, the data integrity is guaranteed by our (t,n)(t,n) threshold signature based public auditing and the data privacy during public auditing is protected by the blinding technology. The generation of signatures can be accelerated by our blockchain-aided incentive mechanism that mobilizes the initiative of signers in the signature generation by rewarding the contributed signers. We formally prove the security of our IPANM and conduct numerical analysis and evaluation study to validate its high efficiency. The experimental results demonstrate that IPANM has lower overheads of storage, communication, and computation as compared to the state-of-the-art technique IAID-PDP and NPP.
Huang, P, Huang, Y, Li, JJ, Li, Y, Luo, K, Tang, G, Tang, L, Wu, Y-L, Yang, Z & Yu, B 2020, 'Outstanding Reviewers for Biomaterials Science in 2019', Biomaterials Science, vol. 8, no. 9, pp. 2343-2343.
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Huang, Q-S, Wang, C, Wei, W & Ni, B-J 2020, 'Magnetic poly(aniline-co-5-sulfo-2-anisidine) as multifunctional adsorbent for highly effective co-removal of aqueous Cr(VI) and 2,4-Dichlophenol', Chemical Engineering Journal, vol. 387, pp. 124152-124152.
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© 2020 Elsevier B.V. The common coexistence of heavy metal ions (HMIs) and toxic organic matters (OMs) arouses public concerns for their combined toxicity and carcinogenicity. The magnetic poly[aniline(AN)-co-5-sulfo-2-anisidine(SA)] (AN-SA/Fe3O4) was synthesized by an oxidative copolymerization method for the highly-effectively simultaneous removal of Cr(VI) and 2,4-dichlorophenol (2,4-DCP) from aqueous solution. The novel adsorbent exhibited ultra-strong adsorption capacities for sole Cr(VI) and sole 2,4-DCP. The mechanism studies revealed that Cr(VI) species (HCrO4− and Cr2O72− in solution pH as 5) were reduced to Cr(III) by the –NH–/–NH2 groups after attaching to the protonated binding sites of AN-SA/Fe3O4 through electrostatic attraction. By contrast, multiple reactions involving the n-π electron donor-acceptor (EDA) interaction, π-π stacking and hydrogen bond contributed to the elimination of 2,4-DCP. In binary system, the coexistent Cr(VI) and 2,4-DCP elevated mutual adsorption capacities by 88.1% and 102.1%, respectively. Specially, 2,4-DCP can form bridge interactions with both Cr(VI) and Cr(III) due to conjugate effect. This property enabled Cr(VI) to additionally link to the hydrophobic sites, except for the hydrophilic sites, via 2,4-DCP bridges. Moreover, the produced Cr(III) can forcefully captured 2,4-DCP with the electron-rich groups (i.e., [sbnd]NH[sbnd], [sbnd]N[dbnd], [sbnd]SO3H, [sbnd]OCH3) on AN-SA/Fe3O4 to form the multi-components complexes. The bridge interactions (i.e., n-π EDA interaction, complexation) created the newly available sites for Cr(VI) and 2,4-DCP, resulting in enlarged adsorbance and synchronous removal on AN-SA/Fe3O4 in coexisting system. In addition, the high proportion of [sbnd]N[dbnd] groups generated by Cr(VI) oxidation also devoted to the uptake enhancement due to its strong affinity for 2,4-DCP. Overall, the high-performance and synergistic removal qualified AN-SA/Fe3O4 as a multifunctional adsorbent for the...
Huang, Q-S, Wu, W, Wei, W, Song, L, Sun, J & Ni, B-J 2020, 'Highly-efficient Pb2+ removal from water by novel K2W4O13 nanowires: Performance, mechanisms and DFT calculation', Chemical Engineering Journal, vol. 381, pp. 122632-122632.
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© 2019 Elsevier B.V. As one of the most toxic heavy metals, lead ions (Pb2+) contamination arouses increasing public concern for high carcinogenicity and neurotoxicity. In this study, a modified hydrothermal method was designed to fabricate novel hexagonal K2W4O13 nanowires to achieve highly-efficient Pb2+ removal from water. Attractively, the as-prepared K2W4O13 exhibited large uptake capacity (228.83 mg/g), fast kinetic (141.67 mg/g in 30 min), superior acid-resistance (75% of removal at pH = 2) and excellent reusability (over 95% of removal after 5 runs) toward Pb2+ adsorption. The Langmuir isotherm and pseudo-second-order kinetic model gave a better fit to the adsorption experimental data. The Pb2+ adsorption process on K2W4O13 was revealed to be a spontaneous, exothermic, film diffusion limited chemisorption reaction. The mechanism studied elucidated that both ion-exchange and complexation were involved in Pb2+ adsorption, with each accounting for approximate 50% of Pb2+ elimination. Through density functional theory (DFT) calculation, the equatorial oxygen was found to be more accessible for Pb attachment than the axial corner oxygen from [WO6] octahedra. Electron pairs from the adjacent O atoms would transfer to the empty orbitals of Pb atoms after adsorption, causing the Pb2+ removal via metal-ligand complexation.
Huang, S, Xu, Z, Tsang, IW & Kang, Z 2020, 'Auto-weighted multi-view co-clustering with bipartite graphs', Information Sciences, vol. 512, pp. 18-30.
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© 2019 Co-clustering aims to explore coherent patterns by simultaneously clustering samples and features of data. Several co-clustering methods have been proposed in the past decades. However, in real-world applications, datasets are often with multiple modalities or composed of multiple representations (i.e., views), which provide different yet complementary information. Hence, it is essential to develop multi-view co-clustering models to solve the multi-view application problems. In this paper, a novel multi-view co-clustering method based on bipartite graphs is proposed. To make use of the duality between samples and features of multi-view data, a bipartite graph for each view is constructed such that the co-occurring structure of data can be extracted. The key point of utilizing the bipartite graphs to deal with the multi-view co-clustering task is to reasonably integrate these bipartite graphs and obtain an optimal consensus one. As for this point, the proposed method can learn an optimal weight for each bipartite graph automatically without introducing an additive parameter as previous methods do. Furthermore, an efficient algorithm is proposed to optimize this model with theoretically guaranteed convergence. Extensive experimental results on both toy data and several benchmark datasets have demonstrated the effectiveness of the proposed model.
Huang, T, Li, C, Wen, S, He, X & Wen, G 2020, 'Special issue: Theoretical analysis of deep learning editorial', Neurocomputing, vol. 416, pp. 45-46.
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Huang, Y, Mok, W-C, Yam, Y-S, Zhou, JL, Surawski, NC, Organ, B, Chan, EFC, Mofijur, M, Mahlia, TMI & Ong, HC 2020, 'Evaluating in-use vehicle emissions using air quality monitoring stations and on-road remote sensing systems', Science of The Total Environment, vol. 740, pp. 139868-139868.
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Huang, Y, Ng, ECY, Surawski, NC, Yam, Y-S, Mok, W-C, Liu, C-H, Zhou, JL, Organ, B & Chan, EFC 2020, 'Large eddy simulation of vehicle emissions dispersion: Implications for on-road remote sensing measurements', Environmental Pollution, vol. 259, pp. 113974-113974.
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© 2020 Elsevier Ltd On-road remote sensing technology measures the concentration ratios of pollutants over CO2 in the exhaust plume in half a second when a vehicle passes by a measurement site, providing a rapid, non-intrusive and economic tool for vehicle emissions monitoring and control. A key assumption in such measurement is that the emission ratios are constant for a given plume. However, there is a lack of study on this assumption, whose validity could be affected by a number of factors, especially the engine operating conditions and turbulence. To guide the development of the next-generation remote sensing system, this study is conducted to investigate the effects of various factors on the emissions dispersion process in the vehicle near-wake region and their effects on remote sensing measurement. The emissions dispersion process is modelled using Large Eddy Simulation (LES). The studied factors include the height of the remote sensing beam, vehicle speed, acceleration and side wind. The results show that the measurable CO2 and NO exhaust plumes are relatively short at 30 km/h cruising speed, indicating that a large percentage of remote sensing readings within the measurement duration (0.5 s) are below the sensor detection limit which would distort the derived emission ratio. In addition, the valid measurement region of NO/CO2 emission ratio is even shorter than the measurable plume and is at the tailpipe height. The effect of vehicle speed (30–90 km/h) on the measurable plume length is insignificant. Under deceleration condition, the length of the valid NO/CO2 measurement region is shorter than under cruising and acceleration conditions. Side winds from the far-tailpipe direction have a significant effect on remote sensing measurements. The implications of these findings are discussed and possible solutions to improve the accuracy of remote sensing measurement are proposed.
Huang, Y, Song, R, Argha, A, Savkin, AV, Celler, BG & Su, SW 2020, 'Continuous Description of Human 3D Motion Intent Through Switching Mechanism', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 1, pp. 277-286.
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© 2001-2011 IEEE. Post-stroke motor recovery highly relies on voluntarily participating in active rehabilitation as early as possible for promoting the reorganization of the patient's brain. In this paper, a new method is proposed which manipulates cable-based rehabilitation robots to assist multi-joint body motions. This uses an electromyography (EMG) decoder for continuous estimation of voluntary motion intention to establish a cooperative human-machine interface for promoting the participation in rehabilitation exercises. In particular, for multi-joint complex tasks in three-dimensional space, a switching mechanism has been developed which can carve up tasks into separate simple motions. For each simple motion, a linear six-inputs and three-outputs time-invariant model is established respectively. The inputs are the processed muscle activations of six arm muscles, and the outputs are voluntary forces of participants when executing a multi-directional tracking task with visual feedback. The experiments for examining the decoder model and EMG-based controller include model training, testing and controller application phases with seven healthy participants. Experimental results demonstrate that the decoder model with the switching mechanism could effectively recognize arm movement intention and provide appropriate assistance to the participants. This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks.
Huang, Y, Surawski, NC, Yam, Y-S, Lee, CKC, Zhou, JL, Organ, B & Chan, EFC 2020, 'Re-evaluating effectiveness of vehicle emission control programmes targeting high-emitters', Nature Sustainability, vol. 3, no. 11, pp. 904-907.
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© 2020, The Author(s), under exclusive licence to Springer Nature Limited. Estimating emission distribution within a vehicle fleet is critical for air pollution control. Previous studies reported that more than half of total fleet emissions were produced by only the highest 10% emitters, making repairing or deregistering a small percentage of high-emitters the most cost-effective measure to control vehicle emissions. With diesel emissions data from chassis dynamometer testing and on-road remote sensing, we show that such a strategy may be oversimplified.
Huang, Y, Xu, J, Wu, Q, Zhong, Y, Zhang, P & Zhang, Z 2020, 'Beyond Scalar Neuron: Adopting Vector-Neuron Capsules for Long-Term Person Re-Identification', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 10, pp. 3459-3471.
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Current person re-identification (re-ID) works mainly focus on the short-term scenario where a person is less likely to change clothes. However, in the long-term re-ID scenario, a person has a great chance to change clothes. A sophisticated re-ID system should take such changes into account. To facilitate the study of long-term re-ID, this paper introduces a large-scale re-ID dataset called “Celeb-reID” to the community. Unlike previous datasets, the same person can change clothes in the proposed Celeb-reID dataset. Images of Celeb-reID are acquired from the Internet using street snap-shots of celebrities. There is a total of 1,052 IDs with 34,186 images making Celeb-reID being the largest long-term re-ID dataset so far. To tackle the challenge of cloth changes, we propose to use vector-neuron (VN) capsules instead of the traditional scalar neurons (SN) to design our network. Compared with SN, one extra-dimensional information in VN can perceive cloth changes of the same person. We introduce a well-designed ReIDCaps network and integrate capsules to deal with the person re-ID task. Soft Embedding Attention (SEA) and Feature Sparse Representation (FSR) mechanisms are adopted in our network for performance boosting. Experiments are conducted on the proposed long-term re-ID dataset and two common short-term re-ID datasets. Comprehensive analyses are given to demonstrate the challenge exposed in our datasets. Experimental results show that our ReIDCaps can outperform existing state-of-the-art methods by a large margin in the long-term scenario. The new dataset and code will be released to facilitate future researches.
Huang, Y, Yu, Y, Yam, Y-S, Zhou, JL, Lei, C, Organ, B, Zhuang, Y, Mok, W-C & Chan, EFC 2020, 'Statistical evaluation of on-road vehicle emissions measurement using a dual remote sensing technique', Environmental Pollution, vol. 267, pp. 115456-115456.
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On-road remote sensing (RS) is a rapid, non-intrusive and economical tool to monitor and control the emissions of in-use vehicles, and currently is gaining popularity globally. However, a majority of studies used a single RS technique, which may bias the measurements since RS only captures a snapshot of vehicle emissions. This study aimed to use a unique dual RS technique to assess the characteristics of on-road vehicle emissions. The results show that instantaneous vehicle emissions are highly dynamic under real-world driving conditions. The two emission factors measured by the dual RS technique show little correlation, even under the same driving condition. This indicates that using the single RS technique may be insufficient to accurately represent the emission level of a vehicle based on one measurement. To increase the accuracy of identifying high-emitting vehicles, using the dual RS technique is essential. Despite little correlation, the dual RS technique measures the same average emission factors as the single RS technique does when a large number of measurements are available. Statistical analysis shows that both RS systems demonstrate the same Gamma distribution with ≥200 measurements, leading to converged mean emission factors for a given vehicle group. These findings point to the need for a minimum sample size of 200 RS measurements in order to generate reliable emission factors for on-road vehicles. In summary, this study suggests that using the single or dual RS technique will depend on the purpose of applications. Both techniques have the same accuracy in calculating average emission factors when sufficient measurements are available, while the dual RS technique is more accurate in identifying high-emitters based on one measurement only.
Huang, Y, Zhou, J, Yu, Y, Mok, W-C, Lee, C & Yam, Y-S 2020, 'Uncertainty in the Impact of the COVID-19 Pandemic on Air Quality in Hong Kong, China', Atmosphere, vol. 11, no. 9, pp. 914-914.
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Strict social distancing rules are being implemented to stop the spread of COVID-19 pandemic in many cities globally, causing a sudden and extreme change in the transport activities. This offers a unique opportunity to assess the effect of anthropogenic activities on air quality and provides a valuable reference to the policymakers in developing air quality control measures and projecting their effectiveness. In this study, we evaluated the effect of the COVID-19 lockdown on the roadside and ambient air quality in Hong Kong, China, by comparing the air quality monitoring data collected in January–April 2020 with those in 2017–2019. The results showed that the roadside and ambient NO2, PM10, PM2.5, CO and SO2 were generally reduced in 2020 when comparing with the historical data in 2017–2019, while O3 was increased. However, the reductions during COVID-19 period (i.e., February–April) were not always higher than that during pre-COVID-19 period (i.e., January). In addition, there were large seasonal variations in the monthly mean pollutant concentrations in every year. This study implies that one air pollution control measure may not generate obvious immediate improvements in the air quality monitoring data and its effectiveness should be evaluated carefully to eliminate the effect of seasonal variations.
Huang, Z, Huang, L, Wang, C, Zhu, S, Qi, X, Chen, Y, Zhang, Y, Cowley, MA, Veldhuis, JD & Chen, C 2020, 'Dapagliflozin restores insulin and growth hormone secretion in obese mice', Journal of Endocrinology, vol. 245, no. 1, pp. 1-12.
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The well-documented hormonal disturbance in a general obese population is characterised by an increase in insulin secretion and a decrease in growth hormone (GH) secretion. Such hormonal disturbance promotes an increase in fat mass, which deteriorates obesity and accelerates the development of insulin resistance and type 2 diabetes. While the pathological consequence is alarming, the pharmaceutical approach attempting to correct such hormonal disturbance remains limited. By applying an emerging anti-diabetic drug, the sodium-glucose cotransporter 2 inhibitor, dapagliflozin (1 mg/kg/day for 10 weeks), to a hyperphagic obese mouse model, we observed a significant improvement in insulin and GH secretion as early as 4 weeks after the initiation of the treatment. Restoration of pathological disturbance of insulin and GH secretion reduced fat accumulation and preserved lean body mass in the obese animal model. Such phenotypic improvement followed with concurrent improvements in glucose and lipid metabolism, insulin sensitivity, as well as the expression of metabolic genes that were regulated by insulin and GH. In conclusion, 10 weeks of treatment with dapagliflozin effectively reduces hyperinsulinemia and restores pulsatile GH secretion in the hyperphagic obese mice with considerable improvement in lipid and glucose metabolism. Promising outcomes from this study may provide insights into drug intervention to correct hormonal disturbance in obesity to delay the diabetes progression.
Huang, Z, Wang, S, Dewhurst, RD, Ignat'ev, NV, Finze, M & Braunschweig, H 2020, 'Bor in energiebezogenen Prozessen und Anwendungen', Angewandte Chemie, vol. 132, no. 23, pp. 8882-8900.
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AbstractDirekt an der Grenze zwischen Metallen und Nichtmetallen angesiedelt, nimmt das Element Bor eine einzigartige Position im Periodensystem ein. Diese besondere Stellung ermöglicht eine enorme Vielfalt an chemischen Reaktionen und Anwendungen. Auch in Hinblick auf die stetig steigende Nachfrage an erneuerbaren und sauberen Energien bzw. energieeffizienten Prozessen ist das Element Bor mehr und mehr in den Fokus der energiebezogenen Forschung gerückt und umfasst mittlerweile Bereiche wie 1) die Aktivierung und Synthese kleiner energiereicher Moleküle, 2) die Speicherung von chemischer und elektrischer Energie und 3) die Umwandlung von elektrischer Energie zu Licht. Diese Anwendungen basieren hierbei auf den besonderen Eigenschaften des Elements Bor, d. h. vor allem auf dessen Elektronenmangel in Verbindung mit der Gegenwart eines unbesetzten p‐Orbitals, was die Ausbildung unzähliger Verbindungen mit gezielt beeinflussbaren chemischen und physikalischen Eigenschaften ermöglicht. So erreicht Bor beispielsweise mit vier kovalenten Bindungen und einer negativen Ladung relativ einfach ein Elektronenoktett, wodurch die Verbindungsklasse der Boratanionen zugänglich wird, welche eine außergewöhnlich hohe chemische und elektrochemische Stabilität aufweisen. Besonders hervorzuheben ist in diesem Zusammenhang die synthetisch wertvolle Klasse der schwach‐koordinierenden Anionen. Dieser Aufsatz soll die Bedeutung von Borverbindungen für energiebezogene Prozesse und Anwendungen verdeutlichen und fasst die Fortschritte der letzten Jahre auf diesem Gebiet zusammen.
Huang, Z, Wang, S, Dewhurst, RD, Ignat'ev, NV, Finze, M & Braunschweig, H 2020, 'Boron: Its Role in Energy‐Related Processes and Applications', Angewandte Chemie International Edition, vol. 59, no. 23, pp. 8800-8816.
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AbstractBoron's unique position in the Periodic Table, that is, at the apex of the line separating metals and nonmetals, makes it highly versatile in chemical reactions and applications. Contemporary demand for renewable and clean energy as well as energy‐efficient products has seen boron playing key roles in energy‐related research, such as 1) activating and synthesizing energy‐rich small molecules, 2) storing chemical and electrical energy, and 3) converting electrical energy into light. These applications are fundamentally associated with boron's unique characteristics, such as its electron‐deficiency and the availability of an unoccupied p orbital, which allow the formation of a myriad of compounds with a wide range of chemical and physical properties. For example, boron's ability to achieve a full octet of electrons with four covalent bonds and a negative charge has led to the synthesis of a wide variety of borate anions of high chemical and electrochemical stability—in particular, weakly coordinating anions. This Review summarizes recent advances in the study of boron compounds for energy‐related processes and applications.
Huo, X, Liu, H, Luo, Q, Sun, G & Li, Q 2020, 'On low-velocity impact response of foam-core sandwich panels', International Journal of Mechanical Sciences, vol. 181, pp. 105681-105681.
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© 2020 Elsevier Ltd This study aimed to investigate low-velocity impact responses and crashworthiness of different aluminum foam-core sandwich structures. Several drop-weight dynamic impact tests were first conducted on both sandwich structures and their individual components to explore the mechanism of energy absorption and interactive effect between the foam core and facesheets. Different shapes and sizes of impactors were used in the experiments. The full-field deflection distribution was acquired by a 3D optical scanner to assess the failure patterns. A full-scale finite element model was then created to simulate the low-velocity impacting response of the foam-core sandwich panels. After the finite element model was validated against the experimental results, it was used to further explore the crash behavior of multi-layered sandwiches. It was found that multi-layer sandwich structure had much better performance in the crush force efficiency than those with single-layer foam core. Based upon the energy principle, an energy-based analytical model was also derived to estimate the initial peak load. It was demonstrated that the analytical predictions were in good agreement with the experimental and numerical results. The presented experimental, numerical and analytical studies are anticipated to provide systematic understanding and new knowledge for design of multilayer sandwich configurations aiming at more desirable impact resistance and better lightweight characteristics.
Hussain, F, Soudagar, MEM, Afzal, A, Mujtaba, MA, Fattah, IMR, Naik, B, Mulla, MH, Badruddin, IA, Khan, TMY, Raju, VD, Gavhane, RS & Rahman, SMA 2020, 'Enhancement in Combustion, Performance, and Emission Characteristics of a Diesel Engine Fueled with Ce-ZnO Nanoparticle Additive Added to Soybean Biodiesel Blends', Energies, vol. 13, no. 17, pp. 4578-4578.
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This study considered the impacts of diesel–soybean biodiesel blends mixed with 3% cerium coated zinc oxide (Ce-ZnO) nanoparticles on the performance, emission, and combustion characteristics of a single cylinder diesel engine. The fuel blends were prepared using 25% soybean biodiesel in diesel (SBME25). Ce-ZnO nanoparticle additives were blended with SBME25 at 25, 50, and 75 ppm using the ultrasonication process with a surfactant (Span 80) at 2 vol.% to enhance the stability of the blend. A variable compression ratio engine operated at a 19.5:1 compression ratio (CR) using these blends resulted in an improvement in overall engine characteristics. With 50 ppm Ce-ZnO nanoparticle additive in SBME25 (SBME25Ce-ZnO50), the brake thermal efficiency (BTE) and heat release rate (HRR) increased by 20.66% and 18.1%, respectively; brake specific fuel consumption (BSFC) by 21.81%; and the CO, smoke, and hydrocarbon (HC) decreased by 30%, 18.7%, and 21.5%, respectively, compared to SBME25 fuel operation. However, the oxides of nitrogen slightly rose for all the nanoparticle added blends. As such, 50 ppm of Ce-ZnO nanoparticle in the blend is a potent choice for the enhancement of engine performance, combustion, and emission characteristics.
Hussain, T, Muhammad, K, Ullah, A, Cao, Z, Baik, SW & de Albuquerque, VHC 2020, 'Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM', IEEE Transactions on Industrial Informatics, vol. 16, no. 1, pp. 77-86.
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Hussain, W, Sohaib, O, Naderpour, M & Gao, H 2020, 'Cloud Marginal Resource Allocation: A Decision Support Model.', Mob. Networks Appl., vol. 25, no. 4, pp. 1418-1433.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. One of the significant challenges for cloud providers is how to manage resources wisely and how to form a viable service level agreement (SLA) with consumers to avoid any violation or penalties. Some consumers make an agreement for a fixed amount of resources, these being the required resources that are needed to execute its business. Consumers may need additional resources on top of these fixed resources, known as– marginal resources that are only consumed and paid for in case of an increase in business demand. In such contracts, both parties agree on a pricing model in which a consumer pays upfront only for the fixed resources and pays for the marginal resources when they are used. A marginal resource allocation is a challenge for service provider particularly small- to medium-sized service providers as it can affect the usage of their resources and consequently their profits. This paper proposes a novel marginal resource allocation decision support model to assist cloud providers to manage the cloud SLAs before its execution, covering all possible scenarios, including whether a consumer is new or not, and whether the consumer requests the same or different marginal resources. The model relies on the capabilities of the user-based collaborative filtering method with an enhanced top-k nearest neighbor algorithm and a fuzzy logic system to make a decision. The proposed framework assists cloud providers manage their resources in an optimal way and avoid violations or penalties. Finally, the performance of the proposed model is shown through a cloud scenario which demonstrates that our proposed approach can assists cloud providers to manage their resources wisely to avoid violations.
Huy Tran, V, Lim, S, Jun Park, M, Suk Han, D, Phuntsho, S, Park, H, Matsuyama, H & Kyong Shon, H 2020, 'Fouling and performance of outer selective hollow fiber membrane in osmotic membrane bioreactor: Cross flow and air scouring effects', Bioresource Technology, vol. 295, pp. 122303-122303.
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© 2019 Elsevier Ltd This study assessed impacts of cross-flow velocity (CFV) and air scouring on the performance and membrane fouling mitigation of a side-stream module containing outer-selective hollow fiber thin film composite forward osmosis membrane in osmosis membrane bioreactor (OMBR) system for urban wastewater treatment. CFV of draw solution was optimized, followed by the impact assessment of three CFVs on feed solution (FS) stream and periodic injection of air scouring into the side-stream module. Overall, the OMBR system exhibited high and stable performance with initial water flux of approximately 15 LMH, high removal efficiencies of bulk organic matter and nutrients. While FS's CFVs insignificantly affected the performance and membrane fouling, regular air scouring showed substantial impact with better performance and high efficiency in mitigating membrane fouling. These results indicated that periodic air scouring can be applied into the side-stream membrane module for efficient fouling mitigation without interruption the operation of the OMBR system.
Huynh, NV, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2020, 'DeepFake: Deep Dueling-based Deception Strategy to Defeat Reactive Jammers'.
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In this paper, we introduce DeepFake, a novel deep reinforcementlearning-based deception strategy to deal with reactive jamming attacks. Inparticular, for a smart and reactive jamming attack, the jammer is able tosense the channel and attack the channel if it detects communications from thelegitimate transmitter. To deal with such attacks, we propose an intelligentdeception strategy which allows the legitimate transmitter to transmit 'fake'signals to attract the jammer. Then, if the jammer attacks the channel, thetransmitter can leverage the strong jamming signals to transmit data by usingambient backscatter communication technology or harvest energy from the strongjamming signals for future use. By doing so, we can not only undermine theattack ability of the jammer, but also utilize jamming signals to improve thesystem performance. To effectively learn from and adapt to the dynamic anduncertainty of jamming attacks, we develop a novel deep reinforcement learningalgorithm using the deep dueling neural network architecture to obtain theoptimal policy with thousand times faster than those of the conventionalreinforcement algorithms. Extensive simulation results reveal that our proposedDeepFake framework is superior to other anti-jamming strategies in terms ofthroughput, packet loss, and learning rate.
Huynh, P, Phan, KT, Liu, B & Ross, R 2020, 'Throughput Analysis of Buffer-Aided Decode-and-Forward Wireless Relaying with RF Energy Harvesting', Sensors, vol. 20, no. 4, pp. 1222-1222.
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In this paper, we investigated a buffer-aided decode-and-forward (DF) wireless relaying system over fading channels, where the source and relay harvest radio-frequency (RF) energy from a power station for data transmissions. We derived exact expressions for end-to-end throughput considering half-duplex (HD) and full-duplex (FD) relaying schemes. The numerical results illustrate the throughput and energy efficiencies of the relaying schemes under different self-interference (SI) cancellation levels and relay deployment locations. It was demonstrated that throughput-optimal relaying is not necessarily energy efficiency-optimal. The results provide guidance on optimal relaying network deployment and operation under different performance criteria.
Ibrahim, IA, Hossain, MJ & Duck, BC 2020, 'An Optimized Offline Random Forests-Based Model for Ultra-Short-Term Prediction of PV Characteristics', IEEE Transactions on Industrial Informatics, vol. 16, no. 1, pp. 202-214.
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© 2005-2012 IEEE. The fluctuation of meteorological data causes random changes in photovoltaic (PV) performance, which may negatively affect the stability and reliability of the electrical grid. This paper proposes a new ultra-short-term offline hybrid prediction model for PV I-V characteristic curves based on the dynamic characteristics of the meteorological data on a 15-min basis. The proposed hybrid prediction model is a combination of the random forests (RFs) prediction technique and the ant-lion optimizer (ALO). ALO is used to optimize the hyper-parameters of the RFs model, which aims to improve its performance in terms of accuracy and computational time. The performance of the proposed hybrid prediction model is compared with that of conventional RFs, RFs-iteration, generalized regression neural network (GRNN), GRNN-iteration, GRNN-ALO, a cascade-forward neural network (CFNN), CFNN-iteration, CFNN-ALO, feed-forward neural network (FFNN), FFNN-iteration, and FFNN-ALO models. The result shows that the I-V characteristic-curve prediction accuracy, in terms of the root-mean-squared error, mean bias error, and mean absolute percentage error of the proposed model are 0.0091 A, 0.0028 A, and 0.1392%, respectively, with an accuracy of 99.86%. Moreover, the optimization, training, and testing times are 162.15, 10.1919, and 0.1237 s, respectively. Therefore, the proposed model performs better than the aforementioned models and the other existing models in the literature. Accordingly, the proposed hybrid (RFs-ALO) offline model can significantly improve the accuracy of PV performance prediction, especially in grid-connected PV system applications.
Ibrahim, IA, Hossain, MJ, Duck, BC & Fell, CJ 2020, 'An Adaptive Wind-Driven Optimization Algorithm for Extracting the Parameters of a Single-Diode PV Cell Model', IEEE Transactions on Sustainable Energy, vol. 11, no. 2, pp. 1054-1066.
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© 2010-2012 IEEE. This paper presents a new methodology to extract the unknown parameters of a single-diode photovoltaic (PV) cell model. The first contribution of this paper is the development and implementation of a new version of the wind-driven optimization algorithm, called an adaptive wind-driven optimization (AWDO) algorithm. The advantages of the AWDO algorithm are: 1) accurate extraction of the global values of the optimized PV parameters in changing weather conditions, which is achieved by building solutions from random operations; and 2) capability of handling the given complex multi-modal and multi-dimensional optimization problems. The second contribution is the identification of a generalization model to generalize the extracted parameters of a single-diode PV cell model. That provides an ability of the proposed methodology to work with any I-V characteristic curve of PV cells and at any weather condition on a 15-min basis. To validate the proposed methodology, it has been tested for 1307 I-V characteristic curves of a PV module at various weather conditions on a 15-min basis. Additionally, its accuracy and computational efficiency are verified and compared with five well-known existing extraction methods: Villalva's model, particle swarm optimization, biogeography-based optimization, Gang's model, and bacterial foraging optimization by both simulation and outdoor measurements. The results show that the AWDO algorithm can provide the extracted five parameters with an acceptable range of accuracy and faster than the aforementioned models. Therefore, the proposed methodology (AWDO based on Chenlo's model) can be confidently recommended as a reliable, feasible, valuable, and fast optimization algorithm for parameter extraction of a single-diode PV cell model.
Ibrahim, IA, Hossain, MJ, Duck, BC & Nadarajah, M 2020, 'An improved wind driven optimization algorithm for parameters identification of a triple-diode photovoltaic cell model', Energy Conversion and Management, vol. 213, pp. 112872-112872.
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© 2020 Elsevier Ltd The double-diode photovoltaic cell model is insufficient to accurately characterize the different current components of a photovoltaic cell. Therefore, the triple-diode model of a photovoltaic cell is considered to model its complicated physical characteristics by clearly defining the different current components of the photovoltaic cell. The identification of its unknown parameters is a complex, multi-modal and multi-variable optimization problem. An improved wind driven optimization algorithm is proposed in this paper to identify its nine unknown parameters. The proposed method is a combination of the mutation strategy of the differential evolution algorithm and the covariance matrix adaptation evolution strategy of the wind driven optimization algorithm. The mutation strategy aims to bolster the exploration ability of the improved wind driven optimization algorithm, while the covariance matrix adaptation evolution strategy based on wind driven optimization algorithm aims to improve the searching of the classical wind driven optimization algorithm. Therefore, improved wind driven optimization algorithm is more accurate and faster than the classical wind driven optimization algorithm in finding the global optimum and balancing exploration and exploitation. The proposed model has been utilized on 15-minute interval data to identify the unknown parameters of three commercial photovoltaic technologies, namely, mono-crystalline, poly-crystalline and thin-film. To show the effectiveness of the proposed model, its performance is validated by comparing it with that obtained by the classical wind driven optimization, the adaptive wind driven optimization, moth-flame optimizer, sunflower optimization and the improved opposition-based whale optimization algorithms. The results demonstrate that improved wind driven optimization outperforms the aforementioned models in accuracy, convergence speed and feasibility. In addition, improved wind driv...
Ibrar, I, Yadav, S, Altaee, A, Hawari, A, Nguyen, V & Zhou, J 2020, 'A novel empirical method for predicting concentration polarization in forward osmosis for single and multicomponent draw solutions', Desalination, vol. 494, pp. 114668-114668.
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Ibrar, I, Yadav, S, Altaee, A, Samal, AK, Zhou, JL, Nguyen, TV & Ganbat, N 2020, 'Treatment of biologically treated landfill leachate with forward osmosis: Investigating membrane performance and cleaning protocols', Science of The Total Environment, vol. 744, pp. 140901-140901.
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This study presents systematic investigations to evaluate the performance, rejection rate, fouling, cleaning protocols and impact of physical and chemical cleaning strategies on the performance of commercial cellulose triacetate (CTA) membrane. The treatment of landfill leachate (LFL) solution was performed in the active layer facing feed solution and support layer facing the draw solution (AL-FS mode), and active layer facing the draw solution and support layer facing the feed solution (AL-DS mode). Compared to the AL-FS mode, a higher flux for AL-DS mode was achieved, but membrane fouling was more severe in the latter. In both membrane orientations, the rejection rate of the FO membrane to heavy ions and contaminants in the wastewater was between 93 and 99%. Physical and chemical cleaning strategies were investigated to recover the performance of the FO membrane and to study the impact of cleaning methods on the membrane rejection rate. Physical cleaning with hot water at 35 °C and osmotic backwashing with 1.5 M NaCl demonstrated excellent water flux recovery compared to chemical cleaning. In the chemical cleaning, an optimal concentration of 3% hydrogen peroxide was determined for 100% flux recovery of the fouled membrane. However, slight membrane damage was achieved at this concentration on the active layer side. Alkaline cleaning at pH 11 was more effective than acid cleaning at pH 4, although both protocols compromised the membrane rejection rate for some toxic ions. A comparison of the membrane long-term performance found that cleaning with osmotic backwashing and hot water were effective methods to restore water flux without comprising the membrane rejection rate. Overall, it was found that physical cleaning protocols are superior to chemical cleaning protocols for forward osmosis membrane fouled by landfill leachate wastewater.
Indraratna, B, Israr, J & Vaughan, LPR 2020, 'From Particles to Constrictions: Scientific Evolution of Enhanced Criteria for Internal Stability Assessment of Soils', Geotechnical Engineering, vol. 51, no. 3, pp. 65-72.
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Internal instability occurs when steady seepage forces erode the finer fractions from non-uniform soils along pre-existing openings such as cracks in cohesive soils and voids in non-cohesive soil to induce permanent changes in the original particle size distribution. Given that the drainage characteristics of soils are significantly influenced by the shape, packing arrangement, compaction, and size distribution of their particles, even limited erosion can markedly alter their drainage characteristics. The geometrical assessment of internal instability potential is normally conducted using classical filter retention criterion based on mere particle size distribution and without giving due consideration to the above factors. These methods would determine the risk of instability by approximating the soil’s constrictions based on its particle size distribution; these constrictions are pore channels connecting neighbouring void spaces that would control both permeability and retention phenomena. However, recent advances in mathematical computations have facilitated the exact delineation of constriction sizes and the introduction of more accurate constriction based methods. This study purports to shed light on the scientific evolution of particle and constriction based methods over the past four decades, including the enhanced accuracy, reduced bias, and robustness associated with the latter. An interesting case study from our experience of using these approaches for a permeable barrier design at Bomaderry, NSW (Australia) for subsurface flow treatment is presented, and recommendations for their use by practicing engineers are made to conclude this study.
Indraratna, B, Korkitsuntornsan, W & Nguyen, TT 2020, 'Influence of Kaolin content on the cyclic loading response of railway subgrade', Transportation Geotechnics, vol. 22, pp. 100319-100319.
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© 2020 Elsevier Ltd Rail tracks passing through saturated subgrade soil often face a serious deterioration of bearing capacity and excessive deformation. One major reason is the excessive cyclic pore pressure that accumulates under the track that leads to soil softening and infiltration over the surface, i.e., subgrade mud pumping (fluidization). Although this issue has received considerable attention in recent decades, how the cyclic stress ratio and soil properties such as plasticity and void ratio influence the cyclic loading response of soft subgrade soil is still not properly understood. In this study, Kaolin – an artificial cohesive fines soil is used to modify a low plasticity subgrade soil to examine how the Kaolin content (cK) can affect its cyclic response. Soil specimens including the original soil and its mixture with 10 and 30% of Kaolin have been subjected to undrained cyclic testing. A cyclic stress ratio (CSR) varying from 0.2 to 1.2 is used and a low initial confining pressure of 20 kPa is applied. The results show 3 different responses of soil, i.e., (i) stable, (ii) cyclic undrained failure, and (iii) fluidization, depending on the magnitude of CSR. Where fluidization becomes imminent, the shear stress rapidly decreases at early stages. Adding cohesive fines, i.e., Kaolin reduces the static undrained shear strength and increases the plasticity index. This enables the test specimen to undergo a larger number of cycles (N) before failure, thus enhancing its resistance to fluidization. Specimens with a smaller initial void ratio, i.e., greater level of compaction, are less susceptible to fluidization because they can withstand larger CSR and N. Moreover, this study shows where there is potential fluidization upon cyclic loading, a significant redistribution of the water content seems to occur over the height of the test specimens.
Indraratna, B, Medawela, S, Rowe, K, Thamwattana, N & Heitor, A 2020, 'Bio-Geochemical clogging of Permeable Reactive Barriers in Acid Sulphate Soil Floodplain', Journal of Geotechnical and Geoenvironmental Engineering, vol. 146, no. 5.
Indraratna, B, Medawela, S, Rowe, RK, Thamwattana, N & Heitor, A 2020, 'Biogeochemical Clogging of Permeable Reactive Barriers in Acid-Sulfate Soil Floodplain', Journal of Geotechnical and Geoenvironmental Engineering, vol. 146, no. 5, pp. 04020015-04020015.
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Column experiments that investigate the use of calcitic limestone as a potential material for permeable reactive barriers (PRBs), as well as its clogging behavior, are conducted under conditions that involve continuous acidic flow containing Al, Fe, and acidophilic bacteria. Results show that nonhomogenous biogeochemical clogging occurred toward the outlet, resulting in a 45% reduction of hydraulic conductivity at the inlet and 10% reduction at the outlet after the bicarbonate buffering period. A mathematical model developed to capture the reductions in longevity is presented. The model, which considers the effects of time-varying porosity, hydraulic conductivity, and head at a particular point on the horizontal flow path, is used for assessing the effect of coupled clogging in a calcitic porous medium.
Indraratna, B, Ngo, T & Rujikiatkamjorn, C 2020, 'Performance of Ballast Influenced by Deformation and Degradation: Laboratory Testing and Numerical Modeling', International Journal of Geomechanics, vol. 20, no. 1, pp. 04019138-04019138.
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© 2019 American Society of Civil Engineers. This paper presents a study on the deformation and degradation responses of railway ballast using large-scale laboratory testing and computational modeling approaches. A series of large-scale triaxial tests were carried out to investigate the ballast breakage responses under cyclic train loading subjected to varying frequencies, f=10-40 Hz. The role of recycled rubber energy-absorbing mats (REAMs) on reducing ballast breakage was also examined. Laboratory test results show that the ballast experiences significant degradation (breakage) and deformation, while the inclusion of REAMs can reduce the ballast breakage up to about 35%. Numerical modeling using the coupled discrete-continuum approach [coupled discrete-element method-finite-difference method (DEM-FDM)] is introduced to provide insightful understanding on the deformation and breaking of ballast under cyclic loading. Discrete ballast grains were simulated by bonding of many circular elements together at appropriate sizes and locations. Selected cylinders located at corners, surfaces, and sharp edges of the simulated particles were connected by parallel bonds; and when those bonds were broken, they were considered to represent ballast breakage. The subgrade and rubber mat were simulated as a continuum media using FDM. The predicted axial strain ϵa and volumetric strain ϵv obtained from the coupled DEM-FDM model are in good agreement with those measured in the laboratory. The model was then used to explore micromechanical aspects of ballast aggregates including the evolution of particle breakage, contact force distributions, and orientation of contacts during cyclic loading. These findings are imperative for a more insightful understanding of the breakage behavior of ballast from the perspective of microstructure characteristics of discrete particle assemblies.
Indraratna, B, Ngo, T, Bessa Ferreira, F, Rujikiatkamjorn, C & Shahkolahi, A 2020, 'Laboratory examination of ballast deformation and degradation under impact loads with synthetic inclusions', Transportation Geotechnics, vol. 25, pp. 100406-100406.
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© 2020 Elsevier Ltd This paper presents a laboratory study on alleviating the deformation and degradation (breakage) of ballast subjected to impact loads using geogrids and rubber mats. A series of drop hammer impact tests are carried out to determine how well the geogrid, under-ballast mat (UBM) or under-sleeper pad (USP) can attenuate impact loads and mitigate ballast degradation. Geogrids to be placed at different locations in a ballast assembly, in combination either with a UBM or a USP are tested. Laboratory test results prove that the inclusion of rubber mats and geogrids decrease the dynamic impact loads transferred to the ballast aggregates and subsequently decrease the degradation (breakage) and deformation of ballast. The tensile strength of geogrids and subgrade stiffness are found to considerably influence the performance of geogrid-reinforced ballast under impact loading conditions. The measured impact forces and accelerations of ballast with and without an artificial inclusion show that rubber mats definitely reduce track vibration (acceleration) and the subsequent deformation and breakage of ballast. These inclusions will not only increase safety and passenger comfort they will also lead to more economical and efficient track designs.
Indraratna, B, Singh, M & Nguyen, TT 2020, 'The mechanism and effects of subgrade fluidisation under ballasted railway tracks', Railway Engineering Science, vol. 28, no. 2, pp. 113-128.
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AbstractThe rapid growth in railway infrastructure and the construction of high-speed heavy-haul rail network, especially on ground that is basically unsuitable, poses challenges for geotechnical engineers because a large part of the money invested in the development of railway lines is often spent on track maintenance. In fact around the world, the mud pumping of subgrade fines is one of the common reasons why track performance deteriorates and track stability is hindered. This article presents a series of laboratory tests to examine following aspects of mud pumping: (1) the mechanisms of subgrade fluidisation under undrained condition, (2) the effects of mud pumping on the engineering characteristics of ballast, and (3) the use of vertical drains to stabilize subgrade under cyclic loads. The undrained cyclic triaxial testing on vulnerable soft subgrade was performed by varying the cyclic stress ratio (CSR) from 0.2 to 1.0 and the loading frequency f from 1.0 to 5.0 Hz. It is seen from the test results that for a specimen compacted at an initial dry density of 1790 kg/m3, the top portion of the specimen fluidises at CSR = 0.5, irrespective of the applied loading frequency. Under cyclic railway loading, the internal redistribution of water at the top of the subgrade layer softens the soil and also reduces its stiffness. In response to these problems, this paper explains how the inclusion of vertical drains in soft subgrade will help to prevent mud pumping by alleviating the build-up of excess pore pressures under moving train loads.
Indraratna, B, Singh, M, Nguyen, TT, Leroueil, S, Abeywickrama, A, Kelly, R & Neville, T 2020, 'Laboratory study on subgrade fluidization under undrained cyclic triaxial loading', Canadian Geotechnical Journal, vol. 57, no. 11, pp. 1767-1779.
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A long-term issue that has hampered the efficient operation of heavy-haul tracks is the migration of fluidized fines from the shallow soft subgrade to the overlying ballast, i.e., mud pumping. This paper presents a series of undrained cyclic triaxial tests where realistic cyclic loading conditions were simulated at low confining pressure that is typical of shallow subgrade beneath a ballast track. Subgrade soil specimens with a low-plasticity index collected from a field site with recent history of mud pumping were tested at frequencies from 1.0 to 5.0 Hz and a cyclic stress ratio (CSR) from 0.1 to 1.0. The experimental results indicate that under adverse loading conditions of critical cyclic stress ratio (CSRc) and frequency, there is upward migration of moisture and the finest particles towards the specimen top and this causes the uppermost part of the soil specimen to soften and fluidize. Conversely, a smaller value of CSR tends to maintain stability of the specimen despite the increasing number of loading cycles. It is noteworthy that for any given combination of CSR and frequency, the relative compaction has a significant influence on the cyclic behaviour of the soil and its potential for fluidization.
Irga, PJ, Dominici, L & Torpy, FR 2020, 'The mycological social network a way forward for conservation of fungal biodiversity', Environmental Conservation, vol. 47, no. 4, pp. 243-250.
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SummaryBecause knowledge of fungal diversity is very incomplete, it is possible that anthropogenic impacts are driving species to extinction before they have been discovered. Fungal inventories are still incomplete and do not reflect the complete diversity of this large taxon. Whilst molecular advancements are leading to an increased rate of species discovery, there is still much to be done to understand the diversity of fungi, identify rare species and establish conservation goals. Citizen science via social media could play an increasingly important role in mycological research, and its continued development should be supported and encouraged. The involvement of non-professionals in data collection helps increase public awareness, as well as extending the scope and efficiency of fungal surveys. Future academic mycological research could benefit from social media interaction and engagement with the amateur mycological community, which may accelerate the achievement of more effective conservation goals.
Islam, M, Nadarajah, M & Hossain, MJ 2020, 'A Grid-Support Strategy With PV Units to Boost Short-Term Voltage Stability Under Asymmetrical Faults', IEEE Transactions on Power Systems, vol. 35, no. 2, pp. 1120-1131.
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Islam, M, Nadarajah, M & Hossain, MJ 2020, 'Dynamic voltage stability of unbalanced DNs with high penetration of roof‐top PV units', International Transactions on Electrical Energy Systems, vol. 30, no. 12.
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Islam, MR, Liu, S, Wang, X & Xu, G 2020, 'Deep learning for misinformation detection on online social networks: a survey and new perspectives', Social Network Analysis and Mining, vol. 10, no. 1.
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© 2020, Springer-Verlag GmbH Austria, part of Springer Nature. Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.
Islam, MR, Lu, H, Hossain, J, Islam, MR & Li, L 2020, 'Multiobjective Optimization Technique for Mitigating Unbalance and Improving Voltage Considering Higher Penetration of Electric Vehicles and Distributed Generation', IEEE Systems Journal, vol. 14, no. 3, pp. 3676-3686.
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© 2007-2012 IEEE. The increasing penetration of distributed generations (DGs) and electric vehicles (EVs) offers not only several opportunities but also introduces many challenges for the distribution system operators (DSOs) regarding power quality. This article investigates the network performances due to uncoordinated DG and EV distribution. It also considers power quality-related performances such as the neutral current, energy loss, voltage imbalance, and bus voltage as a multiobjective optimization problem. The differential evolution optimization algorithm is employed to solve the multiobjective optimization problem to coordinate EV and DG in a distribution grid. This article proposed a method to coordinate EV and DG distribution. The proposed method allows DSOs to jointly optimize the phase sequence and optimal dispatch of DGs to improve the network's performance. If the network requires further improvement, the EV charging or discharging rate is coordinated for a particular location. The efficacy of the proposed method is tested in an Australian low-voltage distribution grid considering the amount of imbalance due to higher penetration of DG and EV. It is observed that the proposed method reduces voltage unbalance factor by up to 98.24%, neutral current up to 94%, and energy loss by 59.45%, and improve bus voltage by 10.42%.
Islam, MR, Lu, H, Islam, MR, Hossain, J & Li, L 2020, 'An IoT- Based Decision Support Tool for Improving the Performance of Smart Grids Connected with Distributed Energy Sources and Electric Vehicles', IEEE Transactions on Industry Applications, vol. 56, no. 4, pp. 1-1.
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Islam, MS, Gu, Y, Farkas, A, Paul, G & Saha, SC 2020, 'Helium–Oxygen Mixture Model for Particle Transport in CT-Based Upper Airways', International Journal of Environmental Research and Public Health, vol. 17, no. 10, pp. 3574-3574.
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The knowledge of respiratory particle transport in the extra-thoracic pathways is essential for the estimation of lung health-risk and optimization of targeted drug delivery. The published literature reports that a significant fraction of the inhaled aerosol particles are deposited in the upper airways, and available inhalers can deliver only a small amount of drug particles to the deeper airways. To improve the targeted drug delivery efficiency to the lungs, it is important to reduce the drug particle deposition in the upper airways. This study aims to minimize the unwanted aerosol particle deposition in the upper airways by employing a gas mixture model for the aerosol particle transport within the upper airways. A helium–oxygen (heliox) mixture (80% helium and 20% oxygen) model is developed for the airflow and particle transport as the heliox mixture is less dense than air. The mouth–throat and upper airway geometry are extracted from CT-scan images. Finite volume based ANSYS Fluent (19.2) solver is used to simulate the airflow and particle transport in the upper airways. Tecplot software and MATLAB code are employed for the airflow and particle post-processing. The simulation results show that turbulence intensity for heliox breathing is lower than in the case of air-breathing. The less turbulent heliox breathing eventually reduces the deposition efficiency (DE) at the upper airways than the air-breathing. The present study, along with additional patient-specific investigation, could improve the understanding of particle transport in upper airways, which may also increase the efficiency of aerosol drug delivery.
Islam, MS, Paul, G, Ong, HX, Young, PM, Gu, YT & Saha, SC 2020, 'A Review of Respiratory Anatomical Development, Air Flow Characterization and Particle Deposition', International Journal of Environmental Research and Public Health, vol. 17, no. 2, pp. 380-380.
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The understanding of complex inhalation and transport processes of pollutant particles through the human respiratory system is important for investigations into dosimetry and respiratory health effects in various settings, such as environmental or occupational health. The studies over the last few decades for micro- and nanoparticle transport and deposition have advanced the understanding of drug-aerosol impacts in the mouth-throat and the upper airways. However, most of the Lagrangian and Eulerian studies have utilized the non-realistic symmetric anatomical model for airflow and particle deposition predictions. Recent improvements to visualization techniques using high-resolution computed tomography (CT) data and the resultant development of three dimensional (3-D) anatomical models support the realistic representation of lung geometry. Yet, the selection of different modelling approaches to analyze the transitional flow behavior and the use of different inlet and outlet conditions provide a dissimilar prediction of particle deposition in the human lung. Moreover, incorporation of relevant physical and appropriate boundary conditions are important factors to consider for the more accurate prediction of transitional flow and particle transport in human lung. This review critically appraises currently available literature on airflow and particle transport mechanism in the lungs, as well as numerical simulations with the aim to explore processes involved. Numerical studies found that both the Euler–Lagrange (E-L) and Euler–Euler methods do not influence nanoparticle (particle diameter ≤50 nm) deposition patterns at a flow rate ≤25 L/min. Furthermore, numerical studies demonstrated that turbulence dispersion does not significantly affect nanoparticle deposition patterns. This critical review aims to develop the field and increase the state-of-the-art in human lung modelling.
Jabbari Ghadi, M, Azizivahed, A, Rajabi, A, Ghavidel, S, Li, L, Zhang, J, Shafie-Khah, M & Catalao, JPS 2020, 'Day-Ahead Market Participation of an Active Distribution Network Equipped With Small-Scale CAES Systems', IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 2966-2979.
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© 2010-2012 IEEE. Large-scale compressed air energy storage (CAES) is conventionally used in power systems. However, application of CAESs at the distribution level is limited because of differences in design and efficiency. On the other hand, application of electrical batteries suited for distribution networks (DNs) faces also challenges from high investment cost and significant degradation. In this regard, this paper presents the participation of an active distribution system equipped with a small-scale CAES (SCAES) in the day-ahead wholesale market. To make CAES applicable to DNs, thermal-electrical setting design of the SCAES coupled with a packed-bed heat exchanger is adopted in the operation of the grid, where SCAES performs as an energy storage for DNs to surpass existing deficiencies of battery banks. The electrical/thermal conversion rate has been modeled for the SCAES operation. Moreover, the operation strategy of the SCAES is optimally coordinated with an electric vehicle charging station (EVCS) as an alternative energy storage technology in deregulated DNs. To make EVCS simulation more realistic, Gaussian Copula probability distribution function is used to model the behavior of the EVCS. The results obtained from different case studies confirm the value of SCAES as a reliable energy storage technology for DNs.
Jafarizadeh, S, Tofigh, F, Lipman, J & Abolhasan, M 2020, 'Optimizing synchronizability in networks of coupled systems', Automatica, vol. 112, pp. 108711-108711.
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© 2019 Elsevier Ltd Of collective behaviors in networks of coupled systems, synchronization is of central importance and an extensively studied area. This is due to the fact that it is essential for the proper functioning of a wide variety of natural and engineered systems. Traditionally, uniform coupling strength has been the default choice and the synchronizability measure has been employed for analysis and enhancement of synchronizability. The main drawback of optimizing the synchronizability measure is that it can reach the Pareto frontier but not necessarily a unique point on the Pareto frontier. Additionally, the shortcoming of uniform coupling strength is that it can reach Pareto frontier in specific topologies including edge-transitive graphs. To achieve a unique optimal answer on the Pareto frontier, this paper takes a different approach and addresses the synchronizability in networks of coupled dynamical systems with nonuniform coupling strength and optimizing the synchronizability via maximizing the minimum distance between the nonzero eigenvalues of the Laplacian and the acceptable boundaries for the stability of the system. Furthermore, two solution methods, namely the concave–convex fractional programming and the Semidefinite Programming (SDP) formulations of the problem have been provided. The proposed solution methods have been compared over different topologies and branches of an arbitrary network, where the SDP based approach has shown to be less restricted and more suitable for a wider range of topologies.
Jafarzadeh, M, Wu, Y-D, Sanders, YR & Sanders, BC 2020, 'Randomized benchmarking for qudit Clifford gates', New Journal of Physics, vol. 22, no. 6, pp. 063014-063014.
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Abstract We introduce unitary-gate randomized benchmarking (URB) for qudit gates by extending single- and multi-qubit URB to single- and multi-qudit gates. Specifically, we develop a qudit URB procedure that exploits unitary 2-designs. Furthermore, we show that our URB procedure is not simply extracted from the multi-qubit case by equating qudit URB to URB of the symmetric multi-qubit subspace. Our qudit URB is elucidated by using pseudocode, which facilitates incorporating into benchmarking applications.
Jahangoshai Rezaee, M, Yousefi, S, Eshkevari, M, Valipour, M & Saberi, M 2020, 'Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA', Stochastic Environmental Research and Risk Assessment, vol. 34, no. 1, pp. 201-218.
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Jahed Armaghani, D, Hasanipanah, M, Bakhshandeh Amnieh, H, Tien Bui, D, Mehrabi, P & Khorami, M 2020, 'Development of a novel hybrid intelligent model for solving engineering problems using GS-GMDH algorithm', Engineering with Computers, vol. 36, no. 4, pp. 1379-1391.
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Jain, R, Klauck, H, Kundu, S, Lee, T, Santha, M, Sanyal, S & Vihrovs, J 2020, 'Quadratically Tight Relations for Randomized Query Complexity', Theory of Computing Systems, vol. 64, no. 1, pp. 101-119.
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Jamborsalamati, P, Hossain, MJ, Taghizadeh, S, Konstantinou, G, Manbachi, M & Dehghanian, P 2020, 'Enhancing Power Grid Resilience Through an IEC61850-Based EV-Assisted Load Restoration', IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1799-1810.
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© 2005-2012 IEEE. Contrary to reliability analysis in power systems with the main mission on safely and securely withstanding credible contingencies in day-to-day operations, resilience assessments are centered on high-impact low probability (HILP) events in the grid. This paper proposes an autonomous load restoration architecture founded on IEC 61850-8-1 GOOSE communication protocol to engender an enhanced feeder-level resilience in active power distribution grids. Different from the past research on outage management solutions, most of which 1) are not resilience-driven; 2) are reactive solutions to local single-fault events; and 3) do not address both network built-in flexibilities and flexible resources. The proposed solution harnesses 1) the imported power and flexibility from the neighboring networks; 2) distributed energy resources; and 3) vehicle to grid capacity of electric vehicles aggregations to enhance the feeder-level resourcefulness for agile response and recovery. Through real-time self-reconfiguration strategies, the suggested solution is capable of coping both single and subsequent outage events, and will engender a heightened resilience before and during the contingency period. Moreover, a resilience evaluation framework, which quantifies the contribution of all resources involved in service restoration, is developed. Real-time performance of the designed architecture is evaluated on a real-world power distribution grid using a real-time hardware-in-the-loop platform. Numerical case studies through a number of diverse scenarios demonstrate the efficacy of the proposed restoration solution in practicing an enhanced resilience in power distribution systems in response to HILP scenarios.
Jamil, S, Loganathan, P, Kandasamy, J, Listowski, A, McDonald, JA, Khan, SJ & Vigneswaran, S 2020, 'Removal of organic matter from wastewater reverse osmosis concentrate using granular activated carbon and anion exchange resin adsorbent columns in sequence', Chemosphere, vol. 261, pp. 127549-127549.
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Reverse osmosis concentrate (ROC) generated as a waste stream during reverse osmosis treatment of reclaimed wastewater, presents significant disposal challenges. This is because it causes environmental pollution when it is disposed to lands and natural water bodies. A long-term dynamic adsorption experiment was conducted by passing ROC from a wastewater reclamation plant, firstly through a granular activated carbon (GAC) column, and subsequently through an anion exchange resin (Purolite) column, for the removal of two major ROC pollutants, namely dissolved organic carbon (DOC) and microorganic pollutants (MOP). GAC removed most of the smaller-sized low molecular weight neutrals and building block fractions as well as the hydrophobic fraction of DOC with much less removal by the subsequent Purolite column. In contrast, the humics fraction was less well removed by the GAC column; however, Purolite column removed all that was remaining of this fraction. This study demonstrated that combining adsorbents having different affinities towards a variety of DOC fractions constitute an effective method of taking advantage of their different properties and achieving larger DOC removals. Almost 100% of all 17 MOPs were removed by the GAC column, even after 2880 bed volumes of continuous use. This contrasted with the DOC fractions' removal which was much lower.
Jamshaid, M, Masjuki, HH, Kalam, MA, Zulkifli, NWM, Arslan, A & Zulfattah, ZM 2020, 'Effect of Fatty Acid Methyl Ester on Fuel-Injector Wear Characteristics', Journal of Biobased Materials and Bioenergy, vol. 14, no. 3, pp. 327-339.
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This paper presents the experimental results carried out to evaluate the fatty acid methyl ester (FAME) obtained from cotton-seed oil and palm oil on fuel-injector wear characteristics. The cottonseed oil methyl ester (COME) and palm oil methyl ester (POME) were produced in the laboratory using alkaline transesterification. Gas chromatography based on 'BS EN 14103:2011' standard was used to analyze the percentage of fatty acids in COME and POME. The physicochemical properties of the two methyl esters were measured based on ASTM and EN standards. Various unique blends using cottonseed–palm oil methyl ester (CPME) were tested. Thirteen (13) different types of fuel blends were prepared from COME, POME, and petroleum diesel fuel (DF100). The wear and lubricity characteristics were measured using a high-frequency reciprocating rig (HFRR) based on ASTM D6079 standard. The worn surfaces of the specimen plates were evaluated by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). The COME100, POME100, and CPME100 showed excellent lubricity properties for the fuel injector in terms of lower COF and wear coefficient when compared with DF100. COME100, POME100 and CPME100 showed lower average COF compared to DF100 by 16.9%, 13.9% and 16.1%, respectively. This may be due to the presence of unsaturated fatty acids in the methyl esters composition. Consequently, the fatty acid methyl esters can be used to reduce the friction and wear of the fuel injectors due to the improvement in the tribological properties of the fuel.
Jayasuriya, C, Indraratna, B & Ferreira, FB 2020, 'The Use of Under Sleeper Pads to Improve the Performance of Rail Tracks', Indian Geotechnical Journal, vol. 50, no. 2, pp. 204-212.
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© 2020, Indian Geotechnical Society. In recent years, with the growing demand for both passenger and freight mobility, faster and heavier rail traffic has been the norm rather than the exception in many countries. As a result, track geometry and the safety of ballasted rail tracks have been adversely affected, leading to exacerbated maintenance costs. Increased stresses in granular foundation induce progressive track degradation, which can result in excessive vertical and lateral deformation, ballast and subballast fouling and impeded drainage. These effects tend to be more severe at specific locations, such as bridges, level crossings and tunnels (i.e. over stiff subgrade). Finding an economical strategy to mitigate ballast degradation has been a challenging task for practitioners, and the inclusion of energy-absorbing rubber pads underneath the sleepers (under sleeper pads—USPs) to minimise track damage is an attractive solution. This paper presents a laboratory study conducted at the University of Wollongong to investigate the use of USPs as resilient elements in ballasted rail tracks involving a stiff subgrade. Test results have shown a significant improvement in track performance resulting from the use of USPs, whereby it is demonstrated that ballast damage induced by the applied cyclic loads can be reduced due to the favourable damping characteristics of these rubber pads. A significant attenuation in particle breakage was observed along with a reduction in both the vertical settlement and lateral movement of the ballast layer, thereby suggesting that USPs can be an effective means of improving the stability and serviceability of the track system.
Jayawardane, VS, Anggraini, V, Li-Shen, AT, Paul, SC & Nimbalkar, S 2020, 'Strength Enhancement of Geotextile-Reinforced Fly-Ash-Based Geopolymer Stabilized Residual Soil', International Journal of Geosynthetics and Ground Engineering, vol. 6, no. 4.
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© 2020, Springer Nature Switzerland AG. Soils in their natural form are often deemed unsatisfactory to be directly used as a construction material for their respective applications. Under such circumtances, employment of ground improvement techniques to better suit the soil for its function is typically the most economical approach. Consequently, the present research investigated into the beneficial effect of modernized soil treatment techniques, i.e., geopolymer stabilization using fly ash as the precursor and geotextile reinforcement, on the strength enhancement of natural residual soil. A series of unconsolidated undrained (UU) triaxial compression tests were carried out to assess variation of geopolymer stabilized residual soil strength based on the varying number of geotextile layers, geotextile arrangement, and confining pressures. It was found that the increase in the number of geotextile layers resulted in a corresponding rise in soil strength and stiffness. It was also discovered that placement of geotextile layers at sample regions which suffered the maximum tensile stress–strain during loading was more effective compared to random placement. Soil strength was observed to reduce with increasing confining pressure which demonstrated the effectiveness of utilizing geotextile reinforcement at greater depths below the ground to be less. Failure patterns showed that while unreinforced soil resulted in failure along a shear plane at an approximate angle of 45 + φ/2 (φ: angle of internal friction), reinforced samples demonstrated a bulging failure where the soil between adjacent layers of geotextiles appeared to bulge. The findings deemed the employment of geopolymer stabilization and geotextile reinforcement on natural residual soil very effective with regards to the enhancement of soil strength and stiffness.
Jayawickrama, B & Huss, F 2020, 'Generation of Independent Rayleigh Faders for Discrete Signal', IEEE Communications Letters, vol. 24, no. 6, pp. 1155-1158.
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© 1997-2012 IEEE. Generating Rayleigh fading is a well investigated subject. However, most existing methods are fundamentally based on a sum-of-sinusoids, hence have a high computational complexity and become impractical when modelling massive MIMO systems. In this letter we present a novel recursive complex number multiplication method to generate Rayleigh fading for discrete signals. The computational complexity of the method is 75-94% less than all prominent sum-of-sinusoid methods. It also preserves the theoretically expected Bessel autocorrelation of a fading channel, zero cross-correlation between different fading channels and the power spectral density.
Jayawickreme, N, Atefi, E, Jayawickreme, E, Qin, J & Gandomi, AH 2020, 'Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka', International Journal of Environmental Research and Public Health, vol. 17, no. 8, pp. 2850-2850.
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Research indicates that psychopathology in disaster survivors is a function of both experienced trauma and stressful life events. However, such studies are of limited utility to practitioners who are about to go into a new post-disaster setting as (1) most of them do not indicate which specific traumas and stressors are especially likely to lead to psychopathology; and (2) each disaster is characterized by its own unique traumas and stressors, which means that practitioners have to first collect their own data on common traumas, stressors and symptoms of psychopathology prior to planning any interventions. An easy-to-use and easy-to-interpret data analytical method that allows one to identify profiles of trauma and stressors that predict psychopathology would be of great utility to practitioners working in post-disaster contexts. We propose that association rule learning (ARL), a big data mining technique, is such a method. We demonstrate the technique by applying it to data from 337 survivors of the Sri Lankan civil war who completed the Penn/RESIST/Peradeniya War Problems Questionnaire (PRPWPQ), a comprehensive, culturally-valid measure of experienced trauma, stressful life events, anxiety and depression. ARL analysis revealed five profiles of traumas and stressors that predicted the presence of some anxiety, three profiles that predicted the presence of severe anxiety, four profiles that predicted the presence of some depression and five profiles that predicted the presence of severe depression. ARL allows one to identify context-specific associations between specific traumas, stressors and psychological distress, and can be of great utility to practitioners who wish to efficiently analyze data that they have collected, understand the output of that analysis, and use it to provide psychosocial aid to those who most need it in post-disaster settings.
Jena, R & Pradhan, B 2020, 'A Model for Visual Assessment of Fault Plane Solutions and Active Tectonics Analysis Using the Global Centroid Moment Tensor Catalog', Earth Systems and Environment, vol. 4, no. 1, pp. 197-211.
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In this study, individual fault plane solutions are developed using various methods to improve the understanding of active tectonics on a regional scale. The comparative analysis of a focal mechanism solution (FMS) has not elicited the attention of researchers. Therefore, this study aims (1) to visually analyze the fault plane solution for 20 local faults that are responsible for all the earthquakes that occurred using visualization techniques such as: fault parameters, the linked Bingham method, the ad hoc pressure (P) axis and tension (T) axis method, and the moment tensor method; (2) to identify the best method for FMS; and (3) to understand the active tectonics of a fault population. A comparative analysis of the models is systematically documented to improve the understanding of the methods. An analysis of the overall fault mechanism is conducted for the analytic determination of fault movement using fault population data from the Global Centroid Moment Tensor catalog. The approach used in this work is a newly designed method for analyzing the reliability of various techniques for fault mechanism and overall fault movement research. Findings show that for the fault mechanism analysis, the P and T axes method and the moment tensor method are better than the fault plane solution from the fault parameters and the linked Bingham method based on the input parameters, output information, model outfit, and accuracy. The moment tensor method is one of the best approaches for analyzing fault mechanism because the errors in the nine components used as input data for the modeling are negligible. Meanwhile, the P and T axes method is one of the best techniques for the overall analysis of fault movement. P and T dihedral analysis using Kamb contouring is modeled. It indicates that the overall mechanisms of compression and dilation are features at the NW–SE and E–W directions, respectively. This comprehensive and consistent analysis of the fault mechanism provides an over...
Jena, R & Pradhan, B 2020, 'Integrated ANN-cross-validation and AHP-TOPSIS model to improve earthquake risk assessment', International Journal of Disaster Risk Reduction, vol. 50, pp. 101723-101723.
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© 2020 Elsevier Ltd The current study presents a novel combination of artificial neural network cross-validation (fourfold ANN-CV) with a hybrid analytic hierarchy process-Technique for Order of Preference by Similarity to Ideal Solution (AHP-TOPSIS) method to improve the earthquake risk assessment (ERA) and applied it to Aceh, Indonesia, to test the model. Recent studies have suggested that neural networks improve probability mapping in a city scale. The network architecture design with probability index remains unexplored in earthquake-based probability studies. This study explored and specified the major indicators needed to improve the predictive accuracy in probability mapping. First, probability mapping was conducted and used for hazard assessment in the next step. Second, a vulnerability map was created based on social and structural factors. Finally, hazard and vulnerability indices were multiplied to produce the ERA, and the population and areas under risk were calculated. Results show that the proposed model achieves 85.4% accuracy, and its consistency ratio is 0.06. Risk varies from very high to high in the city center, approximately covering an area of 23% (14.82 km2) and a total population of 54,695. The model's performance changes on the basis of the input parameters, indicating the selection and importance of input layers on network architecture selection. The proposed model is found to generalize better results than traditional and some existing probabilistic models. The proposed model is simple and transferable to other regions by localizing the input parameters that contribute to earthquake risk mitigation and prevention planning.
Jena, R, Pradhan, B & Alamri, AM 2020, 'Geo-structural stability assessment of surrounding hills of Kuala Lumpur City based on rock surface discontinuity from geological survey data', Arabian Journal of Geosciences, vol. 13, no. 2.
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Jena, R, Pradhan, B & Alamri, AM 2020, 'Susceptibility to Seismic Amplification and Earthquake Probability Estimation Using Recurrent Neural Network (RNN) Model in Odisha, India', Applied Sciences, vol. 10, no. 15, pp. 5355-5355.
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The eastern region of India, including the coastal state of Odisha, is a moderately seismic-prone area under seismic zones II and III. However, no major studies have been conducted on earthquake probability (EPA) and hazard assessment (EHA) in Odisha. This paper had two main objectives: (1) to assess the susceptibility of seismic wave amplification (SSA) and (2) to estimate EPA in Odisha. In total, 12 indicators were employed to assess the SSA and EPA. Firstly, using the historical earthquake catalog, the peak ground acceleration (PGA) and intensity variation was observed for the Indian subcontinent. We identified high amplitude and frequency locations for estimated PGA and the periodograms were plotted. Secondly, several indicators such as slope, elevation, curvature, and amplification values of rocks were used to generate SSA using predefined weights of layers. Thirdly, 10 indicators were implemented in a developed recurrent neural network (RNN) model to create an earthquake probability map (EPM). According to the results, recent to quaternary unconsolidated sedimentary rocks and alluvial deposits have great potential to amplify earthquake intensity and consequently lead to acute ground motion. High intensity was observed in coastal and central parts of the state. Complicated morphometric structures along with high intensity variation could be other parameters that influence deposits in the Mahanadi River and its delta with high potential. The RNN model was employed to create a probability map (EPM) for the state. Results show that the Mahanadi basin has dominant structural control on earthquakes that could be found in the western parts of the state. Major faults were pointed towards a direction of WNW–ESE, NE–SW, and NNW–SSE, which may lead to isoseismic patterns. Results also show that the western part is highly probable for events while the eastern coastal part is highly susceptible to seismic amplification. The RNN model achieved an accura...
Jena, R, Pradhan, B & Beydoun, G 2020, 'Earthquake vulnerability assessment in Northern Sumatra province by using a multi-criteria decision-making model', International Journal of Disaster Risk Reduction, vol. 46, pp. 101518-101518.
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© 2020 Elsevier Ltd The prerequisite for earthquake risk estimation is vulnerability assessment. Therefore, estimating vulnerability is necessary to reduce future fatalities. This study aims to evaluate the earthquake vulnerability assessment (EVA) in Banda Aceh by using the multi-criteria decision-making approach through an analytical hierarchy process and VIseKriterijumska Optimizacija I Kompromisno Resenje method using a geographical information system. Banda Aceh City is located close to the Great Sumatran Fault in North Sumatra. Several factors were used to produce social vulnerability, structural vulnerability, and geotechnical vulnerability indices. Subsequently, the adopted approaches were integrated and applied to estimate the criteria weight, priority ranking, and alternatives of criterion by applying the pair-wise comparison at all levels. Finally, vulnerability layers were superimposed to estimate the earthquake vulnerability index and produce the vulnerability map. Results showed that the central part of the city exhibits high to very high vulnerability. A tiny part of the northern–central part is under severe vulnerability conditions. The consistency ratios for all three vulnerability layers were 1.9%, 4.6% and 5.5%. The consistency ratios for the final EVA was 1.9%. The developed map revealed that 3.39% of Banda Aceh City falls under very high, 11.86% high, 23.73% medium, 28.82% low, and 32.20% of very low vulnerability areas. The proposed method for the EVA provides useful information that could assist in earthquake disaster mitigation.
Jena, R, Pradhan, B, Beydoun, G, Al-Amri, A & Sofyan, H 2020, 'Seismic hazard and risk assessment: a review of state-of-the-art traditional and GIS models', Arabian Journal of Geosciences, vol. 13, no. 2.
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© 2020, Saudi Society for Geosciences. The historical records of earthquakes play a vital role in seismic hazard and risk assessment. During the last decade, geophysical, geotechnical, geochemical, topographical, geomorphological, geological data, and various satellite images have been collected, processed, and well-integrated into qualitative and quantitative spatial databases using geographical information systems (GIS). Various types of modeling approaches, such as traditional and GIS-based models, are used. Progressively, seismic studies can improve and modify systematic models and standardize the inventory map of earthquake-susceptible regions. Therefore, this paper reviews different approaches, which are organized and discussed on various models primarily used to create an earthquake scenario focusing on hazard and risk assessment. The reviews are divided into two major parts. The first part is the basic principles, data, and the methodology of various models used for seismic hazard and risk assessment. In the second part, a comparative analysis in terms of the limitations and strengths of the models, as well as application variability is presented. Furthermore, the paper includes the descriptions of software, data resources, and major conclusions. The main findings of this review explain that the capability of machine learning techniques regularly enhances the state of earthquake research, which will provide research opportunities in the future. The model suitability depends on the improvement of parameters, data, and methods that could help to prevent future risk. This paper will help researchers further understand the models based on their strengths, limitations, and applicability.
Jena, R, Pradhan, B, Beydoun, G, Alamri, AM, Ardiansyah, Nizamuddin & Sofyan, H 2020, 'Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia', Science of The Total Environment, vol. 749, pp. 141582-141582.
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© 2020 Elsevier B.V. On 28th September 2018, a very high magnitude of earthquake Mw 7.5 struck the Palu city in the Island of Sulawesi, Indonesia. The main objective of this research is to estimate the earthquake risk based on probability and hazard in Palu region using cross-correlation among the derived parameters, Silhouette clustering (SC), pure locational clustering (PLC) based on hierarchical clustering analysis (HCA), convolutional neural network (CNN) and analytical hierarchy process (AHP) techniques. There is no specific or simple way of identifying risks as the definition of risk varies with time and space. The main aim of this study is: i) to conduct the clustering analysis to identify the earthquake-prone areas, ii) to develop a CNN model for probability estimation, and iii) to estimate and compare the risk using two calculation equations (Risk A and B). Owing to its high prediction ability, the CNN model assessed the probability while SC and PLC were implemented to understand the spatial clustering, Euclidean distance among clusters, spatial relationship and cross-correlation among the estimated Mw, PGA and intensity including events depth. Finally, AHP was implemented for the vulnerability assessment. To this end, earthquake probability assessment (EPA), susceptibility to seismic amplification (SSA) and earthquake vulnerability assessment (EVA) results were employed to generate risk A, while earthquake hazard assessment (EHA), SSA and EVA were used to generate risk B. The risk maps were compared and the differences in results were obtained. This research concludes that in the case of earthquake risk assessment (ERA), results obtained in Risk B are better than the risk A. This study achieved 89.47% accuracy for EPA while for EVA a consistency ratio of 0.07. These results have important implications for future large-scale risk assessment, land use planning and hazard mitigation.
Jena, R, Pradhan, B, Beydoun, G, Nizamuddin, Ardiansyah, Sofyan, H & Affan, M 2020, 'Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia', Geoscience Frontiers, vol. 11, no. 2, pp. 613-634.
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© 2019 China University of Geosciences (Beijing) and Peking University Catastrophic natural hazards, such as earthquake, pose serious threats to properties and human lives in urban areas. Therefore, earthquake risk assessment (ERA) is indispensable in disaster management. ERA is an integration of the extent of probability and vulnerability of assets. This study develops an integrated model by using the artificial neural network–analytic hierarchy process (ANN–AHP) model for constructing the ERA map. The aim of the study is to quantify urban population risk that may be caused by impending earthquakes. The model is applied to the city of Banda Aceh in Indonesia, a seismically active zone of Aceh province frequently affected by devastating earthquakes. ANN is used for probability mapping, whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering. The risk map is subsequently created by combining the probability, hazard, and vulnerability maps. Then, the risk levels of various zones are obtained. The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%. Furthermore, results show that the central and southeastern regions of the city have moderate to very high risk classifications, whereas the other parts of the city fall under low to very low earthquake risk classifications. The findings of this research are useful for government agencies and decision makers, particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.
Jena, R, Pradhan, B, Jung, HS, Rai, AK & Rizeei, HM 2020, 'Seasonal water change assessment at Mahanadi River, India using multi-temporal data in Google Earth engine', Korean Journal of Remote Sensing, vol. 36, no. 1, pp. 1-13.
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Seasonal changes in river water vary seasonally as well as locationally, and the assessment is essential. In this study, we used the recent technique of post-classification by using the Google earth engine (GEE) to map the seasonal changes in Mahanadi river of Odisha. However, some fixed problems results during the rainy season that affects the livelihood system of Cuttack such as flooding, drowning of children and waste material deposit. Therefore, this study conducted 1) to map and analyse the water density changes and 2) to analyse the seasonal variation of river water to resolve and prevent problem shortcomings. Our results showed that nine types of variation can be found in the Mahanadi River each year. The increase and decrease of intensity of surface water analysed, and it varies in between -130 to 70 m3/nf. The highest frequency change is 2900 Hz near Cuttack city. The pi diagram provides the percentage of seasonal variation that can be observed as permanent water (30%), new seasonal (28%), ephemeral (12%), permanent to seasonal (7%) and seasonal (10%). The analysis is helpful and effective to assess the seasonal variation that can provide a platform for the development of Cuttack city that lies in Mahanadi delta.
Jenifer A, A, Chandran, T, Muthunarayanan, V, Ravindran, B, Nguyen, VK, Nguyen, XC, Bui, X-T, Ngo, HH, Nguyen, XH, Chang, SW & Nguyen, DD 2020, 'Evaluation of efficacy of indigenous acidophile- bacterial consortia for removal of pollutants from coffee cherry pulping wastewater', Bioresource Technology Reports, vol. 11, pp. 100533-100533.
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The efficiency of indigenous bacteria to remove colour, TDS and COD pollutants from coffee cherry pulping wastewater (CCPWW) in an acidic pH without any manipulation of the effluent was studied. For the removal of such pollutants, the CCPWW was subjected to treatment with four indigenous microbial test strains isolated from CCPWW and characterised using 16S rRNA molecular technique, namely Enterobacter ludwigii, Bacilllus cereus, Enterobacter aerogenes and Enterobacter cloacae. Among the individual microbial treatments, the Enterobacter cloacae bacterial strain removed higher amount of TDS (37.6%) and COD (40.1%). Treatment with the bacterial consortia removed about 40.9% TDS, 48.7% COD from CCPWW after 48 h. The correlation coefficient ‘r’ between TDS and COD removal for each individual treatment was 1, showed the positive linear relationship. The microbes had endured in the harsh–low pH environment of the effluent and effectively removed the pollutants without any addition of other nutrient support.
Ji, M, Hu, Z, Hou, C, Liu, H, Ngo, HH, Guo, W, Lu, S & Zhang, J 2020, 'New insights for enhancing the performance of constructed wetlands at low temperatures', Bioresource Technology, vol. 301, pp. 122722-122722.
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Constructed wetlands (CWs) have been widely utilized for various types of wastewater treatment due to their merits, including high cost-effectiveness and easy operation. However, a few intrinsic drawbacks have always restricted their application and long-term stability, especially their weak performance at temperatures under 10 °C (low temperatures) due to the deterioration of microbial assimilation and plant uptake processes. The existing modifications to improve CWs performance from the direct optimization of internal components to the indirect adjunction of external resources promoted the wastewater treatment efficiency to a certain degree, but the sustainability and sufficiency of pollutants removal remains a challenge. With the goal of optimizing CW components, the integrity of the CW ecosystem and the removal of emerging pollutants, future directions for research should include radiation plant breeding, improvements to CW ecosystems, and the combination or integration of certain treatment processes with CWs to enhance wastewater treatment effects at low temperatures.
Ji, Z, Leung, D & Vidick, T 2020, 'A three-player coherent state embezzlement game', Quantum, vol. 4, pp. 349-349.
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We introduce a three-player nonlocal game, with a finite number of classical questions and answers, such that the optimal success probability of1in the game can only be achieved in the limit of strategies using arbitrarily high-dimensional entangled states. Precisely, there exists a constant0<c≤1such that to succeed with probability1−εin the game it is necessary to use an entangled state of at leastΩ(ε−c)qubits, and it is sufficient to use a state of at mostO(ε−1)qubits. The game is based on the coherent state exchange game of Leung et al.\ (CJTCS 2013). In our game, the task of the quantum verifier is delegated to a third player by a classical referee. Our results complement those of Slofstra (arXiv:1703.08618) and Dykema et al.\ (arXiv:1709.05032), who obtained two-player games with similar (though quantitatively weaker) properties based on the representation theory of finitely presented groups andC∗-algebras respectively.
Jiang, J, Ji, S & Long, G 2020, 'Decentralized Knowledge Acquisition for Mobile Internet Applications', World Wide Web, vol. 23, no. 5, pp. 2653-2669.
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Jiang, K, Xie, W, Li, Y, Lei, J, He, G & Du, Q 2020, 'Semisupervised Spectral Learning With Generative Adversarial Network for Hyperspectral Anomaly Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 7, pp. 5224-5236.
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Jiang, N, Fu, F, Zuo, H, Zheng, X & Zheng, Q 2020, 'A Municipal PM2.5 Forecasting Method Based on Random Forest and WRF Model', ENGINEERING LETTERS, vol. 28, no. 2, pp. 312-321.
Jiang, P, Li, R, Lu, H & Zhang, X 2020, 'Modeling of electricity demand forecast for power system', Neural Computing and Applications, vol. 32, no. 11, pp. 6857-6875.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. The emerging complex circumstances caused by economy, technology, and government policy and the requirement of low-carbon development of power grid lead to many challenges in the power system coordination and operation. However, the real-time scheduling of electricity generation needs accurate modeling of electricity demand forecasting for a range of lead times. In order to better capture the nonlinear and non-stationary characteristics and the seasonal cycles of future electricity demand data, a new concept of the integrated model is developed and successfully applied to research the forecast of electricity demand in this paper. The proposed model combines adaptive Fourier decomposition method, a new signal preprocessing technology, for extracting useful element from the original electricity demand series through filtering the noise factors. Considering the seasonal term existing in the decomposed series, it should be eliminated through the seasonal adjustment method, in which the seasonal indexes are calculated and should multiply the forecasts back to restore the final forecast. Besides, a newly proposed moth-flame optimization algorithm is used to ensure the suitable parameters of the least square support vector machine which can generate the forecasts. Finally, the case studies of Australia demonstrated the efficacy and feasibility of the proposed integrated model. Simultaneously, it can provide a better concept of modeling for electricity demand prediction over different forecasting horizons.
Jiang, Y, He, N, Zhou, Y, Xu, B, Zhan, X & Ding, Y 2020, 'Investigation on in situ test and measurement technique of groundwater level in vacuum preloading', Bulletin of Engineering Geology and the Environment, vol. 79, no. 3, pp. 1209-1223.
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Jin, D, Zhang, B, Song, Y, He, D, Feng, Z, Chen, S, Li, W & Musial, K 2020, 'ModMRF: A modularity-based Markov Random Field method for community detection', Neurocomputing, vol. 405, pp. 218-228.
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© 2020 Elsevier B.V. Complex networks are widely used in the research of social and biological fields. Analyzing real community structure in networks is the key to the study of complex networks. Modularity optimization is one of the most popular techniques in community detection. However, due to its greedy characteristic, it leads to a large number of incorrect partitions and more communities than in reality. Existing methods use the modularity as a Hamiltonian at the finite temperature to solve the above problem. Nevertheless, modularity is not formalized as a statistical model in the method, which makes many statistical inference methods limited and cannot be used. Moreover, the method uses the sum-product version of belief propagation (BP) and its performance is not as good as the max-sum version, since it calculates per-variable marginal probabilities rather than the joint probability. To address these issues, we propose a novel Markov Random Field (MRF) method by formalizing modularity as an energy function based on the rich structures of MRF to represent properties and constraints of this problem, and use the max-sum BP to infer model parameters. In order to analyze our method and compare it with existing methods, we conducted experiments on both real-world and synthetic networks with ground-truth of communities, showing that the new method outperforms the state-of-the-art methods.
Jin, J, Zhang, R, Lin, Z, Guo, Y, Zhu, J, Chen, X & Shen, B 2020, 'Modelling analysis of periodically arranged high-temperature superconducting tapes', Physica C: Superconductivity and its Applications, vol. 578, pp. 1353747-1353747.
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Jin, P, Tian, Y, Lu, Y, Guo, Y, Lei, G & Zhu, J 2020, '3-D Analytical Magnetic Field Analysis of the Eddy Current Coupling With Halbach Magnets', IEEE Transactions on Magnetics, vol. 56, no. 1, pp. 1-4.
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Jose, S, Kochandra, R & Daniel, S 2020, 'Instructional Videos, Conceptual Understanding and Self-Efficacy in the Time of COVID', International Journal of Innovation in Science and Mathematics Education, vol. 29, no. 3.
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Advances in technology offer new opportunities for teaching. Many students engage with online videos that enable them to watch, and re-watch these support materials flexibly and at their own pace. In our large-enrolment introductory first-year physics unit, many students find the content very challenging. To support their learning, we have developed short videos of 4-7 minutes explaining concepts and providing demonstrations of the problem-solving process. Our study was originally designed to evaluate and compare the effect on conceptual understanding and self-efficacy of students engaging with two different types of videos: screencasts (e.g. Khan Academy style) and lightboard videos, where the teacher presents direct to the camera on a writable transparent board (the image is then inverted to be the right way round). Then COVID struck, and all our learning was moved online. Thus, in the second semester of the study, we only used screencasts, and focused our research on exploring the relationship between online engagement, self-efficacy and conceptual understanding of students. We found that students preferred lightboards, and that both semesters’ average survey scores on self-efficacy and conceptual understanding were generally stable or increased only slightly. This is at odds with other studies of similar cohorts. However, the small number of paired responses in our study meant that a self-selection bias may have skewed results. Scores on the conceptual understanding were weakly correlated with assessment performance, suggesting the presence of other contributing variables. Initial self-efficacy scores did not predict subsequent engagement. Instead, missing multiple early assessments was identified as a stronger predictor of failing to pass the subject.
Ju, M, Ding, C & Guo, YJ 2020, 'VROHI: Visibility Recovery for Outdoor Hazy Image in Scattering Media', IEEE Photonics Journal, vol. 12, no. 6, pp. 1-15.
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© 2009-2012 IEEE. Additive haze model (AHM), due to its high simplicity, has a potential to increase the efficiency of the restoration procedure of images degraded by scattering media. However, AHM is designed for hazy remote sensing data and is not suitable to be used on outdoor images. In this paper, according to the low-frequency feature (LFC) of haze, AHM is modified via gamma correction technique to make it suitable for modeling outdoor images. Benefitting from the modified AHM (MAHM), a simple yet effective method called VROHI is proposed to enhance the visibility of an outdoor hazy image. In specific, a low complexity LFC extraction method is designed by utilizing characteristic of the discrete cosine transform. Subsequently, by constructing the linear function of unknown parameters and imposing the saturation prior on MAHM, the image dehazing problem can be derived into a global optimization function. Experiments reveal that the proposed VROHI is superior to the other state-of-the-art techniques in terms of both the processing efficiency and recovery quality.
Ju, M, Ding, C, Guo, YJ & Zhang, D 2020, 'IDGCP: Image Dehazing Based on Gamma Correction Prior', IEEE Transactions on Image Processing, vol. 29, pp. 3104-3118.
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© 1992-2012 IEEE. This paper introduces a novel and effective image prior, i.e., gamma correction prior (GCP), which leads to an efficient image dehazing method, i.e., IDGCP. A step-by-step procedure of the proposed IDGCP is as follows. First, an input hazy image is preprocessed by the proposed GCP, resulting in a homogeneous virtual transformation of the hazy image. Then, from the original input hazy image and its virtual transformation, the depth ratio is extracted based on atmospheric scattering theory. Finally, a 'global-wise' strategy and a vision indicator are employed to recover the scene albedo, thus restoring the hazy image. Unlike other image dehazing methods, IDGCP is based on the 'global-wise' strategy, and it only needs to determine one unknown constant without any refining process to attain a high-quality restoration, thereby leading to significantly reduced processing time and computation cost. Each step of IDGCP is tested experimentally to validate its robustness. Moreover, a series of experiments are conducted on a number of challenging images with IDGCP and other state-of-the-art technologies, demonstrating the superiority of IDGCP over the others in terms of restoration quality and implementation efficiency.
Jung, H-S, Lee, S & Pradhan, B 2020, 'Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations', Sustainability, vol. 12, no. 6, pp. 2390-2390.
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The Special Issue on “Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations” is published. A total of 20 qualified papers are published in this Special Issue. The topics of the papers are the application of remote sensing and geospatial information systems to Earth observations in various fields such as (1) object change detection, (2) air pollution, (3) earthquakes, (4) landslides, (5) mining, (6) biomass, (7) groundwater, and (8) urban development using the techniques of remote sensing and geospatial information systems. More than 100 researchers have participated in this Special Issue. We hope that this Special Issue is helpful for sustainable applications.
Kalantar, B, Ueda, N, Al-Najjar, HAH & Halin, AA 2020, 'Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images', Remote Sensing, vol. 12, no. 21, pp. 3529-3529.
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In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earthquakes. Automatic and visual techniques are considered as typical methods to produce building damage maps using RS images. The visual technique, however, is time-consuming due to manual sampling. The automatic method is able to detect the damaged building by extracting the defect features. However, various design methods and widely changing real-world conditions, such as shadow and light changes, cause challenges to the extensive appointing of automatic methods. As a potential solution for such challenges, this research proposes the adaption of deep learning (DL), specifically convolutional neural networks (CNN), which has a high ability to learn features automatically, to identify damaged buildings from pre- and post-event RS imageries. Since RS data revolves around imagery, CNNs can arguably be most effective at automatically discovering relevant features, avoiding the need for feature engineering based on expert knowledge. In this work, we focus on RS imageries from orthophoto imageries for damaged-building detection, specifically for (i) background, (ii) no damage, (iii) minor damage, and (iv) debris classifications. The gist is to uncover the CNN architecture that will work best for this purpose. To this end, three CNN models, namely the twin model, fusion model, and composite model, are applied to the pre- and post-orthophoto imageries collected from the 2016 Kumamoto earthquake, Japan. The robustness of the models was evaluated using four evaluation metrics, namely overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and F1 score. According to the obtained results, the twin model achieved higher accuracy (OA = 76.86%; F1 score = 0.761) compare to the fusion model (OA = 72.27%; F1...
Kalhori, H, Alamdari, MM, Li, B, Halkon, B, Hosseini, SM, Ye, L & Li, Z 2020, 'Concurrent Identification of Impact Location and Force Magnitude on a Composite Panel', International Journal of Structural Stability and Dynamics, vol. 20, no. 10, pp. 2042004-2042004.
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Simultaneous estimation of both the location and force history of an impact applied on a lattice truss core sandwich panel is inversely carried out utilizing velocity signals collected by means of a scanning laser Doppler vibrometer. The algorithm assumes that several impact forces are exerted concurrently on a number of specified locations on a panel, provided that the magnitude of all impact forces but one is actually equal to zero. This condition equates to a scenario where an impact occurs at only one location. The purpose is therefore to detect the actual impact location among all potential locations, together with its force history, through minimizing error functions. Two algorithms, the one-to-one (even-determined) approach and the superposition approach, are considered. The one-to-one approach solves the reconstruction problem independently for each pair of impact and measurement points. However, in the superposition approach, the impact forces at all potential locations are concurrently reconstructed through a single matrix equation. It is shown that the one-to-one approach fails to detect the true impact location while the superposition approach recognizes the actual impact location based on some qualitative evaluating criteria. Adopting the superposition approach, for a problem with four possible impact locations, two scenarios one with four and one with 12 measurement points, are investigated. It is observed that the additional measurement points do not necessarily enhance the efficiency and accuracy of the proposed method. It is found that different arrangements of measuring points lead to identification of the location and the magnitude of the impact force, though the use of four evenly distributed measurement points seems to be most effective in simultaneous identification of the location and magnitude of the impact force. Further, a quantitative index based on the concept of similarity search for time-series using wavelet transf...
Kapeleris, J, Kulasinghe, A, Warkiani, ME, Oleary, C, Vela, I, Leo, P, Sternes, P, O’Byrne, K & Punyadeera, C 2020, 'Ex vivo culture of circulating tumour cells derived from non-small cell lung cancer', Translational Lung Cancer Research, vol. 9, no. 5, pp. 1795-1809.
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Background
Tumour tissue-based information is limited. Liquid biopsy can provide valuable real-time information through circulating tumour cells (CTCs). Profiling and expanding CTCs may provide avenues to study transient metastatic disease.
Methods
Seventy non-small cell lung cancer (NSCLC) patients were recruited. CTCs were enriched using the spiral microfluidic chip and a RosetteSep™ using bloods from NSCLC patients. CTC cultures were carried out using the Clevers media under hypoxic conditions. CTCs were characterized using immunofluorescence and mutation-specific antibodies for samples with known mutation profiles. Exome sequencing was used to characterized CTC cultures.
Results
CTCs (>2 cells) were detected in 38/70 (54.3%) of patients ranging from 0 to 385 CTCs per 7.5 mL blood. In 4/5 patients where primary tumours harboured an EGFR exon 19 deletion, this EGFR mutation was also captured in CTCs. ALK translocation was confirmed on CTCs from a patient harbouring an ALK-rearrangement in the primary tumour. Short term CTC cultures were successfully generated in 9/70 NSCLC patients. Whole exome sequencing (WES) confirmed the presence of somatic mutations in the CTC cultures with mutational signatures consistent with NSCLC.
Conclusions
We were able to detect CTCs in >50% of NSCLC patients. NSCLC patients with >2 CTCs had a poor prognosis. The short-term CTC culture success rate was 12.9%. Further optimization of this culture methodology may provide a means by which to expand CTCs derived from NSCLC patient's bloods. CTC cultures allow for expansion of cells to a critical mass, allowing for functional characterization of CTCs with the goal of drug sensitivity testing and the creation of CTC cell lines.
Karimi, M, Croaker, P, Maxit, L, Robin, O, Skvortsov, A, Marburg, S & Kessissoglou, N 2020, 'A hybrid numerical approach to predict the vibrational responses of panels excited by a turbulent boundary layer', Journal of Fluids and Structures, vol. 92, pp. 102814-102814.
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© 2019 Elsevier Ltd In this work, a hybrid numerical approach to predict the vibrational responses of planar structures excited by a turbulent boundary layer is presented. The approach combines an uncorrelated wall plane wave technique with the finite element method. The wall pressure field induced by a turbulent boundary layer is obtained as a set of uncorrelated wall pressure plane waves. The amplitude of these plane waves are determined from the cross spectrum density function of the wall pressure field given either by empirical models from literature or from experimental data. The response of the planar structure subject to a turbulent boundary layer excitation is then obtained from an ensemble average of the different realizations. The numerical technique is computationally efficient as it rapidly converges using a small number of realizations. To demonstrate the method, the vibrational responses of two panels with simply supported or clamped boundary conditions and excited by a turbulent flow are considered. In the case study comprising a plate with simply supported boundary conditions, an analytical solution is employed for verification of the method. For both cases studies, numerical results from the hybrid approach are compared with experimental data measured in two different anechoic wind tunnels.
Karimi, M, Maxit, L, Croaker, P, Robin, O, Skvortsov, A, Marburg, S, Atalla, N & Kessissoglou, N 2020, 'Analytical and numerical prediction of acoustic radiation from a panel under turbulent boundary layer excitation', Journal of Sound and Vibration, vol. 479, pp. 115372-115372.
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The vibroacoustic response of a simply supported panel excited by turbulent flow is analytically and numerically investigated. In the analytical model, the radiated sound power is described in terms of the cross spectrum density of the wall pressure field and sensitivity functions for the acoustic pressure and fluid particle velocity. For the numerical model, a hybrid approach based on the finite element method is described in which the cross spectrum of the wall pressure field is represented by a set of uncorrelated wall plane waves. Realisations of the wall pressure field are used as deterministic input loads to the panel. The structural and acoustic responses of the panel subject to turbulent boundary layer excitation are then obtained from an ensemble average of the different realisations. Analytical and numerical results are compared with experimental data measured in an anechoic wind tunnel, showing good agreement. The effect of adding stiffeners on the vibroacoustic response of the panel is also examined using the proposed numerical approach.
Karimi, M, Maxit, L, Meyer, V, Marburg, S & Kirby, R 2020, 'Non-negative intensity for planar structures under stochastic excitation', Journal of Sound and Vibration, vol. 488, pp. 115652-115652.
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Karimidastenaei, Z, Torabi Haghighi, A, Rahmati, O, Rasouli, K, Rozbeh, S, Pirnia, A, Pradhan, B & Kløve, B 2020, 'Fog-water harvesting Capability Index (FCI) mapping for a semi-humid catchment based on socio-environmental variables and using artificial intelligence algorithms', Science of The Total Environment, vol. 708, pp. 135115-135115.
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Fog is an important component of the water cycle in northern coastal regions of Iran. Having accurate tools for mapping the precise spatial distribution of fog is vital for water harvesting within integrated water resources management in this semi-humid region. In this study, environmental variables were considered in prediction mapping of areas with high concentrations of fog in the Vazroud watershed, Iran. Fog probability maps were derived from four artificial intelligence algorithms (Generalized Linear Model, Generalized Additive Model, Generalized Boosted Model, and Generalized Dissimilarity Model). Models accuracy were assessed using Receiver Operating characteristic Curve (ROC). Three social variables were also selected according to their relevance for fog suitability mapping. Finally, Fog-water harvesting Capability Index (FCI) maps were produced by multiplying fog probability by fog suitability maps. The results showed high accuracy in fog probability mapping for the study area, with all models proving capable of identifying areas with high fog concentrations in the south and southeast. For all models, the highest values of importance were obtained for sky view factor and the lowest for slope curvature. Analytic Hierarchy Process results showed the relative importance of social conditioning factors in fog suitability mapping, with the highest weight given to distance to residential area, followed by distance to livestock buildings and distance to road. Based on the fog suitability map, southeast and southern parts of the study area are most suitable for fog water harvesting. The fog spatial distribution maps obtained can increase fog water harvesting efficiency. They also indicate areas for future study with regions where fog is a critical component in the water cycle.
Karmokar, DK, Chen, S-L, Thalakotuna, D, Qin, P-Y, Bird, TS & Guo, YJ 2020, 'Continuous Backward-to-Forward Scanning 1-D Slot-Array Leaky-Wave Antenna With Improved Gain', IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 1, pp. 89-93.
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Karmokar, DK, Guo, YJ, Chen, S-L & Bird, TS 2020, 'Composite Right/Left-Handed Leaky-Wave Antennas for Wide-Angle Beam Scanning With Flexibly Chosen Frequency Range', IEEE Transactions on Antennas and Propagation, vol. 68, no. 1, pp. 100-110.
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© 2019 IEEE. A composite right/left-handed (CRLH) leaky-wave antenna (LWA) can effectively scan the radiation beam from backward-to-forward direction. However, in most cases, a large range of frequency sweep is required to achieve a wide-angle beam scan, which could limit their applications. An in-depth study is conducted on an equivalent circuit model for a CRLH LWA unit cell to find the controlling parameters on the frequency sweeping range. A systematic design guideline is given for a CRLH LWA for a wide-angle beam scan in a flexibly chosen frequency range. It is shown that beam scanning by sweeping frequency in a target range can be achieved by systematically designing the unit cell parameters. To verify our approach, a novel CRLH unit cell is developed and used to design an LWA for a wide-angle beam scan in a narrow frequency range. Finally, the concept is validated through realization of the antenna and its measurement. The measured results show that the antenna prototype can scan its beam from -56° to +51° when frequency sweeps from 5.1 to 6.11 GHz (i.e., 18.02% of fractional bandwidth).
Kashani, AR, Saneirad, A & Gandomi, AH 2020, 'Optimum design of reinforced earth walls using evolutionary optimization algorithms', Neural Computing and Applications, vol. 32, no. 16, pp. 12079-12102.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. This study addresses the optimum cost design of mechanically stabilized earth (MSE) using geosynthetics. The design process of MSEs is mathematically programmed based on an objective function depending on the length of reinforcements and vertical distance of reinforced layers. Design restrictions control the final design to be valid in terms of constraints. The aim is to explore the efficiency of evolutionary-based algorithms in dealing with MSE optimization problem along with automating the minimum cost design of MSE walls. To this end, three evolutionary algorithms, differential evolution (DE), evolution strategy, and biogeography-based optimization algorithm (BBO), are tackled to solve this problem. Comprehensive computational simulations confirm the impact of different effective parameters variation on the final design. Finally, the BBO algorithm performed the best, while DE recorded the most unsatisfactory results.
Kashif, M, Hossain, MJ, Fernandez, E, Taghizadeh, S, Sharma, V, Ali, SMN & Irshad, UB 2020, 'A Fast Time-Domain Current Harmonic Extraction Algorithm for Power Quality Improvement Using Three-Phase Active Power Filter', IEEE Access, vol. 8, pp. 103539-103549.
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© 2013 IEEE. Harmonic current estimation is the key aspect of Active Power Filter (APF) control algorithms to generate a reference current for harmonic compensation. This paper proposes a novel structure for harmonic current estimation scheme based on Trigonometric Orthogonal Principle (TOP) and Self Tuning Filter (STF). The key advantages of the proposed method are its simplicity, low computational burden and faster execution time in comparison to the conventional harmonic current estimation approaches. The TOP method provides a simple and fast approach to extract the reference current, while STF provides a simplified structure to generate the required synchronization signal that eliminates the need of a Phase Locked Loop (PLL) algorithm for synchronization. As a result, it exhibits less complexity in implementation and less consumption of microcontroller's resources; thus, the proposed method can be implemented using a low-cost microcontroller. It is shown in the paper that the proposed method provides 10 times gain in processing speed as compared to the conventional DQ method. The proposed approach is analyzed in detail, and its effectiveness and superior performance are verified using simulation and experimental results.
Kaur, G, Singh, SK, Kumar, R, Kumar, B, Kumari, Y, Gulati, M, Pandey, NK, Gowthamarajan, K, Ghosh, D, Clarisse, A, Wadhwa, S, Mehta, M, Satija, S, Dua, K, Dureja, H, Gupta, S, Singh, PK, Kapoor, B, Chitranshi, N, Kumar, A & Porwal, O 2020, 'Development of modified apple polysaccharide capped silver nanoparticles loaded with mesalamine for effective treatment of ulcerative colitis', Journal of Drug Delivery Science and Technology, vol. 60, pp. 101980-101980.
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© 2020 Elsevier B.V. The objective of study was to develop modified apple polysaccharide (MAP) based silver nanoparticles (AgNPs) loaded with mesalamine (MES) for effective treatment of ulcerative colitis in acetic acid induced rat model. AgNPs were prepared by reducing silver nitrate using MAP solution. The size and zeta potential of AgNPs was 89 ± 3 nm and −16.3 ± 1.54 mV and AgNPs loaded with MES (AgNPs-MES) was 101 ± 9 nm and −14.27 ± 2.16 mV. The dissolution study revealed about 54% drug release after 5 h indicating release of drug at the colonic site. The in vivo study was carried out on acetic acid induced ulcerative colitis rats and efficacy of treatment was assessed through evaluation of disease activity index and level of antioxidants as well as tumor necrosis factor-α after 7th and 14th day of induction of colitis. Histopathological evaluation of colonic tissue was also carried out. The results revealed that AgNPs-MES (high dose) provided better therapeutic efficacy for the treatment of UC as compared to its low dose, MES alone, MES-MAP, AgNPs alone and MAP alone. It was concluded that MAP based AgNPs loaded with MES were successfully formulated and found to be effective in treating ulcerative colitis.
Kavadi, DP, Patan, R, Ramachandran, M & Gandomi, AH 2020, 'Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19', Chaos, Solitons & Fractals, vol. 139, pp. 110056-110056.
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The recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous epidemic. Such predictions can be useful for managing and intercepting the outbreak of such diseases. The foremost advantages of applying ML methods are handling a wide variety of data and easy identification of trends and patterns of an undetermined nature.In this study, we propose a partial derivative regression and nonlinear machine learning (PDR-NML) method for global pandemic prediction of COVID-19. We used a Progressive Partial Derivative Linear Regression model to search for the best parameters in the dataset in a computationally efficient manner. Next, a Nonlinear Global Pandemic Machine Learning model was applied to the normalized features for making accurate predictions. The results show that the proposed ML method outperformed state-of-the-art methods in the Indian population and can also be a convenient tool for making predictions for other countries.
Kennedy, P 2020, 'Editorial for “Diagnostic Value of Gd‐EOB‐DTPA‐Enhanced MRI for the Expression of Ki67 and Microvascular Density in Hepatocellular Carcinoma”', Journal of Magnetic Resonance Imaging, vol. 51, no. 6, pp. 1764-1765.
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Kennedy, P, Bane, O, Hectors, SJ, Gordic, S, Berger, M, Delaney, V, Salem, F, Lewis, S, Menon, M & Taouli, B 2020, 'Magnetic resonance elastography vs. point shear wave ultrasound elastography for the assessment of renal allograft dysfunction', European Journal of Radiology, vol. 126, pp. 108949-108949.
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Kerferd, B, Eggler, D, Karimi, M & Kessissoglou, N 2020, 'Active acoustic cloaking of cylindrical shells in low Mach number flow', Journal of Sound and Vibration, vol. 479, pp. 115400-115400.
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The vibro-acoustic responses of a two-dimensional cylindrical shell in low Mach number flow are herein derived. The analytical model takes into account the structural elasticity and coupling of the shell vibration with its interior and exterior acoustic fields in the presence of a moving fluid. The cylindrical shell is modelled using Donnell-Mushtari theory. Taylor transformations are employed to transfer the convected wave equation into the ordinary wave equation which was then solved using scattering theory. Three excitation cases corresponding to a plane wave, an external monopole source and a radial point force applied directly to the shell are considered. Shell circumferential resonances and interior acoustic resonances are identified. Two active control strategies are then applied to acoustically cloak the cylindrical shell at its acoustic and structural resonances. The first control approach employs acoustic control sources in the exterior fluid domain. In the second approach, control forces are applied to directly excite the elastic shell, whereby the structural response is actively modified to manipulate the scattered and radiated acoustic fields arising from plane wave excitation of the shell. Results show that the second approach is superior in terms of both reduced control effort and cloaking of the global exterior domain. For both control approaches, the performance of the active cloak is shown to deteriorate if the convected flow field is not accounted for in the control process.
Ketprasit, N, Cheng, IS, Deutsch, F, Tran, N, Imwong, M, Combes, V & Palasuwan, D 2020, 'The characterization of extracellular vesicles-derived microRNAs in Thai malaria patients', Malaria Journal, vol. 19, no. 1.
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Abstract Background Extracellular vesicles (EVs) have been broadly studied in malaria for nearly a decade. These vesicles carry various functional biomolecules including RNA families such as microRNAs (miRNA). These EVs-derived microRNAs have numerous roles in host-parasite interactions and are considered promising biomarkers for disease severity. However, this field lacks clinical studies of malaria-infected samples. In this study, EV specific miRNAs were isolated from the plasma of patients from Thailand infected with Plasmodium vivax and Plasmodium falciparum. In addition, it is postulated that these miRNAs were differentially expressed in these groups of patients and had a role in disease onset through the regulation of specific target genes. Methods EVs were purified from the plasma of Thai P. vivax-infected patients (n = 19), P. falciparum-infected patients (n = 18) and uninfected individuals (n = 20). EV-derived miRNAs were then prepared and abundance of hsa-miR-15b-5p, hsa-miR-16-5p, hsa-let-7a-5p and hsa-miR-150-5p was assessed in these samples. Quantitative polymerase chain reaction was performed, and relative expression of each miRNA was calculated using hsa-miR-451a as endogenous control. Then, the targets of up-regulated miRNAs and relevant pathways were predicted by using bioinformatics. Receiver Operating Characteristic with Area under the Curve (AUC) was then calculated to assess their diagnostic potential. Results The relative expression of hsa-miR-150-5p and hsa-miR-15b-5p was higher in P. vivax
Khaleque, A, Alam, MM, Hoque, M, Mondal, S, Haider, JB, Xu, B, Johir, MAH, Karmakar, AK, Zhou, JL, Ahmed, MB & Moni, MA 2020, 'Zeolite synthesis from low-cost materials and environmental applications: A review', Environmental Advances, vol. 2, pp. 100019-100019.
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Zeolites with the three-dimensional structures occur naturally or can be synthesized in the laboratory. Zeolites have versatile applications such as environmental remediation, catalytic activity, biotechnological application, gas sensing and medicinal applications. Although, naturally occurring zeolites are readily available, nowadays, more emphasis is given on the synthesis of the zeolites due to their easy synthesis in the pure form, better ion exchange capabilities and uniform in size. Recently, much attention has also been paid on how zeolite is being synthesized from low-cost material (e.g., rice husk), particularly, by resolving the major environmental issues. Hence, the main purpose of this review is to make an effective resolution of zeolite synthesis methods together with potential applications in environmental engineering. Among different synthesis methods, hydrothermal method is commonly found to be used widely in the synthesis of various zeolites from inexpensive raw materials such as fly ash, rice husk ash, blast furnace slag, municipal solid waste, paper sludge, lithium slag and kaolin. Besides, future expectation in the field of synthetic zeolites research is also included.
Khalilpour, KR & Lusis, P 2020, 'Network capacity charge for sustainability and energy equity: A model-based analysis', Applied Energy, vol. 266, pp. 114847-114847.
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Khalilpour, KR & Zafaranloo, A 2020, 'Generic techno-economic optimization methodology for concurrent design and operation of solvent-based PCC processes', International Journal of Greenhouse Gas Control, vol. 99, pp. 103079-103079.
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Khalilpour, KR, Pace, R & Karimi, F 2020, 'Retrospective and prospective of the hydrogen supply chain: A longitudinal techno-historical analysis', International Journal of Hydrogen Energy, vol. 45, no. 59, pp. 34294-34315.
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© 2020 Hydrogen Energy Publications LLC The objective of this study was to investigate the evolution of hydrogen research and its international scientific collaboration network. From the Scopus database, 58,006 relevant articles, published from 1935 until mid-2018, were retrieved. To review this massive volume of publication records, we took a scientometric network analysis approach and investigated the social network of the publication contents based on keywords co-occurrence as well as international collaboration ties. An interesting observation is that despite publications on hydrogen occurring since 1935, the growth of this research field ignited with the Kyoto Protocol of 1997. The publication profile reveals that more than 93% of the existing records have been published over the last two decades. More recently, the accelerated growth of renewables has further motivated hydrogen research with almost 36,000 academic records having been indexed from 2010 till mid-2018. This accounts for ~62% of the total historical publications on hydrogen. The conventional hydrogen production pathway is fossil fuel-based, involving fossil fuel reforming for synthesis gas generation. The keyword analysis also shows a paradigm shift in hydrogen generation to renewables. While all components of hydrogen supply chain research are now growing, the topic areas of biohydrogen and photocatalysis seem to be growing the fastest. Analysis of international collaboration networks also reveals a strong correlation between the increase of collaboration ties on hydrogen research and the publications. Until the 1970s, only 25 countries had collaborated, while this has reached 108 countries as of 2018, with over 17,500 collaboration ties. The collaborations have also evolved into a substantially more integrated network, with a few strong clusters involving China, the United States, Germany, and Japan. The longitudinal network evolution maps also reveal a shift, over the last two decades, from ...
Khan, HA, Castel, A & Khan, MSH 2020, 'Corrosion investigation of fly ash based geopolymer mortar in natural sewer environment and sulphuric acid solution', Corrosion Science, vol. 168, pp. 108586-108586.
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© 2020 Elsevier Ltd The objective of this research is to estimate the durability of low-calcium fly ash based geopolymer mortar (FA-GPm) in comparison with sulphate resistant Portland cement mortars (SRPCm) exposed to natural sewer environment. Their performance is also investigated in the sulphuric acid (H2SO4) solution to highlight the difference in the corrosion mechanisms between these two exposure conditions. Mortar samples were removed from natural sewer and 1.5 % sulphuric acid solution after 12, 24 months and 6 months of exposure, respectively. Visual and physical analyses showed greater neutralization and loss in alkalinity in FA-GPm compared to SRPCm. However, mass loss and strength reduction observed for SRPCm was greater compared to FA-GPm. Microstructural analysis showed widespread gypsum crystallization within SRPCm matrix compared to FA-GPm, leading to more severe matrix deterioration. Differences in corrosion mechanism were identified between natural and sulphuric acid exposure conditions which led to the variation in estimated corrosion depth. Data collected from these microstructural and physical investigations were utilized to develop simplified linear models to express the depth of corrosion, surface pH, mass loss and neutralization depth of FA-GPm and SRPCm as a dependent of exposure time, temperature and H2S concentration in natural sewer environments.
Khan, HU, ARUYA, JOYA & Gill, AQ 2020, 'Web 2.0 Technologies Adoption Barriers for External Contacts and Participation: A Case Study of Federal Establishment of Africa', International Journal of Business Information Systems, vol. 1, no. 1, pp. 1-1.
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Khan, I, Xu, T, Khan, MSH, Castel, A & Gilbert, RI 2020, 'Effect of Various Supplementary Cementitious Materials on Early-Age Concrete Cracking', Journal of Materials in Civil Engineering, vol. 32, no. 4, pp. 04020049-04020049.
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© 2020 American Society of Civil Engineers. This paper focuses on the effect of supplementary cementitious materials on early-age mechanical and viscoelastic properties of concrete, restrained shrinkage-induced cracking, and time to cracking. Compressive strength, indirect tensile strength, and the elastic modulus were measured with different percentage of ordinary portland cement (OPC) replacement using either fly ash, ground-granulated blast-furnace slag (GGBFS), or ferronickel slag (FNS). Tensile creep and drying shrinkage were measured on dog-bone-shaped specimens. Restrained shrinkage-induced stresses and concrete cracking age were assessed by using the ring test. Results revealed that early-age strength development of fly ash-, GGBFS-, and FNS-blended concrete is lower than that of the corresponding OPC concrete. Similar tensile creep coefficients were observed for fly ash-blended concrete and OPC reference concrete whereas GGBFS- and FNS-blended concretes showed significantly higher tensile creep. Drying shrinkage was not altered to a great extent when OPC was replaced by fly ash. However, concrete containing GGBFS and FNS showed more shrinkage than OPC concrete. Partial replacement of OPC by supplementary cementitious materials resulted in a shorter time to cracking. 30% OPC replacement by FNS had the lowest influence on time to cracking with only 20% reduction compared to the reference OPC concrete. 20% replacement by fly ash and 30% replacement by GGBFS led to a reduction in time to cracking of about 33% and 40%, respectively, compared to the reference OPC concrete.
Khan, JA, Nguyen, LN, Duong, HC & Nghiem, LD 2020, 'Acetic acid extraction from rumen fluid by forward osmosis', Environmental Technology & Innovation, vol. 20, pp. 101083-101083.
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Khan, MA, Zhu, Y, Yao, Y, Zhang, P, Agrawal, A & Reece, PJ 2020, 'Impact of metal crystallinity-related morphologies on the sensing performance of plasmonic nanohole arrays', Nanoscale, vol. 12, no. 14, pp. 7577-7585.
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Low surface roughness and large metal grain sizes improve the sensitivity of a plasmonic nanohole array sensor.
Khan, MNH, Forouzesh, M, Siwakoti, YP, Li, L & Blaabjerg, F 2020, 'Switched Capacitor Integrated (2n + 1)-Level Step-Up Single-Phase Inverter', IEEE Transactions on Power Electronics, vol. 35, no. 8, pp. 8248-8260.
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© 1986-2012 IEEE. This article presents a novel switched capacitor (SC) based (2n + 1)-level single-phase inverter with a reduced number of components and input dc voltage supply. This inverter is designed in a way that just one dc source is required to generate different voltage levels. The circuit consists of three major parts, i.e., front-end boost stage, active SC cell(s) in the middle, and H-bridge inverter at the end. The total number of output voltage levels is up to (2n + 1) levels, where n ≥ 2 is the number of switching cells, which consists of three active switches and two capacitors. Compared with conventional SC-based multilevel inverter topologies, the proposed topology features many advantages, such as low number of semiconductor devices, quasi-resonant charging of capacitors that reduce the inrush current and current stress on the devices, self-balancing of capacitor, and reduced voltage stress on the switches. Moreover, a simple sinusoidal pulsewidth modulation technique is employed here to generate the modulation signals for the proposed inverter. The operating principle is presented in detail followed by comparative analysis, thermal modeling, and design guidelines. Finally, computer simulation and laboratory test results are carried out for a five-level inverter with one SC cells as well as a seven-level inverter with two SC cells as two examples to verify the performance of the proposed (2n + 1)-level inverter. Measurement results show that the proposed inverter has the 96.5 ± 1% efficiency over a wide range of load with a peak efficiency of 98.56%.
Khan, MNH, Forouzesh, M, Siwakoti, YP, Li, L, Kerekes, T & Blaabjerg, F 2020, 'Transformerless Inverter Topologies for Single-Phase Photovoltaic Systems: A Comparative Review', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 8, no. 1, pp. 805-835.
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© 2013 IEEE. In photovoltaic (PV) applications, a transformer is often used to provide galvanic isolation and voltage ratio transformations between input and output. However, these conventional iron-and copper-based transformers increase the weight/size and cost of the inverter while reducing the efficiency and power density. It is therefore desirable to avoid using transformers in the inverter. However, additional care must be taken to avoid safety hazards such as ground fault currents and leakage currents, e.g., via the parasitic capacitor between the PV panel and ground. Consequently, the grid connected transformerless PV inverters must comply with strict safety standards such as IEEE 1547.1, VDE0126-1-1, EN 50106, IEC61727, and AS/N ZS 5033. Various transformerless inverters have been proposed recently to eliminate the leakage current using different techniques such as decoupling the dc from the ac side and/or clamping the common mode (CM) voltage (CMV) during the freewheeling period, or using common ground configurations. The permutations and combinations of various decoupling techniques with integrated voltage buck-boost for maximum power point tracking (MPPT) allow numerous new topologies and configurations which are often confusing and difficult to follow when seeking to select the right topology. Therefore, to present a clear picture on the development of transformerless inverters for the next-generation grid-connected PV systems, this paper aims to comprehensively review and classify various transformerless inverters with detailed analytical comparisons. To reinforce the findings and comparisons as well as to give more insight on the CM characteristics and leakage current, computer simulations of major transformerless inverter topologies have been performed in PLECS software. Moreover, the cost and size are analyzed properly and summarized in a table. Finally, efficiency and thermal analysis are provided with a general summary as well as a tec...
Khan, MSH, Nguyen, QD & Castel, A 2020, 'Performance of limestone calcined clay blended cement-based concrete against carbonation', Advances in Cement Research, vol. 32, no. 11, pp. 481-491.
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The aim of this work is to investigate the carbonation resistance of limestone and calcined clay blended cement-based concrete. Two limestone and calcined clay concretes with an average 28 d compressive strength of about 36 MPa were considered. Limestone and calcined clay (with a ratio of 2 : 1) were blended with a general purpose (GP) cement. The GP cement substitution rates considered were 30% and 45%.A low-grade calcined clay was used with about 50% amorphous phase. Accelerated and natural carbonation tests were performed. Mercury intrusion porosimetry and X-ray diffraction were carried out, to assist in the analysis of the experimental results. Results show that the early-age compressive strength is only marginally affected by the limestone and calcined clay substitution up to 45% and a significant refinement of the pore structure was observed compared to the reference GP cement concrete. The resistance of concrete against carbonation reduces with increase in the GP cement substitution rate. Overall, this study shows that a limestone and calcined clay blend used as a simple substitution for GP cement in concrete can provide adequate protection against carbonation-induced steel reinforcement corrosion if the ordinary Portland cement content in the mix is at least 60%.
Khan, NU, Wan, W & Yu, S 2020, 'Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China', ISPRS International Journal of Geo-Information, vol. 9, no. 2, pp. 76-76.
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The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of check-ins, and venue classes, based on users’ check-in behavior and the city’s characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data are translated into the Geographical Information Systems (GIS) format, and after analysis the results are presented in the form of statistical graphs, tables, and spatial heatmaps. SPSS is used for temporal analysis, and Kernel Density Estimation (KDE) is applied based on users’ check-ins with the help of ArcMap and OpenStreetMap for spatial analysis. The findings show various patterns, including more frequent use of LBSN while visiting entertainment and shopping locations, a substantial number of check-ins from educational institutions, and that the density extends to suburban areas mainly because of educational institutions and residential areas. Through analytical results, the usage patterns based on hours of the day, days of the week, and for an entire six months, including by gender, venue category, and frequency distribution of the classes, as well as check-in density all over Shanghai city, are thoroughly demonstrated.
Khan, NU, Wan, W, Yu, S, Muzahid, AAM, Khan, S & Hou, L 2020, 'A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data', ISPRS International Journal of Geo-Information, vol. 9, no. 12, pp. 733-733.
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The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, spatial and visualization techniques by classifying users’ check-ins into different venue categories. This article investigates the use of Weibo for big data analysis and its efficiency in various categories instead of manually collected datasets, by exploring the relation between time, frequency, place and category of check-in based on location characteristics and their contributions. The data used in this research was acquired from a famous Chinese microblogs called Weibo, which was preprocessed to get the most significant and relevant attributes for the current study and transformed into Geographical Information Systems format, analyzed and, finally, presented with the help of graphs, tables and heat maps. The Kernel Density Estimation was used for spatial analysis. The venue categorization was based on nature of the physical locations within the city by comparing the name of venue extracted from Weibo dataset with the function such as education for schools or shopping for malls and so on. The results of usage patterns from hours to days, venue categories and frequency distribution into these categories as well as the density of check-in within the Shanghai and contribution of each venue category in its diversity are thoroughly demonstrated, uncovering interesting spatio-temporal patterns including frequency and density of users from different venues at different time intervals, and significance of using geo-data from Weibo to study human behavior in variety of studies like education, tourism and city dynamics based on location-based social networks. Our findings uncover various aspects of activity patterns in human behavior, the significance of venue classes and its effects in Shanghai...
Khan, SA, Guo, Y, Siwakoti, YP, Lu, DD-C & Zhu, J 2020, 'A Disturbance Rejection-Based Control Strategy for Five-Level T-Type Hybrid Power Converters With Ripple Voltage Estimation Capability', IEEE Transactions on Industrial Electronics, vol. 67, no. 9, pp. 7364-7374.
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© 1982-2012 IEEE. This article proposes a robust control strategy for five-level T-type (5L-T) hybrid power converters to achieve superior dynamic performance for effectively regulating the dc-bus voltage under external disturbances and generating high-quality grid current at the same time. A new filter-less dc-bus ripple voltage estimation method and a simple technique to remove this ripple component from the measured dc-bus voltage of a single-phase converter are developed. A sliding-mode control (SMC) incorporated with an extended-state observer (ESO) is employed for the outer voltage control loop, and to dynamically calculate the input (i.e., the active power reference) for the inner current-tracking controller. The proposed SMC-ESO approach presents a high disturbance rejection capability and robustness against the dc-bus load variation, and thus, significantly improves the dynamic and steady-state performance during system uncertainties. Moreover, a finite control set-model predictive control algorithm is derived as the inner current controller to track their references while balancing the dc-bus capacitor voltages. The effectiveness of the proposed controller is demonstrated and verified through measurement results.
Khan, TA & Ling, SH 2020, 'A survey of the state-of-the-art swarm intelligence techniques and their application to an inverse design problem', Journal of Computational Electronics, vol. 19, no. 4, pp. 1606-1628.
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Khanzada, NK, Farid, MU, Kharraz, JA, Choi, J, Tang, CY, Nghiem, LD, Jang, A & An, AK 2020, 'Removal of organic micropollutants using advanced membrane-based water and wastewater treatment: A review', Journal of Membrane Science, vol. 598, pp. 117672-117672.
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© 2019 Elsevier B.V. The rising consumption of pharmaceuticals, personal care products, and endocrine disruptive compounds for healthcare purposes and improving living standards has resulted in the widespread occurrence of organic micropollutants (MPs) in water and wastewater. Conventional water/wastewater treatment plants are faced with inherent limitations in tackling these compounds, leading to difficulties in the provision of secure and safe water supplies. In this context, membrane technology has been found to be a promising method for resolving this emerging concern. To ensure the suitability of membrane-based treatment processes in full-scale applications, we first need to develop a better understanding of the behavior of MPs and the mechanisms behind their removal using advanced membrane technologies. This review provides a thorough overview of the advanced membrane-based treatment methods available for the effective removal of MPs, including reverse osmosis, nanofiltration, ultrafiltration, forward osmosis, and membrane distillation.
Khari, M, Garg, AK, Gandomi, AH, Gupta, R, Patan, R & Balusamy, B 2020, 'Securing Data in Internet of Things (IoT) Using Cryptography and Steganography Techniques', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 1, pp. 73-80.
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Internet of Things (IoT) is a domain wherein which the transfer of data is taking place every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, the elliptic Galois cryptography protocol is introduced and discussed. In this protocol, a cryptography technique is used to encrypt confidential data that came from different medical sources. Next, a Matrix XOR encoding steganography technique is used to embed the encrypted data into a low complexity image. The proposed work also uses an optimization algorithm called Adaptive Firefly to optimize the selection of cover blocks within the image. Based on the results, various parameters are evaluated and compared with the existing techniques. Finally, the data that is hidden in the image is recovered and is then decrypted.
Khari, M, Jahed Armaghani, D & Dehghanbanadaki, A 2020, 'Prediction of Lateral Deflection of Small-Scale Piles Using Hybrid PSO–ANN Model', Arabian Journal for Science and Engineering, vol. 45, no. 5, pp. 3499-3509.
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Khlaifat, N, Altaee, A, Zhou, J & Huang, Y 2020, 'A review of the key sensitive parameters on the aerodynamic performance of a horizontal wind turbine using Computational Fluid Dynamics modelling', AIMS Energy, vol. 8, no. 3, pp. 493-524.
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© 2020 the Author(s), licensee AIMS Press. Renewable energy technologies are receiving much attention to replacing power plants operated by fossil and nuclear fuels. Of all the renewable technologies, wind power has been successfully implemented in several countries. There are several parameters in the aerodynamic characteristics and design of the horizontal wind turbine. This paper highlights the key sensitive parameters that affect the aerodynamic performance of the horizontal wind turbine, such as environmental conditions, blade shape, airfoil configuration and tip speed ratio. Different turbulence models applied to predict the flow around the horizontal wind turbine using Computational Fluid Dynamics modeling are reviewed. Finally, the challenges and concluding remarks for future research directions in wind turbine design are discussed.
Khlaifat, N, Altaee, A, Zhou, J & Huang, Y 2020, 'A review of the key sensitive parameters on the aerodynamic performance of a horizontal wind turbine using computational fluid dynamics modelling', AIMS Energy, vol. 8, no. 3, pp. 493-524.
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© 2020 the Author(s), licensee AIMS Press. Renewable energy technologies are receiving much attention to replacing power plants operated by fossil and nuclear fuels. Of all the renewable technologies, wind power has been successfully implemented in several countries. There are several parameters in the aerodynamic characteristics and design of the horizontal wind turbine. This paper highlights the key sensitive parameters that affect the aerodynamic performance of the horizontal wind turbine, such as environmental conditions, blade shape, airfoil configuration and tip speed ratio. Different turbulence models applied to predict the flow around the horizontal wind turbine using Computational Fluid Dynamics modeling are reviewed. Finally, the challenges and concluding remarks for future research directions in wind turbine design are discussed.
Khlaifat, N, Altaee, A, Zhou, J, Huang, Y & Braytee, A 2020, 'Optimization of a Small Wind Turbine for a Rural Area: A Case Study of Deniliquin, New South Wales, Australia', Energies, vol. 13, no. 9, pp. 2292-2292.
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The performance of a wind turbine is affected by wind conditions and blade shape. This study aimed to optimize the performance of a 20 kW horizontal-axis wind turbine (HAWT) under local wind conditions at Deniliquin, New South Wales, Australia. Ansys Fluent (version 18.2, Canonsburg, PA, USA) was used to investigate the aerodynamic performance of the HAWT. The effects of four Reynolds-averaged Navier–Stokes turbulence models on predicting the flows under separation condition were examined. The transition SST model had the best agreement with the NREL CER data. Then, the aerodynamic shape of the rotor was optimized to maximize the annual energy production (AEP) in the Deniliquin region. Statistical wind analysis was applied to define the Weibull function and scale parameters which were 2.096 and 5.042 m/s, respectively. The HARP_Opt (National Renewable Energy Laboratory, Golden, CO, USA) was enhanced with design variables concerning the shape of the blade, rated rotational speed, and pitch angle. The pitch angle remained at 0° while the rising wind speed improved rotor speed to 148.4482 rpm at rated speed. This optimization improved the AEP rate by 9.068% when compared to the original NREL design.
Khorsand, M, Tavakoli, J, Guan, H & Tang, Y 2020, 'Artificial intelligence enhanced mathematical modeling on rotary triboelectric nanogenerators under various kinematic and geometric conditions', Nano Energy, vol. 75, pp. 104993-104993.
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© 2020 Elsevier Ltd The triboelectric nanogenerator (TENG) has been introduced as a revolutionary technology in the renewable electrical energy generation at micro/nanoscale. In the current study, experimental and theoretical models for augmented rotary TENGs are presented. The power generated by TENGs is found to be a function of the number of segments, rotational speed, and tribo-surface spacing. Mathematical modeling combined with artificial intelligence is applied to characterize the TENG output under various kinematics and geometric conditions. Sensitivity analysis reveals that the generated energy and the matched resistance depend highly on segmentation and angular velocity rate. It is shown that the optimized harvested energy reaches 0.369 mJ at each cycle. The TENG dynamic outputs for various structural parameters are found and described. This study enhances understanding of rotation-induced periodic TENGs and reveals optimized characteristics for disk-shaped TENG energy harvesters.
Khosravi, F, Hosseini, SA & Hayati, H 2020, 'Free and forced axial vibration of single walled carbon nanotube under linear and harmonic concentrated forces based on nonlocal theory', International Journal of Modern Physics B, vol. 34, no. 08, pp. 2050067-2050067.
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The aim of this paper is to investigate the free and forced axial vibrations under the two various linear and harmonic axial concentrated forces in zigzag single-walled carbon nanotube (SWCNT). Two different boundary conditions, namely clamped–clamped and clamped-free, are established. Eringen’s nonlocal elasticity is employed to justify the nonlocal behavior of constitutive relations. The governing equation and the associated boundary condition are derived based on Hamilton’s principle. In order to solve the derived equation numerically, the assumed modes method is utilized. In the free axial vibration section, the first three natural frequencies are obtained for the various values of the nonlocal parameter. The results are in good agreement in comparison with another study. The fundamental natural frequencies with respect to the nonlocal parameter of the case study as a semiconducting nanotube with boron nitride nanotube (BNNT) as a semiconducting nanotube and SWCNT (5,5) as a metallic nanotube are compared. The effects of the nonlocal parameter, thickness and ratio of the excitation-to-natural frequencies overtime on dimensional and nondimensional axial displacements are studied.
Khosravi, K, Panahi, M, Golkarian, A, Keesstra, SD, Saco, PM, Bui, DT & Lee, S 2020, 'Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran', Journal of Hydrology, vol. 591, pp. 125552-125552.
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Khoury, S & Tran, N 2020, 'qPCR multiplex detection of microRNA and messenger RNA in a single reaction', PeerJ, vol. 8, no. 6, pp. e9004-e9004.
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Reverse Transcription-Quantitative PCR (RT-qPCR) is one of the standards for analytical measurement of different RNA species in biological models. However, current Reverse Transcription (RT) based priming strategies are unable to synthesize differing RNAs and ncRNAs especially miRNAs, within a single tube. We present a new methodology, referred to as RNAmp, that measures in parallel miRNA and mRNA expression. We demonstrate this in various cell lines, then evaluate clinical utility by quantifying several miRNAs and mRNA simultaneously in sera. PCR efficiency in RNAmp was estimated between 1.8 and 1.9 which is comparable to standard miRNA and random primer RT approaches. Furthermore, when using RNAmp to detect selected mRNA and miRNAs, the quantification cycle (Cq) was several cycles lower. This low volume single-tube duplex protocol reduces technical variation and reagent usage and is suitable for uniform analysis of single or multiple miRNAs and/or mRNAs within a single qPCR reaction.
Khuat, TT & Gabrys, B 2020, 'Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule', Information Sciences, vol. 547, pp. 887-909.
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This paper proposes a method to accelerate the training process of a generalfuzzy min-max neural network. The purpose is to reduce the unsuitablehyperboxes selected as the potential candidates of the expansion step ofexisting hyperboxes to cover a new input pattern in the online learningalgorithms or candidates of the hyperbox aggregation process in theagglomerative learning algorithms. Our proposed approach is based on themathematical formulas to form a branch-and-bound solution aiming to remove thehyperboxes which are certain not to satisfy expansion or aggregationconditions, and in turn, decreasing the training time of learning algorithms.The efficiency of the proposed method is assessed over a number of widely useddata sets. The experimental results indicated the significant decrease intraining time of the proposed approach for both online and agglomerativelearning algorithms. Notably, the training time of the online learningalgorithms is reduced from 1.2 to 12 times when using the proposed method,while the agglomerative learning algorithms are accelerated from 7 to 37 timeson average.
Khuat, TT & Gabrys, B 2020, 'Random Hyperboxes', IEEE Transactions on Neural Networks and Learning Systems (2021).
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This paper proposes a simple yet powerful ensemble classifier, called RandomHyperboxes, constructed from individual hyperbox-based classifiers trained onthe random subsets of sample and feature spaces of the training set. We alsoshow a generalization error bound of the proposed classifier based on thestrength of the individual hyperbox-based classifiers as well as thecorrelation among them. The effectiveness of the proposed classifier isanalyzed using a carefully selected illustrative example and comparedempirically with other popular single and ensemble classifiers via 20 datasetsusing statistical testing methods. The experimental results confirmed that ourproposed method outperformed other fuzzy min-max neural networks, popularlearning algorithms, and is competitive with other ensemble methods. Finally,we identify the existing issues related to the generalization error bounds ofthe real datasets and inform the potential research directions.
Khuat, TT & Le, MH 2020, 'Evaluation of Sampling-Based Ensembles of Classifiers on Imbalanced Data for Software Defect Prediction Problems', SN Computer Science, vol. 1, no. 2.
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Kiani, M, Momeni, A, Tayarani, M & Ding, C 2020, 'Spatial wave control using a self-biased nonlinear metasurface at microwave frequencies', Optics Express, vol. 28, no. 23, pp. 35128-35128.
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Recently, investigation of metasurfaces has been extended to wave control through exploiting nonlinearity. Among all of the ways to achieve tunable metasurfaces with multiplexed performances, nonlinearity is one of the promising choices. Although several proposals have been reported to obtain nonlinear architectures at visible frequencies, the area of incorporating nonlinearity in form of passive-designing at microwave metasurfaces is open for investigation. In this paper, a passive wideband nonlinear metasurface is manifested, which is composed of embedded L−shape and Γ −shape meta-atoms with PIN-diode elements. The proposed self-biased nonlinear metasurface has two operational states: at low power intensities, it acts as a Quarter Wave Plate (QWP) in the frequency range from 13.24 GHz to 16.38 GHz with an Axial Ratio (AR) of over 21.2%. In contrast, at high power intensities, by using the polarization conversion property of the proposed PIN-diode based meta-atoms, the metasurface can act as a digital metasurface. It means that by arranging the meta-atoms with a certain coding pattern, the metasurface can manipulate the scattered beams and synthesize well-known patterns such as diffusion-like and chessboard patterns at an ultra-wide frequency range from 8.12 GHz to 19.27 GHz (BW=81.4%). Full-wave and nonlinear simulations are carried out to justify the performance of the wideband nonlinear metasurface. We expect the proposed self-biased nonlinear metasurface at microwave frequencies reveals excellent opportunities to design limiter metasurfaces and compact reconfigurable imaging systems.
Kieu, L-M, Ou, Y, Truong, LT & Cai, C 2020, 'A class-specific soft voting framework for customer booking prediction in on-demand transport', Transportation Research Part C: Emerging Technologies, vol. 114, pp. 377-390.
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© 2020 Elsevier Ltd Customer booking prediction is essential for On-Demand Transport services, especially for those in rural and suburban areas where the demand is low, variable and often regarded as unpredictable. Existing literature tends to focus more on the prediction of demand for traffic, classical public transport, and urban On-Demand Transport service such as taxi, Uber or Lyft, in areas with higher and less variable demand, in which popular time-series prediction methods can be employed. This paper proposes an ensemble learning framework to predict the customer booking behaviour and demand using the observed data of a suburban On-Demand Transport service where data scarcity is a challenge. The proposed method, which is called as Class-specific Soft Voting, is found to be the most accurate prediction method when compared to popular supervised classification methods such as Logistic Regression, Random Forest, Support Vector Machine and other ensemble techniques.
Kim, DI, Gonzales, RR, Dorji, P, Gwak, G, Phuntsho, S, Hong, S & Shon, H 2020, 'Efficient recovery of nitrate from municipal wastewater via MCDI using anion-exchange polymer coated electrode embedded with nitrate selective resin', Desalination, vol. 484, pp. 114425-114425.
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Kim, SY, Choo, Y, Bilodeau, RA, Yuen, MC, Kaufman, G, Shah, DS, Osuji, CO & Kramer-Bottiglio, R 2020, 'Sustainable manufacturing of sensors onto soft systems using self-coagulating conductive Pickering emulsions', Science Robotics, vol. 5, no. 39, p. eaay3604.
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An ethanol-based Pickering emulsion that spontaneously forms conductive composites is used to sustainably manufacture compliant strain sensors.
King, J-T, Prasad, M, Tsai, T, Ming, Y-R & Lin, C-T 2020, 'Influence of Time Pressure on Inhibitory Brain Control During Emergency Driving', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 11, pp. 4408-4414.
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IEEE It is believed that failures of people's reaction to emergencies occurred during driving are closely related to the inhibitory mechanism of brain's operations. To investigate the role of this function in emergency driving, two virtual realistic driving conditions based on stop signal task were designed and time limitation was manipulated to increase the stress in one condition. Sixteen subjects with behavioral encephalography recordings were collected and analyzed. By comparing successful and unsuccessful stop trials with event-related spectral perturbation analysis, δ and θ band power increases in frontal and central areas are correlated with driving inhibitory control of the brain. Moreover, β and ɣ band power in frontal and central areas showed more increases upon stress condition. Time pressure in driving could adjust the operation of brain's inhibition control, to benefit the people's reactive ability upon emergency.
Kishore Kumar, D, Kříž, J, Bennett, N, Chen, B, Upadhayaya, H, Reddy, KR & Sadhu, V 2020, 'Functionalized metal oxide nanoparticles for efficient dye-sensitized solar cells (DSSCs): A review', Materials Science for Energy Technologies, vol. 3, pp. 472-481.
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Dye-sensitized solar cells (DSSCs) are a next-generation photovoltaic energy conversion technology due to their low cost, ability to fabrication on various substrates, structural modifications, excellent transparency, photovoltaic output and its potential applications in wearable devices, energy sustainable buildings, solar-powered windows, etc. DSSC working devices consist of components such as conductive oxide substrates, photoanodes with wide bandgap semiconductors, dye molecules (sensitizers), counter electrodes and redox electrolytes, etc. High-efficiency DSSC devices can be fabricated suitable functionalization of semiconducting metal oxides with quantum dots, organic conjugated polymers, etc. In this review, we discuss different photovoltaic technologies, working principles of DSSCs, fabrication process of devices using various novel inorganic nanostructured materials, influencing parameters on the performance of DSC-device such as photoconversion efficiency (PCE), short circuit current (Jsc), open-circuit voltage (Voc) and fill factor (FF).
Kishore Kumar, D, Loskot, J, Kříž, J, Bennett, N, Upadhyaya, HM, Sadhu, V, Venkata Reddy, C & Reddy, KR 2020, 'Synthesis of SnSe quantum dots by successive ionic layer adsorption and reaction (SILAR) method for efficient solar cells applications', Solar Energy, vol. 199, pp. 570-574.
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© 2020 International Solar Energy Society Quantum dots (QDs) are one of the promising materials in the development of third-generation photovoltaics. QDs have the advantage of multiple exciton generation (MEG), high absorption coefficient and tuneable bandgap, low cost and easy synthesis. The QDs act as analogues to dye molecules in QD sensitized solar cells (QDSSCs) when compared with traditional dye-sensitized solar cells (DSSCs). Extending the absorption range of quantum dots is one of potential solutions for enhancing photoconversion efficiencies. The sensitization of SnSe quantum dots on theTiO2 mesoporous layers is carried by a successive ionic layer adsorption and reaction (SILAR) method in a glove box. The advantages of SILAR method are a high loading rate and wide coverage of the TiO2 matrix by the quantum dots. The device has exhibited a photoconversion efficiency of 0.78% which is the known best among the SnSe quantum dot-based solar cells.
Kocaballi, AB, Ijaz, K, Laranjo, L, Quiroz, JC, Rezazadegan, D, Tong, HL, Willcock, S, Berkovsky, S & Coiera, E 2020, 'Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners', Journal of the American Medical Informatics Association, vol. 27, no. 11, pp. 1695-1704.
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Abstract Objective The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. Materials and Methods Co-design workshops with general practitioners were conducted. The workshops focused on (1) understanding the current consultation context and identifying existing problems, (2) ideating future solutions to these problems, and (3) discussing future roles for AI in primary care. The workshop activities included affinity diagramming, brainwriting, and video prototyping methods. The workshops were audio-recorded and transcribed verbatim. Inductive thematic analysis of the transcripts of conversations was performed. Results Two researchers facilitated 3 co-design workshops with 16 general practitioners. Three main themes emerged: professional autonomy, human-AI collaboration, and new models of care. Major implications identified within these themes included (1) concerns with medico-legal aspects arising from constant recording and accessibility of full consultation records, (2) future consultations taking place out of the exam rooms in a distributed system involving empowered patients, (3) human conversation and empathy remaining the core tasks of doctors in any future AI-enabled consultations, and (4) questioning the current focus of AI initiatives on improved efficiency as opposed to patient care. ...
Kocaballi, AB, Quiroz, JC, Rezazadegan, D, Berkovsky, S, Magrabi, F, Coiera, E & Laranjo, L 2020, 'Responses of Conversational Agents to Health and Lifestyle Prompts: Investigation of Appropriateness and Presentation Structures', Journal of Medical Internet Research, vol. 22, no. 2, pp. e15823-e15823.
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Background Conversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely used examples include voice-activated systems such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. The use of CAs in health care has been on the rise, but concerns about their potential safety risks often remain understudied. Objective This study aimed to analyze how commonly available, general-purpose CAs on smartphones and smart speakers respond to health and lifestyle prompts (questions and open-ended statements) by examining their responses in terms of content and structure alike. Methods We followed a piloted script to present health- and lifestyle-related prompts to 8 CAs. The CAs’ responses were assessed for their appropriateness on the basis of the prompt type: responses to safety-critical prompts were deemed appropriate if they included a referral to a health professional or service, whereas responses to lifestyle prompts were deemed appropriate if they provided relevant information to address the problem prompted. The response structure was also examined according to information sources (Web search–based or precoded), response content style (informative and/or directive), confirmation of prompt recognition, and empathy. Results The 8 studied CAs provided in total 240 responses to 30 prompts. They collectively responded appropriately to 41% (46/112) of the safety-critical and 39% (37/96) of the lifestyle prompts. The ratio of appropriate responses deteriorated when safety-critical prompts were rephrased or when ...
Koli, MNY, Afzal, MU, Esselle, KP & Hashmi, RM 2020, 'A Radial Line Slot-Array Antenna With Low Side Lobes and a Uniform-Phase, Tapered-Amplitude Aperture Field Distribution', IEEE Access, vol. 8, pp. 208532-208542.
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Koli, MNY, Afzal, MU, Esselle, KP & Hashmi, RM 2020, 'An All-Metal High-Gain Radial-Line Slot-Array Antenna for Low-Cost Satellite Communication Systems', IEEE Access, vol. 8, pp. 139422-139432.
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Kong, FH & Manchester, IR 2020, 'Contraction analysis of nonlinear noncausal iterative learning control', Systems & Control Letters, vol. 136, pp. 104599-104599.
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Iterative learning control (ILC) is a method for learning input signals for repetitive control tasks. In this paper, we provide a new method based on convex optimization for certifying convergence and estimating convergence rate in ILC schemes involving a nonlinear plant and a noncausal update law, which are common in practice. Using sum-of-squares (SOS) optimization, we compute the convergence rate of an example nonlinear, noncausal ILC system and verify its accuracy in experiment.
Kong, FH, Zhao, J, Zhao, L & Huang, S 2020, 'Analysis of Minima for Geodesic and Chordal Cost for a Minimal 2-D Pose-Graph SLAM Problem', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 323-330.
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© 2016 IEEE. In this letter, we show that for a minimal 2D pose-graph SLAM problem, even in the ideal case of perfect measurements and spherical covariance, using geodesic distance (in 2D, the 'wrap function') to compare angles results in multiple suboptimal local minima. We numerically estimate regions of attraction to these local minima for some examples, give evidence to show that they are of nonzero measure, and that these regions grow in size as noise is added. In contrast, under the same assumptions, we show that the chordal distance representation of angle error has a unique minimum up to periodicity. For chordal cost, we find that initial conditions failing to converge to the global minimum are far fewer, fail because of numerical issues, and do not seem to grow with noise in our examples.
Kong, L, Liu, H, Zhu, X, Boon, CC, Li, C, Liu, Z & Yeo, KS 2020, 'Design of a Wideband Variable-Gain Amplifier With Self-Compensated Transistor for Accurate dB-Linear Characteristic in 65 nm CMOS Technology', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 12, pp. 4187-4198.
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© 2004-2012 IEEE. A simple yet effective approach for variable-gain amplifier (VGA) design with accurate dB-linear characteristic is presented. In order to extend the bandwidth of the designed VGA with a minimized footprint, an inductorless-based approach is adopted. Moreover, a unique approach that exploits a self-compensated transistor to compensate dB-linear gain error is proposed. Consequently, the overall VGA has an accurate dB-linear inherent characteristic without using any additional exponential generator for gain control. To prove the concept, the designed VGA is fabricated in a standard 65 nm CMOS technology. The measured results show that the voltage gain of the designed VGA can be controlled from -19 dB to 21 dB with a gain error less than 1 dB. Meanwhile, more than 4 GHz of bandwidth can be achieved for the entire gain range. The power consumption of the VGA, excluding the output buffer, is 3.9 mW. The core circuit of this design only occupies an area of 0.012 mm2.
Kong, L, Qu, W, Yu, J, Zuo, H, Chen, G, Xiong, F, Pan, S, Lin, S & Qiu, M 2020, 'Distributed Feature Selection for Big Data Using Fuzzy Rough Sets', IEEE Transactions on Fuzzy Systems, vol. 28, no. 5, pp. 846-857.
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Koopialipoor, M, Fahimifar, A, Ghaleini, EN, Momenzadeh, M & Armaghani, DJ 2020, 'Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance', Engineering with Computers, vol. 36, no. 1, pp. 345-357.
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Koopialipoor, M, Murlidhar, BR, Hedayat, A, Armaghani, DJ, Gordan, B & Mohamad, ET 2020, 'The use of new intelligent techniques in designing retaining walls', Engineering with Computers, vol. 36, no. 1, pp. 283-294.
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Koopialipoor, M, Tootoonchi, H, Marto, A, Faizi, K & Jahed Armaghani, D 2020, 'Various effective factors on peak uplift resistance of pipelines in sand: a comparative study', International Journal of Geotechnical Engineering, vol. 14, no. 7, pp. 820-827.
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Korniejenko, K, Miernik, K, Lin, W-T & Castel, A 2020, 'The influence of microstructure on mechanical properties of 3D printable geopolymer composites', MATEC Web of Conferences, vol. 322, pp. 01011-01011.
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The additive manufacturing technologies are fast-developing industrial sector and, potentially, a ground-breaking technology. They have many advantages such as the saving of resources and energy efficiency. However, the full exploitation of 3D printing technology for ceramic materials is currently limited; a lot of research is being conducted in this area. A promising solution seems to be geopolymers, but its application requires a better understanding of the behaviour this group of materials. This article analyses the influence of microstructure on mechanical properties whilst taking the production method into consideration. The paper is based on comparative analysis – the investigation is focused on the influence of material structure on the mechanical properties and fracture mechanism of these kinds of composites, including those reinforced with different kind of fibres. As a raw material for the matrix, fly ash from the Skawina coal power plant (located in: Skawina, Lesser Poland, Poland) was used. The investigation was made by SEM analysis. The results show that the microstructural analysis did not sufficiently explain the underlying reasons for the observed differences in the mechanical properties of the composites.
Kou, J, Xin, TY, McCarron, P, Gupta, G, Dureja, H, Satija, S, Mehta, M, Bakshi, HA, Tambuwala, MM, Collet, T, Dua, K & Chellappan, DK 2020, 'Going Beyond Antibiotics: Natural Plant Extracts as an Emergent Strategy to Combat Biofilm-Associated Infections', Journal of Environmental Pathology, Toxicology and Oncology, vol. 39, no. 2, pp. 125-136.
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Biofilms are a collective of multiple types of bacteria that develop on a variety of surfaces. Biofilm development results in heightened resistance to antibiotics. Quorum sensing plays an important role in biofilm development as it is one of the common communication mechanisms within cells, which balances and stabilizes the environment, when the amount of bacteria increases. Because of the important implications of the roles biofilms play in infectious diseases, it is crucial to investigate natural antibacterial agents that are able to regulate biofilm formation and development. Various studies have suggested that natural plant products have the potential to suppress bacterial growth and exhibit chemopreventive traits in the modulation of biofilm development. In this review, we discuss and collate potential antibiofilm drugs and biological molecules from natural sources, along with their underlying mechanisms of action. In addition, we also discuss the antibiofilm drugs that are currently under clinical trials and highlight their potential future uses.
Kovaleva, M, Bulger, D & Esselle, KP 2020, 'Comparative Study of Optimization Algorithms on the Design of Broadband Antennas', IEEE Journal on Multiscale and Multiphysics Computational Techniques, vol. 5, pp. 89-98.
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IEEE Broadband antennas find many applications in modern communication systems, such as Wi-Fi, 5G and SatCom. Multiple competing optimization methods are available for the application to antenna design, while it would be preferable to know in advance if any method is superior. Here, an application of the cross-entropy method, along with particle swarm optimization and covariance matrix adaptation evolutionary strategy, to the design of broadband antennas is presented. The first example is an aperture-coupled microstrip patch antenna that has 9.5~dBi peak directivity and 53\% bandwidth after optimization. It is then used as a feed in a high-gain broadband resonant cavity antenna. Using an all-dielectric superstrate with a transverse permittivity gradient, a compact thin resonant cavity antenna with a peak directivity of 19~dBi and 40\% 3-dB bandwidth was designed. A comparative analysis of the cross-entropy method, particle swarm optimization and covariance matrix adaptation evolutionary strategy applied to these two problems was carried out to provide the basis for further optimization of antennas in radio frequency and microwave frequency bands. We found that although all three methods reached a similar solution, the cross-entropy method has a speed advantage. It improves our ability to optimize existing designs and has wider applicability beyond antenna engineering.
Kovaleva, M, Bulger, D & Esselle, KP 2020, 'Cross-Entropy Method for Design and Optimization of Pixelated Metasurfaces', IEEE Access, vol. 8, pp. 224922-224931.
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Kramer, I, Hooning, MJ, Mavaddat, N, Hauptmann, M, Keeman, R, Steyerberg, EW, Giardiello, D, Antoniou, AC, Pharoah, PDP, Canisius, S, Abu-Ful, Z, Andrulis, IL, Anton-Culver, H, Aronson, KJ, Augustinsson, A, Becher, H, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bogdanova, NV, Bojesen, SE, Bolla, MK, Bonanni, B, Brauch, H, Bremer, M, Brucker, SY, Burwinkel, B, Castelao, JE, Chan, TL, Chang-Claude, J, Chanock, SJ, Chenevix-Trench, G, Choi, J-Y, Clarke, CL, Collée, JM, Couch, FJ, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Dörk, T, dos-Santos-Silva, I, Dunning, AM, Dwek, M, Eccles, DM, Evans, DG, Fasching, PA, Flyger, H, Gago-Dominguez, M, García-Closas, M, García-Sáenz, JA, Giles, GG, Goldgar, DE, González-Neira, A, Haiman, CA, Håkansson, N, Hamann, U, Hartman, M, Heemskerk-Gerritsen, BAM, Hollestelle, A, Hopper, JL, Hou, M-F, Howell, A, Ito, H, Jakimovska, M, Jakubowska, A, Janni, W, John, EM, Jung, A, Kang, D, Kets, CM, Khusnutdinova, E, Ko, Y-D, Kristensen, VN, Kurian, AW, Kwong, A, Lambrechts, D, Le Marchand, L, Li, J, Lindblom, A, Lubiński, J, Mannermaa, A, Manoochehri, M, Margolin, S, Matsuo, K, Mavroudis, D, Meindl, A, Milne, RL, Mulligan, AM, Muranen, TA, Neuhausen, SL, Nevanlinna, H, Newman, WG, Olshan, AF, Olson, JE, Olsson, H, Park-Simon, T-W, Peto, J, Petridis, C, Plaseska-Karanfilska, D, Presneau, N, Pylkäs, K, Radice, P, Rennert, G, Romero, A, Roylance, R, Saloustros, E, Sawyer, EJ, Schmutzler, RK, Schwentner, L, Scott, C, See, M-H, Shah, M, Shen, C-Y, Shu, X-O, Siesling, S, Slager, S, Sohn, C, Southey, MC, Spinelli, JJ, Stone, J, Tapper, WJ, Tengström, M, Teo, SH, Terry, MB, Tollenaar, RAEM, Tomlinson, I, Troester, MA, Vachon, CM, van Ongeval, C, van Veen, EM, Winqvist, R, Wolk, A, Zheng, W, Ziogas, A, Easton, DF, Hall, P, Schmidt, MK, Børresen-Dale, A-L, Sahlberg, K, Ottestad, L, Kåresen, R, Schlichting, E, Holmen, MM, Sauer, T, Haakensen, V, Engebråten, O, Naume, B, Fosså, A, Kiserud, C, Reinertsen, K, Helland, Å, Riis, M, Geisler, J, Alnæs, GG, Clarke, C, Marsh, D, Scott, R, Baxter, R, Yip, D, Carpenter, J, Davis, A, Pathmanathan, N, Simpson, P, Graham, JD, Sachchithananthan, M, Amor, D, Andrews, L, Antill, Y, Balleine, R, Beesley, J, Bennett, I, Bogwitz, M, Botes, L, Brennan, M, Brown, M, Buckley, M, Burke, J, Butow, P, Caldon, L, Campbell, I, Chauhan, D, Chauhan, M, Chenevix-Trench, G & et al. 2020, 'Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk', The American Journal of Human Genetics, vol. 107, no. 5, pp. 837-848.
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Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.
Kridalukmana, R, Lu, HY & Naderpour, M 2020, 'A supportive situation awareness model for human-autonomy teaming in collaborative driving', Theoretical Issues in Ergonomics Science, vol. 21, no. 6, pp. 658-683.
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Driving has become a collaborative activity and a form of human-autonomy teaming (HAT) with the addition of autonomy to the advanced driver assistance system (ADAS), which makes situational decisions and sensible actions (e.g., autopilot and collision avoidance). However, it has been identified that in many fatal road accidents involving collaborative driving, over-reliance on the ADAS becomes the primary factor. To overcome this issue, the underlying situation awareness (SA) concept is investigated to identify an appropriate SA model for collaborative driving that could impact the intelligent agent’s design in an HAT context. The formalization of existing SA model characteristics is defined and compared with those in collaborative driving. As a result, existing SA models are inadequate for explaining collaborative driving. Therefore, a new supportive SA (SSA) model is proposed. Based on the nature of this new model, applying transparency during SA development of the ADAS is suggested as a mechanism to comprehend ADAS behaviours. The proposed SA model is a significant expansion of multiple-agent SA models, and a transparent-based system can be a future direction of ADAS development to calibrate drivers’ trust.
Kulandaivelu, J, Choi, PM, Shrestha, S, Li, X, Song, Y, Li, J, Sharma, K, Yuan, Z, Mueller, JF, Wang, C & Jiang, G 2020, 'Assessing the removal of organic micropollutants from wastewater by discharging drinking water sludge to sewers', Water Research, vol. 181, pp. 115945-115945.
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Discharging drinking water treatment sludge (DWTS) to sewers could be an efficient waste management strategy with the potential to replace chemical dosing for pollutant control. This study for the first time investigated the fate of 28 different organic micropollutants (MPs) due to the dosing of iron-rich and aluminum-rich DWTS in a pilot rising main sewer. Nine MPs had an initial rapid removal within 1-hr (i.e., 10-80%) due to Fe-DWTS dosing. The formation of FeS particles due to Fe-DWTS dosing was responsible for the removal of dissolved sulfides (80% reduction comparing to control sewer). Further particle characterization using SEM-EDS, XRD and ATR-FTIR confirmed that FeS particles formation played an important role in the removal of MPs from wastewater. Adsorption of MPs onto the FeS particles was likely the possible mechanism for their rapid removal. In comparison to iron-rich DWTS, aluminum-rich DWTS had very limited beneficial effects in removing MPs from wastewater. The degradability of degradable MPs, including caffeine, paraxanthine, paracetamol, metformin, cyclamate, cephalexin, and MIAA were not affected by the DWTS dosing. Some non-degradable MPs, including cotinine, hydroxycotinine, tramadol, gabapentin, desvenlafaxine, hydrochlorothiazide, carbamazepine, fluconazole, sulfamethoxazole, acesulfame, saccharin and sucralose were also not impacted by the DWTS dosing. This study systematically assessed the additional benefits of discharging Fe-DWTS to the sewer network i.e., the removal of MPs from the liquid phase thereby reducing its load to the treatment plant. The results corroborate the discharge of Fe-rich DWTS in sewers as an effective and beneficial way of managing the waste by-product.
Kulasinghe, A, Lim, Y, Kapeleris, J, Warkiani, M, O’Byrne, K & Punyadeera, C 2020, 'The Use of Three-Dimensional DNA Fluorescent In Situ Hybridization (3D DNA FISH) for the Detection of Anaplastic Lymphoma Kinase (ALK) in Non-Small Cell Lung Cancer (NSCLC) Circulating Tumor Cells', Cells, vol. 9, no. 6, pp. 1465-1465.
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Tumor tissue biopsy is often limited for non-small cell lung cancer (NSCLC) patients and alternative sources of tumoral information are desirable to determine molecular alterations such as anaplastic lymphoma kinase (ALK) rearrangements. Circulating tumor cells (CTCs) are an appealing component of liquid biopsies, which can be sampled serially over the course of treatment. In this study, we enrolled a cohort of ALK-positive (n = 8) and ALK-negative (n = 12) NSCLC patients, enriched for CTCs using spiral microfluidic technology and performed DNA fluorescent in situ hybridization (FISH) for ALK. CTCs were identified in 12/20 NSCLC patients ranging from 1 to 26 CTCs/7.5 mL blood. Our study revealed that 3D imaging of CTCs for ALK translocations captured a well-defined separation of 3′ and 5′ signals indicative of ALK translocations and overlapping 3′/5′ signal was easily resolved by imaging through the nuclear volume. This study provides proof-of-principle for the use of 3D DNA FISH in the determination of CTC ALK translocations in NSCLC.
Kumari, N, Saco, PM, Rodriguez, JF, Johnstone, SA, Srivastava, A, Chun, KP & Yetemen, O 2020, 'The Grass Is Not Always Greener on the Other Side: Seasonal Reversal of Vegetation Greenness in Aspect‐Driven Semiarid Ecosystems', Geophysical Research Letters, vol. 47, no. 15.
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AbstractOur current understanding of semiarid ecosystems is that they tend to display higher vegetation greenness on polar‐facing slopes (PFS) than on equatorial‐facing slopes (EFS). However, recent studies have argued that higher vegetation greenness can occur on EFS during part of the year. To assess whether this seasonal reversal of aspect‐driven vegetation is a common occurrence, we conducted a global‐scale analysis of vegetation greenness on a monthly time scale over an 18‐year period (2000–2017). We examined the influence of climate seasonality on the normalized difference vegetation index (NDVI) values of PFS and EFS at 60 different catchments with aspect‐controlled vegetation located across all continents except Antarctica. Our results show that an overwhelming majority of sites (70%) display seasonal reversal, associated with transitions from water‐limited to energy‐limited conditions during wet winters. These findings highlight the need to consider seasonal variations of aspect‐driven vegetation patterns in ecohydrology, geomorphology, and Earth system models.
Kusumo, F, Shamsuddin, AH, Ahmad, AR, Silitonga, AS & Fazril, I 2020, 'Optimization of biodiesel production from mixed ceiba pentandra and rice bran oil assisted by ultrasound', Journal of Green Engineering, vol. 10, no. 12, pp. 13549-13564.
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The current research aims to examine the feasibility of production of biodiesel with non-edible mixed oils, Ceiba Pentandra Oil (CPO) and Rice Bran Oil (RBO).Several blends of CPO and RBO, ranging from 10:90 to 50:50% w/w were put under evaluation. The transesterification process variables of CP50RB50 as the suitable blend using exposure surface methodology, they were enhanced (RSM). The proportion of methanol to gasoline, the time of reaction and the concentration of the catalyst were both the key process parameters tested.An response surface transesterification process conditions such as KOH catalyst concentration are preferable: 0.83 percentage wt, methanol to oil ratio: 55.36%, reacted for 18.58 min18.58 min, with methyl ester yield of 98.7 %. The result indicates that the CP50RB50 methyl ester properties satisfy the biodiesel requirements as laid in standards, ASTM D6751 and EN 14214.
La Paz, A, Merigó, JM, Powell, P, Ramaprasad, A & Syn, T 2020, 'Twenty‐five years of the Information Systems Journal: A bibliometric and ontological overview', Information Systems Journal, vol. 30, no. 3, pp. 431-457.
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AbstractThe Information Systems Journal (ISJ) published its first issue in 1991, and in 2015, the journal celebrated its 25th anniversary. This study presents an overview of the leading research trends in the papers that the journal has published during its first quarter of a century via a bibliometric and ontological analysis. From a bibliometric perspective, the analysis considers the publication and citation structure of the journal. The study then develops a graphical analysis of the bibliographic material by using visualization of similarities software that employs bibliographic coupling and cocitation analysis. The work produces an ontological framework of impact and analyses the journal papers to assess qualitatively ISJ's impact. The results indicate that the journal has grown significantly over time and is now recognized as one of the leading journals in information systems. Yet challenges remain if the journal is to meet its aims in impacting and setting the agenda for the development of the Information Systems field.
Laccone, F, Malomo, L, Froli, M, Cignoni, P & Pietroni, N 2020, 'Automatic Design of Cable-Tensioned Glass Shells.', Comput. Graph. Forum, vol. 39, pp. 260-273.
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© 2019 The Authors Computer Graphics Forum © 2019 The Eurographics Association and John Wiley & Sons Ltd. We propose an optimization algorithm for the design of post-tensioned architectural shell structures, composed of triangular glass panels, in which glass has a load-bearing function. Due to its brittle nature, glass can fail when it is subject to tensile forces. Hence, we enrich the structure with a cable net, which is specifically designed to post-tension the shell, relieving the underlying glass structure from tension. We automatically derive an optimized cable layout, together with the appropriate pre-load of each cable. The method is driven by a physically based static analysis of the shell subject to its service load. We assess our approach by applying non-linear finite element analysis to several real-scale application scenarios. Such a method of cable tensioning produces glass shells that are optimized from the material usage viewpoint since they exploit the high compression strength of glass. As a result, they are lightweight and robust. Both aesthetic and static qualities are improved with respect to grid shell competitors.
Laccone, F, Malomo, L, Pérez, J, Pietroni, N, Ponchio, F, Bickel, B & Cignoni, P 2020, 'A bending-active twisted-arch plywood structure: computational design and fabrication of the FlexMaps Pavilion', SN Applied Sciences, vol. 2, no. 9, pp. 1505-9.
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Bending-active structures are able to efficiently produce complex curved shapes from flat panels. The desired deformation of the panels derives from the proper selection of their elastic properties. Optimized panels, called FlexMaps, are designed such that, once they are bent and assembled, the resulting static equilibrium configuration matches a desired input 3D shape. The FlexMaps elastic properties are controlled by locally varying spiraling geometric mesostructures, which are optimized in size and shape to match specific bending requests, namely the global curvature of the target shape. The design pipeline starts from a quad mesh representing the input 3D shape, which defines the edge size and the total amount of spirals: every quad will embed one spiral. Then, an optimization algorithm tunes the geometry of the spirals by using a simplified pre-computed rod model. This rod model is derived from a non-linear regression algorithm which approximates the non-linear behavior of solid FEM spiral models subject to hundreds of load combinations. This innovative pipeline has been applied to the project of a lightweight plywood pavilion named FlexMaps Pavilion, which is a single-layer piecewise twisted arch that fits a bounding box of 3.90x3.96x3.25 meters. This case study serves to test the applicability of this methodology at the architectural scale. The structure is validated via FE analyses and the fabrication of the full scale prototype.
Laengle, S, Merigó, JM, Modak, NM & Yang, J-B 2020, 'Bibliometrics in operations research and management science: a university analysis', Annals of Operations Research, vol. 294, no. 1-2, pp. 769-813.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Many universities around the World have made important contributions in the field of operations research and management science. This article presents the most productive and influential universities between 1991 and 2015. For doing so, we use the Web of Science database in order to search for the information which is usually regarded as the most relevant for scientific research. The results show the country of origin of the leading universities being mainly from North America and Asia and especially from USA and China. The Centre National de la Recherche Scientifique (CNRS) of France is the most productive university while the Massachusetts Institute of Technology (MIT) of USA is the most influential one. The temporal evolution shows that USA is trailing its dominancy while China progressing quickly. The evaluation also reveals that Asian universities outperform North American universities during the last 5 years.
Lai, S, Fan, X, Ye, Q, Tan, Z, Zhang, Y, He, X & Nanda, P 2020, 'FairEdge: A Fairness-Oriented Task Offloading Scheme for Iot Applications in Mobile Cloudlet Networks', IEEE Access, vol. 8, pp. 13516-13526.
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Lalbakhsh, A, Afzal, MU, Esselle, KP & Smith, SL 2020, 'Low-Cost Nonuniform Metallic Lattice for Rectifying Aperture Near-Field of Electromagnetic Bandgap Resonator Antennas', IEEE Transactions on Antennas and Propagation, vol. 68, no. 5, pp. 3328-3335.
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© 1963-2012 IEEE. This article addresses a critical issue, which has been overlooked, in relation to the design of phase-correcting structures (PCSs) for electromagnetic bandgap (EBG) resonator antennas (ERAs). All the previously proposed PCSs for ERAs are made using either several expensive radio frequency (RF) dielectric laminates or thick and heavy dielectric materials, contributing to very high fabrication cost, posing an industrial impediment to the application of ERAs. This article presents a new industrial-friendly generation of PCS, in which dielectrics, known as the main cause of high manufacturing cost, are removed from the PCS configuration, introducing an all-metallic PCS (AMPCS). Unlike existing PCSs, a hybrid topology of fully metallic spatial phase shifters are developed for the AMPCS, resulting in an extremely lower prototyping cost as that of other state-of-the-art substrate-based PCSs. The APMCS was fabricated using laser technology and tested with an ERA to verify its predicted performance. The results show that the phase uniformity of the ERA aperture has been remarkably improved, resulting in 8.4 dB improvement in the peak gain of the antenna and improved sidelobe levels (SLLs). The antenna system including APMCS has a peak gain of 19.42 dB with a 1 dB gain bandwidth of around 6%.
Lama, S, Pradhan, S & Shrestha, A 2020, 'Exploration and implication of factors affecting e-tourism adoption in developing countries: a case of Nepal', Information Technology & Tourism, vol. 22, no. 1, pp. 5-32.
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E-tourism plays a pivotal role in delivering services to tourists by enhancing travel experiences. While small tourism service providers in the developed economies can efficiently take advantage of e-tourism by partnering with global online platforms such as TripAdvisor or Booking.com, small tourism service providers in developing countries often struggle to reach global markets due to factors that are unique to the country they operate in. The aim of this study, therefore, is to identify the key barriers and motivators of e-tourism adoption for small and medium tourism enterprises (SMTEs) in developing countries. Nepal is selected as the case study due to its enormous tourism potential that is plagued by typical challenges. Based on the ‘Technology, Organization and Environment’ framework and ‘e-readiness’ model, this study identified ten key factors that affect e-tourism adoption by SMTEs in Nepal. A mixed-method approach, using interviews with seven key stakeholders and a survey with 198 SMTEs, were employed for data collection and validation of the proposed factors. Finally, an e-tourism adoption model highlighting the barriers and motivators for e-tourism by SMTEs is presented. This research found that e-tourism adoption by SMTEs in Nepal is affected by environmental factors related to national infrastructure, market size, country-specific contextual factors and organizational factors comprising e-tourism awareness, ICT resources, value proposition, and top management support. This study offers implications for policy and practice towards effective e-tourism adoption in other developing countries.
Langarica, S, Pizarro, G, Poblete, PM, Radrigan, F, Pereda, J, Rodriguez, J & Nunez, F 2020, 'Denoising and Voltage Estimation in Modular Multilevel Converters Using Deep Neural-Networks', IEEE Access, vol. 8, pp. 207973-207981.
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Lau, AKS, Bilad, MR, Nordin, NAHM, Faungnawakij, K, Narkkun, T, Wang, DK, Mahlia, TMI & Jaafar, J 2020, 'Effect of membrane properties on tilted panel performance of microalgae biomass filtration for biofuel feedstock', Renewable and Sustainable Energy Reviews, vol. 120, pp. 109666-109666.
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© 2019 Elsevier Ltd Efficient membrane-based technology for microalgae harvesting can be achieved via application effective membrane fouling control coupled with appropriate membrane materials. This study explores the combined impact of membrane properties and the tilted panel system on filterability of Euglena sp broth, a potential source of biofuel feedstock. Four membranes from polyvinylidene difluoride (PVDF) and polysulfone (PSF) of PVDF-1, PVDF-3, PSF-1 and PSF-3 were evaluated. Generally, increasing aeration rate, tilting angle and lowering switching period enhance the system performance for all the tested membranes to give the highest permeances of 660, 724, 743 L/m2 h bar, respectively. Those values are among the highest reported in literature. The magnitude of the effect is affected by the membrane properties, mainly by pore size. Tilting without switching configuration is desirable for the membrane with a large pore size (PVDF-1, 0.42 μm) which produced the highest panel permeability of 724.3 (L/m2 h bar), which is >23% higher than the tilted with switching. For this membrane, intermittent aeration applied under switching mode worsened the pore blocking. Membranes with low pore sizes (0.11, 0.04 and 0.03 μm for PVDF-3, PSF-1 and PSF-3, respectively) excelled under switching mode since they are less prone to pore blocking due to smaller pore apertures. Overall results suggest that to gain the full benefit of the tilted panel, operational system of either one-sided without switching or two-sided involving switching must be tailored in conjunction with the desirable properties of the membranes. This finding can help to lower the energy input for microalgae-based biofuel production.
Law, AMK, Valdes-Mora, F & Gallego-Ortega, D 2020, 'Myeloid-Derived Suppressor Cells as a Therapeutic Target for Cancer', Cells, vol. 9, no. 3, pp. 561-561.
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The emergence of immunotherapy has been an astounding breakthrough in cancer treatments. In particular, immune checkpoint inhibitors, targeting PD-1 and CTLA-4, have shown remarkable therapeutic outcomes. However, response rates from immunotherapy have been reported to be varied, with some having pronounced success and others with minimal to no clinical benefit. An important aspect associated with this discrepancy in patient response is the immune-suppressive effects elicited by the tumour microenvironment (TME). Immune suppression plays a pivotal role in regulating cancer progression, metastasis, and reducing immunotherapy success. Most notably, myeloid-derived suppressor cells (MDSC), a heterogeneous population of immature myeloid cells, have potent mechanisms to inhibit T-cell and NK-cell activity to promote tumour growth, development of the pre-metastatic niche, and contribute to resistance to immunotherapy. Accumulating research indicates that MDSC can be a therapeutic target to alleviate their pro-tumourigenic functions and immunosuppressive activities to bolster the efficacy of checkpoint inhibitors. In this review, we provide an overview of the general immunotherapeutic approaches and discuss the characterisation, expansion, and activities of MDSCs with the current treatments used to target them either as a single therapeutic target or synergistically in combination with immunotherapy.
Lazaar, A, Hammouti, KE, Naiji, Z, Pradhan, B, Gourfi, A, Andich, K & Monir, A 2020, 'The manifestation of VIS-NIRS spectroscopy data to predict and map soil texture in the Triffa plain (Morocco)', Kuwait Journal of Science, vol. 48, no. 1, pp. 111-121.
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The use of standard laboratory methods to estimate the soil texture is complicated, expensive, and time-consuming and needs considerable effort. The reflectance spectroscopy represents an alternative method for predicting a large range of soil physical properties and provides an inexpensive, rapid, and reproducible analytical method. This study aimed to assess the feasibility of Visible (VIS: 350-700 nm) and Near-Infrared and Short-Wave-Infrared (NIRS: 701-2500 nm) spectroscopy for predicting and mapping the clay, silt, and sand fractions of the soils of Triffa plain (north-east of Morocco). A total of 100 soil samples were collected from the non-root zone of soil (0-20 cm) and then analyzed for texture using the VIS-NIRS spectroscopy and the traditional laboratory method. The partial least squares regression (PLSR) technique was used to assess the ability of spectral data to predict soil texture. The results of prediction models showed excellent performance for the VIS-NIRS spectroscopy to predict the sand fraction with a coefficient of determination R2 = 0.93 and Root Mean Squares Error (RMSE) =3.72, good prediction for the silt fraction (R2=0.87; RMSE = 4.55), and acceptable prediction for the clay fraction (R2 = 0.53; RMSE = 3.72). Moreover, the range situated between 2150 and 2450 nm is the most significant for predicting the sand and silt fractions, while the spectral range between 2200 and 2440 nm is the optimal to predict the clay fraction. However, the maps of predicted and measured soil texture showed an excellent spatial similarity for the sand fraction, a certain difference in the variability of clay fraction, while the maps of silt fraction show a lower difference.
Lazaar, A, Mouazen, AM, EL Hammouti, K, Fullen, M, Pradhan, B, Memon, MS, Andich, K & Monir, A 2020, 'The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco', International Soil and Water Conservation Research, vol. 8, no. 2, pp. 195-204.
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© 2020 Soil organic matter (SOM) is a fundamental soil constituent. The estimation of this parameter in the laboratory using the classical method is complex time-consuming and requires the use of chemical reagents. The objectives of this study were to assess the accuracy of two laboratory measurement setups of the VIS-NIR spectroscopy in estimating SOM content and determine the important spectral bands in the SOM estimation model. A total of 115 soil samples were collected from the non-root zone (0–20 cm) of soil in the study area of the Triffa Plain and then analysed for SOM in the laboratory by the Walkley–Black method. The reflectance spectra of soil samples were measured by two protocols, Contact Probe (CP) and Pistol Grip (PG)) of the ASD spectroradiometer (350–2500 nm) in the laboratory. Partial least squares regression (PLSR) was used to develop the prediction models. The results of coefficient of determination (R2) and the root mean square error (RMSE) showed that the pistol grip offers reasonable accuracy with an R2 = 0.93 and RMSE = 0.13 compared to the contact probe protocol with an R2 = 0.85 and RMSE = 0.19. The near-Infrared range were more accurate than those in the visible range for predicting SOM using the both setups (CP and PG). The significant wavelengths contributing to the prediction of SOM for (PG) setup were at: 424, 597, 1432, 1484, 1830,1920, 2200, 2357 and 2430 nm, while were at 433, 587, 1380, 1431, 1929, 2200 and 2345 nm for (CP) setup.
Le Gentil, C, Vidal-Calleja, T & Huang, S 2020, 'Gaussian Process Preintegration for Inertial-Aided State Estimation', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2108-2114.
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© 2020 IEEE. In this letter, we present Gaussian Process Preintegration, a preintegration theory based on continuous representations of inertial measurements. A novel use of linear operators on Gaussian Process kernels is employed to generate the proposed Gaussian Preintegrated Measurements (GPMs). This formulation allows the analytical integration of inertial signals on any time interval. Consequently, GPMs are especially suited for asynchronous inertial-aided estimation frameworks. Unlike discrete preintegration approaches, the proposed method does not rely on any explicit motion-model and does not suffer from numerical integration noise. Additionally, we provide the analytical derivation of the Jacobians involved in the first-order expansion for postintegration bias and inter-sensor time-shift correction. We benchmarked the proposed method against existing preintegration methods on simulated data. Our experiments show that GPMs produce the most accurate results and their computation time allows close-to-real-time operations. We validated the suitability of GPMs for inertial-aided estimation by integrating them into a lidar-inertial localisation and mapping framework.
Le, AT, Tran, LC, Huang, X & Guo, YJ 2020, 'Analog Least Mean Square Loop for Self-Interference Cancellation: A Practical Perspective', Sensors, vol. 20, no. 1, pp. 270-270.
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Self-interference (SI) is the key issue that prevents in-band full-duplex (IBFD) communications from being practical. Analog multi-tap adaptive filter is an efficient structure to cancel SI since it can capture the nonlinear components and noise in the transmitted signal. Analog least mean square (ALMS) loop is a simple adaptive filter that can be implemented by purely analog means to sufficiently mitigate SI. Comprehensive analyses on the behaviors of the ALMS loop have been published in the literature. This paper proposes a practical structure and presents an implementation of the ALMS loop. By employing off-the-shelf components, a prototype of the ALMS loop including two taps is implemented for an IBFD system operating at the carrier frequency of 2.4 GHz. The prototype is firstly evaluated in a single carrier signaling IBFD system with 20 MHz and 50 MHz bandwidths, respectively. Measured results show that the ALMS loop can provide 39 dB and 33 dB of SI cancellation in the radio frequency domain for the two bandwidths, respectively. Furthermore, the impact of the roll-off factor of the pulse shaping filter on the SI cancellation level provided by the prototype is presented. Finally, the experiment with multicarrier signaling shows that the performance of the ALMS loop is the same as that in the single carrier system. These experimental results validate the theoretical analyses presented in our previous publications on the ALMS loop behaviors.
Le, AT, Tran, LC, Huang, X & Guo, YJ 2020, 'Beam-Based Analog Self-Interference Cancellation in Full-Duplex MIMO Systems', IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2460-2471.
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Le, AT, Tran, LC, Huang, X, Ritz, CH, Dutkiewicz, E, Phung, SL, Bouzerdoum, A & Franklin, DR 2020, 'Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks.', Sensors, vol. 20, no. 14, pp. 3838-3838.
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Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors.
Le, AT, Tran, LC, Huang, X, Ritz, CH, Dutkiewicz, E, Phung, SL, Bouzerdoum, A & Franklin, DR 2020, 'Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks.', Sensors, vol. 20, pp. 3838-3838.
Le, TP, Smith, BH, Lee, Y, Litofsky, JH, Aplan, MP, Kuei, B, Zhu, C, Wang, C, Hexemer, A & Gomez, ED 2020, 'Enhancing Optoelectronic Properties of Conjugated Block Copolymers through Crystallization of Both Blocks', Macromolecules, vol. 53, no. 6, pp. 1967-1976.
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Lee, C, Nguyen, T-T, Adha, RS, Shon, HK & Kim, IS 2020, 'Influence of hydrodynamic operating conditions on organic fouling of spiral-wound forward osmosis membranes: Fouling-induced performance deterioration in FO-RO hybrid system', Water Research, vol. 185, pp. 116154-116154.
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© 2020 Elsevier Ltd The forward osmosis-reverse osmosis (FO-RO) hybrid process has been extensively researched as part of attempts to reduce the high energy consumption of conventional seawater reverse osmosis in recent years. FO operating conditions play a substantial role in the hybrid process, dictating not only the performance of the entire system but also the propensity for fouling, which deteriorates performance in long-term field operations. Therefore, determining the optimal FO operating conditions with regard to membrane fouling may promote sustainable operation through efficient fouling control. This study thus evaluated the influence of each hydrodynamic operating condition (feed flowrate, draw flowrate, and hydraulic pressure difference) and their synergistic effects on fouling propensity in a pilot-scale FO operation under seawater and municipal wastewater conditions. Fouling-induced variation in water flux, channel pressure drop, diluted concentration, and the resulting specific energy consumption (SEC) were comparatively analyzed and utilized to project performance variation in a full-scale FO-RO system. Fouling-induced performance reduction significantly varied depending on hydrodynamic operating conditions and the resultant fouling propensity during 15 days of continuous operation. A high feed flowrate demonstrated a clear ability to mitigate fouling-induced performance deterioration in all conditions. A high draw flowrate turned out to be detrimental for fouling propensity since its high reverse solute flux accelerated fouling growth. Applying additional hydraulic pressure during FO operation caused a faster reduction of water flux, and thus feed recovery and water production; however, these drawbacks could be compensated for by a 10% reduction in the required FO membrane area and an additional reduction in RO SEC.
Lee, D, Woo, YC, Park, KH, Phuntsho, S, Tijing, LD, Yao, M, Shim, W-G & Shon, HK 2020, 'Polyvinylidene fluoride phase design by two-dimensional boron nitride enables enhanced performance and stability for seawater desalination', Journal of Membrane Science, vol. 598, pp. 117669-117669.
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© 2019 The instability of polyvinylidene fluoride (PVDF) membranes in membrane distillation (MD) for seawater desalination is still a problem, despite the tremendous effort expended to resolve this issue. Here, a simple and feasible approach for improving desalination performance through the incorporation of two-dimensional boron nitride nanosheets (BNNSs) in polyvinylidene fluoride-co-hexafluoropropene (PVDF-co-HFP) electrospun nanofiber membrane (BNs-PH) is proposed as well as demonstrate its origin for fundamental understanding. The BNs-PH membrane exhibits a stable water vapor flux (18 LMH) and superior salt rejection (99.99%), even after operation for 280 h (commercial PVDF: steep decay within 28 h; neat PH: wetting within 4 h). From structural/chemical analyses, the BNNSs play a crucial role in forming favorable phases of the PH polymer crystal structure, inducing a superhydrophobic surface with greater nanoporosity and higher heterogeneity as well as enhanced mechanical properties (increase of UTS: 13.4%; modulus: 1.2%) for long-term operation. Theoretical modeling results of an air-gap MD system are consistent with our experimental results. The approach introduced in this study can be applied to other desalination systems to boost various water treatment applications.
Lee, H, Phillips, JB, Hall, RM & Tipper, JL 2020, 'Neural cell responses to wear debris from metal-on-metal total disc replacements', European Spine Journal, vol. 29, no. 11, pp. 2701-2712.
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PurposeAbstractTotal disc replacements, comprising all-metal articulations, are compromised by wear and particle production. Metallic wear debris and ions trigger a range of biological responses including inflammation, genotoxicity, cytotoxicity, hypersensitivity and pseudotumour formation, therefore we hypothesise that, due to proximity to the spinal cord, glial cells may be adversely affected.MethodsClinically relevant cobalt chrome (CoCr) and stainless steel (SS) wear particles were generated using a six-station pin-on-plate wear simulator. The effects of metallic particles (0.5–50 μm3 debris per cell) and metal ions on glial cell viability, cellular activity (glial fibrillary acidic protein (GFAP) expression) and DNA integrity were investigated in 2D and 3D culture using live/dead, immunocytochemistry and a comet assay, respectively.ResultsCoCr wear particles and ions caused significant reductions in glial cell viability in both 2D and 3D culture systems. Stainless steel particles did not affect glial cell viability or astrocyte activation. In contrast, ions released from SS caused significant reductions in glial cell viability, an effect that was especially noticeable when astrocytes were cultured in isolation without microglia. DNA damage was observed in both cell types and with both biomaterials tested. CoCr wear particles had a dose-dependent effect on astrocyte activation, measured through expression of GFAP.ConclusionsThe results from this study suggest that microglia influence the effects that metal particles have on astrocytes, that SS ions and particles play a role in the adverse effects observed and that SS is a less toxic biomaterial than CoCr alloy f...
Lee, SS, Lim, CS, Siwakoti, YP & Lee, K-B 2020, 'Dual-T-Type Five-Level Cascaded Multilevel Inverter With Double Voltage Boosting Gain', IEEE Transactions on Power Electronics, vol. 35, no. 9, pp. 9522-9529.
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Lee, SS, Lim, CS, Siwakoti, YP & Lee, K-B 2020, 'Hybrid 7-Level Boost Active-Neutral-Point- Clamped (H-7L-BANPC) Inverter', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 10, pp. 2044-2048.
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The emerging active-neutral-point-clamped (ANPC) inverters with voltage-boosting capability are attractive for their low dc-link voltage requirement. These low voltage requirements enable a single-stage dc-ac power conversion, which improves the overall efficiency, reliability, and power density of the system. A high voltage gain of 1.5 was demonstrated in recent boost type ANPC topology; however, it was achieved at the expense of high voltage stress on some of its switching devices. This brief proposes an improved topology with reduced voltage stress and a lower number of components while retaining the merits of high voltage gain. The proposed topology is a hybrid of a T-type inverter and an H-bridge, which require only one floating capacitor and one less power switch than the aforementioned topology. One floating capacitor with self-voltage balancing capability is integrated to generate 7 output voltage levels. The proposed topology is analyzed and compared with recent boost ANPC topologies. Experimental results are presented for validation.
Lee, SS, Siwakoti, YP, Lim, CS & Lee, K-B 2020, 'An Improved PWM Technique to Achieve Continuous Input Current in Common-Ground Transformerless Boost Inverter.', IEEE Trans. Circuits Syst., vol. 67-II, no. 12, pp. 3133-3136.
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Lee, XJ, Ong, HC, Gan, YY, Chen, W-H & Mahlia, TMI 2020, 'State of art review on conventional and advanced pyrolysis of macroalgae and microalgae for biochar, bio-oil and bio-syngas production', Energy Conversion and Management, vol. 210, pp. 112707-112707.
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© 2020 Elsevier Ltd Algal biomass including macroalgae and microalgae show great potential as pyrolysis feedstock in generating energy-dense and valuable pyrolytic products such as bio-oil, biochar and bio-syngas. The chemical constituents of macroalgae and microalgae show great variations, especially their lipid, carbohydrate and protein contents, which could affect the qualities of the pyrolytic products. From the established conventional pyrolysis, the products produced from both macroalgae and microalgae show moderate energy contents (<34 MJ/kg). The review identifies the issues associated with development of conventional pyrolysis such as flash and intermediate pyrolysis. To enhance the production of biofuels from algal biomass, advanced or non-conventional pyrolysis techniques have been employed. Catalytic pyrolysis on algal biomass could reduce the nitrogenates and oxygenates in the biofuels. On top of that, co-pyrolysis with suitable feedstock shows great enhancement on the bio-oil yield. As for hydropyrolysis of algal biomass, their generated biofuels can produce up to 48 MJ/kg with high yield of bio-oil up to 50 wt%, comparable to conventional fuels. Microwave-assisted pyrolysis of algal biomass greatly shortens the processing time through advanced heating; however, favours the formation of bio-syngas by improving the yield up to 84 wt% depending on the feedstock used. Therefore, formation of biofuel fraction suitable for energy generation highly depends on the selected pyrolysis technologies.
Lei, B, Li, W, Liu, H, Tang, Z & Tam, VWY 2020, 'Synergistic Effects of Polypropylene and Glass Fiber on Mechanical Properties and Durability of Recycled Aggregate Concrete', International Journal of Concrete Structures and Materials, vol. 14, no. 1.
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AbstractTo better understand the synergistic effects of combined fibers on mechanical properties and durability of recycled aggregate concrete (RAC), different types of fibers with various lengths and mass ratios were adopted in this study. Experimental investigations were conducted to study the 28-day compressive strength and strength loss after exposed to salt-solution freeze–thaw cycles and the coupled action of mechanical loading and salt-solution freeze–thaw cycles. The microstructure was also characterized to evaluate the mechanism of this synergistic effect. To determine the effectiveness of the combined fibers on improving the mechanical properties and durability of RAC, the synergistic coefficient was proposed and applied for various combinations of fibers. The results indicate that the incorporation of fibers slightly decreased the 28-day compressive strength of RAC, but combining different sizes and types of fibers can mitigate this negative effect. Moreover, the incorporation of fibers greatly improves the freeze–thaw resistance of RAC. The combining different fibers exhibited a synergistic effect on the enhancement in properties of RAC, which could not be predicted with only one simplistic rule of fibre mixtures. In addition, microstructural characterization shows that the bonding strength of the interfacial transition zone (ITZ) between the fiber and cement matrix is mainly determined by the chemical bonding force which is due to the hydration reaction between fiber surface and cement matrix.
Lei, B, Li, W, Luo, Z, Tam, VWY, Dong, W & Wang, K 2020, 'Performance Enhancement of Permeable Asphalt Mixtures With Recycled Aggregate for Concrete Pavement Application', Frontiers in Materials, vol. 7.
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© Copyright © 2020 Lei, Li, Luo, Tam, Dong and Wang. The incorporation of recycled concrete aggregate (RCA) in permeable asphalt mixtures (PAMs) is an efficient method of utilizing construction demolished waste. It not only conforms to the trend of building sponge cities, but also alleviates the problem of overexploitation of natural aggregate resources. As the performance of PAM containing recycled aggregate is not comparable to natural aggregate, modification treatments and the addition of hybrid fibers are adopted as two enhancement methods to improve the performance of PAM with RAC in this study. It is found that replacing natural aggregate with recycled aggregate increases the optimum asphalt content (OAC) but decreases the residual stability. The OAC is increased by 45% when the RCA ratio is 100%, whereas applying silicone resin can give a 16.2% decrease in the OAC. Enhancing RCA with silicone resin can increase the water stability to be comparable with natural aggregate. Moreover, with modification treatment using calcium hydroxide solution, the mechanical strength of PAM is enhanced to even higher than that of natural coarse aggregate mixture alone. Improvements in both mechanical strength and water stability are also achieved by strengthening recycled aggregate with cement slurry, although the performance is less effective than using silicone resin. With the increase in the content of RCA, the permeability coefficients of PAM first decrease and then exhibit an increasing trend. The results indicate that the PAM with RCA and modification treatments can perform satisfactorily as a pavement material in practice. Applying probable modification, PAM incorporating RCA meets the criteria for use in concrete pavement applications.
Lei, B, Li, W, Tang, Z, Li, Z & Tam, VWY 2020, 'Effects of environmental actions, recycled aggregate quality and modification treatments on durability performance of recycled concrete', Journal of Materials Research and Technology, vol. 9, no. 6, pp. 13375-13389.
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© 2020 The Author(s). The durability performance of recycled concrete (RC) subjected to different environmental actions, including salt-solution, mechanical load, salt-solution freeze-thaw cycles, and coupled mechanical load and salt-solution freeze-thaw cycles was investigated in this paper. To evaluate the effects of recycled aggregate (RA) quality on the RC durability, modeled recycled concrete (MRC) containing modeled recycled aggregate (MRA) with various thickness and coverage of old mortar, along with different degrees of initial damage, was fabricated and tested. Moreover, several modification treatments were employed to study the effects of modification treatments on the RC durability, which included the impregnation of RA with polyvinyl alcohol (PVA) emulsion or nano-SiO2 solutions, and the enhancement of RC with the incorporations of fly ash or hybrid fly ash and silica fume. The results reveal that the deterioration of RC under coupled actions of mechanical load and salt-solution freeze-thaw cycles was the most severe, which was followed by the salt-solution freeze-thaw cycles, mechanical load and salt-solution. The old interface in RA was determined as the weakest zone in RC. With the increase in the thickness or coverage of old mortar, or the initial damage of RA, the durability performance of RC declined, and the effect of initial damage of RA was more significant compared to the thickness or coverage of old mortar. Additionally, modifying RC with 1.5% nano-SiO2 solution or PVA emulsion, and replacing cement with 10% fly ash can significantly enhance the RC durability.
Lei, F, Lv, X, Fang, J, Sun, G & Li, Q 2020, 'Multiobjective discrete optimization using the TOPSIS and entropy method for protection of pedestrian lower extremity', Thin-Walled Structures, vol. 152, pp. 106349-106349.
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Lei, J, Fang, S, Xie, W, Li, Y & Chang, C-I 2020, 'Discriminative Reconstruction for Hyperspectral Anomaly Detection With Spectral Learning', IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 10, pp. 7406-7417.
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León-Castro, E, Espinoza-Audelo, LF, Merigó, JM, Gil-Lafuente, AM & Yager, RR 2020, 'The ordered weighted average inflation', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1901-1913.
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Li, BK, Vasiljevic, A, Dufour, C, Yao, F, Ho, BLB, Lu, M, Hwang, EI, Gururangan, S, Hansford, JR, Fouladi, M, Nobusawa, S, Laquerriere, A, Delisle, M-B, Fangusaro, J, Forest, F, Toledano, H, Solano-Paez, P, Leary, S, Birks, D, Hoffman, LM, Szathmari, A, Faure-Conter, C, Fan, X, Catchpoole, D, Zhou, L, Schultz, KAP, Ichimura, K, Gauchotte, G, Jabado, N, Jones, C, Loussouarn, D, Mokhtari, K, Rousseau, A, Ziegler, DS, Tanaka, S, Pomeroy, SL, Gajjar, A, Ramaswamy, V, Hawkins, C, Grundy, RG, Hill, DA, Bouffet, E, Huang, A & Jouvet, A 2020, 'Pineoblastoma segregates into molecular sub-groups with distinct clinico-pathologic features: a Rare Brain Tumor Consortium registry study', Acta Neuropathologica, vol. 139, no. 2, pp. 223-241.
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Pineoblastomas (PBs) are rare, aggressive pediatric brain tumors of the pineal gland with modest overall survival despite intensive therapy. We sought to define the clinical and molecular spectra of PB to inform new treatment approaches for this orphan cancer. Tumor, blood, and clinical data from 91 patients with PB or supratentorial primitive neuroectodermal tumor (sPNETs/CNS-PNETs), and 2 pineal parenchymal tumors of intermediate differentiation (PPTIDs) were collected from 29 centres in the Rare Brain Tumor Consortium. We used global DNA methylation profiling to define a core group of PB from 72/93 cases, which were delineated into five molecular sub-groups. Copy number, whole exome and targeted sequencing, and miRNA expression analyses were used to evaluate the clinico-pathologic significance of each sub-group. Tumors designated as group 1 and 2 almost exclusively exhibited deleterious homozygous loss-of-function alterations in miRNA biogenesis genes (DICER1, DROSHA, and DGCR8) in 62 and 100% of group 1 and 2 tumors, respectively. Recurrent alterations of the oncogenic MYC-miR-17/92-RB1 pathway were observed in the RB and MYC sub-group, respectively, characterized by RB1 loss with gain of miR-17/92, and recurrent gain or amplification of MYC. PB sub-groups exhibited distinct clinical features: group 1-3 arose in older children (median ages 5.2-14.0 years) and had intermediate to excellent survival (5-year OS of 68.0-100%), while Group RB and MYC PB patients were much younger (median age 1.3-1.4 years) with dismal survival (5-year OS 37.5% and 28.6%, respectively). We identified age < 3 years at diagnosis, metastatic disease, omission of upfront radiation, and chr 16q loss as significant negative prognostic factors across all PBs. Our findings demonstrate that PB exhibits substantial molecular heterogeneity with sub-group-associated clinical phenotypes and survival. In addition to revealing novel biology and therapeutics, molecular sub-grouping of PB...
Li, C, Xie, H-B, Mengersen, K, Fan, X, Da Xu, RY, Sisson, SA & Van Huffel, S 2020, 'Bayesian Nonnegative Matrix Factorization With Dirichlet Process Mixtures', IEEE Transactions on Signal Processing, vol. 68, pp. 3860-3870.
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Nonnegative Matrix Factorization (NMF) is valuable in many applications of blind source separation, signal processing and machine learning. A number of algorithms that can infer nonnegative latent factors have been developed, but most of these assume a specific noise kernel. This is insufficient to deal with complex noise in real scenarios. In this paper, we present a hierarchical Dirichlet process nonnegative matrix factorization (DPNMF) model in which the Gaussian mixture model is used to approximate the complex noise distribution. Moreover, the model is cast in the nonparametric Bayesian framework by using Dirichlet process mixture to infer the necessary number of Gaussian components. We derive a mean-field variational inference algorithm for the proposed nonparametric Bayesian model. We first test the model on synthetic data sets contaminated by Gaussian, sparse and mixed noise. We then apply it to extract muscle synergies from the electromyographic (EMG) signal and to select discriminative features for motor imagery single-trial electroencephalogram (EEG) classification. Experimental results demonstrate that DPNMF performs better in extracting the latent nonnegative factors in comparison with state-of-the-art methods.
Li, D, Armaghani, DJ, Zhou, J, Lai, SH & Hasanipanah, M 2020, 'A GMDH Predictive Model to Predict Rock Material Strength Using Three Non-destructive Tests', Journal of Nondestructive Evaluation, vol. 39, no. 4.
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Li, D, Li, JJ, Zhu, Y, Hou, F, Li, Y, Zhao, B & Wang, B 2020, 'Large autologous ilium with periosteum for tibiotalar joint reconstruction in Rüedi-Allgöwer III or AO/OTA type C3 pilon fractures: a pilot study', BMC Musculoskeletal Disorders, vol. 21, no. 1, p. 632.
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Abstract Background Management of Rüedi-Allgöwer III or AO/OTA type C3 pilon fracture presents numerous challenges to the orthopaedic surgeon. A joint preservation technique using a large autologous ilium with periosteum in combination with internal implant fixation was reported to improve the outcome of reconstruction. Methods Twenty-five patients according to Tscherne/Oestern FxCO-I closed fracture and FxOI open fractures classification after Rüedi-Allgöwer III or AO/OTA type C3 pilon fracture received a large autologous ilium with periosteum for tibiotalar joint reconstruction and open reduction and internal fixation (ORIF), between March 2015 and September 2018. The visual analog scale (VAS), American Orthopaedic Foot & Ankle Society (AOFAS) score, and Burwell and Charnley criteria were used for outcome analysis. Results Twenty patients with an average age of 45.2 years were followed for an average of 18.3 months. The VAS and AOFAS scores, and Burwell and Charnley ratings were recorded at the last follow-up after reconstructive surgery. Two patients developed redness and swelling at the wound site, but recovered after local care and dressing changes. No patient displayed deep surgical site infection, donor site complication, non-union or local complication during the final follow-up. The average bone union time was 18.3 months (range 3–36). Conclusions Large autologous ilium with periosteum in combination with ORIF can be performed for tibiotalar joint reconstruction. This experimental procedure reduc...
Li, D, Wen, S, Kong, M, Liu, Y, Hu, W, Shi, B, Shi, X & Jin, D 2020, 'Highly Doped Upconversion Nanoparticles for In Vivo Applications Under Mild Excitation Power', Analytical Chemistry, vol. 92, no. 16, pp. 10913-10919.
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One of the major challenges in using upconversion nanoparticles (UCNPs) is to improve their brightness. This is particularly true for in vivo studies, as the low power excitation is required to prevent the potential photo toxicity to live cells and tissues. Here, we report that the typical NaYF4:Yb0.2,Er0.02 nanoparticles can be highly doped, and the formula of NaYF4:Yb0.8,Er0.06 can gain orders of magnitude more brightness, which is applicable to a range of mild 980 nm excitation power densities, from 0.005 W/cm2 to 0.5 W/cm2. Our results reveal that the concentration of Yb3+ sensitizer ions plays an essential role, while increasing the doping concentration of Er3+ activator ions to 6 mol % only has incremental effect. We further demonstrated a type of bright UCNPs 12 nm in total diameter for in vivo tumor imaging at a power density as low as 0.0027 W/cm2, bringing down the excitation power requirement by 42 times. This work redefines the doping concentrations to fight for the issue of concentration quenching, so that ultrasmall and bright nanoparticles can be used to further improve the performance of upconversion nanotechnology in photodynamic therapy, light-triggered drug release, optogenetics, and night vision enhancement.
Li, G, Feng, B, Zhou, H, Zhang, Y, Sood, K & Yu, S 2020, 'Adaptive service function chaining mappings in 5G using deep Q-learning', Computer Communications, vol. 152, pp. 305-315.
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© 2020 Elsevier B.V. With introduction of Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies, mobile network operators are able to provide on-demand Service Function Chaining (SFC) to meet various needs from users. However, it is challenging to map multiple SFCs to substrate networks efficiently, particularly in a number of key scenarios of forthcoming 5G, where user requests have different priorities and various resource demands. To this end, we first formulate the mapping of multiple SFCs with priorities as a multi-step Linear Integer Programming (ILP) problem, of which the mapping strategy (i.e., the objective function) in each step is configurable to improve overall CPU and bandwidth resource utilization rates. Secondly, to solve the strategy selection problem in each step and alleviate the complexity of ILP, we propose an adaptive deep Q-learning based SFC mapping approach (ADAP), where an agent is learned to make decisions from two low-complexity heuristic SFC mapping algorithms. Finally, we conduct extensive simulations using multiple SFC requests with randomly generated CPU and bandwidth demands in a real-world substrate network topology. Related results demonstrate that compared with a single strategy or random selections of strategies under the ILP-based approach or the proposed heuristic algorithms, our ADAP approach can improve whole-system resource efficiency by scheduling this two simply designed heuristic algorithms properly after limited training episodes.
Li, G, Guo, G, Peng, S, Wang, C, Yu, S, Niu, J & Mo, J 2020, 'Matrix Completion via Schatten Capped p Norm', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-1.
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The low-rank matrix completion problem is fundamental in both machine learning and computer vision fields with many important applications, such as recommendation system, motion capture, face recognition, and image inpainting. In order to avoid solving the rank minimization problem which is NP-hard, several surrogate functions of the rank have been proposed in the literature. However, the matrix restored from the optimization problem based on the existing surrogate functions seriously deviates from the original one. In this paper, we first design a new non-convex Schatten capped p norm which generalizes several existing non-convex matrix norms and balances between the rank and the nuclear norm of the matrix. Then, a matrix completion method based on the Schatten capped p norm is proposed by exploiting the framework of the alternating direction method of multipliers. Meanwhile, the Schatten capped p norm regularized least squares subproblem is analyzed in detail and is solved explicitly. Finally, we evaluate the performance of the proposed matrix completion method based on extensive experiments in the field of image inpainting. All the experimental results demonstrate that the proposed method can indeed improve the accuracy of matrix completion compared with the existing methods.
Li, G, Zhang, Y, Dong, Y, Liang, J, Zhang, J, Wang, J, Mcguffin, MJ & Yuan, X 2020, 'BarcodeTree: Scalable Comparison of Multiple Hierarchies', IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 1022-1032.
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© 1995-2012 IEEE. We propose BarcodeTree (BCT), a novel visualization technique for comparing topological structures and node attribute values of multiple trees. BCT can provide an overview of one hundred shallow and stable trees simultaneously, without aggregating individual nodes. Each BCT is shown within a single row using a style similar to a barcode, allowing trees to be stacked vertically with matching nodes aligned horizontally to ease comparison and maintain space efficiency. We design several visual cues and interactive techniques to help users understand the topological structure and compare trees. In an experiment comparing two variants of BCT with icicle plots, the results suggest that BCTs make it easier to visually compare trees by reducing the vertical distance between different trees. We also present two case studies involving a dataset of hundreds of trees to demonstrate BCT's utility.
Li, G, Zhou, L, Yu, N, Ding, Y, Ying, M & Xie, Y 2020, 'Projection-based runtime assertions for testing and debugging Quantum programs.', Proc. ACM Program. Lang., vol. 4, no. OOPSLA, pp. 150:1-150:1.
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© 2020 Owner/Author. In this paper, we propose Proq, a runtime assertion scheme for testing and debugging quantum programs on a quantum computer. The predicates in Proq are represented by projections (or equivalently, closed subspaces of the state space), following Birkhoff-von Neumann quantum logic. The satisfaction of a projection by a quantum state can be directly checked upon a small number of projective measurements rather than a large number of repeated executions. On the theory side, we rigorously prove that checking projection-based assertions can help locate bugs or statistically assure that the semantic function of the tested program is close to what we expect, for both exact and approximate quantum programs. On the practice side, we consider hardware constraints and introduce several techniques to transform the assertions, making them directly executable on the measurement-restricted quantum computers. We also propose to achieve simplified assertion implementation using local projection technique with soundness guaranteed. We compare Proq with existing quantum program assertions and demonstrate the effectiveness and efficiency of Proq by its applications to assert two sophisticated quantum algorithms, the Harrow-Hassidim-Lloyd algorithm and Shor's algorithm.
Li, H, Li, Y & Li, J 2020, 'Negative stiffness devices for vibration isolation applications: A review', Advances in Structural Engineering, vol. 23, no. 8, pp. 1739-1755.
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In recent years, negative stiffness vibration isolation device with nonlinear characteristic has become an emerging research area and attracted a significant amount of attentions in the community due to the promising potentials it brought into the field. Its high-static-low-dynamic stiffness property endows the capacity to realize effective vibration isolation and in the meantime to maintain the system stability. This article presents a comprehensive review of the recent research and developments on negative stiffness vibration isolation device. It begins with an introduction on the concept of negative stiffness and then provides a summary and discussion regarding the realization and characteristics of negative stiffness vibration isolation device. The article places its special interest on the principles, structure design, and device characterisation of different types of negative stiffness vibration isolation devices, including spring type, pre-bucked beam type, magnetism type, geometrically nonlinear structural type, and composite structural type. Besides, the applications of negative stiffness vibration isolation device, as well as negative stiffness damper, are summarized and discussed based on the current state-of-the-art. Finally, the conclusions and further discussion provide highlights of the investigation.
Li, H, Xu, W, Zhang, H, Zhang, J & Liu, Y 2020, 'Polynomial regressors based data-driven control for autonomous underwater vehicles', Peer-to-Peer Networking and Applications, vol. 13, no. 5, pp. 1767-1775.
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Li, H, Yang, Y, Dai, Y, Yu, S & Xiang, Y 2020, 'Achieving Secure and Efficient Dynamic Searchable Symmetric Encryption over Medical Cloud Data', IEEE Transactions on Cloud Computing, vol. 8, no. 2, pp. 484-494.
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© 2013 IEEE. In medical cloud computing, a patient can remotely outsource her medical data to the cloud server. In this case, only authorized doctors are allowed to access the data since the medical data is highly sensitive. Before outsourcing, the data is commonly encrypted, where the corresponding secret key is sent to authorized doctors. However, performing searches on encrypted medical data is difficult without decryption. In this paper, we propose two Secure and Efficient Dynamic Searchable Symmetric Encryption (SEDSSE) schemes over medical cloud data. First, we utilize the secure k-Nearest Neighbor (kNN) and Attribute-Based Encryption (ABE) techniques to construct a dynamic searchable symmetric encryption scheme, which can achieve forward privacy and backward privacy simultaneously. These tow security properties are vital and very challenging in the area of dynamic searchable symmetric encryption. Then, we propose an enhanced scheme to solve the key sharing problem which widely exists in the kNN based searchable encryption scheme. Compared with existing proposals, our schemes are better in terms of storage, search and updating complexity. Extensive experiments demonstrate the efficiency of our schemes on storage overhead, index building, trapdoor generating and query.
Li, H-Y, Xu, J-X, Yang, Y & Zhang, XY 2020, 'Novel Switchable Filtering Circuit With Function Reconfigurability Between SPQT Filtering Switch and Four-Way Filtering Power Divider', IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 3, pp. 867-876.
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© 2019 IEEE. In this article, we propose a circuit model for designing the switchable filtering circuit with reconfigurable functions between a single-pole quad-throw (SPQT) filtering switch (State 1) and a four-way filtering power divider (State 2) without additional switchable impedance matching network (IMN). The proposed circuit model can be utilized as the feeding network of a switchable directive and omni-directional antenna system to eliminate the omni-directional antenna in conventional solutions to reduce the size and cost. It consists of four filter channels with a common port. In State 1, it works as a SPQT filtering switch, where only one channel is turned on to realize good filtering responses and the impedances at the other three channels are adapted to infinity to realize high isolation. The four channels are turned on simultaneously in State 2 to work as a four-way filtering power divider, and the input impedance of the common port is the same as that in State 1. Thus, a switchable IMN is not required for port matching when switching between the two states. Transmission zeros are generated to enhance the skirt selectivity. For verification, the filtering circuit is constructed using high- Q coaxial resonators. Experimental results show excellent performance in both states acting as a SPQT filtering switch and a four-way filtering power divider.
Li, J, Pan, Y, Sui, Y & Tsang, IW 2020, 'Secure Metric Learning via Differential Pairwise Privacy', IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3640-3652.
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© 2005-2012 IEEE. Distance Metric Learning (DML) has drawn much attention over the last two decades. A number of previous works have shown that it performs well in measuring the similarities of individuals given a set of correctly labeled pairwise data by domain experts. These important and precisely-labeled pairwise data are often highly sensitive in real world (e.g., patients similarity). This paper studies, for the first time, how pairwise information can be leaked to attackers during distance metric learning, and develops differential pairwise privacy (DPP), generalizing the definition of standard differential privacy, for secure metric learning. Unlike traditional differential privacy which only applies to independent samples, thus cannot be used for pairwise data, DPP successfully deals with this problem by reformulating the worst case. Specifically, given the pairwise data, we reveal all the involved correlations among pairs in the constructed undirected graph. DPP is then formalized that defines what kind of DML algorithm is private to preserve pairwise data. After that, a case study employing the contrastive loss is exhibited to clarify the details of implementing a DPP- DML algorithm. Particularly, the sensitivity reduction technique is proposed to enhance the utility of the output distance metric. Experiments both on a toy dataset and benchmarks demonstrate that the proposed scheme achieves pairwise data privacy without compromising the output performance much (Accuracy declines less than 0.01 throughout all benchmark datasets when the privacy budget is set at 4).
Li, J, Xu, T, Hou, W, Liu, F, Qing, W, Huang, L, Ma, G, Mu, Y & Weng, J 2020, 'The response of host blood vessels to graded distribution of macro-pores size in the process of ectopic osteogenesis', Materials Science and Engineering: C, vol. 109, pp. 110641-110641.
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Li, J, Zhu, X, Law, S-S & Samali, B 2020, 'A Two-Step Drive-By Bridge Damage Detection Using Dual Kalman Filter', International Journal of Structural Stability and Dynamics, vol. 20, no. 10, pp. 2042006-2042006.
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Drive-by bridge inspection using acceleration responses of a passing vehicle has great potential for bridge structural health monitoring. It is, however, known that the road surface roughness is a big challenge for the practical application of this indirect approach. This paper presents a new two-step method for the bridge damage identification from only the dynamic responses of a passing vehicle without the road surface roughness information. A state-space equation of the vehicle model is derived based on the Newmark-[Formula: see text] method. In the first step, the road surface roughness is estimated from the dynamic responses of a passing vehicle using the dual Kalman filter (DKF). In the second step, the bridge damage is identified based on the interaction force sensitivity analysis with Tikhonov regularization. A vehicle–bridge interaction model with a wireless monitoring system has been built in the laboratory. Experimental investigation has been carried out for the interaction force and bridge surface roughness identification. Results show that the proposed method is effective and reliable to identify the interaction force and bridge surface roughness. Numerical simulations have also been conducted to study the effectiveness of the proposed method for bridge damage detection. The vehicle is modeled as a 4-degrees-of-freedom half-car and the bridge is modeled as a simply-supported beam. The local bridge damage is simulated as an elemental flexural stiffness reduction. Effects of measurement noise, surface roughness and vehicle speed on the identification are discussed.The results show that the proposed drive-by inspection strategy is efficient and accurate for a quick review on the bridge conditions.
Li, J, Zhu, X, Law, S-S & Samali, B 2020, 'Time-varying characteristics of bridges under the passage of vehicles using synchroextracting transform', Mechanical Systems and Signal Processing, vol. 140, pp. 106727-106727.
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© 2020 Elsevier Ltd The vehicle and bridge responses in a vehicle-bridge interaction (VBI) system have been widely studied with some aiming at the bridge health monitoring. The extraction of bridge modal frequencies from bridge or vehicle responses was mostly conducted with the assumption of an invariant vehicle and bridge system and/or the responses are stationary during the interaction. This assumption may be appropriate when the vehicle mass is negligible compared with the bridge mass. The vehicle and bridge frequencies are time-varying in practice during the VBI process and these time-varying characteristics are potential indicators for bridge condition assessment. This paper presents a new method to extract the time-varying characteristics of the bridge under the passage of vehicles. A time-frequency (TF) analysis method, the synchroextracting transform, is adopted for the purpose. It is a post-processing procedure with short-time Fourier transform to improve the TF resolution on the time-varying features of the signal. The instantaneous frequency of mono-components related to the vehicle and bridge frequencies can then be extracted from the time-frequency representation of the responses. Numerical investigation is conducted to study the effect of measurement noise, vehicle properties and road surface roughness on the identified results. Laboratory and field tests are also conducted to validate the proposed approach. Results show that the time-varying characteristics are good indicators for bridge condition assessment.
Li, K, Liu, AX & Yu, S 2020, 'Special issue on natural computation, fuzzy systems and knowledge discovery from the ICNC&FSKD 2017', Neurocomputing, vol. 393, pp. 112-114.
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Li, K, Ni, W, Emami, Y, Shen, Y, Severino, R, Pereira, D & Tovar, E 2020, 'Design and Implementation of Secret Key Agreement for Platoon-based Vehicular Cyber-physical Systems', ACM Transactions on Cyber-Physical Systems, vol. 4, no. 2, pp. 1-20.
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In a platoon-based vehicular cyber-physical system (PVCPS), a lead vehicle that is responsible for managing the platoon’s moving directions and velocity periodically disseminates control messages to the vehicles that follow. Securing wireless transmissions of the messages between the vehicles is critical for privacy and confidentiality of the platoon’s driving pattern. However, due to the broadcast nature of radio channels, the transmissions are vulnerable to eavesdropping. In this article, we propose a cooperative secret key agreement (CoopKey) scheme for encrypting/decrypting the control messages, where the vehicles in PVCPS generate a unified secret key based on the quantized fading channel randomness. Channel quantization intervals are optimized by dynamic programming to minimize the mismatch of keys. A platooning testbed is built with autonomous robotic vehicles, where a TelosB wireless node is used for onboard data processing and multi-hop dissemination. Extensive real-world experiments demonstrate that CoopKey achieves significantly low secret bit mismatch rate in a variety of settings. Moreover, the standard NIST test suite is employed to verify randomness of the generated keys, where the p-values of our CoopKey pass all the randomness tests. We also evaluate CoopKey with an extended platoon size via simulations to investigate the effect of system scalability on performance.
Li, K, Ni, W, Tovar, E & Guizani, M 2020, 'Optimal Rate-Adaptive Data Dissemination in Vehicular Platoons', IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 10, pp. 4241-4251.
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Li, L, Ju, N, He, C, Li, C & Sheng, D 2020, 'A computationally efficient system for assessing near-real-time instability of regional unsaturated soil slopes under rainfall', Landslides, vol. 17, no. 4, pp. 893-911.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. The objective of this paper is to obtain an applicable assessment method and a Web-GIS-based prediction system for regional landslides. The traditional Richards function is reconstructed using the soil-water characteristic curve (SWCC) and the coordinate transformation technique. The analytical pore pressure for a slope model is derived by solving the modified Richard equation via the Green function and Fourier transformation. The obtained transient pore pressure field is then incorporated with Brakensiek’s matric suction theory, to build a conceptual model for rainfall-induced shallow landslides. The safety factor is obtained by solving the limit equilibrium equation of the conceptual model. The method is then implemented in a Web-GIS system, considering influence of slope geometry features, geology parent material, and near-real-time rainfall intensity of the study area. It is verified that this method is computationally efficient and reliable for gentle slopes and short rainfall durations. Moreover, an extensive parameter study shows that the two commonly used coefficients in the intensity-duration equation are both correlated to rainfall inter-event time via exponential functions, and rainfall event time via power functions. The primary influential factor for regional landslides is the initial water content, followed by the rainfall duration and intensity, and least by soil thickness.
Li, L, Song, K, Yeerken, S, Geng, S, Liu, D, Dai, Z, Xie, F, Zhou, X & Wang, Q 2020, 'Effect evaluation of microplastics on activated sludge nitrification and denitrification', Science of The Total Environment, vol. 707, pp. 135953-135953.
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A large amount of microplastics have entered conventional wastewater treatment plants, and their effects on activated sludge nitrification and denitrification are rarely reported. This study investigated the effects of microplastics on activated sludge nitrification and denitrification using five typical microplastics, namely, polyvinyl chloride (PVC), polypropylene, polyethylene, polystyrene, and polyester (PES) with concentrations of 0, 1000, 5000, and 10,000 particles/L. Results indicated that microplastics had negative effects on ammonia oxidation rate and low effect on nitrite oxidation rate during nitrification. The total inorganic nitrogen did not have much difference during 3 h nitrification under all the tested conditions. The addition of microplastics showed positive effects on denitrification, especially for PVC and PES at microplastic concentration of 5000 particles/L. Nitrification and denitrification did not evidently stop under all the tested conditions, indicating that the selected microplastic types and concentrations were not toxic to nitrification and denitrification within 3 h. The high abundance of PVC microplastics remarkably increased the nitrous oxide (N2O) emission during denitrification. The N2O emission in the test with 10,000 particle/L of PVC was 4.6times higher than the blank control. This study indicated that microplastics with <10,000 particle/L concentration in wastewater had low effects on nitrification and denitrification, whereas they had high effects on the N2O emission during denitrification.
Li, M, Xu, RY, Xin, J, Zhang, K & Jing, J 2020, 'Fast non-rigid points registration with cluster correspondences projection', Signal Processing, vol. 170, pp. 107425-107425.
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Li, M, Yang, Y, Iacopi, F, Nulman, J & Chappel-Ram, S 2020, '3D-Printed Low-Profile Single-Substrate Multi-Metal Layer Antennas and Array With Bandwidth Enhancement', IEEE Access, vol. 8, no. 99, pp. 217370-217379.
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This paper presents a few single-substrate multi-metal layer antennas using additively manufactured electronics (AME) solution based on piezoelectric additive fabrication. By vertically stacking metal layers in a 3D printed single substrate, the designed antenna prototype exhibits the advantages of wide bandwidth and ultra-low profile. For proof-of-concept, multi-layer linear polarization (LP) patch antenna elements and 2×2 LP antenna arrays are designed, fabricated, and measured. It verifies that the feeding network can be integrated into the same substrate of the antenna array element without increasing the size and profile of the array. Compared with the traditional single-layer LP patch antenna, the proposed LP patch antenna can improve the impedance bandwidth from 5.9% to 10.6% (three layers) and 83% (seven layers), respectively. All these designs can be fabricated in a single substrate with a thickness of 1.5 mm ( 0.031 λg ), which is an ideal solution for the applications where ultra-low profile and wideband patch antenna are expected. Finally, circular polarization (CP) patch antenna elements and 2×2 CP antenna arrays are fabricated and measured. Good agreements between the simulated and the measured results verify that wider impedance bandwidth and broader frequency range of under 3-dB axial ratio can be obtained by vertically stacking metal layers. The antennas are designed at sub-6GHz, which have great potentials for 5G consumer mobile electronics.
Li, P, Gao, X, Wang, K, Tam, VWY & Li, W 2020, 'Hydration mechanism and early frost resistance of calcium sulfoaluminate cement concrete', Construction and Building Materials, vol. 239, pp. 117862-117862.
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© 2019 Elsevier Ltd This study investigated the hydration mechanism and mechanical properties of ordinary Portland cement (OPC) blended with calcium sulfoaluminate (CSA) cement. Heat evolution, hydration products, pore size distribution, and microstructure were investigated for OPC-CSA blends concrete with different contents of CSA cement. Macroscopic properties, such as internal temperature, dynamic elastic modulus, and compressive strength, are also studied through concrete subjected to early frost conditions. The results show that the OPC-CSA blended cement displayed a higher early strength and exhibited enhanced resistance to the early frost damage compared to OPC. The OPC-CSA blended cement also exhibits a higher hydration rate and a larger amount of heat of hydration than that in the OPC at the early stage. The increased heat of hydration can effectively prolong the hydration duration at sub-zero temperatures. However, incorporating CSA delayed the hydration of C3S at the late stage, thus affecting the development of compressive strength and dynamic elastic modulus. On the other hand, the hardened blended cement exhibited an higher porosity, which was corresponding to the increasing proportion of macropores (diameter over 1000 nm). If concrete directly is suffered from early frost after casting, blended cement with 20% of CSA can effectively reduce strength loss from frost damage by 100% at −5 °C, and that from frost damage by 80% at −15 °C respectively. Furthermore, when the calcium nitrite is incorporated as the antifreeze admixture with OPC-CSA blended concrete, the early stage frost resistance of concrete infrastructures can be significantly improved.
Li, P, Guo, S, Yu, S & Zhuang, W 2020, 'Cross-Cloud MapReduce for Big Data', IEEE Transactions on Cloud Computing, vol. 8, no. 2, pp. 375-386.
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© 2013 IEEE. MapReduce plays a critical role as a leading framework for big data analytics. In this paper, we consider a geo-distributed cloud architecture that provides MapReduce services based on the big data collected from end users all over the world. Existing work handles MapReduce jobs by a traditional computation-centric approach that all input data distributed in multiple clouds are aggregated to a virtual cluster that resides in a single cloud. Its poor efficiency and high cost for big data support motivate us to propose a novel data-centric architecture with three key techniques, namely, cross-cloud virtual cluster, data-centric job placement, and network coding based traffic routing. Our design leads to an optimization framework with the objective of minimizing both computation and transmission cost for running a set of MapReduce jobs in geo-distributed clouds. We further design a parallel algorithm by decomposing the original large-scale problem into several distributively solvable subproblems that are coordinated by a high-level master problem. Finally, we conduct real-world experiments and extensive simulations to show that our proposal significantly outperforms the existing works.
Li, P, Li, W, Yu, T, Qu, F & Tam, VWY 2020, 'Investigation on early-age hydration, mechanical properties and microstructure of seawater sea sand cement mortar', Construction and Building Materials, vol. 249, pp. 118776-118776.
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© 2020 Elsevier Ltd Using seawater for concrete manufacturing promisingly provides significant economical and environmental benefits. In this study, ordinary Portland cement (OPC) hydration in distilled water and seawater and the corresponding evolution of solid phases was investigated by heat evolution, hydrated phase, hydration kinetics, and microstructure characterization. The results show that seawater can promote the early hydration of tricalcium silicate (C3S) during the hydration acceleration period. The hydrated phase assemblage was affected by the dissolved ions in seawater. Friedel's salt was detected as a specific hydration phase in seawater, which was formed by chemical combination between the aluminate ferrite monosulfate (AFm) phase and chloride ions. The monocarboaluminate can be converted into a stable phase as Friedel's salt in the seawater, due to the reaction with chloride ions. Furthermore, the ettringite becomes more stable when coexists with Friedel's salt than that with monocarboaluminate, and thus ettringite formed in seawater remains 67% higher than that formed in distilled water at the later curing age. Moreover, additional unhydrated cement and less amorphous calcium silicate hydrate (C-S-H) were formed in seawater, which might be responsible for the slightly lower compressive strength of cement mortar prepared by seawater and sea sand. A modeled evolution of the solid phase and pore solution have been established, which agrees well with the characteristics of the dissolution of mineral phase, precipitation of hydration products and changes of pore solution. The related results can provide an insight into the applications of seawater and sea sand concrete for marine infrastructures.
Li, Q, Cao, Z, Ding, W & Li, Q 2020, 'A multi-objective adaptive evolutionary algorithm to extract communities in networks', Swarm and Evolutionary Computation, vol. 52, pp. 100629-100629.
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Li, Q, Cao, Z, Tanveer, M, Pandey, HM & Wang, C 2020, 'A Semantic Collaboration Method Based on Uniform Knowledge Graph', IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4473-4484.
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© 2014 IEEE. The Semantic Internet of Things (SIoT) is the extension of the Internet of Things (IoT) and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the IoT. However, the SIoT has the characteristics of both the IoT and the Semantic Web environment, and the corresponding semantic data present many new data features. In this article, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph (UKG), allowing us to be applied to the environment of the SIoT better. Here, we design a semantic collaboration method based on a UKG. It can take the UKG as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing the semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the SIoT. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes.
Li, Q, Cao, Z, Tanveer, M, Pandey, HM & Wang, C 2020, 'An Effective Reliability Evaluation Method for Power Communication Network Based on Community Structure', IEEE Transactions on Industry Applications, vol. 56, no. 4, pp. 1-1.
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The reliability evaluation of the power communication network is beneficial for the improvement of the stable operation of the power system and the robustness of the power grid. However, the existing reliability evaluation models of the power communication network cannot meet the current situation of timeliness performance, due to rapidly increasing scale and complexity of information across varying services. In this study, we used the complex network theory to analyze the structure of the power communication network. Then we constructed the evaluation index of node (link) reliability of the power communication network based on community reliability. Compared with the traditional reliability indexes, our index not only considers the influence of the environment of the node (link) on the single structure of the power communication network, but also possesses the reliability evaluation rate of the node (link), which have the opportunities for improving the performance of the reliability evaluation of the wide-area power communication network. To verify the rationality of the index, we developed random, low reliability, and high-betweenness deliberate attacks to attack the designated node (link), and compared the network efficiency before and after the attack. Based on the simulation results, it can verify the rationality and superiority of our proposed evaluation index.
Li, Q, Wu, D, Gao, W & Tin-Loi, F 2020, 'Size-dependent instability of organic solar cell resting on Winkler–Pasternak elastic foundation based on the modified strain gradient theory', International Journal of Mechanical Sciences, vol. 177, pp. 105306-105306.
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© 2019 The present study employs the modified strain gradient theory (MSGT) in conjunction with the refined shear deformation plate theory to explore the buckling behaviour of simply supported and clamped OSC. The Winkler-Pasternak elastic foundation is implemented to idealise the foundation. The size-dependent effect of the OSC is captured by the three length scale parameters within the MSGT. The Hamilton principle is used to derive the equations of motion and the boundary conditions, and the Galerkin procedure is subsequently implemented to obtain the critical buckling load. Subsequently, the framework is extended to the thermally induced buckling behaviour, and three types of temperature rise patterns, namely uniform, linear and nonlinear temperature variations, along the thickness of the OSC are considered. Several verification studies are conducted to illustrate the accuracy of the present method. Besides, size-dependent material properties are taken into consideration during the numerical experiments. Thorough studies are conducted to demonstrate the difference between critical buckling loads obtained from the MSGT, the modified couple stress theory (MCST), and the classical plate theory (CPT) models. Furthermore, the effects of length scale parameter (h/l), the aspect ratio (a/b), the length-to-thickness ratio (a/h) and the Winkler-Pasternak elastic foundation parameters on the buckling behaviour of the OSC are also revealed by the numerical results.
Li, Q, Zhong, J, Cao, Z & Li, X 2020, 'Optimizing streaming graph partitioning via a heuristic greedy method and caching strategy', Optimization Methods and Software, vol. 35, no. 6, pp. 1144-1159.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Graph partitioning is an important method for accelerating large distributed graph computation. Streaming graph partitioning is more efficient than offline partitioning, and it has been developed continuously in the application of graph partitioning in recent years. In this work, we first introduce a heuristic greedy streaming partitioning method and show that it outperforms the state-of-the-art streaming partitioning methods, leading to exact balance and fewer cut edges. Second, we propose a cache structure for streaming partitioning, called an adjacent edge structure, which can improve the partition efficiency several times on a single commodity type computer without affecting the partition quality. Regardless as to whether the memory capacity is limited (local cache) or not (global cache), our strategy can also improve the partition quality by restreaming partitioning. Taking linear weight greedy streaming algorithm as an example, the experimental results on 19 real-world graphs show that the average partitioning time of the new method is 4.9 times faster than that of the original method, which proves the effectiveness and superiority of the cache structure mentioned in this paper.
Li, R, Gao, S, Qin, L, Wang, G, Yang, W & Yu, JX 2020, 'Ordering Heuristics for k-clique Listing.', Proc. VLDB Endow., vol. 13, pp. 2536-2548.
Li, R, Wu, B, Ying, M, Sun, X & Yang, G 2020, 'Quantum Supremacy Circuit Simulation on Sunway TaihuLight.', IEEE Trans. Parallel Distributed Syst., vol. 31, no. 4, pp. 805-816.
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© 1990-2012 IEEE. With the rapid progress made by industry and academia, quantum computers with dozens of qubits or even larger size are being realized. However, the fidelity of existing quantum computers often sharply decreases as the circuit depth increases. Thus, an ideal quantum circuit simulator on classical computers, especially on high-performance computers, is needed for benchmarking and validation. We design a large-scale simulator of universal random quantum circuits, often called 'quantum supremacy circuits', and implement it on Sunway TaihuLight. The simulator can be used to accomplish the following two tasks: 1) Computing a complete output state-vector; 2) Calculating one or a few amplitudes. We target the simulation of 49-qubit circuits. For task 1), we successfully simulate such a circuit of depth 39, and for task 2) we reach the 55-depth level. To the best of our knowledge, both of the simulation results reach the largest depth for 49-qubit quantum supremacy circuits.
Li, S, Liang, Y, Li, Y, Li, J & Zhou, Y 2020, 'Investigation of dynamic properties of isotropic and anisotropic magnetorheological elastomers with a hybrid magnet shear test rig', Smart Materials and Structures, vol. 29, no. 11, pp. 114001-114001.
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Abstract Magnetorheological elastomers (MREs) exhibit instantaneous and reversible adaptability of stiffness and damping properties under the influence of magnetic field, which can be implemented in the development of controllable devices. The main MRE components are normally elastomeric matrix and magnetisable particles. Depending on the distribution of the particles in the matrix, MREs can be classified into isotropic and anisotropic. This work experimentally explored, compared, and modelled the dynamic characteristics of both isotropic and anisotropic MREs with different iron particle weight fractions (17%, 22%, and 32%). A novel shear test rig was designed with hybrid magnets system, i.e. permanent magnet and electromagnets, to fulfil the characterisation tasks. The involvement of the hybrid magnets effectively cuts down the maximum electric current and energy consumption of the rig. The tests were conducted under sinusoidal shear motions with excitation frequency ranging from 0.1 Hz to 2 Hz and shear strain varying from 20% to 60% to record the force-displacement hysteresis of MRE samples. Four different levels of magnetic field (0.02, 0.54, 0.77, 1.01 T) were supplied by the hybrid magnetic system and were considered in the tests to evaluate the influence of the magnetic fields. Furthermore, characterised hysteretic behaviours for both isotropic and anisotropic MRE were modelled by a strain stiffening phenomenological model with ideal accuracy under the shear excitation inputs and magnetic fields considered.
Li, S, Tian, T, Wang, H, Li, Y, Li, J, Zhou, Y & Wu, J 2020, 'Development of a four-parameter phenomenological model for the nonlinear viscoelastic behaviour of magnetorheological gels', Materials & Design, vol. 194, pp. 108935-108935.
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Li, S, Watterson, PA, Li, Y, Wen, Q & Li, J 2020, 'Improved magnetic circuit analysis of a laminated magnetorheological elastomer device featuring both permanent magnets and electromagnets', Smart Materials and Structures, vol. 29, no. 8, pp. 085054-085054.
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As an essential and critical step, magnetic circuit modelling is usually implemented in the design of efficient and compact magnetorheological (MR) devices, such as MR dampers and MR elastomer isolators. Conventional magnetic circuit analysis simplifies the analysis by ignoring the magnetic flux leakage and magnetic fringing effect. These assumptions are sufficiently accurate in dealing with less complicated designs, featuring short magnetic path lengths such as in an MR damper. However, when dealing with MR elastomer devices, such simplification in magnetic circuit analysis results in inaccuracy of dimensioning and performance estimation of the devices due to their sophisticated design and complex magnetic paths. Modelling permanent magnets also imposes challenges in the magnetic circuit analysis. This work proposes an improved approach to include magnetic flux fringing effect in magnetic circuit analysis for MR elastomer devices. An MRE-based isolator containing multiple MRE layers and both a permanent magnet and an exciting coil was designed and built as a case study. The results of the proposed method are compared to those of conventional magnetic circuit modelling, finite element analysis and experimental measurements to demonstrate the effectiveness of the proposed approach.
Li, T, Fong, S, Li, X, Lu, Z & Gandomi, AH 2020, 'Swarm Decision Table and Ensemble Search Methods in Fog Computing Environment: Case of Day-Ahead Prediction of Building Energy Demands Using IoT Sensors', IEEE Internet of Things Journal, vol. 7, no. 3, pp. 2321-2342.
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© 2014 IEEE. Building energy demand prediction (BEDP) concerns sensing the environment using the Internet of Things (IoT), making seamless decisions and responding and controlling certain devices automatically, intelligently, and quickly. Typically, the BEDP application can be empowered by fog computing where the sensed data are processed at the edge nodes rather than in a central cloud. The challenge is that in this decentralized IoT environment, the machine learning algorithm implemented at the fog node must learn a model from the incoming data accurately and fast. Which type of incremental learning algorithms, combined with traditional or swarm types of stochastic feature selection methods, are more suitable for BEDP? In this article, this topic is investigated in detail by introducing a new incremental learning model, the swarm decision table (SDT) in comparison with the classical decision tree. The simulation experiments using an empirical energy consumption data set that represent a typical IoT-connected BEDP scenario are tested, and the SDT shows superior results in terms of accuracy and time, demonstrating it as a suitable machine learning candidate in a fog computing environment.
Li, W, Cao, H, Liao, J, Xia, J, Cao, L & Knoll, A 2020, 'Parking Slot Detection on Around-View Images Using DCNN', Frontiers in Neurorobotics, vol. 14, p. 46.
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Due to the complex visual environment and incomplete display of parking slots on around-view images, vision-based parking slot detection is a major challenge. Previous studies in this field mostly use the existing models to solve the problem, the steps of which are cumbersome. In this paper, we propose a parking slot detection method that uses directional entrance line regression and classification based on a deep convolutional neural network (DCNN) to make it robust and simple. For parking slots with different shapes and observed from different angles, we represent the parking slot as a directional entrance line. Subsequently, we design a DCNN detector to simultaneously obtain the type, position, length, and direction of the entrance line. After that, the complete parking slot can be easily inferred using the detection results and prior geometric information. To verify our method, we conduct experiments on the public ps2.0 dataset and self-annotated parking slot dataset with 2,135 images. The results show that our method not only outperforms state-of-the-art competitors with a precision rate of 99.68% and a recall rate of 99.41% on the ps2.0 dataset but also performs a satisfying generalization on the self-annotated dataset. Moreover, it achieves a real-time detection speed of 13 ms per frame on Titan Xp. By converting the parking slot into a directional entrance line, the specially designed DCNN detector can quickly and effectively detect various types of parking slots.
Li, W, Dong, W, Shen, L, Castel, A & Shah, SP 2020, 'Conductivity and piezoresistivity of nano-carbon black (NCB) enhanced functional cement-based sensors using polypropylene fibres', Materials Letters, vol. 270, pp. 127736-127736.
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© 2020 Elsevier B.V. The cement-based sensors have a great potential for structural health monitoring, especially functional sensors filled with nano-carbon black (NCB) particles. To improve the sensing efficiency of NCB filled cementitious composite, polypropylene (PP) fibres were premixed with NCB during the manufacturing of cement-based sensor. Although the compressive strength is slightly decreased, the electrical conductivity and piezoresistivity of the NCB filled cementitious composite are improved by PP fibres. Microstructural characterization indicated that NCB attached to the surface of PP fibres significantly promotes the generation of conductive paths and contact points in cement-based sensors. The results can provide a new insight into the application of nonconductive fibres to enhance the conductivity and piezoresistivity of spherical conductors filled cement-based sensor.
Li, W, Huang, L & Ji, J 2020, 'Globally exponentially stable periodic solution in a general delayed predator-prey model under discontinuous prey control strategy', Discrete & Continuous Dynamical Systems - B, vol. 25, no. 7, pp. 2639-2664.
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This paper studies the solution behaviour of a general delayed predator-prey model with discontinuous prey control strategy. The positiveness and boundeness of the solution of the system is firstly investigated using the comparison theorem. Then the sufficient conditions are derived for the existence of positive periodic solutions using the differential inclusion theory and the topological degree theory. Furthermore, the positive periodic solution is proved to be globally exponentially stable by employing the generalized Lyapunov approach. The global finite-time convergence is also discussed for the system state. Finally, the numerical simulations of four examples are given to validate the correctness of the theoretical results.
Li, W, Huang, L, Guo, Z & Ji, J 2020, 'Global dynamic behavior of a plant disease model with ratio dependent impulsive control strategy', Mathematics and Computers in Simulation, vol. 177, pp. 120-139.
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© 2020 International Association for Mathematics and Computers in Simulation (IMACS) In this paper, we consider the dynamics of a plant disease model with a ratio-dependent state impulsive control strategy. It is shown that the boundary equilibrium point of the controlled system is globally asymptotically stable. By combining LaSalle's invariant theorem, Brouwer's fixed point theorem and some analysis techniques, we are able to determine the basic reproduction number, confirm the well-posedness of the model, describe the structure of possible equilibria as well as establish the stability of the equilibria. Most interestingly, we find that in the case that the basic reproduction number is more than unity and the endemic equilibrium locates above the impulsive control strategy, we can obtain a unique k-order periodic solution and the critical values between 1-order and 2-order periodic solutions. Furthermore, it is found that the endemic equilibrium point is also globally asymptotically stable under the control strategy. Finally, we present a numerical example to substantiate the effectiveness of the theoretical results.
Li, W, Ji, J & Huang, L 2020, 'Dynamics of a controlled discontinuous computer worm system', Proceedings of the American Mathematical Society, vol. 148, no. 10, pp. 4389-4403.
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© 2020 American Mathematical Society This paper studies the dynamic behaviour of a computer worm system under a discontinuous control strategy. Some conditions for globally asymptotically stable solutions of the discontinuous system are obtained by using the Bendixson–Dulac theorem, Green’s formula, and the Lyapunov function. It is found that the solutions of the controlled computer worm system can converge to either of two local equilibrium points or the sliding equilibrium point on the discontinuous surface. It is shown that a threshold control strategy can effectively control the spread of computer viruses. The research results may be applicable to control other types of virus systems.
Li, W, Ji, J & Huang, L 2020, 'Global dynamic behavior of a predator–prey model under ratio-dependent state impulsive control', Applied Mathematical Modelling, vol. 77, pp. 1842-1859.
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© 2019 This paper studies the global dynamic behavior of a prey–predator model with square root functional response under ratio-dependent state impulsive control strategy. It is shown that the boundary equilibrium point of the controlled system is globally asymptotically stable. An order-k periodic orbit is obtained by employing the Brouwer's fixed point theorem. Furthermore, the critical values are determined for the existence of orbitally asymptotically stable order-1 and order-2 periodic orbits in finite time. These critical values play an important role in determining different kinds of order-k periodic orbits and can also be used for designing the control parameters to obtain the desirable dynamic behavior of the controlled prey–predator system. Moreover, it is found that the local equilibrium point is also globally asymptotically stable under the control strategy. Numerical examples are provided to validate the effectiveness and feasibility of the theoretical results.
Li, W, Ji, J, Huang, L & Wang, J 2020, 'Bifurcations and dynamics of a plant disease system under non-smooth control strategy', Nonlinear Dynamics, vol. 99, no. 4, pp. 3351-3371.
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© 2020, Springer Nature B.V. Mathematical models and analyses can assist in designing the control strategies to prevent the spread of infectious disease. The present paper investigates the bifurcations and dynamics of a plant disease system under non-smooth control strategy. The generalized Lyapunov approach is employed to perform the analysis of the plant disease model with non-smooth control. It is found that the controlled disease system can have three types of equilibria. The globally asymptotically attractor for each of three types of equilibria is determined by constructing Lyapunov functions and using Green’s Theorem. It is shown that the disease system can exhibit rich dynamic behaviors including globally stable equilibrium, stable pseudo-equilibrium and sliding mode bifurcations. The solution of the disease system can converge to the disease-free equilibrium, endemic equilibrium or sliding equilibrium on discontinuous surfaces. Biological implications of the obtained results are discussed for implementing the control strategies to the infectious plant diseases.
Li, W, Tang, Z, Tam, VWY, Zhao, X & Wang, K 2020, 'A Review on Durability of Alkali-activated System from Sustainable Construction Materials to Infrastructures', ES Materials & Manufacturing, vol. 4, pp. 2-19.
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Li, W, Wen, W, Chen, X, Ni, B, Lin, X & Fan, W 2020, 'Functional Evolving Patterns of Cortical Networks in Progression of Alzheimer’s Disease: A Graph-Based Resting-State fMRI Study', Neural Plasticity, vol. 2020, pp. 1-11.
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AD is a common chronic progressive neurodegenerative disorder. However, the understanding of the dynamic longitudinal change of the brain in the progression of AD is still rough and sometimes conflicting. This paper analyzed the brain networks of healthy people and patients at different stages (EMCI, LMCI, and AD). The results showed that in global network properties, most differences only existed between healthy people and patients, and few were discovered between patients at different stages. However, nearly all subnetwork properties showed significant differences between patients at different stages. Moreover, the most interesting result was that we found two different functional evolving patterns of cortical networks in progression of AD, named ‘temperature inversion’ and “monotonous decline,” but not the same monotonous decline trend as the external functional assessment observed in the course of disease progression. We suppose that those subnetworks, showing the same functional evolving pattern in AD progression, may have something the same in work mechanism in nature. And the subnetworks with ‘temperature inversion’ evolving pattern may play a special role in the development of AD.
Li, X, Bond, PL, O’Moore, L, Wilkie, S, Hanzic, L, Johnson, I, Mueller, K, Yuan, Z & Jiang, G 2020, 'Increased Resistance of Nitrite-Admixed Concrete to Microbially Induced Corrosion in Real Sewers', Environmental Science & Technology, vol. 54, no. 4, pp. 2323-2333.
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Microbially induced concrete corrosion is a major deterioration process in sewers, causing a huge economic burden, and improved mitigating technologies are required. This study reports a novel and promising effective solution to attenuate the corrosion in sewers using calcium nitrite-admixed concrete. This strategy aims to suppress the development and activity of corrosion-inducing microorganisms with the antimicrobial free nitrous acid, which is generated in situ from calcium nitrite that is added to the concrete. Concrete coupons with calcium nitrite as an admixture were exposed in a sewer manhole, together with control coupons that had no nitrite admixture, for 18 months. The corrosion process was monitored by measuring the surface pH, corrosion product composition, concrete corrosion loss, and the microbial community on the corrosion layer. During the exposure, the corrosion loss of the admixed concrete coupons was 30% lower than that of the control coupons. The sulfide uptake rate of the admixed concrete was also 30% lower, leading to a higher surface pH (0.5-0.6 unit), in comparison to that of the control coupons. A negative correlation between the calcium nitrite admixture in concrete and the abundance of sulfide-oxidizing microorganisms was determined by DNA sequencing. The results obtained in this field study demonstrated that this novel use of calcium nitrite as an admixture in concrete is a promising strategy to mitigate the microbially induced corrosion in sewers.
Li, X, Chen, L, Ji, Y, Li, M, Dong, B, Qian, G, Zhou, J & Dai, X 2020, 'Effects of chemical pretreatments on microplastic extraction in sewage sludge and their physicochemical characteristics', Water Research, vol. 171, pp. 115379-115379.
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© 2019 Elsevier Ltd Sewage sludge is a primary pathway for microplastics (MPs) entering into terrestrial ecosystems. However, a standardized method to analyze MP in sludge is lacking due to its high organic matter. This study investigated the extraction efficiency of six MPs in five solid matrices, i.e. sewage sludge, cattle manure, soil, sediment and silicon dioxide. Results show lower extraction efficiency of 87.2% for MPs in sludge compared with that in other matrices, especially polyethylene terephthalate (PET) (only 27.8%). The possible reason was that the presence of extracellular polymeric substances within the sludge hinders the MPs to float. Therefore, five protocols, i.e. hydrogen peroxide (H2O2), Fenton, nitric acid (HNO3), hydrochloric acid (HCl) and sodium hydroxide (NaOH) were used to pretreat the sludge and optimize the MP extraction. The sludge pretreated by H2O2, Fenton and 1 M of acids had higher MP extraction efficiency than the raw sludge due to higher extraction of the PET. The MP extraction efficiency in the sludge first increased, and subsequently decreased with the soluble chemical oxygen demand (SCOD) content, implying that moderate dissolution of sludge organic matter is beneficial to the MP extraction. Quantitative analysis of the changes in the MP physicochemical characteristics after the pretreatments indicated that polyamide (PA) and PET are not resistant to acid and alkali treatment, respectively. Principal component analysis shows that the effect of pretreatments on the MPs follows a decreasing sequence: alkali > high concentration of acids > low concentration of acids > H2O2 and Fenton. Additionally, the susceptibility of the MPs to the pretreatments follows a decreasing sequence: PET, PA and polymethyl methacrylate (PMMA) > polystyrene (PS) > polyethylene (PE) and polypropylene (PP). The findings supply novel insights into the effect of chemical pretreatments on MP extraction in sewage sludge.
Li, X, Ji, M, Nghiem, LD, Zhao, Y, Liu, D, Yang, Y, Wang, Q, Trinh, QT, Vo, D-VN, Pham, VQ & Tran, NH 2020, 'A novel red mud adsorbent for phosphorus and diclofenac removal from wastewater', Journal of Molecular Liquids, vol. 303, pp. 112286-112286.
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© 2019 Elsevier B.V. The ubiquitous occurrence of nutrients (e.g. phosphorus) and micropollutants (e.g. pharmaceuticals and endocrine disrupting chemicals) in wastewater and urban stormwater runoff sources may cause adverse effects on aquatic ecosystems and human health. Therefore, the removal of these pollutants from wastewater, treated effluent, and urban stormwater runoff is critically needed. In this study, a novel modified red mud with polypyrrole (RM-PPy) was successfully synthesized with improved functional groups (–OH, –N=, –NH–, N+), specific area (SBET 102.24 m2/g), and mesopore structure (i.e. average pore diameter of 3.29 nm), which are assumed to enhance the adsorptive removal of diclofenac (DCF) and phosphorus (P) in aqueous solution. The measured maximum adsorption capacity of RM-PPy towards diclofenac (195 mg/g) in single adsorbate system was higher than that (115.7 mg/g) in the binary adsorbates system (i.e. in the presence of P), indicating that the presence of pollutants such as P in water hampered the adsorptive removal of DCF. The adsorption of DCF and P was largely dependent on solution pH values. Higher adsorptive removals of DCF and P were observed at acidic conditions (pH 2–5). Adsorption isotherm of DCF and P was better fitted to Freundlich model compared to Langmuir isotherm model, suggesting multilayer coverage. Adsorption of DCF onto RM-PPy might take place via anion exchange and electrostatic interactions. For P adsorption, apart from anion exchange and electrostatic interactions, the chemical precipitation via ligand exchange between P and hydroxyl (–OH) in RM-PPy can be considered as one of the main adsorption mechanisms. Further studies on the competitive adsorption of other anionic micropollutants at environmentally relevant concentrations (ng/L–μg/L) in water samples by RM-PPy are needed to evaluate the potential application of RM-PPy for the removal of other anionic micropollutants (i.e. antibiotics) in treated wastew...
Li, X, Kuang, Z, Zhang, J, Liu, X, Hu, J, Xu, Q, Wang, D, Liu, Y, Wang, Q, Yang, Q & Li, H 2020, 'Performance and Mechanism of Potassium Ferrate(VI) Enhancing Dark Fermentative Hydrogen Accumulation from Waste Activated Sludge', ACS Sustainable Chemistry & Engineering, vol. 8, no. 23, pp. 8681-8691.
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© 2020 American Chemical Society. Potassium ferrate (K2FeO4, PF), as a multifunctional green oxidant, has been used for oxidative degradation of pollutants and recovery of resources in sludge. However, its impact on the generation of hydrogen in anaerobic fermentation of waste activated sludge (WAS) is still unclear. The purpose of this work is to study the influence of PF on the dark fermentative hydrogen production. Experimental result suggested that as PF increased from 0 to 0.09 g/g of TSS (total suspended solids), the maximal hydrogen production increased from 1.47 to 8.35 mL/g VSS (volatile suspended solids). A further increase to 0.12 g/g of TSS resulted in a decrease in hydrogen yield. Mechanism studies revealed that that the addition of PF not only facilitated the disruption of sludge cell and extracellular polymeric substances (EPS) but also increased the proportion of biodegradable organics, providing more bioavailable organics for subsequent reactions involved in hydrogen accumulation. Although the activities of microorganisms relevant to dark fermentation were suppressed to a certain extent in the presence of PF, the induced suppression to hydrogen consumers was more severe. Microbial studies indicated that the relative abundances of hydrogen producers (such as Petrimonas and Proteiniborus) were augmented while hydrogen consumers (such as Methanosaeta and Methylocaldum) decreased in the presence of PF.
Li, X, Li, J, Zhang, X, Gao, J & Zhang, C 2020, 'Simplified analysis of cable-stayed bridges with longitudinal viscous dampers', Engineering, Construction and Architectural Management, vol. 27, no. 8, pp. 1993-2022.
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PurposeViscous dampers are commonly used in large span cable-stayed bridges to mitigate seismic effects and have achieved great success.Design/methodology/approachHowever, the nonlinear analysis on damper parameters is usually computational intensive and nonobjective. To address these issues, this paper proposes a simplified method to determine the viscous damper parameters for double-tower cable-stayed bridges. An empirical formula of the equivalent damping ratio of viscous dampers is established through decoupling nonclassical damping structures and linearization of nonlinear viscous dampers. Shaking table tests are conducted to verify the feasibility of the proposed method. Moreover, this simplified method has been proved in long-span cable-stayed bridges.FindingsThe feasibility of this method is verified by the simplified model shaking table test. This simplified method for determining the parameters of viscous dampers is verified in cable-stayed bridges with different spans.Originality/valueThis simplified method has been validated in cable-stayed bridges with various spans.
Li, X, Ling, SH & Su, S 2020, 'A Hybrid Feature Selection and Extraction Methods for Sleep Apnea Detection Using Bio-Signals', Sensors, vol. 20, no. 15, pp. 4323-4323.
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People with sleep apnea (SA) are at increased risk of having stroke and cardiovascular diseases. Polysomnography (PSG) is used to detect SA. This paper conducts feature selection from PSG signals and uses a support vector machine (SVM) to detect SA. To analyze SA, the Physionet Apnea Database was used to obtain various features. Electrocardiography (ECG), oxygen saturation (SaO2), airflow, abdominal, and thoracic signals were used to provide various frequency-, time-domain and non-linear features (n = 87). To analyse the significance of these features, firstly, two evaluation measures, the rank-sum method and the analysis of variance (ANOVA) were used to evaluate the significance of the features. These features were then classified according to their significance. Finally, different class feature sets were presented as inputs for an SVM classifier to detect the onset of SA. The hill-climbing feature selection algorithm and the k-fold cross-validation method were applied to evaluate each classification performance. Through the experiments, we discovered that the best feature set (including the top-five significant features) obtained the best classification performance. Furthermore, we plotted receiver operating characteristic (ROC) curves to examine the performance of the SVM, and the results showed the SVM with Linear kernel (regularization parameter = 1) outperformed other classifiers (area under curve = 95.23%, sensitivity = 94.29%, specificity = 96.17%). The results confirm that feature subsets based on multiple bio-signals have the potential to identify patients with SA. The use of a smaller subset avoids dimensionality problems and reduces the computational load.
Li, X, O'Moore, L, Wilkie, S, Song, Y, Wei, J, Bond, PL, Yuan, Z, Hanzic, L & Jiang, G 2020, 'Nitrite admixed concrete for wastewater structures: Mechanical properties, leaching behavior and biofilm development', Construction and Building Materials, vol. 233, pp. 117341-117341.
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This study systematically investigated the impacts of calcium nitrite addition on the mechanical properties and biofilm communities of concrete-based wastewater infrastructures using sulfate resistant cement through standard tests and DNA sequencing, respectively. The results revealed that setting time and water demand for normal consistency were reduced, but slump, drying shrinkage, and apparent volume of permeable voids increased with calcium nitrite dosage up to 4% weight of cement. The cumulative leached fraction of nitrite, 28-day compressive strength and biofilm communities were not significantly affected by calcium nitrite dosages. The addition of calcium nitrite into concrete is environmentally friendly to wastewater infrastructures.
Li, XL, Dong, Z, Tse, CK & Lu, DD-C 2020, 'Single-Inductor Multi-Input Multi-Output DC–DC Converter With High Flexibility and Simple Control', IEEE Transactions on Power Electronics, vol. 35, no. 12, pp. 13104-13114.
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© 1986-2012 IEEE. Multi-input multi-output (MIMO) dc-dc converters can integrate multiple input sources and output loads simultaneously. This article proposes a new single-inductor MIMO dc-dc converter with a wide conversion ratio. The proposed converter allows input sources to be added or removed seamlessly with no cross-regulation problem. Meanwhile, the outputs are independently controlled, i.e., the load change at one output cell will not affect the other interconnected output cells. Constant current control is the main control requirement. When constant current control is applied to all input cells, the power provided by each input source is proportional to the voltage magnitude of the source. When the constant current control is applied to some of the input cells, the input sources with direct duty-cycle controlled input cells can provide specific power through controlling the duty cycles of the switches of the corresponding input cells. Moreover, the switching time of switches is irrelevant. Therefore, it is easy to realize the high extension capability for arbitrary inputs/outputs. A dual-input dual-output prototype is constructed to illustrate the performance of the proposed converter. The corresponding component design is presented.
Li, Y & Qiao, Y 2020, 'Group-theoretic generalisations of vertex and edge connectivities', Proceedings of the American Mathematical Society, vol. 148, no. 11, pp. 4679-4693.
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Let p p be an odd prime. Let P P be a finite p p -group of class 2 2 and exponent p p , whose commutator quotient P / [ P , P ] Pattern Recognition, vol. 103, pp. 107258-107258.
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© 2020 In this paper, we introduce a method of exploring temporal information for estimating human poses in videos. The current state-of-the-art methods utilizing temporal information can be categorized into two major branches. The first category is a model-based method that captures the temporal information entirely by using a learnable function such as RNN or 3D convolution. However, these methods are limited in exploring temporal consistency, which is essential for estimating human joint positions in videos. The second category is the posterior enhancement method, where an independent post-processing step (e.g., using optical flow) is applied to enhance the prediction. However, operations such as optical flow estimation can be susceptible to the occlusion and motion blur problems, which will adversely affect the final performance. We propose a novel Temporal Consistency Exploration (TCE) module to address both shortcomings. Compared to previous approaches, the TCE module is more efficient as it captures the temporal consistency at the feature level without having to post-process and calculate extra optical flow. Further, to capture the rich spatial context in video data, we design a multi-scale TCE to explore the time consistency information at multi-scale spatial levels. Finally, a video-based pose estimation network is designed, which is based on the encoder-decoder architecture and extended with the powerful multi-scale TCE module. We comprehensively evaluate the proposed model on two video datasets, Sub-JHMDB and Penn, and our model achieves state-of-the-art performance on both datasets.
Li, Y, Wang, D, Yang, G, Yuan, X, Xu, Q, Yang, Q, Liu, Y, Wang, Q, Ni, B-J, Tang, W & Jiang, L 2020, 'Enhanced dewaterability of anaerobically digested sludge by in-situ free nitrous acid treatment', Water Research, vol. 169, pp. 115264-115264.
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As the protonated form of nitrite, free nitrous acid (FNA) is a renewable chemical that can be produced on site from the anaerobic digestion liquor by nitritation, and has been widely employed to improve the fermentation of waste activated sludge (WAS). However, it is not clear whether and how FNA improves the dewaterability of anaerobically digested sludge (ADS). This work therefore aims to provide such supports through comparing the dewaterability of ADS treated by nitrite at different concentrations (0-250 mg/L) under three pH values (5.5, 6.3, or 7.2). Environmental results showed that nitrite was completely denitrified within 12 h, and its addition improved the dewaterability of ADS in all the cases. The optimal normalized capillary suction time of 18.0 ± 0.4 s L/g VSS was obtained at nitrite 50 mg/L and pH 5.5 (equivalent of 0.35 mg/L FNA) in comparison with corresponding value of 23.2 ± 0.4 s L/g·VSS at pH 5.5 (equivalent of 0 mg/L FNA). Under this scenario, 80.5% ± 2.0% of water content was obtained in the FNA-treated sample after press filtration while the corresponding value was 88.5% ± 1.7% in the control. The mechanism investigations showed that FNA treatment reduced surface negative charge of ADS flocs and caused disruption of extracellular polymeric substances and release of intracellular substances, which enhanced the flocculability, hydrophobicity, and flowability, but decreased the bound water content, fractal dimension, and viscosity of ADS. Additionally, FNA treatment altered the secondary structure of proteins through destroying the hydrogen bond, which led to a loose structure of protein, benefiting the exposure of hydrophobic sites or groups in EPS proteins. The findings obtained deepen our understanding of FNA affecting sludge dewatering and provide strong supports to sustainable operation of wastewater treatment plants.
Li, Y, Zeng, X, Zhou, J, Liu, H, Gu, Y, Pan, Z, Zeng, Y & Zeng, Y 2020, 'Incorporation of disposed oil-contaminated soil in cement-based materials', Resources, Conservation and Recycling, vol. 160, pp. 104838-104838.
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© 2020 Elsevier B.V. To realize the win-win objective of environmental conservation and waste recycling, oil-contaminated soil was utilized as an additive in cement-based materials. The effect of diesel and engine oil and the corresponding oil-contaminated soil on cement-based materials were studied, including the heat release of cement hydration, rheological and flow properties, flexural and compressive strength, hydration products and oil leaching values. The results showed that oil-contaminated soil increased the heat release of hydration of unit mass cement and reduced rheological and flow properties of cement paste and mortars. However, when the dosage of oil-contaminated soil is about 4%, the optimum values of the flexural and compressive strength of mortar, in standard curing 7 and 28 days, were obtained. The leaching values of oil in the disposition satisfied the requirement of China standards. The results confirmed that utilizing an appropriate dosage of oil-contaminated soil in cement-based materials improved the flexural and compressive strength, which is stable to dispose of the waste. This shows that using disposed oil-contaminated soil in cement-based materials will serve as a cost-effective and environmental solution.
Li, Y, Zhu, J, Zhu, L, Li, Y & Lei, G 2020, 'A Dynamic Magnetostriction Model of Grain-Oriented Sheet Steels Based on Becker–Döring Crystal Magnetization Model and Jiles–Atherton Theory of Magnetic Hysteresis', IEEE Transactions on Magnetics, vol. 56, no. 3, pp. 1-5.
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Li, Y, Zhu, Y, Wang, D, Yang, G, Pan, L, Wang, Q, Ni, B-J, Li, H, Yuan, X, Jiang, L & Tang, W 2020, 'Fe(II) catalyzing sodium percarbonate facilitates the dewaterability of waste activated sludge: Performance, mechanism, and implication', Water Research, vol. 174, pp. 115626-115626.
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In this work, Fe(II) catalyzing sodium percarbonate (Fe(II)/SPC) was managed to facilitate waste activated sludge (WAS) dewatering for the first time. The results showed that after WAS was treated by 20 mg/g total suspended solids (TSS) Fe(II) and 50 mg/g TSS SPC, the water content of sludge cake (WCSC) by press filtration and capillary suction time (CST) dropped from 90.8% ± 1.6% and 96.1 ± 4.0 s (the control) to 55.6% ± 1.4% and 30.1 ± 2.5 s, respectively. The mechanism investigations indicated that four intermediates or products (i.e., •OH, H2O2, Fe(II), and Fe(III)) generated in the Fe(II)/SPC process were responsible for the improved WAS dewaterability, and •OH and Fe(III) were the two major contributors. It was found that •OH collapsed and fragmented extracellular polymeric substances, damaged cell wall and permeabilized cytoplasmic membrane, and transformed conformation of the extracellular proteins secondary structure via both affecting the hydrogen bond maintaining α-helix and cracking disulfide bond in cysteine residues while Fe(III), the oxidization product of Fe(II), decreased the surface electronegativity and water-affinity surface areas of WAS flocs. As a result, the bound water release, flocculability, surface hydrophobicity, drain capability, and flowability of WAS flocs were strengthened whereas the compact surface structure, colloidal forces, network strength, gel-like structure, and apparent viscosity of WAS flocs were weakened. In addition, Fe(II)/SPC process also reduced the recalcitrant organics and fecal coliforms in sludge, which facilitated land application of dewatered sludge. The findings acquired in this work not only deepens our understanding of Fe(II)/SPC-involved WAS treatment process but also may guide engineers to develop both effective and promising strategies to better condition WAS for dewatering in the future.
Li, Z, Tang, X, Yang, Y, Lu, D & Cai, Z 2020, 'Three‐pole wide‐tuning‐range balanced frequency‐agile bandpass filter with constant absolute bandwidth and 3 transmission zeros', Microwave and Optical Technology Letters, vol. 62, no. 7, pp. 2480-2487.
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AbstractThis paper proposes a 3‐pole wide‐tuning‐range, balanced frequency‐agile bandpass filter (BAL‐FA‐BPF) with constant absolute bandwidth (CABW) and 3 transmission zeros (TZs). Under the differential‐mode (DM) operation, the cascaded trisection coupling topology with source‐load coupling is constructed. Thus, 3 adaptive TZs are generated for the DM responses, which improves the passband selectivity and stopband suppression. Under the common‐mode (CM) operation, the short stubs and lumped elements are added at the symmetric plane which effectively improves the CM suppression. Using the advanced matrix synthesis, a demonstrative BAL‐FA‐BPF is designed with stable CABW and controlled by single‐DC‐bias. The measured frequency tuning range of the DM passband is 2.0 to 3.0 GHz (40%) and 3 dB‐bandwidth is kept at 174 ± 3 MHz (±1.7%). The CM suppression is better than 25 dB.
Li, Z, Tao, M, Du, K, Cao, W & Wu, C 2020, 'Dynamic stress state around shallow-buried cavity under transient P wave loads in different conditions', Tunnelling and Underground Space Technology, vol. 97, pp. 103228-103228.
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Lian, J-W, Ban, Y-L, Zhu, H & Guo, YJ 2020, 'Reduced-Sidelobe Multibeam Array Antenna Based on SIW Rotman Lens', IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 1, pp. 188-192.
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© 2002-2011 IEEE. A multibeam array antenna (MAA) fed by a Rotman lens with a reduced sidelobe level (SLL) is designed using a substrate integrate waveguide (SIW) technology. The designed MAA is composed of a Rotman lens and a 12 × 8 slot array, which functions as the beamforming network and the radiation part, respectively. To reduce the SLL in E-plane, dual-port excitations (DPEs) are applied, instead of single-port excitations (SPEs), at each feeding port of the Rotman lens. By using DPEs, a more tapered amplitude distribution can be obtained on the array elements as compared to using SPEs; therefore, the SLL is reduced from about -11 to -18 dB. The SLL in H-plane is controlled by introducing a Chebyshev distribution to the designed eight-element slot array. Based on the designed MAA, a fabricated prototype is measured to test the discrepancy between simulation and experiment.
Lian, J-W, Ban, Y-L, Zhu, H & Guo, YJ 2020, 'Uniplanar Beam-Forming Network Employing Eight-Port Hybrid Couplers and Crossovers for 2-D Multibeam Array Antennas', IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 11, pp. 4706-4718.
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Lian, J-W, Zhu, H, Ban, Y-L, Karmokar, DK & Guo, YJ 2020, 'Uniplanar High-Gain 2-D Scanning Leaky-Wave Multibeam Array Antenna at Fixed Frequency', IEEE Transactions on Antennas and Propagation, vol. 68, no. 7, pp. 5257-5268.
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Liang, Q, Wu, W, Yang, Y, Zhang, R, Peng, Y & Xu, M 2020, 'Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm', Applied Sciences, vol. 10, no. 3, pp. 864-864.
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Sports analysis has recently attracted increasing research efforts in computer vision. Among them, basketball video analysis is very challenging due to severe occlusions and fast motions. As a typical tracking-by-detection method, k-shortest paths (KSP) tracking framework has been well used for multiple-person tracking. While effective and fast, the neglect of the appearance model would easily lead to identity switches, especially when two or more players are intertwined with each other. This paper addresses this problem by taking the appearance features into account based on the KSP framework. Furthermore, we also introduce a similarity measurement method that can fuse multiple appearance features together. In this paper, we select jersey color and jersey number as two example features. Experiments indicate that about 70% of jersey color and 50% of jersey number over a whole sequence would ensure our proposed method preserve the player identity better than the existing KSP tracking method.
Liang, S, Teng, J, Shan, F & Zhang, S 2020, 'A Numerical Model of Vapour Transfer and Phase Change in Unsaturated Freezing Soils', Advances in Civil Engineering, vol. 2020, no. 1, pp. 1-11.
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In recent studies, vapour transfer is reported to lead to remarkable frost heave in unsaturated soils, but how to better model this process has not been answered. In order to avoid the great uncertainty caused by the phase change term of vapour‐water‐ice in the numerical iteration process, a new numerical model is developed based on the coupled thermal and hydrological processes. The new model avoids using the local equilibrium assumption and the hydraulic relations that accounts for liquid water flow, which provides a new way for the water‐heat coupling movement problem. The model is established by using COMSOL Multiphysics, which is a multiphysics simulation software through finite element analysis. The model is evaluated by comparing simulated results with data from column freezing experiments for unsaturated coarse‐grained soils. Simulated values of the total water content compare well with experimental values. The model is proved to be applicable and numerically stable for a high‐speed railway subgrade involving simultaneous heat and moisture transport. An agreement can be found between the predicted and measured frost/thawed depth and soil moisture profiles, demonstrating that the model is able to simulate rapidly changing boundary conditions and nonlinear water content profiles in the soil.
Liao, J, Xiang, G, Cao, L, Xia, J & Yue, L 2020, 'The left-behind human detection and tracking system based on vision with multi-model fusion and microwave radar inside the bus', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 234, no. 9, pp. 2342-2354.
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Left-behind humans inside the car or bus have caused a lot of accidents, so it is essential to detect the humans in vehicle. Current human detection methods rely on wearable devices, oxygen sensors, and special seat designs in vehicles, but those sensors cannot adapt to ever-changing environments. To solve those problems and especially to improve passengers’ safety on the bus, we propose a method to accomplishing human detection by fusion vision and microwave radar information in various environments in vehicle. For vision information, we use different networks to extract human and human face features, and fusion of the detection results in different models to improve human detection accuracy. The human detection model is MobileNet-V2, and the human face detection model is MTCNN. A new matching schedule and tracking objects management rule based on the Kernelized Correlation Filter tracker are designed to track the human and human face detection boxes. The microwave radar information is used to detect moving objects. Finally, the fusion vision and microwave radar detection results are implemented. Experiments show that our method has improved the human detection accuracy in vehicle, and this method can be used for detection of left-behind children on the school bus.
Lim, S, Akther, N, Tran, VH, Bae, T-H, Phuntsho, S, Merenda, A, Dumée, LF & Shon, HK 2020, 'Covalent organic framework incorporated outer-selective hollow fiber thin-film nanocomposite membranes for osmotically driven desalination', Desalination, vol. 485, pp. 114461-114461.
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Lim, S, Park, KH, Tran, VH, Akther, N, Phuntsho, S, Choi, JY & Shon, HK 2020, 'Size-controlled graphene oxide for highly permeable and fouling-resistant outer-selective hollow fiber thin-film composite membranes for forward osmosis', Journal of Membrane Science, vol. 609, pp. 118171-118171.
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© 2020 Elsevier B.V. Size-controlled graphene oxide (SGO) nanosheets, which are uniform and smaller in size than 2 μm, were successfully incorporated into a polyamide (PA) layer for preparing an outer-selective hollow fiber (OSHF) thin-film nanocomposite (TFN) membrane for forward osmosis (FO) applications by vacuum-assisted interfacial polymerization (VAIP). Here, we specifically demonstrate that the SGO nanosheets in amine aqueous solution were horizontally aligned and stacked on the surface of a membrane substrate by vacuum suction from outside to inside in the VAIP; the SGO nanosheets were then well-incorporated into the thin PA layer with less physical damage. In addition, the SGO nanosheets' effective loading inside the PA layer under the VAIP was much higher than that under the typical interfacial polymerization (IP), since there is no issue about the particle loss from air or nitrogen blowing to remove excess amine solution. The benefit would be highly cost-effective in terms of the nanomaterial's use in a TFN membrane production. As a result, the optimum OSHF TFN membrane incorporated with SGO at 0.0005 wt% (SGO5) exhibited outstanding FO performance, including higher water flux at 39.0 L m-2 h-1 and lower specific reverse solute flux at 0.16 g L-1, using a 1 M NaCl draw solution. Furthermore, this study demonstrates the effect of graphene oxide (GO)'s lateral size toward the short water pathway, and GO's stable incorporation and hydrophilicity of the PA thin film. In the fouling test using artificial wastewater, SGO-incorporated membranes exhibited enhanced fouling resistance and cleaning efficiency against the foulant-rich solution. This novel TFN membrane is therefore a good candidate to address FO's challenges for wastewater treatment or desalination.
Lim, WYB, Luong, NC, Hoang, DT, Jiao, Y, Liang, Y-C, Yang, Q, Niyato, D & Miao, C 2020, 'Federated Learning in Mobile Edge Networks: A Comprehensive Survey', IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 2031-2063.
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© 1998-2012 IEEE. In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.
Lin, A, Lu, J, Xuan, J, Zhu, F & Zhang, G 2020, 'A Causal Dirichlet Mixture Model for Causal Inference from Observational Data', ACM Transactions on Intelligent Systems and Technology, vol. 11, no. 3, pp. 1-29.
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Estimating causal effects by making causal inferences from observational data is common practice in scientific studies, business decision-making, and daily life. In today’s data-driven world, causal inference has become a key part of the evaluation process for many purposes, such as examining the effects of medicine or the impact of an economic policy on society. However, although the literature contains some excellent models, there is room to improve their representation power and their ability to capture complex relationships. For these reasons, we propose a novel prior called Causal DP and a model called CDP. The prior captures the complex relationships between covariates, treatments, and outcomes in observational data using a rational probabilistic dependency structure. The model is Bayesian, nonparametric, and generative and is not based on the assumption of any parametric distribution. CDP is designed to estimate various kinds of causal effects—average, conditional average, average treated, quantile, and so on. It performs well with missing covariates and does not suffer from overfitting. Comparative experiments on synthetic datasets against several state-of-the-art methods demonstrate that CDP has a superior ability to capture complex relationships. Further, a simple evaluation to infer the effect of a job training program on trainee earnings from real-world data shows that CDP is both effective and useful for causal inference.
Lin, B, Zhao, L, Suraweera, HA, Luan, TH, Niyato, D & Hoang, DT 2020, 'Guest Editorial Special Issue on Internet of Things for Smart Ocean', IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9675-9677.
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Lin, C-T, King, J-T, Chuang, C-H, Ding, W, Chuang, W-Y, Liao, L-D & Wang, Y-K 2020, 'Exploring the Brain Responses to Driving Fatigue Through Simultaneous EEG and fNIRS Measurements', International Journal of Neural Systems, vol. 30, no. 01, pp. 1950018-1950018.
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Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO2) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO2 decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO2 oscillations suggest that activations in HbO2 are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain’s responses as evidence of state changes during fatigue driving.
Lin, C-T, Yu, Y-H, King, J-T, Liu, C-H & Liao, L-D 2020, 'Augmented Wire-Embedded Silicon-Based Dry-Contact Sensors for Electroencephalography Signal Measurements', IEEE Sensors Journal, vol. 20, no. 7, pp. 3831-3837.
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© 2001-2012 IEEE. The aim of this study was to develop a novel dry electroencephalography (EEG) sensor with a soft, pliable pad that conforms to the contours of the skin and skull, providing a suitable surface contact area for collecting electrical potential signals and ensuring a reliable connection. In this study, based on our experience in developing flexible silver/silicon-based dry-contact sensors (SBDSs) for biosignal measurements, we proposed a new, augmented wire-embedded silicon-based dry-contact sensor (WSBDS) with a long lifespan and better performance in EEG measurements. The following two augmentation concepts were proposed in this design and implemented in fabrication: 1) the addition of a metal stud and 2) the embedding of copper wires into the fingers of an acicular SBDS. The forehead sensor is suitable for forehead EEG measurements, and the acicular sensor is designed for application to hair-covered sites, where it can overcome hair interference to achieve satisfactory scalp contact while maintaining low impedance at the skin-electrode interface. Finally, this augmented WSBDS performed well in human EEG recording in a designed brain-computer interface (BCI) experiment and is feasible for practical applications.
Lin, J, Sun, G, Cui, T, Shen, J, Xu, D, Beydoun, G, Yu, P, Pritchard, D, Li, L & Chen, S 2020, 'From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning.', World Wide Web, vol. 23, pp. 1747-1767.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.
Lin, X, Romanazzo, S, Lin, K, Kelly, C, Gooding, JJ & Roohani, I 2020, 'Elliptical supra-cellular topographies regulate stem cells migratory pattern and osteogenic differentiation', Materialia, vol. 14, pp. 100870-100870.
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Lin, Z, Lv, T, Ni, W, Zhang, JA & Liu, RP 2020, 'Tensor-Based Multi-Dimensional Wideband Channel Estimation for mmWave Hybrid Cylindrical Arrays', IEEE Transactions on Communications, vol. 68, no. 12, pp. 7608-7622.
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© 1972-2012 IEEE. Channel estimation is challenging for hybrid millimeter wave (mmWave) large-scale antenna arrays which are promising in 5G/B5G applications. The challenges are associated with angular resolution losses resulting from hybrid front-ends, beam squinting, and susceptibility to the receiver noises. Based on tensor signal processing, this paper presents a novel multi-dimensional approach to channel parameter estimation with large-scale mmWave hybrid uniform circular cylindrical arrays (UCyAs) which are compact in size and immune to mutual coupling but known to suffer from infinite-dimensional array responses and intractability. We design a new resolution-preserving hybrid beamformer and a low-complexity beam squinting suppression method, and reveal the existence of shift-invariance relations in the tensor models of received array signals at the UCyA. Exploiting these relations, we propose a new tensor-based subspace estimation algorithm to suppress the receiver noises in all dimensions (time, frequency, and space). The algorithm can accurately estimate the channel parameters from both coherent and incoherent signals. Corroborated by the Cramér-Rao lower bound (CRLB), simulation results show that the proposed algorithm is able to achieve substantially higher estimation accuracy than existing matrix-based techniques, with a comparable computational complexity.
Ling, T, Li, JJ, Xu, R-J, Wang, B & Ge, W-H 2020, 'Topical Diclofenac Solution for Osteoarthritis of the Knee: An Updated Meta‐Analysis of Randomized Controlled Trials', BioMed Research International, vol. 2020, no. 1, pp. 1-11.
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This study was performed to assess the efficacy and safety of a topical diclofenac solution in patients with knee osteoarthritis (OA). PubMed, Embase, Cochrane Library, Web of Science, and Scopus databases were searched for randomized controlled trials until June 2020. The WOMAC pain, stiffness, physical function subscales, pain on walking, and the occurrence of adverse events were pooled to comprehensively analyse the efficacy and safety of topical diclofenac solution. All statistical analyses were conducted using Review Manager 5.3 software. Five RCTs were included, which provided high‐quality evidence. In comparison to the vehicle control, the mean differences for WOMAC pain, stiffness, and physical function subscales, as well as pain on walking, were all statistically significant in favor of topical diclofenac solution. The safety of topical diclofenac solution was similar to the vehicle control, apart from adverse events involving application‐site skin reactions. Topical diclofenac solution is effective and safe for use in patients with knee OA, but may cause minor skin reactions.
Lipinska, V, Thinh, LP, Ribeiro, J & Wehner, S 2020, 'Certification of a functionality in a quantum network stage', Quantum Science and Technology, vol. 5, no. 3, pp. 035008-035008.
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Abstract We consider testing the ability of quantum network nodes to execute multi-round quantum protocols. Specifically, we examine protocols in which the nodes are capable of performing quantum gates, storing qubits and exchanging said qubits over the network a certain number of times. We propose a simple ping-pong test, which provides a certificate for the capability of the nodes to run certain multi-round protocols. We first show that in the noise-free regime the only way the nodes can pass the test is if they do indeed possess the desired capabilities. We then proceed to consider the case where operations are noisy, and provide an initial analysis showing how our test can be used to estimate parameters that allow us to draw conclusions about the actual performance of such protocols on the tested nodes. Finally, we investigate the tightness of this analysis using example cases in a numerical simulation.
Liu, B, Chen, C, Di, X, Liao, J, Wen, S, Su, QP, Shan, X, Xu, Z-Q, Ju, LA, Mi, C, Wang, F & Jin, D 2020, 'Upconversion Nonlinear Structured Illumination Microscopy', Nano Letters, vol. 20, no. 7, pp. 4775-4781.
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Video-rate super-resolution imaging through biological tissue can visualize and track biomolecule interplays and transportations inside cellular organisms. Structured illumination microscopy allows for wide-field super resolution observation of biological samples but is limited by the strong extinction of light by biological tissues, which restricts the imaging depth and degrades its imaging resolution. Here we report a photon upconversion scheme using lanthanide-doped nanoparticles for wide-field super-resolution imaging through the biological transparent window, featured by near-infrared and low-irradiance nonlinear structured illumination. We demonstrate that the 976 nm excitation and 800 nm upconverted emission can mitigate the aberration. We found that the nonlinear response of upconversion emissions from single nanoparticles can effectively generate the required high spatial frequency components in the Fourier domain. These strategies lead to a new modality in microscopy with a resolution below 131 nm, 1/7th of the excitation wavelength, and an imaging rate of 1 Hz.
Liu, B, Ni, W, Liu, RP & Zhu, H 2020, 'Optimal Selection of Heterogeneous Network Interfaces for High-Speed Rail Communications', IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 15005-15018.
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Liu, B, Yuan, L, Lin, X, Qin, L, Zhang, W & Zhou, J 2020, 'Efficient (α, β)-core computation in bipartite graphs.', VLDB J., vol. 29, no. 5, pp. 1075-1099.
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Liu, C, Nitschke, P, Williams, SP & Zowghi, D 2020, 'Data quality and the Internet of Things', Computing, vol. 102, no. 2, pp. 573-599.
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© 2019, Springer-Verlag GmbH Austria, part of Springer Nature. The Internet of Things (IoT) is driving technological change and the development of new products and services that rely heavily on the quality of the data collected by IoT devices. There is a large body of research on data quality management and improvement in IoT, however, to date a systematic review of data quality measurement in IoT is not available. This paper presents a systematic literature review (SLR) about data quality in IoT from the emergence of the term IoT in 1999 to 2018. We reviewed and analyzed 45 empirical studies to identify research themes on data quality in IoT. Based on this analysis we have established the links between data quality dimensions, manifestations of data quality problems, and methods utilized to measure data quality. The findings of this SLR suggest new research areas for further investigation and identify implications for practitioners in defining and measuring data quality in IoT.
Liu, C, Zowghi, D & Talaei-Khoei, A 2020, 'An empirical study of the antecedents of data completeness in electronic medical records', International Journal of Information Management, vol. 50, pp. 155-170.
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© 2019 Elsevier Ltd There is a body of research that highlights the role of data management to improve the quality of data, which in return improves organizational performance. The literature in data management has indicated the five theoretical constructs used to understand the factors influencing data quality, including top management support, capability on the regulation and process management, business-IT alignment, staff participation, and integration of information systems. However, it is unclear how these theoretical constructs can be utilized to understand the antecedents of data completeness as a dimension of data quality. Following that stream of research, the current paper examines the factors influencing data completeness in electronic medical records (EMR). The scope of this study is by only surveying medical professionals at healthcare settings in northern Nevada. The empirical results reveal that resources should be added as one of the antecedents of data completeness in EMR.
Liu, C, Zowghi, D, Talaei-Khoei, A & Jin, Z 2020, 'Empirical study of Data Completeness in Electronic Health Records in China', Pacific Asia Journal of the Association for Information Systems, vol. 12, no. 2, pp. 104-130.
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Abstract Background: As a dimension of data quality in electronic health records (EHR), data completeness plays an important role in improving quality of care. Although many studies of data management focus on constructing the factors that influence data quality for the purpose of quality improvement, the constructs that are developed for interpreting factors influencing data completeness in the EHR context have received limited attention. Methods: Based on related studies, we constructed the factors influencing EHR data completeness in a conceptual model. We then examined the proposed model by surveying clinical practitioners in China. Results: Our results show that the data quality management literature can serve as a starting point to derive a conceptual model of factors influencing data completeness in the EHR context. This study also demonstrates that “resources” should be added as a factor that influences data completeness in EHR. Conclusion: Our resulting conceptual model shows a substantial explanation of data completeness in EHR assessed in this study. Although the proposed relationships between the included factors were previously supported in the literature, our work provides the beginning empirical evidence that some relationships may not be always significantly supported. The possible explanation of these differences has been discussed in the present research. This study thus benefits decision makers and EHR program managers in implementing EHR as well as EHR vendors in the EHR integration by addressing data completeness issues.
Liu, D, Huang, Y, Wu, Q, Ma, R & An, P 2020, 'Multi-Angular Epipolar Geometry Based Light Field Angular Reconstruction Network', IEEE Transactions on Computational Imaging, vol. 6, pp. 1507-1522.
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Liu, D, Ouyang, X, Xu, S, Zhou, P, He, K & Wen, S 2020, 'SAANet: Siamese action-units attention network for improving dynamic facial expression recognition', Neurocomputing, vol. 413, pp. 145-157.
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© 2020 Elsevier B.V. Facial expression recognition (FER) has a wide variety of applications ranging from human–computer interaction, robotics to health care. Although FER has made significant progress with the success of Convolutional Neural Network (CNN), it is still challenging especially for the video-based FER due to the dynamic changes in facial actions. Since the specific divergences exists among different expressions, we introduce a metric learning framework with a siamese cascaded structure that learns a fine-grained distinction for different expressions in video-based task. We also develop a pairwise sampling strategy for such metric learning framework. Furthermore, we propose a novel action-units attention mechanism tailored to FER task to extract spatial contexts from the emotion regions. This mechanism works as a sparse self-attention fashion to enable a single feature from any position to perceive features of the action-units (AUs) parts (eyebrows, eyes, nose, and mouth). Besides, an attentive pooling module is designed to select informative items over the video sequences by capturing the temporal importance. We conduct the experiments on four widely used datasets (CK+, Oulu-CASIA, MMI, and AffectNet), and also do experiment on the wild dataset AFEW to further investigate the robustness of our proposed method. Results demonstrate that our approach outperforms existing state-of-the-art methods. More in details, we give the ablation study of each component.
Liu, F, Liu, Y, Han, F, Ban, Y-L & Jay Guo, Y 2020, 'Synthesis of Large Unequally Spaced Planar Arrays Utilizing Differential Evolution With New Encoding Mechanism and Cauchy Mutation', IEEE Transactions on Antennas and Propagation, vol. 68, no. 6, pp. 4406-4416.
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© 1963-2012 IEEE. This article presents a differential evolution algorithm with a new encoding mechanism and Cauchy mutation (DE-NEM-CM) for optimizing large unequally spaced planar array layouts with the minimum element spacing constraint. In the new encoding mechanism, each individual represents a certain element position rather than an entire array layout used in traditional stochastic optimization algorithms. Such an encoding mechanism has the following advantages: 1) in each individual updating, the array pattern can be efficiently evaluated by only considering the radiation contribution variation from one element movement, which can greatly reduce the computational time; 2) it naturally facilitates the generated new array layout in population updating to meet the minimum element spacing constraint, and 3) each individual is searched always in 2-D space as the array size increases. These advantages enable it to be very suitable for synthesizing large arrays. Besides, DE serves as a search engine, and Cauchy mutation with chaotic mapping is proposed to enhance the local search while preserving the diversity of the population. A set of experiments for synthesizing different types of unequally spaced planar arrays in both narrow-and broadband applications are conducted. Synthesis results show that the proposed method achieves much lower sidelobe level than some state-of-the-art stochastic optimization methods for all the test cases. Importantly, the proposed method is much more efficient than conventional stochastic optimization algorithm especially for the case of synthesizing large unequally spaced planar array layouts. A array layout optimization with more than 1000 elements can be achieved within acceptable CPU time cost, which has not yet been reported for the existing stochastic optimization methods without resorting to supercomputing facilities.
Liu, G, Xiao, F, Lin, C-T & Cao, Z 2020, 'A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation', IEEE Transactions on Fuzzy Systems, vol. 28, no. 11, pp. 2677-2690.
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© 1993-2012 IEEE. Forecasting time series is an emerging topic in operational research. Existing time-series models have limited prediction accuracy when faced with the characteristics of nonlinearity and nonstationarity in complex situations related to energy and finance. To enhance overall prediction capabilities and improve forecasting accuracy, in this article we propose a fuzzy interval time-series forecasting model on the basis of network-based multiple time-frequency spaces and the induced-ordered weighted averaging aggregation (IOWA) operation. Specifically, a time-series signal is decomposed into ensemble empirical modes and then reconstructed as various time-frequency spaces, which are transformed into visibility graphs. Then, forecasting intervals in different spaces can be collected after the local random walker link prediction model is adopted. Furthermore, a rule-based representation value function inspired by Yager's golden rule approach is defined, and an appropriate representation value is calculated. Finally, after IOWA is used to aggregate the forecasting outcomes in different time-frequency spaces, the final forecast value can be obtained from the fuzzy forecasting interval. Considering that energy issues are of widespread interest in nature and the social economy, two cases, based on a hydrological time series from the Biliuhe River in China and two well-known sets of financial time-series data, Taiwan Stock Exchange Capitalization Weighted Stock Index and Hang Seng Index, are studied to test the performance of the proposed approach in comparison with existing models. Our results show that the proposed approach can achieve better performance than well-developed models.
Liu, H, Zheng, Q, Luo, M, Chang, X, Yan, C & Yao, L 2020, 'Memory transformation networks for weakly supervised visual classification', Knowledge-Based Systems, vol. 210, pp. 106432-106432.
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The lack of labeled exemplars makes video classification based on supervised neural networks difficult and challenging. Utilizing external memory that contains task-related knowledge is a beneficial way to learn a category from a handful of samples; however, most existing memory-augmented neural networks still struggle to provide a satisfactory solution for multi-modal external data due to the high dimensionality and massive volume. In light of this, we propose a Memory Transformation Network (MTN) to convert external knowledge, by involving embedded and concentrated memories, so as to leverage it feasibly for video classification with weak supervision. Specifically, we employ a multi-modal deep autoencoder to project external visual and textual information onto a shared space to produce joint embedded memory, which can capture the correlation amongst different modalities to enhance the expressive ability. The curse of dimensionality issue can also be alleviated owing to the inherent dimension reduction ability of the autoencoder. Besides, an attention-based compression mechanism is employed to generate concentrated memory, which records useful information related to a specific task. In this way, the obtained concentrated memory is relatively lightweight to mitigate the time-consuming content-based addressing on large-volume memory. Our model outperforms the state-of-the-arts by 5.44% and 1.81% on average in two metrics over three real-world video datasets, demonstrating its effectiveness and superiority on visual classification with limited labeled exemplars.
Liu, J, Li, H, Ji, J & Luo, J 2020, 'Group-Bipartite Consensus in the Networks With Cooperative-Competitive Interactions', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 12, pp. 3292-3296.
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© 2004-2012 IEEE. This brief addresses the group-bipartite consensus problem of multi-agent systems with cooperative-competitive interactions. By combining the characteristics of group consensus and bipartite consensus, the concept of group-bipartite consensus is introduced to specify multiple bipartite consensus behavior. A distributed control protocol is then proposed for the topology graphs with acyclic partition and sign-balanced couples. The network topology studied in this brief eliminates the constraint that negative links can only exist between different groups, and thus the weights between agents in the same group can be either positive or negative. Some necessary and sufficient conditions for solving group-bipartite consensus problems are established by constructing a new form of the Laplacian matrix associated with the directed communication graphs. A simulation example is given to validate the theoretical results.
Liu, J, Taghizadeh, S, Lu, J, Hossain, MJ, Stegen, S & Li, H 2020, 'Three-phase four-wire interlinking converter with enhanced power quality improvement feature in microgrid systems', CSEE Journal of Power and Energy Systems, vol. 7, no. 5, pp. 1064-1077.
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This paper presents an advanced three-phase four-wire interlinking microgrid system with an improved harmonics reduction feature. Due to their robustness and simplicity features, time-domain second-order notch-filter equivalent techniques have drawn a great deal of research attention. However, the drawbacks of non-satisfactory harmonics rejection characteristics and dynamic response limits their applications. In this context, this paper proposes an advanced control system with an enhanced harmonics reduction feature for microgrid applications. The proposed control system exhibits a superior harmonics reduction feature and better dynamic response than the conventional notch-filter based techniques. In addition, a control scheme is developed for a three-phase power system application which presents higher accuracy in compensating both balanced and unbalanced harmonics. The performance of the proposed system is validated through simulations and tested on the hardware of a real microgrid system. From the results, it is evident that the proposed approach provides excellent performance in terms of harmonics reduction in microgrid systems.
Liu, K, Li, Q, Wu, C, Li, X & Li, J 2020, 'Optimization of spherical cartridge blasting mode in one-step raise excavation using pre-split blasting', International Journal of Rock Mechanics and Mining Sciences, vol. 126, pp. 104182-104182.
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© 2019 Elsevier Ltd In one-step raise excavation, spherical cartridge blasting mode is easy to be implemented due to minimal requirement of the hole-deviation. Nevertheless, it may induce cumulative damage to the country rock. This study firstly uses a validated rock model (Johnson-Holmquist model) to simulate the damage evolution process of a raise by spherical cartridge blasting mode in LS-DYNA software. Then a field test is carried out to examine the numerical results of spherical cartridge blasting mode. Both the numerical and test results indicate that because of highly confined rock mass and restricted free face in deep raise, a large charge is required in spherical cartridge blasting mode which leads to extensive damage on the wall of the raise. In order to solve such a problem, the pre-split blasting technique is developed to optimize spherical cartridge blasting mode. According to the subsequent numerical results, the improved spherical cartridge blasting mode is successfully applied in another filling raise. This study provides an effective solution to the difficulties that are encountered in one-step raise excavation by spherical cartridge blasting mode.
Liu, K, Wu, C, Li, X, Li, Q, Fang, J & Liu, J 2020, 'A modified HJC model for improved dynamic response of brittle materials under blasting loads', Computers and Geotechnics, vol. 123, pp. 103584-103584.
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Liu, L, Guo, R, Ji, J, Miao, Z & Zhou, J 2020, 'Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates', International Journal of Robust and Nonlinear Control, vol. 30, no. 10, pp. 3831-3847.
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This paper proposes a sliding-mode control (SMC) method to achieve practical cooperative consensus tracking for a network of multiple nonholonomic wheeled mobile robots (MNWMRs) with input disturbances. A novel SMC surface under the nonholonomic constraints is first formulated to characterize the network communication interactions among the networked robots under the framework of polar coordinates. A unified distributed consensus tracking strategy is then proposed by systematically combining a position controller and a direction controller. Furthermore, a simple yet general criterion is derived to achieve the desired practical consensus of trajectory tracking and posture stabilization for MNWMRs. In particular, for a specific common consensus trajectory, the complete asymptotic tracking in heading direction can be fully guaranteed when the perfect asymptotic position-tracking errors are realized. Accordingly, the developed consensus tracking strategy for MNWMRs demonstrates some advantages of control performance including stability, robustness, and effectiveness over the existing control method proposed for their single-robot counterparts. Some comparative simulation results are given to confirm the effectiveness of the proposed cooperative consensus control method.
Liu, L, Zhou, T, Long, G, Jiang, J & Zhang, C 2020, 'Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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We study many-class few-shot (MCFS) problem in both supervised learning andmeta-learning settings. Compared to the well-studied many-class many-shot andfew-class few-shot problems, the MCFS problem commonly occurs in practicalapplications but has been rarely studied in previous literature. It brings newchallenges of distinguishing between many classes given only a few trainingsamples per class. In this paper, we leverage the class hierarchy as a priorknowledge to train a coarse-to-fine classifier that can produce accuratepredictions for MCFS problem in both settings. The propose model,'memory-augmented hierarchical-classification network (MahiNet)', performscoarse-to-fine classification where each coarse class can cover multiple fineclasses. Since it is challenging to directly distinguish a variety of fineclasses given few-shot data per class, MahiNet starts from learning aclassifier over coarse-classes with more training data whose labels are muchcheaper to obtain. The coarse classifier reduces the searching range over thefine classes and thus alleviates the challenges from 'many classes'. Onarchitecture, MahiNet firstly deploys a convolutional neural network (CNN) toextract features. It then integrates a memory-augmented attention module and amulti-layer perceptron (MLP) together to produce the probabilities over coarseand fine classes. While the MLP extends the linear classifier, the attentionmodule extends the KNN classifier, both together targeting the 'few-shot'problem. We design several training strategies of MahiNet for supervisedlearning and meta-learning. In addition, we propose two novel benchmarkdatasets 'mcfsImageNet' and 'mcfsOmniglot' specially designed for MCFS problem.In experiments, we show that MahiNet outperforms several state-of-the-artmodels on MCFS problems in both supervised learning and meta-learning.
Liu, M, Lu, X, Nothling, MD, Doherty, CM, Zu, L, Hart, JN, Webley, PA, Jin, J, Fu, Q & Qiao, GG 2020, 'Physical Aging Investigations of a Spirobisindane-Locked Polymer of Intrinsic Microporosity', ACS Materials Letters, vol. 2, no. 8, pp. 993-998.
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Polymers of intrinsic microporosity (PIMs) have exceptional gas separation performance for a broad range of applications. However, PIMs are highly susceptible to physical aging, which drastically reduces their long-term performance over time. In this work, we leverage complementary experimental and density functional theory (DFT) studies to decipher the inter-/intrachain changes that occur during aging of the prototypical PIM-1 and its rigidified analogue PIM-C1. By elucidating this hereto unexplored aging behavior, we reveal that the dramatic decrease in gas permeability of PIM materials during aging stems from a loss of fractional free volume (FFV) due to PIM chain relaxations induced by π-πinteractions, hydrogen bonding, or van der Waals' forces. While the PIM-1 based membranes displayed enhanced gas pair selectivities after aging, the PIM-C1 based membranes showed an opposite trend with unexpected reductions for CO2/N2 and CO2/CH4. This is due to the reductions in CO2/N2 and CO2/CH4 solubility (S) selectivities and, unlike PIM-1, the spirobisindane locked PIM-C1 (i.e., maintenance of micropore sizes) has a stable diffusivity (D) selectivities that cannot offset such reductions. These fundamental insights into the intrinsic relaxation of different PIM polymer chains during physical aging can guide the future design of high-performance PIM materials with enhanced anti-aging properties.
Liu, M, Nothling, MD, Tan, SSL, Webley, PA, Qiao, GG & Fu, Q 2020, 'Polyrotaxane-based thin film composite membranes for enhanced nanofiltration performance', Separation and Purification Technology, vol. 246, pp. 116893-116893.
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© 2020 An urgent need exists for the development of advanced water purification technologies to meet the increasing global demand being placed on freshwater resources. Membrane-based separation technologies for size-selective contaminant removal represent a promising approach to achieve this goal. Here, a novel thin film composite nanofiltration membrane is prepared via interfacial polymerization of α-cyclodextrin on a commercially available polyacrylonitrile substrate. Subsequent in-situ inclusion complexation of alkyne-functionalized poly(ethylene glycol) (PEG) is then used to tune the polyrotaxane-based pores for size-dependent filtration. The resultant membrane shows excellent size-selective rejection rates for organic dye (e.g. rhodamine B, >99%) as well as heavy-metal ions (e.g. Co(II), >90%), while crucially maintaining high water permeance (e.g. H2O: 7.1 L h−1 m−2 bar−1). The facile and straightforward synthetic approach to the fabrication of polyrotaxane nanofiltration membranes, combined with their strong nanofiltration separation performance, holds significant promise for membrane-based water purification applications.
Liu, M, Nothling, MD, Webley, PA, Jin, J, Fu, Q & Qiao, GG 2020, 'High-throughput CO2 capture using PIM-1@MOF based thin film composite membranes', Chemical Engineering Journal, vol. 396, pp. 125328-125328.
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© 2020 Elsevier B.V. Carbon capture from power plants represents a powerful technique to mitigate increasing greenhouse gas emissions. In this work, we describe a thin film composite (TFC) membrane incorporating a polymer of intrinsic microporosity (PIM-1) and metal organic framework (MOF) nanoparticles for post-combustion CO2 capture. The novel TFC membrane design consists of three layers: (1) a CO2 selective layer composed of a PIM-1@MOF mixed matrix; (2) an ultrapermeable PDMS gutter layer doped with MOF nanosheets; and (3) a porous polymeric substrate. Notably, the PDMS@MOF gutter layer incorporating amorphous nanosheets provides a CO2 permeance of 10,000–11,000 GPU, suggesting less gas transport resistance in comparison with pristine PDMS gutter layers. In addition, by blending nanosized MOF particles (MOF-74-Ni and NH2-UiO-66) into PIM-1 to afford a selective layer, the resultant TFC membrane assembly delivered improved CO2 permeance of 4660–7460 GPU and CO2/N2 selectivity of 26–33, compared with a pristine PIM-1 counterpart (CO2 permeance of 4320 GPU and CO2/N2 selectivity of 19). Furthermore, PIM-1@MOF based TFC membranes displayed an enhanced resistance to aging effect, maintaining a stable CO2 permeance of 900–1200 GPU and CO2/N2 selectivity of 26–30 after aging for 8 weeks. To the best of our knowledge, the high CO2 separation performance presented here is unprecedented for PIM-1 based TFC membranes reported in the open literature.
Liu, MD, Airey, DW, Indraratna, B, Zhuang, Z & Horpibulsuk, S 2020, 'An extended modified cam clay model for improved accuracy at low and high-end stress levels', Marine Georesources & Geotechnology, vol. 38, no. 4, pp. 423-436.
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Liu, Q, Kang, B, Yu, K, Qi, X, Li, J, Wang, S & Li, H-A 2020, 'Contour-Maintaining-Based Image Adaption for an Efficient Ambulance Service in Intelligent Transportation Systems', IEEE Access, vol. 8, pp. 12644-12654.
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© 2013 IEEE. Ambulance services play a vital role in intelligent transportation systems (ITS). In an intelligent ambulance system, the medical images can help doctors quickly and accurately understand the patients' condition during first aid. On various display devices in different kinds of ambulances, content-aware image adaption can be used to better present the medical image among different display resolutions and aspect ratios. Most existing methods mainly focus on visual protection of salient areas, such as specific organ parts of the human body, with less attention paid to the visual effect of unimportant areas. However, the human visual system is more sensitive to the edge and contour of images, which are important for ambulance services. To improve the visual effect of adapted images, a contour-maintaining-based image adaption method for an efficient ambulance service in ITS is proposed here. Firstly, the proposed method innovatively combines the weighted gradient, saliency, and edge maps into an importance map. Secondly, energy is optimized for reducing contour distortion and interruption according to the visual slope and curvature of contours and edges in non-salient areas. Finally, applying the sub-procedure of a forward seam carving method, the optimal seams can more evenly pass through the contour areas. The experimental results demonstrate that the proposed method is more effective than other similar methods.
Liu, Q, Liufu, K, Cui, Z, Li, J, Fang, J & Li, Q 2020, 'Multiobjective optimization of perforated square CFRP tubes for crashworthiness', Thin-Walled Structures, vol. 149, pp. 106628-106628.
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Liu, T, Zhang, W, Yuwono, M, Zhang, M, Ueland, M, Forbes, SL & Su, SW 2020, 'A data-driven meat freshness monitoring and evaluation method using rapid centroid estimation and hidden Markov models', Sensors and Actuators B: Chemical, vol. 311, pp. 127868-127868.
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Liu, W, Chang, X, Chen, L, Phung, D, Zhang, X, Yang, Y & Hauptmann, AG 2020, 'Pair-based Uncertainty and Diversity Promoting Early Active Learning for Person Re-identification', ACM Transactions on Intelligent Systems and Technology, vol. 11, no. 2, pp. 1-15.
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The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and practical problem called Early Active Learning, which is applied to the early stage of experiments when there is no pre-labeled sample available as references for human annotating. Previous early active learning methods suffer from two limitations for Re-ID. First, these instance-based algorithms select instances rather than pairs, which can result in missing optimal pairs for Re-ID. Second, most of these methods only consider the representativeness of instances, which can result in selecting less diverse and less informative pairs. To overcome these limitations, we propose a novel pair-based active learning for Re-ID. Our algorithm selects pairs instead of instances from the entire dataset for annotation. Besides representativeness, we further take into account the uncertainty and the diversity in terms of pairwise relations. Therefore, our algorithm can produce the most representative, informative, and diverse pairs for Re-ID data annotation. Extensive experimental results on five benchmark Re-ID datasets have demonstrated the superiority of the proposed pair-based early active learning algorithm.
Liu, W, Shen, X, Ong, Y-S, Tsang, IW, Gong, C & Pavlovic, V 2020, 'Guest Editorial Special Issue on Structured Multi-Output Learning: Modeling, Algorithm, Theory, and Applications', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 7, pp. 2236-2239.
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Liu, X, He, D, Wu, Y, Xu, Q, Wang, D, Yang, Q, Liu, Y, Ni, B-J, Wang, Q & Li, X 2020, 'Freezing in the presence of nitrite pretreatment enhances hydrogen production from dark fermentation of waste activated sludge', Journal of Cleaner Production, vol. 248, pp. 119305-119305.
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© 2019 Elsevier Ltd Due to the poor biodegradability of released organics and the rapid consumption of hydrogen, hydrogen production from the untreated waste activated sludge (WAS) and/or inocula is still limited. In this study, it was found that the dark fermentative hydrogen production was largely enhanced from WAS pretreated by freezing in the presence of nitrite. With an increase of nitrite addition from 100 to 400 mg NO2−-N/L during freezing pretreatment (−5 °C for 4 h), the maximal hydrogen yield increased from 7.96 to 19.40 mL/g VS (volatile solids), which was 5.5–13.4 times of that in the control (without freezing and nitrite addition). Mechanism explorations revealed that the proposed pretreatment not only accelerated the disintegration of sludge but also promoted the proportion of biodegradable organics released, thereby provided more bio-available substrates for subsequent hydrogen production. Proposed pretreatment severely suppressed the sludge microorganisms responding to homoacetogenesis (−32.1%), methanogenesis (−58.4%), and sulfate-reducing process (−51.5%), inhibited the consumption of hydrogen. Moreover, there was more acetic and butyric (76% versus 57.5%) but less propionic acid (22.6% versus 13.4%) in this pretreated fermenter, which was in correspondence with the theory of fermentation type affecting hydrogen production. Long-term fermentation experiments indicated that the proposed pretreatment boosted the [FeFe]-hydrogenase activities while suppressed the activities of carbon monoxide dehydrogenase, coenzyme F420, and adenylyl sulfate reductase.
Liu, X, Huang, X, Wu, Y, Xu, Q, Du, M, Wang, D, Yang, Q, Liu, Y, Ni, B-J, Yang, G, Yang, F & Wang, Q 2020, 'Activation of nitrite by freezing process for anaerobic digestion enhancement of waste activated sludge: Performance and mechanisms', Chemical Engineering Journal, vol. 387, pp. 124147-124147.
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© 2020 Elsevier B.V. Nitrite-based pretreatment was demonstrated to effectively improve anaerobic digestion of waste activated sludge. It was found in this work that the freezing activated nitrite pretreatment could further enhance the performances. With the increase of nitrite addition from 0 to 600 mg NO2−-N/L during freezing process, the biochemical methane potential of pretreated-sludge gradually increased from 191.3 ± 8.0 to 233.2 ± 10.6 mL per gram volatile solid (VS), while only 178.6 ± 7.3 mL/g VS was obtained in the raw sludge. Mechanism explorations revealed that the freezing activated nitrite pretreatment remarkably facilitated the disintegration of sludge. Excitation emission matrix and fluorescence regional integration analyses further revealed that nitrite addition during freezing process promoted the proportion of biodegradable organics released, thereby providing more bio-available substrates for subsequent anaerobic digestion. Freezing condition induced reactive derivatives from nitrite (e.g., free nitrite acid, NO2[rad], N2O3) were assumed to be the major contributors to the enhanced sludge disintegration and recalcitrant organics (e.g., humic acid-like substances) degradation. It was also found that 600 mg NO2−-N/L addition activated by freezing pretreatment produced an anaerobically digested sludge with an improved dewaterability, as indicated by the decrease of the specific resistance to filterability and moisture content of dewatered cake. Moreover, 600 mg NO2−-N/L addition activated by freezing pretreatment and subsequent anaerobic digestion largely inactivated the pathogens to the levels below Class A biosolids requirements. Considering that nitrite can be in-situ produced in wastewater treatment plants through nitritation of the digestion liquid, this nitrite-based freezing process for sludge pretreatment was environmental-friendly and economically attractive.
Liu, X, Song, W, Musial, K, Zhao, X, Zuo, W & Yang, B 2020, 'Semi-supervised stochastic blockmodel for structure analysis of signed networks', Knowledge-Based Systems, vol. 195, pp. 105714-105714.
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© 2020 Elsevier B.V. Finding hidden structural patterns is a critical problem for all types of networks, including signed networks. Among all of the methods for structural analysis of complex network, stochastic blockmodel (SBM) is an important research tool because it is flexible and can generate networks with many different types of structures. However, most existing SBM learning methods for signed networks are unsupervised, leading to poor performance in terms of finding hidden structural patterns, especially when handling noisy and sparse networks. Learning SBM in a semi-supervised way is a promising avenue for overcoming the above difficulty. In this type of model, a small number of labelled nodes and a large number of unlabelled nodes, coupled with their network structures, are simultaneously used to train SBM. We propose a novel semi-supervised signed stochastic blockmodel and its learning algorithm based on variational Bayesian inference, with the goal of discovering both assortative (the nodes connect more densely in same clusters than that in different clusters) and disassortative (the nodes link more sparsely in same clusters than that in different clusters) structures from signed networks. The proposed model is validated through a number of experiments wherein it compared with the state-of-the-art methods using both synthetic and real-world data. The carefully designed tests, allowing to account for different scenarios, show our method outperforms other approaches existing in this space. It is especially relevant in the case of noisy and sparse networks as they constitute the majority of the real-world networks.
Liu, X, Wei, W, Xu, J, Wang, D, Song, L & Ni, B-J 2020, 'Photochemical decomposition of perfluorochemicals in contaminated water', Water Research, vol. 186, pp. 116311-116311.
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Perfluorochemicals (PFCs) are a set of chemicals containing C-F bonds, which are concerned due to their bioaccumulation property, persistent and toxicological properties. Photocatalytic approaches have been widely studied for the effective removal of PFCs due to the mild operation conditions. This review aims to provide a comprehensive and up-to-date summary on the homogenous and heterogeneous photocatalytic processes for PFCs removal. Specifically, the homogenous photocatalytic methods for remediating PFCs are firstly discussed, including generation of hydrated electrons (eaq‒) and its performance and mechanisms for photo-reductive destruction of PFCs, the active species responsible for photo-oxidative degradation of PFCs and the corresponding mechanisms, and metal-ion-mediated (Fe(III) mainly used) processes for the remediation of PFCs. The influences of molecular structures of PFCs and water matrix, such as dissolved oxygen, humic acid, nitrate, chloride on the homogenous photocatalytic degradation of PFCs are also discussed. For heterogeneous photocatalytic processes, various semiconductor photocatalysts used for the decomposition of perfluorooctanoic acid (PFOA) are then discussed in terms of their specific properties benefiting photocatalytic performances. The preparation methods for optimizing the performance of photocatalysts are also overviewed. Moreover, the photo-oxidative and photo-reductive pathways are summarized for remediating PFOA in the presences of different semiconductor photocatalysts, including active species responsible for the degradation. We finally put forward several key perspectives for the photocatalytic removal of PFCs to promote its practical application in PFCs-containing wastewater treatment, including the treatment of PFCs degradation products such as fluoride ion, and the development of noble-metal free photocatalysts that could efficiently remove PFCs under solar light irradiation.
Liu, X, Zhou, A, Shen, S-L, Li, J & Sheng, D 2020, 'A micro-mechanical model for unsaturated soils based on DEM', Computer Methods in Applied Mechanics and Engineering, vol. 368, pp. 113183-113183.
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© 2020 Elsevier B.V. A micro-mechanical model to study the microscopic and macroscopic behavior of unsaturated soils under different suctions is proposed in this study. In the model, a novel pore-scale numerical method for simulating the liquid–solid interfaces is proposed first. A discretization of the particle surface using Fibonacci-Lattice is then introduced to calculate the capillary forces from the complex liquid–solid interfaces. The joint influence of capillary forces and the interparticle contact forces on the motion of the particles are handled by the discrete element method (DEM). The effective stress parameter estimated by the model is compared with the experimental results for unsaturated soils, which confirms the validity of the proposed micro-mechanical model. The microscopic responses (liquid–solid interfaces, capillary forces, contact forces and coordination numbers) and macroscopic responses (strength, stress–strain relationship and volume change) of unsaturated soils in desaturation tests and triaxial tests are studied by the proposed model.
Liu, Y, Chen, L, Zhu, C, Ban, Y-L & Guo, YJ 2020, 'Efficient and Accurate Frequency-Invariant Beam Pattern Synthesis Utilizing Iterative Spatiotemporal Fourier Transform', IEEE Transactions on Antennas and Propagation, vol. 68, no. 8, pp. 6069-6079.
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© 1963-2012 IEEE. An iterative spatiotemporal Fourier transform (STFT) method is presented to efficiently design finite-impulse-response (FIR) filter coefficients for generating a desired frequency-invariant (FI) beam pattern of an antenna array. In this method, by introducing the concepts of normalized temporal angular frequency and spatial angular frequency, the broadband pattern is treated as a spatiotemporal spectral distribution. The relationship between the FIR coefficient distribution and the spatiotemporal spectral distribution can be built as an STFT, and consequently the 2-D fast Fourier transform (2D-FFT) and inverse 2D-FFT (2D-IFFT) can be utilized to efficiently accomplish the transformation between the FIR coefficient distribution and the spatiotemporal spectral distribution. Thus, the proposed synthesis method starts from an initial spatiotemporal spectral distribution and then adopt an iterative modification-and-transformation strategy to successively update the obtained spatiotemporal spectral distribution and the corresponding FIR coefficients. Two kinds of pattern modification techniques including the mainlobe FI modification and broadband sidelobe control are adopted in each iteration. Several examples for synthesizing different FI patterns are conducted. Synthesis results show that the proposed method can obtain much better FI pattern performance in terms of both mainlobe FI property and sidelobe control than the original FT method whilst costing less CPU time than the convex optimization method especially for the case of large FI arrays.
Liu, Y, Lan, C, Blumenstein, M & Li, J 2020, 'Bi-Level Error Correction for PacBio Long Reads', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 17, no. 3, pp. 899-905.
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IEEE The latest sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines can generate long reads at the length of thousands of nucleic bases which is much longer than the reads at the length of hundreds generated by Illumina machines. However, these long reads are prone to much higher error rates, for example 15%, making downstream analysis and applications very difficult. Error correction is a process to improve the quality of sequencing data. Hybrid correction strategies have been recently proposed to combine Illumina reads of low error rates to fix sequencing errors in the noisy long reads with good performance. In this paper, we propose a new method named Bicolor, a bi-level framework of hybrid error correction for further improving the quality of PacBio long reads. At the first level, our method uses a de Bruijn graph-based error correction idea to search paths in pairs of solid < formula > < tex > $k$ < /tex > < /formula > -mers iteratively with an increasing length of < formula > < tex > $k$ < /tex > < /formula > -mer. At the second level, we combine the processed results under different parameters from the first level. In particular, a multiple sequence alignment algorithm is used to align those similar long reads, followed by a voting algorithm which determines the final base at each position of the reads. We compare the superior performance of Bicolor with three state-of-the-art methods on three real data sets. Results demonstrate that Bicolor always achieves the highest identity ratio. Bicolor also achieves a higher alignment ratio ( < formula > < tex > $ & #x003E; 1.3\%$ < /tex > < /formula > ) and a higher number of aligned reads than the current methods on two data sets. On the third data set, our method is closely competitive to the current methods in terms of number of aligned reads and genome coverage. The C++ source codes of our algorithm are freely available at https://github.com/yuansliu/Bicolor.
Liu, Y, Li, H-W & Huang, Z 2020, 'Editorial: Metal Hydride-Based Energy Storage and Conversion Materials', Frontiers in Chemistry, vol. 8, p. 675.
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Liu, Y, Li, M, Haupt, RL & Guo, YJ 2020, 'Synthesizing Shaped Power Patterns for Linear and Planar Antenna Arrays Including Mutual Coupling by Refined Joint Rotation/Phase Optimization', IEEE Transactions on Antennas and Propagation, vol. 68, no. 6, pp. 4648-4657.
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© 1963-2012 IEEE. In this article, a novel strategy based on refined joint element rotation/phase optimization is presented to obtain vectorial shaped power patterns for antenna arrays with arbitrary element structures including mutual coupling. The active element pattern (AEP) is used for each antenna element, and then the rotation of an element is approximately described by mathematically rotating its AEP under the assumption that the mutual coupling variation does not change the AEP considerably. Optimal element rotations and phases for an array can be found by solving a vectorial shaped pattern synthesis problem such that the obtained array pattern has the desired co-polarization mainlobe shape while maintaining constrained sidelobe and cross-polarization levels. However, due to the variation of mutual coupling, this synthesized pattern may deviate from the real array pattern. To reduce the pattern discrepancy, successive refined joint element rotation/phase optimizations are adopted. As the number of refining steps increases, the allowable element rotation range is set to be smaller and smaller so that the synthesized array pattern can get closer and closer to the real one. Such a shaped power pattern synthesis technique does not need nonuniform amplitude weighting, thus saving many unequal power dividers. Three examples for synthesizing rotated linear and planar arrays with different antenna structures and different pattern shape requirements are provided to validate the effectiveness and advantages of the proposed method.
Liu, Y, Luo, G, Ngo, HH, Guo, W & Zhang, S 2020, 'Advances in thermostable laccase and its current application in lignin-first biorefinery: A review', Bioresource Technology, vol. 298, pp. 122511-122511.
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© 2019 Elsevier Ltd As the most abundant aromatic polymers on the Earth, lignin has great potential to produce biofuels and aromatic chemicals due to their high carbon content and low oxygen content. Lignin-first biorefinery methods have attracted increasing attention recently for their high-value of aromatic chemicals, and high biofuels productivity from lignocellulosic wastes. Thermostable laccase has proven to be an excellent alternative catalyst in degrading lignin for its versatile catalytic abilities under industrial conditions and pollution-free by-products. Thermostable laccases can be found in native extreme environments or modified by biologically based technologies such as gene recombination expression and enzyme direct evolution. This review demonstrated thermostable laccases and their application in lignin degradation. Future research should focus more on the investigation of the reaction of thermostable laccases with lignin substrates.
Liu, Y, Ngo, HH, Guo, W, Wang, D, Peng, L, Wei, W & Ni, B-J 2020, 'Impact of coexistence of sludge flocs on nitrous oxide production in a granule-based nitrification system: A model-based evaluation', Water Research, vol. 170, pp. 115312-115312.
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© 2019 Elsevier Ltd A common operational status of granule-based reactor is the inevitable coexistence of sludge flocs. Such hybrid system could have a profound impact on nitrous oxide (N2O) production in nitrifying process. In this work, a mathematical model is employed to evaluate the key role of the coexistence of sludge flocs on N2O production in a granule-based nitrifying system for the first time, by considering both nitrifier denitrification and hydroxylamine oxidation pathways. The modelling results show that the N2O production gradually decreases with the increase of the percentage of sludge flocs in the total biomass (10–60%). More N2O is tended to be generated in sludge flocs which has lower N2O production capacity compared to granular biomass, thus lowering the total N2O production. The relative contributions of two N2O production pathways are only affected by bulk dissolved oxygen (DO) for the sludge flocs in the hybrid system, whereas those are affected by both bulk DO and the fractions of sludge flocs for the granular biomass. The results reveal a substantial effect of the coexistence of sludge flocs on N2O production in granule-based nitrifying process, which should not be ignored in future design and operation.
Liu, Y, Wang, F, Lu, H, Fang, G, Wen, S, Chen, C, Shan, X, Xu, X, Zhang, L, Stenzel, M & Jin, D 2020, 'Cancer Spheroids: Super‐Resolution Mapping of Single Nanoparticles inside Tumor Spheroids (Small 6/2020)', Small, vol. 16, no. 6, pp. 2070030-2070030.
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Liu, Y, Wang, F, Lu, H, Fang, G, Wen, S, Chen, C, Shan, X, Xu, X, Zhang, L, Stenzel, M & Jin, D 2020, 'Super‐Resolution Mapping of Single Nanoparticles inside Tumor Spheroids', Small, vol. 16, no. 6, pp. 1905572-1905572.
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AbstractCancer spheroids have structural, functional, and physiological similarities to the tumor, and have become a low‐cost in vitro model to study the physiological responses of single cells and therapeutic efficacy of drugs. However, the tiny spheroid, made of a cluster of high‐density cells, is highly scattering and absorptive, which prevents light microscopy techniques to reach the depth inside spheroids with high resolution. Here, a method is reported for super‐resolution mapping of single nanoparticles inside a spheroid. It first takes advantage of the self‐healing property of a “nondiffractive” doughnut‐shaped Bessel beam from a 980 nm diode laser as the excitation, and further employs the nonlinear response of the 800 nm emission from upconversion nanoparticles, so that both excitation and emission at the near‐infrared can experience minimal loss through the spheroid. These strategies lead to the development of a new nanoscopy modality with a resolution of 37 nm, 1/26th of the excitation wavelength. This method enables mapping of single nanoparticles located 55 µm inside a spheroid, with a resolution of 98 nm. It suggests a solution to track single nanoparticles and monitor their release of drugs in 3D multicellar environments.
Liu, Y, Wong, L & Li, J 2020, 'Allowing mutations in maximal matches boosts genome compression performance', Bioinformatics, vol. 36, no. 18, pp. 4675-4681.
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Abstract Motivation A maximal match between two genomes is a contiguous non-extendable sub-sequence common in the two genomes. DNA bases mutate very often from the genome of one individual to another. When a mutation occurs in a maximal match, it breaks the maximal match into shorter match segments. The coding cost using these broken segments for reference-based genome compression is much higher than that of using the maximal match which is allowed to contain mutations. Results We present memRGC, a novel reference-based genome compression algorithm that leverages mutation-containing matches (MCMs) for genome encoding. MemRGC detects maximal matches between two genomes using a coprime double-window k-mer sampling search scheme, the method then extends these matches to cover mismatches (mutations) and their neighbouring maximal matches to form long and MCMs. Experiments reveal that memRGC boosts the compression performance by an average of 27% in reference-based genome compression. MemRGC is also better than the best state-of-the-art methods on all of the benchmark datasets, sometimes better by 50%. Moreover, memRGC uses much less memory and de-compression resources, while providing comparable compression speed. These advantages are of significant benefits to genome data storage and transmission. Availability and implementation https://github.com/yuansliu/memRGC. Supplementary information Supplementary data are available at Bioinformatics online.
Liu, Y, Yang, Y, Han, F, Liu, QH & Guo, YJ 2020, 'Improved Beam-Scannable Ultra-Wideband Sparse Antenna Arrays by Iterative Convex Optimization Based on Raised Power Series Representation', IEEE Transactions on Antennas and Propagation, vol. 68, no. 7, pp. 5696-5701.
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Liu, Y, Zhao, T, Su, Z, Zhu, T & Ni, B-J 2020, 'Evaluating the roles of coexistence of sludge flocs on nitrogen removal and nitrous oxide production in a granule-based autotrophic nitrogen removal system', Science of The Total Environment, vol. 730, pp. 139018-139018.
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Certain levels of sludge flocs would always coexist in granule-based reactors due to the biomass detachment from granules. Such inevitable coexistence could affect both total nitrogen (TN) removal and nitrous oxide (N2O) production in autotrophic nitrogen removal systems. This work utilized a mathematical approach to systematically study the influence of the coexisting sludge flocs on TN removal and N2O production in a granular nitritation-anaerobic ammonium oxidation (Anammox) process for the first time, based on a 2-pathway N2O production model concept. The modelling results reveal that the highest TN removal efficiency decreases from ca. 87-88% to ca. 41-49% as the fraction of sludge flocs in the system increases from 10% to 40%, while the N2O production rate gradually increases with such increase. Meanwhile, both bulk dissolved oxygen (DO, 0.05-0.3 mg/L) and the size of granule (200-400 μm) could also influence the TN removal efficiency and N2O production. As the fraction of sludge flocs increases from 10% to 40%, the contribution of granular biomass to total N2O production is reduced due to increase of N2O-producing ammonia-oxidizing bacteria (AOB) in the sludge flocs, and the increase of granule size could intensify such decrease. In addition, the hydroxylamine oxidation pathway dominates the nitrifier denitrification pathway in both granules and sludge flocs under various testing conditions, whereas the increasing contribution of the latter would occur at a certain DO range, higher fraction of sludge flocs and smaller granule size. These results disclose an important influence of the coexisting sludge flocs on the performance of granular nitritation-Anammox systems.
Liu, Y, Zheng, J, Li, M, Luo, Q, Rui, Y & Guo, YJ 2020, 'Synthesizing Beam-Scannable Thinned Massive Antenna Array Utilizing Modified Iterative FFT for Millimeter-Wave Communication', IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 11, pp. 1983-1987.
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Liu, Y, Zou, Y, Feng, C, Lee, A, Yin, J, Chung, R, Park, JB, Rizos, H, Tao, W, Zheng, M, Farokhzad, OC & Shi, B 2020, 'Charge Conversional Biomimetic Nanocomplexes as a Multifunctional Platform for Boosting Orthotopic Glioblastoma RNAi Therapy', Nano Letters, vol. 20, no. 3, pp. 1637-1646.
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Liu, Z, Cao, J, Tan, Y, Xiao, Q & Prasad, M 2020, 'Planning Above the API Clouds Before Flying Above the Clouds: A Real-Time Personalized Air Travel Planning Approach', International Journal of Parallel Programming, vol. 48, no. 1, pp. 137-156.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. The rapid growth of the airline industry has resulted in the availability of a large number of flights, however this can also create a paralyzing problem. Flight information on all airlines across the world can be obtained via the Internet. Today, passengers trend to be interested in user personalized service. How to effectively find a passenger’s most preferred air travel plan, which might include multiple transfers from millions of possible choices with certain constraints, such as time and price, is a critical challenge. This paper presents an efficient air travel planning approach, which can find a number of air travel plans by invoking the APIs offered by airline companies. At the same time, these plans also best match the customer’s preference based on an analysis of historical orders. An algorithm to extract user preference features is introduced and heuristic rules to speed up the K path search process under constraints are presented. The experiment results show that the proposed model finds optimal air travel plans efficiently on a real-world dataset.
Liu, Z, Zhang, L, Ni, W & Collings, IB 2020, 'Uncoordinated Pseudonym Changes for Privacy Preserving in Distributed Networks', IEEE Transactions on Mobile Computing, vol. 19, no. 6, pp. 1465-1477.
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© 2002-2012 IEEE. Pseudonyms have been adopted to preserve identity privacy of nodes in distributed networks. Frequent and unlinkable changes of pseudonyms need to be enabled by having at least $k$k nodes change together to confuse potential eavesdroppers. Existing approaches either depend on the coordination from central controllers, or involve interactive signaling between the nodes. This can potentially compromise privacy. This paper proposes a fully uncoordinated approach to change pseudonyms in distributed networks, where each node uses a pseudonym until its expiration and then changes after a random delay. We develop a new model to analyse the time-varying population of changing pseudonyms. Critical conditions are analytically established, under which individual nodes can independently change their pseudonyms while their identity privacy is preserved. The conditions are validated by illustrative examples. Corroborated by simulations, the accuracy of the analytical model improves, as the number of nodes increases. The analysis confirms that, the $k$k-anonymity can be achieved at a negligible throughput loss in the case of large networks.
Livesu, M, Pietroni, N, Puppo, E, Sheffer, A & Cignoni, P 2020, 'LoopyCuts: practical feature-preserving block decomposition for strongly hex-dominant meshing.', ACM Trans. Graph., vol. 39, no. 4, pp. 121-121.
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© 2020 ACM. We present a new fully automatic block-decomposition algorithm for feature-preserving, strongly hex-dominant meshing, that yields results with a drastically larger percentage of hex elements than prior art. Our method is guided by a surface field that conforms to both surface curvature and feature lines, and exploits an ordered set of cutting loops that evenly cover the input surface, defining an arrangement of loops suitable for hex-element generation. We decompose the solid into coarse blocks by iteratively cutting it with surfaces bounded by these loops. The vast majority of the obtained blocks can be turned into hexahedral cells via simple midpoint subdivision. Our method produces pure hexahedral meshes in approximately 80% of the cases, and hex-dominant meshes with less than 2% non-hexahedral cells in the remaining cases. We demonstrate the robustness of our method on 70+ models, including CAD objects with features of various complexity, organic and synthetic shapes, and provide extensive comparisons to prior art, demonstrating its superiority.
Liyanaarachchi, S, Jegatheesan, V, Shu, L, Shon, HK, Muthukumaran, S & Li, CQ 2020, 'Evaluating the Feasibility of Forward Osmosis in Diluting RO Concentrate Using Pretreatment Backwash Water', Membranes, vol. 10, no. 3, pp. 35-35.
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Forward osmosis (FO) is an excellent membrane process to dilute seawater (SW) reverse osmosis (RO) concentrate for either to increase the water recovery or for safe disposal. However, the low fluxes through FO membranes as well the biofouling/scaling of FO membranes are bottlenecks of this process requiring larger membrane area and membranes with anti-fouling properties. This study evaluates the performance of hollow fibre and flat sheet membranes with respect to flux and biofouling. Ferric hydroxide sludge was used as impaired water mimicking the backwash water of a filter that is generally employed as pretreatment in a SWRO plant and RO concentrate was used as draw solution for the studies. Synthetic salts are also used as draw solutions to compare the flux produced. The study found that cellulose triacetate (CTA) flat sheet FO membrane produced higher flux (3–6 L m−2 h−1) compared to that produced by polyamide (PA) hollow fibre FO membrane (less than 2.5 L m−2 h−1) under the same experimental conditions. Therefore, long-term studies conducted on the flat sheet FO membranes showed that fouling due to ferric hydroxide sludge did not allow the water flux to increase more than 3.15 L m−2 h−1.
Loganathan, P, Gradzielski, M, Bustamante, H & Vigneswaran, S 2020, 'Progress, challenges, and opportunities in enhancing NOM flocculation using chemically modified chitosan: a review towards future development', Environmental Science: Water Research & Technology, vol. 6, no. 1, pp. 45-61.
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Natural organic matter (NOM) occurs ubiquitously in water bodies and this can greatly affect feed or raw water quality (taste, colour, odour, bacterial growth). Chemically modified chitosan can effectively remove NOM by the flocculation process.
Logeswaran, J, Shamsuddin, AH, Silitonga, AS & Mahlia, TMI 2020, 'Prospect of using rice straw for power generation: a review', Environmental Science and Pollution Research, vol. 27, no. 21, pp. 25956-25969.
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With the ever-increasing energy demands, fossil fuels are gradually depleting and eventually, these nonrenewable sources of energy will be exhausted. Hence, there is an urgent need to formulate alternative fuels that are both renewable and sustainable. Biomass is one of the reliable sources of energy because it is replenishable. Rice is the staple food in many countries, particularly in Asia. The number of paddy fields has increased tremendously over the years and is expected to increase in the future in response to the growing world population. This will lead to significant amounts of agricultural wastes annually, particularly rice straw. In some countries, open burning and soil incorporation are used to manage agricultural wastes. Open burning is the preferred method because it is inexpensive. However, this method is highly undesirable because of its detrimental impact on the environment resulting from the release of carbon dioxide and methane gas. Hence, it is important to develop an energy-harvesting method from rice straw for power generation. More studies need to be carried out on the availability and characteristics of rice straw as well as logistic analysis to assess the potential of rice straw for power generation. This paper is focused on reviewing studies pertaining to the characteristics and potential of rice straw for power generation, current rice straw management practices, and logistic analysis in order to develop a suitable energy-harvesting method from rice straw in Malaysia.
Lomax, BA, Conti, M, Khan, N, Bennett, NS, Ganin, AY & Symes, MD 2020, 'Proving the viability of an electrochemical process for the simultaneous extraction of oxygen and production of metal alloys from lunar regolith', Planetary and Space Science, vol. 180, pp. 104748-104748.
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© 2019 Elsevier Ltd The development of an efficient process to simultaneously extract oxygen and metals from lunar regolith by way of in-situ resource utilisation (ISRU) has the potential to enable sustainable activities beyond Earth. The Metalysis-FFC (Fray, Farthing, Chen) process has recently been proven for the industrial-scale production of metals and alloys, leading to the present investigation into the potential application of this process to regolith-like materials. This paper provides a proof-of-concept for the electro-deoxidation of powdered solid-state lunar regolith simulant using an oxygen-evolving SnO2 anode, and constitutes the first in-depth study of regolith reduction by this process that fully characterises and quantifies both the anodic and cathodic products. Analysis of the resulting metallic powder shows that 96% of the total oxygen was successfully extracted to give a mixed metal alloy product. Approximately a third of the total oxygen in the sample was detected in the off-gas, with the remaining oxygen being lost to corrosion of the reactor vessel. We anticipate, with appropriate adjustments to the experimental set-up and operating parameters, to be able to isolate essentially all of the oxygen from lunar regolith simulants using this process, leading to the exciting possibility of concomitant oxygen generation and metal alloy production on the lunar surface.
Long, G, Xie, Y, Luo, Z, Qu, L, Zhou, JL & Li, W 2020, 'Deterioration mechanism of steam-cured concrete subjected to coupled environmental acid and drying action', Journal of Infrastructure Preservation and Resilience, vol. 1, no. 1, p. 5.
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AbstractIn order to investigate the deterioration mechanism of steam-cured concrete under severe environmental actions such as acid rain corrosion, salt corrosion, and cyclic thermal loading, accelerated corrosion tests were conducted in this study. Surface damage as well as deteriorative kinetics of steam-cured concrete and cement paste suffering from coupled acid-thermal actions was investigated by soaking-drying cycle experiments. The effects of mineral admixture, curing regime and corrosion condition on the durability were all comparatively studied, and the X-ray diffractograms and nanoindentation were applied to analyse the mechanism of corrosion deterioration. The results revealed that compared with the cementitious materials under standard curing, larger depth and faster corrosion were observed for steam-cured concrete and cement paste, which might be partly attributed to the lower content of hydrated production presented in steam-cured specimens. Besides, under acid solution soaking-drying cycle regime, there was significant higher corrosion depth compared to only soaking in acid solution. The corrosion depth under steam curing and soaking-drying condition increased by 156.68% and 44.17%, respectively, compared with those under standard curing and only soaking treatment. In addition, fly ash effectively decreased the corrosion depth of steam-cured cement paste and concrete by 64.98% and 16.33%, respectively.
Long, Z, Meng, H, Li, T & Li, S 2020, 'Compact geometric representation of qualitative directional knowledge', Knowledge-Based Systems, vol. 195, pp. 105616-105616.
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Lu, J, Liu, A, Song, Y & Zhang, G 2020, 'Data-driven decision support under concept drift in streamed big data', Complex & Intelligent Systems, vol. 6, no. 1, pp. 157-163.
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Abstract Data-driven decision-making ($$\mathrm {D^3}$$D3M) is often confronted by the problem of uncertainty or unknown dynamics in streaming data. To provide real-time accurate decision solutions, the systems have to promptly address changes in data distribution in streaming data—a phenomenon known as concept drift. Past data patterns may not be relevant to new data when a data stream experiences significant drift, thus to continue using models based on past data will lead to poor prediction and poor decision outcomes. This position paper discusses the basic framework and prevailing techniques in streaming type big data and concept drift for $$\mathrm {D^3}$$D3M. The study first establishes a technical framework for real-time $$\mathrm {D^3}$$D3M under concept drift and details the characteristics of high-volume streaming data. The main methodologies and approaches for detecting concept drift and supporting $$\mathrm {D^3}$$D3M are highlighted and presented. Lastly, further resea...
Lu, J, Zheng, X, Sheng, M, Jin, J & Yu, S 2020, 'Efficient Human Activity Recognition Using a Single Wearable Sensor', IEEE Internet of Things Journal, vol. 7, no. 11, pp. 11137-11146.
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Lu, J, Zuo, H & Zhang, G 2020, 'Fuzzy Multiple-Source Transfer Learning', IEEE Transactions on Fuzzy Systems, vol. 28, no. 12, pp. 3418-3431.
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Transfer learning is gaining increasing attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy transfer learning, which is based on fuzzy systems and particularly fuzzy rule-based models, was developed due to its capacity to deal with uncertainty. However, one issue with fuzzy transfer learning, even in the area of general transfer learning, has not been resolved: how to combine and then use knowledge when multiple-source domains are available. This study presents new methods for merging fuzzy rules from multiple domains for regression tasks. Two different settings are separately explored: homogeneous and heterogeneous space. In homogeneous situations, knowledge from the source domains is merged in the form of fuzzy rules. In heterogeneous situations, knowledge is merged in the form of both data and fuzzy rules. Experiments on both synthetic and real-world datasets provide insights into the scope of applications suitable for the proposed methods and validate their effectiveness through comparisons with other state-of-the-art transfer learning methods. An analysis of parameter sensitivity is also included.
Lu, S, Zhang, Q, Liu, Y, Liu, L, Zhu, Q & Jing, K 2020, 'Retrieval of Multiple Spatiotemporally Correlated Images on Tourist Attractions Based on Image Processing', Traitement du Signal, vol. 37, no. 5, pp. 847-854.
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The thriving of information technology (IT) has elevated the demand for intelligent query and retrieval of information about the tourist attractions of interest, which are the bases for preparing convenient and personalized itineraries. To realize accurate and rapid query of tourist attraction information (not limited to text information), this paper proposes a spatiotemporal feature extraction method and a ranking and retrieval method for multiple spatiotemporally correlated images (MSCIs) on tourist attractions based on deeply recursive convolutional network (DRCN). Firstly, the authors introduced the acquisition process of candidate spatiotemporally correlated images on tourist attractions, including both coarse screening and fine screening. Next, the workflow of spatiotemporal feature extraction from tourist attraction images was explained, as well as he proposed convolutional long short-term memory (ConvLSTM) algorithm. After that, the ranking model of MSCIs was constructed and derived. Experimental results demonstrate that our strategy is effective in the retrieval of tourist attraction images. The research results shed light on the fast and accurate retrieval of other types of images.
Lu, W & Liu, D 2020, 'A2: Extracting cyclic switchings from DOB-nets for rejecting excessive disturbances', Neurocomputing, vol. 400, pp. 161-172.
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© 2020 Reinforcement Learning (RL) is limited in practice by its poor explainability, which is responsible for insufficient trustiness from users, unsatisfied interpretation for human intervention, inadequate analysis for future improvement, etc. This paper seeks to partially characterize the interplay between dynamical environments and a previously-proposed Disturbance OBserver net (DOB-net). The DOB-net is trained via RL and offers optimal control for a set of Partially Observable Markovian Decision Processes (POMDPs). The transition function of each POMDP is largely determined by the environments (excessive external disturbances). This paper proposes an Attention-based Abstraction (A2) approach to extract a finite-state automaton, referred to as a Key Moore Machine Network (KMMN), to capture the switching mechanisms exhibited by the DOB-net in dealing with multiple such POMDPs. A2 first quantizes the controlled platform by learning continuous-discrete interfaces. Then it extracts the KMMN by finding the key hidden states and transitions that attract sufficient attention from the DOB-net. Within the resultant KMMN, three patterns of cyclic switchings (between key hidden states) are found, and saturated controls are shown synchronized with unknown disturbances. Interestingly, the found switchings have previously appeared in the control design for often-saturated systems. They are interpreted via an analogy to the discrete-event subsystem of hybrid control.
Lu, Z-H, Wang, H-J, Qu, F, Zhao, Y-G, Li, P & Li, W 2020, 'Novel empirical model for predicting residual flexural capacity of corroded steel reinforced concrete beam', Frontiers of Structural and Civil Engineering, vol. 14, no. 4, pp. 888-906.
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© 2020, Higher Education Press. In this study, a total of 177 flexural experimental tests of corroded reinforced concrete (CRC) beams were collected from the published literature. The database of flexural capacity of CRC beam was established by using unified and standardized experimental data. Through this database, the effects of various parameters on the flexural capacity of CRC beams were discussed, including beam width, the effective height of beam section, ratio of strength between longitudinal reinforcement and concrete, concrete compressive strength, and longitudinal reinforcement corrosion ratio. The results indicate that the corrosion of longitudinal reinforcement has the greatest effect on the residual flexural capacity of CRC beams, while other parameters have much less effect. In addition, six available empirical models for calculating the residual flexural strength of CRC beams were also collected and compared with each other based on the established database. It indicates that though five of six existing empirical models underestimate the flexural capacity of CRC beams, there is one model overestimating the flexural capacity. Finally, a newly developed empirical model is proposed to provide accurate and effective predictions in a large range of corrosion ratio for safety assessment of flexural failure of CRC beams confirmed by the comparisons.
Luo, C, Wong, S, Chen, R, Zhu, X, Yang, Y, Lin, J, Tu, Z & Xue, Q 2020, 'Compact on‐chip millimetre wave bandpass filters with meandered grounding resonator in 0.13‐μm (Bi)‐CMOS technology', IET Microwaves, Antennas & Propagation, vol. 14, no. 6, pp. 559-565.
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In this study, an ultra‐compact meandered grounding resonator is proposed to design two millimetre wave bandpass filters (BPFs) in a standard 0.13‐µm silicon‐germanium (Bi)‐complementary metal oxide semiconductor (CMOS) technology. The fundamental second‐order prototype, namely BPF‐I, consists of a pair of proposed resonators and a pair of grounded metal‐insulator‐metal (MIM) capacitors. To better understand the principle of the second‐order BPF‐I, an equivalent LC‐circuit model and theoretical analysis method are presented in this study. Based on BPF‐I, the second‐order BPF‐II is proposed by adding the additional two pairs of MIM capacitors to improve the frequency selectivity, by means of introducing a transmission zero at lower stopband. Finally, both of the two second‐order BPFs are fabricated. The measured results show a good agreement with the full‐wave simulation results. The insertion loss of the first BPF‐I is 1.79 dB at the centre frequency of 46.6 GHz, and the fractional bandwidth is up to 96.5%. The second BPF‐II has a centre frequency at 46.8 GHz with a fractional bandwidth of 94.1%. The minimum insertion loss is 2.08 dB and the lower stopband attenuation is up to 42.7 dB. Moreover, the die sizes of the two compact BPFs, excluding the test pads, are only 0.0197 mm2 (0.104 × 0.190 mm2).
Luo, C, Wong, S-W, Lin, J-Y, Yang, Y, Li, Y, Yu, X-Z, Feng, L-P, Tu, Z-H & Zhu, L 2020, 'Quasi-Reflectionless Microstrip Bandpass Filters Using Bandstop Filter for Out-of-Band Improvement', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 10, pp. 1849-1853.
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© 2004-2012 IEEE. In this brief, a class of microstrip bandpass filters (BPFs) with quasi-reflectionless behavior on both input and output ports is presented. A single-port reflectionless BPF is proposed by shunt connecting one bandstop filter cell namely one-cell. This one-cell single-port reflectionless BPF is made up of a high impedance transmission line as a series band-pass filter section and a shunt-connected band-stop section with resistively terminated. In the operation passband, the bandpass filter section is used to transmit spectral energy, while the bandstop section is regarded as an open circuit. Besides, the out-of-band energy is absorbed by resistively component in the bandstop filter section. In order to achieve better out-of-band reflectionless characteristic, a two-cell two-port reflectionless BPF is constructed by shunt connecting two cells at input/output ports and one series bandpass filter section in the center. In addition, a three-cell reflectionless BPF is designed to further verify the improvement of out-of-band performance. Finally, two-cell and three-cell two-port reflectionless BPFs are fabricated and tested to verify the design concept. The measured results of these two reflectionless BPFs are in good agreement with the simulation ones.
Luo, Y, Zhang, JA, Huang, X, Ni, W & Pan, J 2020, 'Multibeam Optimization for Joint Communication and Radio Sensing Using Analog Antenna Arrays', IEEE Transactions on Vehicular Technology, vol. PP, no. 99, pp. 1-1.
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Multibeam technology enables the use of two or more subbeams for jointcommunication and radio sensing, to meet different requirements of beamwidthand pointing directions. Generating and optimizing multibeam subject to therequirements is critical and challenging, particularly for systems using analogarrays. This paper develops optimal solutions to a range of multibeam designproblems, where both communication and sensing are considered. We first studythe optimal combination of two pre-generated subbeams, and their beamformingvectors, using a combining phase coefficient. Closed-form optimal solutions arederived to the constrained optimization problems, where the received signalpowers for communication and the beamforming waveforms are alternatively usedas the objective and constraint functions. We also develop global optimizationmethods which directly find optimal solutions for a single beamforming vector.By converting the original intractable complex NP-hard global optimizationproblems to real quadratically constrained quadratic programs, near-optimalsolutions are obtained using semidefinite relaxation techniques. Extensivesimulations validate the effectiveness of the proposed constrained multibeamgeneration and optimization methods.
Luo, Z 2020, 'Rational Design of Pentamode Metamaterials by Topology Optimization', Video Proceedings of Advanced Materials, vol. 1, no. 1.
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Pentamode metamaterials (PMMs)[1,2], categorized as a new class of three-dimensional solid mechanical metamaterials, refer to artificially engineered lattice materials exhibiting a vanishing shear modulus, mimicking the behaviour of fluids but are solid, and hard to compress yet easy to deform. Here ‘penta’ denotes five, referring to only one non-zero but five vanishing eigenvalues in the elasticity tensor of isotropic materials. The extraordinary elastic properties of PMMs are dominantly determined by the geometries (i.e. shapes and topologies) of rationally designed microstructures, instead of the composition of their base materials. Compared to most up-to-date design methods based on conventional rigid-body double-cone concept related to diamond lattice, this lecture is more focused an innovative computational design methodology using topology optimization to uncover new micro architectures with novel geometries over a range of effective properties and relative densities. The overall elastic deformation of the microstructure is utilised to achieve the pentamode properties, rather than making use of the highly localised point-point deformation gained from rigid-body links (tiny tips). The design problem is then formulated to make the microstructure have a large but realistically attainable ratio of effective bulk modulus compared to the shear modulus, corresponding to the isotropic microstructure with the effective Poisson’s ratio approaching to 0.5. The larger of the ratio, the better of the PMM solids to simulate liquids. The whole microstructure is discretised with a high-resolution finite element mesh, in order to capture the fine geometric features of the ultralight microstructure in the space. The SIMP-topology optimisation method [3] is firstly applied to find the topologically optimised pentaomode microstructure, and then based on the skeleton of the topological design the size optimisation approach is further used to investigate the impact of...
Luo, Z, Li, W, Gan, Y, Mendu, K & Shah, SP 2020, 'Applying grid nanoindentation and maximum likelihood estimation for N-A-S-H gel in geopolymer paste: Investigation and discussion', Cement and Concrete Research, vol. 135, pp. 106112-106112.
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© 2020 Elsevier Ltd Static nanoindentation and Maximum Likelihood Estimation (MLE) were applied for the nano/micromechanical properties investigation of alkali-activated fly ash (AAFA) in this study. Some critical issues of statistical nanoindentation were fully discussed, including properties of pure gel phase, influence of bin size when using least-square estimation (LSE), and suitable number of components for deconvolution. Results indicate that the model estimated by MLE method can effectively reflect the micromechanical distribution of AAFA. The number of components needed to separate sodium aluminosilicate hydrate (N-A-S-H) gels is sometimes more than the normally used 3 or 4, depending on the sample and testing factors. The gel phase does not always display as a prominent peak in the histogram and is easy to be mixed with other adjacent peaks even if the bin size is small, indicating the challenges of employing the LSE method to investigate the gel phase in highly heterogeneous materials, such as geopolymer.
Luo, Z, Li, W, Gan, Y, Mendu, K & Shah, SP 2020, 'Maximum likelihood estimation for nanoindentation on sodium aluminosilicate hydrate gel of geopolymer under different silica modulus and curing conditions', Composites Part B: Engineering, vol. 198, pp. 108185-108185.
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© 2020 Elsevier Ltd As an important inorganic material, geopolymer has been widely used for ceramics and sustainable cement in concrete. Sodium aluminosilicate hydrate (N-A-S-H) gel known as the zeolite precursor gel has the most critical impact on the performance of geopolymer. The nano/micromechanical properties of N-A-S-H have been investigated in several studies, but the resutls are always inconsistent. A novel “compromise approach” using Maximum Likelihood Estimation (MLE) for deconvolution of nanoindentation data is introduced to fundamentally further understand this issue in this study. Correlation and difference of different statistical techniques are compared to clarify the rationality of this method. Multiple characterization techniques including microstructure observation at micro -and nano-scale, element analysis, and crystal identification are applied to reveal the mechanisms. The results indicate that the elastic modulus and hardness of the N-A-S-H gel in geopolymer under different silica modulus and curing conditions vary in a small range from 10.50 to 14.30 GPa and from 0.40 to 0.57 GPa, respectively. When applying statistical nanoindentation in geopolymer, two kinds of spurious phases, mixed phases and sub-phases are unavoidable. For the MLE method adopted, the errors generated from analytical technique were estimated to be only 0.68 and 0.13 GPa for elastic modulus and hardness, respectively.
Lv, X, Chen, SJ, Galehdar, A, Withayachumnankul, W & Fumeaux, C 2020, 'Fast Semi-Analytical Design for Single-FSS-Layer Circuit-Analog Absorbers', IEEE Open Journal of Antennas and Propagation, vol. 1, pp. 483-492.
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Ly, QV, Matindi, C, Kuvarega, AT, Ngo, HH, Le, QV, Nam, VH & Li, J 2020, 'Exploring the novel PES/malachite mixed matrix membrane to remove organic matter for water purification', Chemical Engineering Research and Design, vol. 160, pp. 63-73.
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Lyu, B & Hoang, DT 2020, 'Optimal Time Scheduling in Relay Assisted Batteryless IoT Networks', IEEE Wireless Communications Letters, vol. 9, no. 5, pp. 706-710.
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© 2012 IEEE. In this letter, we propose a novel relay transmission scheme in a batteryless IoT network for practical implementation and high energy-efficiency, where communications between a hybrid access point (HAP) and multiple batteryless sensors are assisted by energy-constrained gateways. In the proposed system, while a batteryless sensor backscatters the incident signals from the HAP to transmit data to its gateway, other gateways can simultaneously harvest energy from the HAP. Then, the gateways can use their harvested energy to forward the received signals to the HAP. Under this setup, we formulate the achievable sum-rate maximization problem by optimizing the time allocation between data backscattering, energy harvesting, and data forwarding. Then, an efficient method is proposed to find the optimal solution. Simulation results show that the proposed relay transmission scheme can achieve up to 34% sum-rate gain over two benchmark schemes.
Lyu, B, Hoang, DT & Yang, Z 2020, 'Backscatter Then Forward: A Relaying Scheme for Batteryless IoT Networks', IEEE Wireless Communications Letters, vol. 9, no. 4, pp. 562-566.
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IEEE In this paper, we introduce a novel relaying scheme together with a joint energy beamforming (EB) and time allocation optimization to meet requirements about energy efficiency and hardware constraints of batteryless IoT networks. First, we propose an intelligent relaying scheme using RF-powered gateways as relay nodes to deliver information from batteryless IoT devices to a hybrid access point (HAP). The HAP can also transfer energy to the gateways and batteryless devices using EB techniques. The energy from HAP will be then used to supply power for gateways and as a communications means to transmit data for batteryless devices. We then formulate a sum-rate maximization problem by jointly optimizing the EB vectors, time scheduling, and power allocation. Since the optimization problem is non-convex, we exploit EB characteristics for data backscattering and employ variable substitutions and semidefinite relaxation techniques to transform it into a convex one. After that, a low-complexity method is proposed to obtain the optimal solution in a closed-form. Simulation results confirm that the proposed scheme can achieve significant sum-rate gain.
Lyu, B, Qin, L, Lin, X, Zhang, Y, Qian, Z & Zhou, J 2020, 'Maximum Biclique Search at Billion Scale.', Proc. VLDB Endow., vol. 13, no. 9, pp. 1359-1372.
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© 2020, VLDB Endowment. Maximum biclique search, which finds the biclique with the maximum number of edges in a bipartite graph, is a fun-damental problem with a wide spectrum of applications in different domains, such as E-Commerce, social analysis, web services, and bioinformatics. Unfortunately, due to the dif-ficulty of the problem in graph theory, no practical solution has been proposed to solve the issue in large-scale real-world datasets. Existing techniques for maximum clique search on a general graph cannot be applied because the search objec-tive of maximum biclique search is two-dimensional, i.e., we have to consider the size of both parts of the biclique simul-taneously. In this paper, we divide the problem into several subproblems each of which is specified using two parameters. These subproblems are derived in a progressive manner, and in each subproblem we can restrict the search in a very small part of the original bipartite graph. We prove that a loga-rithmic number of subproblems is enough to guarantee the algorithm correctness. To minimize the computational cost, we show how to reduce significantly the bipartite graph size for each subproblem while preserving the maximum biclique satisfying certain constraints by exploring the properties of one-hop and two-hop neighbors for each vertex. We use several real datasets from various application domains, one of which contains over 300 million vertices and 1:3 billion edges, to demonstrate the high efficiency and scalability of our proposed solution. It is reported that 50% improve-ment on recall can be achieved after applying our method in Alibaba Group to identify the fraudulent transactions in their e-commerce networks. This further demonstrates the usefulness of our techniques in practice.
Lyu, H, Dong, Z, Roobavannan, M, Kandasamy, J & Pande, S 2020, 'Prospects of interventions to alleviate rural–urban migration in Jiangsu Province, China based on sensitivity and scenario analysis', Hydrological Sciences Journal, vol. 65, no. 13, pp. 2175-2184.
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© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Rural–urban migration is an adaptive response to location-specific environmental or socio-economic stressors. Jiangsu Province, China is witnessing rapid economic growth fuelled by manufacturing and services sector. Rural–urban migration in Jiangsu, which brings higher stress to resource-carrying capacity of urban areas, is driven by rural “push” factors, principally labour surplus and unemployment in agriculture. This study investigates possible policy interventions aimed at relieving the rapid rural–urban migration in Jiangsu based on a sensitivity analysis of driving factors in rural agricultural production. It shows that rural–urban migration is sensitive to input elasticities of precipitation and labour. Two groups of scenario analysis corresponding to possible policy interventions are implemented. The first policy focuses on providing government subsidies to rural non-agricultural industries then compensate for the shrinking agricultural production. Another policy supports education in rural areas to provide more skilled labour resource which can be absorbed by non-agricultural industries. Both two policies are effective in reducing rural unemployment and alleviating rural–urban migration.
Lyu, X, Ren, C, Ni, W, Tian, H & Liu, RP 2020, 'Cooperative Computing Anytime, Anywhere: Ubiquitous Fog Services', IEEE Wireless Communications, vol. 27, no. 1, pp. 162-169.
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M. Vanaki, S, Holmes, D, Saha, SC, Chen, J, Brown, RJ & Jayathilake, PG 2020, 'Muco-ciliary clearance: A review of modelling techniques', Journal of Biomechanics, vol. 99, pp. 109578-109578.
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Ma, B, Zheng, J, Lei, G, Zhu, J, Jin, P & Guo, Y 2020, 'Topology Optimization of Ferromagnetic Components in Electrical Machines', IEEE Transactions on Energy Conversion, vol. 35, no. 2, pp. 786-798.
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IEEE This paper presents the topology optimization of ferromagnetic components in electrical machines by using the density method. Explicit expressions of machine performance based on the principle of electromechanical energy conversion are derived and incorporated in the finite element model. Because the gradient-based algorithm is employed for efficient optimization, the performance sensitivities with respect to the design variables are derived subsequently. An optimization framework is then proposed to optimize effectively the electrical machine performance, such as the flux linkage, back electromotive force, torque profile including torque density and torque ripples. Two design examples are reported to confirm the feasibility and effectiveness of the presented topology optimization approach. The accuracy of optimal solutions is verified by using the commercial electromagnetic analysis software ANSYS MAXWELL.
Ma, B, Zheng, J, Zhu, J, Wu, J, Lei, G & Guo, Y 2020, 'Robust Design Optimization of Electrical Machines Considering Hybrid Random and Interval Uncertainties', IEEE Transactions on Energy Conversion, vol. 35, no. 4, pp. 1815-1824.
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© 1986-2012 IEEE. For robust design optimization (RDO) of electrical machines, cases with random uncertainty and interval uncertainty are generally investigated separately. Accordingly, the performance fluctuation analysis under uncertainty is based on random method and interval method respectively. However, the problem with hybrid uncertainties can also be met yet rarely researched. Under this circumstance, the uncertainty analysis methods for a single type of uncertainty may no longer be applicable, which challenges the robust optimization conduction. For effective RDO of electrical machines with hybrid uncertainties, this article presents a robust optimizer based on evolutionary algorithms and the polynomial chaos Chebyshev interval (PCCI) method. The PCCI method is utilized for effectively modeling the fluctuations caused by the hybrid uncertainty with a small number of samples. As additional enhancements, the filtering strategy for the algorithm with deterministic constraints is proposed to reduce the solutions that require robustness analysis in each iteration and accelerate the optimization further while not affecting the global convergence ability. A design example of a brushless DC motor considering hybrid uncertainties is analyzed and optimized. The results confirm the feasibility of the proposed method.
Ma, C, Gao, Y, Degano, M, Wang, Y, Fang, J, Gerada, C, Zhou, S & Mu, Y 2020, 'Eccentric position diagnosis of static eccentricity fault of external rotor permanent magnet synchronous motor as an in‐wheel motor', IET Electric Power Applications, vol. 14, no. 11, pp. 2263-2272.
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An eccentric position diagnosis method of static eccentricity (SE) fault of external rotor permanent magnet synchronous motor (ER‐PMSM) is presented. Firstly, an analytical model of no‐load radial magnetic field of ER‐PMSM is established. Analytical models of no‐load Back‐EMF of both unit motors and the whole motor are carried out and are verified by finite element method (FEM) and experimental measurements. Then, the influences of SE ratio, SE circumferential angle, winding distribution mode and number of parallel branches on no‐load radial magnetic field and no‐load Back‐EMF are analyzed based on these analytical models. The results show that SE does not affect the frequency characteristics of no‐load radial magnetic field, but changes space order characteristics. On one hand, for ER‐PMSM, of which the number of unit motors is equal to 1, SE causes no‐load Back‐EMF distortion. On the other hand, for ER‐PMSM, of which the number of unit motors is greater than 1, SE does not affect no‐load Back‐EMF of the whole motor, but it still leads to no‐load Back‐EMF distortion of unit motors. Therefore, based on total harmonic distortion (THD) of no‐load Back‐EMF of unit motor, a projection method of intersection lines for SE fault diagnosis of ER‐PMSM is proposed finally.
Ma, C, Zhou, S, Yang, N, Degano, M, Gerada, C, Fang, J & Liu, Q 2020, 'Characteristic analysis and direct measurement for air gap magnetic field of external rotor permanent magnet synchronous motors in electric vehicles', IET Electric Power Applications, vol. 14, no. 10, pp. 1784-1794.
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In this study, the air gap magnetic field characteristics of external rotor permanent magnet synchronous motors (PMSMs) under both the stator and rotor coordinate systems considering low‐order current harmonics and high‐order sideband current harmonics are analysed. A direct measurement technique (DMT) for air‐gap magnetic field is proposed. First, an analytical model of air gap magnetic field of external rotor PMSMs is established. The spatial order and frequency characteristics of stator/rotor air gap magnetic field are revealed. Then, a 24‐pole 27‐slot external rotor PMSM is taken as an example. The analytical and finite element (FE) results are compared and analysed. The difference of the spatial order and frequency characteristics between the stator and rotor air gap magnetic field are verified. Next, a new DMT is proposed, which can detect the precise distribution and local microscopic characteristics on the order of 10−1 mm with high resolution. The accuracy of analytical and FE model are verified by the DMT and an indirect experimental test of no‐load back electromotive force. Finally, the mechanical challenges of in‐wheel motors and the practicablility of DMT for eccentricity detection are further discussed.
Ma, H, Wang, Y, Xiong, R, Kodagoda, S & Tang, L 2020, 'DeepGoal: Learning to drive with driving intention from human control demonstration', Robotics and Autonomous Systems, vol. 127, pp. 103477-103477.
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© 2020 Elsevier B.V. Recent research on automotive driving has developed an efficient end-to-end learning mode that directly maps visual input to control commands. However, it models distinct driving variations in a single network, which increases learning complexity and is less adaptive for modular integration. In this paper, we re-investigate human's driving style and propose to learn an intermediate driving intention region to relax the difficulties in end-to-end approach. The intention region follows both road structure in image and direction towards goal in public route planner, which addresses visual variations only and figures out where to go without conventional precise localization. Then the learned visual intention is projected on vehicle local coordinate and fused with reliable obstacle perception to render a navigation score map that is widely used for motion planning. The core of the proposed system is a weakly-supervised cGAN-LSTM model trained to learn driving intention from human demonstration. The adversarial loss learns from limited demonstration data with one local planned route and enables reasoning of multi-modal behaviors with diverse routes while testing. Comprehensive experiments are conducted with real-world datasets. Results indicate the proposed paradigm can produce more consistent motion commands with human demonstration and shows better reliability and robustness to environment change. Our code is available at https://github.com/HuifangZJU/visual-navigation.
Ma, M, Tam, VWY, Le, KN & Li, W 2020, 'Challenges in current construction and demolition waste recycling: A China study', Waste Management, vol. 118, pp. 610-625.
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China produced a large amount of construction and demolition (C&D) waste, owing to the rapid development of construction industry. Although a set of policies and regulations are being drafted in China for promoting C&D waste recycling, execution of these policies in practice seems to be far from effective. Currently, approximately 75% of Chinese cities are still surrounded by large volumes of C&D waste. Therefore, identification of challenges in the development of C&D waste management, specially recycling, is essential. This paper employs site visits to 10 recycling plants in 10 Chinese cities (Shanghai, Hangzhou, Suzhou, Chongqing, Chengdu, Xi'an, Changsha, Shenzhen, Nanjing, and Zhoukou) and interviews with 25 industry practitioners for examining the challenges. Eight challenges are identified: (1) unstable source of C&D waste for recycling, (2) absence of subsidies for recycling activities and high cost for land use, (3) insufficient attention paid to design for waste minimisation, (4) absence of regulations on on-site sorting, (5) unregulated landfill activities, (6) a lack of coordination among different government administration departments, (7) a lack of accurate estimation of waste quantity and distribution, and (8) a lack of an effective waste tracing system. Recommendations to address these challenges are presented. The results of this study are expected to aid policy makers in formulation of proper C&D waste management in China and provide a useful reference for researchers who are interested in C&D waste recycling industry.
Ma, R, Li, T, Bo, D, Wu, Q & An, P 2020, 'Error sensitivity model based on spatial and temporal features', Multimedia Tools and Applications, vol. 79, no. 43-44, pp. 31913-31930.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Packet loss and error propagation induced by it are significant causes of visual impairments in video applications. Most of the existing video quality assessment models are developed at frame or sequence level, which can not accurately describe the impact of packet loss on the local regions in one frame. In this paper, we propose an error sensitivity model to evaluate the impact of a single packet loss. We also make full use of the spatio-temporal correlation of the video and analyze a set of features that directly impact the perceptual quality of videos, based on the specific situation of video packet loss. With the aid of the support vector regression (SVR), these features are used to predict the error sensitivity of the local region. The proposed model is tested on six video sequences. Experimental results show that the proposed model predicts sensitivity of videos to different packet loss cases with certain reasonable accuracy, and provides good generalization ability, which turns out outperform the state-of-art image and video quality assessment methods.
Ma, XY, Dong, K, Tang, L, Wang, Y, Wang, XC, Ngo, HH, Chen, R & Wang, N 2020, 'Investigation and assessment of micropollutants and associated biological effects in wastewater treatment processes', Journal of Environmental Sciences, vol. 94, pp. 119-127.
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Currently, the wastewater treatment plants (WWTPs) attempt to achieve the shifting from general pollution parameters control to reduction of organic micropollutants discharge. However, they have not been able to satisfy the increasing ecological safety needs. In this study, the removal of micropollutants was investigated, and the ecological safety was assessed for a local WWTP. Although the total concentration of 31 micropollutants detected was reduced by 83% using the traditional biological treatment processes, the results did not reflect chemicals that had poor removal efficiencies and low concentrations. Of the five categories of micropollutants, herbicides, insecticides, and bactericides were difficult to remove, pharmaceuticals and UV filters were effectively eliminated. The specific photosynthesis inhibition effect and non-specific bioluminescence inhibition effect from wastewater were detected and evaluated using hazardous concentration where 5% of aquatic organisms are affected. The photosynthesis inhibition effect from wastewater in the WWTP was negligible, even the untreated raw wastewater. However, the bioluminescence inhibition effect from wastewater which was defined as the priority biological effect, posed potential ecological risk. To decrease non-specific biological effects, especially of macromolecular dissolved organic matter, overall pollutant reduction strategy is necessary. Meanwhile, the ozonation process was used to further decrease the bioluminescence inhibition effects from the secondary effluent; ≥ 0.34 g O3/g DOC of ozone dose was recommended for micropollutants elimination control and ecological safety.
Mahlia, TMI, Syazmi, ZAHS, Mofijur, M, Abas, AEP, Bilad, MR, Ong, HC & Silitonga, AS 2020, 'Patent landscape review on biodiesel production: Technology updates', Renewable and Sustainable Energy Reviews, vol. 118, pp. 109526-109526.
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© 2019 Elsevier Ltd Biodiesel is a renewable fuel made from vegetable oils and animal fats. Compared with fossil fuels, it has the potential to alleviate environmental pressures and achieve sustainable development. In this paper, 1660 patents related to biodiesel production were reviewed. They were published between January 1999 and July 2018 and were retrieved from the Derwent Innovation patent database. The patents were grouped into five categories depending on whether they related to starting materials, pre-treatment methods, catalysts, reactors and processing methods, or testing methods. Their analysis shows that the availability of biodiesel starting materials depends on climate, geographical location, local soil conditions, and local agricultural practices. Starting materials constitute 75% of overall production costs and, therefore, it is crucial to select the best feedstock. Pre-treatment of feedstock can improve its suitability for processing and increase extraction effectiveness and oil yield. Catalysts can enhance the solubility of alcohol, leading to higher reaction rates, faster biodiesel production processes, and lower biodiesel production costs. Moreover, the apparatus and processes used strongly affect the oil yield and quality, and production cost. In order to be commercialized and marketed, biodiesel should pass either the American Society for Testing and Materials (ASTM) standards or European Standards (EN). Due to increases in environmental awareness, it is likely that the number of published patents on biodiesel production will remain stable or even increase.
Mahmood, AH, Foster, SJ & Castel, A 2020, 'Development of high-density geopolymer concrete with steel furnace slag aggregate for coastal protection structures', Construction and Building Materials, vol. 248, pp. 118681-118681.
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© 2020 Elsevier Ltd Anticipated changes in coastal wave conditions due to various climate change impact scenarios along coastlines may expose coastal protection structures to greater wave energies and higher damage rates than designed for, especially during episodic storm events. Some existing coastal breakwaters need upgrading to withstand the projected conditions. Breakwater armour unit design equations and physical model tests predict a large gain in stability with a modest increase in the armour material density and indicate reduced armour unit size requirements when utilising high-density concrete. In this study, a high-density geopolymer concrete mix with steel furnace slag (SFS) aggregate was developed based on several trials; the material properties were evaluated for on-site applications under ambient curing conditions. The use of SFS aggregate offers higher bulk density to concrete and mixes were proportioned to achieve good workability and setting time. Most importantly, the fly ash-blast furnace slag blended binder used in this study leads to adequate strength gain in ambient curing and allows the diffusion of the free lime associated with the SFS aggregate into the geopolymer matrix to eliminate the delayed hydration and expansion of the aggregate. This research provides a pathway to both upgradings of existing breakwaters and construction of new structures with a reduction to the carbon footprint in breakwater construction.
Mahmood, AH, Foster, SJ & Castel, A 2020, 'High-density geopolymer concrete for Port Kembla breakwater upgrade', Construction and Building Materials, vol. 262, pp. 120920-120920.
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© 2020 Elsevier Ltd Marine environments can pose continuous threats to coastal infrastructure, especially during episodic storm events. Thus, monitoring and maintenance of rock or concrete units-armoured coastal breakwaters are required, especially under present climate change scenarios, where changes in wave behaviour are anticipated and coastal structures are likely to be exposed to even greater wave energy resulting in higher rates of damage. A possible pathway towards the upgrading of existing breakwaters is to introduce high-density armour units, suggested by theoretical estimates and physical model test findings. In this project, a unique, sustainable high-density geopolymer concrete (GPC) mix was developed and trialled in fabricating armour units for upgrading existing coastal breakwaters. The system developed in laboratories was upscaled for field applications and is being tested at the Northern breakwater of NSW Ports’ Port Kembla Harbour. The concrete uses steel furnace slag (SFS) aggregate in an alkali-activated blended fly ash-blast furnace slag binder proportioned to facilitate the elimination of delayed expansion of the aggregate. The concrete properties were measured and microstructural analyses undertaken. The results show that SFS aggregate offers higher bulk density to the concrete alongside satisfactory strength in on-site curing and can reduce armour mass requirements at the same level of structural stability. Microstructural analyses confirm the elimination of SFS aggregate free-lime expansion. This important result provides a novel approach to both repair of existing structures and construction of new structures with reductions to both cost and carbon footprint.
Mahmoudi, T, Pirpour Tazehkand, A, Pourhassan-Moghaddam, M, Alizadeh-Ghodsi, M, Ding, L, Baradaran, B, Razavi Bazaz, S, Jin, D & Ebrahimi Warkiani, M 2020, 'PCR-free paper-based nanobiosensing platform for visual detection of telomerase activity via gold enhancement', Microchemical Journal, vol. 154, pp. 104594-104594.
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© 2020 Elsevier B.V. Telomerase activity has been demonstrated in a wide variety of most solid tumors and considered as a well-known cancer biomarker. The commonly utilized method for its detection is polymerase chain reaction (PCR)-based telomeric repeat amplification protocol (TRAP). However, the TRAP technique suffers from false-negative results caused by the failure of PCR step. Moreover, it requires advanced equipment with a tedious and time-consuming procedure. Herein, we presented a portable nitrocellulose paper-based nanobiosensing platform for ultrafast and equipment-free detection of telomerase activity based on a simple colorimetric assay that enabled naked-eye visualization of the color change in response to enzyme activity. In this platform, hybridization was initially performed between telomere complementary oligonucleotide immobilized on gold nanoparticles (GNPs) and telomerase elongated biotinylated probe. Thereafter, the assembly was attached on activated paper strip via avidin-biotin interaction. The signal amplification was carried out by enlargement of the attached GNPs on the paper strip, forming tightly compact rod-shaped submicron structures of gold representing a visual color formation. Thanks to significant sensitivity enhancement, the color change was occurred for down to 6 cells, which can be easily observed by the naked eye. Due to the desired aspects of the developed assay including PCR-free, low cost, simple, and high sensitivity, it can be used for evaluation of telomerase activity in cell extracts for future clinical applications. Furthermore, this design has the ability to be easily integrated into lab-on-chip devices for point-of-care telomerase sensing.
Mahmoudi, Z, Mohammadnejad, J, Razavi Bazaz, S, Abouei Mehrizi, A, Saidijam, M, Dinarvand, R, Ebrahimi Warkiani, M & Soleimani, M 2020, 'Promoted chondrogenesis of hMCSs with controlled release of TGF-β3 via microfluidics synthesized alginate nanogels', Carbohydrate Polymers, vol. 229, pp. 115551-115551.
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The field of cartilage tissue engineering has been evolved in the last decade and a myriad of scaffolding biomaterials and bioactive agents have been proposed. Controlled release of growth factors encapsulated in the polymeric nanomaterials has been of interest notably for the repair of damaged articular cartilage. Here, we proposed an on-chip hydrodynamic flow focusing microfluidic approach for synthesis of alginate nanogels loaded with the transforming growth factor beta 3 (TGF-β3) through an ionic gelation method in order to achieve precise release profile of these bioactive agents during chondrogenic differentiation of mesenchymal stem cells (MSCs). Alginate nanogels with adjustable sizes were synthesized by fine-tuning the flow rate ratio (FRR) in the microfluidic device consisting of cross-junction microchannels. The result of present study showed that the proposed approach can be a promising tool to synthesize bioactive -loaded polymeric nanogels for applications in drug delivery and tissue engineering.
Mahmud, K, Nizami, MSH, Ravishankar, J, Hossain, MJ & Siano, P 2020, 'Multiple Home-to-Home Energy Transactions for Peak Load Shaving', IEEE Transactions on Industry Applications, vol. 56, no. 2, pp. 1074-1085.
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Mahmud, K, Rahman, MS, Ravishankar, J, Hossain, MJ & Guerrero, JM 2020, 'Real-Time Load and Ancillary Support for a Remote Island Power System Using Electric Boats', IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1516-1528.
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Mahmud, K, Ravishankar, J & Hossain, J 2020, 'Rebound behaviour of uncoordinated EMS and their impact minimisation', IET Smart Grid, vol. 3, no. 2, pp. 237-245.
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In this paper, the impacts of uncoordinated energy management systems (EMS), with a rebound effect, on a renewable energy‐dependent microgrid are discussed and feasible solutions are presented. Two different approaches, i.e. load‐based and price‐based EMS are modelled which consider PV units, battery energy storage systems (BESS), and electric vehicles (EVs). Taking account of each component's boundary conditions, the load‐based approach intelligently charges the EV and BESS from the grid/PV during off‐peak hours, and provides a combined discharge response during peak load hours. In the price‐based approach, the charging‐discharging of BESSs and EVs from/to grid and PV depends on the time‐of‐use tariff signal. The primary objective of both models is to minimise the customers' peak electricity consumption and the saturation issues of distribution transformers. It is observed that the simultaneous response of the EMS due to the identical behaviour of load or price curves, and the rebound effect after mode switching transition create large power demand spikes. To mitigate its negative consequence, an improved locking and randomisation technique is designed and implemented. Additionally, the impact of the PV power fluctuations on the load‐support systems due to fast‐moving clouds and their consequences to the behaviour of the EMS response are investigated.
Mahmud, K, Ravishankar, J, Hossain, MJ & Dong, ZY 2020, 'The Impact of Prediction Errors in the Domestic Peak Power Demand Management', IEEE Transactions on Industrial Informatics, vol. 16, no. 7, pp. 4567-4579.
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Majid, ESA, Garcia, JA, Nordin, AI & Raffe, WL 2020, 'Staying Motivated During Difficult Times: A Snapshot of Serious Games for Paediatric Cancer Patients', IEEE Transactions on Games, vol. 12, no. 4, pp. 367-375.
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© 2018 IEEE. Research on the use of digital games for cancer patients suggests positive impact in the form of the reduction of depressive symptoms, anxiety, and the feeling of nausea after chemotherapy treatment. This can take the childs focus off their condition and their treatment process and direct it toward other aspects of their childhood. In this article, a comprehensive review of the current literature was conducted to assess how serious games could positively impact paediatric cancer patients. Inclusion criteria were used during data extraction to find the most relevant literature, including the need for a game prototype to have been developed and for the game to specifically target children with cancer as a target audience. Data were extracted including age ranges, treatment and procedure plan, time context, users, purpose, and technology. The resulting serious games were grouped based on their purpose and were classified in three main categories; motivation, education, and distraction. This review demonstrates the positive use of serious games as an intervention for paediatric cancer patients that undergo treatment in hospital. The results suggest that the design of these serious games should consider the purpose of the game within the treatment plan of target audience; the accessibility and suitability of the technology used for the game; and social connection during play.
Makhdoom, I, Zhou, I, Abolhasan, M, Lipman, J & Ni, W 2020, 'PrivySharing: A blockchain-based framework for privacy-preserving and secure data sharing in smart cities', Computers & Security, vol. 88, pp. 101653-101653.
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© 2019 Elsevier Ltd The ubiquitous use of Internet of Things (IoT) ranges from industrial control systems to e-Health, e-commerce, smart cities, agriculture, supply chain management, smart cars, cyber-physical systems and a lot more. However, the data collected and processed by IoT systems especially the ones with centralized control are vulnerable to availability, integrity, and privacy threats. Hence, we present “PrivySharing,” a blockchain-based innovative framework for privacy-preserving and secure IoT data sharing in a smart city environment. The proposed scheme is distinct from existing strategies on many aspects. The data privacy is preserved by dividing the blockchain network into various channels, where every channel comprises a finite number of authorized organizations and processes a specific type of data such as health, smart car, smart energy or financial details. Moreover, access to users’ data within a channel is controlled by embedding access control rules in the smart contracts. In addition, data within a channel is further isolated and secured by using private data collection and encryption respectively. Likewise, the REST API that enables clients to interact with the blockchain network has dual security in the form of an API Key and OAuth 2.0. The proposed solution conforms to some of the significant requirements outlined in the European Union General Data Protection Regulation. We also present a system of reward in the form of a digital token named “PrivyCoin” for users sharing their data with stakeholders/third parties. Lastly, the experimental outcomes advocate that a multi-channel blockchain scales well as compared to a single-channel blockchain system.
Maldonado, S, Merigo, J & Miranda, J 2020, 'IOWA-SVM: A Density-Based Weighting Strategy for SVM Classification via OWA Operators', IEEE Transactions on Fuzzy Systems, vol. 28, no. 9, pp. 2143-2150.
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© 1993-2012 IEEE. A weighting strategy for handling outliers in binary classification using support vector machine (SVM) is proposed in this article. The traditional SVM model is modified by introducing an induced ordered weighted averaging (IOWA) operator, in which the hinge loss function becomes an ordered weighted sum of the SVM slack variables. These weights are defined using IOWA quantifiers, while the order is induced via fuzzy density-based methods for outlier detection. The proposal is developed for both linear and kernel-based classification using the duality theory and the kernel trick. Our experimental results on well known benchmark datasets demonstrate the virtues of the proposed IOWA-SVM, which achieved the best average performance compared to other machine learning approaches of similar complexity.
Malik, N, Nanda, P, He, X & Liu, RP 2020, 'Vehicular networks with security and trust management solutions: proposed secured message exchange via blockchain technology', Wireless Networks, vol. 26, no. 6, pp. 4207-4226.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. In vehicular ad hoc networks (VANET), effective trust establishment with authentication is an important requirement. Trust management among communicating vehicles is significant for secure message transmission; however, very less contributions have been made towards evaluating the trustworthiness of the node. This research work intends to introduce a new trust management system in VANET with two major phases: secured message transmission and node trustability prediction. The security assured message passing is carried out by incorporating the privacy preservation model under the data sanitization process. The key used for the sanitization process is optimally tuned by a new hybrid algorithm termed Sea Lion Explored-Whale Optimization Algorithm, which is the combination of Whale Optimization Algorithm and Sea Lion Optimization Algorithm, respectively. The blockchain technology is assisted to handle the key generated by the nodes. Subsequently, the trustability of the node is evaluated under novel specifics “two-level evaluation process” with a rule-based and machine learning-based evaluation process. Finally, the performance of the proposed model is verified and proved over other conventional methods for certain measures.
Malisetty, RS, Indraratna, B & Vinod, J 2020, 'Behaviour of ballast under principal stress rotation: Multi-laminate approach for moving loads', Computers and Geotechnics, vol. 125, pp. 103655-103655.
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© 2020 Elsevier Ltd Railway tracks are subjected to millions of loading cycles over time and at high speeds, these moving trains induce dynamic amplification of vertical stresses and rotation of principal stress axes in the track layers. It is important to predict and analyse the behaviour of ballast under these loads with complex stress paths involving principal stress rotation. In this paper, a constitutive model based on a multi-laminate framework is used to predict the deformation and degradation of ballast under complex stress paths. The yield and plastic potential surfaces are developed based on a non-linear critical state and bounding surface plasticity concepts. The proposed model is validated with independent test data to capture the influence of confining stress, loading frequency, Cyclic Stress Ratio (CSR) and Shear Stress Ratio (ητ) on the permanent strain response. Furthermore, the response of ballast under traffic loading stress paths with different CSR and ητ is analysed. These model predictions show that higher CSRand ητ values lead to exacerbated particle breakage of ballast, large and unstable axial strains and dilatant volumetric strains. Furthermore, a stability surface is proposed based on model predictions, to estimate the allowable CSR and ητ for a stable response.
Malisetty, RS, Indraratna, B & Vinod, JS 2020, 'Multilaminate Mathematical Framework for Analyzing the Deformation of Coarse Granular Materials', International Journal of Geomechanics, vol. 20, no. 6, pp. 06020004-06020004.
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© 2020 American Society of Civil Engineers. Coarse granular materials such as railway ballast and rockfill are often subjected to three-dimensional (3D) stress conditions including the influence of intermediate principal stress. Modeling the deformation and breakage of these materials under the presence of intermediate principal stress is important for assessing their long-term performance. This paper presents a mathematical model to describe the mechanical behavior of granular materials incorporating the intermediate principal stress and capture particle breakage. The model formulation encompasses interparticle contact planes using a multilaminate mathematical framework based on generalized plasticity and associated critical state concepts. The model that has been calibrated based on recent experimental data on latite basalt, captures the stress-strain and volumetric strain behavior for a range of confining pressures under triaxial compression. This paper also describes the influence of intermediate principal stress on the strength and deformation response of selected granular materials following 3D stress paths. It is evident from the results that the current modeling technique successfully captured the effects of particle breakage, intermediate principal stress, and confining pressure on the shear behavior of various granular assemblies. The results also highlight the influence of intermediate principal stress in reducing the peak deviatoric strength of the material. The model predictions were validated using four independent sets of past experimental data on crushed basalt, limestone, sandstone, and granite aggregates.
Mani, N, Rifai, A, Houshyar, S, Booth, MA & Fox, K 2020, 'Diamond in medical devices and sensors: An overview of diamond surfaces', MEDICAL DEVICES & SENSORS, vol. 3, no. 6.
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AbstractSignificant challenges arise when the human body is damaged, diseased and unable to repair itself. Current biomaterials for biomedical devices have limitations to restore function, while materials for implants and sensors often invoke a large foreign body response. Therefore, there is a need to develop suitable biomaterials in the fields of medical devices and sensors. Diamond is emerging due to its many favourable properties including biocompatibility, antimicrobial capability, antifouling properties, electrical conductivity and chemical functionalization capability. Thin film coatings of diamond can be fabricated by chemical vapour deposition, or by particle coatings with nanodiamond materials. Hybrid/composite diamond materials include soft materials such as those processed by electrospinning and melt extrusion, as well as hard materials such as those processed by additive manufacturing. Additive manufacturing is a developing area for diamond biomaterial fabrication and can include both hard and soft materials. The fabrication method used will depend on the properties required of the biomaterial, as well as the application. In this mini‐review, recent progress on using diamond in medical devices and sensors is outlined, with particular emphasis on fabrication methods. We highlight selected applications from recent literature and, in closing, make comments and suggestions to advance the field and direction of diamond application in medical devices and sensors.
Manikandan, R, Patan, R, Gandomi, AH, Sivanesan, P & Kalyanaraman, H 2020, 'Hash polynomial two factor decision tree using IoT for smart health care scheduling', Expert Systems with Applications, vol. 141, pp. 112924-112924.
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© 2019 The steady growth of an aging population and increased frequency of chronic disease led to the development of Smart Health Care (SHC) systems. While patient prioritization is the core of any SHC system, handling the response time by medical practitioners is a prevailing challenge. With advancements in information technology, the concept of the Internet of Things (IoT) has made it possible to integrate SHC systems with the Cloud environment to not only ensure patient prioritization according to disease prevalence, but also to minimize response time. In this work, an IoT-based scheduling method, called the Hash Polynomial Two-factor Decision Tree (HP-TDT) is proposed to increase scheduling efficiency and reduce response time by classifying patients as being normal or in a critical state in minimal time. The HP-TDT scheduling method involves three stages including the registration stage, the data collection stage, and the scheduling stage. The registration phase is carried out through Open Address Hashing (OAH) model for reducing the key generation response time. Next, the data collection stage is performed using the Polynomial Data Collection (PDC) algorithm. By incorporating PDC, computation overhead is reduced because a number of operations are considered during data collection. Finally, scheduling is performed by applying two-factor, entropy and information gain according to a decision tree. With this, scheduling efficiency is improved due to the classification of patients as being normal or in a critical state. The proposed method minimizes response time, computational overhead, and improves essential scheduling efficiency.
Mann, RL, Mathieson, L & Greenhill, C 2020, 'On the Parameterised Complexity of Induced Multipartite Graph Parameters'.
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We introduce a family of graph parameters, called induced multipartite graphparameters, and study their computational complexity. First, we consider thefollowing decision problem: an instance is an induced multipartite graphparameter $p$ and a given graph $G$, and for natural numbers $k\geq2$ and$\ell$, we must decide whether the maximum value of $p$ over all induced$k$-partite subgraphs of $G$ is at most $\ell$. We prove that this problem isW[1]-hard. Next, we consider a variant of this problem, where we must decidewhether the given graph $G$ contains a sufficiently large induced $k$-partitesubgraph $H$ such that $p(H)\leq\ell$. We show that for certain parameters thisproblem is para-NP-hard, while for others it is fixed-parameter tractable.
Mannina, G, Ni, B-J, Ferreira Rebouças, T, Cosenza, A & Olsson, G 2020, 'Minimizing membrane bioreactor environmental footprint by multiple objective optimization', Bioresource Technology, vol. 302, pp. 122824-122824.
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This paper presents a modelling study aimed at minimizing the environmental foot print of a membrane bioreactor (MBR) for wastewater treatment. Specifically, an integrated model for MBR was employed in view of the management optimization of an MBR biological nutrient removal (BNR) pilot plant in terms of operational costs and direct greenhouse gases emissions. The influence of the operational parameters (OPs) on performance indicators (PIs) was investigated by adopting the Extended-FAST sensitivity analysis method. Further, a multi-objective analysis was performed by applying the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results show-up that the sludge retention time is the OP mostly affecting all the investigated PIs. By applying the set of optimal OPs, there was a reduction of 48% and 10% of the operational costs and direct emissions, respectively.
Manuel, AJ, Deverajan, GG, Patan, R & Gandomi, AH 2020, 'Optimization of Routing-Based Clustering Approaches in Wireless Sensor Network: Review and Open Research Issues', Electronics, vol. 9, no. 10, pp. 1630-1630.
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In today’s sensor network research, numerous technologies are used for the enhancement of earlier studies that focused on cost-effectiveness in addition to time-saving and novel approaches. This survey presents complete details about those earlier models and their research gaps. In general, clustering is focused on managing the energy factors in wireless sensor networks (WSNs). In this study, we primarily concentrated on multihop routing in a clustering environment. Our study was classified according to cluster-related parameters and properties and is subdivided into three approach categories: (1) parameter-based, (2) optimization-based, and (3) methodology-based. In the entire category, several techniques were identified, and the concept, parameters, advantages, and disadvantages are elaborated. Based on this attempt, we provide useful information to the audience to be used while they investigate their research ideas and to develop a novel model in order to overcome the drawbacks that are present in the WSN-based clustering models.
Manzoor, H, Selam, MA, Adham, S, Shon, HK, Castier, M & Abdel-Wahab, A 2020, 'Energy recovery modeling of pressure-retarded osmosis systems with membrane modules compatible with high salinity draw streams', Desalination, vol. 493, pp. 114624-114624.
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Mao, Y, Jianxi, Y, Ji, J, Xu, W & Guo, Q 2020, 'An analytical solution of Reynolds equation for evaluating the characteristics of surface textured bearing', Industrial Lubrication and Tribology, vol. 72, no. 9, pp. 1075-1085.
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PurposeCurrently, there is a lack of fast and highly accurate on analytical solution of Reynolds equation for evaluating the characteristics of surface textured bearing. This paper aims to develop such an analytical solution of Reynolds equation for an effective analysis of the characteristics of surface textured bearings.Design/methodology/approachBy using the separation of variables method and mean eigenvalue method, the analytical solution is constructed. The CFD simulations and experimental results are used to validate the correctness of the analytical solution.FindingsThe analytical solution can accurately evaluate the characteristics of textured bearings. It is found that the larger the eccentricity ratio and aspect ratio, the greater the oil film force. It also found that the smaller the eccentricity ratio, the larger the Sommerfeld number S. When eccentricity ratio e = 0.65, the attitude angles of different oil boundaries are same. The effect of different aspect ratios on dynamic stiffness and damping coefficient generally follows a same trend. It is numerically shown that the critical speed of rotor-bearing is 3500 rpm.Originality/valueThe analytical solution provides a simple yet effective way to study the characteristics of surface textured bearings.
Marin, JG, Baba, AA, Hesselbarth, J, Hashmi, RM & Esselle, KP 2020, 'Millimeter-Wave Low-Loss Multifeed Superstrate-Based Antenna', IEEE Transactions on Antennas and Propagation, vol. 68, no. 5, pp. 3387-3396.
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Marjanovic, O & Cecez-Kecmanovic, D 2020, 'Open government data platforms – A complex adaptive sociomaterial systems perspective', Information and Organization, vol. 30, no. 4, pp. 100323-100323.
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Martínez-López, FJ, Merigó, JM, Gázquez-Abad, JC & Ruiz-Real, JL 2020, 'Industrial marketing management: Bibliometric overview since its foundation', Industrial Marketing Management, vol. 84, pp. 19-38.
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© 2019 Elsevier Inc. Industrial Marketing Management (IMM) is an outstanding journal in the field of business-to-business marketing. This paper focuses on this journal, with an extensive bibliometric analysis of IMM from its foundation in 1971 to 2017, the last year analyzed in this study. It identifies, among others, the annual evolution of publications, the most influential countries, the most relevant authors, the most prominent institutions supporting research, as well as the citations of IMM papers in major marketing, but also other, business and management journals. To do so, this research uses the Web of Science Core Collection and Scopus databases, and analyzes a wide range of bibliometric indicators, including the total number of publications and citations, citations per paper, the h-index, m-value and citation thresholds, and also develops a graphical analysis of the bibliographical material using the visualization of similarities (VOS) viewer software. Finally, by applying a cluster analysis by fractional accounting, this research identifies trends and proposes future topics and research lines, such as: trust, innovation, performance, relationship marketing, the future role of new technologies in industrial marketing research, online marketing and corporate image.
Mateos, MK, Tulstrup, M, Quinn, MCJ, Tuckuviene, R, Marshall, GM, Gupta, R, Mayoh, C, Wolthers, BO, Barbaro, PM, Ruud, E, Sutton, R, Huttunen, P, Revesz, T, Trakymiene, SS, Barbaric, D, Tedgård, U, Giles, JE, Alvaro, F, Jonsson, OG, Mechinaud, F, Saks, K, Catchpoole, D, Kotecha, RS, Dalla-Pozza, L, Chenevix-Trench, G, Trahair, TN, MacGregor, S & Schmiegelow, K 2020, 'Genome-Wide Association Meta-Analysis of Single-Nucleotide Polymorphisms and Symptomatic Venous Thromboembolism during Therapy for Acute Lymphoblastic Leukemia and Lymphoma in Caucasian Children', Cancers, vol. 12, no. 5, pp. 1285-1285.
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Symptomatic venous thromboembolism (VTE) occurs in five percent of children treated for acute lymphoblastic leukemia (ALL), but whether a genetic predisposition exists across different ALL treatment regimens has not been well studied. Methods: We undertook a genome-wide association study (GWAS) meta-analysis for VTE in consecutively treated children in the Nordic/Baltic acute lymphoblastic leukemia 2008 (ALL2008) cohort and the Australian Evaluation of Risk of ALL Treatment-Related Side-Effects (ERASE) cohort. A total of 92 cases and 1481 controls of European ancestry were included. Results: No SNPs reached genome-wide significance (p < 5 × 10−8) in either cohort. Among the top 34 single-nucleotide polymorphisms (SNPs) (p < 1 × 10−6), two loci had concordant effects in both cohorts: ALOX15B (rs1804772) (MAF: 1%; p = 3.95 × 10−7) that influences arachidonic acid metabolism and thus platelet aggregation, and KALRN (rs570684) (MAF: 1%; p = 4.34 × 10−7) that has been previously associated with risk of ischemic stroke, atherosclerosis, and early-onset coronary artery disease. Conclusion: This represents the largest GWAS meta-analysis conducted to date associating SNPs to VTE in children and adolescents treated on childhood ALL protocols. Validation of these findings is needed and may then lead to patient stratification for VTE preventive interventions. As VTE hemostasis involves multiple pathways, a more powerful GWAS is needed to detect combination of variants associated with VTE.
Maxit, L, Guasch, O, Meyer, V & Karimi, M 2020, 'Noise radiated from a periodically stiffened cylindrical shell excited by a turbulent boundary layer', Journal of Sound and Vibration, vol. 466, pp. 115016-115016.
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© 2019 Elsevier Ltd This work proposes a semi-analytical method to model the vibroacoustic behavior of submerged cylindrical shells periodically stiffened by axisymmetric frames and excited by a homogeneous and fully developed turbulent boundary layer (TBL). The process requires the computation of the TBL wall-pressure cross spectral density function and the sensitivity functions for stiffened cylindrical shells. The former is deduced from an existent TBL model and the latter are derived from a wavenumber-point reciprocity principle and a spectral formulation of the problem. The stiffeners' dynamic behavior is introduced in the formulation through circumferential admittances that are computed by a standard finite element code using shell elements. Four degrees of freedom are taken into account for the coupling between the shell and the stiffeners: three translation directions and one tangential rotation. To investigate the effect of the stiffeners on the radiated noise, two case studies are considered. The first one examines a fluid-loaded cylindrical shell with regularly spaced simple supports. The influence of Bloch-Floquet waves and the support spacing on the noise radiation are highlighted. The second case study inspects the fluid-loaded cylindrical shell with two different periodic ring stiffeners, namely stiffeners with T-shaped and I-shaped cross-sections. Their influence on the vibroacoustics of the shell is thoroughly analyzed.
Maxit, L, Karimi, M, Meyer, V & Kessissoglou, N 2020, 'Vibroacoustic responses of a heavy fluid loaded cylindrical shell excited by a turbulent boundary layer', Journal of Fluids and Structures, vol. 92.
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Mazzurco, A & Daniel, S 2020, 'JEE Selects: Where expertise counts in humanitarian engineering', ASEE Prism, vol. 29, pp. 49-50.
Mazzurco, A & Daniel, S 2020, 'Socio‐technical thinking of students and practitioners in the context of humanitarian engineering', Journal of Engineering Education, vol. 109, no. 2, pp. 243-261.
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AbstractBackgroundHumanitarian engineering (HE) is rapidly emerging in universities and professional workplaces worldwide. In HE, socio‐technical thinking is fundamental as HE projects exist at the intersection of engineering and sustainable community development. However, the literature still lacks an understanding of the key features of socio‐technical thinking.Purpose/HypothesisThe purpose of this article is to investigate the key characteristics that distinguish the socio‐technical thinking of an expert from a novice in the context of HE projects.Design/MethodWe distributed the Energy Conversion Playground (ECP) design task to students starting their engineering degree (n = 26) and practitioners (n = 16). We iteratively and inductively analyzed the responses to develop a rubric characterizing the key features of expert socio‐technical thinking. We then scored participants' responses and compared them to identify differences between students and practitioners.ResultsThe analysis showed that expert socio‐technical thinkers can provide high‐quality considerations across three domains: technology, people, and broader context. The comparison of the participants' scores showed that both students and practitioners scored highly in the technology domain. In contrast, students scored poorly in the people and broader contexts domains, identifying only simplistic considerations in these non‐technical areas, if at all.ConclusionsThis s...
McCarthy, PX, Gong, X, Eghbal, S, Falster, DS & Rizoiu, M-A 2020, 'Evolution of diversity and dominance of companies in online activity', PLOS ONE, 16(4), e0249993, 2021.
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Ever since the web began, the number of websites has been growingexponentially. These websites cover an ever-increasing range of online servicesthat fill a variety of social and economic functions across a growing range ofindustries. Yet the networked nature of the web, combined with the economics ofpreferential attachment, increasing returns and global trade, suggest that overthe long run a small number of competitive giants are likely to dominate eachfunctional market segment, such as search, retail and social media. Here weperform a large scale longitudinal study to quantify the distribution ofattention given in the online environment to competing organisations. In twolarge online social media datasets, containing more than 10 billion posts andspanning more than a decade, we tally the volume of external links postedtowards the organisations' main domain name as a proxy for the online attentionthey receive. We also use the Common Crawl dataset -- which contains thelinkage patterns between more than a billion different websites -- to study thepatterns of link concentration over the past three years across the entire web.Lastly, we showcase the linking between economic, financial and market data byexploring the relationships between online attention on social media and thegrowth in enterprise value in the electric carmaker Tesla. Our analysis showsthat despite the fact that we observe consistent growth in all the macroindicators -- the total amount of online attention, in the number oforganisations with an online presence, and in the functions they perform -- wealso observe that a smaller number of organisations account for anever-increasing proportion of total user attention, usually with one largeplayer dominating each function. These results highlight how evolution of theonline economy involves innovation, diversity, and then competitive dominance.
McCauley, JI, Labeeuw, L, Jaramillo-Madrid, AC, Nguyen, LN, Nghiem, LD, Chaves, AV & Ralph, PJ 2020, 'Management of Enteric Methanogenesis in Ruminants by Algal-Derived Feed Additives', Current Pollution Reports, vol. 6, no. 3, pp. 188-205.
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© 2020, Springer Nature Switzerland AG. Purpose of Review: In this paper, we critically review the current state of nutritional management strategies to reduce methane emissions resulting from enteric fermentation in livestock production. In this context, it highlights the novel strategy regarding the use of macroalgal- and microalgal-derived feed additives. Recent Findings: Several feed management strategies for ruminants focus on the inclusion of nutritional supplements, increasing proportion of starch, or supplementation with high-energy lipids. These strategies aim to improve animal productivity, whilst at the same time reduce methane emissions. Algae supplements are currently investigated as novel ingredients for decreasing methanogenesis, with the potential production of algal biomass also contributing to reducing greenhouse gas emissions. Thus, utilisation of algal biomass as a feed concentrate in dietary supplementation presents a sustainable and environmentally friendly strategy. Summary: This review summarises the current stage of research on dietary strategies and their influences on the metabolic processes during enteric fermentation. This information is essential for developing strategies to mitigate methane emissions in the livestock industry. We specifically present the opportunities that algae could offer as a feed additive for methanogenic reduction in cattle. The data compiled from the peer-reviewed literature revealed synergistic effects of algal biomass on methane reduction and animal productivity. However, the challenges regarding the mass cultivation of macro- and microalgae were noticed. Considering the diversity of algal species, future research should increase screening efforts to include more species and dosage evaluation, along with efforts to see if such effects are sustained over time.
McConnell, D, Hale, CL, Lenc, E, Banfield, JK, Heald, G, Hotan, AW, Leung, JK, Moss, VA, Murphy, T, O’Brien, A, Pritchard, J, Raja, W, Sadler, EM, Stewart, A, Thomson, AJM, Whiting, M, Allison, JR, Amy, SW, Anderson, C, Ball, L, Bannister, KW, Bell, M, Bock, DC-J, Bolton, R, Bunton, JD, Chippendale, AP, Collier, JD, Cooray, FR, Cornwell, TJ, Diamond, PJ, Edwards, PG, Gupta, N, Hayman, DB, Heywood, I, Jackson, CA, Koribalski, BS, Lee-Waddell, K, McClure-Griffiths, NM, Ng, A, Norris, RP, Phillips, C, Reynolds, JE, Roxby, DN, Schinckel, AET, Shields, M, Tremblay, C, Tzioumis, A, Voronkov, MA & Westmeier, T 2020, 'The Rapid ASKAP Continuum Survey I: Design and first results', Publications of the Astronomical Society of Australia, vol. 37.
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AbstractThe Rapid ASKAP Continuum Survey (RACS) is the first large-area survey to be conducted with the full 36-antenna Australian Square Kilometre Array Pathfinder (ASKAP) telescope. RACS will provide a shallow model of the ASKAP sky that will aid the calibration of future deep ASKAP surveys. RACS will cover the whole sky visible from the ASKAP site in Western Australia and will cover the full ASKAP band of 700–1800 MHz. The RACS images are generally deeper than the existing NRAO VLA Sky Survey and Sydney University Molonglo Sky Survey radio surveys and have better spatial resolution. All RACS survey products will be public, including radio images (with$\sim$15 arcsec resolution) and catalogues of about three million source components with spectral index and polarisation information. In this paper, we present a description of the RACS survey and the first data release of 903 images covering the sky south of declination$+41^\circ$made over a 288-MHz band centred at 887.5 MHz.
McCourt, LR, Ruppert, MG, Routley, BS, Indirathankam, SC & Fleming, AF 2020, 'A comparison of gold and silver nanocones and geometry optimisation for tip‐enhanced microscopy', Journal of Raman Spectroscopy, vol. 51, no. 11, pp. 2208-2216.
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AbstractIn this article, boundary element method simulations are used to optimise the geometry of silver and gold nanocone probes to maximise the localised electric field enhancement and tune the near‐field resonance wavelength. These objectives are expected to maximise the sensitivity of tip‐enhanced Raman microscopes. Similar studies have used limited parameter sets or used a performance metric other than localised electric field enhancement. In this article, the optical responses for a range of nanocone geometries are simulated for excitation wavelengths ranging from 400 to 1000 nm. Performance is evaluated by measuring the electric field enhancement at the sample surface with a resonant illumination wavelength. These results are then used to determine empirical models and derive optimal nanocone geometries for a particular illumination wavelength and tip material. This article concludes that gold nanocones are expected to provide similar performance to silver nanocones at red and near‐infrared wavelengths, which is consistent with other results in the literature. In this article, 633 nm is determined to be the shortest usable illumination wavelength for gold nanocones. Below this limit, silver nanocones will provide superior enhancement. The use of gold nanocone probes is expected to dramatically improve probe lifetime, which is currently measured in hours for silver coated probes. Furthermore, the elimination of passivation coatings is expected to enable smaller probe radii and improved topographical resolution.
Medawela, S & Indraratna, B 2020, 'Computational modelling to predict the longevity of a permeable reactive barrier in an acidic floodplain', Computers and Geotechnics, vol. 124, pp. 103605-103605.
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Meena, NK, Nimbalkar, S, Fatahi, B & Yang, G 2020, 'Effects of soil arching on behavior of pile-supported railway embankment: 2D FEM approach', Computers and Geotechnics, vol. 123, pp. 103601-103601.
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Mehmood, A, Zameer, A, Chaudhary, NI, Ling, SH & Raja, MAZ 2020, 'Design of meta-heuristic computing paradigms for Hammerstein identification systems in electrically stimulated muscle models.', Neural Comput. Appl., vol. 32, no. 16, pp. 12469-12497.
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Mehmood, A, Zameer, A, Ling, SH, Rehman, AU & Raja, MAZ 2020, 'Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming.', Neural Comput. Appl., vol. 32, no. 14, pp. 10337-10357.
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Mehrabi, M, Pradhan, B, Moayedi, H & Alamri, A 2020, 'Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques', Sensors, vol. 20, no. 6, pp. 1723-1723.
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Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area.
Melnikov, A, Maeder, M, Friedrich, N, Pozhanka, Y, Wollmann, A, Scheffler, M, Oberst, S, Powell, D & Marburg, S 2020, 'Acoustic metamaterial capsule for reduction of stage machinery noise', The Journal of the Acoustical Society of America, vol. 147, no. 3, pp. 1491-1503.
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Noise mitigation of stage machinery can be quite demanding and requires innovative solutions. In this work, an acoustic metamaterial capsule is proposed to reduce the noise emission of several stage machinery drive trains, while still allowing the ventilation required for cooling. The metamaterial capsule consists of c-shape meta-atoms, which have a simple structure that facilitates manufacturing. Two different metamaterial capsules are designed, simulated, manufactured, and experimentally validated that utilize an ultra-sparse and air-permeable reflective meta-grating. Both designs demonstrate transmission loss peaks that effectively suppress gear mesh noise or other narrow band noise sources. The ventilation by natural convection was numerically verified, and was shown to give adequate cooling, whereas a conventional sound capsule would lead to overheating. The noise spectra of three common stage machinery drive trains are numerically modelled, enabling one to design meta-gratings and determine their noise suppression performance. The results fulfill the stringent stage machinery noise limits, highlighting the benefit of using metamaterial capsules of simple c-shape structure.
Mendelson, N, Doherty, M, Toth, M, Aharonovich, I & Tran, TT 2020, 'Strain‐Induced Modification of the Optical Characteristics of Quantum Emitters in Hexagonal Boron Nitride', Advanced Materials, vol. 32, no. 21, pp. 1908316-1908316.
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AbstractQuantum emitters in hexagonal boron nitride (hBN) are promising building blocks for the realization of integrated quantum photonic systems. However, their spectral inhomogeneity currently limits their potential applications. Here, tensile strain is applied to quantum emitters embedded in few‐layer hBN films and both red and blue spectral shifts are realized with tuning magnitudes up to 65 meV, a record for any 2D quantum source. Reversible tuning of the emission and related photophysical properties is demonstrated. Rotation of the optical dipole in response to strain is also observed, suggesting the presence of a second excited state. A theoretical model is derived to describe strain‐based tuning in hBN, and the rotation of the optical dipole. The study demonstrates the immense potential for strain tuning of quantum emitters in layered materials to enable their employment in scalable quantum photonic networks.
Meng, Q, Wu, C, Hao, H, Li, J, Wu, P, Yang, Y & Wang, Z 2020, 'Steel fibre reinforced alkali-activated geopolymer concrete slabs subjected to natural gas explosion in buried utility tunnel', Construction and Building Materials, vol. 246, pp. 118447-118447.
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© 2020 Elsevier Ltd Accidental gas explosions in the buried utility tunnels around the world have caused massive losses in economy and human lives. The buried utility tunnel with adequate blast resistance capacity is therefore required to withstand the possible accidental gas explosions. In this study, a novel construction material, alkali-activated steel fibre reinforced geopolymer composite is introduced and the blast resistance capacity of slabs made of this material is studied in a full-scale buried utility tunnel. Fly ash and S95 grade ground granulated blast-furnace slag powder (GGBS) were used as the major binders in this geopolymer concrete. The plain geopolymer concrete had a compressive strength of 61 MPa and the steel fibre reinforced geopolymer concrete had a compressive strength of 74 MPa. The elastic modulus of the plain geopolymer concrete was found to be lower than the conventional C30 concrete. The methane gas explosion test was conducted in a full-scale (12 m × 1.8 m × 0.6 m) tunnel segment to investigate the structural performance of selected slab specimen (1.8 m × 0.4 m × 0.09 m). The test results and numerical simulations of structural responses subjected to methane gas explosion are presented. The results indicate the fibre reinforced geopolymer concrete slab has good capacity to resist methane gas explosion load.
Meng, Q, Wu, C, Li, J, Liu, Z, Wu, P, Yang, Y & Wang, Z 2020, 'Steel/basalt rebar reinforced Ultra-High Performance Concrete components against methane-air explosion loads', Composites Part B: Engineering, vol. 198, pp. 108215-108215.
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© 2020 Elsevier Ltd Ultra-High Performance Concrete (UHPC) is a relatively new construction material, which has been investigated over the past few decades. Despite its exceptional mechanical strength, UHPC still requires passive steel reinforcement to maximise its bending capacity and the overall material cost will be high. The basalt fibre rebar has a higher mechanical strength than steel rebar with lower cost. In addition, it also has better alkali resistance and good cost-effectiveness. The basalt fibre rebar is therefore considered as a potential alternative reinforcement in the structural member. In this study, a recently developed UHPC formula was adopted, the conventional steel rebar and basalt fibre rebar were used as reinforcement. The developed components were tested against static flexural and methane-air explosion loads. In the four-point flexural tests, the basalt fibre rebar reinforced specimen (400 mm × 100 mm × 100 mm) performed more ductile structural behaviour with higher flexural strength. Two large scale methane-air explosion tests were conducted in buried utility tunnels with different length (i.e., 12000 mm × 1800 mm × 600 mm and 20000 mm × 1800 mm × 600 mm). The experimental test in shorter tunnel yielded lower explosion pressure [1] with marginal structural response. The test in longer tunnel achieved a higher explosion pressure on concrete elements. The C30 and UHPC specimens (1800 mm × 400 mm × 90 mm) with steel/basalt fibre rebar reinforcement were tested. The pressure and deflection data revealed that basalt fibre rebar reinforced UHPC component had a more ductile structural behaviour against accidental gas explosion.
Merigó, JM, Linares-Mustaros, S & Ferrer-Comalat, JC 2020, 'Fuzzy systems in management and information science', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5319-5322.
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Merigó, JM, Mulet-Forteza, C, Martorell, O & Merigó-Lindahl, C 2020, 'Scientific research in the tourism, leisure and hospitality field: a bibliometric analysis', Anatolia, vol. 31, no. 3, pp. 494-508.
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Mery, D, Saavedra, D & Prasad, M 2020, 'X-Ray Baggage Inspection With Computer Vision: A Survey', IEEE Access, vol. 8, pp. 145620-145633.
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© 2013 IEEE. In the last decades, baggage inspection based on X-ray imaging has been established to protect environments in which access control is of vital significance. In several public entrances, like airports, government buildings, stadiums and large event venues, security checks are carried out on all baggage to detect suspicious objects (e.g., handguns and explosives). Although improvements in X-ray technology and computer vision have made many X-ray detection tasks that were previously unfeasible a reality, the progress that has been made in automated baggage inspection is very limited compared to what is needed. For this reason, X-ray screening systems are usually being manipulated by human inspectors. Research and development experts who focus on X-ray testing are moving towards new approaches that can be used to aid human operators. This paper reports the state of the art in baggage inspection identifying three research fields that have been used to deal with this problem: i) X-ray energies, because there is enough research evidence to show that multi-energy X-ray testing must be used when the material characterization is required; ii) X-ray multi-views, because they can be an effective option for examining complex objects where the uncertainty of only one view can lead to misinterpretation; and iii) X-ray computer vision algorithms, because there are a plethora of computer vision approaches that can address many 3D object recognition problems. Besides, this paper presents useful public datasets that can be used for training and testing, and also summarizes the reported experimental results in this field. Finally, this paper addresses the general limitations and show new avenues for future research.
Mesgari, S, Akbarnezhad, A & Xiao, JZ 2020, 'Recycled geopolymer aggregates as coarse aggregates for Portland cement concrete and geopolymer concrete: Effects on mechanical properties', Construction and Building Materials, vol. 236, pp. 117571-117571.
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Messaoud, M, Trabelsi, F, Kumari, P, Merenda, A & Dumée, LF 2020, 'Recrystallization and coalescence kinetics of TiO2 and ZnO nano-catalysts towards enhanced photocatalytic activity and colloidal stability within slurry reactors', Materials Chemistry and Physics, vol. 252, pp. 123235-123235.
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Advanced oxidation processes rely on the development of stable photocatalytic materials, offering specific band-gap attained upon reaching appropriate crystalline phases. A key research gap is related to the recrystallization of nano-catalysts and their impact on performance. This paper describes the systematic preparation of TiO2 and ZnO nanoparticles by hydrothermal-assisted sol-gel method followed by systematic calcination steps with the aim to shed light on the recrystallization process to engineer higher photocatalytic activity. Spherical anatase-TiO2 nanoparticles with a 20 nm diameter size present higher photocatalytic activity than 70 nm of ZnO NPs calcined at 500 °C. A photocatalytic yield of 84% within 30 min of irradiation for the degradation of model dyes was observed for the titania particles, which were also less sensitive to a gglomeration, a key challenge when designing slurry reactors. The variation in zeta potential of TiO2 and ZnO with pH exhibited isoelectric points (IEP) in aqueous media at 5.1 and 6.5, respectively, suggesting amphoteric behaviors while X ray photo-electron spectroscopy and diffusive reflectance spectroscopy data were used to characterize the changes in surface vacancies and the band gap of the materials. These data open the door to the development of advanced oxidation processes in complex industrial environments.
Metia, S, Ha, QP, Duc, HN & Scorgie, Y 2020, 'Urban air pollution estimation using unscented Kalman filtered inverse modeling with scaled monitoring data', Sustainable Cities and Society, vol. 54, pp. 101970-101970.
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© 2019 The increasing rate of urbanization requires effective and reliable techniques for air quality monitoring and control. For this, the Air Pollution Model and Chemical Transport Model (TAPM-CTM) has been developed and used in Australia with emissions inventory data, synoptic data and terrain data used as its input parameters. Since large uncertainties exist in the emissions inventory (EI), further refinements and improvements are required for accurate air quality prediction. This study evaluates the performance of urban air quality forecasting, using TAPM-CTM, and improves accuracy of air pollution estimation by using a two-stage optimization technique to upgrade EI with validation from monitoring data. The first stage is based on statistical analysis for EI correction and the second stage is based on the unscented Kalman filter (UKF) to take into account the spatio-temporal distributions of air pollutant levels utilizing a Matérn covariance function. The predicted nitrogen monoxide (NO) and nitrogen dioxide (NO2) concentrations with a priori emissions are first compared with observations at monitoring stations in the New South Wales (NSW). Ozone (O3) is also considered since at the ground level it represents a major air pollutant affecting human health and the environment. In the second stage, with the improved EI, TAPM-CTM model errors are reduced further by using the UKF to calibrate EI. Results obtained show effectiveness of the proposed technique, which is promising for air quality inverse modeling, an important aspect of air pollution control in smart cities to achieve environmental sustainability.
Meyer, S, Gonzalez de Vega, R, Xu, X, Du, Z, Doble, PA & Clases, D 2020, 'Characterization of Upconversion Nanoparticles by Single-Particle ICP-MS Employing a Quadrupole Mass Filter with Increased Bandpass', Analytical Chemistry, vol. 92, no. 22, pp. 15007-15016.
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This work introduces new methods to characterize dispersions of small-diameter or low-mass-fraction nanoparticles (NPs) by single-particle inductively coupled plasma-mass spectrometry (SP ICP-MS). The optimization of ion extraction, ion transport, and the operation of the quadrupole with increased mass bandwidth improved the signal-to-noise ratios significantly and decreased the size detection limits for all NP dispersions investigated. As a model system, 10.9 ± 1.0 nm Au NPs were analyzed to demonstrate the effects of increasing ion transmission. Specifically, increasing the mass bandwidth of the quadrupole improved the size detection limit to 4.2 nm and enabled the resolution of NP signals from ionic background and noise. Subsequently, the methods were applied to the characterization of lanthanide-doped upconversion nanoparticles (UCNPs) by SP ICP-MS. Three different types of UCNPs (90 nm NaYF4: 20% Yb, 2% Er; 20 nm NaGdF4: 20% Yb, 1% Er; 15 nm NaYF4: 20% Yb, 2% Er) were investigated. Y showed the best signal-to-noise ratios with optimized ion extraction and transport parameters only, whereas the signal-to-noise ratios of Gd, Er, and Yb were further improved by increasing the mass bandwidth of a quadrupole mass filter. The novel methods were suitable for detailed characterization of diluted UCNP dispersions including particle stoichiometries and size distributions. A Poisson model was further applied to assess particle-particle interactions in the aqueous dispersions. The methods have considerable potential for the characterization of small-diameter and/or low-mass-fraction nanoparticles.
Mhiesan, H, Wei, Y, Siwakoti, YP & Mantooth, HA 2020, 'A Fault-Tolerant Hybrid Cascaded H-Bridge Multilevel Inverter', IEEE Transactions on Power Electronics, vol. 35, no. 12, pp. 12702-12715.
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Mi, Y, Liu, Z, Wang, W, Yang, Y & Wu, C 2020, 'Experimental study on residual axial bearing capacity of UHPFRC-filled steel tubes after lateral impact loading', Structures, vol. 26, pp. 549-561.
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Micheli, GJL, Cagno, E, Mustillo, G & Trianni, A 2020, 'Green supply chain management drivers, practices and performance: A comprehensive study on the moderators', Journal of Cleaner Production, vol. 259, pp. 121024-121024.
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© 2020 Elsevier Ltd The growing level of attention toward global warming, reduction of non-renewable resources and pollution calls manufacturing firms to implement sustainable, and specifically green initiatives into their supply chains (Green Supply Chain Management, GSCM). So far, too little studies have provided clear empirical evidence on the actual impact of these initiatives on firms’ performance, especially within the European manufacturing context, and on the actual impact of possible drivers on the implementation of the above-mentioned initiative. Thus, the aim of this study is to analyse possible moderation factors that affect the relationships between drivers-practices and practices-performance through a survey carried out in 169 Italian manufacturing firms belonging to a range of different sectors. The moderation analysis shows that some drivers strongly influence the relationships between drivers-practices and practices-performance, and a few contributions from the existing literature are challenged and discussed. Our findings may be particularly interesting for managers and supply chain specialists, as well as for policymakers, who could be inspired by the role of particular drivers on the implementation of GSCM practices, and by the level of performance achievable thanks to the adoption of a set of green practices. As for the academic impact, the issue has been tackled for the first time in an attempt of a comprehensive view, which paves the way to a number of research lines to further investigate both the confirmed and unconfirmed moderations, so as to understand the related rationales in the comprehensive view proposed by the authors.
Mihandoust, A, Razavi Bazaz, S, Maleki-Jirsaraei, N, Alizadeh, M, A. Taylor, R & Ebrahimi Warkiani, M 2020, 'High-Throughput Particle Concentration Using Complex Cross-Section Microchannels', Micromachines, vol. 11, no. 4, pp. 440-440.
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High throughput particle/cell concentration is crucial for a wide variety of biomedical, clinical, and environmental applications. In this work, we have proposed a passive spiral microfluidic concentrator with a complex cross-sectional shape, i.e., a combination of rectangle and trapezoid, for high separation efficiency and a confinement ratio less than 0.07. Particle focusing in our microfluidic system was observed in a single, tight focusing line, in which higher particle concentration is possible, as compared with simple rectangular or trapezoidal cross-sections with similar flow area. The sharper focusing stems from the confinement of Dean vortices in the trapezoidal region of the complex cross-section. To quantify this effect, we introduce a new parameter, complex focusing number or CFN, which is indicative of the enhancement of inertial focusing of particles in these channels. Three spiral microchannels with various widths of 400 µm, 500 µm, and 600 µm, with the corresponding CFNs of 4.3, 4.5, and 6, respectively, were used. The device with the total width of 600 µm was shown to have a separation efficiency of ~98%, and by recirculating, the output concentration of the sample was 500 times higher than the initial input. Finally, the investigation of results showed that the magnitude of CFN relies entirely on the microchannel geometry, and it is independent of the overall width of the channel cross-section. We envision that this concept of particle focusing through complex cross-sections will prove useful in paving the way towards more efficient inertial microfluidic devices.
Miner, AS, Laranjo, L & Kocaballi, AB 2020, 'Chatbots in the fight against the COVID-19 pandemic', npj Digital Medicine, vol. 3, no. 1.
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Ming, Y, Pelusi, D, Fang, C-N, Prasad, M, Wang, Y-K, Wu, D & Lin, C-T 2020, 'EEG data analysis with stacked differentiable neural computers', Neural Computing and Applications, vol. 32, no. 12, pp. 7611-7621.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. Differentiable neural computer (DNC) has demonstrated remarkable capabilities in solving complex problems. In this paper, we propose to stack an enhanced version of differentiable neural computer together to extend its learning capabilities. Firstly, we give an intuitive interpretation of DNC to explain the architectural essence and demonstrate the stacking feasibility by contrasting it with the conventional recurrent neural network. Secondly, the architecture of stacked DNCs is proposed and modified for electroencephalogram (EEG) data analysis. We substitute the original Long Short-Term Memory network controller by a recurrent convolutional network controller and adjust the memory accessing structures for processing EEG topographic data. Thirdly, the practicability of our proposed model is verified by an open-sourced EEG dataset with the highest average accuracy achieved; then after fine-tuning the parameters, we show the minimal mean error obtained on a proprietary EEG dataset. Finally, by analyzing the behavioral characteristics of the trained stacked DNCs model, we highlight the suitableness and potential of utilizing stacked DNCs in EEG signal processing.
Mirzaaghaian, A, Ramiar, A, Ranjbar, AA & Warkiani, ME 2020, 'Application of level-set method in simulation of normal and cancer cells deformability within a microfluidic device', Journal of Biomechanics, vol. 112, pp. 110066-110066.
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Application of microfluidic systems for the study of cellular behaviors has been a flourishing area of research in the past decade. In the process of probing cell biomechanics the passage of a cell through a narrow microchannel or a small pore has attracted much attention during the recent years. And the study of cellular deformability and transportability using these systems with enhanced resolution and accuracy has opened a new paradigm for high-throughput characterization of both healthy and diseased cell populations.Here we use the level-set method to explore the relationship between the transit time and mechanical properties of normal white blood cells (WBCs) and breast cancer epithelial cells (MCF7) under different microenvironmental parameters (i.e., pressure difference, cell size, effective cell surface tension, constriction size and taper angle) in a 2-D computational domain by considering the cell as a viscous drop. The novel biomechanical relations are obtained for each cell type by the Response Surface Method (RSM), relating microenvironmental parameters to the dimensionless entry time of the normal and cancer cells. Our results revealed that MCF7 cells show asignificantly different behavior (a bifurcating behavior when the pressure difference of inlet/outlet increases) in regards to the dimensionless entry time as a function of microchannel taper angle in comparison with the WBC. These results suggest that the microenvironmental parameters have a significant effect on the transportability of the cells and different cells have different behaviors in response to a specific microenvironmental parameter. Finally, it can be claimed that this method can be also utilized to distinguish between benign and cancerous cells or even to probe tumor heterogeneity toward high throughput cell cytometry.
Mishra, AK, Das, SR, Ray, PK, Mallick, RK, Mohanty, A & Mishra, DK 2020, 'PSO-GWO Optimized Fractional Order PID Based Hybrid Shunt Active Power Filter for Power Quality Improvements', IEEE Access, vol. 8, pp. 74497-74512.
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This paper presents a Hybrid Shunt Active Power Filter (HSAPF) optimized by hybrid Particle Swarm Optimization-Grey Wolf Optimization (PSO-GWO) and Fractional Order Proportional-Integral-Derivative Controller (FOPIDC) for reactive power and harmonic compensation under balance and unbalance loading conditions. Here, the parameters of FOPID controller are tuned by PSO-GWO technique to mitigate the harmonics. Comparing Passive with Active Filters, the former is tested to be bulky and design is complex and the later is not cost effective for high rating. Hence, a hybrid structure of shunt active and passive filter is designed using MATLAB/Simulink and in real time experimental set up. The compensation process for shunt active filter is different from predictable methods such as (p-q) or (id-i ) theory, in which only the source current is to be sensed. The performance of the proposed controller is tested under different operating conditions such as steady and transient states and indices like Total Harmonic Distortion (THD), Input Power Factor (IPF), Real Power (P) and Reactive Power (Q) are estimated and compared with that of other controllers. The parameters of FOPIDC and Conventional PID Controller (CPIDC) are optimized by the techniques such as PSO, GWO and hybrid PSO-GWO. The comparative simulation/experiment results reflect the better performance of PSO-GWO optimized FOPIDC based HSAPF with respect to PSO/GWO optimized FOPIDC/CPIDC based HSAPF under different operating conditions. q
Mishra, B, Varjani, S, Agrawal, DC, Mandal, SK, Ngo, HH, Taherzadeh, MJ, Chang, J-S, You, S & Guo, W 2020, 'Engineering biocatalytic material for the remediation of pollutants: A comprehensive review', Environmental Technology & Innovation, vol. 20, pp. 101063-101063.
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Mishra, B, Varjani, S, Pradhan, I, Ekambaram, N, Teixeira, JA, Ngo, HH & Guo, W 2020, 'Insights into Interdisciplinary Approaches for Bioremediation of Organic Pollutants: Innovations, Challenges and Perspectives', Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, vol. 90, no. 5, pp. 951-958.
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© 2020, The National Academy of Sciences, India. Modern industrialization has originated a tremendous industrial growth. Discharge of industrial effluent is a critical threat to a safe environment. Removal of various pollutants from industrial wastewater is obligatory for controlling environmental pollution. Bioremediation using biotechnological interventions has attracted greater attention among the researchers in the field of control and abatement of environmental pollution. This review is aimed to introduce methods for bioremediation on the removal of organic pollutants from industrial wastewater that have been discussed, and the kinetic models that are related to it have been introduced. In addition, biotechnological interventions on bioremediation of pollutants have been discussed fingerprinting of microbial sp. present at polluted sites. Microbial electrochemical technologies such as a green technology for the removal of pollutants from industrial effluents and simultaneous resource recovery from industrial waste have been discussed to generate up-to-date scientific literature. This review also provides detailed knowledge gaps, challenges and research perspectives related to the topic.
Mishra, PK 2020, 'Study of Tool Wear Rate (TWR) During the Electric Discharge Machining (EDM) of Hybrid Al-6061 Metal Matrix Composite', Journal of Advanced Research in Dynamical and Control Systems, vol. 12, no. SP3, pp. 915-922.
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© 2020, Institute of Advanced Scientific Research, Inc.. All rights reserved. Hybrid Aluminum metal matrix composites have become leading materials due to their excellent engineering characteristics and applications. In this experimental work, Al6061 based hybrid metal matrix composite is fabricated by stir casting process, where the ‘SiCp ’ and ‘Grp’ are used as reinforcements. Due to the abrasive nature of reinforcements, the hardness of fabricated samples is increased, which was very much difficult to machine by traditional methods. Therefore, in this study an effective machining process (EDM-Electric Discharge Machining) is used for machining the developed metal matrix composite. This paper investigates the significant effect of EDM machining parameters like pulse-on time (T-on), pulse-off time (T-off), voltage (V) and current (I) on a response variable (TWR-tool wear rate). For machining, the fabricated samples, three different electrodes materials; Steel-304, Brass and Copper with a Ø12mm each were used. The design matrix is developed by Taguchi L27 approach in Minitab software. The ANOVA technique is used to check the signification of the model. The SEM (Scanning electron microscope) and EDS (Energy-dispersive X-ray spectrometer) were done to study the surface characteristics and elements analysis, respectively of the machined electrodes.
Mishra, PN, Zhang, Y, Bhuyan, MH & Scheuermann, A 2020, 'Anisotropy in volume change behaviour of soils during shrinkage', Acta Geotechnica, vol. 15, no. 12, pp. 3399-3414.
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Moayedi, H, Mehrabi, M, Bui, DT, Pradhan, B & Foong, LK 2020, 'Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility', Journal of Environmental Management, vol. 260, pp. 109867-109867.
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Forests are important dynamic systems which are widely affected by fire worldwide. Due to the complexity and non-linearity of the forest fire problem, employing hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threat. Three fuzzy-metaheuristic ensembles, based on adaptive neuro-fuzzy inference systems (ANFIS) incorporated with genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) evolutionary algorithms are used to produce the forest fire susceptibility map (FFSM) of a fire-prone region in Iran. A sensitivity analysis is also executed to evaluate the effectiveness of the proposed ensembles in terms of time and complexity. The results revealed that all models produce FFSMs with acceptable accuracy. However, the superiority of the GA-ANFIS was shown in both recognizing the pattern (AUROCtrain = 0.912 and Error = 0.1277) and predicting unseen fire events (AUROCtest = 0.850 and Error = 0.1638). The optimized structures of the proposed GA-ANFIS and PSO-ANFIS ensembles could be good alternatives to traditional forest fire predictive models, and their FFSMs can be promisingly used for future planning and decision making in the proposed area.
Modak, NM, Lobos, V, Merigó, JM, Gabrys, B & Lee, JH 2020, 'Forty years of computers & chemical engineering: A bibliometric analysis', Computers & Chemical Engineering, vol. 141, pp. 106978-106978.
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© 2020 Elsevier Ltd Computers & Chemical Engineering (CCE) is one of the premier international journals in the field of chemical engineering. CCE published its first issue in 1977 and completed forty years in 2016. More than four decades of continuous and successful journey influenced us to celebrate its contribution through a comprehensive bibliometric study. Using the Web of Science Core Collection database we depict trends of the journal in terms of papers, topics, authors, institutions, and countries. Networks visualization of co-citation of journals and authors, bibliographic coupling institutions and countries, and co-occurrence of author keywords are prepared using the visualization of similarities (VOS) viewer software. The present analysis explores publication and citation patterns of the journal. Professor Ignacio E. Grossmann, Carnegie Mellon University, and USA respectively appear as the most productive and influential author, institution, and country in CCE publications. Optimization based research topics received most attention in CCE publications.
Modak, NM, Sinha, S, Raj, A, Panda, S, Merigó, JM & Lopes de Sousa Jabbour, AB 2020, 'Corporate social responsibility and supply chain management: Framing and pushing forward the debate', Journal of Cleaner Production, vol. 273, pp. 122981-122981.
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© 2020 Elsevier Ltd Corporate social responsibility (CSR) in supply chain management (SCM) is one of the burgeoning fields of the last decade. Significant interest in this area has led to a large number of publications in recent times. For this reason, this study has been carried out to provide a comprehensive framework and future research directions for this topic. This work presents a bibliometric analysis of relevant publications dealing with CSR in SCM up to April 2019. As well as the presentation of an overview of publications and citation structures, it also explores journals and countries based on a bibliometric study. To collect the relevant data for this study, we have utilized the reliable SCOPUS database. Our results highlight the significant contributions of journals, authors, universities, and countries on this topic. With the help of “Visualization of similarities (VOS)” viewer software, this study investigates bibliographic coupling of sources and countries. It also presents co-occurrence of keywords and graphic representations of the bibliographic materials. Finally, it provides an overview of all relevant review papers in this field and a comprehensive view of related research fields.
Mofijur, M, Kusumo, F, Fattah, IMR, Mahmudul, HM, Rasul, MG, Shamsuddin, AH & Mahlia, TMI 2020, 'Resource Recovery from Waste Coffee Grounds Using Ultrasonic-Assisted Technology for Bioenergy Production', Energies, vol. 13, no. 7, pp. 1770-1770.
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Biodiesel is a proven alternative fuel that can serve as a substitute for petroleum diesel due to its renewability, non-toxicity, sulphur-free nature and superior lubricity. Waste-based non-edible oils are studied as potential biodiesel feedstocks owing to the focus on the valorisation of waste products. Instead of being treated as municipal waste, waste coffee grounds (WCG) can be utilised for oil extraction, thereby recovering an energy source in the form of biodiesel. This study evaluates oil extraction from WCG using ultrasonic and Soxhlet techniques, followed by biodiesel conversion using an ultrasonic-assisted transesterification process. It was found that n-hexane was the most effective solvent for the oil extraction process and ultrasonic-assisted technology offers a 13.5% higher yield compared to the conventional Soxhlet extraction process. Solid-to-solvent ratio and extraction time of the oil extraction process from the dried waste coffee grounds (DWCG) after the brewing process was optimised using the response surface methodology (RSM). The results showed that predicted yield of 17.75 wt. % of coffee oil can be obtained using 1:30 w/v of the mass ratio of DWCG-ton-hexane and 34 min of extraction time when 32% amplitude was used. The model was verified by the experiment where 17.23 wt. % yield of coffee oil was achieved when the extraction process was carried out under optimal conditions. The infrared absorption spectrum analysis of WCG oil determined suitable functional groups for biodiesel conversion which was further treated using an ultrasonic-assisted transesterification process to successfully convert to biodiesel.
Mofijur, M, Rizwanul Fattah, IM, Saiful Islam, ABM, Uddin, MN, Ashrafur Rahman, SM, Chowdhury, MA, Alam, MA & Uddin, MA 2020, 'Relationship between Weather Variables and New Daily COVID-19 Cases in Dhaka, Bangladesh', Sustainability, vol. 12, no. 20, pp. 8319-8319.
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The present study investigated the relationship between the transmission of COVID-19 infections and climate indicators in Dhaka, Bangladesh, using coronavirus infections data available from the Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh. The Spearman rank correlation test was carried out to study the association of seven climate indicators, including humidity, air quality, minimum temperature, precipitation, maximum temperature, mean temperature, and wind speed with the COVID-19 outbreak in Dhaka, Bangladesh. The study found that, among the seven indicators, only two indicators (minimum temperature and average temperature) had a significant relationship with new COVID-19 cases. The study also found that air quality index (AQI) had a strong negative correlation with cumulative cases of COVID-19 in Dhaka city. The results of this paper will give health regulators and policymakers valuable information to lessen the COVID-19 spread in Dhaka and other countries around the world.
Moghaddam, HA, Sarmadian, A, Asnaashari, A, Joushani, HAN, Islam, MS, Saha, SC, Ghasemi, G & Shafaee, M 2020, 'Condensation heat transfer and pressure drop characteristics of Isobutane in horizontal channels with twisted tape inserts', International Journal of Refrigeration, vol. 118, pp. 31-40.
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Mohamad, ET, Li, D, Murlidhar, BR, Jahed Armaghani, D, Kassim, KA & Komoo, I 2020, 'The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production', Engineering with Computers, vol. 36, no. 4, pp. 1355-1370.
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Mohamadzade, B, Simorangkir, RBVB, Maric, S, Lalbakhsh, A, Esselle, KP & Hashmi, RM 2020, 'Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas', Electronics, vol. 9, no. 9, pp. 1375-1375.
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Reconfigurable antennas have gained tremendous interest owing to their multifunctional capabilities while adhering to minimalistic space requirements in ever-shrinking electronics platforms and devices. A stark increase in demand for flexible and conformal antennas in modern and emerging unobtrusive and space-limited electronic systems has led to the development of the flexible and conformal reconfigurable antennas era. Flexible and conformal antennas rely on non-conventional materials and realization approaches, and thus, despite the mature knowledge available for rigid reconfigurable antennas, conventional reconfigurable techniques are not translated to a flexible domain in a straight forward manner. There are notable challenges associated with integration of reconfiguration elements such as switches, mechanical stability of the overall reconfigurable antenna, and the electronic robustness of the resulting devices when exposed to folding of sustained bending operations. This paper reviews various approaches demonstrated thus far, to realize flexible reconfigurable antennas, categorizing them on the basis of reconfiguration attributes, i.e., frequency, pattern, polarization, or a combination of these characteristics. The challenges associated with development and characterization of flexible and conformal reconfigurable antennas, the strengths and limitations of available methods are reviewed considering the progress in recent years, and open challenges for the future research are identified.
Mojiri, A, Zhou, J, Vakili, M & Van Le, H 2020, 'Removal performance and optimisation of pharmaceutical micropollutants from synthetic domestic wastewater by hybrid treatment', Journal of Contaminant Hydrology, vol. 235, pp. 103736-103736.
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Occurrence of pharmaceutical micropollutants in aquatic environments has been one amongst serious environmental problems. During this study, two reactors, including a sequencing batch reactor (SBR) + powdered composite adsorbent (CA) (first reactor, SBR + CA) and a sequencing batch reactor (second reactor, SBR), were designed to treat synthetic wastewater. Powdered CA was added with a dosage of 4.8 g L-1 to the first reactor. Tap water was contaminated with chemical oxygen demand (COD), ammonia and three pharmaceuticals, namely, atenolol (ATN), ciprofloxacin (CIP) and diazepam (DIA) to produce synthetic wastewater. The SBR + CA illustrated a better performance during synthetic municipal wastewater treatment. Up to 138.6 mg L-1 (92.4%) of COD and up to 114.2 mg L-1 (95.2%) of ammonia were removed by the first reactor. Moreover, optimisation of pharmaceuticals removal was conducted through response surface methodology (RSM) and artificial neural network (ANN). Based on the RSM, the best elimination of ATN (90.2%, 2.26 mg L-1), CIP (94.0%, 2.35 mg L-1) and DIA (95.5%, 2.39 mg L-1) was detected at the optimum initial concentration of MPs (2.51 mg L-1) and the contact time (15.8 h). In addition, ANN represented a high R2 value (>0.99) and a rational mean squared error (<1.0) during the optimisation of micropollutants removal by both reactors. Moreover, adsorption isotherm study showed that the Freundlich isotherm could justify the abatement of micropollutants by using CA better than the Langmuir isotherm.
Momeni, E, Dowlatshahi, MB, Omidinasab, F, Maizir, H & Armaghani, DJ 2020, 'Gaussian Process Regression Technique to Estimate the Pile Bearing Capacity', Arabian Journal for Science and Engineering, vol. 45, no. 10, pp. 8255-8267.
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Mong, GR, Chong, CT, Ng, J-H, Chong, WWF, Lam, SS, Ong, HC & Ani, FN 2020, 'Microwave pyrolysis for valorisation of horse manure biowaste', Energy Conversion and Management, vol. 220, pp. 113074-113074.
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© 2020 Elsevier Ltd Biomass-based feedstock is an attractive alternative to fossil fuel due to its sustainability and potential as a clean energy source. The present work focuses on the valorisation of horse manure biowaste to produce bioenergy via microwave-assisted pyrolysis technique. The thermal decomposition process is conducted by considering the effects of pyrolysis temperature, catalyst loading and carrier gas flow rate on the yield and quality of end products. The pyrolysed gaseous product contains up to 73.1 vol% of syngas components. The solid biochar obtained contains a heating value of 35.5 MJ/kg with high surface to pore volume ratio. The relatively high specific energy contents of gaseous products and biochar indicate their potential as biofuels. The liquid product is found to contain oxygenated phenolic compound of up to 79.4 wt%. In spite of an overall energy deficit achieved when comparing the total energy of end products with the feedstock, the energy balance analysis indicates the optimum production parameters. The least energy deficit is achieved at the reactive conditions of 350–450 °C and manure-to-catalyst ratio of 1:1. A reaction mechanism pathway for the pyrolysis of horse manure is presented to show the production route for bioenergy and valuable chemicals.
Monkman, J, Taheri, T, Ebrahimi Warkiani, M, O’Leary, C, Ladwa, R, Richard, D, O’Byrne, K & Kulasinghe, A 2020, 'High-Plex and High-Throughput Digital Spatial Profiling of Non-Small-Cell Lung Cancer (NSCLC)', Cancers, vol. 12, no. 12, pp. 3551-3551.
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Profiling the tumour microenvironment (TME) has been informative in understanding the underlying tumour–immune interactions. Multiplex immunohistochemistry (mIHC) coupled with molecular barcoding technologies have revealed greater insights into the TME. In this study, we utilised the Nanostring GeoMX Digital Spatial Profiler (DSP) platform to profile a non-small-cell lung cancer (NSCLC) tissue microarray for protein markers across immune cell profiling, immuno-oncology (IO) drug targets, immune activation status, immune cell typing, and pan-tumour protein modules. Regions of interest (ROIs) were selected that described tumour, TME, and normal adjacent tissue (NAT) compartments. Our data revealed that paired analysis (n = 18) of matched patient compartments indicate that the TME was significantly enriched in CD27, CD3, CD4, CD44, CD45, CD45RO, CD68, CD163, and VISTA relative to the tumour. Unmatched analysis indicated that the NAT (n = 19) was significantly enriched in CD34, fibronectin, IDO1, LAG3, ARG1, and PTEN when compared to the TME (n = 32). Univariate Cox proportional hazards indicated that the presence of cells expressing CD3 (hazard ratio (HR): 0.5, p = 0.018), CD34 (HR: 0.53, p = 0.004), and ICOS (HR: 0.6, p = 0.047) in tumour compartments were significantly associated with improved overall survival (OS). We implemented both high-plex and high-throughput methodologies to the discovery of protein biomarkers and molecular phenotypes within biopsy samples, and demonstrate the power of such tools for a new generation of pathology research.
Moon, DH, Chung, WJ, Chang, SW, Lee, SM, Kim, SS, Jeung, JH, Ro, YH, Ahn, JY, Guo, W, Ngo, HH & Nguyen, DD 2020, 'Fabrication and characterization of Ni-Ce-Zr ternary disk-shaped catalyst and its application for low-temperature CO2 methanation', Fuel, vol. 260, pp. 116260-116260.
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Moore, SI, Ruppert, MG & Yong, YK 2020, 'AFM Cantilever Design for Multimode Q Control: Arbitrary Placement of Higher Order Modes', IEEE/ASME Transactions on Mechatronics, vol. 25, no. 3, pp. 1389-1397.
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Moreira, C, Fell, L, Dehdashti, S, Bruza, P & Wichert, A 2020, 'Towards a quantum-like cognitive architecture for decision-making', Behavioral and Brain Sciences, vol. 43.
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Abstract We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information compared to classical models. This framework can accommodate and predict several cognitive biases reported in Lieder & Griffiths without heavy reliance on heuristics or on assumptions of the computational resources of the mind.
Moreira, C, Tiwari, P, Pandey, HM, Bruza, P & Wichert, A 2020, 'Quantum-like influence diagrams for decision-making', Neural Networks, vol. 132, pp. 190-210.
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Morris, A, Hastings, C, Mitchell, E & Ramia, G 2020, 'The pandemic and international students in the private rental sector', Parity, vol. 33, no. 5, pp. 19-20.
Mortazavi, M, Sharafi, P, Kildashti, K & Samali, B 2020, 'Prefabricated hybrid steel wall panels for mid-rise construction in seismic regions', Journal of Building Engineering, vol. 27, pp. 100942-100942.
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Mostafaeipour, A, Shakeriravesh, M, Naderpour, M & Owlia, MS 2020, 'A new conceptual model for CO<SUB align='right'>2 reduction in hot and dry urban areas: a case study of Mashhad in Iran', International Journal of Global Warming, vol. 21, no. 4, pp. 325-325.
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Mousavi, M, Holloway, D, Olivier, JC & Gandomi, AH 2020, 'A Spline Method based on the Crack Induced Deflection for Bridge Damage Detection', Advances in Engineering Software, vol. 149, pp. 102894-102894.
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© 2020 Elsevier Ltd This paper proposes a novel method for damage detection on a simply supported beam subjected to a moving mass using multiple vibration data measured from a vehicle-bridge interaction (VBI) model. It is shown first that the integral of the difference between the deflection time history of the healthy and damaged beam reaches a local maximum at the location of the crack. As such, the integral of the difference between the vibration time history of the healthy and damaged beam at some dense points on the beam is required. However, practically the number of measured points is limited. Therefore, the data from VBI measured at a limited set of points have been interpolated at each time instant to estimate the deflection of other locations on the beam. It has been demonstrated through several simulations that the proposed method can detect both the location and severity of damage on the beam while considering the interaction between the moving mass and the bridge, as well as the effect of road roughness. It is also shown that the proposed method is fairly robust to noise in both damage localisation and quantification.
Mujtaba, MA, Kalam, MA, Masjuki, HH, Gul, M, Soudagar, MEM, Ong, HC, Ahmed, W, Atabani, AE, Razzaq, L & Yusoff, M 2020, 'Comparative study of nanoparticles and alcoholic fuel additives-biodiesel-diesel blend for performance and emission improvements', Fuel, vol. 279, pp. 118434-118434.
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© 2020 Elsevier Ltd This study aims to investigate a CI diesel engine characteristic of diesel-biodiesel blend with oxygenated alcohols and nanoparticle fuel additives. Biodiesel was synthesized from a complementary palm-sesame oil blend using an ultrasound-assisted transesterification process. B30 was mixed with fuel additives as the base fuel to form ternary blends in different proportions before engine testing. The oxygenated alcohols (DMC and DEE) and nanoparticles (CNT and TiO2) were used to improve both the fuel characteristics and engine emission and performance parameters. B30 fuel was mixed with 5% (DEE) and 10% (DMC) by volume and 100 ppm concentration of CNT and TiO2 nanoparticles, respectively, which are kept constant during this study. Engine performance and emissions characteristics were studied using a CI diesel engine with variable engine rpm at full load condition. The results were compared with B30 fuel and B10 (commercial diesel). The main findings indicated that the B30 + TiO2 ternary blend shows an overall decrease in brake specific fuel consumption up to 4.1% among all tested fuels. B30 + DMC produced a higher 9.88% brake thermal efficiency, among other fuels. B30 + DMC ternary blend showed a maximum decrease in CO and HC emissions by 29.9% and 21.4%, respectively, collated to B30. B30 + CNT ternary blend showed a maximum reduction of 3.92% in NOx emissions compared to B30.
Mujtaba, MA, Masjuki, HH, Kalam, MA, Noor, F, Farooq, M, Ong, HC, Gul, M, Soudagar, MEM, Bashir, S, Rizwanul Fattah, IM & Razzaq, L 2020, 'Effect of Additivized Biodiesel Blends on Diesel Engine Performance, Emission, Tribological Characteristics, and Lubricant Tribology', Energies, vol. 13, no. 13, pp. 3375-3375.
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This research work focuses on investigating the lubricity and analyzing the engine characteristics of diesel–biodiesel blends with fuel additives (titanium dioxide (TiO2) and dimethyl carbonate (DMC)) and their effect on the tribological properties of a mineral lubricant. A blend of palm–sesame oil was used to produce biodiesel using ultrasound-assisted transesterification. B30 (30% biodiesel + 70% diesel) fuel was selected as the base fuel. The additives used in the current study to prepare ternary fuel blends were TiO2 and DMC. B30 + TiO2 showed a significant reduction of 6.72% in the coefficient of friction (COF) compared to B30. B10 (Malaysian commercial diesel) exhibited very poor lubricity and COF among all tested fuels. Both ternary fuel blends showed a promising reduction in wear rate. All contaminated lubricant samples showed an increment in COF due to the dilution of combustible fuels. Lub + B10 (lubricant + B10) showed the highest increment of 42.29% in COF among all contaminated lubricant samples. B30 + TiO2 showed the maximum reduction (6.76%) in brake-specific fuel consumption (BSFC). B30 + DMC showed the maximum increment (8.01%) in brake thermal efficiency (BTE). B30 + DMC exhibited a considerable decline of 32.09% and 25.4% in CO and HC emissions, respectively. The B30 + TiO2 fuel blend showed better lubricity and a significant improvement in engine characteristics.
Mujtaba, MA, Masjuki, HH, Kalam, MA, Ong, HC, Gul, M, Farooq, M, Soudagar, MEM, Ahmed, W, Harith, MH & Yusoff, MNAM 2020, 'Ultrasound-assisted process optimization and tribological characteristics of biodiesel from palm-sesame oil via response surface methodology and extreme learning machine - Cuckoo search', Renewable Energy, vol. 158, pp. 202-214.
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© 2020 Elsevier Ltd The purpose of this study was the improvement of cold flow and lubricity characteristics of biodiesel produced from the palm-sesame oil blend. Extreme learning machine (ELM) and response surface methodology (RSM) techniques were used to model the production process and the input variables (time, catalyst amount, methanol to oil ratio, and duty cycle) were optimized using cuckoo search algorithm. The mean absolute percentage error (MAPE), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and standard error of prediction (SEP) were calculated to evaluate the performance of RSM and ELM. The results showed that ELM model had better performance in prediction than RSM model. The optimum yield of P50S50 biodiesel obtained was 96.6138% under operating parameters of time (38.96 min), duty cycle (59.52%), methanol to oil ratio (60 V/V %) and catalyst amount (0.70 wt%). The cold flow characteristics of P50S50 biodiesel are significantly improved like cloud point (7.89 °C), pour point (3.80 °C), and cold filter plugging point (- 1.77 °C) with better oxidation stability 6.89 h. The average coefficient of friction P50S50 biodiesel was lower than palm biodiesel (B100) and B10 commercial diesel by 2.29% and 12.37% respectively.
Mujtaba, MA, Muk Cho, H, Masjuki, HH, Kalam, MA, Ong, HC, Gul, M, Harith, MH & Yusoff, MNAM 2020, 'Critical review on sesame seed oil and its methyl ester on cold flow and oxidation stability', Energy Reports, vol. 6, pp. 40-54.
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© 2019 The demand for renewable energy is steadily increasing due to rapid population growth and economic development worldwide. An additional reason is that fossil fuel reserves are limited, and this situation results in their non-uniform availability globally. Furthermore, the attitudes of the society, energy policies and technology choices are constantly changing. Thus, renewable energy resources are now considered good alternatives to fossil fuels. In the meantime, liquid energy, such as methyl ester from locally produced vegetable oils, is well accepted by many countries, even though it is currently being blended up to 20% with petroleum fuels. Recently, the industrialisation of biodiesel is a major problem because of its poor cold flow properties and oxidative stability. Vegetable oils are also being blended in an appropriate proportion before transesterification to obtain the desired properties in biodiesel. Similarly, poor cold flow properties and oxidative stability can be improved by choosing suitable vegetable oils for making blends. Amongst all available vegetable oils, sesame seed oil (SSO) has unique cold flow properties and oxidation stability, particularly because of naturally occurring antioxidants and preservatives, which enhance the stability of oil towards rancidity. Therefore, SSO can be used as a potential feedstock for blending with other vegetable oils to enhance the overall cold flow and oxidation stability properties. This overview summarises sesame cultivation, SSO production, the physicochemical properties of SSO and its potential as an alternative renewable fuel source. In this review, the physicochemical properties of sesame biodiesel are compared with those of biodiesel derived from other vegetable oils. Results show that blending SSO with palm oil before transesterification will successfully improve the cold flow properties and oxidation stability of palm methyl ester (biodiesel).
Mukai, H, Sakata, K, Devitt, SJ, Wang, R, Zhou, Y, Nakajima, Y & Tsai, J-S 2020, 'Pseudo-2D superconducting quantum computing circuit for the surface code: proposal and preliminary tests', New Journal of Physics, vol. 22, no. 4, pp. 043013-043013.
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Abstract Among the major hardware platforms for large-scale quantum computing, one of the leading candidates is superconducting quantum circuits. Current proposed architectures for quantum error-correction with the promising surface code require a two-dimensional layout of superconducting qubits with nearest-neighbor interactions. A major hurdle for the scalability in such an architecture using superconducting systems is the so-called wiring problem, where qubits internal to a chipset become difficult to access by the external control/readout lines. In contrast to the existing approaches which address the problem through intricate three-dimensional wiring and packaging technology, leading to a significant engineering challenge, here we address this problem by presenting a modified microarchitecture in which all the wiring can be realized through a newly introduced pseudo two-dimensional resonator network which provides the inter-qubit connections via airbridges. Our proposal is completely compatible with current standard planar circuit technology. We carried out experiments to examine the feasibility of the new airbridge component. The measured quality factor of the airbridged resonator is below the simulated surface-code threshold required for a coupling resonator, and it should not limit simulated gate fidelity. The measured crosstalk between crossed resonators is at most −49 dB in resonance. Further spatial and frequency separation between the resonators should result in relatively limited crosstalk between them, which would not increase as the size of the chipset increases. This architecture and the preliminary tests indicate the possibility that a large-scale, fully error-corrected quantum computer could be constructed by monolithic integration technologies without additional overhead or special packaging know-how.
Muniyasamy, A, Sivaporul, G, Gopinath, A, Lakshmanan, R, Altaee, A, Achary, A & Velayudhaperumal Chellam, P 2020, 'Process development for the degradation of textile azo dyes (mono-, di-, poly-) by advanced oxidation process - Ozonation: Experimental & partial derivative modelling approach', Journal of Environmental Management, vol. 265, pp. 110397-110397.
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Munot, S, Redfern, J, Bray, J, Marschner, S, Von Huben, A, Semsarian, C, Jennings, G, Bauman, A, Angell, B, Coggins, A, Kumar, S, Middleton, P, Ferry, C, Kovoor, P, Lai, K, Oppermann, I, Vukasovic, M, Nelson, M, Denniss, A, Ware, S & Chow, C 2020, '046 Bystander Cardiopulmonary Resuscitation (CPR) and use of Automated External Defibrillator (AED) for Out-of-hospital Cardiac Arrest (OHCA): Urban Versus Regional NSW', Heart, Lung and Circulation, vol. 29, pp. S58-S59.
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Murlidhar, BR, Kumar, D, Jahed Armaghani, D, Mohamad, ET, Roy, B & Pham, BT 2020, 'A Novel Intelligent ELM-BBO Technique for Predicting Distance of Mine Blasting-Induced Flyrock', Natural Resources Research, vol. 29, no. 6, pp. 4103-4120.
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Mwasakifwa, GE, Amin, J, White, CP, Center, JR, Kelleher, A & Boyd, MA 2020, 'Early changes in bone turnover and inflammatory biomarkers and clinically significant bone mineral density loss over 48 weeks among HIV‐infected patients with virological failure of a standard first‐line antiretroviral therapy regimen in the SECOND‐LINE study', HIV Medicine, vol. 21, no. 8, pp. 492-504.
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ObjectivesWe assessed whether changes at week 12 in markers of bone turnover, inflammation, and immune activation were associated with clinically important (≥ 5%) bone mineral density (BMD) loss from baseline to week 48 at the proximal femur (hip) and lumbar spine in the SECOND‐LINE study.MethodsWe measured concentrations of procollagen type 1 pro‐peptide (P1NP), carboxyl‐terminal collagen crosslinks (CTX), high‐sensitivity C‐reactive protein (hs‐CRP), D‐dimer, interleukin (IL)‐6, tumor necrosis factor (TNF), neopterin, and soluble CD14 and 163 at weeks 0, 12, and 48 in 123 SECOND‐LINE dual‐energy X‐ray absorptiometry (DXA) substudy participants. Linear regression was used to compare changes in biomarkers. Predictors of ≥ 5% BMD loss were examined using multivariable regression.ResultsThe mean age was 38 years, the mean CD4 T‐cell count was 252 cells/µL and the mean viral load was 4.2 log HIV‐1 RNA copies/mL; 56% of participants were female and 47% were randomized to receive a nucleos(t)ide reverse transcriptase inhibitor [N(t)RTI]‐based regimen [91% (53/58) were randomized to receive a tenofovir disoproxil fumarate (TDF)‐containing regimen]. Over 48 weeks, 71% in the N(t)RTI arm experienced ≥ 5% hip BMD loss vs. 29% in the raltegravir arm (P = 0.001). Week 12 changes in P1NP and CTX were significantly greater among patients experiencing ≥ 5% hip BMD loss, patients randomized to N(t)RTI, and male patients. Predictors of ≥ 5% hip BMD loss at week 48 were P1NP increase [odds ratio (OR) 5.0; 95% confidence interval (CI) 1.1–27; P < 0.043]; N(t)RTI randomization (OR 6.7; 95% CI 2.0–27.1; P < 0.003), being African, higher baseline CD4 T cell count , and smoking.
Nag, S, Shivakumara, P, Pal, U, Lu, T & Blumenstein, M 2020, 'A new unified method for detecting text from marathon runners and sports players in video (PR-D-19-01078R2)', Pattern Recognition, vol. 107, pp. 107476-107476.
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© 2020 Detecting text located on the torsos of marathon runners and sports players in video is a challenging issue due to poor quality and adverse effects caused by flexible/colorful clothing, and different structures of human bodies or actions. This paper presents a new unified method for tackling the above challenges. The proposed method fuses gradient magnitude and direction coherence of text pixels in a new way for detecting candidate regions. Candidate regions are used for determining the number of temporal frame clusters obtained by K-means clustering on frame differences. This process in turn detects key frames. The proposed method explores Bayesian probability for skin portions using color values at both pixel and component levels of temporal frames, which provides fused images with skin components. Based on skin information, the proposed method then detects faces and torsos by finding structural and spatial coherences between them. We further propose adaptive pixels linking a deep learning model for text detection from torso regions. The proposed method is tested on our own dataset collected from marathon/sports video and three standard datasets, namely, RBNR, MMM and R-ID of marathon images, to evaluate the performance. In addition, the proposed method is also tested on the standard natural scene datasets, namely, CTW1500 and MS-COCO text datasets, to show the objectiveness of the proposed method. A comparative study with the state-of-the-art methods on bib number/text detection of different datasets shows that the proposed method outperforms the existing methods.
Nagasubramanian, G, Sakthivel, RK, Patan, R, Gandomi, AH, Sankayya, M & Balusamy, B 2020, 'RETRACTED ARTICLE: Securing e-health records using keyless signature infrastructure blockchain technology in the cloud', Neural Computing and Applications, vol. 32, no. 3, pp. 639-647.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. Health record maintenance and sharing are one of the essential tasks in the healthcare system. In this system, loss of confidentiality leads to a passive impact on the security of health record whereas loss of integrity leads can have a serious impact such as loss of a patient’s life. Therefore, it is of prime importance to secure electronic health records. Health records are represented by Fast Healthcare Interoperability Resources standards and managed by Health Level Seven International Healthcare Standards Organization. Centralized storage of health data is attractive to cyber-attacks and constant viewing of patient records is challenging. Therefore, it is necessary to design a system using the cloud that helps to ensure authentication and that also provides integrity to health records. The keyless signature infrastructure used in the proposed system for ensuring the secrecy of digital signatures also ensures aspects of authentication. Furthermore, data integrity is managed by the proposed blockchain technology. The performance of the proposed framework is evaluated by comparing the parameters like average time, size, and cost of data storage and retrieval of the blockchain technology with conventional data storage techniques. The results show that the response time of the proposed system with the blockchain technology is almost 50% shorter than the conventional techniques. Also they express the cost of storage is about 20% less for the system with blockchain in comparison with the existing techniques.
Naghibi, SA, Vafakhah, M, Hashemi, H, Pradhan, B & Alavi, SJ 2020, 'Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms', Natural Resources Research, vol. 29, no. 3, pp. 1915-1933.
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Nagy, Z, Cheung, BB, Tsang, W, Tan, O, Herath, M, Ciampa, OC, Shadma, F, Carter, DR & Marshall, GM 2020, 'Withaferin A activates TRIM16 for its anti-cancer activity in melanoma', Scientific Reports, vol. 10, no. 1.
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AbstractAlthough selective BRAF inhibitors and novel immunotherapies have improved short-term treatment responses in metastatic melanoma patients, acquired resistance to these therapeutics still represent a major challenge in clinical practice. In this study, we evaluated the efficacy of Withaferin A (WFA), derived from the medicinal plant Withania Somnifera, as a novel therapeutic agent for the treatment of melanoma. WFA showed selective toxicity to melanoma cells compared to non-malignant cells. WFA induced apoptosis, significantly reduced cell proliferation and inhibited migration of melanoma cells. We identified that repression of the tumour suppressor TRIM16 diminished WFA cytotoxicity, suggesting that TRIM16 was in part responsible for the cytotoxic effects of WFA in melanoma cells. Together our data indicates that WFA has potent cytopathic effects on melanoma cells through TRIM16, suggesting a potential therapeutic application of WFA in the disease.
Naidu, G, Tijing, L, Johir, MAH, Shon, H & Vigneswaran, S 2020, 'Hybrid membrane distillation: Resource, nutrient and energy recovery', Journal of Membrane Science, vol. 599, pp. 117832-117832.
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© 2020 Elsevier B.V. Membrane distillation (MD) is a promising alternative thermal-based membrane process that can achieve high-quality freshwater across various impaired water sources. However, the performance of MD as a stand-alone system remains a challenge for attaining commercialization. Hybrid MD - the integration of MD with other processes, offers a practical approach for performance enhancement as well as the possibility to achieve valuable resource recovery. This review details the performance and related challenges of various hybrid MD systems with a focus on resource recovery. On the basis of recovering valuable salt/element from impaired water sources, hybrid MD-crystallizer is limited to the recovery of major salts. Comparatively, MD-adsorbent exhibits potential for selectively recovering valuable elements, which may offset treatment cost. Meanwhile, hybrid MD-bioreactor (MDBR) and MD-forward osmosis (MD-FO) are especially favorable combinations for attaining water reclamation from the wastewater industry and recovering nutrients and biogas that mitigates environmental pollution. Simultaneous recovery of water and energy can be attained with hybrid MD-pressure retarded osmosis (MD-PRO) and MD-reverse electrodialysis (MD-RED). Overall, this review highlights the favorable potential of hybrid MD for recovering resources in niche applications. Future suggestions for improving hybrid MD are discussed, specifically pilot-scale application, module configuration and membrane development.
Naji, M, Braytee, A, Al-Ani, A, Anaissi, A, Goyal, M & Kennedy, PJ 2020, 'Design of airport security screening using queueing theory augmented with particle swarm optimisation', Service Oriented Computing and Applications, vol. 14, no. 2, pp. 119-133.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. Designing an efficient and reliable airport security screening system is a critical and challenging task. It is an essential element of airline and passenger safety which aims to provide the expected level of confidence and to ensure the safety of passengers and the aviation industry. In recent years, security at airports has gone through noticeable improvements with the utilisation of advanced technology and highly trained security officers. However, for many airports, it is important to find the best compromise between the capacity of the security area, the number of passengers and the number of screening machines and officers to maintain a high level of security and to ensure that the cost and waiting times for passengers and airlines are at acceptable levels. This paper proposes a novel method based on queueing theory augmented with particle swarm optimisation (QT-PSO) to predict passenger waiting times in a security screening context. This model consists of multiple servers operating in parallel and takes into consideration the complete scenario such as normal, slow and express lanes. Such an approach has the potential to be a reliable model that is able to assimilate variations in the number of passengers, security officers and security machines on the service time. To evaluate our proposed method, we collected real-world security screening data from an Australian airport from December to March for the two consecutive years of 2016 and 2017. The results show that our proposed QT-PSO method is superior to predict the average waiting time of passengers compared to the state of the art.
Nakagawa, K, Uchida, K, Wu, JLC, Shintani, T, Yoshioka, T, Sasaki, Y, Fang, L-F, Kamio, E, Shon, HK & Matsuyama, H 2020, 'Fabrication of porous polyketone forward osmosis membranes modified with aromatic compounds: Improved pressure resistance and low structural parameter', Separation and Purification Technology, vol. 251, pp. 117400-117400.
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Nan, Y, Huang, X & Guo, YJ 2020, 'A Millimeter-Wave GCW-SAR Based on Deramp-on-Receive and Piecewise Constant Doppler Imaging', IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 1, pp. 680-690.
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© 2019 IEEE. A novel generalized continuous-wave synthetic aperture radar (GCW-SAR) based on deramp-on-receive operating in millimeter-wave frequency is proposed in this article. With deramp-on-receive, the receiver sampling rate is drastically reduced, and the downsampled 1-D raw data can be obtained from the received beat signal. Further adopting piecewise constant Doppler (PCD) imaging in the digital domain, a GCW-SAR image can be easily reconstructed by using the existing frequency-modulated continuous-wave (FMCW) radar system. The effects of deramp-on-receive in PCD imaging are analyzed accordingly. The short wavelength of the millimeter-wave carrier used in the proposed GCW-SAR enables high azimuth resolution as well as a short synthetic aperture, which, in turn, significantly reduces the imaging computational complexity. Simulation and experimental results confirm the advantages of the proposed GCW-SAR.
Nan, Y, Huang, X & Guo, YJ 2020, 'Piecewise Constant Doppler Algorithm: Performance Analysis, Further Simplification, and Motion Compensation', IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 5, pp. 3613-3631.
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IEEE The piecewise constant Doppler (PCD) algorithm is a novel radar imaging process recently proposed for the generalized continuous wave synthetic aperture radar (GCW-SAR). This paper presents a detailed theoretical analysis on the PCD algorithm's performance and proposes a further complexityreduced PCD algorithm with motion compensation (MOCO) suitable for practical applications. Firstly, the difference between conventional SAR imaging and PCD imaging, i.e., the zeroth order versus the first order slant range approximation, is revealed. Exact ambiguity function expressions of the PCD imaging in range and azimuth directions respectively are then derived. An error function of the PCD imaging as compared with the ideal matched filtering method is further defined and shown to be a function of an image quality factor which can be used to quantify the PCD imaging performance. Finally, a faster and more flexible imaging process, called decimated PCD algorithm, is proposed, by which the image azimuth spacing can be easily extended and hence the computational complexity can be significantly reduced. The decimated PCD implementation incorporated with the MOCO is developed for practical GCWSAR applications and its imaging error lower-bounded by the PCD imaging error function is analyzed accordingly. Simulation and experimental results validate the theoretical analysis of the PCD imaging and show that the decimated PCD algorithm can achieve a high imaging quality at low cost.
Nanda, A, Nanda, P, He, X, Jamdagni, A & Puthal, D 2020, 'A hybrid encryption technique for Secure-GLOR: The adaptive secure routing protocol for dynamic wireless mesh networks', Future Generation Computer Systems, vol. 109, pp. 521-530.
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© 2018 Elsevier B.V. As we progress in into a digital era where most aspects of our life depend upon a network of computers, it is essential to focus on digital security. Each component of a network, be it a physical network, virtual network or social network requires security when transmitting data. Hence the dynamic wireless mesh network must also deploy high levels of security as found in current legacy networks. This paper presents a secure Geo-Location Oriented Routing (Secure-GLOR) protocol for wireless mesh networks, which incorporates a hybrid encryption scheme for its multilevel security framework. The hybrid encryption technique improves the network's overall performance compared to the basic encryption by using a combination of symmetric key as well as asymmetric key encryption. Using the combination of the two encryption schemes, the performance of the network can be improved by reducing the transmitted data size, reduced computational overhead and faster encryption–decryption cycles. In this paper discussed multiple encryption schemes for both symmetric and asymmetric encryption, compare their performance in various experimental scenarios. Proposed security scheme achieves better performance based on the results obtained with most viable options for our network model.
Nanda, P, He, X & Yang, LT 2020, 'Security, Trust and Privacy in Cyber (STPCyber): Future trends and challenges', Future Generation Computer Systems, vol. 109, pp. 446-449.
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© 2020 Today's world experiences massively interconnected devices to share information across variety of platforms between traditional computers (machines), Smart IoT devices used across smart homes, smart interconnected vehicles etc. and of course the social networks apps such as Facebook, Linkdn, twitter etc. We experience the growth has been skyrocketing and the trend will continue exponentially to the future. At one end, we find life becomes easier with such developments and at the other end; we experience more and more cyber threats on our privacy, security and trustworthiness with organizations holding our data. In this special issue, we summarize contributions by authors in advanced topics related to security, trust and privacy based on a range of applications and present a selection of the most recent research efforts in these areas.
Naseem, U, Razzak, I, Musial, K & Imran, M 2020, 'Transformer based Deep Intelligent Contextual Embedding for Twitter sentiment analysis', Future Generation Computer Systems, vol. 113, pp. 58-69.
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© 2020 Elsevier B.V. Along with the emergence of the Internet, the rapid development of handheld devices has democratized content creation due to the extensive use of social media and has resulted in an explosion of short informal texts. Although a sentiment analysis of these texts is valuable for many reasons, this task is often perceived as a challenge given that these texts are often short, informal, noisy, and rich in language ambiguities, such as polysemy. Moreover, most of the existing sentiment analysis methods are based on clean data. In this paper, we present DICET, a transformer-based method for sentiment analysis that encodes representation from a transformer and applies deep intelligent contextual embedding to enhance the quality of tweets by removing noise while taking word sentiments, polysemy, syntax, and semantic knowledge into account. We also use the bidirectional long- and short-term memory network to determine the sentiment of a tweet. To validate the performance of the proposed framework, we perform extensive experiments on three benchmark datasets, and results show that DICET considerably outperforms the state of the art in sentiment classification.
Naseer, A, Rani, M, Naz, S, Razzak, MI, Imran, M & Xu, G 2020, 'RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning', Neural Computing and Applications, vol. 32, no. 3, pp. 839-854.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work.
Nasir, AA, Tuan, HD, Duong, TQ & Hanzo, L 2020, 'Transmitter-Side Wireless Information- and Power-Transfer in Massive MIMO Systems', IEEE Transactions on Vehicular Technology, vol. 69, no. 2, pp. 2322-2326.
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© 1967-2012 IEEE. Both time-switching (TS) and power splitting has been used at the receiver for wireless information and power transfer in the downlink of massive multiple-input-multiple-output systems. By contrast, this correspondence adopts the transmit-TS approach, where the energy and information are transferred over different fractions of a time slot. Our goal is to jointly optimize the transmit-TS factor and power allocation coefficients during energy and information transfer for maximizing the users' minimum throughput subject to transmit power and minimum harvested energy constraints. This nonconvex problem is solved by our path following algorithm. Our simulation results demonstrate the benefits of the proposed transmit-TS algorithm, which easily doubles the throughput compared to that of the existing techniques.
Nasir, AA, Tuan, HD, Duong, TQ & Poor, HV 2020, 'MIMO-OFDM-Based Wireless-Powered Relaying Communication With an Energy Recycling Interface', IEEE Transactions on Communications, vol. 68, no. 2, pp. 811-824.
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© 2019 IEEE. This paper considers wireless-powered relaying multiple-input-multiple-output (MIMO) communication, where all four nodes (information source, energy source, relay, and destination) are equipped with multiple antennas. Orthogonal frequency division multiplexing (OFDM) is applied for information processing to compensate the frequency selectivity of communication channels between the information source and the relay and between the relay and the destination as these nodes are assumed to be located far apart from each. The relay is equipped with a full-duplexing interface for harvesting energy not only from the wireless transmission of the dedicated energy source but also from its own transmission while relaying the source information to the destination. The problem of designing the optimal power allocation over OFDM subcarriers and transmit antennas to maximize the overall spectral efficiency is addressed. Due to a very large number of subcarriers, this design problem poses a large-scale nonconvex optimization problem involving a few thousand variables of power allocation, which is very computationally challenging. A novel path-following algorithm is proposed for computation. Based on the developed closed-form calculation of linear computational complexity at each iteration, the proposed algorithm rapidly converges to an optimal solution. Compared to the best existing solvers, the computational complexity of the proposed algorithm is reduced at least 105 times, making it very efficient and practical for online computation while existing solvers are ineffective. Numerical results for a practical simulation setting show promising results by achieving high spectral efficiency.
Nasir, AA, Tuan, HD, Duong, TQ, Poor, HV & Hanzo, L 2020, 'Hybrid Beamforming for Multi-User Millimeter-Wave Networks', IEEE Transactions on Vehicular Technology, vol. 69, no. 3, pp. 2943-2956.
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© 1967-2012 IEEE. This paper considers hybrid beamforming by combining an analog beamformer with a new regularized zero forcing baseband one, for multi-user millimeter-wave networks under a limited number of radio frequency (RF) chains. Three popular scenarios are examined: i) the number of users is up to the number of RF chains in a single-cell network, ii) the number of users is up to twice the number of RF chains in a single-cell network, and iii) the number of users is up to twice the number of RF chains in each cell of a two-cell network. In the second and third scenarios, we group the users into two categories of cell-center users as well as cell-edge users and serve them in two different time fractions. In the third scenario, we propose to suppress the inter-cell interference by serving the cell-center and cell-edge users in alternate fractional-time slots. In all the three scenarios, we determine the optimal power allocation maximizing the users' minimum rate. Finally, low-complexity path-following algorithms having rapid convergence are developed for the computation of the optimal power. Our simulation results show that the proposed algorithms achieve a clear performance gain over the existing benchmarkers.
Nasir, AA, Tuan, HD, Nguyen, HH, Duong, TQ & Poor, HV 2020, 'Signal Superposition in NOMA With Proper and Improper Gaussian Signaling', IEEE Transactions on Communications, vol. 68, no. 10, pp. 6537-6551.
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© 1972-2012 IEEE. Recent studies of single-cell two-user networks have shown that a higher network throughput is achieved by using a common message to be decoded by both users and conveying partial information for both users, rather than using the common message to convey the entire information for one of the two users. The latter is essentially the conventional non-orthogonal multiple access (NOMA), which performs better than orthogonal multiple access (OMA) only under users' dissimilar channel conditions. Unlike NOMA, the former performs consistently better than OMA. This paper generalizes such a signaling strategy to a general multi-cell multiuser network, which leads to a new NOMA approach (called n-NOMA) in which each pair of users decodes a message that conveys partial information for one of them only. Unlike the conventional NOMA, whose performance is dependent on the users' pairing strategy, the proposed n-NOMA consistently outperforms both NOMA and OMA schemes. Both proper and improper Gaussian signaling is considered for all the concerned schemes and it is shown that the latter is clearly more advantageous than the former.
Nasouri Gilvaei, M, Jafari, H, Jabbari Ghadi, M & Li, L 2020, 'A novel hybrid optimization approach for reactive power dispatch problem considering voltage stability index', Engineering Applications of Artificial Intelligence, vol. 96, pp. 103963-103963.
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© 2020 Elsevier Ltd This paper proposes a novel, reliable, and effective hybrid approach based on the integration of the firefly algorithm (FA) and the adaptive particularly tunable fuzzy particle swarm optimization (APT-FPSO) method to address reactive power dispatch (RPD) problem, a crucial optimization problem in the operation of power systems. Similar to many other original meta-heuristic optimization techniques, the standard FA suffers from some severe drawbacks, most importantly being easily trapped into a locally optimal solution. In order to tackle these difficulties, in the current study, an improved version of fuzzy-based particle swarm optimization is utilized in the internal structure of the original FA. The developed hybrid approach, which is capable of avoiding premature convergence of the original FA by enhancing exploration and exploitation procedures, is employed to determine the optimum control variables (i.e., the voltage of generation buses, tap positions of tap-changer transformers, and reactive power output of shunt compensators) through optimizing three distinct objective functions consisting of total transmission real power loss, the voltage magnitude deviations as well as voltage stability index. To validate the accuracy and competency of the proposed hybrid approach, it is firstly used for solving several benchmark optimization functions and then applied to three test systems at different scales, consisting of IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus power systems, for solving the RPD problem. Eventually, the results of the presented hybrid method will be compared to those obtained by other implemented swarm intelligence-based approaches. The statistical analysis of this research substantiates the robustness and effectiveness of the developed algorithm to handle sophisticated optimization problems, particularly the RPD problem.
Nasser, A, Castel, A & Merimi, I 2020, 'Influence of steel-concrete interface and pre-existing oxides layer on passive reinforcing steel corrosion', ARPN Journal of Engineering and Applied Sciences, vol. 15, no. 15, pp. 1622-1631.
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This paper deals with the influence of the steel-concrete interface quality and preexisting oxides layer on reinforcement corrosion in passive state. In passive state, steel corrosion rate in concrete is considered null for conventional civil engineering structures due to the relatively short design service life time. On the contrary, for the nuclear waste facilities, due to a very long design life time, this low corrosion rate can become a risk. Previous studies, dealing with chloride induced steel corrosion in concrete, have clearly shown that the quality of the steel-concrete interface is a predominant factor for corrosion propagation. The purpose of this work is to study the influence of steel-concrete interface defaults and preexisting oxides layer on steel passivity and the consequences on the corrosion rate. Electrochemical methods and destructive surface analysis techniques were used to assess the corrosion rate of the embedded steel bars. Results confirm that the quality of the steel-concrete interface and the preexisting oxides layer affect the steel corrosion rate in passive state.
Natarajan, S, Vairavasundaram, S, Natarajan, S & Gandomi, AH 2020, 'Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data', Expert Systems with Applications, vol. 149, pp. 113248-113248.
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© 2020 The web contains a huge volume of data, and it's populating every moment to the point that human beings cannot deal with the vast amount of data manually or via traditional tools. Hence an advanced tool is required to filter such massive data and mine the valuable information. Recommender systems are among the most excellent tools for such a purpose in which collaborative filtering is widely used. Collaborative filtering (CF) has been extensively utilized to offer personalized recommendations in electronic business and social network websites. In that, matrix factorization is an efficient technique; however, it depends on past transactions of the users. Hence, there will be a data sparsity problem. Another issue with the collaborative filtering method is the cold start issue, which is due to the deficient information about new entities. A novel method is proposed to overcome the data sparsity and the cold start problem in CF. For cold start issue, Recommender System with Linked Open Data (RS-LOD) model is designed and for data sparsity problem, Matrix Factorization model with Linked Open Data is developed (MF-LOD). A LOD knowledge base “DBpedia” is used to find enough information about new entities for a cold start issue, and an improvement is made on the matrix factorization model to handle data sparsity. Experiments were done on Netflix and MovieLens datasets show that our proposed techniques are superior to other existing methods, which mean recommendation accuracy is improved.
Navaratnarajah, SK & Indraratna, B 2020, 'Stabilisation of Stiffer Rail Track Substructure Using Artificial Inclusion', Indian Geotechnical Journal, vol. 50, no. 2, pp. 196-203.
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© 2020, Indian Geotechnical Society. The railway transport system in many countries plays a significant role in the passage of bulk freight and passengers. However, increased train speeds and higher freight loads (large dynamic wheel loads) accelerate the deterioration of rail track substructure. This problem is more critical in isolated rail track locations where the track substructure is much stiffer than the regular surface track assembly such as track at the bridges and tunnels. Ballast is a key track foundation material placed underneath the sleepers which provides structural support against high cyclic and impact stresses caused by moving trains. Inclusion of rubber mats called under ballast mats (UBMs) placed between the ballast and stiffer base layer is one of the measures to minimise the ballast deterioration. In this study, cyclic loads representing fast and heavy haul trains were simulated on stiffer track foundation condition using a large-scale process simulation prismoidal triaxial apparatus to investigate the mitigation of strain, stress and degradation characteristics of ballast stabilised with UBM. These UBMs were locally manufactured from recycled tyre wastes. The results show that ballast on a stiff foundation substructure stabilised with UBM experienced significantly less vertical and lateral deformation, ballast interface and inter-particle stresses and degradation. This study also confirmed that the recycled tyre UBMs used in this study had adequate damping to absorb the energy transmitted to the moving train to the track, thus preventing excessive plastic deformation and degradation of the ballast layer.
Nayak, DR, Dash, R, Chang, X, Majhi, B & Bakshi, S 2020, 'Automated Diagnosis of Pathological Brain Using Fast Curvelet Entropy Features', IEEE Transactions on Sustainable Computing, vol. 5, no. 3, pp. 416-427.
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Automated diagnosis of pathological brain not only reduces the diagnostic error significantly but also improves the patient's quality of life, thereby addressing the sustainability issues. The last few decades have witnessed an intensive research on binary classification of brain magnetic resonance (MR) images. Multiclass classification of pathological brain MR images is a more challenging task and the literature on this problem is still in its infancy. In this paper, we propose a new automated diagnosis system to classify the brain MR images into five different categories. Texture features within MR images play a significant role in accurate and efficient pathological brain detection. This work presents the extraction of such vital texture features by calculating the entropy over the curvelet subbands. Two faster and simpler strategies of fast curvelet transform are separately employed for feature extraction and the derived features are termed as FCEntF-I and FCEntF-II. The features are finally subjected to kernel extreme learning machine (K-ELM) for classification. The effectiveness of the proposed scheme is evaluated on multiclass as well as binary brain MR datasets. Comparisons with state-of-the-art methods indicate the superiority of the proposed scheme. The discriminatory potential of FCEntF-I and FCEntF-II features is found better than its counterparts.
Neophytou, N, Vargiamidis, V, Foster, S, Graziosi, P, de Sousa Oliveira, L, Chakraborty, D, Li, Z, Thesberg, M, Kosina, H, Bennett, N, Pennelli, G & Narducci, D 2020, 'Hierarchically nanostructured thermoelectric materials: challenges and opportunities for improved power factors', The European Physical Journal B, vol. 93, no. 11.
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AbstractThe field of thermoelectric materials has undergone a revolutionary transformation over the last couple of decades as a result of the ability to nanostructure and synthesize myriads of materials and their alloys. TheZTfigure of merit, which quantifies the performance of a thermoelectric material has more than doubled after decades of inactivity, reaching values larger than two, consistently across materials and temperatures. Central to thisZTimprovement is the drastic reduction in the material thermal conductivity due to the scattering of phonons on the numerous interfaces, boundaries, dislocations, point defects, phases, etc., which are purposely included. In these new generation of nanostructured materials, phonon scattering centers of different sizes and geometrical configurations (atomic, nano- and macro-scale) are formed, which are able to scatter phonons of mean-free-paths across the spectrum. Beyond thermal conductivity reductions, ideas are beginning to emerge on how to use similar hierarchical nanostructuring to achieve power factor improvements. Ways that relax the adverse interdependence of the electrical conductivity and Seebeck coefficient are targeted, which allows power factor improvements. For this, elegant designs are required, that utilize for instance non-uniformities in the underlying nanostructured geometry, non-uniformities in the dopant distribution, or potential barriers that form at boundaries between materials. A few recent reports, both theoretical and experimental, indicate that extremely high power factor values can be achieved, even for the same geometries that also provide ultra-low thermal conductivities. Despite the experimental complications that can arise in having the required control in nanostructure realization, in this colloquium, we aim to demonstrate, mostly theoretically, that it is a very promising path wort...
Nerse, C, Wang, S & Goo, S 2020, 'Effect of damping distribution on coupling in panel–cavity systems: Conditions for optimality through a modal approach', International Journal of Mechanical Sciences, vol. 187, pp. 105908-105908.
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© 2020 In this study, we examine an acoustic–structure interaction problem for nonproportionally damped systems. Coupled responses for a structure and acoustic enclosure are derived using a modified modal coupling formulation. Uncoupled modal patterns are used to assess the coupling effectiveness. Comparison with a proportional damping case reveals characteristics that associate complex modes with damping optimality. Such an interrelation is investigated through topology optimization of a damping layer to minimize the acoustic pressure in the cavity. The findings of this numerical study indicate a spatial relation between the imaginary part of the coupling coefficient and the optimal damping layout. Further investigation of complex modal patterns with wave interpretation shows that the optimal damping characteristics of the panel can be expressed by a spatially varying nonproportional damping index. Case studies involving various nonproportional damping configurations are presented to confirm the significant correlation with respect to observed phenomena.
Neshat, M, Alexander, B & Wagner, M 2020, 'A hybrid cooperative co-evolution algorithm framework for optimising power take off and placements of wave energy converters', Information Sciences, vol. 534, pp. 218-244.
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Neshat, M, Alexander, B, Sergiienko, NY & Wagner, M 2020, 'New insights into position optimisation of wave energy converters using hybrid local search', Swarm and Evolutionary Computation, vol. 59, pp. 100744-100744.
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Ng, PQ, Ling, LSC, Chellian, J, Madheswaran, T, Panneerselvam, J, Kunnath, AP, Gupta, G, Satija, S, Mehta, M, Hansbro, PM, Collet, T, Dua, K & Chellappan, DK 2020, 'Applications of Nanocarriers as Drug Delivery Vehicles for Active Phytoconstituents', Current Pharmaceutical Design, vol. 26, no. 36, pp. 4580-4590.
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Many plant-based bioactive compounds have been serving as the origin of drugs since long ago andmany of them have been proven to have medicinal value against various chronic diseases, including, cancer,arthritis, hepatic diseases, type-2 diabetes and cardiovascular diseases. However, their clinical applications havebeen limited due to their poor water solubility, stability, low bioavailability and extensive transformation due tothe first-pass metabolism. The applications of nanocarriers have been proven to be able to improve the delivery ofbioactive phytoconstituents, resulting in the enhancement of various pharmacokinetic properties and therebyincreasing the therapeutic value of phytoconstituents. These biocompatible nanocarriers also exert low toxicity tohealthy cells. This review focuses on the uses and applications of different types of nanocarriers to enhance thedelivery of phytoconstituents for the treatment of various chronic diseases, along with comparisons related tobioavailability and therapeutic efficacy of nano phytoconstituents with native phytoconstituents.
Nghiem, LD, Morgan, B, Donner, E & Short, MD 2020, 'The COVID-19 pandemic: Considerations for the waste and wastewater services sector', Case Studies in Chemical and Environmental Engineering, vol. 1, pp. 100006-100006.
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Ngo, CQ, Chai, R, Nguyen, TV, Jones, TW & Nguyen, HT 2020, 'Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia', IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 5, pp. 1237-1245.
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Hypoglycemia or low blood glucose is the most feared complication of insulin treatment of diabetes. For people with diabetes, the mismatch between the insulin therapy and the body's physiology could increase the risk of hypoglycemia. Nocturnal hypoglycemia is particularly dangerous for type-1 diabetes patients because its symptoms may obscure during sleep. The early onset detection of hypoglycemia at night time is necessary because it can result in unconsciousness and even death. This paper presents new electroencephalogram spectral features for nocturnal hypoglycemia detection. The system uses high-order spectral moments for feature extraction and Bayesian neural network for classification. From a clinical study of hypoglycemia of eight patients with type-1 diabetes at night, we find that these spectral moments of theta band and alpha band changed significantly. During hypoglycemia episodes, the theta moments increased significantly (P < 0.001) while the features of alpha band reduced significantly (P < 0.001). Using the optimal Bayesian neural network, the classification results were 85% and 52% in sensitivity and specificity, respectively. The significant correlation (P < 0.001) with real blood glucose profiles shows the effectiveness of the proposed features for the detection of nocturnal hypoglycemia.
Ngo, QT, Dang, DNM & Le‐Trung, Q 2020, 'Extreme power saving directional MAC protocol in IEEE 802.11ah networks', IET Networks, vol. 9, no. 4, pp. 180-188.
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In this study, a novel directional medium access control scheme with an extreme power saving mechanism is developed for traffic indication map stations in the IEEE 802.11ah networks. To improve the aggregate throughput, stations are managed based on their geographical locations in combination with the employment of directional operation mode on the access point. In addition to the restricted access window mechanism used in the IEEE 802.11ah standard for power saving, an adaptive transmission power scheme is proposed for uplink transmission to leverage the power‐saving efficiency. With the proposed access scheme, the network performance is significantly enhanced compared to the IEEE 802.11ah standard. The analytical models for throughput and energy consumption are formulated using Markov Chains under unsaturated conditions. The simulation results indicate the effectiveness of the proposed directional access scheme under various network scenarios.
Ngo, T & Indraratna, B 2020, 'Analysis of Deformation and Degradation of Fouled Ballast: Experimental Testing and DEM Modeling', International Journal of Geomechanics, vol. 20, no. 9, pp. 06020020-06020020.
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© 2020 American Society of Civil Engineers. The deformation and degradation of fouled ballast have been examined by large-scale triaxial tests and discrete element modeling (DEM) to understand how clay fouling changes the shear strength and micromechanical aspects of ballast. Particle shape analysis using 3D aggregate imaging and a laser scanner is introduced to construct more realistic polyhedral discrete elements that will represent natural ballast particles. Shear stress-strain and volumetric changes of fresh and clay-fouled ballast are analyzed. Micromechanical analysis of the fouled ballast is carried out and the effects of fines are quantified by considering the changes of ballast breakage, particle connectivity number Cn, and the associated distribution of contact forces that could not be measured experimentally. These findings enable a more insightful understanding of the load-deformation of fouled ballast from a micromechanical perspective.
Ngo, T & Indraratna, B 2020, 'Mitigating ballast degradation with under-sleeper rubber pads: Experimental and numerical perspectives', Computers and Geotechnics, vol. 122, pp. 103540-103540.
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© 2020 Elsevier Ltd This paper presents a study on mitigating the degradation of ballast by placing an under-sleeper rubber pad (USP) beneath a sleeper. Large-scale track process simulation apparatus (TPSA) tests have been carried out on ballast assemblies (with and without USP) subjected to cyclic loadings. Numerical modelling has been performed using a coupled discrete-continuum modelling (coupled DEM-FDM) approach to investigate the role of USP from a micromechanical perspective. Ballast grains are simulated in DEM by bonding of many cylinders together at appropriate sizes and locations; and when those bonds break, they are considered to represent ballast breakage. The capping and subgrade layers are simulated as continuum media using the finite difference method (FDM). Interface elements were developed for transmitting forces and displacements between the discrete and continuum domains. The coupled model is validated by comparing the predicted load-deformation responses with those measured from large-scale TPSA tests. The model is then used to explore changes in the micromechanical aspects of ballast subjected to cyclic loading, including particle connectivity number, contact force distributions, and contact orientations and associated particle breakage. These findings are needed to gain a better insight as to how USPs help to attenuate the load applied in a ballast assembly.
Ngoc, TP, Fatahi, B, Khabbaz, H & Sheng, D 2020, 'Impacts of matric suction equalization on small strain shear modulus of soils during air drying', Canadian Geotechnical Journal, vol. 57, no. 12, pp. 1982-1997.
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In this study, a weight-control bender element system has been developed to investigate the impact of matric suction equalization on the measurement of small strain shear modulus (Gmax) during an air-drying process. The setup employed is capable of measuring the shear wave velocity and the corresponding Gmaxof the soil sample in either an open system in which the soil sample evaporates freely or in a closed system that allows the process of matric suction equalization. The comparison between measurements of Gmaxin the open and closed systems revealed underestimations of Gmaxwhen matric suction equalization was ignored due to the nonuniform distribution of water content across the sample cross-sectional area. This study also investigated the time required for matric suction equalization tseto be established for samples with different sizes. The experimental results indicated two main mechanisms driving the matric suction equalization in a closed system during an air-drying process, namely the hydraulic flow of water and the flow of vapour. While the former played the key role when the micropores were still saturated at the high range of water content, effects of the latter increased and finally dominated when more air invaded the micropores at lower water contents.
Nguyen, AQ, Nguyen, LN, Johir, MAH, Ngo, H-H, Chaves, AV & Nghiem, LD 2020, 'Derivation of volatile fatty acid from crop residues digestion using a rumen membrane bioreactor: A feasibility study', Bioresource Technology, vol. 312, pp. 123571-123571.
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Nguyen, CT, Saputra, YM, Huynh, NV, Nguyen, N-T, Khoa, TV, Tuan, BM, Nguyen, DN, Hoang, DT, Vu, TX, Dutkiewicz, E, Chatzinotas, S & Ottersten, B 2020, 'A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies', IEEE Access, vol. 8, pp. 153479-153507.
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Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice.
Nguyen, CT, Saputra, YM, Huynh, NV, Nguyen, N-T, Khoa, TV, Tuan, BM, Nguyen, DN, Hoang, DT, Vu, TX, Dutkiewicz, E, Chatzinotas, S & Ottersten, B 2020, 'Enabling and Emerging Technologies for Social Distancing: A Comprehensive Survey and Open Problems', IEEE Access, vol. 8, pp. 153479-153507.
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Social distancing plays a pivotal role in preventing the spread of viraldiseases illnesses such as COVID-19. By minimizing the close physical contactamong people, we can reduce the chances of catching the virus and spreading itacross the community. This paper aims to provide a comprehensive survey on howemerging technologies, e.g., wireless and networking, artificial intelligence(AI) can enable, encourage, and even enforce social distancing practice. Tothat end, we first provide a comprehensive background of social distancingincluding basic concepts, measurements, models, and propose various practicalsocial distancing scenarios. We then discuss enabling wireless technologieswhich are especially effective and can be widely adopted in practice to keepdistance, encourage, and enforce social distancing in general. After that,other emerging and related technologies such as machine learning, computervision, thermal, ultrasound, etc., are introduced. These technologies open manynew solutions and directions to deal with problems in social distancing, e.g.,symptom prediction, detection and monitoring quarantined people, and contacttracing. Finally, we provide important open issues and challenges (e.g.,privacy-preserving, scheduling, and incentive mechanisms) in implementingsocial distancing in practice. As an example, instead of reacting with ad-hocresponses to COVID-19-like pandemics in the future, smart infrastructures(e.g., next-generation wireless systems like 6G, smart home/building, smartcity, intelligent transportation systems) should incorporate a pandemic mode inits standard architecture/design.
Nguyen, CT, Saputra, YM, Van Huynh, N, Nguyen, N-T, Khoa, TV, Tuan, BM, Nguyen, DN, Hoang, DT, Vu, TX, Dutkiewicz, E, Chatzinotas, S & Ottersten, B 2020, 'A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues', IEEE Access, vol. 8, pp. 154209-154236.
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This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs
Nguyen, DM, Ding, G & Runeson, G 2020, 'Energy and economic analysis of environmental upgrading of existing office buildings', Construction Economics and Building, vol. 20, no. 4, pp. 82-102.
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Over many decades, buildings have been recognised as a significant area contributing to the negative impacts on the environment over their lifecycle, accelerating climate change. In return, climate change also impacts on buildings with extreme heatwaves occurring more frequently and raising the earth’s temperature. The operation phase is the most extended period over a building’s lifespan. In this period, office buildings consume most energy and emit the highest amount of greenhouse gas pollution into the environment. Building upgrading to improve energy efficiency seems to be the best way to cut pollution as the existing building stock is massive. The paper presents an economic analysis of energy efficiency upgrade of buildings with a focus of office buildings. The paper identifies upgrading activities that are commonly undertaken to upgrade energy efficiency of office buildings and a case study of three office buildings in Sydney, Australia has been used to analyse the results. The upgrading activities can improve the energy performance of the case study buildings from 3 stars to 5 stars NABERS energy rating in compliance with the mandatory requirement in the Australian government’s energy policy. With the potential increase in energy price, energy efficiency upgrading will become more affordable, but currently, most of them, except solar panels and motion sensors show a negative return and would not be undertaken if they did not also contribute to higher rental income and an increased life span of the building. The upgrading discussed in the paper represent a potentially attractive alternative to demolition and building anew.
Nguyen, H, Moayedi, H, Foong, LK, Al Najjar, HAH, Jusoh, WAW, Rashid, ASA & Jamali, J 2020, 'Optimizing ANN models with PSO for predicting short building seismic response', Engineering with Computers, vol. 36, no. 3, pp. 823-837.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. The present study aimed to optimize the artificial neural network (ANN) with one of the well-established optimization algorithms called particle swarm optimization (PSO) for the problem of ground response approximation in short structures. Various studies showed that ANN-based solutions are a reliable method for complex engineering problems. Predicting the ground surface respond to seismic loading is one of the engineering problems that still has not received any ANN solution. Therefore, this paper aimed to assess the application of hybrid PSO-based ANN models to the calculation of horizontal deflection of columns in short building after being subjected to a significant seismic loading (e.g., The Chi-Chi earthquake used as one of the input databases). To prepare both of the training and testing datasets, for the ANN and PSO-ANN network models, a series of finite element (FE) modeling were performed. The used FEM simulation database consists of 8324 training datasets and 2081 testing datasets that is equal to 80% and 20% of the whole database, respectively. The input includes Chi-Chi earthquake dynamic time (s), friction angle (φ), dilation angle (ψ), unit weight (γ), soil elastic modulus (E), Poisson’s ratio (v), structure axial stiffness (EA), and bending stiffness (EI) where the output was taken horizontal deflection of the columns at their highest level (Ux). The result indicates higher reliability of the PSO-ANN model in estimating the ground response and horizontal deflection of structural columns in short structures after being subjected to earthquake loading.
Nguyen, HC, Ong, HC, Pham, TTT, Dinh, TKK & Su, C 2020, 'Microwave‐mediated noncatalytic synthesis of ethyl levulinate: A green process for fuel additive production', International Journal of Energy Research, vol. 44, no. 3, pp. 1698-1708.
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Nguyen, HC, Wang, F-M, Dinh, KK, Pham, TT, Juan, H-Y, Nguyen, NP, Ong, HC & Su, C-H 2020, 'Microwave-Assisted Noncatalytic Esterification of Fatty Acid for Biodiesel Production: A Kinetic Study', Energies, vol. 13, no. 9, pp. 2167-2167.
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This study developed a microwave-mediated noncatalytic esterification of oleic acid for producing ethyl biodiesel. The microwave irradiation process outperformed conventional heating methods for the reaction. A highest reaction conversion, 97.62%, was achieved by performing esterification with microwave irradiation at a microwave power of 150 W, 2:1 ethanol:oleic acid molar ratio, reaction time of 6 h, and temperature of 473 K. A second-order reaction model (R2 of up to 0.997) was established to describe esterification. The reaction rate constants were promoted with increasing microwave power and temperature. A strong linear relation of microwave power to pre-exponential factors was also established, and microwave power greatly influenced the reaction due to nonthermal effects. This study suggested that microwave-assisted noncatalytic esterification is an efficient approach for biodiesel synthesis.
Nguyen, HG & Nguyen, TV 2020, 'An epidemiologic profile of COVID-19 patients in Vietnam'.
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AbstractBackground and AimThere is a paucity of data on the COVID-19 pandemic in Vietnam. In this paper, we sought to provide an epidemiologic description of patients who were infected with SARS-Cov-2 in Vietnam.MethodsData were abstracted from the wikipedia’s COVID-19 information resource and Johns Hopkins University Dashboard. Demographic data and treatment status were obtained for each patient in each day. The coverage period was from 23/1/2020 to 10/4/2020. Descriptive analyses of incident cases were stratified by gender and age group. The estimation of the reproduction ratio was done with a bootstrap method using the R statistical environment.ResultsDuring the coverage period, Vietnam has recorded 257 cases of COVID-19. Approximately 54% of the cases were women. The median age of patients was 30 years (range: 3 months to 88 years), with 78% of patients aged 49 or younger. About 66% (n = 171) of patients were overseas tourists (20%) and Vietnamese students or workers returning from overseas (46%). Approximately 57% (n = 144) of patients have been recovered and discharged from hospitals. There have been no mortality. The reproduction ratio was estimated to range between 0.95 and 1.24.ConclusionThese data indicate that a majority of COVID-19 patients in Vietnam was imported cases in overseas tourists and young students and workers who had returned from overseas.
Nguyen, HG, Pham, MTD, Ho-Pham, LT & Nguyen, TV 2020, 'Lean mass and peak bone mineral density', Osteoporosis and Sarcopenia, vol. 6, no. 4, pp. 212-216.
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Nguyen, HT, Tuan, HD, Duong, TQ, Poor, HV & Hwang, W-J 2020, 'Joint D2D Assignment, Bandwidth and Power Allocation in Cognitive UAV-Enabled Networks', IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 3, pp. 1084-1095.
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© 2015 IEEE. This paper considers a cognitive communication network, which consists of a flying base station deployed by an unmanned aerial vehicle (UAV) to serve its multiple downlink ground terminals (GTs), and multiple underlaid device-to-device (D2D) users. To support the GTs' throughput while guaranteeing the quality-of-service for the D2D users, the paper proposes the joint design of D2D assignment, bandwidth, and power allocation. This design task poses a computationally challenging mixed-binary optimization problem, for which a new computational method for its solution is developed. Multiple binary (discrete) constraints for the D2D assignment are equivalently expressed by continuous constraints to leverage systematic processes of continuous optimization. As a result, this problem of mixed-binary optimization is reformulated by an exactly penalized continuous optimization problem, for which an alternating descent algorithm is proposed. Each round of the algorithm invokes two simple convex optimization problems of low computational complexity. The theoretical convergence of the algorithm can be easily proved and the provided numerical results demonstrate its rapid convergence to an optimal solution. Such a cognitive network is even more desirable as it outperforms a non-cognitive network, which uses a partial bandwidth for D2D users only.
Nguyen, HT, Tuan, HD, Duong, TQ, Poor, HV & Hwang, W-J 2020, 'Nonsmooth Optimization Algorithms for Multicast Beamforming in Content-Centric Fog Radio Access Networks', IEEE Transactions on Signal Processing, vol. 68, pp. 1455-1469.
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© 1991-2012 IEEE. This paper considers a content-centric fog radio access network (F-RAN). Its multi-antenna remote radio heads (RRHs) are capable of caching and executing signal processing for content delivery to its users. The fronthaul traffic is thus saved since its baseband processing unit (BBU) needs to transfer only the cache-missed content items to the RRHs via limited-capacity fronthaul links. The problem of beamforming design maximizing the energy efficiency in content delivery subject to the quality-of-content-service constraints in terms of content throughput and fronthaul limited-capacity is addressed. Unlike the user's throughput in user-centric networks, the content throughput in content-centric networks is no longer a differentiable function of the beamforming vectors. The problem is inherently high-dimensional due to the involvement of many beamforming vectors even in simple cases of three RRHs serving three users. Path-following algorithms, which invoke a simple convex quadratic optimization problem to generate a better feasible point, are proposed for computation of this nonsmooth and high-dimensional optimization problem. We also employ generalized zero-forcing beamforming, which forces the multi-content interference to zero or nearly to zero to reduce the problem dimensionality for computational efficiency. Numerical results are provided to demonstrate their computational effectiveness. They also reveal that when the fronthaul traffic becomes more flexible, hard-transfer fronthauling is more energy efficient than soft-transfer fronthauling.
Nguyen, L & Miro, JV 2020, 'An Efficient 3-D Model for Remaining Wall Thicknesses of Cast Iron Pipes in Nondestructive Testing', IEEE Sensors Letters, vol. 4, no. 7.
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© 2017 IEEE. Over 50% of global pipes have been made of cast iron, and most of them are aging. In order to effectively estimate possibilities of their failures, which is paramount for efficiently managing asset infrastructures, it requires the remaining wall thickness (RWT) of a pipe to be known. In fact, RWT of the cast iron pipes can be primarily measured by the magnetism based nondestructive testing technologies though they are quite slow. To speed up the inspection process, it is proposed to sense RWT of a part of a pipe and then employ a model to predict RWT in the rest. Thus, this letter introduces a 3-D model to efficiently represent RWT of a pipe given measurements collected intermittently on the pipe's surface. The proposed model first transforms 3-D cylindrical coordinates to 3-D Cartesian coordinates before modeling RWT by Gaussian processes (GP). The transformation allows GP to work properly on RWT data gathered on a cylindrical pipe and effectively predict RWT at unmeasured locations. Moreover, periodicity of RWT along circumference of the pipe is naturally integrated. The effectiveness of the proposed approach is demonstrated by implementation in two real life inservice aging cast iron pipes, where the obtained results are highly promising.
Nguyen, L & Miro, JV 2020, 'An Efficient 3-D Model for Remaining Wall Thicknesses of Cast Iron Pipes in Nondestructive Testing', IEEE Sensors Letters, vol. 4, no. 7, pp. 1-4.
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© 2017 IEEE. Over 50% of global pipes have been made of cast iron, and most of them are aging. In order to effectively estimate possibilities of their failures, which is paramount for efficiently managing asset infrastructures, it requires the remaining wall thickness (RWT) of a pipe to be known. In fact, RWT of the cast iron pipes can be primarily measured by the magnetism based nondestructive testing technologies though they are quite slow. To speed up the inspection process, it is proposed to sense RWT of a part of a pipe and then employ a model to predict RWT in the rest. Thus, this letter introduces a 3-D model to efficiently represent RWT of a pipe given measurements collected intermittently on the pipe's surface. The proposed model first transforms 3-D cylindrical coordinates to 3-D Cartesian coordinates before modeling RWT by Gaussian processes (GP). The transformation allows GP to work properly on RWT data gathered on a cylindrical pipe and effectively predict RWT at unmeasured locations. Moreover, periodicity of RWT along circumference of the pipe is naturally integrated. The effectiveness of the proposed approach is demonstrated by implementation in two real life inservice aging cast iron pipes, where the obtained results are highly promising.
Nguyen, L & Miro, JV 2020, 'Efficient Evaluation of Remaining Wall Thickness in Corroded Water Pipes Using Pulsed Eddy Current Data', IEEE Sensors Journal, vol. 20, no. 23, pp. 14465-14473.
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© 2001-2012 IEEE. In order to analyse failures of an ageing water pipe, some methods such as the loss-of-section require remaining wall thickness (RWT) along the pipe to be fully known, which can be measured by the magnetism based non-destructive evaluation sensors though they are practically slow due to the magnetic penetrating process. That is, fully measuring RWT at every location in a water pipe is not really practical if RWT inspection causes disruption of water supply to customers. Thus, this paper proposes a new data prediction approach that can increase amount of RWT data of a corroded water pipe collected in a given period of time by only measuring RWT on a part (e.g. 20%) of the total pipe surface area and then employing the measurements to predict RWT at unmeasured area. It is proposed to utilize a marginal distribution to convert the non-Gaussian RWT measurements to the standard normally distributed data, which can then be input into a 3-dimensional Gaussian process model for efficiently predicting RWT at unmeasured locations on the pipe. The proposed approach was implemented in two real-life in-service pipes, and the obtained results demonstrate its practicality.
Nguyen, LN, Commault, AS, Kahlke, T, Ralph, PJ, Semblante, GU, Johir, MAH & Nghiem, LD 2020, 'Genome sequencing as a new window into the microbial community of membrane bioreactors – A critical review', Science of The Total Environment, vol. 704, pp. 135279-135279.
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Recent developed sequencing techniques have resulted in a new and unprecedented way to study biological wastewater treatment, in which most organisms are uncultivable. This review provides (i) an insight on state-of-the-art sequencing techniques and their limitations; (ii) a critical assessment of the microbial community in biological reactor and biofouling layer in a membrane bioreactor (MBR). The data from high-throughput sequencing has been used to infer microbial growth conditions and metabolisms of microorganisms present in MBRs at the time of sampling. These data shed new insight to two fundamental questions about a microbial community in the MBR process namely the microbial composition (who are they?) and the functions of each specific microbial assemblage (what are their function?). The results to date also highlight the complexity of the microbial community growing on MBRs. Environmental conditions are dynamic and diverse, and can influence the diversity and structural dynamics of any given microbial community for wastewater treatment. The benefits of understanding the structure of microbial communities on three major aspects of the MBR process (i.e. nutrient removal, biofouling control, and micropollutant removal) were symmetrically delineated. This review also indicates that the deployment of microbial community analysis for a practical engineering context, in terms of process design and system optimization, can be further realized.
Nguyen, LN, Truong, MV, Nguyen, AQ, Johir, MAH, Commault, AS, Ralph, PJ, Semblante, GU & Nghiem, LD 2020, 'A sequential membrane bioreactor followed by a membrane microalgal reactor for nutrient removal and algal biomass production', Environmental Science: Water Research & Technology, vol. 6, no. 1, pp. 189-196.
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A hybrid process combining a single compartment aerobic membrane bioreactor (MBR) and a membrane microalgal reactor (MMR) was evaluated for nutrient removal and microalgal biomass production.
Nguyen, LN, Vu, MT, Johir, MAH, Pathak, N, Zdarta, J, Jesionowski, T, Semblante, GU, Hai, FI, Khanh Dieu Nguyen, H & Nghiem, LD 2020, 'A Novel Approach in Crude Enzyme Laccase Production and Application in Emerging Contaminant Bioremediation', Processes, vol. 8, no. 6, pp. 648-648.
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Laccase enzyme from white-rot fungi is a potential biocatalyst for the oxidation of emerging contaminants (ECs), such as pesticides, pharmaceuticals and steroid hormones. This study aims to develop a three-step platform to treat ECs: (i) enzyme production, (ii) enzyme concentration and (iii) enzyme application. In the first step, solid culture and liquid culture were compared. The solid culture produced significantly more laccase than the liquid culture (447 vs. 74 µM/min after eight days), demonstrating that white rot fungi thrived on a solid medium. In the second step, the enzyme was concentrated 6.6 times using an ultrafiltration (UF) process, resulting in laccase activity of 2980 µM/min. No enzymatic loss due to filtration and membrane adsorption was observed, suggesting the feasibility of the UF membrane for enzyme concentration. In the third step, concentrated crude enzyme was applied in an enzymatic membrane reactor (EMR) to remove a diverse set of ECs (31 compounds in six groups). The EMR effectively removed of steroid hormones, phytoestrogen, ultraviolet (UV) filters and industrial chemical (above 90%). However, it had low removal of pesticides and pharmaceuticals.
Nguyen, MH, Hà, MH, Nguyen, DN & Tran, TT 2020, 'Solving the k-dominating set problem on very large-scale networks', Computational Social Networks, vol. 7, no. 1.
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AbstractThe well-known minimum dominating set problem (MDSP) aims to construct the minimum-size subset of vertices in a graph such that every other vertex has at least one neighbor in the subset. In this article, we study a general version of the problem that extends the neighborhood relationship: two vertices are called neighbors of each other if there exists a path through no more thankedges between them. The problem called “minimumk-dominating set problem” (MkDSP) becomes the classical dominating set problem ifkis 1 and has important applications in monitoring large-scale social networks. We propose an efficient heuristic algorithm that can handle real-world instances with up to 17 million vertices and 33 million edges. This is the first time such large graphs are solved for the minimumk-dominating set problem.
Nguyen, NC, Duong, HC, Nguyen, HT, Chen, S-S, Le, HQ, Ngo, HH, Guo, W, Duong, CC, Le, NC & Bui, XT 2020, 'Forward osmosis–membrane distillation hybrid system for desalination using mixed trivalent draw solution', Journal of Membrane Science, vol. 603, pp. 118029-118029.
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Nguyen, PTK, Tran, HT, Tran, TS, Fitzgerald, DA, Graham, SM & Marais, BJ 2020, 'Predictors of Unlikely Bacterial Pneumonia and Adverse Pneumonia Outcome in Children Admitted to a Hospital in Central Vietnam', Clinical Infectious Diseases, vol. 70, no. 8, pp. 1733-1741.
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Abstract Background Pneumonia is the leading cause of antibiotic use and hospitalization in Vietnam. There is a need for better prediction of unlikely bacterial pneumonia and adverse pneumonia outcome in order to guide hospital admission and improve rational antibiotic use. Methods All children under 5 admitted with pneumonia (per clinician assessment) to the Da Nang Hospital for Women and Children were prospectively enrolled. Children were classified as having likely or unlikely bacterial pneumonia and followed for outcome assessment. A Bayesian model averaging approach was used to identify predictors of unlikely bacterial pneumonia and adverse pneumonia outcome, which guided the development of a pragmatic management algorithm. Results Of 3817 patients assessed, 2199 (57.6%) met World Health Organization (WHO) pneumonia criteria. In total, 1594 (41.7%) children were classified as having unlikely and 129 (3.4%) as having likely bacterial pneumonia. The remainder (2399; 62.9%) were considered to have disease of uncertain etiology. Factors predictive of unlikely bacterial pneumonia were no fever, no consolidation on chest radiograph, and absolute neutrophil count <5 × 109/L at presentation, which had a negative predictive value (NPV) for likely bacterial pneumonia of 99.0%. Among those who met WHO pneumonia criteria, 8.6% (189/2199) experienced an adverse outcome. Not having any WHO danger sign or consolidation on chest radiograph had an NPV of 96.8% for adverse pneumonia outcome. Co...
Nguyen, QD & Castel, A 2020, 'Reinforcement corrosion in limestone flash calcined clay cement-based concrete', Cement and Concrete Research, vol. 132, pp. 106051-106051.
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© 2020 Elsevier Ltd Limestone calcined clay cement (LC3) concrete has attracted world-wide attention as a newly promising low-carbon concrete. In this study, long-term reinforcement corrosion in LC3 concrete was investigated. Both chloride and carbonation-induced reinforcing bar corrosion were examined. Open circuit corrosion potential, polarization resistance, Tafel constants were monitored at regular intervals up to 500 days. Gravimetric mass loss was measured and compared to the loss of mass calculated using electrochemical methods. The performance of concrete with flash calcined clay and limestone was similar to that of traditional Portland cement concrete in long-term investigation. Traditional corrosion methods and classifications used widely to assess of steel in concrete can be applied to concrete containing LC3 providing a recalibration of polarization resistance range for passitivity condition.
Nguyen, QD, Afroz, S & Castel, A 2020, 'Influence of Calcined Clay Reactivity on the Mechanical Properties and Chloride Diffusion Resistance of Limestone Calcined Clay Cement (LC3) Concrete', Journal of Marine Science and Engineering, vol. 8, no. 5, pp. 301-301.
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Calcined clay plays an important role in the performance of limestone calcined clay cement (LC3) concrete. In this study, the performance of two different types of calcined clay produced from different calcination processes were investigated in chloride environment. The characteristics of the calcined clays, including mineral composition, chemical composition, particle size distribution, specific surface area and particle morphology, were evaluated. Based on the reactivity of the calcined clays, the compressive strength of concretes after up to 28 days of curing was adopted as the best measure to determine the appropriate replacement levels of Portland cement by LC3 to satisfy standards requirements for concrete in chloride environments. The chloride bulk diffusion test was conducted to investigate the performance of LC3 concretes in comparison with reference Portland cement concrete. Similar chloride diffusion resistance could be achieved by using the two different calcined clays in LC3 concrete. The performance of both LC3 concretes was much better than that of reference concrete. However, the Portland cement substitution rate for each calcined clay was governed by the compressive strength standard requirements.
Nguyen, QD, Khan, MSH & Castel, A 2020, 'Chloride Diffusion in Limestone Flash Calcined Clay Cement Concrete', ACI Materials Journal, vol. 117, no. 6, pp. 165-175.
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Nguyen, QD, Khan, MSH & Castel, A 2020, 'Chloride Diffusion in Limestone Flash Calcined Clay Cement Concrete', ACI Materials Journal, vol. 117, no. 6.
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Nguyen, QD, Kim, T & Castel, A 2020, 'Mitigation of alkali-silica reaction by limestone calcined clay cement (LC3)', Cement and Concrete Research, vol. 137, pp. 106176-106176.
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© 2020 Elsevier Ltd The study aims to investigate the influence of limestone calcined clay cement (LC3) on the alkali-silica reaction (ASR). Kinetics and sequence of ASR formation were monitored using the model reactant method. Accelerated mortar bar test (AMBT) was also conducted to evaluate the effect LC3 in the ASR expansion. 30 wt% replacement by flash calcined clay and limestone in binder reduced the mortar expansion lower than the limit of Australian Standard. From the model reactant method, the additional calcium rich phases in LC3 model reactant system seem to delay the ASR gel formation or produce high Ca/Si ASR products, relatively rigid C-S-H and C-A-S-H that has less expansive capability. The current results reveal the possibility to utilize model reactant experiments to monitor the formation sequence of ASR gels with the presence of calcined clay and limestone due to the consistent results observed between model reactant experiments and real LC3-based specimens.
Nguyen, TAH, Ngo, HH, Guo, WS, Nguyen, THH, Soda, S, Vu, ND, Bui, TKA, Vo, TDH, Bui, XT, Nguyen, TT & Pham, TT 2020, 'White hard clam (Meretrix lyrata) shells media to improve phosphorus removal in lab-scale horizontal sub-surface flow constructed wetlands: Performance, removal pathways, and lifespan', Bioresource Technology, vol. 312, pp. 123602-123602.
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This work examined the phosphorus (P) removal from the synthetic pretreated swine wastewater using lab-scale horizontal sub-surface flow constructed wetlands (HSSF-CWs). White hard clam (Meretrix lyrata) shells (WHC) and Paspalum atratum were utilized as substrate and plant, respectively. The focus was placed on treatment performance, removal mechanisms and lifespan of the HSSF-CWs. Results indicated that WHC-based HSSF-CW with P. atratum exhibited a high P removal (89.9%). The mean P efluent concentration and P removal rate were 1.34 ± 0.95 mg/L and 0.32 ± 0.03 g/m2/d, respectively. The mass balance study showed that media sorption was the dominant P removal pathway (77.5%), followed by microbial assimilation (14.5%), plant uptake (5.4%), and other processes (2.6%). It was estimated the WHC-based bed could work effectively for approximately 2.84 years. This WHC-based HSSF-CWs technology will therefore pave the way for recycling Ca-rich waste materials as media in HSSF-CWs to enhance P-rich wastewater purification.
Nguyen, TAH, Ngo, HH, Guo, WS, Nguyen, TT, Vu, ND, Soda, S, Nguyen, THH, Nguyen, MK, Tran, TVH, Dang, TT, Nguyen, VH & Cao, TH 2020, 'White hard clam (Meretrix lyrata) shells as novel filter media to augment the phosphorus removal from wastewater', Science of The Total Environment, vol. 741, pp. 140483-140483.
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It is well recognized that filter media play a crucial role in constructed wetlands (CWs) for decontamination of phosphorus (P)-rich wastewater. This study investigates the suitability of raw white hard clam shells (WHC) and white hard clam shells thermally modified at 800 °C (WHC-M800) as potential media to enhance P treatment performance in CWs. The results indicated that both WHC and WHC-M800 displayed appropriate physicochemical properties, such as high porosity, excellent hydraulic conductivity, and rich Ca content. WHC-M800 exhibited a superior P adsorption capacity (38.7 mg/g) to WHC (12.8 mg/g). However, the practical utilization of WHC-M800 as filter media in CWs may be compromised, due to certain limitations, for example: extremely high pH values in the post-adsorption solutions; high weight losses during calcination and adsorption processes; low mechanical strength; and intensive energy consumption. In contrast, the WHC demonstrated significant advantages of reasonably high P adsorption capacity, locally abundant availability, low cost, and marginal side effects. The fractionation of inorganic P of WHC and WHC-M800 revealed that Ca-bounded P was the most dominant binding form, followed by loosely bound P, Fe-P, occluded P, and Al-P. The present study demonstrates that recycling of WHC shells as a potential substrate in CWs provides a feasible method for upgrading P removal in CWs. Additionally, it helps to reduce waste WHC shells in a simple, cheap, and eco-friendly way, thus can double environmental benefits.
Nguyen, TH, Tran, HN, Vu, HA, Trinh, MV, Nguyen, TV, Loganathan, P, Vigneswaran, S, Nguyen, TM, Trinh, VT, Vu, DL & Nguyen, THH 2020, 'Laterite as a low-cost adsorbent in a sustainable decentralized filtration system to remove arsenic from groundwater in Vietnam', Science of The Total Environment, vol. 699, pp. 134267-134267.
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© 2019 Elsevier B.V. In the Red River Delta, Vietnam, arsenic (As) contamination of groundwater is a serious problem where more than seventeen million people are affected. Millions of people in this area are unable to access clean water from the existing centralized water treatment systems. They also cannot afford to buy expensive household water filters. Similar dangerous situations exist in many other countries and for this reason there is an urgent need to develop a cost-effective decentralized filtration system using new low-cost adsorbents for removing arsenic. In this study, seven locally available low-cost materials were tested for arsenic removal by conducting batch adsorption experiments. Of these materials, a natural laterite (48.7% Fe2O3 and 18.2% Al2O3) from Thach That (NLTT) was deemed the most suitable adsorbent based on arsenic removal performance, local availability, stability/low risk and cost (US$ 0.10/kg). Results demonstrated that the adsorption process was less dependent on the solution pH from 2.0 to 10. The coexisting anions competed with As(III) and As(V) in the order, phosphate > silicate > bicarbonate > sulphate > chloride. The adsorption process reached a fast equilibrium at approximately 120–360 min, depending on the initial arsenic concentrations. The Langmuir maximum adsorption capacities of NLTT at 30 °C were 512 μg/g for As(III) and 580 μg/g for As(V), respectively. Thermodynamic study conducted at 10 °C, 30 °C, and 50 °C suggested that the adsorption process of As(III) and As(V) was spontaneous and endothermic in nature. A water filtration system packed with NLTT was tested in a childcare centre in the most disadvantaged community in Ha Nam province, Vietnam, to determine arsenic removal performance in an operation lasting six months. Findings showed that the system reduced total arsenic concentration in groundwater from 122 to 237 μg/L to below the Vietnam drinking water standard of 10 μg/L.
Nguyen, TK, Nguyen, HH & Tuan, HD 2020, 'Max-Min QoS Power Control in Generalized Cell-Free Massive MIMO-NOMA With Optimal Backhaul Combining', IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 10949-10964.
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© 1967-2012 IEEE. This paper studies the uplink (UL) transmission of a generalized cell-free massive multiple-input multiple-output (massive MIMO) system in which multiple base stations (or access points), each equipped with a multiple-antenna array and connected to a central processing unit (CPU) over a backhaul network, simultaneously serve multiple users in a cell-free service area. The paper focuses on the non-orthogonal multiple access (NOMA) approach for sharing pilot sequences among users. Unlike the conventional cell-free massive MIMO-NOMA systems in which the UL signals from different access points are equally combined over the backhaul network, this paper first develops an optimal backhaul combining (OBC) method to maximize the UL signal-to-interference-plus-noise ratio (SINR). It is shown that, by using OBC, the correlated interference can be effectively mitigated if the number of users assigned to each pilot sequence is less than or equal to the number of base stations (BSs). As a result, the cell-free massive MIMO-NOMA system with OBC can enjoy unlimited performance when the number of antennas at each BS tends to infinity. A closed-form SINR expression is derived under Rayleigh fading and used to formulate a max-min quality-of-service (QoS) power control problem to further enhance the system performance. To deal with the NP-hardness of the concerned optimization problem, a successive inner approximation technique is applied to convert the original problem into a series of convex optimizations, which can be solved iteratively. In addition, a user grouping algorithm is also developed and shown to be better than random user grouping and a grouping method recently proposed in the literature. Numerical results are presented to corroborate the analysis and demonstrate the superiority of the proposed optimal backhaul combining over both equal-gain backhaul combining and zero-forcing backhaul combining.
Nguyen, TKL, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Nguyen, TV & Nghiem, DL 2020, 'Contribution of the construction phase to environmental impacts of the wastewater treatment plant', Science of The Total Environment, vol. 743, pp. 140658-140658.
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This study aims to investigate the environmental issues regarding the construction phase of the wastewater treatment plant (WWTP) and explore the roles of different materials through their environmental impacts. Detailed inventories of the two WWTPs were conducted by involving materials and transportation for civil works undertaken. EPD 2018 and ReCiPe life cycle impact assessment methods were employed to measure all the impact categories. Five treatment processes - (1) pumping, (2) primary treatment, (3) secondary treatment, (4) sludge line, and (5) building landscape - were considered for the assessment. It was found that concrete and reinforcing steel played similarly vital roles in most of the EPD 2018 impacts. The significant score of reinforcing steel was found on human cancer toxicity, which contributed more than 90% of the impacts. The contribution of diesel on ozone formation was 5% higher than that of reinforcing steel. Glassfiber was responsible for 70% of the burdens on ozone depletion, showing much higher than the total share of concrete and reinforcing steel. Primary treatment units only contributed 9.5% of the construction impacts in the Girona WWTP but up to 43.8% in Mill Creek WWTP mainly because of the proportion of consumed materials. In short, the comprehensive data inventories were necessary when evaluating the total environmental impacts of the WWTP.
Nguyen, TKL, Ngo, HH, Guo, WS, Chang, SW, Nguyen, DD, Nghiem, LD & Nguyen, TV 2020, 'A critical review on life cycle assessment and plant-wide models towards emission control strategies for greenhouse gas from wastewater treatment plants', Journal of Environmental Management, vol. 264, pp. 110440-110440.
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© 2020 Elsevier Ltd For decades, there has been a strong interest in mitigating greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs). Numerous models were developed to measure the emissions and propose the quantification. Existing studies looked at the relationship between GHG emissions and operational cost (OCI), which is one of the most important indicators for decision-makers. Other parameters that can influence the control strategies include the effluent quality (EQI) and total environmental impacts. Plant-wide models are reliable methods to examine the OCI, EQI and GHG emissions while Life cycle assessment (LCA) works to assess the potential environmental impacts. A combined LCA and plant-wide model proved to be a valuable tool evaluating and comparing strategies for the best performance of WWTPs. For this study involving a WWTP, the benchmark model is used while LCA is the decision tool to find the most suitable treatment strategy. LCA adds extra criteria that complement the existing criteria provided by such models. Complementing the cost/performance criteria is proposed for plant-wide models, including environmental evaluation, based on LCA, which provides an overall better assessment of WWTPs. It can capture both the dynamic effects and potential environmental impacts. This study provides an overview of the integration between plant-wide models and LCA.
Nguyen, TN, Emre Erkmen, R, Sanchez, LFM & Li, J 2020, 'Stiffness Degradation of Concrete Due to Alkali-Silica Reaction: A Computational Homogenization Approach', ACI Materials Journal, vol. 117, no. 6, pp. 65-76.
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Alkali-silica reaction (ASR) is one of the most harmful distress mechanisms affecting concrete infrastructure worldwide. ASR is a chemical reaction that generates a secondary product, which induces expansive pressure within the reacting aggregate material and adjacent cement paste upon moisture uptake, leading to cracking, loss of material integrity, and functionality of the affected structure. In this work, a computational homogenization approach is proposed to model the impact of ASR-induced cracking on concrete stiffness as a function of its development. A representative volume element (RVE) of the material at the mesoscale is developed, which enables the input of the cracking pattern and extent observed from a series of experimental testing. The model is appraised on concrete mixtures presenting different mechanical properties and incorporating reactive coarse aggregates. The results have been compared with experimental results reported in the literature. The case studies considered for the analysis show that stiffness reduction of ASR-affected concrete presenting distinct damage degrees can be captured using the proposed mesoscale model as the predictions of the proposed methodology fall in between the upper and lower bounds of the experimental results.
Nguyen, TT & Indraratna, B 2020, 'A Coupled CFD–DEM Approach to Examine the Hydraulic Critical State of Soil under Increasing Hydraulic Gradient', International Journal of Geomechanics, vol. 20, no. 9, pp. 04020138-04020138.
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© 2020 American Society of Civil Engineers. Increasing hydraulic gradients and associated seepage in a soil foundation accompanied by a reduction in effective stress, degradation of soil stiffness, and diminished internal stability contribute to adverse conditions in engineered earth structures, including dams and transport infrastructure. Although much attention has been drawn into these geotechnical challenges, most previous analytical and experimental studies could not properly capture the detailed response of fluid and soil particles, especially the localized or microscopic fluid-soil perspectives. In this regard, this paper aims to apply a numerical approach to analyze the response of a soil-fluid system under increasing hydraulic gradients. Soils with different gradation properties and porosities are created using the discrete element method (DEM), which is then coupled with computational fluid dynamics (CFD) based on Navier-Stokes equations. This numerical investigation reveals different stages in the development of hydraulic critical state, that is, from localized erosion (e.g., piping) to overall heave and fluidization. The transformation of fluid and particle characteristics, such as particle migration, the erosion rate, and hydraulic conductivity associated with porosity when soil approaches critical state, is discussed in detail. Micromechanical degradation within the contact network and the associated reduction in effective stress of soil due to an increasing hydraulic gradient are also analyzed in this study. A number of key factors that govern the soil response, such as friction, porosity, and grain uniformity, are addressed through numerical investigations. This study demonstrates acceptable numerical predictions for hydraulic behavior and erosion rates that are in good agreement with previous experimental data.
Nguyen, TT & Indraratna, B 2020, 'The energy transformation of internal erosion based on fluid-particle coupling', Computers and Geotechnics, vol. 121, pp. 103475-103475.
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© 2020 Elsevier Ltd The transformation of energy is an intrinsic process that is needed to trigger the internal erosion of soils subjected to a fluid flow, but how to capture this process is not understood very well. This is why this study aims to address these complex processes through a numerical fluid-particle coupling simulation. The computational fluid dynamics (CFD) is used to model fluid flows which is coupled with the discrete element method (DEM) employed to simulate soil particles. Detailed migration of particles and fluid variables are recorded to enable their kinetic energy to be computed. Successful experiments are used to demonstrate how the numerical method can be used to model the internal erosion associated with energy computation. This study shows a good agreement between the numerical and experimental results in terms of the hydraulic conductivity and erosion rate of soils subjected to upward flows. A significant loss in energy is also found as fluid flows through the soil whereas only a small amount of kinetic energy is needed to make particles migrate at a considerable degree. The influence that the porosity and uniformity of soils has on the transformation of energy is also discussed in the paper.
Nguyen, TT & Indraratna, B 2020, 'The role of particle shape on hydraulic conductivity of granular soils captured through Kozeny–Carman approach', Géotechnique Letters, vol. 10, no. 3, pp. 398-403.
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Previous studies indicate that particle shape plays an important role in the hydraulic conductivity (k) of granular materials, often represented through the Kozeny–Carman (KC) concept. Several recent studies have improved the accuracy of the KC approach using the particle-size distribution (PSD) to estimate the specific surface area of particles but overly simplifying the effect of particle shape. This current study innovatively adopts the micro-computed tomography technique to compute particle shape parameters of different granular materials (e.g. glass beads, sand and crushed gravel) and then incorporate these parameters into the KC equation to estimate k more accurately, which is then validated with experimental data. The results indicate that k varies significantly according to different particle shapes even if the same mean porosity and PSD are retained. Particles that are less spherical and rounded have a larger fluid–particle contact area (i.e. larger shape factor), hence a smaller hydraulic conductivity. The study suggests a shape factor of 1·28–1·52 for natural sand and 1·84–2·1 for crushed sand and gravel can be used for KC method to estimate k while a porosity-dependent equation is proposed to estimate the tortuosity for different shaped materials.
Nguyen, TT, Indraratna, B & Baral, P 2020, 'Biodegradable prefabricated vertical drains: From laboratory to field studies', Geotechnical Engineering, vol. 51, no. 2, pp. 39-46.
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Biodegradable prefabricated vertical drains (BPVDs) made from natural fibres have been in use for several decades to improve soft soil, especially in East and Southeast Asia despite the fact that this type of drain has still not been fully addressed and evaluated. This study presents a series of laboratory tests where a drain made from coconut cores wrapped in Indian jute sheath filters is compared to conventional synthetic prefabricated vertical drains (SPVDs). Discharge volume tests are carried out with and without soil clogging to understand how jute drains can resist soil clogging under increasing confining pressure. Along with these macro-hydraulic tests, the influence that the micro-characteristics of natural fibre drains can have on their hydraulic conductivity is also examined using micro-CT scanning and an optical microscopic to capture the micro-details of these drains. This study shows that the porous structure of BPVDs is much more complex than SPVDs, which causes them to have a lower discharge capacity. Unlike SPVDs, micro-properties also play an important role in the hydraulic properties of BPVDs. A pilot project in soft soil at Ballina, Australia, where BPVDs were installed in parallel to SPVDs, was used to evaluate their performance in assisting soil consolidation considering the biodegradation of natural fibres. The identical performance of these two types of PVDs added further evidence to prove how well BPVDs can facilitate soil consolidation.
Nguyen, TT, Ngo, HH, Guo, W & Wang, XC 2020, 'A new model framework for sponge city implementation: Emerging challenges and future developments', Journal of Environmental Management, vol. 253, pp. 109689-109689.
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© 2019 Sponge City concept is emerging as a new kind of integrated urban water systems, which aims to address urban water problems. However, its implementation has encountered a variety of challenges. The lack of an integrated comprehensive model to assist Sponge City planning, implementation and life cycle assessment is one of the most challenging factors. This review briefly analyses the opportunity of existing urban water management models and discusses the limitation of recent studies in the application of current integrated models for Sponge City implementation. Furthermore, it proposes a new Sponge City model framework by integrating four main sub-models including MIKE-URBAN, LCA, W045-BEST, and MCA in which environmental, social, and economic aspects of Sponge City infrastructure options are simulated. The new structure of Sponge City model that includes the sub-model layer, input layer, module layer, output layer, and programing language layer is also illustrated. Therefore, the proposed model could be applied to optimize different Sponge City practices by not only assessing the drainage capacity of stormwater infrastructure but also pays attention to multi-criteria analysis of urban water system (including the possibility of assessing Sponge City ecosystem services for urban areas and watershed areas) as well. Balancing between simplification and innovation of integrated models, increasing the efficiency of spatial data sharing systems, defining the acceptability of model complexity level and improving the corporation of multiple stakeholders emphasizing on possible future directions of a proper Sponge City design and construction model.
Nguyen, T-T-D, Nguyen, T-T, An Binh, Q, Bui, X-T, Ngo, HH, Vo, HNP, Andrew Lin, K-Y, Vo, T-D-H, Guo, W, Lin, C & Breider, F 2020, 'Co-culture of microalgae-activated sludge for wastewater treatment and biomass production: Exploring their role under different inoculation ratios', Bioresource Technology, vol. 314, pp. 123754-123754.
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In this study, mixed culture (microalgae:activated sludge) of a photobioreactor (PBR) were investigated at different inoculation ratios (1:0, 9:1, 3:1, 1:1, 0:1 wt/wt). This work was not only to determine the optimal ratio for pollutant remediation and biomass production but also to explore the role of microorganisms in the co-culture system. The results showed high total biomass concentrations were obtained from 1:0 and 3:1 ratio being values of 1.06, 1.12 g L-1, respectively. Microalgae played a dominant role in nitrogen removal via biological assimilation while activated sludge was responsible for improving COD removal. Compared with the single culture of microalgae, the symbiosis between microalgae and bacteria occurred at 3:1 and 1:1 ratio facilitated a higher COD removal by 37.5-45.7 %. In general, combined assessment based on treatment performance and biomass productivity facilitated to select an optimal ratio of 3:1 for the operation of the co-culture PBR.
Nguyen, TTQ, Loganathan, P, Nguyen, TV & Vigneswaran, S 2020, 'Removing arsenate from water using modified manganese oxide ore: Column adsorption and waste management', Journal of Environmental Chemical Engineering, vol. 8, no. 6, pp. 104491-104491.
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© 2020 Elsevier Ltd. There is a need to remove arsenic (As) in drinking water supplies by simple and cost-effective techniques. A column adsorption study was conducted to remove As(V) from water employing an iron (Fe) and zirconium (Zr) grafted Vietnamese manganese oxide ore (Fea-VMO and Zra-VMO). At a flow rate of 0.15 L/h, the bed volumes of water (As(V) concentration 0.1 mg/L) treated by Zra-VMO and Fea-VMO to produce water with As(V) concentration below the WHO guideline concentration (10 μg/L) were 6 and 8 times higher than for VMO, respectively. An increase in influent As concentration increased the adsorption capacity, but the increase of flow rate reduced the adsorption capacity. The maximum adsorption capacities derived from the Thomas model for VMO, Fea-VMO, and Zra-VMO at an influent concentration of 0.25 mg As(V)/L and flow rate of 0.15 L/h were 0.151, 1.145, and 0.925 mg/g, respectively. These values fell when influent As concentration decreased or the flow rate increased. Solidification/stabilisation method was applied to immobilise As(V) in the exhausted absorbent wastes by replacing 5, 10, 15, and 20 % of sand in a sand/cement concrete mixture by the adsorbent waste. This solidified material had satisfactory compressive strength, rapid chloride penetrability test, and volume of permeable voids, which indicated the material had good stability, making it suitable for use as a building material in construction work. The As(V) leaching from these materials, as measured by Method 1313 of the Leaching Environmental Assessment Framework of USEPA, proved to be very negligible.
Nguyen, TTQ, Loganathan, P, Nguyen, TV & Vigneswaran, S 2020, 'Removing arsenic from water with an original and modified natural manganese oxide ore: batch kinetic and equilibrium adsorption studies', Environmental Science and Pollution Research, vol. 27, no. 5, pp. 5490-5502.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Arsenic contamination of drinking water is a serious water quality problem in many parts of the world. In this study, a low-cost manganese oxide ore from Vietnam (Vietnamese manganese oxide (VMO)) was firstly evaluated for its performance in arsenate (As(V)) removal from water. This material contains both Mn (25.6%) and Fe (16.1%) mainly in the form of cryptomelane and goethite minerals. At the initial As(V) concentration of 0.5 mg/L, the adsorption capacity of original VMO determined using the Langmuir model was 0.11 mg/g. The modified VMOs produced by coating VMO with iron oxide (Fea-VMO) and zirconium oxide (Zra-VMO) at 110 °C and 550 °C achieved the highest As(V) adsorption capacity when compared to three other methods of VMO modifications. Langmuir maximum adsorption capacities of Fea-VMO and Zra-VMO at pH 7.0 were 2.19 mg/g and 1.94 mg/g, respectively, nearly twenty times higher than that of the original VMO. Batch equilibrium adsorption data fitted well to the Langmuir, Freundlich, and Temkin models and batch kinetics adsorption data to pseudo-first order, pseudo-second order, and Elovich models. The increase of pH progressively from 3 to 10 reduced As(V) adsorption with a maximum reduction of 50–60% at pH 10 for both original and modified VMOs. The co-existing oxyanions considerably weakened the As(V) removal efficiency because they competed with As(V) anions. The competition order was PO43− > SiO32− > CO32− > SO42−. The characteristics of the original and modified VMOs evaluated using SEM, FTIR, XRD, XRF, surface area, and zeta potential explained the As(V) adsorption behaviour.
Nguyen, TTQ, Loganathan, P, Nguyen, TV, Vigneswaran, S & Ngo, HH 2020, 'Iron and zirconium modified luffa fibre as an effective bioadsorbent to remove arsenic from drinking water', Chemosphere, vol. 258, pp. 127370-127370.
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Porous luffa plant fibre (LF) was grafted with Fe and Zr, and the ability of the fabricated adsorbents to remove arsenate (As(V)) from water was investigated in batch and column adsorption experiments. The Langmuir adsorption capacity (mg g-1) at pH 7 of LF was found to be 0.035, which increased to 2.55 and 2.89 after being grafted with Fe (FLF-3) and Zr (ZLF-3), respectively. Grafting with Fe and Zr increased the zeta potential and zero point of charge (ZPC) of LF (from pH 3.9 to 7.4 for Fe grafting and to 7.6 for Zr grafting), due to chemical bonding of the metals, possibly with the hydroxyl and carboxylic groups in LF as indicated in FTIR peaks. Zeta potential and ZPC decreased after As adsorption owing to inner-sphere complexation mechanism of adsorption. The increase of pH from 3 to 10 progressively reduced the adsorbents' adsorption capacity. Co-existing anions weakened the As(V) removal efficiency in the order, PO43- > SiO32- > CO32- > SO42-. Adsorption kinetics data fitted well to the Weber and Morris model, which revealed initial fast and subsequent slow rates of intra-particle As diffusion into the bigger pores and smaller pores, respectively. Column adsorption data fitted well to the Thomas model with the predicted adsorption capacities in the same order as in the batch adsorption experiment (ZLF-3 > FLF-3 > LF).
Nguyen, TV 2020, 'Common methodological issues and suggested solutions in bone research', Osteoporosis and Sarcopenia, vol. 6, no. 4, pp. 161-167.
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Bone research is a dynamic area of scientific investigation that usually encompasses multidisciplines. Virtually all basic cellular research, clinical research and epidemiologic research rely on statistical concepts and methodology for inference. This paper discusses common issues and suggested solutions concerning the application of statistical thinking in bone research, particularly in clinical and epidemiological investigations. The issues are sample size estimation, biases and confounders, analysis of longitudinal data, categorization of continuous data, selection of significant variables, over-fitting, P-values, false positive finding, confidence interval, and Bayesian inference. It is hoped that by adopting the suggested measures the scientific quality of bone research can improve.
Nguyen, TV 2020, 'Toward the era of precision fracture risk assessment', The Journal of Clinical Endocrinology & Metabolism, vol. 105, no. 7, pp. e2636-e2638.
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Nguyen, TV & Eisman, JA 2020, 'Post‐GWAS Polygenic Risk Score: Utility and Challenges', JBMR Plus, vol. 4, no. 11, p. e10411.
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ABSTRACTOver the past decade, through genome‐wide association studies, more than 300 genetic variants have been identified to be associated with either BMD or fracture risk. These genetic variants are common in the general population, but they exert small to modest effects on BMD, suggesting that the utility of any single variant is limited. However, a combination of effect sizes from multiple variants in the form of the polygenic risk score (PRS) can provide a useful indicator of fracture risk beyond that obtained by conventional clinical risk factors. In this perspective, we review the progress of genetics of osteoporosis and approaches for creating PRSs, their uses, and caveats. Recent studies support the idea that the PRS, when integrated into existing fracture prediction models, can help clinicians and patients alike to better assess the fracture risk for an individual, and raise the possibility of precision risk assessment. © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
Nguyen, XC, Tran, TCP, Hoang, VH, Nguyen, TP, Chang, SW, Nguyen, DD, Guo, W, Kumar, A, La, DD & Bach, Q-V 2020, 'Combined biochar vertical flow and free-water surface constructed wetland system for dormitory sewage treatment and reuse', Science of The Total Environment, vol. 713, pp. 136404-136404.
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A two-stage treatment system that included vertical flow (VF) and free-water surface (FWS) constructed wetlands was investigated for the dual purposes of sewage treatment and reuse. The VF included four layers (biochar, sand, gravel, and sandy soil), and the FWS was installed after the VF and used as a polishing tank. Two types of local plants, namely Colocasia esculenta and Canna indica, were planted in the VF and FWS, respectively. The system operated for approximately six months, and the experimental period was categorized into four stages that corresponded to changes in the hydraulic loading rate (HLR) (0.02-0.12 m/d). The removal efficiencies for total suspended solids (TSS), chemical oxygen demand (COD), biological oxygen demand (BOD5), ammonia (NH4-N), and total coliform (Tcol) were 71 ± 11%, 73 ± 13%, 79 ± 11%, 91 ± 3%, and 70 ± 20%, respectively. At HLRs of 0.04-0.06 m/d, the COD and BOD5 levels satisfied Vietnam's irrigation standards, with removable rates of 64% and 88%, respectively, and the TSS and Tcol levels satisfied Vietnam's standards for potable water. Furthermore, the NO3-N levels satisfied the reuse limits, whereas the NH4-N levels exceeded the reuse standards. At high HLRs (e.g., 0.12 m/d), all the effluent parameters, except Tcol and NO3-N, exceeded the standards.
Ni, B-J, Yan, X, Dai, X, Liu, Z, Wei, W, Wu, S-L, Xu, Q & Sun, J 2020, 'Ferrate effectively removes antibiotic resistance genes from wastewater through combined effect of microbial DNA damage and coagulation', Water Research, vol. 185, pp. 116273-116273.
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The widespread of antibiotic resistance genes (ARGs) in the environment can pose severe threats to public health. The wastewater treatment plant (WWTP) is regarded as an important hotspot of ARGs in the urban environment, but the removal of ARGs through conventional treatment techniques has been proven not sufficient. In this study, ferrate (Fe(VI)) was applied for the first time to remove intracellular ARGs from the secondary effluent of the WWTP. The results showed that Fe(VI) treatment could effectively remove 15 ARGs covering eight different types as well as intI1, the most common integron important to ARGs horizontal transfer. The removal efficiencies of tested genes could reach 1.10-4.37 log at the Fe(VI) dosage of 10 mg-Fe/L, which is significantly higher than those achieved through traditional disinfection methods. The DNA gel electrophoresis suggested that Fe(VI) could induce microbial DNA damage and consequently resulted in ARGs elimination. The presence of ARGs in settled residues indicated that coagulation initiated by Fe(VI) reduction products also contributed to ARGs removal from wastewater. In addition, the viability and relative abundances of potential ARGs hosts in the wastewater were decreased after Fe(VI) treatment. This study suggested a promising prospect for applying Fe(VI) to efficiently remove ARGs from wastewater, and consequently to control their proliferation and transfer in the environment.
Ni, B-J, Zeng, S, Wei, W, Dai, X & Sun, J 2020, 'Impact of roxithromycin on waste activated sludge anaerobic digestion: Methane production, carbon transformation and antibiotic resistance genes', Science of The Total Environment, vol. 703, pp. 134899-134899.
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The macrolide antibiotic roxithromycin is widely detected in varying aquatic environments, especially in the wastewater systems, as an emerging contaminant and leads to significant impacts on the microorganisms involved. In this study, the impact of a shock load of roxithromycin on waste activated sludge (WAS) anaerobic digestion was comprehensively investigated. The biochemical methane potential tests showed that the methane production from WAS anaerobic digestion was significantly inhibited by roxithromycin. With the dosage of roxithromycin increasing from 0 to 1000 μg/L, the maximum cumulative methane production decreased from 163.5 ± 2.6 mL/g VS to 150.9 ± 4.5 mL/g VS. In particular, roxithromycin inhibited the acidogenesis and methanogenesis in WAS anaerobic digestion, leading to the decreased methane production. The methanogenic archaea in the studied system mainly belonged to the genera of Methanoseata, Candidatus Methanofastidiosum and Methanolinea and their relative abundances also decreased with roxithromycin addition. The analysis of antibiotic resistance genes (ARGs) in the digested sludge indicated that the abundances of most ARGs detected in this study were increased with roxithromycin exposure, suggesting the potential of growing antibiotic resistance, which was probably caused by enhancing the effect of esterases, methylases and phosphorylases. This work reveals how roxithromycin affects the WAS anaerobic digestion and the change of ARGs in the anaerobic digestion with roxithromycin exposure, and provides useful information for practical operation.
Ni, B-J, Zhu, Z-R, Li, W-H, Yan, X, Wei, W, Xu, Q, Xia, Z, Dai, X & Sun, J 2020, 'Microplastics Mitigation in Sewage Sludge through Pyrolysis: The Role of Pyrolysis Temperature', Environmental Science & Technology Letters, vol. 7, no. 12, pp. 961-967.
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© 2020 American Chemical Society. All rights reserved. Sewage sludge is an important source of introducing microplastics into the environment, and thus, effective mitigation of microplastics in the sludge is in urgent need. Herein, the effect of pyrolysis on microplastics reduction in sewage sludge was investigated through a lab-scale study. The micro-Raman analysis showed that the microplastics concentrations in sludge residues decreased significantly from 550.8 to 960.9 particles/g to 1.4-2.3 particles/g with the pyrolysis temperature increasing to 500 °C, and no tiny (10-50 μm) microplastics remained. Polyethylene and polypropylene, the two most abundant microplastics in sewage sludge, were entirely degraded when the pyrolysis temperature reached 450 °C. However, during the pyrolysis process, new plastic polymers could be produced through the reaction between original microplastics with organics in sludge, and heavy metals in sludge can also be combined. Moreover, scanning electron microscopy analysis of spiked microplastics showed that incomplete pyrolysis at low temperatures could result in rough surface morphology of microplastics, making it more readily to adsorb contaminants. Overall, the results of this study provide the first insight into the effectiveness of microplastics control in sewage sludge through pyrolysis, but to avoid potential environmental risks induced by incomplete pyrolysis, a pyrolysis temperature of 450 °C should be reached at least.
Ni, X, Cao, Y, Guo, Z, Huang, T & Wen, S 2020, 'Global exponential anti-synchronization for delayed memristive neural networks via event-triggering method', Neural Computing and Applications, vol. 32, no. 17, pp. 13521-13535.
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This paper studies the exponential anti-synchronization problem of memristive delayed neural networks under the event-triggered controller.To reduce the recalculation of the control signals, two event-triggered control strategies including static and dynamic are proposed. A novel Lyapunov function is constructed to analyze the global exponential anti-synchronization problem. By analysis, we can choose the suitable parameter of the controller to realize global exponential anti-synchronization with a given convergence rate γ without wasting a lot of control resources. Moreover, under event-triggering conditions given in our theorem, we derive that the Zeno behavior will not happen. Finally, numerical examples are given to validate our theorem.
Ni, Z, Zhang, A, Yang, K, Gao, F & An, J 2020, 'Low-complexity Subarray-based RF Precoding for Wideband Multiuser Millimeter Wave Systems', IEEE Transactions on Vehicular Technology, vol. 69, no. 7, pp. 1-1.
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© 1967-2012 IEEE. This correspondence paper proposes a novel low-complexity radio-frequency (RF) precoding and combining scheme for wideband multiuser millimeter wave hybrid-array systems, targeting at maximizing the system energy efficiency. We first derive a nearly-optimal fully-connected RF precoder, via minimizing the correlation across different users and subcarriers. We then extend the optimization solution to subarray-based architectures by exploiting the unitary matrix feature of subarrays. With the obtained phase values of the precoder, we optimize the power allocation on each subcarrier of the baseband precoder. Simulation results are provided and validate the effectiveness of our proposed hybrid precoding scheme.
Ni, Z, Zhang, JA, Yang, K, Gao, F & An, J 2020, 'Estimation of Multiple Angle-of-Arrivals With Localized Hybrid Subarrays for Millimeter Wave Systems', IEEE Transactions on Communications, vol. 68, no. 3, pp. 1897-1910.
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IEEE Angle of Arrival (AoA) estimation with localized hybrid arrays is challenging in millimeter-wave (mmWave) communication systems. Most existing solutions quantize AoAs into limited values with relatively low accuracy. This paper presents a multi-AoA estimation scheme which is capable of estimating multiple AoAs from multiple users with low complexity. Specifically, we design a path filter via combining the received signals for each subarray. Each path filter enables a certain range of AoAs to pass through while suppressing the rest. Then we can use low-complexity cross-correlation operations to obtain continuous AoA estimates. Association of paths to users is further achieved by a follow-up pseudo-random codes based correlation operation. The scheme is first presented for a narrowband system and then extended to wideband with frequency selectivity. We also introduce new metrics and derive the lower bound of mean square error for evaluating the accuracy of AoA estimates, as conventional metrics face difficulties in the presence of multiple closely located AoAs. Extensive simulation results are provided and validate the effectiveness of the proposed multi-AoA estimation scheme.
Ni, Z, Zhang, JA, Yang, K, Gao, F & An, J 2020, 'Hybrid Precoder Design With Minimum-Subspace-Distortion Quantization in Multiuser mmWave Communications', IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 11055-11065.
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© 1967-2012 IEEE. Hybrid precoding has been a promising technique in millimeter wave (mmWave) communications, providing a balanced tradeoff between system performance and hardware complexity. Existing hybrid precoding schemes either require full channel state information (CSI) or use codebook-based design, causing large feedback overhead or degraded system performance, respectively. In this paper, we propose a balanced scheme with limited feedback CSI and element-level quantization. Our key idea is to maximize the system sum rate by using an adaptive baseband precoder and an eigenbeam radio frequency (RF) precoder. The adaptive baseband precoder can balance the effective channel gain and the multiuser interference according to the transmitted power. The eigenbeam RF precoder is optimized with only requiring a limited length of feedback vector. Considering the practical constraints on RF precoders, we then propose a quantization algorithm that minimizes the subspace distortion between the optimized eigenbeam RF precoder and the quantized one. The quantization algorithm also works for the case when RF chains are more than users. Extensive simulation results are provided and validate the effectiveness of the proposed hybrid precoding scheme.
Niamir, L, Ivanova, O, Filatova, T, Voinov, A & Bressers, H 2020, 'Demand-side solutions for climate mitigation: Bottom-up drivers of household energy behavior change in the Netherlands and Spain', Energy Research & Social Science, vol. 62, pp. 101356-101356.
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Niamir, L, Kiesewetter, G, Wagner, F, Schöpp, W, Filatova, T, Voinov, A & Bressers, H 2020, 'Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions', Climatic Change, vol. 158, no. 2, pp. 141-160.
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AbstractIn the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and ...
Nicolas, C, Valenzuela-Fernández, L & Merigó, JM 2020, 'Research Trends of Marketing: A Bibliometric Study 1990–2017', Journal of Promotion Management, vol. 26, no. 5, pp. 674-703.
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© 2020, © 2020 Taylor & Francis Group, LLC. Interest in the role of marketing has grown in recent decades due to its impact in brand value, value creation for customers, profitability of customer base, and organizational results. The paper shows an overall view on marketing research to explore the development of research trends, showing the high-frequency keywords at different time periods. Using bibliometric methods, the research analyzes publications between 1990 and 2017 found in the Web of Science and Scopus databases. The paper shows the evolution of keywords to reveal emerging topics as demonstrated in the connections network which includes “advertising,” “consumer behavior,” “trust,” “innovation,” and “customer satisfaction.”.
Nikoloska, R, Bykerk, L, Vitanage, D, Valls Miro, J, Chen, F, Wang, Y & Liang, B 2020, 'Enhancing Sydney Water’s leak prevention through acoustic monitoring', Water e-Journal, vol. 5, no. 2, pp. 1-15.
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Nimbalkar, S, Kolay, PK & Sun, Y 2020, 'Editorial: Geotechnical Innovation for Transport Infrastructures', Frontiers in Built Environment, vol. 6.
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Nimbalkar, S, Pain, A & Annapareddy, VSR 2020, 'A Strain Dependent Approach for Seismic Stability Assessment of Rigid Retaining Wall', Geotechnical and Geological Engineering, vol. 38, no. 6, pp. 6041-6055.
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© 2020, Springer Nature Switzerland AG. A new method is proposed to evaluate the seismic stability of a rigid retaining wall undergoing translation or rotational failure. In the present method, strain-dependent dynamic properties are used to assess the seismic stability of rigid retaining walls against sliding and overturning failure conditions. The effect of foundation soil properties on the stability of retaining walls is also considered. From the parametric study, it is observed that the foundation soil properties have a significant effect on both sliding and rotational stability of rigid retaining walls. This can be attributed to the use of strain-dependent dynamic properties and the consideration of foundation soil properties. The predictions of the proposed method are compared and verified against the results from other methods proposed in the past. The percentage increase in the results compared to the existing literature is a maximum of 10 and 28% for rigid (bedrock) and flexible (sand deposit) foundation, respectively.
Ning, X, Yac, L, Wang, X, Benatallah, B, Dong, M & Zhang, S 2020, 'Rating prediction via generative convolutional neural networks based regression', Pattern Recognition Letters, vol. 132, pp. 12-20.
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Ratings are an essential criterion for evaluating the quality of movies and a critical indicator of whether a customer would watch a movie. Therefore, an important related research challenge is to predict the rating of a movie before it is released in cinema or even before it is produced. Many existing approaches fail to address this challenge because they predict movie ratings based on post-production factors such as review comments from social media. Consequently, they are generally inapplicable until a movie has been released for a certain period of time when a sufficient number of review comments have become available. In this paper, we propose a regression model based on generative convolutional neural networks for movie rating prediction. Instead of post-production factors widely used by previous work, this model learns from movies’ intrinsic pillars such as genres, budget, cast, director and plot information, which are obtainable before the production of movies. In particular, the model explores the correlations between the rating of a movie and its intrinsic attributes to predict its rating. The results can serve as a reference for investors and movie studios to determine an optimal portfolio for movie production and a guidance to the interested users to choose the movie to watch. Extensive experiments on a real dataset are benchmarked against a set of baselines and state of the art approaches. The results demonstrate the effectiveness of our approach. The proposed model is also general to be extended to handle other prediction tasks.
Nithya, S, Sangeetha, M, Prethi, KNA, Sahoo, KS, Panda, SK & Gandomi, AH 2020, 'SDCF: A Software-Defined Cyber Foraging Framework for Cloudlet Environment', IEEE Transactions on Network and Service Management, vol. 17, no. 4, pp. 2423-2435.
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© 2004-2012 IEEE. The cloudlets can be deployed over mobile devices or even fixed state powerful servers that can provide services to its users in physical proximity. Executing workloads on cloudlets involves challenges centering on limited computing resources. Executing Virtual Machine (VM) based workloads for cloudlets does not scale due to the high computational demands of a VM. Another approach is to execute container-based workloads on cloudlets. However, container-based methods suffer from the cold-start problem, making it unfit for mobile edge computing scenarios. In this work, we introduce executing serverless functions on Web-assembly as workloads for both mobile and fixed state cloudlets. To execute the serverless workload on mobile cloudlets, we built a lightweight Web-assembly runtime. The orchestration of workloads and management of cloudlets or serverless runtime is done by introducing software-defined Cyber Foraging (SDCF) framework, which is a hybrid controller including a control plane for local networks and cloudlets. The SDCF framework integrates the management of cloudlets by utilizing the control plane traffic of the underlying network and thus avoids the extra overhead of cloudlet control plane traffic management. We evaluate SDCF using three use cases: (1) Price aware resource allocation (2) Energy aware resource scheduling for mobile cloudlets (3) Mobility pattern aware resource scheduling in mobile cloudlets. Through the virtualization of cloudlet resources, SDCF preserves minimal maintenance property by providing a centralized approach for configuring and management of cloudlets.
Niu, F, Zhao, S, Qiu, X & Zhang, D 2020, 'A note on wind velocity and pressure spectra inside compact spherical porous microphone windscreens', The Journal of the Acoustical Society of America, vol. 147, no. 1, pp. EL43-EL49.
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Simultaneous measurements of wind velocity and pressure fluctuations were conducted in a wind tunnel to investigate the wind noise source inside compact spherical open celled porous windscreens. The existing outdoor wind noise models are found to be inadequate to predict the wind noise inside a wind tunnel. This paper proposes a model to predict the interior stagnation pressure, which agrees with the wind noise measured inside the windscreen within a bandwidth, where the exterior turbulence-turbulence interaction pressure overestimates the wind noise level. The limitations of the proposed model and other potential sources for wind noise inside porous windscreens are discussed.
Niu, K, Guo, J, Pan, Y, Gao, X, Peng, X, Li, N & Li, H 2020, 'Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data', Complexity, vol. 2020, pp. 1-9.
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Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k-fold cross validation experiment. Additional k-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.
Niu, T, Wang, J, Lu, H, Yang, W & Du, P 2020, 'Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting', Expert Systems with Applications, vol. 148, pp. 113237-113237.
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Nizami, MSH, Hossain, MJ & Fernandez, E 2020, 'Multiagent-Based Transactive Energy Management Systems for Residential Buildings With Distributed Energy Resources', IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1836-1847.
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© 2005-2012 IEEE. Proper management of building loads and distributed energy resources (DER) can offer grid assistance services in transactive energy (TE) frameworks besides providing cost savings for the consumer. However, most TE models require building loads and DER units to be managed by external entities (e.g., aggregators), and in some cases, consumers need to provide critical information related to their electricity demand and usage, which hampers their privacy. This article introduces a transactive energy management framework for the buildings in a residential neighborhood to address grid overloading and cost optimization of the buildings. The decentralized coordination for the energy management system is realized by using a multiagent system architecture, which provides the consumers with full decision-making authority and preserves their privacy. A new event-triggered transactive market algorithm is developed, where the buildings trade energy to maximize profits, while the regional grid operator procures energy-supply flexibility of active consumers to prevent transformer overloading. A two-stage energy management system is developed for the residential buildings that schedules building loads and DER units in day-ahead stage to minimize cost and inconveniences for the consumer while participating in the real-time transactive market to maximize profits. An optimal bidding model is developed for the buildings that incorporates the degradation of residential storage devices for energy trading. Case studies and analyses with actual Australian building data and electricity tariff structures indicate the efficacy of the proposed methodology for effective mitigation of transformer overloading at a negligible cost compared to transformer replacement cost. Results also indicate that the proposed system can provide 15-20% cost savings for the consumers while minimizing their inconveniences and degradation of storage devices.
Nizami, MSH, Hossain, MJ, Amin, BMR & Fernandez, E 2020, 'A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading', Applied Energy, vol. 261, pp. 114322-114322.
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Nobakht, M, Sui, Y, Seneviratne, A & Hu, W 2020, 'PGFit: Static permission analysis of health and fitness apps in IoT programming frameworks', Journal of Network and Computer Applications, vol. 152, pp. 102509-102509.
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© 2020 Popular Internet of Things (IoT) programming frameworks, such as Google Fit, enable third-party developers to build apps that store and retrieve user data from a variety of data sources such as wearable devices. Most of these apps, particularly those that are health and fitness-related, collect potentially sensitive personal data and send it to cloud servers. Analogous to Android OS, IoT programming frameworks often follow similar permission model; third-party apps on IoT platforms prompt users to grant the apps the access to their private data stored on cloud servers of IoT programming frameworks. Most users have a poor understanding of why these permissions are being asked. This can often lead to unnecessary permissions being granted, which in turn grant these apps with excessive privileges. Over-privileged apps might not be harmful to users when they are used as designed, however, they can potentially be exploited by a malicious actor in a cyber security attack. This is of particular concern with health and fitness apps, which may be exploited to leak highly sensitive personal information. This paper presents PGFIT, a static permission analysis tool that precisely and efficiently identifies privilege escalation in third-party apps built on top of a popular IoT programming framework, Google Fit. PGFIT extracts the set of requested permission scopes and the set of used data types in Google Fit-enabled apps to determine whether the requested permission scopes are actually necessary. PGFIT performs graph reachability analysis on inter-procedural control flow graph. PGFIT serves as a quality assurance tool for developers and a privacy checker for app users. We evaluated PGFIT using a set of 20 popular Google Fit-enabled apps downloaded from Google Play. Our tool successfully identified the unnecessary permission scopes granted in our data set apps and found that 6 (30%) of the 20 apps are over-privileged.
Nolte, V, Sindram, T, Mazarov, J & Deuse, J 2020, 'Industrial Data Science erfolgreich implementieren', ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 115, no. 10, pp. 734-737.
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Nolte, V, Sindram, T, Mazarov, J & Deuse, J 2020, 'Industrial Data Science erfolgreich implementieren', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 115, no. 10, pp. 734-737.
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Abstract Die Potenziale von Industrial Data Science haben Unternehmen unlängst erkannt, scheitern jedoch an deren Umsetzung. In diesem Beitrag werden die Ergebnisse einer branchenübergreifenden Interviewstudie mit über 50 Führungskräften und Fachexperten vorgestellt, wobei Durchführungshemmnisse und Erfolgsfaktoren identifiziert werden. Zudem werden Anforderungen an das Change Management diskutiert sowie konkrete Handlungsempfehlungen für Unternehmen gegeben.
Nosouhi, MR, Sood, K, Yu, S, Grobler, M & Zhang, J 2020, 'PASPORT: A Secure and Private Location Proof Generation and Verification Framework', IEEE Transactions on Computational Social Systems, vol. 7, no. 2, pp. 293-307.
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© 2014 IEEE. Recently, there has been a rapid growth in location-based systems and applications in which users submit their location information to service providers in order to gain access to a service, resource, or reward. We have seen that in these applications, dishonest users have an incentive to cheat on their location. Unfortunately, no effective protection mechanism has been adopted by service providers against these fake location submissions. This is a critical issue that causes severe consequences for these applications. Motivated by this, we propose the Privacy-Aware and Secure Proof Of pRoximiTy (PASPORT) scheme in this article to address the problem. Using PASPORT, users submit a location proof (LP) to service providers to prove that their submitted location is true. PASPORT has a decentralized architecture designed for ad hoc scenarios in which mobile users can act as witnesses and generate LPs for each other. It provides user privacy protection as well as security properties, such as unforgeability and nontransferability of LPs. Furthermore, the PASPORT scheme is resilient to prover-prover collusions and significantly reduces the success probability of Prover-Witness collusion attacks. To further make the proximity checking process private, we propose P-TREAD, a privacy-aware distance bounding protocol and integrate it into PASPORT. To validate our model, we implement a prototype of the proposed scheme on the Android platform. Extensive experiments indicate that the proposed method can efficiently protect location-based applications against fake submissions.
Nosouhi, MR, Yu, S, Zhou, W, Grobler, M & Keshtiar, H 2020, 'Blockchain for secure location verification', Journal of Parallel and Distributed Computing, vol. 136, pp. 40-51.
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© 2019 Elsevier Inc. In location-sensitive applications, dishonest users may submit fake location claims to illegally access a service or obtain benefit. To address this issue, a number of location proof mechanisms have been proposed in literature. However, they confront various security and privacy challenges, including Prover–Prover collusions (Terrorist Frauds), Prover–Witness collusions, and location privacy threats. In this paper, we utilize the unique features of the blockchain technology to design a decentralized scheme for location proof generation and verification. In the proposed scheme, a user who needs a location proof (called a prover) broadcasts a request to the neighbor devices through a short-range communication interface, e.g. Bluetooth. Those neighbor devices that decide to respond (called witnesses) start to authenticate the requesting user. We integrate an incentive mechanism into the proposed scheme to reward such witnesses. Upon successful authentication, a transaction is generated as a location proof and is broadcast onto a peer-to-peer network where it can be picked up by verifiers for final verification. Our security analysis shows that the proposed scheme achieves a reliable performance against Prover–Prover and Prover–Witness collusions. Moreover, our prototype implementation on the Android platform shows that the proposed scheme outperforms other currently deployed location proof schemes.
Nosratabadi, S, Mosavi, A, Duan, P, Ghamisi, P, Filip, F, Band, S, Reuter, U, Gama, J & Gandomi, A 2020, 'Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods', Mathematics, vol. 8, no. 10, pp. 1799-1799.
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This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.
Nothling, MD, Fu, Q, Reyhani, A, Allison‐Logan, S, Jung, K, Zhu, J, Kamigaito, M, Boyer, C & Qiao, GG 2020, 'Progress and Perspectives Beyond Traditional RAFT Polymerization', Advanced Science, vol. 7, no. 20, pp. 2001656-2001656.
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AbstractThe development of advanced materials based on well‐defined polymeric architectures is proving to be a highly prosperous research direction across both industry and academia. Controlled radical polymerization techniques are receiving unprecedented attention, with reversible‐deactivation chain growth procedures now routinely leveraged to prepare exquisitely precise polymer products. Reversible addition‐fragmentation chain transfer (RAFT) polymerization is a powerful protocol within this domain, where the unique chemistry of thiocarbonylthio (TCT) compounds can be harnessed to control radical chain growth of vinyl polymers. With the intense recent focus on RAFT, new strategies for initiation and external control have emerged that are paving the way for preparing well‐defined polymers for demanding applications. In this work, the cutting‐edge innovations in RAFT that are opening up this technique to a broader suite of materials researchers are explored. Emerging strategies for activating TCTs are surveyed, which are providing access into traditionally challenging environments for reversible‐deactivation radical polymerization. The latest advances and future perspectives in applying RAFT‐derived polymers are also shared, with the goal to convey the rich potential of RAFT for an ever‐expanding range of high‐performance applications.
Noushini, A, Castel, A, Aldred, J & Rawal, A 2020, 'Chloride diffusion resistance and chloride binding capacity of fly ash-based geopolymer concrete', Cement and Concrete Composites, vol. 105, pp. 103290-103290.
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© 2019 This study evaluated the chloride diffusion resistance of low-calcium fly ash-based geopolymer concrete through electrical and bulk diffusion techniques. The geopolymer concretes were prepared using 12 different heat curing conditions; three temperatures of 60, 75 and 90 °C and four curing durations of 8, 12, 18 and 24 h, as well as ambient curing. The mechanical and transport properties and microstructural characteristics of the geopolymer concretes were examined. NT BUILD 492 chloride migration and ASTM C1556 bulk diffusion tests were carried out. Results showed that the chloride diffusion resistance and the chloride binding capacity of fly ash-based geopolymer concrete is very low. The fly ash-based geopolymer concrete appears to be suitable for applications where there are little or no chloride-related durability concerns.
Nurek, M, Michalski, R, Lizardo, O & Rizoiu, M-A 2020, 'Predicting Relationship Labels and Individual Personality Traits from Telecommunication History in Social Networks using Hawkes Processes'.
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Mobile phones contain a wealth of private information, so we try to keep themsecure. We provide large-scale evidence that the psychological profiles ofindividuals and their relations with their peers can be predicted fromseemingly anonymous communication traces -- calling and texting logs thatservice providers routinely collect. Based on two extensive longitudinalstudies containing more than 900 college students, we use point processmodeling to describe communication patterns. We automatically predict the peerrelationship type and temporal dynamics, and assess user personality based onthe modeling. For some personality traits, the results are comparable to thegold-standard performances obtained from survey self-report data. Findingsillustrate how information usually residing outside the control of individualscan be used to reconstruct sensitive information.
Nuruzzaman, M, Ren, J, Liu, Y, Rahman, MM, Shon, HK & Naidu, R 2020, 'Hollow Porous Silica Nanosphere with Single Large Pore Opening for Pesticide Loading and Delivery', ACS Applied Nano Materials, vol. 3, no. 1, pp. 105-113.
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O’Callaghan, C & Stewart, M 2020, 'Heathcliff, Race and Adam Low’s Documentary, A Regular Black: The Hidden Wuthering Heights (2010)', Brontë Studies, vol. 45, no. 2, pp. 156-167.
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Oberst, S, Halkon, B, Ji, J & Brown, T 2020, 'Preface', Vibration Engineering for a Sustainable Future: Active and Passive Noise and Vibration Control, Vol. 1, vol. 1, pp. v-vi.
Oberst, S, Lai, JCS, Martin, R, Halkon, BJ, Saadatfar, M & Evans, TA 2020, 'Revisiting stigmergy in light of multi-functional, biogenic, termite structures as communication channel', Computational and Structural Biotechnology Journal, vol. 18, pp. 2522-2534.
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Termite mounds are fascinating because of their intriguing composition of nu- merous geometric shapes and materials. However, little is known about these structures, or of their functionalities. Most research has been on the basic com- position of mounds compared with surrounding soils. There has been some targeted research on the thermoregulation and ventilation of the mounds of a few species of fungi-growing termites, which has generated considerable inter- est from human architecture. Otherwise, research on termite mounds has been scattered, with little work on their explicit properties.This review is focused on how termites design and build functional structures as nest, nursery and food storage; for thermoregulation and climatisation; as defence, shelter and refuge; as a foraging tool or building material; and for colony communication, either as in indirect communication (stigmergy) or as an information channel essential for direct communication through vibrations (biotremology).Our analysis shows that systematic research is required to study the prop- erties of these structures such as porosity and material composition. High res- olution computer tomography in combination with nonlinear dynamics and methods from computational intelligence may provide breakthroughs in un- veiling the secrets of termite behaviour and their mounds. In particular, the ex- amination of dynamic and wave propagation properties of termite-built struc- tures in combination with a detailed signal analysis of termite activities is re- quired to better understand the interplay between termites and their nest as superorganism. How termite structures serve as defence in the form of disguis- ing acoustic and vibration signals from detection by predators, and what role local and global vibration synchronisation plays for building are open ques- tions that need to be addressed to provide insights into how termites utilise materials to thrive in a world of predators and competitors.
Ogie, R & Pradhan, B 2020, 'Social vulnerability to natural hazards in Wollongong: Comparing strength-based and traditional methods', Australian Journal of Emergency Management, vol. 35, no. 1, pp. 60-68.
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Social vulnerability is a widely recognised way of assessing the sensitivity of a population to natural hazards and its ability to respond to and recover from them. In the traditional approach to computing social vulnerability, the emphasis is mainly on the weaknesses only (e.g. old age, low income, language barriers). This study presents a strengthbased social vulnerability index that identifies the strengths that communities have that help minimise disaster risk exposure. The strength-based social vulnerability index method is compared with the traditional approach using various statistical procedures like the one-sample T-test and the Wilcoxon signed rank test. This is performed through a case study measuring the social vulnerability for the 108 suburbs of Wollongong in New South Wales. The results show there is a significant difference between the values obtained from measurements using the strength-based social vulnerability index technique and those generated by the traditional approach. The implications of the results for emergency and disaster management are broadly discussed.
Oh, H & Sui, Y 2020, 'Welcome from the Chairs', TAPAS 2020 - Proceedings of the 11th ACM SIGPLAN International Workshop on Tools for Automatic Program Analysis, Co-located with SPLASH 2020, p. III.
Olszak, CM, Zurada, J & Cetindamar, D 2020, 'Introduction to the business intelligence & big data for innovative and sustainable development of organizations minitrack', Proceedings of the Annual Hawaii International Conference on System Sciences, vol. 2020-January, pp. 166-167.
Ong, HC, Chen, W-H, Singh, Y, Gan, YY, Chen, C-Y & Show, PL 2020, 'A state-of-the-art review on thermochemical conversion of biomass for biofuel production: A TG-FTIR approach', Energy Conversion and Management, vol. 209, pp. 112634-112634.
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© 2020 Elsevier Ltd Effective methods of biomass characterization are needed for energy production due to the increase in biomass to bioenergy conversion capacity and the availability of various biomass sources. The utilization of biomass has been enhanced through thermochemical conversion techniques such as torrefaction, pyrolysis, and gasification. The biomass analytical techniques have been developed to decrease the time and energy required for biomass conversion performance. Thermogravimetric analyzer (TG) and Fourier transform infrared spectroscopic (FTIR) analytical techniques facing several limitations when applied individually. Thus, TG coupled with FTIR (TG-FTIR) was used to analyze the main parameters of biomass and improved the energy crop growing developments. In addition, TG-FTIR can determine the suitable ratio for two different biomass or coal blending during the co-pyrolysis and co-gasification to achieve the optimum synergetic interaction. In this review, thermochemical conversion processes such as torrefaction, pyrolysis, and gasification are presented. The analysis of the thermochemical conversion of biomass with the use of TG and FTIR individually are then discussed. Lastly, this review aims to discuss the applications of TG-FTIR techniques that have been applied to the analysis of evolved gas from the thermochemical processing of biomass to biofuels.
Ong, HC, Mofijur, M, Silitonga, AS, Gumilang, D, Kusumo, F & Mahlia, TMI 2020, 'Physicochemical Properties of Biodiesel Synthesised from Grape Seed, Philippine Tung, Kesambi, and Palm Oils', Energies, vol. 13, no. 6, pp. 1319-1319.
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The production of biodiesel using vegetable oil is an effective way to meet growing energy demands, which could potentially reduce the dependency on fossil fuels. The aim of this study was to evaluate grape seed (Vitis vinifera), Philippine tung (Reutealis trisperma), and kesambi (Schleichera oleosa) oils as potential feedstocks for biodiesel production to meet this demand. Firstly, biodiesels from these oils were produced and then their fatty acid methyl ester profiles and physicochemical properties were evaluated and compared with palm biodiesel. The results showed that the biodiesel produced from grape seed oil possessed the highest oxidation stability of 4.62 h. On the other hand, poor oxidation stability was observed for Philippine tung biodiesel at 2.47 h. The poor properties of Philippine tung biodiesel can be attributed to the presence of α-elaeostearic fatty acid. Furthermore, synthetic antioxidants (pyrogallol) and diesel were used to improve the oxidation stability. The 0.2 wt.% concentration of pyrogallol antioxidant could increase the oxidation stability of grape seed biodiesel to 6.24 h, while for kesambi and Philippine tung, biodiesels at higher concentrations of 0.3% and 0.4 wt.%, respectively, were needed to meet the minimum limit of 8 h. The blending of biodiesel with fossil diesel at different ratios can also increase the oxidation stability.
Organ, B, Huang, Y, Zhou, JL, Yam, Y-S, Mok, W-C & Chan, EFC 2020, 'Simulation of engine faults and their impact on emissions and vehicle performance for a liquefied petroleum gas taxi', Science of The Total Environment, vol. 716, pp. 137066-137066.
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© 2020 Elsevier B.V. The deterioration of emissions control systems in a spark ignition engine is predominantly a gradual process of wear and tear occurring as vehicles accumulate mileage. As new innovations in engine and emissions technology have been progressively introduced to meet lower emissions targets, the impact of gradual deterioration of hardware has become more challenging to identify and quantify in the repair industry. When a pioneering emissions control programme utilising remote sensing to detect high emitting gasoline and liquefied petroleum gas (LPG) vehicles was to be introduced in Hong Kong, it became apparent the repair industry needed specialised training to assist with identifying the types of failures which would lead to high vehicle emissions. To identify the impact of hardware deterioration and failures, a Toyota Crown Comfort LPG taxi was used to demonstrate simulated failures of engine hardware systems to measure their impact on emissions, fuel consumption and drivability using a chassis dynamometer. This novel study simulated a broad range of deterioration and failures covering the intake, fuel supply, ignition, and exhaust systems. The results of the study showed significant THC and CO increases of up to 317% (0.604 g/km) and 782% (5.351 g/km) respectively for a simulated oxygen sensor high voltage fault and a sticky mixture control valve. The largest increase in NOx emissions was for restricted main fuel supply in the LPG vapouriser, producing an increase of 282% (1.41 g/km). Fuel consumption varied with increases of up to 15.5%. Drivability was impacted with poor idle from a number of faults and especially by a worn throttlebody which produced rough acceleration characteristics as well. This study clearly highlights the importance of having properly maintained emissions and engine hardware systems to achieve optimal fuel economy and compliant emissions levels, which could be reproduced in other regions for prescribed emiss...
Otte, M, Sofge, D & Fitch, R 2020, 'Guest editorial: Special issue on robot communication challenges: real-world problems, systems, and methods', Autonomous Robots, vol. 44, no. 1, pp. 1-2.
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Ou, T, Wang, D, Xin, Z, Tan, J, Wu, C, Guo, Q & Zhang, Y 2020, 'Full-scale tests on the mechanical behaviour of a continuously welded stainless steel roof under wind excitation', Thin-Walled Structures, vol. 150, pp. 106680-106680.
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© 2020 Elsevier Ltd The wind uplift performance of the continuously welded stainless steel roof (CWSSR) system adopted in the Zhaoqing New District Sports (ZNDS) Center of China is investigated in this study. To determine the optimal welding program and examine the mechanical properties of the continuously welded stainless steel joints, uniaxial tensile testing is first conducted on 27 specimens with tension-shear and tension-bending types. Two CWSSR specimens, one that is square-shaped with a horizontal layout and one that is rectangular-shaped with an inclination layout of 10.71°, are further tested under dynamic and static ultimate wind uplift loadings to explore the wind uplift capacity. All specimens are full-size, and the corresponding materials, structural details and construction technologies are kept the same as the actual building to ensure the authenticity of the testing investigations. The testing results indicate that the integrated and sealed CWSSR system has a clear force transmission mechanism and a remarkable wind resistance performance. The welded joints achieve the best performance, and the mechanical behaviours are equivalent to those of the base material under the continuously welded conditions including an electric current of 65 A and a moving velocity of 750 mm/s. An excellent dynamic wind suction performance is achieved under 5000 five-level cumulative loading cycles with a maximum pressure of 5400 Pa. The static ultimate pressure reaches 9400 Pa for the square specimen and 10,400 Pa for the rectangular specimen. Damage observations show that no tearing or rupture failures are observed for the CWSSR system. The investigation results contribute the most to the safe design of the ZNDS Center and are expected to provide guidelines for future applications of the CWSSR system.
Ouyang, D, Yuan, L, Qin, L, Chang, L, Zhang, Y & Lin, X 2020, 'Efficient Shortest Path Index Maintenance on Dynamic Road Networks with Theoretical Guarantees.', Proc. VLDB Endow., vol. 13, no. 5, pp. 602-615.
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Computing the shortest path between two vertices is a fundamental problem in road networks that is applied in a wide variety of applications. To support efficient shortest path query processing, a plethora of index-based methods have been proposed in the literature, but few of them can support dynamic road networks commonly encountered in practice, as their corresponding index structures cannot be efficiently maintained when the input road network is dynamically updated. Motivated by this, we study the shortest path index maintenance problem on dynamic road networks in this paper. We adopt Contraction Hierarchies (CH) as our underlying shortest path computation method because of its outstanding overall performance in pre-processing time, space cost, and query processing time and aim to design efficient algorithms to maintain the index structure, shortcut index, of CH when the input road network is dynamically updated. To achieve this goal, we propose a shortcut-centric paradigm focusing on exploring a small number of shortcuts to maintain the shortcut index. Following this paradigm, we design an auxiliary data structure named SS-Graph and propose a shortcut weight propagation mechanism based on the SS-Graph. With them, we devise efficient algorithms to maintain the shortcut index in the streaming update and batch update scenarios with non-trivial theoretical guarantees. We experimentally evaluate our algorithms on real road networks and the results demonstrate that our approach achieves 2-3 orders of magnitude speedup compared to the state-of-the-art algorithm for the streaming update.
Ovsepian, SV, Jiang, Y, Sardella, TCP, Malekzadeh-Najafabadi, J, Burton, NC, Yu, X & Ntziachristos, V 2020, 'Visualizing cortical response to optogenetic stimulation and sensory inputs using multispectral handheld optoacoustic imaging', Photoacoustics, vol. 17, pp. 100153-100153.
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Pachauri, RK, Mahela, OP, Sharma, A, Bai, J, Chauhan, YK, Khan, B & Alhelou, HH 2020, 'Impact of Partial Shading on Various PV Array Configurations and Different Modeling Approaches: A Comprehensive Review', IEEE Access, vol. 8, pp. 181375-181403.
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Pain, A, Nimbalkar, S & Hussain, M 2020, 'Applicability of Bouc-Wen Model to Capture Asymmetric Behavior of Sand at High Cyclic Shear Strain', International Journal of Geomechanics, vol. 20, no. 6, pp. 06020009-06020009.
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Pan, L, Hartley, R, Scheerlinck, C, Liu, M, Yu, X & Dai, Y 2020, 'High Frame Rate Video Reconstruction based on an Event Camera', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 5, pp. 1-1.
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Event-based cameras measure intensity changes (called 'events') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the 'active pixel sensor' (APS), the 'Dynamic and Active-pixel Vision Sensor' (DAVIS) allows the simultaneous output of intensity frames and events. However, the output images are captured at a relatively low frame rate and often suffer from motion blur. A blurred image can be regarded as the integral of a sequence of latent images, while events indicate changes between the latent images. Thus, we are able to model the blur-generation process by associating event data to a latent sharp image. Based on the abundant event data alongside a low frame rate, easily blurred images, we propose a simple yet effective approach to reconstruct high-quality and high frame rate sharp videos. Starting with a single blurred frame and its event data from DAVIS, we propose the Event-based Double Integral (EDI) model and solve it by adding regularization terms. Then, we extend it to multiple Event-based Double Integral (mEDI) model to get more smooth results based on multiple images and their events. Furthermore, we provide a new and more efficient solver to minimize the proposed energy model. By optimizing the energy function, we achieve significant improvements in removing blur and the reconstruction of a high temporal resolution video. The video generation is based on solving a simple non-convex optimization problem in a single scalar variable. Experimental results on both synthetic and real datasets demonstrate the superiority of our mEDI model and optimization method compared to the state-of-the-art.
Pan, Y, Tsang, IW, Singh, AK, Lin, C-T & Sugiyama, M 2020, 'Stochastic Multichannel Ranking with Brain Dynamics Preferences', Neural Computation, vol. 32, no. 8, pp. 1499-1530.
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A driver's cognitive state of mental fatigue significantly affects his or her driving performance and more important, public safety. Previous studies have leveraged reaction time (RT) as the metric for mental fatigue and aim at estimating the exact value of RT using electroencephalogram (EEG) signals within a regression model. However, due to the easily corrupted and also nonsmooth properties of RTs during data collection, methods focusing on predicting the exact value of a noisy measurement, RT generally suffer from poor generalization performance. Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels.
Pandey, NK, Singh, SK, Gulati, M, Kumar, B, Kapoor, B, Ghosh, D, Kumar, R, Khursheed, R, Awasthi, A, Kuppusamy, G, Wadhwa, S, Satija, S, Dureja, H, Jain, SK, Chellappan, DK, Anand, K, Mehta, M & Dua, K 2020, 'Overcoming the dissolution rate, gastrointestinal permeability and oral bioavailability of glimepiride and simvastatin co-delivered in the form of nanosuspension and solid self-nanoemulsifying drug delivery system: A comparative study', Journal of Drug Delivery Science and Technology, vol. 60, pp. 102083-102083.
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© 2020 Elsevier B.V. Simvastatin (SIM) and glimepiride (GLM) were co-formulated into nanosuspensions and self-nanoemulsifying drug delivery systems (SNEDDS) to improve their dissolution rate and oral bioavailability. Nanosuspension was prepared by liquid anti-solvent precipitation method, involving supersaturation of a solution by mixing the drug solution in an antisolvent. Liquid SNEDDS were prepared by loading drugs into an isotropic mixture of Capmul MCM, Labrafil M1944CS, Tween-80 and Transcutol P. Both formulations were solidified using spray drying. Enhancement in dissolution rate by 6.4 folds and 4.45 folds was observed for GLM and SIM respectively by preparing their nano-formulations. Drugs’ permeability was also enhanced by loading them into nano-formulations. The pharmacokinetic studies were conducted on rats which revealed increase in oral bioavailability by 6.69- and 4.22-folds for GLM and 1.76- and 2.68-folds for SIM respectively for nanosuspension and solid SNEDDS than their unprocessed forms. Both dissolution rate and oral bioavailability of SIM and GLM got significantly improved through S-SNEDDS and nanosuspension. However, performance of nanosuspension was found better than SNEDDS in terms of dissolution rate and oral bioavailability.
Pandey, P, Satija, S, Wadhwa, R, Mehta, M, Purohit, D, Gupta, G, Prasher, P, Chellappan, DK, Awasthi, R, Dureja, H & Dua, K 2020, 'Emerging trends in nanomedicine for topical delivery in skin disorders: Current and translational approaches', Dermatologic Therapy, vol. 33, no. 3.
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Pang, B, Zhu, Y, Ni, J, Ruan, J, Thompson, J, Malouf, D, Bucci, J, Graham, P & Li, Y 2020, '<p>Quality Assessment and Comparison of Plasma-Derived Extracellular Vesicles Separated by Three Commercial Kits for Prostate Cancer Diagnosis</p>', International Journal of Nanomedicine, vol. Volume 15, pp. 10241-10256.
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Introduction
Current standard biomarkers in clinic are not specific enough for prostate cancer (PCa) diagnosis. Extracellular vesicles (EVs) are nano-scale vesicles released by most mammalian cells. EVs are promising biomarker source for PCa liquid biopsy due to its minimal invasive approach, rich information and improved accuracy compared to the clinical standard prostate-specific antigen (PSA). However, current EV separation methods cannot separate pure EVs and the quality characteristics from these methods remain largely unknown. In this study, we evaluated the quality characteristics of human plasma-derived EVs by comparing three clinical suitable separation kits.
Methods
We combined EV separation by commercial kits with magnetic beads capture and flow cytometry analysis, and compared three kits including ExoQuick Ultra based on precipitation and qEV35 and qEV70 based on size exclusion chromatography (SEC).
Results
Our results indicated that two SEC kits provided higher EV purity and lower protein contamination compared to ExoQuick Ultra precipitation and that qEV35 demonstrated a higher EV yield but lower EV purity compared to qEV70. Particle number correlated very well particularly with CD9/81/63 positive EVs for all three kits, which confirms that particle number can be used as the estimate for EV amount. At last, we found that several EV metrics including total EVs and PSA-specific EVs could not differentiate PCa patients from health controls.
Conclusion
We provided a systematic workflow for the comparison of three separation kits as well as a general analysis process in clinical laboratories for EV-based cancer diagnosis. Better EV-associated cancer biomarkers need to be explored in the future study with a larger cohort.
Pang, B, Zhu, Y, Ni, J, Thompson, J, Malouf, D, Bucci, J, Graham, P & Li, Y 2020, 'Extracellular vesicles: the next generation of biomarkers for liquid biopsy-based prostate cancer diagnosis', Theranostics, vol. 10, no. 5, pp. 2309-2326.
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Prostate cancer (PCa) is a leading cause of cancer death for males in western countries. The current gold standard for PCa diagnosis - template needle biopsies - often does not convey a true representation of the molecular profile given sampling error and complex tumour heterogeneity. Presently available biomarker blood tests have limited accuracy. There is a growing demand for novel diagnostic approaches to reduce both the number of men with an abnormal PSA/ DRE who undergo invasive biopsy and the number of cores collected per biopsy. 'Liquid biopsy' is a minimally invasive biofluid-based approach that has the potential to provide information and improve the accuracy of diagnosis for patients' treatment selection, prognostic counselling and development of risk-adjusted follow-up protocols. Extracellular vesicles (EVs) are lipid bilayer-delimited particles released by tumour cells which may provide a real-time snapshot of the entire tumour in a non-invasive way. EVs can regulate physiological processes and mediate systemic dissemination of various types of cancers. Emerging evidence suggests that EVs have crucial roles in PCa development and metastasis. Most importantly, EVs are directly derived from their parent cells with their information. EVs contain components including proteins, mRNAs, DNA fragments, non-coding RNAs and lipids, and play a critical role in intercellular communication. Therefore, EVs hold promise for the discovery of liquid biopsy-based biomarkers for PCa diagnosis. Here, we review the current approaches for EV isolation and analysis, summarise the recent advances in EV protein biomarkers in PCa and focus on liquid biopsy-based EV biomarkers in PCa diagnosis for personalised medicine.
Pang, G & Cao, L 2020, 'Heterogeneous Univariate Outlier Ensembles in Multidimensional Data', ACM Transactions on Knowledge Discovery from Data, vol. 14, no. 6, pp. 1-27.
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In outlier detection, recent major research has shifted from developing univariate methods to multivariate methods due to the rapid growth of multidimensional data. However, one typical issue of this paradigm shift is that many multidimensional data often mainly contains univariate outliers , in which many features are actually irrelevant. In such cases, multivariate methods are ineffective in identifying such outliers due to the potential biases and the curse of dimensionality brought by irrelevant features. Those univariate outliers might be well detected by applying univariate outlier detectors in individually relevant features. However, it is very challenging to choose a right univariate detector for each individual feature since different features may take very different probability distributions. To address this challenge, we introduce a novel Heterogeneous Univariate Outlier Ensembles (HUOE) framework and its instance ZDD to synthesize a set of heterogeneous univariate outlier detectors as base learners to build heterogeneous ensembles that are optimized for each individual feature. Extensive results on 19 real-world datasets and a collection of synthetic datasets show that ZDD obtains 5%–14% average AUC improvement over four state-of-the-art multivariate ensembles and performs substantially more robustly w.r.t. irrelevant features.
Pardeshi, V, Nimbalkar, S & Khabbaz, H 2020, 'Field Assessment of Gravel Loss on Unsealed Roads in Australia', Frontiers in Built Environment, vol. 6, pp. 1-11.
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The gravel loss is a major limitation for unsealed roads and it needs major maintenance annually. The continual process of gravel loss leads to the unsustainability of these roads. The unsealed road management faces several issues, viz., difficulty to forecast behavior, huge data collection needs, and a vulnerability in the service and maintenance practices. The quality of gravel material also plays a major role in the process of gravel loss. In view of the aforementioned, appropriate revisions to ARRB material specifications are proposed in this study. The gravel material as per modified ARRB specifications is used on the unsealed road network in the Scenic Rim Regional Council in the state of Queensland. Gravel loss monitoring stations were established over the entire region in order to assess the gravel loss and the implication of using a better quality of gravel material. This study discusses the gravel loss monitoring approaches, data analyses, and improved material specification for gravel. It is found that the modified gravel used on unsealed road performs better than conventionally used gravel.
Pardhi, DM, Şen Karaman, D, Timonen, J, Wu, W, Zhang, Q, Satija, S, Mehta, M, Charbe, N, McCarron, PA, Tambuwala, MM, Bakshi, HA, Negi, P, Aljabali, AA, Dua, K, Chellappan, DK, Behera, A, Pathak, K, Watharkar, RB, Rautio, J & Rosenholm, JM 2020, 'Anti-bacterial activity of inorganic nanomaterials and their antimicrobial peptide conjugates against resistant and non-resistant pathogens', International Journal of Pharmaceutics, vol. 586, pp. 119531-119531.
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This review details the antimicrobial applications of inorganic nanomaterials of mostly metallic form, and the augmentation of activity by surface conjugation of peptide ligands. The review is subdivided into three main sections, of which the first describes the antimicrobial activity of inorganic nanomaterials against gram-positive, gram-negative and multidrug-resistant bacterial strains. The second section highlights the range of antimicrobial peptides and the drug resistance strategies employed by bacterial species to counter lethality. The final part discusses the role of antimicrobial peptide-decorated inorganic nanomaterials in the fight against bacterial strains that show resistance. General strategies for the preparation of antimicrobial peptides and their conjugation to nanomaterials are discussed, emphasizing the use of elemental and metallic oxide nanomaterials. Importantly, the permeation of antimicrobial peptides through the bacterial membrane is shown to aid the delivery of nanomaterials into bacterial cells. By judicious use of targeting ligands, the nanomaterial becomes able to differentiate between bacterial and mammalian cells and, thus, reduce side effects. Moreover, peptide conjugation to the surface of a nanomaterial will alter surface chemistry in ways that lead to reduction in toxicity and improvements in biocompatibility.
Park, J, Lim, J, Park, Y, Han, DS, Shon, HK, Hoffmann, MR & Park, H 2020, 'In Situ-Generated Reactive Oxygen Species in Precharged Titania and Tungsten Trioxide Composite Catalyst Membrane Filters: Application to As(III) Oxidation in the Absence of Irradiation', Environmental Science & Technology, vol. 54, no. 15, pp. 9601-9608.
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This study demonstrates that in situ-generated reactive oxygen species (ROSs) in prephotocharged TiO2 and WO3 (TW) composite particle-embedded inorganic membrane filters oxidize arsenite (As(III)) into arsenate (As(V)) without any auxiliary chemical oxidants under ambient conditions in the dark. TW membrane filters have been charged with UV or simulated sunlight and subsequently transferred to a once-through flow-type system. The charged TW filters can transfer the stored electrons to dissolved O2, producing ROSs that mediate As(III) oxidation in the dark. Dramatic inhibition of As(V) production with O2 removal or addition of ROS quenchers indicates an ROS-mediated As(III) oxidation mechanism. Electron paramagnetic spectroscopic analysis has confirmed the formation of the HO2•/O2•- pair in the dark. The WO3 fraction in the TW filter significantly influences the performance of the As(III) oxidation, while As(V) production is enhanced with increasing charging time and solution pH. The As(III) oxidation is terminated when the singly charged TW filter is fully discharged; however, recharging of TW recovers the catalytic activity for As(III) oxidation. The proposed oxidation process using charged TW membrane filters is practical and environmentally benign for the continuous treatment of As(III)-contaminated water during periods of unavailability of sunlight.
Parvin, K, Hannan, MA, Al-Shetwi, AQ, Ker, PJ, Roslan, MF & Mahlia, TMI 2020, 'Fuzzy Based Particle Swarm Optimization for Modeling Home Appliances Towards Energy Saving and Cost Reduction Under Demand Response Consideration', IEEE Access, vol. 8, pp. 210784-210799.
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Paryani, S, Neshat, A, Javadi, S & Pradhan, B 2020, 'Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping', Natural Hazards, vol. 103, no. 2, pp. 1961-1988.
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© 2020, Springer Nature B.V. Abstract: Many landslides occur in the Karun watershed in the Zagros Mountains. In the present study, we employed a novel comparative approach for spatial modeling of landslides given the high potential of landslides in the region. The aim of the study was to combine adaptive neuro-fuzzy inference system (ANFIS) with grey wolf optimizer (GWO) and particle swarm optimizer (PSO) algorithms using the outputs of qualitative stepwise weight assessment ratio analysis (SWARA) and quantitative certainty factor (CF) models. To this end, 264 landslide positions and twelve conditioning factors including slope, aspect, altitude, distance to faults, distance to rivers, distance to roads, land use, lithology, rainfall, plan and profile curvature and TWI were then extracted considering regional characteristics, literature review and available data. In the next step, the multi-criteria SWARA decision-making model and CF probability model were used to evaluate a correlation between landslide distribution and conditioning factors. Ultimately, landslide susceptibility maps were generated by ANFIS-GWO and ANFIS-PSO hybrid models and the accuracy of models was assessed by ROC curve. According to the results, the area under the curve (AUC) for the hybrid models ANFIS - GWO SWARA, ANFIS - PSO SWARA, ANFIS - GWO CF and ANFIS - PSO CF was 0.789, 0.838, 0.850 and 0.879, respectively. The hybrid models ANFIS - PSO CF and ANFIS - GWO SWARA showed the highest and lowest prediction rate, respectively. Moreover, CF outperformed the SWARA method in terms of evaluating correlation between conditioning factors and landslides. The map produced in this study can be used by regional authorities to manage landslide risk. Graphic abstract: [Figure not available: see fulltext.].
Paryani, S, Neshat, A, Javadi, S & Pradhan, B 2020, 'GIS-based comparison of the GA-LR ensemble method and statistical models at Sefiedrood Basin, Iran', Arabian Journal of Geosciences, vol. 13, no. 19.
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Pashaki, PV & Ji, J-C 2020, 'Nonlocal nonlinear vibration of an embedded carbon nanotube conveying viscous fluid by introducing a modified variational iteration method', Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 42, no. 4.
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Pathak, N, Phuntsho, S, Tran, VH, Johir, MAH, Ghaffour, N, Leiknes, T, Fujioka, T & Shon, HK 2020, 'Simultaneous nitrification-denitrification using baffled osmotic membrane bioreactor-microfiltration hybrid system at different oxic-anoxic conditions for wastewater treatment', Journal of Environmental Management, vol. 253, pp. 109685-109685.
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The efficacy of a baffled osmotic membrane bioreactor-microfiltration (OMBR-MF) hybrid system equipped with thin film forward osmosis membrane for wastewater treatment was evaluated at laboratory scale. The novel OMBR-MF hybrid system involved baffles, that separate oxic and anoxic zones in the aerobic reactor for simultaneous nitrification and denitrification (SND), and a bioreactor comprised of thin film composite-forward osmosis (TFC-FO) and polyether sulfone-microfiltration (PES-MF) membranes. The evaluation was conducted under four different oxic-anoxic cycle patterns. Changes in flux, salinity build-up, and microbial activity (e.g., extracellular polymeric substances (EPS) were assessed. Over the course of a 34 d test, the OMBR-MF hybrid system achieved high removal of total organic carbon (TOC) (86-92%), total nitrogen (TN) (63-76%), and PO4-P (57-63%). The oxic-anoxic cycle time of 0.5-1.5 h was identified to be the best operating condition. Incorporation of MF membrane effectively alleviated salinity build-up in the reactor, allowing stable system operation.
Pathak, N, Tran, VH, Merenda, A, Johir, MAH, Phuntsho, S & Shon, H 2020, 'Removal of Organic Micro-Pollutants by Conventional Membrane Bioreactors and High-Retention Membrane Bioreactors', Applied Sciences, vol. 10, no. 8, pp. 2969-2969.
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The ubiquitous presence of organic micropollutants (OMPs) in the environment as a result of continuous discharge from wastewater treatment plants (WWTPs) into water matrices—even at trace concentrations (ng/L)—is of great concern, both in the public and environmental health domains. This fact essentially warrants developing and implementing energy-efficient, economical, sustainable and easy to handle technologies to meet stringent legislative requirements. Membrane-based processes—both stand-alone or integration of membrane processes—are an attractive option for the removal of OMPs because of their high reliability compared with conventional process, least chemical consumption and smaller footprint. This review summarizes recent research (mainly 2015–present) on the application of conventional aerobic and anaerobic membrane bioreactors used for the removal of organic micropollutants (OMP) from wastewater. Integration and hybridization of membrane processes with other physicochemical processes are becoming promising options for OMP removal. Recent studies on high retention membrane bioreactors (HRMBRs) such as osmotic membrane bioreactor (OMBRs) and membrane distillation bioreactors (MDBRs) are discussed. Future prospects of membrane bioreactors (MBRs) and HRMBRs for improving OMP removal from wastewater are also proposed.
Patten, T, Park, K & Vincze, M 2020, 'DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-based Robotic Grasping', Frontiers in Robotics and AI, vol. 7, p. 120.
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This article presents a method for grasping novel objects by learning fromexperience. Successful attempts are remembered and then used to guide futuregrasps such that more reliable grasping is achieved over time. To generalisethe learned experience to unseen objects, we introduce the dense geometriccorrespondence matching network (DGCM-Net). This applies metric learning toencode objects with similar geometry nearby in feature space. Retrievingrelevant experience for an unseen object is thus a nearest neighbour searchwith the encoded feature maps. DGCM-Net also reconstructs 3D-3D correspondencesusing the view-dependent normalised object coordinate space to transform graspconfigurations from retrieved samples to unseen objects. In comparison tobaseline methods, our approach achieves an equivalent grasp success rate.However, the baselines are significantly improved when fusing the knowledgefrom experience with their grasp proposal strategy. Offline experiments with agrasping dataset highlight the capability to generalise within and betweenobject classes as well as to improve success rate over time from increasingexperience. Lastly, by learning task-relevant grasps, our approach canprioritise grasps that enable the functional use of objects.
Pattison, TG, Spanu, A, Friz, AM, Fu, Q, Miller, RD & Qiao, GG 2020, 'Growing Patterned, Cross-linked Nanoscale Polymer Films from Organic and Inorganic Surfaces Using Ring-Opening Metathesis Polymerization', ACS Applied Materials & Interfaces, vol. 12, no. 3, pp. 4041-4051.
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The ability to modify substrates with thin polymer films allows for the tailoring of surface properties, and through combination of patterning finds use in a large variety of applications such as electronics and lab-on-chip devices. Although many techniques can be used to afford polymer-modified surfaces such as surface-initiated polymerization or layer-by-layer methodologies, their stability in a wide range of environments as well as their ability to target specific chemistry are critical factors to enable their successful application. In this paper, we report a facile technique in creating nanoscale polymer thin films using solid-state continuous assembly of polymers via ring-opening metathesis polymerization (ssCAPROMP) directly from surfaces functionalized through silanization. Using a polymeric precursor that includes norbornene moieties, a highly dense cross-linked network of polymer can be grown in a bottom-up fashion to afford thin films from an olefin-terminated silanized planar surface. Such nanotechnology affords films retaining the desirable qualities of previously reported methods while, at the same time, being covalently bound to the substrate: they are virtually pinhole free and can be reinitiated multiple times. By combining this process with microcontact printing, patterned films can be created by either the patterned deposition of a catalyst or by controlling the surface silanization chemistry and placement of olefin-terminated and nonreactive silanes. Additionally, patterned ssCAPROMP films were grown from SU-8 by selectively functionalizing the surface through masking and lift-off processes after the silanization step, thereby spatially controlling the surface-initiation, and subsequent polymer film formation. These patterned films expand the capabilities of the CAPROMP process and offer advantages over other film formation techniques in processes where patterned substrates and modified but robust surface chemistries are utilized.
Paudel, KR, Wadhwa, R, Mehta, M, Chellappan, DK, Hansbro, PM & Dua, K 2020, 'Rutin loaded liquid crystalline nanoparticles inhibit lipopolysaccharide induced oxidative stress and apoptosis in bronchial epithelial cells in vitro', Toxicology in Vitro, vol. 68, pp. 104961-104961.
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Airway inflammation and infections are the primary causes of damage in the airway epithelium, that lead to hypersecretion of mucus and airway hyper-responsiveness. The role of reactive oxygen species (ROS) and their components in the pathophysiological mechanisms of airway inflammation have been well-studied and emphasized for the past several decades. Rutin, a potent bioflavonoid, is well-known for its antioxidant, anti-inflammatory, especially in bronchial inflammation. However, poor solubility and rapid metabolism have led to its low bioavailability in biological systems, and hence limit its application. The present study aims to investigate the beneficial effects of rutin-loaded liquid crystalline nanoparticles (LCNs) against lipopolysaccharide (LPS) induced oxidative damage in human bronchial epithelial cell line (BEAS-2-B) cells in vitro. LPS was used to stimulate BEAS-2-B cells, causing the generation of nitric oxide (NO) and other reactive oxygen species (ROS) that had led to cellular apoptosis. The levels of NO and ROS were detected by, Griess reagent kit and dichlorodihydrofluorescein diacetate (DCFH-DA) respectively, whereas, cell apoptosis was studied by Annexin V-FITC and PI staining. The findings revealed that rutin-loaded LCNs significantly reduced NO, ROS levels and prevented apoptosis in BEAS-2B cells. The observations and findings provide a mechanistic understanding of the effectiveness of rutin-loaded LCNs in protecting the bronchial cells against airway inflammation, thus possessing a promising therapeutic option for the management of airway diseases.
Paull, NJ, Krix, D, Irga, PJ & Torpy, FR 2020, 'Airborne particulate matter accumulation on common green wall plants', International Journal of Phytoremediation, vol. 22, no. 6, pp. 594-606.
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© 2019, © 2019 Taylor & Francis Group, LLC. In order to better design greening systems for effective particulate matter (PM) removal, it is important to understand the impact leaf traits have on PM deposition. There are however, inconsistences amongst the leaf traits that have previously been correlated with PM accumulation. The aim of this paper was to identify vegetation characteristics of green wall plants that were associated with the accumulation of particulate matter. To determine patterns associated with different leaf morphologies, eleven common ornamental plant species were sampled across 15 sites, over a 6 month duration. PM deposition was determined gravimetrically and its associated size fractions determined microscopically. Linear mixed models were used to identify statistical patterns relating to differences in PM deposition across plant species. PM deposition and the relative frequencies of particle size fractions were found to be statistically different among species, sites and months. Green wall plants were shown to be effective at PM accumulation as all of the assessed plant species had equivalent PM removal efficiency, with minimal evidence of influential leaf characteristics that could enhance PM removal.
Pei, T, Li, L, Zhang, J & Hao, X 2020, 'Module block fault locating strategy for large-scale photovoltaic arrays', Energy Conversion and Management, vol. 214, pp. 112898-112898.
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© 2020 Elsevier Ltd In practical grid-connected photovoltaic systems, faulty modules for small-scale photovoltaic arrays need to be accurately located, while the same strategy to locate each faulty module for large-scale arrays would imply high investment cost due to a large number of sensors needed. Therefore, to reduce the number of sensors and save fault monitoring investment, it suffices to identify where fault happens in a module block, which consists of several modules connected in series. For this purpose, this paper proposes a fault-locating strategy to identify faulty module blocks for large-scale arrays, where voltage sensors are deployed by differentiating the parity of string numbers in the array to acquire the terminal voltages of the module blocks between adjacent strings, and the fault locating rules for open circuit, short circuit, degradation and partial shading faults are formulated. With the help of MATLAB/Simulink platform, the proposed strategy is tested in one small-size array and two large-scale arrays, and the testing results demonstrate that the proposed strategy can locate the single fault case, multiple faults of single-type, as well as mixed faults of different sub-arrays. To summarize, the proposed fault-locating method has higher locating accuracy, lower implementation cost and wiring complexity, and is easily integrable with existing photovoltaic systems.
Pei, T, Zhang, J, Li, L & Hao, X 2020, 'A fault locating method for PV arrays based on improved voltage sensor placement', Solar Energy, vol. 201, pp. 279-297.
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© 2020 International Solar Energy Society Solar photovoltaic (PV) power generation has been widely used because of its environment-friendly advantages. However, operational faults in PV arrays have always been one of the critical factors affecting the PV system's power-generation efficiency and life cycle. This paper proposes a fault locating strategy, which does not need the current sensor in every PV string and can also minimize the number of voltage sensors between strings, to accurately locate the faulty PV modules in a PV array. A variety of faults including open circuit, short circuit, degradation and partial shading faults are considered, and a universal method is proposed in this paper to locate the faulty modules under these faults. In this method, the voltage sensors are deployed through differentiating the parity of the number of strings for the PV array, while the fault locating rules are formulated under each of the afore-mentioned faults, respectively. Compared with existing fault locating methods, the proposed locating technique is shown to be effective in the application to PV arrays with any size and capacity, and it can accurately locate each faulty module for those faults, especially for degradation and partial shading faults. Thus, it is helpful in PV array dynamic reconfiguration and maintenance cost reduction.
Pekka, A, da Silva Cruz, LA, da Silva, EAB, Ebrahimi, T, Freitas, PG, Gilles, A, Oh, K-J, Pagliari, C, Pereira, F, Perra, C, Perry, S, Pinheiro, AMG, Schelkens, P, Seidel, I & Tabus, I 2020, 'JPEG Pleno: Standardizing A Coding Framework And Tools For Plenoptic Imaging Modalities', ITU Journal: ICT Discoveries, vol. 3, no. 1, pp. 1-15.
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JPEG Pleno is an upcoming standard from the ISO/IEC JTC 1/SC 29/WG 1 (JPEG) Committee. It aims to provide a standard framework for coding new imaging modalities derived from representations inspired by the plenoptic function. The image modalities addressed by the current standardization activities are light field, holography, and point clouds, where these image modalities describe different sampled representations of the plenoptic function. The applications that may benefit from these emerging image modalities range from supporting varying capture platforms, interactive content viewing, cultural environments exploration and medical imaging to more immersive browsing with novel special effects and more realistic images. These use cases come with a set of requirements addressed by the JPEG Pleno standard. Main requirements envision high compression efficiency, random access, scalability, error-resilience, low complexity, and metadata support. This paper presents a synopsis of the status of the standardization process and provides technical insights as well as the latest performance evaluation results.
Pendrill, A-M & Eager, D 2020, 'Velocity, acceleration, jerk, snap and vibration: forces in our bodies during a roller coaster ride', Physics Education, vol. 55, no. 6, pp. 065012-065012.
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Abstract Changing acceleration and forces are part of the excitement of a roller coaster ride. According to Newton’s second law, F = m a , every part of our body must be exposed to a force to accelerate. Since our bodies are not symmetric, the direction of the force matters, and must be accounted for by ride designers. An additional complication is that not all parts of the body accelerate in the same way when the acceleration is changing, i.e. when there is jerk. Softer parts of the body provide varying levels of damping, and different parts of the body have different frequency responses and different resonance frequencies that should be avoided or reduced by the roller coaster designer. This paper discusses the effect of acceleration, jerk, snap and vibration on the experience and safety of roller coaster rides, using authentic data from a dive coaster as an example.
Peng, L, Nie, W-B, Ding, J, Ni, B-J, Liu, Y, Han, H-J & Xie, G-J 2020, 'Denitrifying Anaerobic Methane Oxidation and Anammox Process in a Membrane Aerated Membrane Bioreactor: Kinetic Evaluation and Optimization', Environmental Science & Technology, vol. 54, no. 11, pp. 6968-6977.
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Denitrifying anaerobic methane oxidation (DAMO) coupled to anaerobic ammonium oxidation (anammox) is a promising technology for complete nitrogen removal with economic and environmental benefit. In this work, a model framework integrating DAMO and anammox process was constructed based on suspended-growth systems. The proposed model was calibrated and validated using experimental data from a sequencing batch reactor and a membrane aerated membrane bioreactor (MAMBR). The model managed to describe removal rates of ammonium (NH4+), nitrite (NO2-), and total nitrogen, as well as biomass changes of DAMO archaea, DAMO bacteria, and anaerobic ammonium oxidizing bacteria (AnAOB) in both reactors. The estimated parameter values revealed that DAMO archaea possessed properties of faster growth and higher biomass yield in suspended-growth systems compared to those in attached-growth systems (e.g., biofilm). Model simulation demonstrated that solid retention time (SRT) was effective in washing out DAMO bacteria, but retaining DAMO archaea and AnAOB in the MAMBR. The optimal SRT and nitritation efficiency (the ratio of the NO2- to the sum of NH4+ and NO2- in the MAMBR influent) were simulated so that 99% of total nitrogen was removed to meet the discharge standard. MAMBR further suggested to be operated with SRT between 15 and 30 days so that the optimal nitritation efficiency could be minimized to 49% for cost savings.
Peng, Y, Wu, J, Wen, S, Feng, Y, Tu, Z & Zou, L 2020, 'A New Supply Chain System and Its Impulsive Synchronization', Complexity, vol. 2020, pp. 1-9.
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The purpose of this paper is to discuss modelling and synchronization of nonlinear supply chain system. Firstly, we present a new supply chain system which is sensitive to various uncertainties along with exogenous disturbances. Synchronization is an important method to reduce the negative impact of uncertainties and disturbances on the supply chain. Since impulsive control can reduce control cost and the amount of transmitted information drastically, we discuss impulsive synchronization behavior of two supply chain systems with the same structure. Finally, simulation experiments are given to show the effectiveness of our analytical results.
Peng, Y, Zhang, Y, Lin, X, Qin, L & Zhang, W 2020, 'Answering Billion-Scale Label-Constrained Reachability Queries within Microsecond.', Proc. VLDB Endow., vol. 13, no. 6, pp. 812-825.
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In this paper, we study the problem of label-constrained reachability (LCR) query which is fundamental in many applications with directed edge-label graphs. Although the classical reachability query (i.e., reachability query without label constraint) has been extensively studied, LCR query is much more challenging because the number of possible label constraint set is exponential to the size of the labels. We observe that the existing techniques for LCR queries only construct partial index for better scalability, and their worst query time is not guaranteed and could be the same as an online breadth-first search (BFS). In this paper, we propose novel label-constrained 2-hop indexing techniques with novel pruning rules and order strategies. It is shown that our worst query time could be bounded by the in-out index entry size. With all these techniques, comprehensive experiments show that our proposed methods significantly outperform the state-of-the-art technique in terms of query response time (up to 5 orders of magnitude speedup), index size and index construction time. In particular, our proposed method can answer LCR queries within microsecond over billion-scale graphs in a single machine.
Perrier, E, Tao, D & Ferrie, C 2020, 'Quantum geometric machine learning for quantum circuits and control', New Journal of Physics, vol. 22, no. 10, pp. 103056-103056.
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Abstract The application of machine learning techniques to solve problems in quantum control together with established geometric methods for solving optimisation problems leads naturally to an exploration of how machine learning approaches can be used to enhance geometric approaches for solving problems in quantum information processing. In this work, we review and extend the application of deep learning to quantum geometric control problems. Specifically, we demonstrate enhancements in time-optimal control in the context of quantum circuit synthesis problems by applying novel deep learning algorithms in order to approximate geodesics (and thus minimal circuits) along Lie group manifolds relevant to low-dimensional multi-qubit systems, such as SU(2), SU(4) and SU(8). We demonstrate the superior performance of greybox models, which combine traditional blackbox algorithms with whitebox models (which encode prior domain knowledge of quantum mechanics), as means of learning underlying quantum circuit distributions of interest. Our results demonstrate how geometric control techniques can be used to both (a) verify the extent to which geometrically synthesised quantum circuits lie along geodesic, and thus time-optimal, routes and (b) synthesise those circuits. Our results are of interest to researchers in quantum control and quantum information theory seeking to combine machine learning and geometric techniques for time-optimal control problems.
Peters, A, Liang, B, Tian, H, Li, Z, Doolan, C, Vitanage, D, Norris, H, Simpson, K, Wang, Y & Chen, F 2020, 'Data-driven water quality prediction in chloraminated systems', Water e-Journal, vol. 5, no. 4, pp. 1-19.
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This paper proposes a data-driven method that provides water quality prediction within the entire Woronora delivery system in Sydney. Specifically, the key factors relating to water quality are identified through factor analysis. A Bayesian parametric decay model is formulated using the key factors to predict water quality. To estimate the water travel time, which links the upstream (reservoir) data to the downstream (resident) data, the hydraulic system is employed to capture the topology of the delivery system. Moreover, the uncertainties of both data and the model are analysed to define the boundaries of prediction for better decision making.
Pham, M, Hoang, DB & Chaczko, Z 2020, 'Congestion-Aware and Energy-Aware Virtual Network Embedding', IEEE/ACM Transactions on Networking, vol. 28, no. 1, pp. 210-223.
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Pham, T-M, Fdida, S, Nguyen, T-T-L & Chu, H-N 2020, 'Modeling and analysis of robust service composition for network functions virtualization', Computer Networks, vol. 166, pp. 106989-106989.
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Pham, TT, Ngo, HH, Tran, VS & Nguyen, MK 2020, 'Removal of As (V) from the aqueous solution by a modified granular ferric hydroxide adsorbent', Science of The Total Environment, vol. 706, pp. 135947-135947.
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A novel adsorbent was prepared in granular form from iron (III) hydroxide and other additives to remove arsenate (As (V)) from aqueous solution. Adsorption of As (V) onto the adsorbent in batch experiments was analyzed to understand the adsorption mechanism, affecting factors, and adsorption isotherms. The optimal working conditions for the developed adsorbent were at pH 3, 30 °C and 50 g/L. The adsorption of arsenate onto the adsorbent occurred rapidly in the first 10 min and reached equilibrium in 2 h. The Langmuir isotherm was found to be best fitted the adsorption. The pre- and post-adsorption adsorbents were characterized by SEM, BET, FTIR, XRD, and Zeta potential techniques. Experimental results clearly demonstrated the potential impact of elemental composition, crystallinity, surface morphology, and other physico-chemical properties of the adsorbent on the adsorption performance variety. The experimental results with the pilot scale treatment system revealed that the adsorbent can be applied successfully and lead to a very efficient drinking water treatment system, at a competitive cost compared to the water market in Hanoi, Vietnam.
Phong Vo, HN, Ngo, HH, Guo, W, Hong Nguyen, TM, Li, J, Liang, H, Deng, L, Chen, Z & Hang Nguyen, TA 2020, 'Poly‐and perfluoroalkyl substances in water and wastewater: A comprehensive review from sources to remediation', Journal of Water Process Engineering, vol. 36, pp. 101393-101393.
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© 2020 Elsevier Ltd Per- and polyfluoroalkyl substances (PFAS) are pollutants have attracted major concern due to their high persistence and bioaccumulation. They are causing increasingly serious epidemiological problems in many communities globally due to consuming PFAS-contaminated water sources. Necessarily, the behavior of PFAS in water and wastewater needs to be understood better. This study attempts to comprehensively review, analyze and discuss PFAS based on the following key aspects: (i) sources, (ii) occurrence in water and wastewater, (iii) transformation, fate and migration, and (iv) remediation technologies. Studies indicated that modern water and wastewater treatment plants cannot deal completely with PFAS and in some cases, the removal efficiency is minus -3500-fold. The main reasons are the high hydrophobicity of PFAS and presence of PFAS precursors. Precursors can account for 33–63% of total PFAS concentration in water and wastewater. Detection and identification of precursors are challenging due to the requirement of advanced analytical instrument and standard chemicals. Several technologies have been developed for PFAS remediation involving two main mechanisms: separation-concentration and destruction. The most widespread in-use technology is adsorption because it is reasonably affordable. Anion exchange resin and synthesized materials are the most effective sorbents having a sorption capacity of 100–2000 mg PFAS/g sorbent, effective within a few hours. The destruction technology such as plasma can also be a promising one for degrading PFAS to below health-based standard in 1 min. However, plasma is costly and not yet ready for full scale application.
Phung, MD & Ha, QP 2020, 'Motion-encoded particle swarm optimization for moving target search using UAVs', Applied Soft Computing, vol. 97, pp. 106705-106705.
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Phuntsho, S, Kim, JE, Tran, VH, Tahara, S, Uehara, N, Maruko, N, Matsuno, H, Lim, S & Shon, HK 2020, 'Free-standing, thin-film, symmetric membranes: Next-generation membranes for engineered osmosis', Journal of Membrane Science, vol. 607, pp. 118145-118145.
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© 2020 Elsevier B.V. The support layer of an asymmetric thin-film composite membrane results in structural resistance (internal concentration polarization) that significantly undermines engineered osmosis. Increasing the porosity and reducing the thickness and tortuosity of the membrane support layer reduces structural resistance; however, internal concentration polarization still impacts membrane performance. A novel, ultrathin, free-standing and symmetric membrane has been synthesized using sulfonated polyether ketone and tested for forward osmosis applications. This membrane is composed of a protonic acid group containing an aromatic polyether resin with sulfonated structural units. Polyether ketone provides high mechanical strength essential for ultrathin free-standing membranes, while sulfonation enhances the membrane hydrophilicity. These sulfonated polyether ketone membranes show promising water flux performances with impressive mechanical strength under the hydraulic operating conditions used for a FO process.
Piggin, CL, Roden, DL, Law, AMK, Molloy, MP, Krisp, C, Swarbrick, A, Naylor, MJ, Kalyuga, M, Kaplan, W, Oakes, SR, Gallego-Ortega, D, Clark, SJ, Carroll, JS, Bartonicek, N & Ormandy, CJ 2020, 'ELF5 modulates the estrogen receptor cistrome in breast cancer', PLOS Genetics, vol. 16, no. 1, pp. e1008531-e1008531.
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Pileggi, SF 2020, 'Is the World Becoming a Better or a Worse Place? A Data-Driven Analysis', Sustainability, vol. 12, no. 1, pp. 88-88.
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Is the World becoming a better or a worse place to live? In this paper, we propose a tool that can help to answer the question by combining a number of global indicators belonging to multiple categories. The proliferation of statistical data about various aspects of the World performance may suggest that it should be “easy” to evaluate the overall success of human enterprise on this planet. Moreover, it also points out the intrinsic importance in the selection of indicators. However, people have different values, biases, and preferences about the importance of various indicators, making it almost impossible to find an objective answer to this question. To address the variety and the heterogeneity of available indicators and world views, we present the analysis of global World performance as a multi-criteria decision problem, making sure that the assessment method remains as transparent as possible. By dynamically selecting a set of indicators of interest, defining the weights that we attach to various indicators and specifying the desired trends associated with each indicator, we make the assessment adaptive to individual values. We also try to deal with the inherent bias that may exist in the set of indicators that are chosen. As a study case, from various data sets that are openly available online, we have selected several that are most relevant and easy to interpret in the context of the question in the title of the paper. We demonstrate how the choice of personal preferences, or weights, can strongly change the result. Our method also provides analysis of the weights space, showing how results for particular value sets compare to the average and extreme (optimistic and pessimistic) combinations of weights that may be chosen by users.
Pileggi, SF & Lamia, SA 2020, 'Climate Change TimeLine: An Ontology to Tell the Story so Far', IEEE Access, vol. 8, pp. 65294-65312.
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Pileggi, SF, Indorf, M, Nagi, A & Kersten, W 2020, 'CoRiMaS—An Ontological Approach to Cooperative Risk Management in Seaports', Sustainability, vol. 12, no. 11, pp. 4767-4767.
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For today’s global value chains, seaports and their operations are indispensable components. In many cases, the cargo handling takes place in close proximity to residential and/or environmentally sensitive areas. Furthermore, seaports are often not operated by a single organization, but need to be considered as communities of sometimes hundreds of internal and external stakeholders. Due to their close cooperation in the cargo handling process, risk management should be a common approach among the internal stakeholders as well in order to effectively mitigate and respond to emerging risks. However, empirical research has revealed that risk management is often limited to the organization itself, which indicates a clear lack of cooperation. Primary reasons in this regard are missing knowledge about the relations and responsibilities within the port and differing terminologies. Therefore, we propose an ontology (CoRiMaS) that implements a developed reference model for risk management that explicitly aims at seaports with a cooperative approach to risk management. CoRiMaS has been designed looking at the Semantic Web and at the Linked Data model to provide a common interoperable vocabulary in the target domain. The key concepts of our ontology comprise the hazard, stakeholder, seaport, cooperation aspect, and risk management process. We validated our ontology by applying it in a case study format to the Port of Hamburg (Germany). The CoRiMaS ontology can be widely applied to foster cooperation within and among seaports. We believe that such an ontological approach has the potential to improve current risk management practices and, thereby, to increase the resilience of operations, as well as the protection of sensitive surrounding areas.
Pineda-Escobar, MA & Merigó, JM 2020, 'A bibliometric analysis of the Base/Bottom of the Pyramid research', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5537-5551.
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Popović, M, Vidal-Calleja, T, Hitz, G, Chung, JJ, Sa, I, Siegwart, R & Nieto, J 2020, 'An informative path planning framework for UAV-based terrain monitoring', Autonomous Robots, vol. 44, no. 6, pp. 889-911.
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AbstractUnmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori. The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task.
Porter, AL, Zhang, Y, Huang, Y & Wu, M 2020, 'Tracking and Mining the COVID-19 Research Literature', Frontiers in Research Metrics and Analytics, vol. 5.
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Pourzolfaghar, H, Abnisa, F, Wan Daud, WMA, Aroua, MK & Mahlia, TMI 2020, 'Catalyst Characteristics and Performance of Silica-Supported Zinc for Hydrodeoxygenation of Phenol', Energies, vol. 13, no. 11, pp. 2802-2802.
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The present investigation aimed to study the physicochemical characteristics of supported catalysts comprising various percentages of zinc dispersed over SiO2. The physiochemical properties of these catalysts were surveyed by N2 physisorption (BET), thermogravimetry analysis (TGA), H2 temperature-programmed reduction, field-emission scanning electron microscopy (FESEM), inductively coupled plasma-optical emission spectrometry (ICP-OES), and NH3 temperature-programmed desorption (NH3-TPD). In addition, to examine the activity and performance of the catalysts for the hydrodeoxygenation (HDO) of the bio-oil oxygenated compounds, the experimental reaction runs, as well as stability and durability tests, were performed using 3% Zn/SiO2 as the catalyst. Characterization of silica-supported zinc catalysts revealed an even dispersion of the active site over the support in the various dopings of the zinc. The acidity of the calcinated catalysts elevated clearly up to 0.481 mmol/g. Moreover, characteristic outcomes indicate that elevating the doping of zinc metal led to interaction and substitution of proton sites on the SiO2 surface that finally resulted in an increase in the desorption temperature peak. The experiments were performed at temperature 500 °C, pressure 1 atm; weight hourly space velocity (WHSV) 0.32 (h−1); feed flow rate 0.5 (mL/min); and hydrogen flow rate 150 (mL/min). Based on the results, it was revealed that among all the prepared catalysts, that with 3% of zinc had the highest conversion efficiency up to 80%. However, the selectivity of the major products, analyzed by gas chromatography flame-ionization detection (GC-FID), was not influenced by the variation in the active site doping.
Prabhu, CSR, Jan, T, Prasad, M & Varadarajan, V 2020, 'FOG ANALYTICS - A SURVEY', Malaysian Journal of Computer Science, vol. 2020, no. Special Issue 1, pp. 140-151.
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Fog computing has emerged as an essential alternative to the cloud. Fog computing is the future as it is nearer to the edge where actually the IOT devices and sensors are located. A Fog Server or Fog Node is located near to the IOT devices, connecting directly (wired or wireless) to them. The Fog Server has a functionality of fast accessibility to the data arising out of IOT devices or sensors, as against cloud server which may be located in data centers (near core Network Centers) located far away from the edge resulting in extreme delays in network transmission and latency, especially when the data is large volume as stream (or ‘Big Data’) arising out of IOT devices or sensors including cameras, etc. Real time response after completing the necessary Analytics on the data generated by IOT devices and sensors becomes critically essential for meeting the real time response requirements of critical applications such as in health care and transportation. What are the relevant techniques for Fog Analytics? In this paper we provide a brief survey of Fog Analytics techniques in stream data analytics, machine learning, deep learning techniques and also game theoretical adversarial learning.
Pradeepkumar, A, Amjadipour, M, Mishra, N, Liu, C, Fuhrer, MS, Bendavid, A, Isa, F, Zielinski, M, Sirikumara, HI, Jayasekara, T, Gaskill, DK & Iacopi, F 2020, 'p-Type Epitaxial Graphene on Cubic Silicon Carbide on Silicon for Integrated Silicon Technologies', ACS Applied Nano Materials, vol. 3, no. 1, pp. 830-841.
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Copyright © 2019 American Chemical Society. The synthesis of graphene on cubic silicon carbide on silicon pseudosubstrates draws enormous interest due to the potential integration of the 2D material with the well-established silicon technology and processing. However, the control of transport properties over large scales on this platform, essential for integrated electronics and photonics applications, has lagged behind so far, due to limitations such as 3C-SiC/Si interface instability and nonuniform graphene coverage. We address these issues by obtaining an epitaxial graphene (EG) onto 3C-SiC on a highly resistive silicon substrate using an alloy-mediated, solid-source graphene synthesis. We report the transport properties of EG grown over large areas directly on 3C-SiC(100) and 3C-SiC(111) substrates, and we present the corresponding physical models. We observe that the carrier transport of EG/3C-SiC is dominated by the graphene-substrate interaction rather than the EG grain size, sharing the same conductivity and same inverse power law as EG on 4H- or 6H-SiC(0001) substrates - although the grain sizes for the latter are vastly different. In addition, we show that the induced oxidation/silicates at the EG/3C-SiC interface generate a p-type charge in this graphene, particularly high for the EG/3C-SiC(001). When silicates are at the interface, the presence of a buffer layer in the EG/3C-SiC(111) system is found to reduce somewhat the charge transfer. This work also indicates that a renewed focus on the understanding and engineering of the EG interfaces could very well enable the long sought-after graphene-based electronics and photonics integrated on silicon.
Pradeepkumar, A, Gaskill, DK & Iacopi, F 2020, 'Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review', Applied Sciences, vol. 10, no. 12, pp. 4350-4350.
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The electronic and transport properties of epitaxial graphene are dominated by the interactions the material makes with its surroundings. Based on the transport properties of epitaxial graphene on SiC and 3C-SiC/Si substrates reported in the literature, we emphasize that the graphene interfaces formed between the active material and its environment are of paramount importance, and how interface modifications enable the fine-tuning of the transport properties of graphene. This review provides a renewed attention on the understanding and engineering of epitaxial graphene interfaces for integrated electronics and photonics applications.
Pradhan, B 2020, 'Preface', Advances in Science, Technology and Innovation, pp. v-x.
Pradhan, B, Al-Najjar, HAH, Sameen, MI, Mezaal, MR & Alamri, AM 2020, 'Landslide Detection Using a Saliency Feature Enhancement Technique From LiDAR-Derived DEM and Orthophotos', IEEE Access, vol. 8, pp. 121942-121954.
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Pradhan, B, Al-Najjar, HAH, Sameen, MI, Tsang, I & Alamri, AM 2020, 'Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks', Remote Sensing, vol. 12, no. 10, pp. 1676-1676.
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Zero-shot learning (ZSL) is an approach to classify objects unseen during the training phase and shown to be useful for real-world applications, especially when there is a lack of sufficient training data. Only a limited amount of works has been carried out on ZSL, especially in the field of remote sensing. This research investigates the use of a convolutional neural network (CNN) as a feature extraction and classification method for land cover mapping using high-resolution orthophotos. In the feature extraction phase, we used a CNN model with a single convolutional layer to extract discriminative features. In the second phase, we used class attributes learned from the Word2Vec model (pre-trained by Google News) to train a second CNN model that performed class signature prediction by using both the features extracted by the first CNN and class attributes during training and only the features during prediction. We trained and tested our models on datasets collected over two subareas in the Cameron Highlands (training dataset, first test dataset) and Ipoh (second test dataset) in Malaysia. Several experiments have been conducted on the feature extraction and classification models regarding the main parameters, such as the network’s layers and depth, number of filters, and the impact of Gaussian noise. As a result, the best models were selected using various accuracy metrics such as top-k categorical accuracy for k = [1,2,3], Recall, Precision, and F1-score. The best model for feature extraction achieved 0.953 F1-score, 0.941 precision, 0.882 recall for the training dataset and 0.904 F1-score, 0.869 precision, 0.949 recall for the first test dataset, and 0.898 F1-score, 0.870 precision, 0.838 recall for the second test dataset. The best model for classification achieved an average of 0.778 top-one, 0.890 top-two and 0.942 top-three accuracy, 0.798 F1-score, 0.766 recall and 0.838 precision for the first test dataset and 0.737 top-one, 0.906 top-two...
Prakash, S, Joshi, S, Bhatia, T, Sharma, S, Samadhiya, D, Shah, RR, Kaiwartya, O & Prasad, M 2020, 'Characteristic of enterprise collaboration system and its implementation issues in business management', International Journal of Business Intelligence and Data Mining, vol. 16, no. 1, pp. 49-49.
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© 2020 Inderscience Enterprises Ltd. Collaboration is an extremely useful area for the most of the enterprise systems particularly within Web 2.0 and Enterprise 2.0. The collaboration provides help in enterprise collaboration system (ECS) to achieve the desired goal by unifying completed tasks of employees or people working on a similar or the same task. Thus, the collaboration systems have witnessed significant attention. The ECS provides consistent and off-the-shelf support to processes and managements within organisations. Management techniques of the ECS may be useful to a community which manages ECS systems for collaboration. In this context, this paper focuses on enterprise collaboration system and answers critical questions related to ECS including: 1) what does collaboration really means for an enterprise system; 2) how can the collaboration help to improve internal processes and management of the system; 3) how it is helpful to improve interactions with customers and partners?
Pramanik, BK, Nghiem, LD & Hai, FI 2020, 'Extraction of strategically important elements from brines: Constraints and opportunities', Water Research, vol. 168, pp. 115149-115149.
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Pugalia, S, Prakash Sai, L & Cetindamar, DK 2020, 'Personal Networks’ Influence on Student Entrepreneurs: A Qualitative Study', International Journal of Innovation and Technology Management, vol. 17, no. 05, pp. 2050037-2050037.
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This study focuses on students who have conceptualized the business idea during their academic studies and created the business venture during or within two years after graduation. The extant literature identifies social networks as a key factor not only for opportunity recognition but also for start-up survival. This study expands the knowledge about the roles of personal networks within the context of student entrepreneurs. By conducting focus group, interviews, and a survey at a top-ranked technological institute of higher learning in India, this study analyzed the role played by the personal networks in facilitating and enabling the creation of a venture by student entrepreneurs. Our study findings indicate that (1) student entrepreneurs expect ten potential roles from their personal networks, (2) the hierarchy of these roles indicates the triggering impact of business networking with a final outcome of motivational support, and (3) business networking, venture financing and the founding team formation are the most important roles in the actual start-up phase.
Pugalia, S, Sai, LP & Cetindamar Kozanoglu, D 2020, 'Personal networks' influence on student entrepreneurs: A qualitative study', International Journal of Innovation and Technology Management, vol. 17, no. 5, pp. 1-25.
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The present study focuses on students who have conceptualised the business idea during their academic studies and created the business venture during or within two years after graduation. The extant literature identifies social networks as a key factor not only for opportunity recognition but also for start-up survival. This study expands the knowledge about the roles of personal networks within the context of student entrepreneurs. By conducting focus group, interviews, and a survey at a top-ranked technological institute of higher learning in India, the current study analysed the role played by the personal networks in facilitating and enabling the creation of a venture by student entrepreneurs. Our study shows (1) student entrepreneurs' expectations from their personal networks are grouped under 10 topics, (2) the hierarchy of these roles indicates the triggering impact of business networking with a final outcome of motivational support, and (3) the degree which these expectations are realised show that business networking, venture financing and the founding team formation are the most important roles in the actual start-up phase. The present empirical study is an earliest attempt to address the gap in the entrepreneurship literature pertaining to analysing student entrepreneurs’ perspectives on the role of personal networks during start-up. With theoretical and practical implications, this study tries to enrich the entrepreneurship literature.
Punetha, P & Samanta, M 2020, 'Modelling of Shear Behaviour of Interfaces Involving Smooth Geomembrane and Nonwoven Geotextile Under Static and Dynamic Loading Conditions', Geotechnical and Geological Engineering, vol. 38, no. 6, pp. 6313-6327.
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The constitutive modelling of geosynthetic–geosynthetic interfaces is essential to predict the performance of the engineering structures such as landfills, flood control dykes and geotextile encapsulated-sand systems for the protection of shore. This article presents a mathematical model to simulate the shear stress/force–displacement behaviour of the interfaces involving smooth geomembrane and nonwoven geotextile under static and dynamic loading conditions. The model is the extension of an existing technique developed for predicting the soil-structure interface shear behaviour under static loading conditions. The proposed model can predict the non-linear pre-peak and the post-peak strain softening/hardening behaviour of the interfaces observed during the laboratory testing. The shear stress/force–displacement response of the interfaces has been modelled by dividing it into three parts: pre-peak, peak and post-peak behaviour. Subsequently, the modelling parameters are obtained using the results from the laboratory direct shear tests and fixed–block type shake table tests conducted on these interfaces. Finally, the shear stress/force–displacement response of the interfaces is evaluated and compared with the experimental results. The predicted shear stress/force–displacement response of the interfaces is found to be in good agreement with the experimental data for both static and dynamic loading conditions.
Punetha, P, Nimbalkar, S & Khabbaz, H 2020, 'Analytical Evaluation of Ballasted Track Substructure Response under Repeated Train Loads', International Journal of Geomechanics, vol. 20, no. 7, pp. 04020093-04020093.
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© 2020 American Society of Civil Engineers. The irrecoverable deformations in the substructure layers are detrimental to the track stability and demand frequent maintenance. With an escalation in axle load and traffic volume, the frequency of maintenance operations has remarkably increased. Consequently, there is an inevitable need to predict the long-term behavior of the track substructure layers. This article presents a methodology to evaluate the recoverable and irrecoverable responses of the substructure layers under the train-induced repetitive loads. The present method utilizes an integrated approach combining track loading, resiliency, and settlement models. The track substructure layers are simulated as lumped masses that are connected by springs and dashpots. The method is successfully validated against the field investigation data reported in the literature. A parametric study is conducted to investigate the influence of substructure layer properties on the track response. The results reveal that the response of each track layer is significantly influenced by the neighboring layer properties and the incorporation of multilayered track structure enables more accurate prediction of track behavior. The present analytical approach is simple, computationally efficient and may assist the practicing engineers in the safer design of the ballasted track.
Punetha, P, Nimbalkar, S & Khabbaz, H 2020, 'Evaluation of additional confinement for three-dimensional geoinclusions under general stress state', Canadian Geotechnical Journal, vol. 57, no. 3, pp. 453-461.
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Three-dimensional cellular geoinclusions (e.g., geocells, scrap tires) offer all-around confinement to the granular infill materials, thus improving their strength and stiffness. The accurate evaluation of extra confinement offered by these geoinclusions is essential for predicting their performance in the field. The existing models to evaluate the additional confinement are based on either a plane-strain or axisymmetric stress state. However, these geoinclusions are more likely to be subjected to the three-dimensional stresses in actual practice. This note proposes a semi-empirical model to evaluate the additional confinement provided by cellular geoinclusions under the three-dimensional stress state. The proposed model is successfully validated against the experimental data. A parametric study is conducted to investigate the influence of input parameters on additional confinement. Results reveal that the simplification of the three-dimensional stress state into axisymmetric or plane-strain condition has resulted in inaccurate and unreliable results. The extra confinement offered by the geoinclusion shows substantial variation along the intermediate and minor principal stress directions depending on the intermediate principal stress, infill soil, and geoinclusion properties. The magnitude of additional confinement increases with an increase in the geoinclusion modulus. The findings are crucial for accurate assessment of the in situ performance of three-dimensional cellular geoinclusions.
Putra, N, Sandi, AF, Ariantara, B, Abdullah, N & Indra Mahlia, TM 2020, 'Performance of beeswax phase change material (PCM) and heat pipe as passive battery cooling system for electric vehicles', Case Studies in Thermal Engineering, vol. 21, pp. 100655-100655.
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© 2020 The Authors. Increasing greenhouse gas (GHG) emissions in the atmosphere and the scarcity of fossil fuel sources have encouraged car manufacturers to develop more environmentally friendly electric vehicles (EVs). The technology advancements of EVs - those with battery systems in particular - have increased their travel distances. Therefore, increasing and maintaining the battery capacity is a key concern in the development of sustainable EVs. In this study, passive cooling systems were constructed with a heat pipe and phase change material (PCM), and their performances were investigated with battery simulators. The aim was to determine the effectiveness of the cooling system and to identify the optimal PCM (beeswax or Rubitherm RT 44 HC) for a temperature range of 25-55 °C. The use of a heat pipe could decrease the battery temperature by 26.62 °C under a 60 W heat load compared to the case without passive cooling system. Furthermore, the addition of RT 44 to a heat pipe resulted in a maximal temperature decrease of 33.42 °C. Thus, an RT 44 HC is more effective than beeswax because its melting temperature lies within the recommended range of the battery working temperature, and its latent heat allows the absorption of more heat compared to beeswax.
Qi, C, Chen, H, Shen, L, Li, X, Fu, Q, Zhang, Y, Sun, Y & Liu, Y 2020, 'Superhydrophobic Surface Based on Assembly of Nanoparticles for Application in Anti-Icing under Ultralow Temperature', ACS Applied Nano Materials, vol. 3, no. 2, pp. 2047-2057.
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© 2020 American Chemical Society. A new class of superhydrophobic surface based on assembly of nanoparticles was fabricated for improving mechanical durability and anti-icing performance under ultralow temperature. Furthermore, the anti-icing performance and mechanism of the yielded superhydrophobic surface were investigated by high speed video and thermal infrared imaging equipment. The frozen time of water droplets could be prolonged to 372.0 s when glass slides with superhydrophobic surface were exposed to an ultralow temperature of -40.0 °C. This outstanding anti-icing performance is attributed to the unique structure of the superhydrophobic surface based on assembly of nanoparticles, which possesses good free-energy barrier and low heat transfer rate. This study thus opens up an avenue for the design and fabrication of superhydrophobic surface with good durability and anti-icing performance under ultralow temperature.
Qi, C, Chen, H, Sun, Y, Fu, Q, shen, L, Li, X & Liu, Y 2020, 'Facile synthesis and anti-icing performance of superhydrophobic flower-like OTS-SiO2 with tunable size', Advanced Powder Technology, vol. 31, no. 11, pp. 4533-4540.
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© 2020 The Society of Powder Technology Japan The superhydrophobic flower-like OTS-SiO2 particles with tunable size were synthesized for application in anti-icing technology, in which the nanosilica fibers were grown on surface of SiO2 sphere. Furthermore, the anti-icing process of flower-like OTS-SiO2 particles was investigated by a high speed video and thermal infrared imaging equipment. It was found that the flower-like OTS-SiO2 particles with a diameter of 300.0 nm showed best anti-icing ability, in which the frozen time of water droplets could be prolonged to 564.0 s at −25.0℃. The good anti-icing ability was attributed to micro-nano hierarchical structure and surface modification of flower-like OTS-SiO2 particles. The work has an important guiding implication for the subsequent design and preparation of superhydrophobic particles for application in anti-icing technology.
Qi, C, Chen, H, Sun, Y, Shen, L, Li, X, Fu, Q & Liu, Y 2020, 'Facile preparation of robust superhydrophobic surface based on multi‐scales nanoparticle', Polymer Engineering & Science, vol. 60, no. 8, pp. 1785-1794.
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AbstractA new superhydrophobic surface based on multi‐scales nanoparticle was designed and prepared to enhance the robustness and reproducibility. The influence of multi‐scale nanoparticles on the structure and property of the superhydrophobic surface was further investigated. The superhydrophobic surface with optimized composition did not only show high contact angle of 160°‐166.3° but also exhibited good durability to the mechanical, chemical, and thermal environments. Furthermore, the superhydrophobic surface was evaluated for application in anticorrosion, anti‐icing, and self‐cleaning. This study provides a new method to prepare robust superhydrophobic surface based on polymer nanocomposite coating for various potential applications.
Qi, Y & Indraratna, B 2020, 'Energy-Based Approach to Assess the Performance of a Granular Matrix Consisting of Recycled Rubber, Steel-Furnace Slag, and Coal Wash', Journal of Materials in Civil Engineering, vol. 32, no. 7, pp. 04020169-04020169.
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© 2020 American Society of Civil Engineers. Ballasted track progressively deteriorates due to ballast degradation and track deformation under dynamic loading, and this process accelerates when train speeds increase and axle loads become heavier as the railways are seeking to serve the enhanced productivity of the mining and agriculture sectors; on this basis, improving track performance is imperative. One effective solution is to incorporate energy-absorbing materials in the rail track, particularly when these materials are recycled from mining waste and recycled rubber. In this paper the performance of the track specimen with a synthetic energy absorbing layer (SEAL) (i.e., a matrix of recycled rubber crumbs with mining waste) is investigated by a series of large-scale (prototype) cubical triaxial tests. The test results indicate that the inclusion of rubber inside the SEAL matrix has a significant influence on the lateral movement, vertical deformation, ballast degradation, and energy distribution of the track specimen. To facilitate a better understanding of the energy-absorbing mechanism with the addition of rubber, an energy-based analysis has been adopted to identify the critical amount of rubber crumbs needed to efficiently distribute the accumulated energy, hence improve track performance. It is shown that adding 10% of rubber into the SEAL matrix will provide superior performance with less ballast breakage, less vibration (as reflected by the elastic energy), and comparable settlement compared to traditional track.
Qian, K, Liu, H, Valls Miro, J, Jing, X & Zhou, B 2020, 'Hierarchical and parameterized learning of pick-and-place manipulation from under-specified human demonstrations', Advanced Robotics, vol. 34, no. 13, pp. 858-872.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan. Imitating manipulation skills through observing human demonstrations in everyday life is promising in allowing service robots to be programed quickly, as well as to perform human-like behaviors. Such a Learning by demonstration (LbD) problem is challenging because robots are expected to adapt their learned behaviors to the changes of task parameters and the environment, rather than simply cloning the human teacher's motion. In this paper, we propose a hierarchical and parameterized LbD framework that combines symbolic and trajectory learning of pick-and-place manipulation tasks. We have extended the two-step parameterized learning method with error compensation for learning Environment-adaptive Action Primitives (EaAPs), which is capable of adapting robot's reproduced trajectories to new task instances as well as environmental changes. To arrive at refined plans in situations of under-specified human demonstrations, we propose to model the semantics of demonstrated activities with PDDL-based skill scripts. Therefore, latent motion primitives that are impossible to be learned directly from observing human demonstration in noisy video data can be inferred. The proposed method is implemented as a hierarchical LbD framework and has been evaluated on real robot hardware to illustrate the effectiveness of the proposed approach.
Qian, W, Yu, X & Qian, C 2020, 'Wireless Powered Encoding and Broadcasting of Frequency Modulated Detection Signals', IEEE Access, vol. 8, pp. 200450-200460.
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Qian, W, Yu, X & Qian, C 2020, 'Wireless Reconfigurable RF Detector Array for Focal and Multiregional Signal Enhancement', IEEE Access, vol. 8, pp. 136594-136604.
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Qiao, Y, Sun, X & Yu, N 2020, 'Local Equivalence of Multipartite Entanglement', IEEE Journal on Selected Areas in Communications, vol. 38, no. 3, pp. 568-574.
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© 2020 IEEE. Let R be an invariant polynomial ring of a reductive group acting on a vector space, and let d be the minimum integer such that R is generated by those polynomials in R of degree no more than d. To upper bound such d is a long standing open problem since the very initial study of the invariant theory in the 19th century. Motivated by its significant role in characterizing multipartite entanglement, we study the invariant polynomial rings of local unitary groups-the direct product of unitary groups acting on the tensor product of Hilbert spaces, and local general linear groups-the direct product of general linear groups acting on the tensor product of Hilbert spaces. For these two group actions, we prove explicit upper bounds on the degrees needed to generate the corresponding invariant polynomial rings. On the other hand, systematic methods are provided to construct all homogeneous polynomials that are invariant under these two groups for any fixed degree. Thus, our results can be regarded as a complete characterization of the invariant polynomial rings. As an interesting application, we show that multipartite entanglement is additive in the sense that two multipartite states are local unitary equivalent if and only if r-copies of them are local unitary equivalent for some r.
Qin, C, Zhang, JA, Huang, X & Guo, YJ 2020, 'Virtual-Subarray-Based Angle-of-Arrival Estimation in Analog Antenna Arrays', IEEE Wireless Communications Letters, vol. 9, no. 2, pp. 194-197.
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© 2012 IEEE. Angle-of-arrival (AoA) estimation is a challenging problem for analog antenna arrays. Typical algorithms are based on beam scanning which can be time-consuming. In this letter, we propose a virtual-subarray based recursive AoA estimation scheme that can get an AoA estimate from every two measurements and recursively improve the performance by updating beamforming weights with soft probability-based information. Simulation results validate the high efficiency of the proposed scheme, demonstrating its superiority for initial AoA estimation in analog arrays.
Qin, C, Zhang, JA, Huang, X, Wu, K & Guo, YJ 2020, 'Fast Angle-of-Arrival Estimation via Virtual Subarrays in Analog Antenna Array', IEEE Transactions on Wireless Communications, vol. 19, no. 10, pp. 6425-6439.
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Qin, H & Stewart, MG 2020, 'Construction defects and wind fragility assessment for metal roof failure: A Bayesian approach', Reliability Engineering & System Safety, vol. 197, pp. 106777-106777.
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Post-damage observations reveal that construction error is one of the major contributors to roof damage for houses subjected to extreme winds. In this study, a Bayesian approach was developed to probabilistically quantify the construction defect rates in roof connections, which enables a systematic integration of expert judgement, human reliability analysis (HRA) techniques and limited construction defect data. The reductions of uplift capacities for defective roof connections were also probabilistically modelled based on experimental evidence and engineering judgement. The developed construction defect model was incorporated in a reliability-based fragility method to assess the wind damage to metal roof cladding and timber roof trusses for contemporary houses in non-cyclonic regions of Australia. It was found that, the effects of construction defects are significant for the predicted roof cladding fragility, whereas for roof truss fragility, such effects are lower.
Qin, H & Stewart, MG 2020, 'Risk-based cost-benefit analysis of climate adaptation measures for Australian contemporary houses under extreme winds', Journal of Infrastructure Preservation and Resilience, vol. 1, no. 1.
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AbstractClimate adaptation measures improve housing resilience to extreme winds, and reduce economic losses associated with wind and rainfall damage under a changing climate. Several adaptation measures are adopted in this study for Australian contemporary houses subjected to non-cyclonic windstorms to either reinforce the building envelope or increase the water resistance of building interior. A risk-based cost-benefit analysis is conducted to evaluate the cost-effectiveness of these adaptation measures that considers the effect of construction defects. It was found that the annual expected losses for houses in Brisbane with construction defects are considerably higher than those without considering construction defects, whereas the influence of construction defects is lower for the Melbourne houses. The cost-benefit analysis reveals that strengthening windows is cost-effective for Brisbane and Melbourne houses. Installing window shutters significantly reduces economic risks associated with extreme winds and is cost-effective for houses in Brisbane. Adaptation measures are generally not cost-effective for Melbourne houses due to lower extreme wind speed and associated rainfall.
Qin, H & Stewart, MG 2020, 'Wind and rain losses for metal-roofed contemporary houses subjected to non-cyclonic windstorms', Structural Safety, vol. 86, pp. 101979-101979.
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Severe windstorms cause millions in losses annually for housing in Southeast Australia that has more than half of Australia's population. The risk assessment for housing in these non-cyclonic regions is the key to assessing the cost-effectiveness of relevant wind mitigation measures to reduce economic losses. This study develops a probabilistic risk assessment framework to evaluate the wind and rain losses for new Australian contemporary houses correctly built and inspected to current standards that are subjected to non-cyclonic windstorms, which integrates the hazard modelling for extreme wind and associated rainfall, reliability-based wind damage assessment, rainwater intrusion evaluation and economic loss modelling. The risk analysis was conducted for metal-roofed contemporary houses in Brisbane and Melbourne, and the efficacy of the proposed risk assessment framework was demonstrated by comparing with inferred insurance loss data. It was found that rainwater damage to building interior and contents is the major contributor to annual expected economic losses associated with windstorms, whereas wind damage to roof cladding and windows comprises a small portion of annual losses. Preliminary model outputs also indicate that houses in Brisbane are generally subject to more losses than houses in Melbourne. However, modelling assumptions that lead to these results have yet been fully validated.
Qin, J, Guo, Y, Xue, B, Shi, P, Chen, Y, Su, QP, Hao, H, Zhao, S, Wu, C, Yu, L, Li, D & Sun, Y 2020, 'ER-mitochondria contacts promote mtDNA nucleoids active transportation via mitochondrial dynamic tubulation', Nature Communications, vol. 11, no. 1, p. 4471.
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AbstractA human cell contains hundreds to thousands of mitochondrial DNA (mtDNA) packaged into nucleoids. Currently, the segregation and allocation of nucleoids are thought to be passively determined by mitochondrial fusion and division. Here we provide evidence, using live-cell super-resolution imaging, that nucleoids can be actively transported via KIF5B-driven mitochondrial dynamic tubulation (MDT) activities that predominantly occur at the ER-mitochondria contact sites (EMCS). We further demonstrate that a mitochondrial inner membrane protein complex MICOS links nucleoids to Miro1, a KIF5B receptor on mitochondria, at the EMCS. We show that such active transportation is a mechanism essential for the proper distribution of nucleoids in the peripheral zone of the cell. Together, our work identifies an active transportation mechanism of nucleoids, with EMCS serving as a key platform for the interplay of nucleoids, MICOS, Miro1, and KIF5B to coordinate nucleoids segregation and transportation.
Qin, J, Wang, W, Xiao, C & Zhang, Y 2020, 'Similarity query processing for high-dimensional data', Proceedings of the VLDB Endowment, vol. 13, no. 12, pp. 3437-3440.
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Similarity query processing has been an active research topic for several decades. It is an essential procedure in a wide range of applications. Recently, embedding and auto-encoding methods as well as pre-trained models have gained popularity. They basically deal with high-dimensional data, and this trend brings new opportunities and challenges to similarity query processing for high-dimensional data. Meanwhile, new techniques have emerged to tackle this long-standing problem theoretically and empirically. In this tutorial, we summarize existing solutions, especially recent advancements from both database (DB) and machine learning (ML) communities, and analyze their strengths and weaknesses. We review exact and approximate methods such as cover tree, locality sensitive hashing, product quantization, and proximity graphs. We also discuss the selectivity estimation problem and show how researchers are bringing in state-of-the-art ML techniques to address the problem. By highlighting the strong connections between DB and ML, we hope that this tutorial provides an impetus towards new ML for DB solutions and vice versa.
Qiu, C, Wei, Z, Yuan, X, Feng, Z & Zhang, P 2020, 'Multiple UAV-Mounted Base Station Placement and User Association With Joint Fronthaul and Backhaul Optimization', IEEE Transactions on Communications, vol. 68, no. 9, pp. 5864-5877.
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Qu, F, Li, W, Tao, Z, Castel, A & Wang, K 2020, 'High temperature resistance of fly ash/GGBFS-based geopolymer mortar with load-induced damage', Materials and Structures, vol. 53, no. 4.
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© 2020, RILEM. This study investigated the effect of elevated temperatures on the residual mechanical behaviors of geopolymer mortars with initial damage induced by mechanical load. Geopolymer mortar was prepared using different fly ash/ ground granulated blast furnace slag (GGBFS) ratios and was activated by sodium silicate and sodium hydroxide solution. The physical properties and residual mechanical strength were investigated and compared with those of Portland cement mortar (PCM). After elevated temperature exposure, microstructure of GSM was studied by various microcharacterizations. The results show that before the exposure to high temperature, the addition of GGBFS increased the compressive strength of GSM, but made it more sensitive to the preloading damage, leading to the increased strength loss. After exposed to combined preloading damage and high temperature exposure, the GSM exhibited lower residual strength than the ones only suffered from preloading damage or high temperature exposure. Compared to the PCM, GSM with GGBFS performed better at temperature of 300 °C, but became worse at temperatures of 500 and 700 °C due to severe damage caused by combined high load level and large heat exposure. Finally, a low percentage of GGBFS (less than 20%) can be considered as an optimal amount for the GSM to achieve excellent fire resistance capacity.
Qu, F, Li, W, Zeng, X, Luo, Z, Wang, K & Sheng, D 2020, 'Effect of microlimestone on properties of self-consolidating concrete with manufactured sand and mineral admixture', Frontiers of Structural and Civil Engineering, vol. 14, no. 6, pp. 1545-1560.
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© 2020, Higher Education Press. Self-consolidating concrete (SCC) with manufactured sand (MSCC) is crucial to guarantee the quality of concrete construction technology and the associated property. The properties of MSCC with different microlimestone powder (MLS) replacements of retreated manufactured sand (TMsand) are investigated in this study. The result indicates that high-performance SCC, made using TMsand (TMSCC), achieved high workability, good mechanical properties, and durability by optimizing MLS content and adding fly ash and silica fume. In particular, the TMSCC with 12% MLS content exhibits the best workability, and the TMSCC with 4% MLS content has the highest strength in the late age, which is even better than that of SCC made with the river sand (Rsand). Though MLS content slightly affects the hydration reaction of cement and mainly plays a role in the nucleation process in concrete structures compared to silica fume and fly ash, increasing MLS content can evidently have a significant impact on the early age hydration progress. TMsand with MLS content ranging from 8% to 12% may be a suitable alternative for the Rsand used in the SCC as fine aggregate. The obtained results can be used to promote the application of SCC made with manufactured sand and mineral admixtures for concrete-based infrastructure.
Qu, K, Wu, C, Liu, J, Yao, Y, Deng, Y & Yi, C 2020, 'Ballistic performance of multi-layered aluminium and UHMWPE fibre laminate targets subjected to hypervelocity impact by tungsten alloy ball', Composite Structures, vol. 253, pp. 112785-112785.
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© 2020 Elsevier Ltd Multi-layered structures have been widely used in defence, marine, aerospace and automotive engineering. It is radical to comprehend the dynamic response of such structures under impact loadings and to design optimal structures to resist ballistic penetration. In this study, hypervelocity projectile impact experiments were conducted to explore the penetration resistance of spaced multi-layered aluminium and ultra-high molecular weight polyethylene (UHMWPE) fibre laminate targets. Experimental results including crater diameter, depth of penetration (DOP) and energy absorption capacity of multi-layered targets were measured and then discussed, from which it was evident that the combined use of aluminium and UHMWPE fibre laminates was the most effective in resisting projectile impact considering both the dynamic performance and light weight. Based on the validated simulation models, further parametric studies were conducted to investigate the effect of air space between adjacent layers, number and order of layers on the ballistic performance of multi-layered aluminium and UHMWPE fibre laminate targets.
Qu, X, Yu, Y, Zhou, M, Lin, C-T & Wang, X 2020, 'Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach', Applied Energy, vol. 257, pp. 114030-114030.
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© 2019 Elsevier Ltd It has been well recognized that human driver's limits, heterogeneity, and selfishness substantially compromise the performance of our urban transport systems. In recent years, in order to deal with these deficiencies, our urban transport systems have been transforming with the blossom of key vehicle technology innovations, most notably, connected and automated vehicles. In this paper, we develop a car following model for electric, connected and automated vehicles based on reinforcement learning with the aim to dampen traffic oscillations (stop-and-go traffic waves) caused by human drivers and improve electric energy consumption. Compared to classical modelling approaches, the proposed reinforcement learning based model significantly reduces the modelling constraints and has the capability of self-learning and self-correction. Experiment results demonstrate that the proposed model is able to improve travel efficiency by reducing the negative impact of traffic oscillations, and it can also reduce the average electric energy consumption.
Qu, Y, Gao, L, Luan, TH, Xiang, Y, Yu, S, Li, B & Zheng, G 2020, 'Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing', IEEE Internet of Things Journal, vol. 7, no. 6, pp. 5171-5183.
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© 2014 IEEE. As the extension of cloud computing and a foundation of IoT, fog computing is experiencing fast prosperity because of its potential to mitigate some troublesome issues, such as network congestion, latency, and local autonomy. However, privacy issues and the subsequent inefficiency are dragging down the performances of fog computing. The majority of existing works hardly consider a reasonable balance between them while suffering from poisoning attacks. To address the aforementioned issues, we propose a novel blockchain-enabled federated learning (FL-Block) scheme to close the gap. FL-Block allows local learning updates of end devices exchanges with a blockchain-based global learning model, which is verified by miners. Built upon this, FL-Block enables the autonomous machine learning without any centralized authority to maintain the global model and coordinates by using a Proof-of-Work consensus mechanism of the blockchain. Furthermore, we analyze the latency performance of FL-Block and further derive the optimal block generation rate by taking communication, consensus delays, and computation cost into consideration. Extensive evaluation results show the superior performances of FL-Block from the aspects of privacy protection, efficiency, and resistance to the poisoning attack.
Qu, Y, Yu, S, Zhou, W & Tian, Y 2020, 'GAN-Driven Personalized Spatial-Temporal Private Data Sharing in Cyber-Physical Social Systems', IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 2576-2586.
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Qu, Y, Zhang, J, Li, R, Zhang, X, Zhai, X & Yu, S 2020, 'Generative adversarial networks enhanced location privacy in 5G networks', Science China Information Sciences, vol. 63, no. 12, p. 220303.
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5G networks, as the up-to-date communication platforms, are experiencing fast booming. Meanwhile, increasing volumes of sensitive data, especially location information, are being generated and shared using 5G networks for various purposes ceaselessly. Location and trajectory information in the published data has always been and will keep courting risks and attacks by malicious adversaries. Therefore, there are still privacy leakage threats by simply sharing the original data, especially data with location information, due to the short cover range of 5G signal tower. To better address these issues, we proposed a generative adversarial networks (GAN) enhanced location privacy protection model to cloak the location and even trajectory information. We use posterior sampling to generate a subset of data, which is proved complying with differential privacy requirements from the end device side. After that, a data augmentation algorithm modified from classic GAN is devised to generate a series of privacy-preserving full-sized synthetic data from the central server side. With the synthetic data generated from a real-world dataset, we demonstrate the superiority of the proposed model in terms of location privacy protection, data utility, and prediction accuracy.
Quiroz, JC, Laranjo, L, Kocaballi, AB, Briatore, A, Berkovsky, S, Rezazadegan, D & Coiera, E 2020, 'Identifying relevant information in medical conversations to summarize a clinician-patient encounter', Health Informatics Journal, vol. 26, no. 4, pp. 2906-2914.
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To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases to be used for generating a summary of the consultation. For all consultations, less than 20% of all words in the transcripts were needed for inclusion in the summary. On average, 9.1% of all words in the transcripts, 26.6% of all medical terms, and 27.3% of all speaker turns were highlighted. The results indicate that communication content used for generating a consultation summary makes up a small portion of GP consultations, and automated summarization solutions—such as digital scribes—must focus on identifying the 20% relevant information for automatically generating consultation summaries.
Rafi, FHM, Hossain, MJ, Rahman, MS & Taghizadeh, S 2020, 'An overview of unbalance compensation techniques using power electronic converters for active distribution systems with renewable generation', Renewable and Sustainable Energy Reviews, vol. 125, pp. 109812-109812.
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Rafique, W, Zhao, X, Yu, S, Yaqoob, I, Imran, M & Dou, W 2020, 'An Application Development Framework for Internet-of-Things Service Orchestration', IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4543-4556.
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© 2014 IEEE. Application development for the Internet of Things (IoT) poses immense challenges due to the lack of standard development frameworks, tools, and techniques to assist end users in dealing with the complexity of IoT systems during application development. These challenges invoke the use of model-driven development (MDD) along with the representational state transfer (REST) architecture to develop IoT applications, supporting model generation at different abstraction levels while generating software implementation artifacts for heterogeneous platforms and ensuring loose coupling in complex IoT systems. This article proposes an IoT application development framework, named IADev, which uses attribute-driven design and MDD to address the above-mentioned challenges. This framework is composed of two major steps, including iterative architecture development using attribute-driven design and generating models to guide the transformation using MDD. IADev uses attribute-driven design to transform the requirements into a solution architecture by considering the concerns of all involved stakeholders, and then, MDD metamodels are generated to hierarchically transform the design components into the software artifacts. We evaluate IADev for a smart vehicle scenario in an intelligent transportation system to generate an executable implementation code for a real-world system. The case study experiments proclaim that IADev achieves higher satisfaction of the participants for the IoT application development and service orchestration, as compared to conventional approaches. Finally, we propose an architecture that uses IADev with the Siemens IoT cloud platform for service orchestration in industrial IoT.
Rahman, ML, Zhang, JA, Huang, X, Guo, YJ & Heath, RW 2020, 'Framework for a Perceptive Mobile Network Using Joint Communication and Radar Sensing', IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 3, pp. 1926-1941.
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In this paper, we develop a framework for a novel perceptive mobile/cellular network that integrates radar sensing function into the mobile communication network. We propose a unified system platform that enables downlink and uplink sensing, sharing the same transmitted signals with communications. We aim to tackle the fundamental sensing parameter estimation problem in perceptive mobile networks, by addressing two key challenges associated with sophisticated mobile signals and rich multipath in mobile networks. To extract sensing parameters from orthogonal frequency division multiple access and spatial division multiple access communication signals, we propose two approaches to formulate it to problems that can be solved by compressive sensing techniques. Most sensing algorithms have limits on the number of multipath signals for their inputs. To reduce the multipath signals, as well as removing unwanted clutter signals, we propose a background subtraction method based on simple recursive computation, and provide a closed-form expression for performance characterization. The effectiveness of these methods is validated in simulations.
Rahman, ML, Zhang, JA, Huang, X, Guo, YJ & Lu, Z 2020, 'Joint communication and radar sensing in 5G mobile network by compressive sensing', IET Communications, vol. 14, no. 22, pp. 3977-3988.
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Radio sensing can be integrated with communication in what the authors call future perceptive mobile networks. Due to the complicated signal structure, it is challenging to estimate sensing parameters such as delay, angle of arrival, and Doppler when joint communication and radar/radio sensing is applied in perceptive mobile networks. Radio sensing with signals compatible with a fifth‐generation (5G) new radio standard using one‐dimension (1D) to 3D compressive sensing (CS) techniques under 5G channel conditions is studied. In the case of 1D–3D CS techniques, they formulate the parameter estimation as a sparse signal recovery problem. These algorithms demonstrate respective advantages, but also show shortcomings in dealing with clustered channels. To effectively exploit the cluster structure in multipath channels, they also propose a 2D cluster Kronecker CS algorithm for significantly improved sensing parameter estimation via introducing a prior probability distribution. Simulation results are provided and they focus the respective advantages and disadvantages of these techniques that validate the effectiveness of the proposed algorithms.
Rahman, MS, Hossain, MJ, Lu, J, Rafi, FHM & Mishra, S 2020, 'A Vehicle-to-Microgrid Framework With Optimization-Incorporated Distributed EV Coordination for a Commercial Neighborhood', IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1788-1798.
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Rahmati, O, Panahi, M, Ghiasi, SS, Deo, RC, Tiefenbacher, JP, Pradhan, B, Jahani, A, Goshtasb, H, Kornejady, A, Shahabi, H, Shirzadi, A, Khosravi, H, Moghaddam, DD, Mohtashamian, M & Tien Bui, D 2020, 'Hybridized neural fuzzy ensembles for dust source modeling and prediction', Atmospheric Environment, vol. 224, pp. 117320-117320.
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Rajabi, A, Eskandari, M, Ghadi, MJ, Li, L, Zhang, J & Siano, P 2020, 'A comparative study of clustering techniques for electrical load pattern segmentation', Renewable and Sustainable Energy Reviews, vol. 120, pp. 109628-109628.
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© 2019 Elsevier Ltd Smart meters have been widely deployed in power networks since the last decade. This trend has resulted in an enormous volume of data being collected from the electricity customers. To gain benefits for various stakeholders in power systems, proper data mining techniques, such as clustering, need to be employed to extract the underlying patterns from energy consumptions. In this paper, a comparative study of different techniques for load pattern clustering is carried out. Different parameters of the methods that affect the clustering results are evaluated and the clustering algorithms are compared for two data sets. In addition, the two suitable and commonly used data size reduction techniques and feature definition/extraction methods for load pattern clustering are analysed. Furthermore, the existing studies on clustering of electricity customers are reviewed and the main results are highlighted. Finally, the future trends and major applications of clustering consumption patterns are outlined to inform industry practitioners and academic researchers to optimize smart meter operational use and effectiveness.
Ramakrishna, VAS, Chamoli, U, Rajan, G, Mukhopadhyay, SC, Prusty, BG & Diwan, AD 2020, 'Smart orthopaedic implants: A targeted approach for continuous postoperative evaluation in the spine', Journal of Biomechanics, vol. 104, pp. 109690-109690.
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Ramezani, A, Ghiasi, M, Dehghani, M, Niknam, T, Siano, P & Alhelou, HH 2020, 'Reduction of Ripple Toothed Torque in the Internal Permanent Magnet Electric Motor by Creating Optimal Combination of Holes in the Rotor Surface Considering Harmonic Effects', IEEE Access, vol. 8, pp. 215107-215124.
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Ramos, A, Gomes Correia, A, Indraratna, B, Ngo, T, Calçada, R & Costa, PA 2020, 'Mechanistic-empirical permanent deformation models: Laboratory testing, modelling and ranking', Transportation Geotechnics, vol. 23, pp. 100326-100326.
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© 2020 Elsevier Ltd Geomaterials exhibit elastoplastic behaviour during dynamic and repeated loading conditions. These loads are induced by the passage of a train or vehicle which then generates recoverable (resilient) deformation and/or permanent (plastic) deformation. Modelling this behaviour is still a challenge for geotechnical engineers as it implies the understanding of the complex deformation mechanism and application of advanced constitutive models. This paper reviews on the major causes of permanent deformation and the factors that influence the long-term performance of materials. It will also present the fundamental concepts of permanent deformation as well as the models and approaches used to characterise this behaviour, including: elastoplastic models, shakedown theory and mechanistic-empirical permanent deformation models. This paper will focus on the mechanistic-empirical approach and highlight the evolution of the models, and the main similarities and differences between them. A comparison between several empirical models as well as the materials used to develop the models is also discussed. These materials are compared by considering the reference conditions on the type of material and its physical state. This approach allows for an understanding of which properties can influence the performance of railway subgrade and pavement structures, as well as the main variables used to characterise this particular behaviour. An innovative ranking of geomaterials that relate to the expected permanent deformation and classification (UIC and ASTM) of soil is also discussed because it can be used as an important tool for the design process.
Ranjan Das, S, Mishra, AK, Ray, PK, Mohanty, A, Mishra, DK, Li, L, Hossain, MJ & Mallick, RK 2020, 'Advanced wavelet transform based shunt hybrid active filter in PV integrated power distribution system for power quality enhancement', IET Energy Systems Integration, vol. 2, no. 4, pp. 331-343.
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Rao, RN, Silitonga, AS, Shamsuddin, AH, Milano, J, Riayatsyah, TMI, Sebayang, AH, Nur, TB, Sabri, M, Yulita, MR & Sembiring, RW 2020, 'Effect of Ethanol and Gasoline Blending on the Performance of a Stationary Small Single Cylinder Engine', Arabian Journal for Science and Engineering, vol. 45, no. 7, pp. 5793-5802.
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Raoufi, MA, Razavi Bazaz, S, Niazmand, H, Rouhi, O, Asadnia, M, Razmjou, A & Ebrahimi Warkiani, M 2020, 'Fabrication of unconventional inertial microfluidic channels using wax 3D printing', Soft Matter, vol. 16, no. 10, pp. 2448-2459.
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A novel workflow for the fabrication of inertial microfluidic devices based on the wax 3D printing method.
Rasouli, H & Fatahi, B 2020, 'Geofoam blocks to protect buried pipelines subjected to strike-slip fault rupture', Geotextiles and Geomembranes, vol. 48, no. 3, pp. 257-274.
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© 2019 Elsevier Ltd This paper proposes using geofoam blocks to improve the safety of buried steel pipelines under permanent ground deformation due to strike-slip fault rupture. Since these geofoam blocks are deformable, they can compress during fault rupture and thus reduce the pressure imposed on the pipeline by the surrounding soil. This means that the pipe can sustain a higher level of tectonic deformations. For the pipeline system adopted in this study, the geofoam blocks consist of two 1 m thick blocks at each side and another on the top of the pipeline. The effectiveness of this configuration is then assessed in comparison to the conventional buried pipeline by three dimensional numerical simulations that consider the interaction between soil and structure and the impact of critical parameters such as the pipeline-fault trace crossing angle, geofoam blocks thickness and the internal pressure of the pipeline. The results indicated that the geofoam blocks reduced the axial tensile strain of non-pressurised pipeline from the unacceptable 4.16% to the safe level of 0.75% when the crossing angle was 135°. In addition, geofoam blocks successfully decreased the maximum ovalisation parameter and compressive strain of the non-pressurised pipeline from 0.237 and −25.8% to 0.065 and −0.47%, respectively when the crossing angle was 65°.
Rasouli, H, Fatahi, B & Nimbalkar, S 2020, 'Liquefaction and post-liquefaction assessment of lightly cemented sands', Canadian Geotechnical Journal, vol. 57, no. 2, pp. 173-188.
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Post-liquefaction response of lightly cemented sands during an earthquake may change and become similar to uncemented sands due to bonding breakage. In the current study, the effect of degree of cementation on liquefaction and post-liquefaction behaviour of lightly cemented sands was studied through a series of cyclic and monotonic triaxial tests. Portland cement with high early strength and Sydney sand were used to reconstitute the lightly cemented specimens with unconfined compression strength ranging from 25 to 220 kPa. A series of multi-stage soil element tests including stress-controlled cyclic loading events with different amplitudes and post-cyclic undrained monotonic shearing tests were carried out on both uncemented and cemented specimens. Furthermore, a series of undrained monotonic shearing tests without cyclic loading history on different types of specimens was conducted to investigate the effect of cyclic loading history on the post-cyclic response of the specimens. The results show that residual excess pore-water pressure is correlated to the cyclic degradation of lightly cemented sands during cyclic loading. In addition, optical microstructure images of the cemented specimens after liquefaction showed that a major proportion of cementation bonds remained unbroken, which resulted in a superior post-liquefaction response with respect to initial stiffness and shear modulus in comparison to the uncemented sand.
Ratiko, R, Wisnubroto, DS, Nasruddin, N & Mahlia, TMI 2020, 'Current and future strategies for spent nuclear fuel management in Indonesia', Energy Strategy Reviews, vol. 32, pp. 100575-100575.
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© 2020 The Author(s) Currently, Indonesia has only three nuclear research reactors. However, Indonesia is the world's fourth most populous country. Owing to the enormous size and rapid growth of the population and the limited availability of fossil fuel and renewable energy resources, the construction of new nuclear power plants (NPPs) has been considered. Because of this, the management policies for long-term spent nuclear fuel in Indonesia have become crucial. This paper reviews the current handling and future management strategies for spent nuclear fuel in Indonesia. With a maximum capacity of 1448 spent fuel elements, Indonesia's interim wet storage of spent fuel (ISSF) is designed to store spent nuclear fuel arising from 25 years of reactor operation at maximum power. However, with the existing low-power reactor operation, the ISSF could be utilized for more than 75 years. The potential problem for long-term storage in the ISSF is system, structure, and component (SSC) aging. Continuous planning, operation, monitoring, and maintenance of the SSC in the ISSF have been conducted to ensure safe long-term utilization of the facility. In accordance with the possibility of NPP construction in the future, three possible scenarios may be considered for future nuclear spent fuel management strategies in Indonesia: 1) wet storage - dry storage - disposal; 2) wet storage -repatriation or sending to other countries; and 3) wet storage - moving to wet- or dry storage of NPP candidate - disposal.
Rawat, S, Mittal, RK & Muthukumar, G 2020, 'Isolated Rectangular Footings under Biaxial Bending: A Critical Appraisal and Simplified Analysis Methodology', Practice Periodical on Structural Design and Construction, vol. 25, no. 3.
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Razavi Bazaz, S, Amiri, HA, Vasilescu, S, Abouei Mehrizi, A, Jin, D, Miansari, M & Ebrahimi Warkiani, M 2020, 'Obstacle-free planar hybrid micromixer with low pressure drop', Microfluidics and Nanofluidics, vol. 24, no. 8.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Planar micromixers with repetitive units have received substantial research interest since they allow low cost, lab-on-a-chip (LOC), and point-of-care (POC) systems to achieve a proper level of mixing for any given process. This paper presents an efficient planar micromixer that combines four types of mixing units, including convergent–divergent, circular, rhombic, and G-shaped micromixers. Their combinations and resulting effects on the mixing efficiency are numerically and experimentally investigated. A comprehensive Taguchi design of experiment method was used to reduce the number of the combinations from 1024 to only 16, among which a micromixer made of rhombic and G-shaped units readily showed a mixing efficiency beyond 80% over a wide range of inlet Reynolds numbers 0.001–0.3 and 35–65; meanwhile, a pressure drop as low as 12 kPa was reported. The velocity and concentration fields and their gradients within the nominated micromixer were analyzed, providing a better understanding of the mixing mechanism. These results offer design insights for further development of planar micromixers with repetitive unites for low-cost LOC and POC devices.
Razavi Bazaz, S, Hazeri, AH, Rouhi, O, Mehrizi, AA, Jin, D & Warkiani, ME 2020, 'Volume-preserving strategies to improve the mixing efficiency of serpentine micromixers', Journal of Micromechanics and Microengineering, vol. 30, no. 11, pp. 115022-115022.
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Abstract In this study, we have proposed volume-preserving strategies to boost chaoticadvection and improve the mixing efficiency of serpentine micromixers. The proposed strategies revolve around the point that the volume of the micromixer is kept constant during the manipulation. The first strategy involves the utilization of a nozzle-diffuser (ND) shaped microchannel. Using this, the velocity of the fluids fluctuates in an alternating pattern, leading to additional chaotic advection, a decrease in the mixing path, and an increase in the mixing index. The second strategy uses non-aligned inlets to generate swirl inducing effects at the microchannel entrance, where the collision of two fluids generates angular momentum in the flow, providing more chaotic advection. These strategies proved to be effective in boosting the mixing efficiency over wide ranges of Re in which 60% enhancement (from 20.53% to 80.31%) was achieved for Re of 30 by applying an ND shaped microchannel, and 20% enhancement (from 12.71% to 32.21%) was achieved for a critical Re of 15 by applying both of the strategies simultaneously.
Razavi Bazaz, S, Mashhadian, A, Ehsani, A, Saha, SC, Krüger, T & Ebrahimi Warkiani, M 2020, 'Computational inertial microfluidics: a review', Lab on a Chip, vol. 20, no. 6, pp. 1023-1048.
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Schematic illustration of various kinds of geometries used for inertial microfluidics.
Razavi Bazaz, S, Rouhi, O, Raoufi, MA, Ejeian, F, Asadnia, M, Jin, D & Ebrahimi Warkiani, M 2020, '3D Printing of Inertial Microfluidic Devices', Scientific Reports, vol. 10, no. 1, p. 5929.
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AbstractInertial microfluidics has been broadly investigated, resulting in the development of various applications, mainly for particle or cell separation. Lateral migrations of these particles within a microchannel strictly depend on the channel design and its cross-section. Nonetheless, the fabrication of these microchannels is a continuous challenging issue for the microfluidic community, where the most studied channel cross-sections are limited to only rectangular and more recently trapezoidal microchannels. As a result, a huge amount of potential remains intact for other geometries with cross-sections difficult to fabricate with standard microfabrication techniques. In this study, by leveraging on benefits of additive manufacturing, we have proposed a new method for the fabrication of inertial microfluidic devices. In our proposed workflow, parts are first printed via a high-resolution DLP/SLA 3D printer and then bonded to a transparent PMMA sheet using a double-coated pressure-sensitive adhesive tape. Using this method, we have fabricated and tested a plethora of existing inertial microfluidic devices, whether in a single or multiplexed manner, such as straight, spiral, serpentine, curvilinear, and contraction-expansion arrays. Our characterizations using both particles and cells revealed that the produced chips could withstand a pressure up to 150 psi with minimum interference of the tape to the total functionality of the device and viability of cells. As a showcase of the versatility of our method, we have proposed a new spiral microchannel with right-angled triangular cross-section which is technically impossible to fabricate using the standard lithography. We are of the opinion that the method proposed in this study will open the door for more complex geometries with the bespoke passive internal flow. Furthermore, the proposed fabrication workflow can be adopted at the production level, enabling large-scale man...
Razzak, I, Saris, RA, Blumenstein, M & Xu, G 2020, 'Integrating joint feature selection into subspace learning: A formulation of 2DPCA for outliers robust feature selection', Neural Networks, vol. 121, pp. 441-451.
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© 2019 Elsevier Ltd Since the principal component analysis and its variants are sensitive to outliers that affect their performance and applicability in real world, several variants have been proposed to improve the robustness. However, most of the existing methods are still sensitive to outliers and are unable to select useful features. To overcome the issue of sensitivity of PCA against outliers, in this paper, we introduce two-dimensional outliers-robust principal component analysis (ORPCA) by imposing the joint constraints on the objective function. ORPCA relaxes the orthogonal constraints and penalizes the regression coefficient, thus, it selects important features and ignores the same features that exist in other principal components. It is commonly known that square Frobenius norm is sensitive to outliers. To overcome this issue, we have devised an alternative way to derive objective function. Experimental results on four publicly available benchmark datasets show the effectiveness of joint feature selection and provide better performance as compared to state-of-the-art dimensionality-reduction methods.
Razzak, I, Zafar, K, Imran, M & Xu, G 2020, 'Randomized nonlinear one-class support vector machines with bounded loss function to detect of outliers for large scale IoT data', Future Generation Computer Systems, vol. 112, pp. 715-723.
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© 2020 Elsevier B.V. Exponential growth of large scale data industrial internet of things is evident due to the enormous deployment of IoT data acquisition devices. Detection of unusual patterns from large scale IoT data is important though challenging task. Recently, one-class support vector machines is extensively being used for anomaly detection. It tries to find an optimal hyperplane in high dimensional data that best separates the data from anomalies with maximum margin. However, the hinge loss of traditional one-class support vector machines is unbounded, which results in larger loss caused by outliers affecting its performance for anomaly detection. Furthermore, existing methods are computationally complex for larger data. In this paper, we present novel anomaly detection for large scale data by using randomized nonlinear features in support vector machines with bounded loss function rather than finding optimized support vectors with unbounded loss function. Extensive experimental evaluation on ten benchmark datasets shows the robustness of the proposed approach against outliers such as 0.8239, 0.7921, 0.7501, 0.6711, 0.6692, 0.4789, 0.6462, 0.6812, 0.7271 and 0.7873 accuracy for Gas Sensor Array, Human Activity Recognition, Parkinson's, Hepatitis, Breast Cancer, Blood Transfusion, Heart, ILPD and Wholesale Customers datasets respectively. In addition to this, introduction of randomized nonlinear feature helps to considerably decrease the computational complexity and space complexity from O(N3) to O(Bkn) and O(N2) to O(Bkn). Thus, very attractive for larger datasets.
Razzak, MI, Imran, M & Xu, G 2020, 'Big data analytics for preventive medicine', Neural Computing and Applications, vol. 32, no. 9, pp. 4417-4451.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.
Regan, B, Aghajamali, A, Froech, J, Tran, TT, Scott, J, Bishop, J, Suarez‐Martinez, I, Liu, Y, Cairney, JM, Marks, NA, Toth, M & Aharonovich, I 2020, 'Plastic Deformation of Single‐Crystal Diamond Nanopillars', Advanced Materials, vol. 32, no. 9, pp. 1906458-1906458.
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AbstractDiamond is known to possess a range of extraordinary properties that include exceptional mechanical stability. In this work, it is demonstrated that nanoscale diamond pillars can undergo not only elastic deformation (and brittle fracture), but also a new form of plastic deformation that depends critically on the nanopillar dimensions and crystallographic orientation of the diamond. The plastic deformation can be explained by the emergence of an ordered allotrope of carbon that is termed O8‐carbon. The new phase is predicted by simulations of the deformation dynamics, which show how the sp3 bonds of (001)‐oriented diamond restructure into O8‐carbon in localized regions of deforming diamond nanopillars. The results demonstrate unprecedented mechanical behavior of diamond, and provide important insights into deformation dynamics of nanostructured materials.
Regan, B, Kim, S, Ly, ATH, Trycz, A, Bray, K, Ganesan, K, Toth, M & Aharonovich, I 2020, 'Photonic devices fabricated from (111)‐oriented single crystal diamond', InfoMat, vol. 2, no. 6, pp. 1241-1246.
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AbstractDiamond is a material of choice in the pursuit of integrated quantum photonic technologies. So far, the majority of photonic devices fabricated from diamond are made from (100)‐oriented crystals. In this work, we demonstrate a methodology for the fabrication of optically active membranes from (111)‐oriented diamond. We use a liftoff technique to generate membranes, followed by chemical vapor deposition of diamond in the presence of silicon to generate homogenous silicon vacancy color centers with emission properties that are superior to those in (100)‐oriented diamond. We further use the diamond membranes to fabricate microring resonators with quality factors exceeding ~ 3000. Supported by finite‐difference time‐domain calculations, we discuss the advantages of (111)‐oriented structures as building blocks for quantum nanophotonic devices.image
Reid, W, Fitch, R, Göktoğan, AH & Sukkarieh, S 2020, 'Sampling‐based hierarchical motion planning for a reconfigurable wheel‐on‐leg planetary analogue exploration rover', Journal of Field Robotics, vol. 37, no. 5, pp. 786-811.
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AbstractReconfigurable mobile planetary rovers are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. Planning paths for these adaptive robots is challenging due to their many degrees of freedom, and the need to consider potentially continuous platform reconfiguration along the length of the path. We propose a novel hierarchical structure for asymptotically optimal (AO) sampling‐based planners and specifically apply it to the state‐of‐the‐art Fast Marching Tree (FMT*) AO planner. Our algorithm assumes a decomposition of the full configuration space into multiple subspaces, and begins by rapidly finding a set of paths through one such subspace. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full configuration space. This technique provides a novel way to incorporate prior knowledge of subspaces to efficiently bias search within existing AO sampling‐based planners. Importantly, probabilistic completeness and asymptotic optimality are preserved. Experimental results in simulation are provided that benchmark the algorithm against state‐of‐the‐art sampling‐based planners without the hierarchical variation. Additional experimental results performed with a physical wheel‐on‐leg platform demonstrate application to planetary rover mobility and showcase how constraints such as actuator failures and sensor pointing may be easily incorporated into the planning problem. In minimizing an energy objective that combines an approximation of the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion. These results illustrate the generality of the planner in exploiting the platform's mechanical ability to fluidly trans...
Ren, C, Lyu, X, Ni, W, Tian, H, Song, W & Liu, RP 2020, 'Distributed Online Optimization of Fog Computing for Internet of Things Under Finite Device Buffers', IEEE Internet of Things Journal, vol. 7, no. 6, pp. 5434-5448.
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Ren, L-F, Ngo, HH, Bu, C, Ge, C, Ni, S-Q, Shao, J & He, Y 2020, 'Novel external extractive membrane bioreactor (EMBR) using electrospun polydimethylsiloxane/polymethyl methacrylate membrane for phenol-laden saline wastewater', Chemical Engineering Journal, vol. 383, pp. 123179-123179.
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© 2019 Elsevier B.V. Phenol-laden saline wastewaters can adversely affect water, groundwater, soil, organisms and ecosystems. Given that frequently-used biodegradation process is generally inhibited by salinity, this work aims to solve the problem through a novel configuration of external extractive membrane bioreactor (EMBR) for the objective of simultaneous phenol permeation, salt rejection and biodegradation. Contact angles of 160.9 ± 2.2° (water) and 0.0° (phenol) were observed on the electrospun polydimethylsiloxane/polymethyl methacrylate (PDMS/PMMA) membrane, suggesting this superhydrophobic/superorganophilic membrane was suitable for separating phenol from water-soluble salt. Phenol ranging from 14.1 ± 2.7 to 290.7 ± 10.4 mg/L (stages 1 to 8) was continuously permeated and completely biodegraded in external EMBR under a hydraulic retention time (HRT) of 24 h, which corresponded with detoxification performance improving from 6.3% to 70.5%. After phenol exposure of 8 stages, Proteobacteria and Saccharibacteria became the main phyla for microorganisms. Enumeration of functional genes (phe, amoA, narG, nirS) confirmed that phenol was mainly consumed by denitrifiers and other heterotrophs as the sole carbon and energy source via oxidation and ring cleavage. As bacterial responses, these genes’ proliferation was promoted under low phenol concentrations but inhibited under high phenol concentrations. Meanwhile, results of extracellular polymeric substances revealed that protein was the key substance in toxicity resistance, phenol adsorption and transfer.
Ren, P, Xiao, Y, Chang, X, Prakash, M, Nie, F, Wang, X & Chen, X 2020, 'Structured Optimal Graph-Based Clustering With Flexible Embedding', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 10, pp. 3801-3813.
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In the real world, the duality of high-dimensional data is widespread. The coclustering method has been widely used because they can exploit the co-occurring structure between samples and features. In fact, most of the existing coclustering methods cluster the graphs in the original data matrix. However, these methods fail to output an affinity graph with an explicit cluster structure and still call for the postprocessing step to obtain the final clustering results. In addition, these methods are difficult to find a good projection direction to complete the clustering task on high-dimensional data. In this article, we modify the flexible manifold embedding theory and embed it into the bipartite spectral graph partition. Then, we propose a new method called structured optimal graph-based clustering with flexible embedding (SOGFE). The SOGFE method can learn an affinity graph with an optimal and explicit clustering structure and does not require any postprocessing step. Additionally, the SOGFE method can learn a suitable projection direction to map high-dimensional data to a low-dimensional subspace. We perform extensive experiments on two synthetic data sets and seven benchmark data sets. The experimental results verify the superiority, robustness, and good projection direction selection ability of our proposed method.
Ren, Y, Hao Ngo, H, Guo, W, Wang, D, Peng, L, Ni, B-J, Wei, W & Liu, Y 2020, 'New perspectives on microbial communities and biological nitrogen removal processes in wastewater treatment systems', Bioresource Technology, vol. 297, pp. 122491-122491.
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Biological nitrogen removal (BNR) is a critical process in wastewater treatment. Recently, there have new microbial communities been discovered to be capable of performing BNR with novel metabolic pathways. This review presents the up-to-date status on these microorganisms, including ammonia oxidizing archaea (AOA), complete ammonia oxidation (COMAMMOX) bacteria, anaerobic ammonium oxidation coupled to iron reduction (FEAMMOX) bacteria, anaerobic ammonium oxidation (ANAMMOX) bacteria and denitrifying anaerobic methane oxidation (DAMO) microorganism. Their metabolic pathways and enzymatic reactions in nitrogen cycle are demonstrated. Generally, these novel microbial communities have advantages over canonical nitrifiers or denitrifiers, such as higher substrate affinities, better physicochemical tolerances and/or less greenhouse gas emission. Also, their recent development and/or implementation in BNR is discussed and outlook. Finally, the key implications of coupling these microbial communities for BNR are identified. Overall, this review illustrates novel microbial communities that could provide new possibilities for high-performance and energy-saving nitrogen removal from wastewater.
Ren, Z, Wen, S, Li, Q, Feng, Y & Tang, N 2020, 'Stability Analysis for Nonlinear Impulsive Control System with Uncertainty Factors', Computational Intelligence and Neuroscience, vol. 2020, pp. 1-10.
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Considering the limitation of machine and technology, we study the stability for nonlinear impulsive control system with some uncertainty factors, such as the bounded gain error and the parameter uncertainty. A new sufficient condition for this system is established based on the generalized Cauchy–Schwarz inequality in this paper. Compared with some existing results, the proposed method is more practically applicable. The effectiveness of the proposed method is shown by a numerical example.
Reza, AM, Tavakoli, J, Zhou, Y, Qin, J & Tang, Y 2020, 'Synthetic fluorescent probes to apprehend calcium signalling in lipid droplet accumulation in microalgae—an updated review', Science China Chemistry, vol. 63, no. 3, pp. 308-324.
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Reza, CMFS & Lu, D 2020, 'Over-Current Protection for Power Packet Dispatching System', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 11, pp. 2617-2621.
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© 2004-2012 IEEE. Power packet dispatching system (PPDS) which integrates information into power distribution has been proposed recently. A PPDS distributes power in a packet form to the loads. It offers reduced power processing stages and smart power handling according to load demand and power generation capacity. The PPDS comprises of a mixer which generates PPs of different voltage levels and a router which distributes power to the designated loads. In existing PPDS, while the mixer generates the voltage packets and the router distributes them, the current of each packet is simply determined by the impedances of the power cable and the loads. Hence, there is no measure to monitor and protect the system during an over-current scenario. In this brief, an over-current protection scheme based on a simple analog circuit and integrated with the PPDS is proposed. In this brief, DC system is considered due to its reliability, efficiency and easy integration of renewable energy sources compared to AC system. It is able to prevent false tripping and provide accurate information for the router to isolate the load when the over-current is genuinely detected. The proposed method has been experimentally verified through a laboratory prototype.
Reza, CMFS & Lu, DD-C 2020, 'Recent Progress and Future Research Direction of Nonlinear Dynamics and Bifurcation Analysis of Grid-Connected Power Converter Circuits and Systems', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 8, no. 4, pp. 3193-3203.
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Rezaei, M, Khalilpour, KR & Jahangiri, M 2020, 'Multi-criteria location identification for wind/solar based hydrogen generation: The case of capital cities of a developing country', International Journal of Hydrogen Energy, vol. 45, no. 58, pp. 33151-33168.
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Rezaei, M, Razavi Bazaz, S, Zhand, S, Sayyadi, N, Jin, D, Stewart, MP & Ebrahimi Warkiani, M 2020, 'Point of Care Diagnostics in the Age of COVID-19', Diagnostics, vol. 11, no. 1, pp. 9-9.
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The recent outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated serious respiratory disease, coronavirus disease 2019 (COVID-19), poses a major threat to global public health. Owing to the lack of vaccine and effective treatments, many countries have been overwhelmed with an exponential spread of the virus and surge in the number of confirmed COVID-19 cases. Current standard diagnostic methods are inadequate for widespread testing as they suffer from prolonged turn-around times (>12 h) and mostly rely on high-biosafety-level laboratories and well-trained technicians. Point-of-care (POC) tests have the potential to vastly improve healthcare in several ways, ranging from enabling earlier detection and easier monitoring of disease to reaching remote populations. In recent years, the field of POC diagnostics has improved markedly with the advent of micro- and nanotechnologies. Due to the COVID-19 pandemic, POC technologies have been rapidly innovated to address key limitations faced in existing standard diagnostic methods. This review summarizes and compares the latest available POC immunoassay, nucleic acid-based and clustered regularly interspaced short palindromic repeats- (CRISPR)-mediated tests for SARS-CoV-2 detection that we anticipate aiding healthcare facilities to control virus infection and prevent subsequent spread.
Rizwanul Fattah, IM, Ong, HC, Mahlia, TMI, Mofijur, M, Silitonga, AS, Rahman, SMA & Ahmad, A 2020, 'State of the Art of Catalysts for Biodiesel Production', Frontiers in Energy Research, vol. 8.
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© Copyright © 2020 Rizwanul Fattah, Ong, Mahlia, Mofijur, Silitonga, Rahman and Ahmad. Biodiesel is one of the potential alternative energy sources that can be derived from renewable and low-grade origin through different processes. One of the processes is alcoholysis or transesterification in the presence of a suitable catalyst. The catalyst can be either homogeneous or heterogeneous. This article reviews various catalysts used for biodiesel production to date, presents the state of the art of types of catalysts, and compares their suitability and associated challenges in the transesterification process. Biodiesel production using homogeneous and heterogeneous catalysis has been studied extensively, and novel heterogeneous catalysts are being continuously investigated. Homogeneous catalysts are generally efficient in converting biodiesel with low free fatty acid (FFA) and water containing single-origin feedstock. Heterogeneous catalysts, on the other hand, provide superior activity, range of selectivity, good FFA, and water adaptability. The quantity and strengths of active acid or basic sites control these properties. Some of the heterogeneous catalysts such as zirconia and zeolite-based catalysts can be used as both basic and acidic catalyst by suitable alteration. Heterogeneous catalysts from waste and biocatalysts play an essential role in attaining a sustainable alternative to traditional homogeneous catalysts for biodiesel production. Recently, high catalytic efficiency at mild operating conditions has drawn attention to nanocatalysts. This review evaluates state of the art and perspectives for catalytic biodiesel production and assesses the critical operational variables that influence biodiesel production along with the technological solutions for sustainable implementation of the process.
Roche, CD & Gentile, C 2020, 'Transplantation of a 3D Bioprinted Patch in a Murine Model of Myocardial Infarction', Journal of Visualized Experiments, vol. 2020, no. 163, pp. 1-12.
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© 2020 JoVE Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Testing regenerative properties of 3D bioprinted cardiac patches in vivo using murine models of heart failure via permanent left anterior descending (LAD) ligation is a challenging procedure and has a high mortality rate due to its nature. We developed a method to consistently transplant bioprinted patches of cells and hydrogels onto the epicardium of an infarcted mouse heart to test their regenerative properties in a robust and feasible way. First, a deeply anesthetized mouse is carefully intubated and ventilated. Following left lateral thoracotomy (surgical opening of the chest), the exposed LAD is permanently ligated and the bioprinted patch transplanted onto the epicardium. The mouse quickly recovers from the procedure after chest closure. The advantages of this robust and quick approach include a predicted 28-day mortality rate of up to 30% (lower than the 44% reported by other studies using a similar model of permanent LAD ligation in mice). Moreover, the approach described in this protocol is versatile and could be adapted to test bioprinted patches using different cell types or hydrogels where high numbers of animals are needed to optimally power studies. Overall, we present this as an advantageous approach which may change preclinical testing in future studies for the field of cardiac regeneration and tissue engineering.
Roche, CD, Brereton, RJL, Ashton, AW, Jackson, C & Gentile, C 2020, 'Current challenges in three-dimensional bioprinting heart tissues for cardiac surgery', European Journal of Cardio-Thoracic Surgery, vol. 58, no. 3, pp. 500-510.
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Abstract Summary Previous attempts in cardiac bioengineering have failed to provide tissues for cardiac regeneration. Recent advances in 3-dimensional bioprinting technology using prevascularized myocardial microtissues as ‘bioink’ have provided a promising way forward. This review guides the reader to understand why myocardial tissue engineering is difficult to achieve and how revascularization and contractile function could be restored in 3-dimensional bioprinted heart tissue using patient-derived stem cells.
Romeijn, T, Wells, B, Wei, D & Paul, G 2020, 'Investigation into the shear property of thin-walled additively manufactured structures using staggered fused filament fabrication', Additive Manufacturing, vol. 35, pp. 101259-101259.
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© 2020 Additive manufacturing techniques, such as Fused Filament Fabrication (FFF), are rapidly revolutionising the manufacturing and mining sectors. This paper presents an investigation into the shear behaviour of thin-walled FFF structures, printed via a proposed ‘offset method’. Firstly, an alternative method of filament positioning in material extrusion is proposed, referred to as the ‘offset method’, which aims to reduce the volume of empty cavities between deposited material. Then the shear properties, density properties, and cross-sectional void surface area are compared to structures printed using the aligned printing method. Experimental results on solid printed (no infill) samples, through four different-sized nozzles, have shown the newly proposed method produces a 6.5 % increase in density and a 7.2 % improvement in maximum in-plane shear stress per millimetre increase in nozzle size, compared with the aligned method of FFF. The offset method was found to produce a material with increased interlayer contact, compared to the aligned method, which results in a higher fictitious shear stress modulus. The effect of the increased interlayer contact on the fictitious shear modulus and real shear stress was investigated using a FEM analysis of the unit cells. In short, using the same feedstock material, the offset method produces a stiffer material with a higher fictitious shear strength than the aligned method of FFF printing.
Romero, E, Valenzuela, VM, Kermany, AR, Sementilli, L, Iacopi, F & Bowen, WP 2020, 'Engineering the Dissipation of Crystalline Micromechanical Resonators', Physical Review Applied, vol. 13, no. 4.
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© 2020 American Physical Society. © 2020 American Physical Society. High-quality micro- and nanomechanical resonators are widely used in sensing, communications, and timing, and have future applications in quantum technologies and fundamental studies of quantum physics. Crystalline thin films are particularly attractive for such resonators due to their prospects for high quality, high intrinsic stress, high yield strength, and low dissipation. However, when such films are grown on a silicon substrate, interfacial defects arising from lattice mismatch with the substrate have been postulated to introduce additional dissipation. Here, we develop a back-side etching process for single-crystal silicon carbide microresonators that allows us to quantitatively verify this prediction. By engineering the geometry of the resonators and removing the defective interfacial layer, we achieve quality factors exceeding a million in silicon carbide trampoline resonators at room temperature, a factor of five higher than those achieved without removal of the interfacial defect layer. We predict that similar devices fabricated from ultrahigh-purity silicon carbide, leveraging its high yield strength, could enable room-temperature quality factors as high as 6×109.
Roobavannan, M, Kandasamy, J, Pande, S, Vigneswaran, S & Sivapalan, M 2020, 'Sustainability of agricultural basin development under uncertain future climate and economic conditions: A socio-hydrological analysis', Ecological Economics, vol. 174, pp. 106665-106665.
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© 2020 Elsevier B.V. A socio-hydrological model is used to forecast future conditions in a river basin arising from changes in climate and the economy in order to learn about macroeconomic conditions that would yield pathways for sustainable development and how they may be affected by changes in climate and the economy. The study uses a system dynamics model with endogenous social values and preferences and exogenous climate and economic drivers. Basin scale sustainability is defined as a function of economic growth, provision of environmental services and equality within the basin. The analysis reveals that a diversified basin economy is important to achieve sustainable development. Under current climate conditions, a higher level of diversification in the basin's economy increases sustainability. Higher current capital growth rates, e.g., >2% of the current rate, would also lead to more sustainable development of a kind that is less affected by the availability of water and robust to vagaries of climate change. The results suggest that policy-makers and resource managers should focus on measures to diversify the economy when it is thriving, but also consider the capacity of society to adapt to unpredictable shocks to the system.
Roobavannan, S, Vigneswaran, S & Naidu, G 2020, 'Enhancing the performance of membrane distillation and ion-exchange manganese oxide for recovery of water and lithium from seawater', Chemical Engineering Journal, vol. 396, pp. 125386-125386.
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© 2020 Elsevier B.V. Recovering lithium (Li) from natural sources such as seawater is a sustainable alternative to meet its high demands. Li recovery from seawater must be enhanced to attain economic efficiency. In this work, the potential of enhancing Li recovery from seawater by acid treated manganese oxide ion sieve (HMO) is evaluated by increasing Li concentration in seawater using direct contact membrane distillation (DCMD) and reducing competitive ions. DCMD achieved enhanced water recovery upon pre-treatment with oxalic acid (88–91%) compared to caustic soda ash (65–68%) and without pre-treatment (47–51%). Caustic soda ash required Na addition in alkaline condition for Ca removal, while, oxalic acid removed Ca in acidic condition without any inorganic ion addition. The low ion concentration in acidic condition upon oxalic acid pre-treatment enabled DCMD to concentrate seawater to high levels, increasing Li concentration by 7 times. In Li solution, HMO achieved a maximum adsorptive capacity (Langmuir Qmax) of 17.8 mg/g in alkaline condition. Multiple cycles of desorption and regeneration of HMO showed only 7–11% decline of Li uptake and minimal Mn dissolution, which, established HMO's reuse capacity. Selective Li mechanism is attributed to H/Li exchange as well as high negative surface charge of HMO. In seawater, Li uptake by HMO reduced by 44–46% due to Mg. Seawater with minimal Mg was favourable for enhancing Li uptake by HMO. Seawater treatment in stages – divalent pretreament and concentrating seawater, followed by HMO, provided a favourable scenario for attaining high quality water, selective Li recovery, and other resources – Ca and Mg.
Roslidar, R, Rahman, A, Muharar, R, Syahputra, MR, Arnia, F, Syukri, M, Pradhan, B & Munadi, K 2020, 'A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection', IEEE Access, vol. 8, pp. 116176-116194.
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© 2013 IEEE. Developing a breast cancer screening method is very important to facilitate early breast cancer detection and treatment. Building a screening method using medical imaging modality that does not cause body tissue damage (non-invasive) and does not involve physical touch is challenging. Thermography, a non-invasive and non-contact cancer screening method, can detect tumors at an early stage even under precancerous conditions by observing temperature distribution in both breasts. The thermograms obtained on thermography can be interpreted using deep learning models such as convolutional neural networks (CNNs). CNNs can automatically classify breast thermograms into categories such as normal and abnormal. Despite their demostrated utility, CNNs have not been widely used in breast thermogram classification. In this study, we aimed to summarize the current work and progress in breast cancer detection based on thermography and CNNs. We first discuss of breast thermography potential in early breast cancer detection, providing an overview of the availability of breast thermal datasets together with publicly accessible. We also discuss characteristics of breast thermograms and the differences between healthy and cancerous thermographic patterns. Breast thermogram classification using a CNN model is described step by step including a simulation example illustrating feature learning. We cover most research related to the implementation of deep neural networks for breast thermogram classification and propose future research directions for developing representative datasets, feeding the segmented image, assigning a good kernel, and building a lightweight CNN model to improve CNN performance.
Rostami, AA, Karimi, V, Khatibi, R & Pradhan, B 2020, 'An investigation into seasonal variations of groundwater nitrate by spatial modelling strategies at two levels by kriging and co-kriging models', Journal of Environmental Management, vol. 270, pp. 110843-110843.
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Nitrate pollution of groundwater through spatial models is investigated in this paper by using a sample of nitrate values at monitoring wells using the data from four seasons of a year, in which data are sparse. Two spatial modelling strategies are formulated at two levels, in which Strategy 1 comprises: three variations of kriging-based models (ordinary kriging, simple kriging and universal kriging), which are constructed at Level 1 to predict nitrate concentrations; and a Multiple Co-Kriging (MCoK) model is used at Level 2 to enhance the accuracy of the predictions. Strategy 2 is also at two levels but employs Indicator Kriging (IK) at Level 1 as a probabilistic spatial model to predict areas at risk of exceeding two thresholds of 37.5 mg/L and 50 mg/L of nitrate concentration, and Multiple Co-Indicator Kriging (MCoIK) at Level 2 for a better accuracy. The improvements at Level 2 for both strategies are remarkable and hence they are used to gain an insight into inherent problems. The results of a study delineate areas with excessive nitrate concentrations, which are in the vicinity of urban areas and hence reflect poor planning practices since the 1990s. The results further reveal the patterns on sensitivities to seasonal variations driven by aquifer recharge and strong dilution processes in spring times; and on the role of pumpage impacting aquifers giving rise to possible hotspots of nitrate concentrations.
Roth, N, Deuse, J & Biedermann, H 2020, 'A framework for System Excellence assessment of production systems, based on lean thinking, business excellence, and factory physics', International Journal of Production Research, vol. 58, no. 4, pp. 1074-1091.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This article proposes a production system framework that synthesises lean production, business excellence, and factory physics. The framework, which draws on a deep state-of-the-art understanding, consists of a performance measurement system supporting the achievement of a target condition based on variability and lead time reduction, as well as approaches of continuous improvement. Based on four types of excellence, a System Excellence value is calculated, indicating the distance from a target condition and thus displaying relevant improvement potential. As a key result, the framework proposed provides a contribution to knowledge, as it combines the aforementioned schools of thought, resulting in a holistic framework for action. The measurement system offers a high level of robustness, as it draws on diverse data sources and reflects on the dynamic behaviour over time. It has been successfully implemented in automotive manufacturing plants worldwide, which may suggest considerable practical relevance. Another key result of this research is that through applying the framework, important bottom-line indicators, such as lead time, failure costs, or productivity, could be improved. As the plants are typical automotive industry high-volume plants, it is proposed that the solutions presented offer a suitable standard for this industry and type of plant.
Rouf, RA, Jahan, N, Alam, KCA, Sultan, AA, Saha, BB & Saha, SC 2020, 'Improved cooling capacity of a solar heat driven adsorption chiller', Case Studies in Thermal Engineering, vol. 17, pp. 100568-100568.
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Roy, P, Chandra Pal, S, Arabameri, A, Chakrabortty, R, Pradhan, B, Chowdhuri, I, Lee, S & Tien Bui, D 2020, 'Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility', Remote Sensing, vol. 12, no. 20, pp. 3284-3284.
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The extreme form of land degradation through different forms of erosion is one of the major problems in sub-tropical monsoon dominated region. The formation and development of gullies is the dominant form or active process of erosion in this region. So, identification of erosion prone regions is necessary for escaping this type of situation and maintaining the correspondence between different spheres of the environment. The major goal of this study is to evaluate the gully erosion susceptibility in the rugged topography of the Hinglo River Basin of eastern India, which ultimately contributes to sustainable land management practices. Due to the nature of data instability, the weakness of the classifier andthe ability to handle data, the accuracy of a single method is not very high. Thus, in this study, a novel resampling algorithm was considered to increase the robustness of the classifier and its accuracy. Gully erosion susceptibility maps have been prepared using boosted regression trees (BRT), multivariate adaptive regression spline (MARS) and spatial logistic regression (SLR) with proposed resampling techniques. The re-sampling algorithm was able to increase the efficiency of all predicted models by improving the nature of the classifier. Each variable in the gully inventory map was randomly allocated with 5-fold cross validation, 10-fold cross validation, bootstrap and optimism bootstrap, while each consisted of 30% of the database. The ensemble model was tested using 70% and validated with the other 30% using the K-fold cross validation (CV) method to evaluate the influence of the random selection of training and validation database. Here, all resampling methods are associated with higher accuracy, but SLR bootstrap optimism is more optimal than any other methods according to its robust nature. The AUC values of BRT optimism bootstrap, MARS optimism bootstrap and SLR optimism bootstrap are 87.40%, 90.40% and 90.60%, respectively. According ...
Rufangura, P, Folland, TG, Agrawal, A, Caldwell, JD & Iacopi, F 2020, 'Towards low- loss on-chip nanophotonics with coupled graphene and silicon carbide: a review', Journal of Physics: Materials, vol. 3, no. 3, pp. 032005-032005.
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Abstract The ability to control the interaction of light and matter at the nanoscale is at the heart of the field of nanophotonics. This subdiffractional confinement of light can be achieved through the stimulation of surface polaritons, most notably surface plasmon polaritons (SPPs). However, the high optical losses and lack of tunability of conventional plasmonic materials have hindered major progress in this field. In the search for alternative low-loss and tunable materials, graphene and polar dielectric materials are viewed as potential alternatives to more common metal-based plasmonic materials. In particular, the possibility of combining the tunable nature of graphene SPPs with the high-quality factors and long lifetimes of surface phonon-polaritons (SPhPs) modes supported in polar dielectric materials (e.g. SiC) offers great promise for advanced nanophotonic applications. The combination of graphene SPPs and SPhPs supported in SiC is even more pertinent as this material system can be realized in the form of epitaxial graphene (EG), whereby sublimation of silicon from a SiC results in a surface reconstruction into a graphene surface termination. This offers an ideal technology platform for realizing hybrid SPP-SPhP modes. In this review, we outline advances in graphene plasmonics and the generation of SPhPs in polar materials, in the context of epitaxial graphene. We review recent attempts at realizing such coupling of graphene SPPs with phonon and SPhP modes in SiC, as well as covering such modes in other polar materials and conclude with an overview of advantages and challenges for further advancement of nanophotonics based on graphene on silicon carbide for on-chip light manipulation.
Rujikiatkamjorn, C 2020, 'Editorial', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 173, no. 4, pp. 187-187.
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Ruppert, MG, Bartlett, NJ, Yong, YK & Fleming, AJ 2020, 'Amplitude noise spectrum of a lock-in amplifier: Application to microcantilever noise measurements', Sensors and Actuators A: Physical, vol. 312, pp. 112092-112092.
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Rush, A, Catchpoole, DR, Ling, R, Searles, A, Watson, PH & Byrne, JA 2020, 'Improving Academic Biobank Value and Sustainability Through an Outputs Focus', Value in Health, vol. 23, no. 8, pp. 1072-1078.
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Although it is generally accepted that human tissue biobanks are important to facilitate progress in health and medical research, many academic biobanks face sustainability challenges. We propose that biobank sustainability is challenged by a lack of available data describing the outputs and benefits that are produced by biobanks, as reflected by a dearth of publications that enumerate biobank outputs. We further propose that boosting the available information on biobank outputs and using a broader range of output metrics will permit economic analyses such as cost-consequence analyses of biobank activity. Output metrics and cost-consequence analyses can allow biobanks to achieve efficiencies, and improve the quality and/or quantity of their outputs. In turn, biobank output measures provide all stakeholders with explicit and accountable data on biobank value, which could contribute to the evolution of biobank operations to best match research needs, and mitigate some threats to biobank sustainability.
Rutherford, H, Chacon, A, Mohammadi, A, Takyu, S, Tashima, H, Yoshida, E, Nishikido, F, Hofmann, T, Pinto, M, Franklin, DR, Yamaya, T, Parodi, K, Rosenfeld, AB, Guatelli, S & Safavi-Naeini, M 2020, 'Dose quantification in carbon ion therapy using in-beam positron emission tomography', Physics in Medicine & Biology, vol. 65, no. 23, pp. 235052-235052.
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Abstract This work presents an iterative method for the estimation of the absolute dose distribution in patients undergoing carbon ion therapy, via analysis of the distribution of positron annihilations resulting from the decay of positron-emitting fragments created in the target volume. The proposed method relies on the decomposition of the total positron-annihilation distributions into profiles of the three principal positron-emitting fragment species - 11C, 10C and 15O. A library of basis functions is constructed by simulating a range of monoenergetic 12C ion irradiations of a homogeneous polymethyl methacrylate phantom and measuring the resulting one-dimensional positron-emitting fragment profiles and dose distributions. To estimate the dose delivered during an arbitrary polyenergetic irradiation, a linear combination of factors from the fragment profile library is iteratively fitted to the decomposed positron annihilation profile acquired during the irradiation, and the resulting weights combined with the corresponding monoenergetic dose profiles to estimate the total dose distribution. A total variation regularisation term is incorporated into the fitting process to suppress high-frequency noise. The method was evaluated with 14 different polyenergetic 12C dose profiles in a polymethyl methacrylate target: one which produces a flat biological dose, 10 with randomised energy weighting factors, and three with distinct dose maxima or minima within the spread-out Bragg peak region. The proposed method is able to calculate the dose profile with mean relative errors of 0.8%, 1.0% and 1.6% from the 11C, 10C, 15O fragment profiles, respectively, and estimate the position of the distal edge of the SOBP to within an average of 0.7 mm, 1.9 mm and...
Ryu, S, Naidu, G, Moon, H & Vigneswaran, S 2020, 'Selective copper recovery by membrane distillation and adsorption system from synthetic acid mine drainage', Chemosphere, vol. 260, pp. 127528-127528.
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Acid mine drainage (AMD) which involves high sulfur and heavy metals concentrations and furthermore are acidic in character, has been a major environmental and economic issue due to the associated toxicity and treatment costs. A large quantity of AMD in nature has a variety of resources including water and heavy metals such as Cu, Al, Fe and Ni. In this study, the valuable resource of Cu was selectively recovered from model AMD solution through membrane distillation and adsorption systems. Direct contact membrane distillation (DCMD) system enabled to concentrate the Cu concentration in AMD by more than 2.5 times while recovering 80% of high-quality water for reuse purposes. For adsorption, mesoporous silica material was used after multi-modification with Mn and amine grafting to enhance the adsorption capacity as well as selectivity for Cu. Under acidic conditions, heavy metals cannot be adsorbed on amine grafted SBA-15. Therefore, the pH of synthetic AMD (pH = 2.2) had to be adjusted to the 5.0-5.2 range, in order to enable adsorption of Cu on modified SBA-15 (this is to prevent protonation of amine groups grafted on prepared SBA-15). Moreover, an increase in pH helped to precipitate more than 99% of Fe and Al (predominant metals in AMD). Cu adsorption on modified SBA-15 was 24.53 mg/g for KOH-treated AMD. However, Cu adsorption on modified SBA-15 decreased by 26% (18.11 mg/g) for NaOH-treated AMD. Cu adsorption with modified SBA-15 significantly improved to 55.75 mg/g when the Cu concentration was concentrated by DCMD.
Rzhevskiy, AS, Razavi Bazaz, S, Ding, L, Kapitannikova, A, Sayyadi, N, Campbell, D, Walsh, B, Gillatt, D, Ebrahimi Warkiani, M & Zvyagin, AV 2020, 'Rapid and Label-Free Isolation of Tumour Cells from the Urine of Patients with Localised Prostate Cancer Using Inertial Microfluidics', Cancers, vol. 12, no. 1, pp. 81-81.
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During the last decade, isolation of circulating tumour cells via blood liquid biopsy of prostate cancer (PCa) has attracted significant attention as an alternative, or substitute, to conventional diagnostic tests. However, it was previously determined that localised forms of PCa shed a small number of cancer cells into the bloodstream, and a large volume of blood is required just for a single test, which is impractical. To address this issue, urine has been used as an alternative to blood for liquid biopsy as a truly non-invasive, patient-friendly test. To this end, we developed a spiral microfluidic chip capable of isolating PCa cells from the urine of PCa patients. Potential clinical utility of the chip was demonstrated using anti-Glypican-1 (GPC-1) antibody as a model of the primary antibody in immunofluorescent assay for identification and detection of the collected tumour cells. The microchannel device was first evaluated using DU-145 cells in a diluted Dulbecco’s phosphate-buffered saline sample, where it demonstrated >85 (±6) % efficiency. The microchannel proved to be functional in at least 79% of cases for capturing GPC1+ putative tumour cells from the urine of patients with localised PCa. More importantly, a correlation was found between the amount of the captured GPC1+ cells and crucial diagnostic and prognostic parameter of localised PCa—Gleason score. Thus, the technique demonstrated promise for further assessment of its diagnostic value in PCa detection, diagnosis, and prognosis.
Saco, PM, Rodríguez, JF, Moreno-de las Heras, M, Keesstra, S, Azadi, S, Sandi, S, Baartman, J, Rodrigo-Comino, J & Rossi, MJ 2020, 'Using hydrological connectivity to detect transitions and degradation thresholds: Applications to dryland systems', CATENA, vol. 186, pp. 104354-104354.
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Sadeghi Rad, H, Bazaz, SR, Monkman, J, Ebrahimi Warkiani, M, Rezaei, N, O'Byrne, K & Kulasinghe, A 2020, 'The evolving landscape of predictive biomarkers in immuno‐oncology with a focus on spatial technologies', Clinical & Translational Immunology, vol. 9, no. 11.
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AbstractImmunotherapies have shown long‐lasting and unparalleled responses for cancer patients compared to conventional therapy. However, they seem to only be effective in a subset of patients. Therefore, it has become evident that a greater understanding of the tumor microenvironment (TME) is required to understand the nuances which may be at play for a favorable outcome to therapy. The immune contexture of the TME is an important factor in dictating how well a tumor may respond to immune checkpoint inhibitors. While traditional immunohistochemistry techniques allow for the profiling of cells in the tumor, this is often lost when tumors are analysed using bulk tissue genomic approaches. Moreover, the actual cellular proportions, cellular heterogeneity and deeper spatial distribution are lacking in characterisation. Advances in tissue interrogation technologies have given rise to spatially resolved characterisation of the TME. This review aims to provide an overview of the current methodologies that are used to profile the TME, which may provide insights into the immunopathology associated with a favorable outcome to immunotherapy.
Sadeghi, F, Li, J & Zhu, X 2020, 'A Steel-Concrete Composite Beam Element for Structural Damage Identification', International Journal of Structural Stability and Dynamics, vol. 20, no. 10, pp. 2042015-2042015.
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The composite action between the layers of steel and concrete is governed by the shear connection. Because of the complicated interconnection behavior of these composite layers, it is difficult to detect damage in the composite structures, especially, the interfacial integrity of the two layers. In this paper, anovel method has been developed for structural damage identification of composite structures based on a steel-concrete composite beam element with bonding interface. In displacement-based finite element (FE) formulation, three damage indicators have been embedded into stiffness matrix of the composite beam that are defined as a stiffness reduction in the concrete, steel and interface layers. An algorithm-based on recursive quadratic programming has been proposed to identify structural damage in the composite beam from static measurements. The analytical FE model is validated by adapting its static responses in undamaged state with those obtained from an equal experimental model as well as a FE model developed in commercial software ABAQUS. A convergence study is conducted to determine the number of the composite beam FEs. To verify the proposed method, the static responses of the FE model with different damage cases at a given loading are calculated, and the measurements are simulated by adding different levels of white noise. Then, the proposed algorithm is applied to identify damage of the composite beam. The effects of measurement noise, loading location and amplitude, measurement numbers and the sizes of FE mesh on the identified results have been investigated. The numerical results show that this method is efficient and accurate to separately identify small damage in the concrete slab, and the steel girder and bonding interface of the composite beam.
Safira, L, Putra, N, Trisnadewi, T, Kusrini, E & Mahlia, TMI 2020, 'Thermal properties of sonicated graphene in coconut oil as a phase change material for energy storage in building applications1', International Journal of Low-Carbon Technologies, vol. 15, no. 4, pp. 629-636.
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Abstract This study aims to investigate the thermal properties of a phase change material (PCM) based on coconut oil for building energy storage applications. Coconut oil is classified as an organic PCM composed of fatty acids made from renewable feedstock. However, low thermal conductivity is one of the major drawbacks of organic PCMs that must be improved. Graphene could be an effective material to enhance the thermal performance of organic PCMs. In this study, coconut oil with a latent heat capacity of 114.6 J/g and a melting point of 17.38°C was used. PCMs were prepared by sonicating graphene into coconut oil, as a supporting material. The mass fractions of the prepared PCMs were 0, 0.1, 0.2, 0.3, 0.4 and 0.5. Thermal conductivity tests were performed using a KD2 thermal property analyser under different ambient temperatures of 5, 10, 15, 20 and 25°C simulated with a circulating thermostatic bath. The latent heat, melting point and freezing point were determined through differential scanning calorimetry, the thermal stability was determined using thermogravimetric analysis (TGA) and the morphology and chemical structure were examined using transmission electron microscopy and Fourier-transform infrared spectroscopy, respectively. The results of this study showed that graphene addition to coconut oil improved the thermal performance, with the highest improvement seen in a 0.3 wt% sample at 20°C. The latent heat decreased by 11% owing to molecular movements within the PCM. However, TGA revealed that the composite PCMs showed good thermal stability in ambient building temperature ranges.
Saha, S, Barua, S, Kushwaha, B, Subedi, S, Hasan, MN & Saha, SC 2020, 'Conjugate natural convection in a corrugated solid partitioned differentially heated square cavity', Numerical Heat Transfer, Part A: Applications, vol. 78, no. 10, pp. 541-559.
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© 2020 Taylor & Francis Group, LLC. Conjugate natural convection heat transfer inside a differentially heated square cavity having a heat conducting and sinusoidal corrugated solid partition has been investigated numerically in the present study. The fluid flow and the heat transfer within the cavity are governed by two-dimensional Navier–Stokes and energy equations, and those are solved using the finite element method. Numerical simulation is carried out for a wide range of Rayleigh number (103 ≤ Ra ≤ 109) with a fixed Prandtl number (Pr = 0.71) since the working fluid in the cavity is considered as air. The variations of both corrugation amplitude and corrugation frequency of the sinusoidal partition wall on the average Nusselt number of the heated wall are observed in order to assess the influence of the roughness of the solid partition on the heat transfer characteristics of the cavity. Moreover, different types of partition material are selected to scrutinize the effect of thermal conductivity of the solid partition on the heat transfer performance. Finally, a correlation is proposed to predict the average Nusselt number of the heated wall of the cavity from the governing parameters (such as Rayleigh number, corrugation frequency and thermal conductivity ratio) within the selected range of the present investigation.
Saha, S, Saha, A, Hembram, TK, Pradhan, B & Alamri, AM 2020, 'Evaluating the Performance of Individual and Novel Ensemble of Machine Learning and Statistical Models for Landslide Susceptibility Assessment at Rudraprayag District of Garhwal Himalaya', Applied Sciences, vol. 10, no. 11, pp. 3772-3772.
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Landslides are known as the world’s most dangerous threat in mountainous regions and pose a critical obstacle for both economic and infrastructural progress. It is, therefore, quite relevant to discuss the pattern of spatial incidence of this phenomenon. The current research manifests a set of individual and ensemble of machine learning and probabilistic approaches like an artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LR), and their ensembles such as ANN-RF, ANN-SVM, SVM-RF, SVM-LR, LR-RF, LR-ANN, ANN-LR-RF, ANN-RF-SVM, ANN-SVM-LR, RF-SVM-LR, and ANN-RF-SVM-LR for mapping landslide susceptibility in Rudraprayag district of Garhwal Himalaya, India. A landslide inventory map along with sixteen landslide conditioning factors (LCFs) was used. Randomly partitioned sets of 70%:30% were used to ascertain the goodness of fit and predictive ability of the models. The contribution of LCFs was analyzed using the RF model. The altitude and drainage density were found to be the responsible factors in causing the landslide in the study area according to the RF model. The robustness of models was assessed through three threshold dependent measures, i.e., receiver operating characteristic (ROC), precision and accuracy, and two threshold independent measures, i.e., mean-absolute-error (MAE) and root-mean-square-error (RMSE). Finally, using the compound factor (CF) method, the models were prioritized based on the results of the validation methods to choose best model. Results show that ANN-RF-LR indicated a realistic finding, concentrating only on 17.74% of the study area as highly susceptible to landslide. The ANN-RF-LR ensemble demonstrated the highest goodness of fit and predictive capacity with respective values of 87.83% (area under the success rate curve) and 93.98% (area under prediction rate curve), and the highest robustness correspondingly. These attempts will play a significant role in ensemble ...
Saharkhiz, MA, Pradhan, B, Rizeei, HM & Jung, HS 2020, 'Land use feature extraction and sprawl development prediction from quickbird satellite imagery using Dempster-Shafer and land transformation model', Korean Journal of Remote Sensing, vol. 36, no. 1, pp. 15-27.
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Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future...
Sahoo, KS, Mishra, P, Tiwary, M, Ramasubbareddy, S, Balusamy, B & Gandomi, AH 2020, 'Improving End-Users Utility in Software-Defined Wide Area Network Systems', IEEE Transactions on Network and Service Management, vol. 17, no. 2, pp. 696-707.
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IEEE Software Defined Networks (SDN) has brought a new form of network architecture that simplifies network management through innovations and programmability. But, the distributed control plane of SD-Wide Area Network is challenged by load imbalance problem due to the dynamic change of the traffic pattern. The packet_in messages are one of the major contributors of the control’s load. When such packet rate exceeds a certain threshold limit, the response time for control request increases non-linearly. In order to achieve better end-user experience, most of the previous works considered the optimal switch to controller association with an objective to minimize the response time on LAN environment but ignores the consequence of large scale network. In this regard, the proposed work realizes the necessity of layer-2 and layer-3 controller in LAN and WAN environment separately. A load prediction based alertness approach has been introduced to reduce the burden of the controllers. This approach may create an additional delay for the initial packets of the flow entry that lead to more prediction error. However, the proposed method reduces the error by selecting an optimal timeout value of the flow. Further, minimization of the response time between router to the controller has been taken care of. An extensive simulation shows the efficacy of the proposed scheme.
Sahoo, S, Dhar, A, Debsarkar, A, Pradhan, B & Alamri, AM 2020, 'Future Water Use Planning by Water Evaluation and Planning System Model', Water Resources Management, vol. 34, no. 15, pp. 4649-4664.
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© 2020, Springer Nature B.V. Assessment of future water availability is a challenging task under changing climatic conditions and anthropogenic interventions. The current research focuses on future water resources scenario generation for contributing areas of proposed hydraulic structures generated from the Water Evaluation and Planning (WEAP) System model. The proposed methodology was implemented for the Dwarakeswar-Gandherswari river basin (India) which needs a long-term future water use plan. Bias-corrected Representative Concentration Pathways (RCPs) data were used for climate change analysis through a hydrological model. Different simulation model outputs [e.g. Dynamic Conversion of Land-Use and its Effects (Dyna-CLUE), Soil and Water Assessment Tool (SWAT), Modular Finite-Difference Flow Model (MODFLOW)] were utilized in water evaluation model for a generation of future water resources scenarios. Four scenarios (2010–2030–2050-2080) were generated for the sustainability of limited water resources management strategies. SWAT simulated results show an increase in river discharge for 2030 or 2080 and a decrease for 2050. MODFLOW simulated results show a visible groundwater storage change for 2030 but minimal change for 2050 and 2080 scenarios. The results also show a decrease in agricultural land and an increase in population for the contributing areas of three hydraulic structures during 2010–2030–2050-2080. These results provide a piece of valuable information for decision-makers in future water management plan preparation.
Saki, M, Abolhasan, M & Lipman, J 2020, 'A Novel Approach for Big Data Classification and Transportation in Rail Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1239-1249.
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This paper introduces a new framework into future data-driven railway condition monitoring systems (RCM). For this purpose, we have proposed an edge processing unit that includes two main parts: a data classification model that classifies Internet of Things (IoT) data into maintenance-critical data (MCD) and maintenance-non-critical data (MNCD) and a data transmission unit that, based on the class of data, employs appropriate communication methods to transmit data to railway control centers. For the transmission of MNCD, we propose a travel pattern method that employs train stations as points of data offloading so that trains can deliver data as well as passengers at stations. The performance of our proposed solution is successfully validated via three various data sets under different operating conditions.
Saki, M, Abolhasan, M, Lipman, J & Jamalipour, A 2020, 'A Comprehensive Access Point Placement for IoT Data Transmission Through Train-Wayside Communications in Multi-Environment Based Rail Networks', IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 11937-11949.
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In this paper, we propose three algorithms for placement of access points (APs) for the purpose of data transportation via train-to-wayside (T2W) communications along a rail network. The first algorithm is proposed to find the minimum number of APs so that the path-loss (PL) does not exceed a desired threshold. Through the second algorithm, the most optimal places for a desired number of APs are determined so that the average PL is minimum. The goal of the third algorithm is to determine the required number and optimal places of APs in a rail network. Furthermore, we propose a model to consider the effects of changes of communication characteristics on the efficiency of the network in different environments. Through such model, the algorithms proposed for placement of APs can be used in different railway scenarios. The proposed algorithms are validated through extensive simulations in Sydney Trains of Australia. The simulation results show that the proposed approach can improve the efficiency of the system at least 21% and up to 165% within 10 different scenarios. We also show that we can approximately transmit over 250 Gigabit data through T2W communications over common WiFi networks.
Salgotra, R, Gandomi, M & Gandomi, AH 2020, 'Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries', Chaos, Solitons & Fractals, vol. 140, pp. 110118-110118.
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COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries.
Salgotra, R, Gandomi, M & Gandomi, AH 2020, 'Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming', Chaos, Solitons & Fractals, vol. 138, pp. 109945-109945.
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COVID-19 declared as a global pandemic by WHO, has emerged as the most aggressive disease, impacting more than 90% countries of the world. The virus started from a single human being in China, is now increasing globally at a rate of 3% to 5% daily and has become a never ending process. Some studies even predict that the virus will stay with us forever. India being the second most populous country of the world, is also not saved, and the virus is spreading as a community level transmitter. Therefore, it become really important to analyse the possible impact of COVID-19 in India and forecast how it will behave in the days to come. In present work, prediction models based on genetic programming (GP) have been developed for confirmed cases (CC) and death cases (DC) across three most affected states namely Maharashtra, Gujarat and Delhi as well as whole India. The proposed prediction models are presented using explicit formula, and impotence of prediction variables are studied. Here, statistical parameters and metrics have been used for evaluated and validate the evolved models. From the results, it has been found that the proposed GEP-based models use simple linkage functions and are highly reliable for time series prediction of COVID-19 cases in India.
Salik, B, Yi, H, Hassan, N, Santiappillai, N, Vick, B, Connerty, P, Duly, A, Trahair, T, Woo, AJ, Beck, D, Liu, T, Spiekermann, K, Jeremias, I, Wang, J, Kavallaris, M, Haber, M, Norris, MD, Liebermann, DA, D'Andrea, RJ, Murriel, C & Wang, JY 2020, 'Targeting RSPO3-LGR4 Signaling for Leukemia Stem Cell Eradication in Acute Myeloid Leukemia', Cancer Cell, vol. 38, no. 2, pp. 263-278.e6.
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Signals driving aberrant self-renewal in the heterogeneous leukemia stem cell (LSC) pool determine aggressiveness of acute myeloid leukemia (AML). We report that a positive modulator of canonical WNT signaling pathway, RSPO-LGR4, upregulates key self-renewal genes and is essential for LSC self-renewal in a subset of AML. RSPO2/3 serve as stem cell growth factors to block differentiation and promote proliferation of primary AML patient blasts. RSPO receptor, LGR4, is epigenetically upregulated and works through cooperation with HOXA9, a poor prognostic predictor. Blocking the RSPO3-LGR4 interaction by clinical-grade anti-RSPO3 antibody (OMP-131R10/rosmantuzumab) impairs self-renewal and induces differentiation in AML patient-derived xenografts but does not affect normal hematopoietic stem cells, providing a therapeutic opportunity for HOXA9-dependent leukemia.
Salomon, R, Martelotto, L, Valdes‐Mora, F & Gallego‐Ortega, D 2020, 'Genomic Cytometry and New Modalities for Deep Single‐Cell Interrogation', Cytometry Part A, vol. 97, no. 10, pp. 1007-1016.
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AbstractIn the past few years, the rapid development of single‐cell analysis techniques has allowed for increasingly in‐depth analysis of DNA, RNA, protein, and epigenetic states, at the level of the individual cell. This unprecedented characterization ability has been enabled through the combination of cytometry, microfluidics, genomics, and informatics. Although traditionally discrete, when properly integrated, these fields create the synergistic field of Genomic Cytometry. In this review, we look at the individual methods that together gave rise to the broad field of Genomic Cytometry. We further outline the basic concepts that drive the field and provide a framework to understand this increasingly complex, technology‐intensive space. Thus, we introduce Genomic Cytometry as an emerging field and propose that synergistic rationalization of disparate modalities of cytometry, microfluidics, genomics, and informatics under one banner will enable massive leaps forward in the understanding of complex biology. © 2020 International Society for Advancement of Cytometry
Samadi-Boroujeni, H, Abbasi, S, Altaee, A & Fattahi-Nafchi, R 2020, 'Numerical and Physical Modeling of the Effect of Roughness Height on Cavitation Index in Chute Spillways', International Journal of Civil Engineering, vol. 18, no. 5, pp. 539-550.
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© 2019, Iran University of Science and Technology. This study presents the results of physical and numerical modeling of the effect of bed roughness height of chute spillways on the cavitation index. A 1:50-scale physical hydraulic model of the chute spillway of Surk Dam was constructed at the hydraulic laboratory of Shahrekord University, Iran. The experiments were conducted for different flow rates and the parameters of pressure, velocity, and flow depth in 26 positions along the chute. Finally, the ANSYS-FLUENT model was calibrated in the chute spillway using the experimental data by assumptions of two-phase volume of fluid and k–ε (RNG) turbulence models. The cavitation index in different sections of the chute spillway was calculated for different values of bed roughness including the roughness heights of 1, 2, and 2.5 mm. Results showed that the minimum values of the cavitation index were 0.2906, 0.2733, and 0.2471 for the roughness heights of 1, 2, and 2.5 mm, respectively. The statistical significance analysis showed that reducing the roughness height from 2.5 to 1 mm would not change significantly the value of the cavitation index at 95% confidence interval.
Samaei, SM, Gato-Trinidad, S & Altaee, A 2020, 'Performance evaluation of reverse osmosis process in the post-treatment of mining wastewaters: Case study of Costerfield mining operations, Victoria, Australia', Journal of Water Process Engineering, vol. 34, pp. 101116-101116.
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© 2019 Elsevier Ltd Reverse Osmosis (RO) membrane has been used for treatment and purification of industrial wastewaters including those from the mining industry before being discharged to receiving body or reuse for applications that are fit for purpose. This study evaluates the performance of Reverse Osmosis (RO) plant as a post-treatment process in mining operations in Victoria, Australia. The data analysis shows that the RO unit significantly improves the quality of the final permeate before discharged to surface waters. Considering average rejection efficiency for the entire evaluated period, turbidity, total dissolved solids (TDS), Antimony, Arsenic, Nickel, Zinc and Iron concentrations are reduced by 85 %, 96 %, 95 %, 66 %, 82 %, 48 % and 10 %, respectively in the RO permeate compared to the feed water. Although the quality of the RO permeate was in a desirable condition in most days of the evaluated years, TDS concentrations on the October 11 and 20,2016 and November 14, 2017 were higher than the limits specified by Environmental Protection Authority (EPA) Victoria. Anomalies regarding antimony levels in RO permeate occurred in September and November 2016 as well as August 2017 due to inconsistency in the RO feed quality. This resulted in fouling of RO membranes and contributed to discharge non-compliance with EPA licence conditions on TDS and antimony. Discharge to waterways was suspended over the period when TDS and antimony contents were above the EPA guidelines. Changes in the pre-treatment reduced the turbidity of the feed water and improved the performance of the RO system to comply with the discharge guidelines.
Sameen, MI, Pradhan, B & Lee, S 2020, 'Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment', CATENA, vol. 186, pp. 104249-104249.
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© 2019 Elsevier B.V. This study developed a deep learning based technique for the assessment of landslide susceptibility through a one-dimensional convolutional network (1D-CNN) and Bayesian optimisation in Southern Yangyang Province, South Korea. A total of 219 slide inventories and 17 slide conditioning variables were obtained for modelling. The data showed a complex scenario. Some past slides have spread over steep lands, while others have spread through flat terrain. Random forest (RF) served to keep only important factors for further analysis as a pre-processing measure. To select CNN hyperparameters, Bayesian optimization was used. Three methods contributed to overcoming the overfitting issue owing to small training data in our research. The selection of key factors by RF helped first of all to reduce information dimensionality. Second, the CNN model with 1D convolutions was intended to considerably decrease the number of its parameters. Third, a high rate of drop-out (0.66) helped reduce the CNN parameters. Overall accuracy, area under the receiver operating characteristics curve (AUROC) and 5-fold cross-validation were used to evaluate the models. CNN performance was compared to ANN and SVM. CNN achieved the highest accuracy on testing dataset (83.11%) and AUROC (0.880, 0.893, using testing and 5-fold CV, respectively). Bayesian optimization enhanced CNN accuracy by~3% (compared with default configuration). CNN could outperform ANN and SVM owing to its complicated architecture and handling of spatial correlations through convolution and pooling operations. In complex situations where some variables make a non-linear contribution to the occurrence of landslides, the method suggested could thus help develop landslide susceptibility maps.
Sameen, MI, Pradhan, B, Bui, DT & Alamri, AM 2020, 'Systematic sample subdividing strategy for training landslide susceptibility models', CATENA, vol. 187, pp. 104358-104358.
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© 2019 Elsevier B.V. Current practice in choosing training samples for landslide susceptibility modelling (LSM) is to randomly subdivide inventory information into training and testing samples. Where inventory data differ in distribution, the selection of training samples by a random process may cause inefficient training of machine learning (ML)/statistical models. A systematic technique may, however, produce efficient training samples that well represent the entire inventory data. This is particularly true when inventory information is scarce. This research proposed a systemic strategy to deal with this problem based on the fundamental distribution of probabilities (i.e. Hellinger) and a novel graphical representation of information contained in inventory data (i.e. inventory information curve, IIC). This graphical representation illustrates the relative increase in available information with the growth of the training sample size. Experiments on a selected dataset over the Cameron Highlands, Malaysia were conducted to validate the proposed methods. The dataset contained 104 landslide inventories and 7 landslide-conditioning factors (i.e. altitude, slope, aspect, land use, distance from the stream, distance from the road and distance from lineament) derived from a LiDAR-based digital elevation model and thematic maps acquired from government authorities. In addition, three ML/statistical models, namely, k-nearest neighbour (KNN), support vector machine (SVM) and decision tree (DT), were utilised to assess the proposed sampling strategy for LSM. The impacts of model's hyperparameters, noise and outliers on the performance of the models and the shape of IICs were also investigated and discussed. To evaluate the proposed method further, it was compared with other standard methods such as random sampling (RS), stratified RS (SRS) and cross-validation (CV). The evaluations were based on the area under the receiving characteristic curves. The results show that IICs a...
Sameen, MI, Sarkar, R, Pradhan, B, Drukpa, D, Alamri, AM & Park, H-J 2020, 'Landslide spatial modelling using unsupervised factor optimisation and regularised greedy forests', Computers & Geosciences, vol. 134, pp. 104336-104336.
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Samiee, A, Ahmed, M, Yang, L & Pereloma, E 2020, 'The effect of continuous heating on microstructure development in thermo-mechanically processed Ti-10V-3Fe-3Al alloy produced by powder metallurgy', Materials Characterization, vol. 161, pp. 110172-110172.
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Sanders, YR, Berry, DW, Costa, PCS, Tessler, LW, Wiebe, N, Gidney, C, Neven, H & Babbush, R 2020, 'Compilation of Fault-Tolerant Quantum Heuristics for Combinatorial Optimization', PRX Quantum, vol. 1, no. 2.
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Here we explore which heuristic quantum algorithms for combinatorial optimization might be most practical to try out on a small fault-tolerant quantum computer. We compile circuits for several variants of quantum-accelerated simulated annealing including those using qubitization or Szegedy walks to quantize classical Markov chains and those simulating spectral-gap-amplified Hamiltonians encoding a Gibbs state. We also optimize fault-tolerant realizations of the adiabatic algorithm, quantum-enhanced population transfer, the quantum approximate optimization algorithm, and other approaches. Many of these methods are bottlenecked by calls to the same subroutines; thus, optimized circuits for those primitives should be of interest regardless of which heuristic is most effective in practice. We compile these bottlenecks for several families of optimization problems and report for how long and for what size systems one can perform these heuristics in the surface code given a range of resource budgets. Our results discourage the notion that any quantum optimization heuristic realizing only a quadratic speedup achieves an advantage over classical algorithms on modest superconducting qubit surface code processors without significant improvements in the implementation of the surface code. For instance, under quantum-favorable assumptions (e.g., that the quantum algorithm requires exactly quadratically fewer steps), our analysis suggests that quantum-accelerated simulated annealing requires roughly a day and a million physical qubits to optimize spin glasses that could be solved by classical simulated annealing in about 4 CPU-minutes.
Sandi, SG, Saco, PM, Rodriguez, JF, Saintilan, N, Wen, L, Kuczera, G, Riccardi, G & Willgoose, G 2020, 'Patch organization and resilience of dryland wetlands', Science of The Total Environment, vol. 726, pp. 138581-138581.
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Saputra, YM, Nguyen, DN, Hoang, DT, Vu, TX, Dutkiewicz, E & Chatzinotas, S 2020, 'Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks', IEEE Transactions on Mobile Computing, pp. 1-1.
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In this paper, we propose a novel energy-efficient framework for an electricvehicle (EV) network using a contract theoretic-based economic model tomaximize the profits of charging stations (CSs) and improve the social welfareof the network. Specifically, we first introduce CS-based and CSclustering-based decentralized federated energy learning (DFEL) approacheswhich enable the CSs to train their own energy transactions locally to predictenergy demands. In this way, each CS can exchange its learned model with otherCSs to improve prediction accuracy without revealing actual datasets and reducecommunication overhead among the CSs. Based on the energy demand prediction, wethen design a multi-principal one-agent (MPOA) contract-based method. Inparticular, we formulate the CSs' utility maximization as a non-collaborativeenergy contract problem in which each CS maximizes its utility under commonconstraints from the smart grid provider (SGP) and other CSs' contracts. Then,we prove the existence of an equilibrium contract solution for all the CSs anddevelop an iterative algorithm at the SGP to find the equilibrium. Throughsimulation results using the dataset of CSs' transactions in Dundee city, theUnited Kingdom between 2017 and 2018, we demonstrate that our proposed methodcan achieve the energy demand prediction accuracy improvement up to 24.63% andlessen communication overhead by 96.3% compared with other machine learningalgorithms. Furthermore, our proposed method can outperform non-contract-basedeconomic models by 35% and 36% in terms of the CSs' utilities and socialwelfare of the network, respectively.
Saqib, M, \bf Anwar, BS, Anwar, A, Petersson, L, Sharma, N & Blumenstein, M 2020, '\enquoteCOVID19 detection from Radiographs: Is Deep Learning able to handle the crisis?', Pattern Recognition (\bf PR).
Sarin, S, Haon, C, Belkhouja, M, Mas-Tur, A, Roig-Tierno, N, Sego, T, Porter, A, Merigó, JM & Carley, S 2020, 'Uncovering the knowledge flows and intellectual structures of research in Technological Forecasting and Social Change: A journey through history', Technological Forecasting and Social Change, vol. 160, pp. 120210-120210.
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Sarker, PC, Guo, Y, Lu, HY & Zhu, JG 2020, 'A generalized inverse Preisach dynamic hysteresis model of Fe-based amorphous magnetic materials', Journal of Magnetism and Magnetic Materials, vol. 514, pp. 167290-167290.
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Fe-based amorphous magnetic materials are attracting more and more attentions in the application of low and medium frequency transformers due to their favorable properties of low core loss and high saturation magnetic flux density. Accurate modelling of their static and dynamic characteristics is required for analysis and design optimization of low and medium frequency transformers. In particular, for numerical analysis using the vectorial magnetic potential, an inverse magnetic hysteresis model is needed to predict the magnetic field strength from the magnetic flux density. When the excitation varies with time, the magnetic hysteresis model must be able to predict the dynamic hysteresis characteristics. This paper presents a generalized inverse Preisach dynamic hysteresis model for dynamic characterization of Fe-based magnetic materials. This model incorporates the reversible magnetization and magnetization dependent hysteresis, as well as all core loss components, including the hysteresis, eddy current, and excess losses. The proposed model can predict accurately the magnetic field strength from the magnetic flux density and hence accurate core losses. The predicted results are verified by experimental measurements.
Sarmadian, A, Moghaddam, HA, Asnaashari, A, Joushani, HAN, Moosavi, M, Islam, MS, Saha, SC & Shafaee, M 2020, 'Flow boiling heat transfer and pressure drop characteristics of Isobutane in horizontal channels with twisted tapes', International Journal of Heat and Mass Transfer, vol. 162, pp. 120345-120345.
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© 2020 Elsevier Ltd Using twisted tapes as a passive method for heat transfer improvement in a two-phase flow heat exchanger is experimentally studied. The test evaporator is a copper channel with a length of 1000 mm and an internal diameter of 8.1 mm which is installed horizontally. Three twisted tapes with twist ratios of 4, 10, and 15 are used at refrigerant vapor qualities in the range of 0.1–0.8 and refrigerant mass velocities between 160-350 kgm−2s−1. The natural refrigerant Isobutane (R600a) is chosen as the working fluid because it is environmentally friendly. According to the experiments, installing twisted tapes inside the channel augments both heat transfer rate and pressure drops over the plain channel. It is also observed that for both plain and twisted tape inserted channels, the values of heat transfer coefficients and pressure losses grow by giving rise to the refrigerant mass velocity and vapor quality. Results showed that the system performance factor varied between 0.44–1.09 offering that using twisted tapes as a turbulator is beneficial under specific operating conditions. The empirical data showed that there is an optimum value of the working fluid mass velocity at which the performance of twisted tape inserted channels is higher.
Sathik, MJ, Bhatnagar, K, Siwakoti, YP, Bassi, HM, Rawa, M, Sandeep, N, Yang, Y & Blaabjerg, F 2020, 'Switched‐capacitor multilevel inverter with self‐voltage‐balancing for high‐frequency power distribution system', IET Power Electronics, vol. 13, no. 9, pp. 1807-1818.
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Switched capacitor multilevel inverter (SCMLI) with reduced components is attractive for higher number of voltage levels due to less implementation complexity and low cost. In this study, a new family of hybrid SCMLI for high frequency power distribution system is presented to eliminate the intermediate power conversion. Firstly, a five‐level SCMLI employing a single voltage source is proposed, which is further extended to nine‐level (9L) with its operation. Further extension/enhancement of the proposed 9L‐SCMLI for generating a higher number of voltage levels with reduced number of components is achieved on the basis of structural modification. The mathematical analysis for determination of capacitance, power loss analysis and comparative analysis has been provided in detail. A comprehensive comparison with other similar topologies is also provided to highlight the merits of the proposed topology. Simulation and experimental results are discussed for various dynamic load conditions with different output frequencies to validate the suitability of the proposed SCMLI for various high‐frequency AC applications, such as renewable energy systems, microgrids, electric vehicles and so on.
Satija, S, Mehta, M, Gupta, G, Chellappan, DK & Dua, K 2020, 'Targeting Interleukins in Chronic Airway Diseases Using Advanced Drug Delivery', Future Medicinal Chemistry, vol. 12, no. 20, pp. 1805-1807.
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Satija, S, Mehta, M, Sharma, M, Prasher, P, Gupta, G, Chellappan, DK & Dua, K 2020, 'Vesicular Drug Delivery Systems As Theranostics in COVID-19', Future Medicinal Chemistry, vol. 12, no. 18, pp. 1607-1609.
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Satija, S, Tambuwala, MM, Pabreja, K, Bakshi, HA, Chellappan, DK, Aljabali, AA, Nammi, S, Singh, TG, Dureja, H, Gupta, G, Dua, K, Mehta, M & Garg, M 2020, 'Development of a novel HPTLC fingerprint method for simultaneous estimation of berberine and rutin in medicinal plants and their pharmaceutical preparations followed by its application in antioxidant assay', JPC – Journal of Planar Chromatography – Modern TLC, vol. 33, no. 3, pp. 313-319.
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Satya, A, Harimawan, A, Haryani, GS, Johir, MAH, Vigneswaran, S, Ngo, HH & Setiadi, T 2020, 'Batch Study of Cadmium Biosorption by Carbon Dioxide Enriched Aphanothece sp. Dried Biomass', Water, vol. 12, no. 1, pp. 264-264.
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The conventional method for cadmium removal in aqueous solutions (1–100 mg/L) is ineffective and inefficient. Therefore, a batch biosorption reactor using a local freshwater microalga (originating from an urban lake, namely, Situ Rawa Kalong-Depok) as dried biosorbent was tested. Biosorbent made from three kinds of cyanobacterium Aphanothece sp. cultivars (A0, A8, and A15) were used to eliminate cadmium (Cd2+) ions in aqueous solution (1–7 mg/L). The biosorbents were harvested from a photobioreactor system enriched with carbon dioxide gas of 0.04% (atmospheric), 8%, and 15% under continuous light illumination of about 5700–6000 lux for 14 d of cultivation. Produced dried biosorbents had Brunauer–Emmet–Teller (BET) surface area ranges of 0.571–1.846 m2/g. Biosorption of Cd2+ was pH and concentration dependent. Sorption was spontaneous (ΔG = −8.39 to −10.88 kJ/mol), exothermic (ΔH = −41.85 to −49.16 kJ/mol), and decreased randomness (ΔS = −0.102 to −0.126 kJ/mol. K) on the interface between solid and liquid phases when the process was completed. The kinetic sorption data fitted best to the pseudo-second-order model (k2 = 2.79 × 10−2, 3.96 × 10−2, and 4.54 × 10−2 g/mg.min). The dried biosorbents of A0, A8, and A15, after modeling with the Langmuir and Dubinin–Radushkevich isotherm models, indicated that cadmium binding occurred through chemisorption (qmax, D-R = 9.74 × 10−4, 4.79 × 10−3, and 9.12 × 10−3 mol/g and mean free energy of 8.45, 11.18, and 11.18 kJ/mol) on the monolayer and homogenous surface (qmax, Langmuir of 12.24, 36.90, and 60.24 mg/g). In addition, the results of SEM, EDX, and FTIR showed that there were at least nine functional groups that interacted with Cd2+ (led to bond formation) after biosorption through cation exchange mechanisms, and morphologically the surfaces changed after biosorption. Biosorbent A15 indicated the best resilient features over three cycles of sorption–desorption using 1 M HCl as the desorbing eluent. These...
Savkin, AV, Huang, H & Ni, W 2020, 'Securing UAV Communication in the Presence of Stationary or Mobile Eavesdroppers via Online 3D Trajectory Planning', IEEE Wireless Communications Letters, vol. 9, no. 8, pp. 1211-1215.
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© 2012 IEEE. Unmanned Aerial Vehicles (UAVs) are expected to play an active role In future wireless communication systems, thanks to their excellent mobility. This letter considers a problem of navigating a UAV to secure Its wireless communication with a stationary or mobile ground node In the presence of stationary or mobile eavesdroppers that can eavesdrop Individually and collaboratively. We optimize online the 3D trajectory of the UAV to minimize the energy expenditure of the UAV subject to the UAV's aeronautic maneuverability, and the ground node can effectively capture the transmitted data while the eavesdroppers are not able to do It. A model predictive control (MPC) based navigation scheme Is developed and Its optimality Is proved. Computer simulations and comparisons against a benchmark method are conducted to demonstrate the effectiveness of the proposed approach.
Saxena, A, Pare, S, Meena, MS, Gupta, D, Gupta, A, Razzak, I, Lin, C-T & Prasad, M 2020, 'A Two-Phase Approach for Semi-Supervised Feature Selection', Algorithms, vol. 13, no. 9, pp. 215-215.
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This paper proposes a novel approach for selecting a subset of features in semi-supervised datasets where only some of the patterns are labeled. The whole process is completed in two phases. In the first phase, i.e., Phase-I, the whole dataset is divided into two parts: The first part, which contains labeled patterns, and the second part, which contains unlabeled patterns. In the first part, a small number of features are identified using well-known maximum relevance (from first part) and minimum redundancy (whole dataset) based feature selection approaches using the correlation coefficient. The subset of features from the identified set of features, which produces a high classification accuracy using any supervised classifier from labeled patterns, is selected for later processing. In the second phase, i.e., Phase-II, the patterns belonging to the first and second part are clustered separately into the available number of classes of the dataset. In the clusters of the first part, take the majority of patterns belonging to a cluster as the class for that cluster, which is given already. Form the pairs of cluster centroids made in the first and second part. The centroid of the second part nearest to a centroid of the first part will be paired. As the class of the first centroid is known, the same class can be assigned to the centroid of the cluster of the second part, which is unknown. The actual class of the patterns if known for the second part of the dataset can be used to test the classification accuracy of patterns in the second part. The proposed two-phase approach performs well in terms of classification accuracy and number of features selected on the given benchmarked datasets.
Sayem, ASM, Esselle, KP & Hashmi, RM 2020, 'Increasing the transparency of compact flexible antennas using defected ground structure for unobtrusive wearable technologies', IET Microwaves, Antennas & Propagation, vol. 14, no. 14, pp. 1869-1877.
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In this paper, new designs of optically transparent wearable antennas have been presented. The explored antennas have the excellent features of high flexibility, small size, low specific absorption rate (SAR) and high optical transparency. The proposed antennas are realised by utilising highly flexible, optically transparent and low‐cost materials. For achieving compactness in antenna dimension and for improved transparency, square‐ring‐shaped radiator is used in the antenna design. To improve the efficiency and gain of the square‐ring patch antennas, a new technique is proposed in this paper. The proposed technique utilises a strip line that connects the middle of the two opposite sides of the ring. Full ground plane is used in antenna design to reduce the back radiation. However, full ground plane reduces optical transparency. To elevate optical transparency without affecting antenna's back radiation significantly, a new technique of defected ground structure is investigated in this study. With this defected ground structure, the optical transparency is improved by about 6% without significantly compromising the SAR. The compatibility of the proposed antennas for wearable applications are investigated by examining the performances on flat and bent phantoms. Moreover, the robustness of the antennas are studied by subjecting the prototypes to multiple bending operations.
Sayem, ASM, Esselle, KP, Hashmi, RM & Liu, H 2020, 'Experimental studies of the robustness of the conductive-mesh-polymer composite towards the development of conformal and transparent antennas', Smart Materials and Structures, vol. 29, no. 8, pp. 085015-085015.
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Sayem, ASM, Le, D, Simorangkir, RBVB, Bjorninen, T, Esselle, KP, Hashmi, RM & Zhadobov, M 2020, 'Optically Transparent Flexible Robust Circularly Polarized Antenna for UHF RFID Tags', IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 12, pp. 2334-2338.
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Sayem, ASM, Simorangkir, RBVB, Esselle, KP, Hashmi, RM & Liu, H 2020, 'A Method to Develop Flexible Robust Optically Transparent Unidirectional Antennas Utilizing Pure Water, PDMS, and Transparent Conductive Mesh', IEEE Transactions on Antennas and Propagation, vol. 68, no. 10, pp. 6943-6952.
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Schlegl, T, Schlegl, S & Deuse, J 2020, 'Detektion von Anomalien in automatisierten Schraubprozessen', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 115, no. 5, pp. 275-278.
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Kurzfassung Die Anwendung maschineller Lernverfahren zur Detektion von Fehlern in automatisierten Verschraubungsprozessen der Motormontage wird durch deren oft unbekannte Fehlerbilder erschwert. In diesem Beitrag wird ein selbstlernendes Verfahren verwendet, um den Normalzustand des Schraubprozesses zu erlernen und Anomalien zu erkennen. Es wird zudem ein Ansatz zur Verbesserung der Sensitivität vorgestellt. Die Erprobung anhand produktiver Daten der Motormontage zeigt eine erhebliche Verbesserung der Fehlererkennung.
Schleif, WS, Goldenberg, NA & Catchpoole, DR 2020, 'The “Federated Pediatric BioCloud” Model: State of the Art and Future Prospects in Pediatric Biospecimen Science', The Journal of Pediatrics, vol. 221, pp. S43-S48.
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Schmitt, J, Bönig, J, Borggräfe, T, Beitinger, G & Deuse, J 2020, 'Predictive model-based quality inspection using Machine Learning and Edge Cloud Computing', Advanced Engineering Informatics, vol. 45, pp. 101101-101101.
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Scimeca, L, Maiolino, P, Bray, E & Iida, F 2020, 'Structuring of tactile sensory information for category formation in robotics palpation', Autonomous Robots, vol. 44, no. 8, pp. 1377-1393.
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AbstractThis paper proposes a framework to investigate the influence of physical interactions to sensory information, during robotic palpation. We embed a capacitive tactile sensor on a robotic arm to probe a soft phantom and detect and classify hard inclusions within it. A combination of PCA and K-Means clustering is used to: first, reduce the dimensionality of the spatiotemporal data obtained through the probing of each area in the phantom; second categorize the re-encoded data into a given number of categories. Results show that appropriate probing interactions can be useful in compensating for the quality of the data, or lack thereof. Finally, we test the proposed framework on a palpation scenario where a Support Vector Machine classifier is trained to discriminate amongst different types of hard inclusions. We show the proposed framework is capable of predicting the best-performing motion strategy, as well as the relative classification performance of the SVM classifier, solely based on unsupervised cluster analysis methods.
Sedehi, O, Katafygiotis, LS & Papadimitriou, C 2020, 'Hierarchical Bayesian operational modal analysis: Theory and computations', Mechanical Systems and Signal Processing, vol. 140, pp. 106663-106663.
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Sedehi, O, Papadimitriou, C & Katafygiotis, LS 2020, 'Data-driven uncertainty quantification and propagation in structural dynamics through a hierarchical Bayesian framework', Probabilistic Engineering Mechanics, vol. 60, pp. 103047-103047.
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Sekandari, M, Masoumi, I, Beiranvand Pour, A, M Muslim, A, Rahmani, O, Hashim, M, Zoheir, B, Pradhan, B, Misra, A & Aminpour, SM 2020, 'Application of Landsat-8, Sentinel-2, ASTER and WorldView-3 Spectral Imagery for Exploration of Carbonate-Hosted Pb-Zn Deposits in the Central Iranian Terrane (CIT)', Remote Sensing, vol. 12, no. 8, pp. 1239-1239.
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The exploration of carbonate-hosted Pb-Zn mineralization is challenging due to the complex structural-geological settings and costly using geophysical and geochemical techniques. Hydrothermal alteration minerals and structural features are typically associated with this type of mineralization. Application of multi-sensor remote sensing satellite imagery as a fast and inexpensive tool for mapping alteration zones and lithological units associated with carbonate-hosted Pb-Zn deposits is worthwhile. Multiple sources of spectral data derived from different remote sensing sensors can be utilized for detailed mapping a variety of hydrothermal alteration minerals in the visible near infrared (VNIR) and the shortwave infrared (SWIR) regions. In this research, Landsat-8, Sentinel-2, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing sensors were used for prospecting Zn-Pb mineralization in the central part of the Kashmar–Kerman Tectonic Zone (KKTZ), the Central Iranian Terrane (CIT). The KKTZ has high potential for hosting Pb-Zn mineralization due to its specific geodynamic conditions (folded and thrust belt) and the occurrence of large carbonate platforms. For the processing of the satellite remote sensing datasets, band ratios and principal component analysis (PCA) techniques were adopted and implemented. Fuzzy logic modeling was applied to integrate the thematic layers produced by image processing techniques for generating mineral prospectivity maps of the study area. The spatial distribution of iron oxide/hydroxides, hydroxyl-bearing and carbonate minerals and dolomite were mapped using specialized band ratios and analyzing eigenvector loadings of the PC images. Subsequently, mineral prospectivity maps of the study area were generated by fusing the selected PC thematic layers using fuzzy logic modeling. The most favorable/prospective zones for hydrothermal ore mineralizations and car...
Selander, J, Sun, J, Tjulin, A & Buys, N 2020, 'Interrelated Factors for Return to Work of Sick-Listed Employees in Sweden', International Journal of Disability Management, vol. 15.
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AbstractPurpose:Long-term sickness absence is a significant human and economic cost in many countries, including Sweden making research on factors which impact on return to work (RTW) relevant. This study has two aims: (1) provide an overview of factors that impact RTW expectations in a national sample of Swedish workers on long-term sickness absence; and (2) gain an understanding of the interrelationships among these factors using a socioecological framework and decision tree analysis.Method:A survey, designed to capture information about demographic variables, health and work ability, workplace contact, supervisor support and expectations of return to work, was mailed to 1,112 randomly selected sick-listed people in Sweden and completed by 534, representing a response rate of 48%.Results:The most important factors affecting RTW expectations were work ability and burnout. Employees reporting high levels of work ability were more likely to expect to RTW compared to those reporting low levels, and this was dependent on their relative burnout score. Those with a high burnout score were less likely to expect to RTW, while for those with a low burnout score RTW expectations were dependent on age, country of birth, and supervisor support. For young employees reporting low work ability and low burnout score, RTW expectations were lower.Conclusions:Our results suggest a more nuanced approach to delivery of RTW services is required, whereby practitioners need to understand the socioecology of the range of factors that impact RTW expectations. The use of decision tree analysis facilitates this understand...
Senanayake, S, Pradhan, B, Huete, A & Brennan, J 2020, 'A Review on Assessing and Mapping Soil Erosion Hazard Using Geo-Informatics Technology for Farming System Management', Remote Sensing, vol. 12, no. 24, pp. 4063-4063.
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Soil erosion is a severe threat to food production systems globally. Food production in farming systems decreases with increasing soil erosion hazards. This review article focuses on geo-informatics applications for identifying, assessing and predicting erosion hazards for sustainable farming system development. Several researchers have used a variety of quantitative and qualitative methods with erosion models, integrating geo-informatics techniques for spatial interpretations to address soil erosion and land degradation issues. The review identified different geo-informatics methods of erosion hazard assessment and highlighted some research gaps that can provide a basis to develop appropriate novel methodologies for future studies. It was found that rainfall variation and land-use changes significantly contribute to soil erosion hazards. There is a need for more research on the spatial and temporal pattern of water erosion with rainfall variation, innovative techniques and strategies for landscape evaluation to improve the environmental conditions in a sustainable manner. Examining water erosion and predicting erosion hazards for future climate scenarios could also be approached with emerging algorithms in geo-informatics and spatiotemporal analysis at higher spatial resolutions. Further, geo-informatics can be applied with real-time data for continuous monitoring and evaluation of erosion hazards to risk reduction and prevent the damages in farming systems.
Senanayake, S, Pradhan, B, Huete, A & Brennan, J 2020, 'Assessing Soil Erosion Hazards Using Land-Use Change and Landslide Frequency Ratio Method: A Case Study of Sabaragamuwa Province, Sri Lanka', Remote Sensing, vol. 12, no. 9, pp. 1483-1483.
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This study aims to identify the vulnerable landscape areas using landslide frequency ratio and land-use change associated soil erosion hazard by employing geo-informatics techniques and the revised universal soil loss equation (RUSLE) model. Required datasets were collected from multiple sources, such as multi-temporal Landsat images, soil data, rainfall data, land-use land-cover (LULC) maps, topographic maps, and details of the past landslide incidents. Landsat satellite images from 2000, 2010, and 2019 were used to assess the land-use change. Geospatial input data on rainfall, soil type, terrain characteristics, and land cover were employed for soil erosion hazard classification and mapping. Landscape vulnerability was examined on the basis of land-use change, erosion hazard class, and landslide frequency ratio. Then the erodible hazard areas were identified and prioritized at the scale of river distribution zones. The image analysis of Sabaragamuwa Province in Sri Lanka from 2000 to 2019 indicates a significant increase in cropping areas (17.96%) and urban areas (3.07%), whereas less dense forest and dense forest coverage are significantly reduced (14.18% and 6.46%, respectively). The average annual soil erosion rate increased from 14.56 to 15.53 t/ha/year from year 2000 to 2019. The highest landslide frequency ratios are found in the less dense forest area and cropping area, and were identified as more prone to future landslides. The river distribution zones Athtanagalu Oya (A-2), Kalani River-south (A-3), and Kalani River- north (A-9), were identified as immediate priority areas for soil conservation.
Sepehrirahnama, S & Lim, K-M 2020, 'Acoustophoretic agglomeration patterns of particulate phase in a host fluid', Microfluidics and Nanofluidics, vol. 24, no. 12.
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Sepehrirahnama, S, Ong, ET, Lee, HP & Lim, KM 2020, 'Numerical Modeling of Free-Surface Wave Effects on Flexural Vibration of Floating Structures', International Journal of Computational Methods, vol. 17, no. 05, pp. 1940016-1940016.
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To investigate flexural vibration of structures in a fluid, a numerical algorithm was developed to relate the added mass and damping effects of the fluid to each mode of vibration. These are separate from the traditional added mass associated with rigid body motion, such as the translational motion along Cartesian axes. In this formulation, small-amplitude free surface waves were accounted for by using a nonsingular implementation of the free-surface Green’s function for a potential flow solver based on Boundary Element Method. The formulation was applied to the forced vibration of structures, namely, a hemispherical shell and a reinforced half cylinder with typical dimensions of ships and offshore structures, to obtain their dynamic response at various excitation frequencies. It is observed that resonance frequency of the structure, in contact with water, decreases due to the added mass effect. The influence of the free-surface wave on the fluid loading was investigated for large structures. It is simpler to relate the fluid added mass to mode shapes rather than distribution of fluid load over wetted surface of the structure in engineering simulations. Moreover, it is found that the vibration energy radiated away by the fluid surface wave has little influence on the vibration response of the shell structures.
Shahabuddin, M, Mofijur, M, Kalam, MA & Masjuki, HH 2020, 'Study on the Friction and Wear Characteristics of Bio-lubricant Synthesized from Second Generation Jatropha Methyl Ester', Tribology in Industry, vol. 42, no. 1, pp. 41-49.
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© 2020 Published by Faculty of Engineering. The demands for eco-friendly bio-lubricants are growing due to the environmental concern and the rapid depletion of petroleum oil. This paper outlines the tribological evaluation of jatropha methyl ester (JME) based bio-lubricant by analyzing its anti-wear (AW) and extreme pressure (EP) characteristics. The AW and EP tests were conducted using a four-ball tribotester with standard test methods of ASTM D 4172 and ASTM D 2783, respectively. After each test, the wear scar diameter, flash temperature parameter, viscosity and viscosity index (VI) were measured. The SEM analysis characterized the surface structure of the worn surface. The properties of formulated bio-lubricants were compared with the commercial lubricant SAE 15W-40. Experimental results showed that under boundary lubrication, the bio-lubricants showed excellent tribological properties up to the initial seizure load (ISL). Over the ISL, the friction and wear were increased slightly as compared to the commercial lubricant. The final seizure load (FSL) found for the bio-lubricant (BL 10), and commercial lubricant was 220 kg. The bio-lubricant with 10 % JME (BL 10) was found to be the most favorable, which met standard ISO requirements except for pour point.
Shahid, I, Thalakotuna, DN, Karmokar, DK & Heimlich, M 2020, 'Asymmetric transversal patch loaded microstrip line based 1-D periodic structure with flexible selection of stopband resonance', AEU - International Journal of Electronics and Communications, vol. 114, pp. 153010-153010.
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© 2019 Elsevier GmbH A reactively loaded microstrip line based 1-D periodic structure is discussed. An off-centered via configuration is introduced to the conventional center-shorted square patch mushroom type electromagnetic bandgap (EBG) structures. In this work, a rectangular transversal patch is used to load a longitudinal microstrip line reactively. Reactive loading is varied by changing the via position away from the patch center while keeping all other structure parameters constant. This asymmetric loading causes the structure to exhibit EBG characteristics at much lower frequency with the same structure dimensions when compared to conventional centered via setup. Propagating modes are investigated using eigenmode dispersion analysis for different via positions. An equivalent circuit model of the proposed unit cell is developed. Transmission matrix (ABCD) based phase constant calculations for the circuit model agrees with the dispersion analysis results. Finally, experimental results confirm that the proposed asymmetric configuration allows the structure to lower the bandgap resonance by 28.6% with negligible impact on other performance attributes of the structure. Using the proposed configuration, compact filter structures can be designed where the stopband resonance can be flexibly placed in the band of interest within a wide frequency range, by optimizing the via position.
Shahid, I, Thalakotuna, DN, Karmokar, DK & Heimlich, M 2020, 'Bandstop Filter Synthesis Scheme for Reactively Loaded Microstrip Line Based 1-D Periodic Structures', IEEE Access, vol. 8, pp. 155492-155505.
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A 1-D finite electromagnetic bandgap (EBG) periodic structure is studied. In the structure, EBG behaviour arises from a unit cell comprised of a metallic patch sandwiched between microstrip line and ground plane. Reactive loading offered by patch size determines the bandgap position. A detailed parametric study of various physical structure parameters is presented as a basis to develop a interrelation between physical parameters of the structure and cutoff frequencies. Closed-form synthesis equations are then formulated using curve fitting techniques. Subsequently, a step-by-step design methodology is presented to get a close first pass approximation of structure dimensions for a given specification. This design method reduces the effort required for a designer to perform extensive electromagnetic simulations at early stages of the design. The proposed synthesis method is tested for a variety of commercially available substrates and different frequency ranges for validation. Comparison with electromagnetic (EM) simulations and measurement show that the proposed synthesis method provides first pass approximation of the physical structure dimensions with 94% accuracy.
Shakor, P, Nejadi, S & Paul, G 2020, 'Investigation into the effect of delays between printed layers on the mechanical strength of inkjet 3DP mortar', Manufacturing Letters, vol. 23, pp. 19-22.
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© 2019 Currently, additive manufacturing have enabled to fabricate the three-dimensional models. 3D-Printing technique is a multipurpose process for producing structural members using a sequential layering approach. The “feature quality” of 3DP specimens can be improved by optimising the build constraints. In this paper, a mortar mix powder-base has been prepared that consists of cementitious materials. Experiments are conducted to investigate the effects of different delays in printing time on the mechanical properties of the scaffolds. It has been shown that the compressive stress and strength of printed specimens with a delay of 200 ms were greater than specimens with other delay values.
Shakor, P, Nejadi, S, Paul, G & Sanjayan, J 2020, 'Dimensional accuracy, flowability, wettability, and porosity in inkjet 3DP for gypsum and cement mortar materials', Automation in Construction, vol. 110, pp. 102964-102964.
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© 2019 Inkjet (powder-based) 3D Printing is a popular and widely used technology, which can be applied to print a wide range of specimens using different powder materials. This paper discusses the use of inkjet 3DP technology for construction applications using custom-made powder instead of commercial gypsum powder (ZP 151). The paper aims to address the differences between ZP 151 and CP (a custom-made construction-specific cement mortar powder) with regard to powder flowability, wettability, powder bed porosity and apparent porosity in 3DP specimens. An inkjet 3D printer is employed and experimental results verify that ZP 151 has a lower angle of repose, a higher contact angle and noticeably less porosity in the powder bed compared with the CP powder. Additionally, specimens printed with ZP 151 have a lower apparent porosity compared with CP specimens. The wettability for each of the powders was tested using contact angle goniometer, while the Optronis Cam-Recorder was used at 1000 fps at 800 × 600 pixel resolution images for the powder flowability tests. The bulk density tester was utilised to find the apparent porosity in the printed specimens. The paper also discusses the details of the printing procedure and dimensional accuracy of printed specimens.
Shakor, P, Nejadi, S, Sutjipto, S, Paul, G & Gowripalan, N 2020, 'Effects of deposition velocity in the presence/absence of E6-glass fibre on extrusion-based 3D printed mortar', Additive Manufacturing, vol. 32, pp. 101069-101069.
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© 2020 Additive Manufacturing (AM) technologies are widely used in various fields of industry and research. Continual research has enabled AM technologies to be considered as a feasible substitute for certain applications in the construction industry, particularly given the advances in the use of glass fibre reinforced mortar. An investigation of the resulting mechanical properties of various mortar mixes extruded using a robotic arm is presented. The nozzle paths were projected via ‘spline’ interpolation to obtain the desired trajectory and deposition velocity in the reference frame of the manipulator. Along each path, various mortar mixes, with and without chopped glass fibre, were deposited at different velocities. Tests were conducted to determine their mechanical performance when incorporated in printed structures with different layers (1, 2, 4 and 6 layers). The results are compared with those of conventional cast-in-place mortar. In this study, the mixes consist of ordinary Portland cement, fine sand, chopped glass fibres (6 mm) and chemical admixtures, which are used to print prismatic- and cubic-shaped specimens. Mechanical strength tests were performed on the printed specimens to evaluate the behaviour of the materials in the presence and absence of glass fibre. Robot end-effector velocity tests were performed to examine the printability and extrudability of the mortar mixes. Finally, horizontal and vertical line printing tests were used to determine the workability, buildability and uniformity of the mortar mix and to monitor the fibre flow directions in the printed specimens. The results show that printed specimens with glass fibre have enhanced compressive strength compared with specimens without glass fibre.
Shalchi, A, Abbasi, M, Abbasi, E, Tousi, B & Gharehpetian, GB 2020, 'New DTR line selection method in a power system comprising DTR, ESS, and RES for increasing RES integration and minimising load shedding', IET Generation, Transmission & Distribution, vol. 14, no. 25, pp. 6319-6329.
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In this study, new technologies such as dynamic thermal rating (DTR) technology and energy storage system (ESS) are simultaneously used to optimise the integration of renewable energy sources (RESs) and minimise the load shedding. For achieving the mentioned aims, a new method is proposed to select the candidate lines for the implementation of DTR technologies. The DTR technology is responsible for increasing lines limited capacity. Moreover, optimally placed and sized ESSs save RESs generated power in non‐peak‐hours and inject it to the network in peak hours. For validating the performance of the proposed solution, comprehensive simulations are performed in several stages on IEEE RTS‐24‐ and 30‐bus test systems networks. To meet the increased power demand, RESs (wind and solar) are optimally allocated by using the genetic algorithm (GA) in the test systems. Then, ESS devices are optimally sized and placed by using GA. Finally, candidate lines are selected based on the proposed method and DTR devices are added to the systems. Comprehensive comparisons are presented for comparing the previously presented solutions and the proposed one. It is proved that using DTR technology and ESSs along with the proposed line selection method is the superior solution for system operators.
Shamshirian, A, Aref, AR, Yip, GW, Ebrahimi Warkiani, M, Heydari, K, Razavi Bazaz, S, Hamzehgardeshi, Z, Shamshirian, D, Moosazadeh, M & Alizadeh-Navaei, R 2020, 'Diagnostic value of serum HER2 levels in breast cancer: a systematic review and meta-analysis', BMC Cancer, vol. 20, no. 1, p. 1049.
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Abstract Background Measurement of serum human epidermal growth factor receptor-2 (HER-2/neu) levels might play an essential role as a diagnostic/screening marker for the early selection of therapeutic approaches and predict prognosis in breast cancer patients. We aimed to undertake a systematic review and meta-analysis focusing on the diagnostic/screening value of serum HER-2 levels in comparison to routine methods. Methods We performed a systematic search via PubMed, Scopus, Cochrane-Library, and Web of Science databases for human diagnostic studies reporting the levels of serum HER-2 in breast cancer patients, which was confirmed using the histopathological examination. Meta-analyses were carried out for sensitivity, specificity, accuracy, area under the ROC curve (AUC), positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), and negative likelihood ratio (NLR). Results Fourteen studies entered into this investigation. The meta-analysis indicated the low sensitivity for serum HER2 levels (Sensitivity: 53.05, 95%CI 40.82–65.28), but reasonable specificity of 79.27 (95%CI 73.02–85.51), accuracy of 72.06 (95%CI 67.04–77.08) and AUC of 0.79 (95%CI 0.66–0.92). We also found a significant differences for PPV (PPV: 56.18, 95%CI 44.16–68.20), NPV (NPV: 76.93, 95%CI 69.56–84.31), PLR (PLR: 2.10, 95%CI 1.69–2.50) and NLR (NLR: 0.58, 95%CI 0.44–0.71). Conclusion Our findings revealed that although serum HER-2 le...
Shang, F, Zhou, K, Liu, H, Cheng, J, Tsang, IW, Zhang, L, Tao, D & Jiao, L 2020, 'VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 1, pp. 188-202.
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© 1989-2012 IEEE. In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD). Unlike the choices of snapshot and starting points in SVRG and its proximal variant, Prox-SVRG, the two vectors of VR-SGD are set to the average and last iterate of the previous epoch, respectively. The settings allow us to use much larger learning rates, and also make our convergence analysis more challenging. We also design two different update rules for smooth and non-smooth objective functions, respectively, which means that VR-SGD can tackle non-smooth and/or non-strongly convex problems directly without any reduction techniques. Moreover, we analyze the convergence properties of VR-SGD for strongly convex problems, which show that VR-SGD attains linear convergence. Different from most algorithms that have no convergence guarantees for non-strongly convex problems, we also provide the convergence guarantees of VR-SGD for this case, and empirically verify that VR-SGD with varying learning rates achieves similar performance to its momentum accelerated variant that has the optimal convergence rate O(1/T2O(1/T2). Finally, we apply VR-SGD to solve various machine learning problems, such as convex and non-convex empirical risk minimization, and leading eigenvalue computation. Experimental results show that VR-SGD converges significantly faster than SVRG and Prox-SVRG, and usually outperforms state-of-the-art accelerated methods, e.g., Katyusha.
Shang, J, Xu, W, Lee, C-H, Yuan, X, Zhang, P & Lin, J 2020, 'REF Codes: Intermediate Performance Oriented Fountain Codes With Feedback', IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 13148-13164.
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Shanmugam, S, Ngo, H-H & Wu, Y-R 2020, 'Advanced CRISPR/Cas-based genome editing tools for microbial biofuels production: A review', Renewable Energy, vol. 149, pp. 1107-1119.
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© 2019 Elsevier Ltd With rapid progress in the fields of synthetic biology and metabolic engineering, there are possible applications to generate a wide range of advanced biofuels with maximized yield and productivity to achieve a more sustainable bioprocess with reduced carbon footprints. Among the diverse molecular biology tools, clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins (CRISPR-Cas) technology stands out with potential targeted genome editing, exhibiting a more precise and accurate gene knock-out and knock-in system better than its predecessors, for example zinc finger nucleases (ZFN) and transcription activator-like effector nucleases (TALEN). There are reports involved in the advanced microbial genome engineering tools for the biofuels production; however, there is lack of a comprehensive review about the CRISPR-Cas based-techniques in improved biofuel production along with the strategies to reduce the off-target effect that ensures the success and safety of this method. Therefore, in this review we attempt to systematically comment on the mechanism of CRISPR-Cas and its application to microbial biofuels production. This includes bioethanol, biobutanol as well as other hydrocarbons that sequentially follow various suggestions on enhancing the efficiency of targeting genes. The role of inducible on/off genetic circuits in response to environmental stimuli in the regulation of targeted genome editing (TGE) by minimizing metabolic burden and maximizing fermentation efficiency is also discussed. The relevant stringent regulatory demands to ensure minimal off-target cleavage with maximum efficiency coupled with complete biosafety of this technology are considered. It can be concluded that the recent development of CRISPR-Cas technology should open a new avenue in creating microbial biorefineries for potentially enhanced biofuel production.
Shanmuganathan, V, Yesudhas, HR, Khan, MS, Khari, M & Gandomi, AH 2020, 'R-CNN and wavelet feature extraction for hand gesture recognition with EMG signals', Neural Computing and Applications, vol. 32, no. 21, pp. 16723-16736.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. This paper demonstrates the implementation of R-CNN in terms of electromyography-related signals to recognize hand gestures. The signal acquisition is implemented using electrodes situated on the forearm, and the biomedical signals are generated to perform the signals preprocessing using wavelet packet transform to perform the feature extraction. The R-CNN methodology is used to map the specific features that are acquired from the wavelet power spectrum to validate and train how the architecture is framed. Additionally, the real-time test is completed to reach the accuracy of 96.48% compared to the related methods. This kind of result proves that the proposed work has the highest amount of accuracy in recognizing the gestures.
Shannon, AG & Deveci, Ö 2020, 'Rising binomial coefficients - Type 1: Extensions of carlitz and riordan', Advanced Studies in Contemporary Mathematics (Kyungshang), vol. 30, no. 2, pp. 263-268.
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Following some ideas initiated by Leonard Carlitz and John Riordan, this paper generalizes some properties of the ordinary binomial coefficient through the use of rising factorials. Properties include connections with Beta and Gamma functions.
Sharbatoghli, M, Vafaei, S, Aboulkheyr Es, H, Asadi-Lari, M, Totonchi, M & Madjd, Z 2020, 'Prediction of the treatment response in ovarian cancer: a ctDNA approach', Journal of Ovarian Research, vol. 13, no. 1.
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AbstractOvarian cancer is the eighth most commonly occurring cancer in women. Clinically, the limitation of conventional screening and monitoring approaches inhibits high throughput analysis of the tumor molecular markers toward prediction of treatment response. Recently, analysis of liquid biopsies including circulating tumor DNA (ctDNA) open new way toward cancer diagnosis and treatment in a personalized manner in various types of solid tumors. In the case of ovarian carcinoma, growing pre-clinical and clinical studies underscored promising application of ctDNA in diagnosis, prognosis, and prediction of treatment response. In this review, we accumulate and highlight recent molecular findings of ctDNA analysis and its associations with treatment response and patient outcome. Additionally, we discussed the potential application of ctDNA in the personalized treatment of ovarian carcinoma.Graphical abstractctDNA-monitoring usage during the ovarian cancer treatments procedures.
Sharifi, S, Abrishami, S & Gandomi, AH 2020, 'Consolidation assessment using Multi Expression Programming', Applied Soft Computing, vol. 86, pp. 105842-105842.
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© 2019 Elsevier B.V. In this study, new approximate solutions for consolidation have been developed in order to hasten the calculations. These solutions include two groups of equations, one can be used to calculate the average degree of consolidation and the other one for computing the time factor (inverse functions). Considering the complicated nature of consolidation, an evolutionary computation technique called Multi-Expression Programming was applied to generate several non-piecewise models which are accurate and straightforward enough for different purposes for calculating the degree of consolidation for each depth and its average as well for the whole soil layer. The parametric study was also performed to investigate the impact of each input parameter on the predicted consolidation degree of developed models for each depth. Moreover, the results of the consolidation test carried out on four different clays attained from the literature showed the proper performance of the proposed models.
Sharma, A, Singh, Y, Singh, NK, Singla, A, Ong, HC & Chen, W-H 2020, 'Corrigendum to “Effective utilization of tobacco (Nicotiana Tabaccum) for biodiesel production and its application on diesel engine using response surface methodology approach” [Fuel 273 (2020) 117793]', Fuel, vol. 276, pp. 118133-118133.
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Sharma, V, Hossain, MJ, Ali, SMN & Kashif, M 2020, 'A Photovoltaic-Fed Z-Source Inverter Motor Drive with Fault-Tolerant Capability for Rural Irrigation', Energies, vol. 13, no. 18, pp. 4630-4630.
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In recent years, photovoltaic (PV) systems have emerged as economical solutions for irrigation systems in rural areas. However, they are characterized by low voltage output and less reliable configurations. To address this issue in this paper, a promising inverter configuration called Impedance (Z)-source inverter (ZSI) is designed and implemented to obtain high voltage output with single-stage power conversion, particularly suitable for irrigation application. An improved and efficient modulation scheme and design specifications of the network parameters are derived. Additionally, a suitable fault-tolerant strategy is developed and implemented to improve reliability and efficiency. It incorporates an additional redundant leg with an improved control strategy to facilitate the fault-tolerant operation. The proposed fault-tolerant circuit is designed to handle switch failures of the inverter modules due to the open-circuit and short-circuit faults. The relevant simulation and experimental results under normal, faulty and post-fault operation are presented. The post-fault operation characteristics are identical to the normal operation. The motor performance characteristics such as load current, torque, harmonic spectrum, and efficiency are thoroughly analysed to prove the suitability of the proposed system for irrigation applications. This study provides an efficient and economical solution for rural irrigation utilized in developing countries, for example, India.
Shaukat, K, Luo, S, Varadharajan, V, Hameed, IA & Xu, M 2020, 'A Survey on Machine Learning Techniques for Cyber Security in the Last Decade', IEEE Access, vol. 8, pp. 222310-222354.
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Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.
Shen, Q, Lin, Y, Kawabata, Y, Jia, Y, Zhang, P, Akther, N, Guan, K, Yoshioka, T, Shon, H & Matsuyama, H 2020, 'Engineering Heterostructured Thin-Film Nanocomposite Membrane with Functionalized Graphene Oxide Quantum Dots (GOQD) for Highly Efficient Reverse Osmosis', ACS Applied Materials & Interfaces, vol. 12, no. 34, pp. 38662-38673.
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In this study, custom-tailored graphene oxide quantum dots (GOQD) were synthesized as functional nanofillers to be embedded into the polyamide (PA) membrane for reverse osmosis (RO) via interfacial polymerization (IP). The heterostructured interface-functionalization of amine/sulfonic decoration on GOQD (N/S-d-GOQD) takes place via the tuning of the molecular design. The embedded N/S-d-GOQD inside the PA matrix contributes to facilitating water molecules quick transport due to the more accessible capturing sites with higher internal polarity, achieving a nearly 3-fold increase in water permeance when compared to the pristine thin-film composite (TFC) membrane. Covalent bonding between the terminal amine groups and the acyl chloride of trimesoyl chloride (TMC) enables the formation of an amplified selective layer, while the sulfonic part assists in maintaining a robust membrane surface negative charge, thus remarkably improving the membrane selectivity toward NaCl. As a result, the newly developed TFN membrane performed remarkably high water permeance up to 5.89 L m-2 h-1 bar-1 without the compromising of its favorable salt (NaCl) rejection ratio of 97.1%, revealing a comparably high separation property when comparing to the state-of-the-art RO membranes, and surpassing the permeability-selectivity trade-off limits. Furthermore, we systematically investigated the GOQDs with different surface decorations but similar configurations (including 3 different nanofillers of pristine GOQD, amine decorated GOQD (N-d-GOQD), and N/S-d-GOQD) to unveil the underlying mechanisms of the swing effects of internal geometry and polarity of the embedded nanofillers on contributing to the uptake, and/or release of aqueous molecules within TFN membranes, providing a fundamental perspective to investigate the impact of embedded nanofillers on the formation of an IP layer and the overall transporting behavior of the RO process.
Shen, X, Zhang, J, Xie, H, Hu, Z, Liang, S, Ngo, HH, Guo, W, Chen, X, Fan, J & Zhao, C 2020, 'Intensive removal of PAHs in constructed wetland filled with copper biochar', Ecotoxicology and Environmental Safety, vol. 205, pp. 111028-111028.
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Sheng, Z, Tuan, HD, Nasir, AA, Duong, TQ & Poor, HV 2020, 'Secure UAV-Enabled Communication Using Han–Kobayashi Signaling', IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 2905-2919.
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© 2002-2012 IEEE. This paper proposes Han-Kobayashi signaling (HKS), under which each pair of users decodes a common message to improve their throughput, for UAV-enabled multi-user communication. Given that only a single transmit antenna is used and thus there is no null space of users' channels for inserting an artificial noise that would effectively help to jam an eavesdropper without interfering the users' desired signals, a new information and artificial noise transfer scheme to address physical layer security (PLS) for the considered networks is investigated. Under this scheme, the UAV sends the confidential information to its users within a fraction of the time slot and sends the artificial noise within the remaining fraction. Accordingly, the problem of jointly optimizing the time-fraction, bandwidth and power allocation to maximize the users' worst secrecy throughput is formulated. New inner approximations are proposed for developing path-following algorithms for its computation. Simulation shows that the proposed information and artificial noise transfer enables not only HKS but also orthogonal multi-access and nonorthogonal multi-access to provide PLS for UAV-enabled communication even when the eavesdropper is in the best channel condition. HKS outperforms the other two schemes in terms of users' worst secrecy throughput.
Shi, K, Gong, C, Lu, H, Zhu, Y & Niu, Z 2020, 'Wide-grained capsule network with sentence-level feature to detect meteorological event in social network', Future Generation Computer Systems, vol. 102, pp. 323-332.
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Shi, K, Lu, H, Zhu, Y & Niu, Z 2020, 'Automatic generation of meteorological briefing by event knowledge guided summarization model', Knowledge-Based Systems, vol. 192, pp. 105379-105379.
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Shi, X, Zhu, J, Li, L & Dah-Chuan LU, D 2020, 'Low-Complexity Dual-Vector-Based Predictive Control of Three-Phase PWM Rectifiers Without Duty-Cycle Optimization', IEEE Access, vol. 8, pp. 77049-77059.
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© 2013 IEEE. The conventional model-predictive-based direct power control (MPDPC) of the three-phase full-bridge AC/DC converters chooses the best single voltage vector for the following control period, which results in variable switching frequency and power distortion, and thus a relatively higher sampling frequency is needed to achieve acceptable results. This paper proposes a simplified dual-vector-based predictive direct duty-cycle-control (SPDDC) with an additional zero vector implemented in contrast to the MPDPC. With the same best vector selection method, the proposed strategy has retained the control simplicity with just one more step added and much better control performance as well as a fixed switching frequency in comparison to the MPDPC. On the other hand, the duty-cycle optimization procedure is eliminated while the negative duration issue is essentially resolved compared with the conventional dual-vector-based model predictive duty-cycle-control (MPDCC). Comprehensive comparisons of various control methods by numerical simulation and experimental testing show that the SPDDC can achieve better steady state and dynamic performance than the MPDPC and simpler algorithms than the MPDCC.
Shi, Y, Cui, Q, Ni, W & Fei, Z 2020, 'Proactive Dynamic Channel Selection Based on Multi-Armed Bandit Learning for 5G NR-U', IEEE Access, vol. 8, pp. 196363-196374.
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Shi, Y, Pan, Y, Xu, D & Tsang, IW 2020, 'Multiview Alignment and Generation in CCA via Consistent Latent Encoding', Neural Computation, vol. 32, no. 10, pp. 1936-1979.
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Multiview alignment, achieving one-to-one correspondence of multiview inputs, is critical in many real-world multiview applications, especially for cross-view data analysis problems. An increasing amount of work has studied this alignment problem with canonical correlation analysis (CCA). However, existing CCA models are prone to misalign the multiple views due to either the neglect of uncertainty or the inconsistent encoding of the multiple views. To tackle these two issues, this letter studies multiview alignment from a Bayesian perspective. Delving into the impairments of inconsistent encodings, we propose to recover correspondence of the multiview inputs by matching the marginalization of the joint distribution of multiview random variables under different forms of factorization. To realize our design, we present adversarial CCA (ACCA), which achieves consistent latent encodings by matching the marginalized latent encodings through the adversarial training paradigm. Our analysis, based on conditional mutual information, reveals that ACCA is flexible for handling implicit distributions. Extensive experiments on correlation analysis and cross-view generation under noisy input settings demonstrate the superiority of our model.
Shi, Y, Tuan, HD & Apkarian, P 2020, 'Parameterized bilinear matrix inequality techniques for gain‐scheduling proportional integral derivative control design', International Journal of Robust and Nonlinear Control, vol. 30, no. 10, pp. 3886-3905.
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SummaryProportional‐integral‐derivative (PID) structured controller is the most popular class of industrial control but still could not be appropriately exploited in gain‐scheduling control systems. To gain the practicability and tractability of gain‐scheduling control systems, this paper addresses thegain‐scheduling PID control. The design of such a controller is based on parameterized bilinear matrix inequalities, which are then solved via a bilinear matrix inequality optimization problem of nonconvex optimization. Several computational procedures are developed for its computation. The merit of the developed algorithms is shown through the benchmark examples.
Shi, Y, Tuan, HD, Duong, TQ, Poor, HV & Savkin, AV 2020, 'PMU Placement Optimization for Efficient State Estimation in Smart Grid', IEEE Journal on Selected Areas in Communications, vol. 38, no. 1, pp. 71-83.
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© 1983-2012 IEEE. This paper investigates phasor measurement unit (PMU) placement for informative state estimation in smart grid by incorporating various constraints for observability. Observability constitutes an important property for PMU placement to characterize the depth of the buses' reachability by the placed PMUs, but addressing it solely by binary linear programming as in many works still does not guarantee a good estimate for the grid state. Some existing works have considered optimization of some estimation indices by ignoring the observability requirements for computational ease and thus potentially lead to trivial results such as acceptance of the estimate for an unobserved state component as its unconditional mean. In this work, the PMU placement optimization problem is considered by minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state subject to observability constraints, which incorporate operating conditions such as presence of zero injection buses, contingency of measurement loss, and limitation of communication channels per PMU. The proposed design is thus free from the fundamental shortcomings in the existing PMU placement designs. The problems are posed as large scale binary nonlinear optimization problems involving thousands of binary variables, for which this paper develops efficient algorithms for computational solutions. Their performance is analyzed in detail through numerical examples on large scale IEEE power networks. The solution method is also shown to be extendable to AC power flow models, which are formulated by nonlinear equations.
Shi, Z, Pan, Q & Xu, M 2020, 'LSTM-Cubic A*-based auxiliary decision support system in air traffic management', Neurocomputing, vol. 391, pp. 167-176.
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Shi, Z, Sun, X, Lei, G, Yang, Z, Guo, Y & Zhu, J 2020, 'Analysis and Optimization of Radial Force of Permanent-Magnet Synchronous Hub Motors', IEEE Transactions on Magnetics, vol. 56, no. 2, pp. 1-4.
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© 1965-2012 IEEE. Permanent-magnet synchronous hub motors (PMSHMs) have been investigated for electric vehicles. However, there are some challenges such as effective cooling and radial force. As PMSHMs are installed on the wheels, the radial force will directly affect the ride comfort of the vehicle. This article presents the analysis and optimization of the radial force for PMSHMs. The radial force densities of the symmetry and asymmetry PMSHMs are analyzed first. It is found that the symmetry PMSHMs have balanced force, while the asymmetry PMSHMs normally have big unbalanced radial force and vibration. To reduce the radial force of the asymmetry PMSHMs, an improved sequential Taguchi optimization method (ISTOM) with mixed orthogonal array is presented for an asymmetry PMSHM in this article. It can be found that the proposed method is efficient and can significantly reduce the radial force of the hub motor.
Shrestha, J, Razavi Bazaz, S, Aboulkheyr Es, H, Yaghobian Azari, D, Thierry, B, Ebrahimi Warkiani, M & Ghadiri, M 2020, 'Lung-on-a-chip: the future of respiratory disease models and pharmacological studies', Critical Reviews in Biotechnology, vol. 40, no. 2, pp. 213-230.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. Recently, organ-on-a-chip models, which are microfluidic devices that mimic the cellular architecture and physiological environment of an organ, have been developed and extensively investigated. The chips can be tailored to accommodate the disease conditions pertaining to many organs; and in the case of this review, the lung. Lung-on-a-chip models result in a more accurate reflection compared to conventional in vitro models. Pharmaceutical drug testing methods traditionally use animal models in order to evaluate pharmacological and toxicological responses to a new agent. However, these responses do not directly reflect human physiological responses. In this review, current and future applications of the lung-on-a-chip in the respiratory system will be discussed. Furthermore, the limitations of current conventional in vitro models used for respiratory disease modeling and drug development will be addressed. Highlights of additional translational aspects of the lung-on-a-chip will be discussed in order to demonstrate the importance of this subject for medical research.
Shu, Y, Li, Q, Liu, S & Xu, G 2020, 'Learning with privileged information for photo aesthetic assessment', Neurocomputing, vol. 404, pp. 304-316.
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Shukla, N, Merigó, JM, Lammers, T & Miranda, L 2020, 'Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017', Computer Methods and Programs in Biomedicine, vol. 183, pp. 105075-105075.
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© 2019 Background and Objective: Computer Methods and Programs in Biomedicine (CMPB) is a leading international journal that presents developments about computing methods and their application in biomedical research. The journal published its first issue in 1970. In 2020, the journal celebrates the 50th anniversary. Motivated by this event, this article presents a bibliometric analysis of the publications of the journal during this period (1970–2017). Methods: The objective is to identify the leading trends occurring in the journal by analysing the most cited papers, keywords, authors, institutions and countries. For doing so, the study uses the Web of Science Core Collection database. Additionally, the work presents a graphical mapping of the bibliographic information by using the visualization of similarities (VOS) viewer software. This is done to analyze bibliographic coupling, co-citation and co-occurrence of keywords. Results: CMPB is identified as a leading and core journal for biomedical researchers. The journal is strongly connected to IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging. Paper from Wang, Jacques, Zheng (published in 1995) is its most cited document. The top author in this journal is James Geoffrey Chase and the top contributing institution is Uppsala U (Sweden). Most of the papers in CMPB are from the USA followed by the UK and Italy. China and Taiwan are the only Asian countries to appear in the top 10 publishing in CMPB. A keyword co-occurrences analysis revealed strong co-occurrences for classification, picture archiving and communication system (PACS), heart rate variability, survival analysis and simulation. Keywords analysis for the last decade revealed that machine learning for a variety of healthcare problems (including image processing and analysis) dominated other research fields in CMPB. Conclusions: It can be concluded that CMPB is a world-renowned publication outlet for biomedical re...
Shukla, N, Pradhan, B, Dikshit, A, Chakraborty, S & Alamri, AM 2020, 'A Review of Models Used for Investigating Barriers to Healthcare Access in Australia', International Journal of Environmental Research and Public Health, vol. 17, no. 11, pp. 4087-4087.
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Understanding barriers to healthcare access is a multifaceted challenge, which is often highly diverse depending on location and the prevalent surroundings. The barriers can range from transport accessibility to socio-economic conditions, ethnicity and various patient characteristics. Australia has one of the best healthcare systems in the world; however, there are several concerns surrounding its accessibility, primarily due to the vast geographical area it encompasses. This review study is an attempt to understand the various modeling approaches used by researchers to analyze diverse barriers related to specific disease types and the various areal distributions in the country. In terms of barriers, the most affected people are those living in rural and remote parts, and the situation is even worse for indigenous people. These models have mostly focused on the use of statistical models and spatial modeling. The review reveals that most of the focus has been on cancer-related studies and understanding accessibility among the rural and urban population. Future work should focus on further categorizing the population based on indigeneity, migration status and the use of advanced computational models. This article should not be considered an exhaustive review of every aspect as each section deserves a separate review of its own. However, it highlights all the key points, covered under several facets which can be used by researchers and policymakers to understand the current limitations and the steps that need to be taken to improve health accessibility.
Si, Y, Li, F, Duan, K, Tao, Q, Li, C, Cao, Z, Zhang, Y, Biswal, B, Li, P, Yao, D & Xu, P 2020, 'Predicting individual decision-making responses based on single-trial EEG', NeuroImage, vol. 206, pp. 116333-116333.
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Decision-making plays an essential role in the interpersonal interactions and cognitive processing of individuals. There has been increasing interest in being able to predict an individual's decision-making response (i.e., acceptance or rejection). We proposed an electroencephalogram (EEG)-based computational intelligence framework to predict individual responses. Specifically, the discriminative spatial network pattern (DSNP), a supervised learning approach, was applied to single-trial EEG data to extract the DSNP feature from the single-trial brain network. A linear discriminate analysis (LDA) trained on the DSNP features was then used to predict the individual response trial-by-trial. To verify the performance of the proposed DSNP, we recruited two independent subject groups, and recorded the EEGs using two types of EEG systems. The performances of the trial-by-trial predictors achieved an accuracy of 0.88 ± 0.09 for the first dataset, and 0.90 ± 0.10 for the second dataset. These trial-by-trial prediction performances suggested that individual responses could be predicted trial-by-trial by using the specific pattern of single-trial EEG networks, and our proposed method has the potential to establish the biologically inspired artificial intelligence decision system.
Sikandar, A, Agrawal, R, Tyagi, MK, Rao, ALN, Prasad, M & Binsawad, M 2020, 'Toward green computing in wireless sensor networks: prediction-oriented distributed clustering for non-uniform node distribution', EURASIP Journal on Wireless Communications and Networking, vol. 2020, no. 1.
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AbstractRecently, researchers and practitioners in wireless sensor networks (WSNs) are focusing on energy-oriented communication and computing considering next-generation smaller and tiny wireless devices. The tiny sensor-enabled devices will be used for the purpose of sensing, computing, and wireless communication. The hundreds/thousands of WSNs sensors are used to monitor specific activities and report events via wireless communication. The tiny sensor-enabled devices are powered by smaller batteries to work independently in distributed environments resulting in limited maximum lifetime of the network constituted by these devices. Considering the non-uniform distribution of sensor-enabled devices in the next-generation mobility centric WSNs environments, energy consumption is imbalanced among the different sensors in the overall network environments. Toward this end, in this paper, a cluster-oriented routing protocol termed as prediction-oriented distributed clustering (PODC) mechanism is proposed for WSNs focusing on non-uniform sensor distribution in the network. A network model is presented, while categorizing PODC mechanism in two activities including setting cluster of nodes and the activity in the steady state. Further cluster set up activity is described while categorizing in four subcategories. The proposed protocol is compared with individual sensor energy awareness and distributed networking mode of clustering (EADC) and scheduled sensor activity-based individual sensor energy awareness and distributed networking mode of clustering (SA-ADC). The metrics including the overall lifetime of the network and nodes individual energy consumption in realistic next-generation WSNs environments are considered in the experimental evaluation. The results attest the reduced energy consumption centric benefits of the proposed framework PODC as compared to the literature. Therefore, the framework will be more applicable for ...
Sili, I, Azarkhov, A, Bukhlal, N & Petrov, V 2020, 'HOME STATIONARY VERTICAL WIND GENERATOR PROTOTYPE', Scientific bulletin of the Tavria State Agrotechnological University, vol. 10, pp. 24-24.
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Silitonga, AS, Shamsuddin, AH, Mahlia, TMI, Milano, J, Kusumo, F, Siswantoro, J, Dharma, S, Sebayang, AH, Masjuki, HH & Ong, HC 2020, 'Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization', Renewable Energy, vol. 146, pp. 1278-1291.
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© 2019 Elsevier Ltd In this study, microwave irradiation-assisted transesterification was used to produce Ceiba pentandra biodiesel, which accelerates the rate of reaction and temperature within a shorter period. The improvement of biodiesel production requires a reliable model that accurately reflects the effects of input variables on output variables. In this study, an extreme learning machine integrated with cuckoo search algorithm was developed to predict and optimize the process parameters. This model will be useful for virtual experimentations in order to enhance biodiesel research and development. The optimum parameters of the microwave irradiation-assisted transesterification process conditions were obtained as follows: (1) methanol/oil ratio: 60%, (2) potassium hydroxide catalyst concentration: 0.84%(w/w), (3) stirring speed: 800 rpm, and (4) reaction time: 388 s. The corresponding Ceiba pentandra biodiesel yield was 96.19%. Three independent experiments were conducted using the optimum process parameters and the average biodiesel yield was found to be 95.42%. In conclusion, microwave irradiation-assisted transesterification is an effective method for biodiesel production because it is more energy-efficient, which will reduce the overall cost of biodiesel production.
Singh, AK, Chen, H-T, Gramann, K & Lin, C-T 2020, 'Intraindividual Completion Time Modulates the Prediction Error Negativity in a Virtual 3-D Object Selection Task', IEEE Transactions on Cognitive and Developmental Systems, vol. 12, no. 2, pp. 354-360.
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IEEE A prediction error negativity (PEN) can be observed in the human electroencephalogram when there is a mismatch between the predicted and the perceived changes in the environment. Our previous study using a virtual object selection task demonstrated an impact of the level of avatar realism on the PEN, reflecting a mismatch between visual and proprioceptive feedback about the object selection. To investigate the role of temporal integration of different sensory information on the PEN, this study investigated the impact of task completion times on the PEN amplitude, using the same virtual object selection task. Trials from each participant were divided into slow trials and fast trials based on the task completion time, and their associated PEN amplitudes were separately aggregated and analyzed. The result shows that PEN amplitudes are significantly more pronounced in slow trials than in fast trials. This finding suggests that task completion times modulate the PEN amplitude -a long task completion time allowed for a better integration of information from both visual and proprioceptive systems as the basis to detect a mismatch between the expected hand trajectory during a reaching motion and the perceived visual feedback in the virtual environment.
Singh, AK, Lv, Z, Lu, H & Chang, X 2020, 'Guest editorial: Recent trends in multimedia data-hiding: a reliable mean for secure communications', Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 5, pp. 1795-1797.
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Singh, AK, Wang, Y-K, King, J-T & Lin, C-T 2020, 'Extended Interaction With a BCI Video Game Changes Resting-State Brain Activity', IEEE Transactions on Cognitive and Developmental Systems, vol. 12, no. 4, pp. 809-823.
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Singh, K, Afzal, MU, Kovaleva, M & Esselle, KP 2020, 'Controlling the Most Significant Grating Lobes in Two-Dimensional Beam-Steering Systems With Phase-Gradient Metasurfaces', IEEE Transactions on Antennas and Propagation, vol. 68, no. 3, pp. 1389-1401.
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© 2019 IEEE. High-directivity antenna systems that provide 2-D beam steering by rotating a pair of phase-gradient metasurfaces (PGMs) in the near field of a fixed-beam antenna, hereafter referred to as near-field meta-steering systems, are efficient, planar, simple, short, require less power to operate, and do not require antenna tilting. However, when steering the beam, such systems generate undesirable dominant grating lobes, which substantially limit their applications. Optimizing a pair of these metasurfaces to minimize the grating lobes using standard methods is nearly impossible due to their large electrical size and thousands of small features leading to high computational costs. This article addresses this challenge as follows. First, it presents a method to efficiently reduce the strength of 'offending' grating lobes by optimizing a supercell using Floquet analysis and multi-objective particle swarm optimization. Second, it investigates the effects of the transmission phase gradient of PGMs on radiation-pattern quality. It is shown that the number of dominant unwanted lobes in a 2-D beam-steering antenna system and their levels can be reduced substantially by increasing the transmission phase gradient of the two PGMs. This knowledge is then extended to 2-D beam-steering systems, where we demonstrate how to substantially reduce all grating lobes to a level below-20 dB for all beam directions, without applying any amplitude tapering to the aperture field. When steering the beam of two meta-steering systems with peak directivities of 30.5 and 31.4 dBi, within a conical volume with an apex angle of 96°, the variation in directivity is 2.4 and 3.2 dB, respectively. We also demonstrate that beam-steering systems with steeper gradient PGMs can steer the beam in a wider range of directions, require less mechanical rotation of metasurfaces to obtain a given scan range, and their beam steering is faster. The gap between the two metasurfaces in a near-fie...
Singh, M, Indraratna, B, Rujikiatkamjorn, C & Kelly, R 2020, 'Cyclic response of railway subgrade prone to mud pumping', Australian Geomechanics Journal, vol. 55, no. 1, pp. 43-54.
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Given the fast pace of growth in today's world, the need for a cost-effective and sustainable mode of transportation is indispensable. Railways provide a mode of mass transportation which facilitates travel between two places. Rails are often laid on subgrade soils with difficult conditions such as low bearing capacity, and high groundwater tables, etc., so when trains pass over these challenging ground conditions, the subgrade softens into a slurry and starts pumping the fines into the upper ballast layers. In Australia, this phenomenon is commonly known as mud pumping or mud holes. This paper investigates the cyclic response of subgrade prone to mud pumping. It is observed that the cyclic stress ratio (CSR) has a threshold value beyond which the cyclic axial strains and mean excess pore pressures rapidly accumulate. An empirical model is proposed to capture the generation of mean excess pore pressure in relation to the applied CSR. Further, numerical simulations have been carried out using PLAXIS2D to model vertical drain inclusions in the railway subsoil. The results indicate that vertical drains not only reduce the accumulation of excess pore pressure but also assist in their dissipation under cyclic loading, thereby providing a viable alternative to mitigate the effects of mud pumping.
Singh, P, Raghav, V, Padhmashali, V, Paul, G, Islam, MS & Saha, SC 2020, 'Airflow and Particle Transport Prediction through Stenosis Airways', International Journal of Environmental Research and Public Health, vol. 17, no. 3, pp. 1119-1119.
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Airflow and particle transport in the human lung system is influenced by biological and other factors such as breathing pattern, particle properties, and deposition mechanisms. Most of the studies to date have analyzed airflow characterization and aerosol transport in idealized and realistic models. Precise airflow characterization for airway stenosis in a digital reference model is lacking in the literature. This study presents a numerical simulation of airflow and particle transport through a stenosis section of the airway. A realistic CT-scan-based mouth–throat and upper airway model was used for the numerical calculations. Three different models of a healthy lung and of airway stenosis of the left and right lung were used for the calculations. The ANSYS FLUENT solver, based on the finite volume discretization technique, was used as a numerical tool. Proper grid refinement and validation were performed. The numerical results show a complex-velocity flow field for airway stenosis, where airflow velocity magnitude at the stenosis section was found to be higher than that in healthy airways. Pressure drops at the mouth–throat and in the upper airways show a nonlinear trend. Comprehensive pressure analysis of stenosis airways would increase our knowledge of the safe mechanical ventilation of the lung. The turbulence intensities at the stenosis sections of the right and left lung were found to be different. Deposition efficiency (DE) increased with flow rate and particle size. The findings of the present study increase our understanding of airflow patterns in airway stenosis under various disease conditions. More comprehensive stenosis analysis is required to further improve knowledge of the field.
Singh, R, Altaee, A & Gautam, S 2020, 'Nanomaterials in the advancement of hydrogen energy storage', Heliyon, vol. 6, no. 7, pp. e04487-e04487.
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The hydrogen economy is the key solution to secure a long-term energy future. Hydrogen production, storage, transportation, and its usage completes the unit of an economic system. These areas have been the topics of discussion for the past few decades. However, its storage methods have conflicted for on-board hydrogen applications. In this review, the promising systems based on solid-state hydrogen storage are discussed. It works generally on the principles of chemisorption and physisorption. The usage of hydrogen packing material in the system enhances volumetric and gravimetric densities of the system and helps in improving ambient conditions and system kinetics. Numerous aspects like pore size, surface area ligand functionalization and pore volume of the materials are intensively discussed. This review also examines the newly developed research based on MOF (Metal-Organic Frameworks). These hybrid clusters are employed for nano-confinement of hydrogen at elevated temperatures. A combination of the various methodologies may give another course to a wide scope in the area of energy storage materials later in the future.
Singh, RP, Nimbalkar, S, Singh, S & Choudhury, D 2020, 'Field assessment of railway ballast degradation and mitigation using geotextile', Geotextiles and Geomembranes, vol. 48, no. 3, pp. 275-283.
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© 2019 Elsevier Ltd Rail tracks continue to deform due to degradation of ballast under the application of heavy train traffic. The resulting track deformations often lead to drainage impairment as well as loss of resiliency. For track replenishment, deep screening of ballast is usually adopted by Indian Railway (IR) either after 10 years or passage of 500 MGT traffic, whichever is earlier. To study the effectiveness of geotextile on track stability and assess possible reductions in maintenance costs, a layer of woven geotextile was installed at the ballast-subgrade interface in Bhusawal-Akola central railway section of IR which is the present study area. The results show that the amount of degradation and fouling are different in UP and DN tracks due to inherent variation in traffic characteristics. This study also shows that the placement of geotextile in the track has led to prolonged maintenance cycle with favorable implications on cost and track shutdown periods. The findings of the present case study results will be useful for IR to reduce the ballast procurement and reuse of discarded material during deep screening in future.
Sinha, A, Chand, S, Wijayaratna, KP, Virdi, N & Dixit, V 2020, 'Comprehensive safety assessment in mixed fleets with connected and automated vehicles: A crash severity and rate evaluation of conventional vehicles', Accident Analysis & Prevention, vol. 142, pp. 105567-105567.
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© 2020 Elsevier Ltd Connected and Automated Vehicle (CAV) technology, although in the development stage, is quickly expanding throughout the vehicle market. However, full market penetration will most likely require considerable planning as key stakeholders, manufacturers, consumers and governing agencies work together to determine optimal deployment strategies. Specifically, road safety is a critical challenge to the widespread deployment and adoption of this disruptive technology. During the transition period fleets will be composed of a combination of CAVs and conventional vehicles, and therefore it is imperative to investigate the repercussions of CAVs on traffic safety at different penetration rates. Since crash severity and frequency in conjunction reflect traffic safety, this study attempts to investigate the effect of CAVs on both crash severity and frequency through a microsimulation modelling exercise. VISSM microsimulation platform is used to simulate a case study of the M1 Geelong Ring Road network (Princes Freeway) in Victoria, Australia. Network performance is evaluated using performance metrics (Total System Travel Time, Delay) and kinematic variables (Speed, acceleration, jerk rate). Surrogate safety measures (time to collision, post encroachment time, etc.) are examined to inspect the safety in the network. The results indicate that the introduction of CAVs does not achieve the expected decrease in crash severity and rates involving manual vehicles, despite the improvement in network performance, given the demand and the set of parameters used in our operational CAV algorithm are intact. Additionally, the study identifies that the safety benefits of CAVs are not proportional to CAV penetration, and full-scale benefits of CAVs can only be achieved at 100 % CAV penetration. Further, considering network efficiency as a performance metric and total crash rate involving conventional vehicles as a safety metric, a Pareto frontier is extracted, for varyi...
Sinha, A, Chand, S, Wijayaratna, KP, Virdi, N & Dixit, V 2020, 'Crash Severity and Rate Evaluation of Conventional Vehicles in Mixed Fleets with Connected and Automated Vehicles', Procedia Computer Science, vol. 170, pp. 688-695.
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© 2020 The Authors. Published by Elsevier B.V.All rights reserved. Connected and Automated Vehicle (CAV) technology, although in the development stage, is quickly expanding its market but a full market penetration might not be rapid. The safety concern is the paramount challenge to widespread adoption of this disruptive technology. During the transition period, fleets will be composed of a combination of CAVs and conventional vehicles, therefore it is germane to investigate the repercussions of CAVs on traffic safety at different penetration. Since crash severity and frequency in conjunction reflect the traffic safety, this study attempts to investigate the effect of CAVs on both crash severity and frequency. PTV VISSM microsimulation platform is used to simulate M1 Geelong Ring Road network (Princes Freeway) in Victoria, Australia, which is the testbed for this study. Network performance is evaluated using performance metrics (Total System Travel Time, Delay and instantaneous speed profiles). Surrogate safety measures (time to collision, post encroachment time, etc.) are examined to inspect the safety in the network. The results showed that CAVs would not inevitably decrease the crash severity and crash rate involving manual vehicles, despite the improvement in network performance, given the demand and the set of parameters used in our operational CAV algorithm are intact. Additionally, the study identifies that the safety benefits of CAVs are not proportional to CAV penetration, a full-scale benefits CAVs can only be achieved at 100% CAV penetration. The results presented in this study provide an insight into the repercussion of CAVs on comprehensive traffic safety to the insurance companies and other industry participants, enabling safety-related services and more enterprising business models.
Siwakoti, YP, Palanisamy, A, Mahajan, A, Liese, S, Long, T & Blaabjerg, F 2020, 'Analysis and Design of a Novel Six-Switch Five-Level Active Boost Neutral Point Clamped Inverter', IEEE Transactions on Industrial Electronics, vol. 67, no. 12, pp. 10485-10496.
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This article presents an analysis and design of a new boost type six-switch five-level (5L) active neutral point clamped (ANPC) inverter based on switched/flying capacitor technique with self-voltage balancing. Compared to major conventional 5L inverter topologies, such as neutral point clamped, flying capacitor, cascaded H-bridge, and ANPC topologies, the new topology reduces the dc-link voltage requirement by 50%. Whilst reducing the dc-link voltage requirement, the number and the size of the active and passive components are also reduced without compromising the reactive power capability. The analysis shows that the proposed topology is suitable for wide range of power conversion applications (for example, rolling mills, fans, pumps, marine appliances, mining, tractions, and most prominently grid-connected renewable energy systems). Experimental results from a 1.2 kVA prototype justifies the concept of the proposed inverter with a conversion efficiency of around 97.5% ± 1% for a wide load range.
Siwakoti, YP, Palanisamy, A, Mahajan, A, Liese, S, Long, T & Blaabjerg, F 2020, 'Analysis and Design of a Novel Six-Switch Five-Level Active Boost Neutral Point Clamped Inverter.', IEEE Trans. Ind. Electron., vol. 67, pp. 10485-10496.
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Skarding, J, Gabrys, B & Musial, K 2020, 'Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey', in IEEE Access, vol. 9, pp. 79143-79168.
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Dynamic networks are used in a wide range of fields, including social networkanalysis, recommender systems, and epidemiology. Representing complex networksas structures changing over time allow network models to leverage not onlystructural but also temporal patterns. However, as dynamic network literaturestems from diverse fields and makes use of inconsistent terminology, it ischallenging to navigate. Meanwhile, graph neural networks (GNNs) have gained alot of attention in recent years for their ability to perform well on a rangeof network science tasks, such as link prediction and node classification.Despite the popularity of graph neural networks and the proven benefits ofdynamic network models, there has been little focus on graph neural networksfor dynamic networks. To address the challenges resulting from the fact thatthis research crosses diverse fields as well as to survey dynamic graph neuralnetworks, this work is split into two main parts. First, to address theambiguity of the dynamic network terminology we establish a foundation ofdynamic networks with consistent, detailed terminology and notation. Second, wepresent a comprehensive survey of dynamic graph neural network models using theproposed terminology
Sohaib, O, Hussain, W, Asif, M, Ahmad, M & Mazzara, M 2020, 'A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption.', IEEE Access, vol. 8, no. 1, pp. 13138-13150.
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© 2013 IEEE. The majority of previous research on new technology acceptance has been conducted with single-step Structural Equation Modeling (SEM) based methods. The primary purpose of the study is to enhance the new technology acceptance based research with the Artificial Neural Network (ANN) method to enable more precise and in-depth research results as compared to the single-step SEM method. This study measures the relation between technology readiness dimension (optimism, innovativeness, discomfort, insecurity) and the technology acceptance (perceived ease of use and perceived usefulness) - and the intention to use cryptocurrency, such as bitcoin. The contribution of this study include the use of a multi-analytical approach by combining Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. First, PLS-SEM was applied to assess which factor has significant influence toward intention to use cryptocurrency. Second, an ANN was employed to rank the relative influence of the significant predictor variables attained from the PLS-SEM. The findings of the two-step PLS-SEM and ANN approach confirm that the use of ANN further verifies the results obtained by the PLS-SEM analysis. Also, ANN is capable of modelling complex linear and non-linear relationships with high predictive accuracy compared to SEM methods. Also, an Importance-Performance Map Analysis (IPMA) of the PLS-SEM results provides a more specific understanding of each factor's importance-performance.
Soltanieh, N, Norouzi, Y, Yang, Y & Karmakar, NC 2020, 'A Review of Radio Frequency Fingerprinting Techniques', IEEE Journal of Radio Frequency Identification, vol. 4, no. 3, pp. 222-233.
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Radio frequency (RF) fingerprinting techniques have been used as an extra security layer for wireless devices. Unique fingerprints are used to identify wireless devices in order to avoid spoofing or impersonating attacks. These unique features can be extracted from imperfections of analog components during the manufacturing. This paper presents a general review of recent progress on RF fingerprinting techniques. Several studies are investigated for RF fingerprinting using different parts of a signal. The majority of these studies have been focused on the transient part of the signal. For this purpose, the transient signal must be extracted precisely. A number of common techniques of transient extraction are theoretically analyzed in this review. Then, some other approaches using the modulated part of the signal are also discussed. For all these approaches, the applied methodologies, the classification algorithms and a taxonomy of features are described. A comprehensive overview of the methods in RF fingerprinting is presented to demonstrate the state-of-the-art works.
Song, B, Wang, X, Ni, W, Song, Y, Liu, RP, Jiang, G-P & Guo, YJ 2020, 'Reliability Analysis of Large-Scale Adaptive Weighted Networks', IEEE Transactions on Information Forensics and Security, vol. 15, pp. 651-665.
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© 2005-2012 IEEE. Disconnecting impaired or suspicious nodes and rewiring to those reliable, adaptive networks have the potential to inhibit cascading failures, such as DDoS attack and computer virus. The weights of disconnected links, indicating the workload of the links, can be transferred or redistributed to newly connected links to maintain network operations. Distinctively different from existing studies focused on adaptive unweighted networks, this paper presents a new mean-field model to analyze the reliability of adaptive weighted networks against cascading failures. By taking mean-field approximation, we develop a new continuous-time Markov model to capture the propagations of cascading failures and the rewiring actions that individual nodes can take to bypass failed neighbors. We analyze the stability of the model to identify the critical conditions, under which the cascading failures can be eventually inhibited or would proliferate. The conditions are evaluated under different link weight distributions and rewiring strategies. Our model reveals that preferentially disconnecting suspicious peers with high weights can effectively inhibit virus and failures.
Song, Y, Lu, J, Lu, H & Zhang, G 2020, 'Fuzzy Clustering-Based Adaptive Regression for Drifting Data Streams', IEEE Transactions on Fuzzy Systems, vol. 28, no. 3, pp. 544-557.
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© 1993-2012 IEEE. Current models and algorithms have been increasingly required to learn in a nonstationary environment because the phenomenon of concept drift (or pattern shift) may occur, that is, the assumption that data are identically distributed may be invalid in data streams. Once the data pattern changes, a well-trained model built on the previous, now obsolete data cannot provide an accurate prediction for future data. To obtain reliable prediction, it is important to understand the existing patterns in the data stream and to know which pattern the current examples belong to during the modeling process. However, it is ambiguous to classify an example to a certain pattern in many real-world cases. In this paper, we propose a novel adaptive regression approach, called FUZZ-CARE, to dynamically recognize, train, and store patterns, and assign the membership degree of the upcoming examples belonging to these patterns. Membership degrees are presented by the membership matrix obtained from a kernel fuzzy c-means clustering, which is synchronously trained and adapted with regression parameters. Rather than designing a complicated procedure to continuously chase the newest pattern, which is a common approach in the literature, FUZZ-CARE abstracts useful past information to help predict newly arrived examples. It thus effectively avoids the risk of insufficient training due to the lack of new data and improves prediction accuracy. Experiments on six synthetic datasets and 21 real-world datasets validate the high accuracy and robustness of our approach.
Song, Z, Zhang, X, Sun, F, Ngo, HH, Guo, W, Wen, H, Li, C & Zhang, Z 2020, 'Specific microbial diversity and functional gene (AOB amoA) analysis of a sponge-based aerobic nitrifying moving bed biofilm reactor exposed to typical pharmaceuticals', Science of The Total Environment, vol. 742, pp. 140660-140660.
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© 2018 Elsevier B.V. Four bench-scale sponge-based aerobic nitrifying moving bed biofilm reactors (MBBRs) were used to treat municipal wastewater containing typical pharmaceuticals (1 mg/L, 2 mg/L and 5 mg/L). This preliminary research aims to investigate the effects of sulfadiazine (SDZ), ibuprofen (IBU) and carbamazepine (CBZ) on nitrification performance and explore specific microbial diversity and functional gene (Ammonia-oxidizing bacteria (AOB), amoA) of MBBRs. After 90 days of operation, the MBBR without pharmaceuticals could remove up to 97.4 ± 1.5% of NH4+-N while the removals of NH4+-N by the MBBRs with SDZ, IBU and CBZ were all suppressed to varying degrees. Based on the Shannon and Chao 1 index, the specific microbial diversity and richness in biofilm samples increased at a range of 1 mg/L to 2 mg/L pharmaceuticals (SDZ, IBU or CBZ) and started decreasing after the pharmaceutical concentration was higher than 2 mg/L. The determination of functional gene (AOB amoA) showed that Proteobacteria was the most dominant bacteria within all biofilms with the relative abundance ranging from 24.81% to 55.32%. Furthermore, Nitrosomonas was the most numerous genus in AOB, followed by Campylobacter and Thauera, whose relative abundance shifted under the pressure of different pharmaceuticals.
Sood, K, Karmakar, KK, Yu, S, Varadharajan, V, Pokhrel, SR & Xiang, Y 2020, 'Alleviating Heterogeneity in SDN-IoT Networks to Maintain QoS and Enhance Security', IEEE Internet of Things Journal, vol. 7, no. 7, pp. 5964-5975.
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© 2014 IEEE. Software-defined networks (SDNs) offer unique and attractive solutions to solve challenging management issues in Internet of Things (IoT)-based large-scale multitechnological networks. SDN-IoT network collaboration is innovative and attractive but expected to be extremely heterogeneous in future generation IoT systems. For example, multitechnology network, network externality, and nodes heterogeneity in SDN-IoT may seriously affect the flow or application-specific quality-of-service (QoS) requirements. Furthermore, it highly influences security adoption in a network of interconnected IoT nodes. We observe that both QoS and security are interdependent and nonnegligible factors, thus we emphasize that in order to alleviate heterogeneity it is inevitable to study both these factors hand to hand (or vice versa). With this aim, first, we discuss significant and reasonable cases to encourage researchers to study QoS and security integrally in order to alleviate heterogeneity at SDN-IoT control plane. Second, we propose a framework which successfully transforms the m heterogeneous controllers to n homogeneous controller groups. The key metric of our observation and analysis is the SDN controller's response time. Following this, to validate our approach, we use the mathematical model and a proof of concept (PoC) in a virtual SDN ecosystem is demonstrated. From performance evaluation, we observe that the proposed framework significantly alleviates heterogeneity which helps to maintain QoS and enhance security. This fundamental analysis will enable network security individuals to deal heterogeneity, QoS, and security, of SDN-IoT, in more successful and promising ways.
Sood, S & Pattinson, H 2020, 'Globotics Driven Digital Transformation: A Bright Future for Internships, Digital Marketing and E-Commerce Education', INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION, vol. 6, no. 3, pp. 20-28.
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This paper introduces a new approach, Globotics (Baldwin 2019), with the main focus directed towards the lack of skills in digital marketing and e-commerce. Globotics is assumed to provide insights for the adoption of a pedagogy of experiential learning. Furthermore, the adoption of globotics (ibid) may potentially lead towards a brighter future for tertiary marketing education, as well as fulfil the diverse needs of Asia and Oceania regarding the acquisition of digital marketing talent. The author conducted in-depth interviews with academics and practitioners in order to gain insight into the overall context of marketing practice. Upon reviewing the data, informats have, recognizing its value, highlighted the differences between digital and its counter point – traditional marketing. We assumed that tracking the online search trends can help solidify and feedback some information where past search demands for digital marketing, social media marketing e-commerce marketing and social commerce. An online service using “globotics” (Baldwin 2019) provides a promising approach towards solving the problems of both digital marketing curriculum and scarce talent linking marketing educators and students with practitioners. Importantly, with globotics marketing students as interns have an opportunity to take on tasks well beyond previous undergrad and postgrad entry- level roles of the last century.
Soon, JL, Lu, DD-C, Peng, JC-H & Xiao, W 2020, 'Reconfigurable Nonisolated DC–DC Converter With Fault-Tolerant Capability', IEEE Transactions on Power Electronics, vol. 35, no. 9, pp. 8934-8943.
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© 1986-2012 IEEE. Malfunction of power semiconductor switches is the dominant cause of failure in power electronic converters. This article proposes a novel dual-switch dc-dc topology for high-reliability applications. The proposed converter is fault tolerant and supports operation under both step-down and step-up modes. The proposed topology can be reconfigured automatically under various switch-fault conditions in order to maintain normal operation. This is enabled by an affine-parametrization-based control design, which minimizes the transient impact of the faults. The reliability performance of the proposed converter is evaluated theoretically using a Markov model, demonstrating its superiority over conventional topologies. Finally, a laboratory prototype is developed and tested to verify the proposed design and control performance under switch faults.
Sornalingam, K, McDonagh, A, Canning, J, Cook, K, Johir, MAH, Zhou, JL & Ahmed, MB 2020, 'Photocatalysis of 17α-ethynylestradiol and estriol in water using engineered immersible optical fibres and light emitting diodes', Journal of Water Process Engineering, vol. 33, pp. 101075-101075.
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© 2019 Elsevier Ltd This research aims to promote photocatalysis of endocrine disrupting chemicals (EDCs) in water. Two reactor setups with (i) modified air-clad optical fibres and (ii) waterproof LED strips were utilised to transmit light to photocatalysts P25 TiO2 and gold-modified TiO2 (Au-TiO2). The performances to photodegrade 17α-ethynylestradiol (EE2) and estriol (E3) under Cool White and UVA high efficacy LEDs were examined. Au-TiO2 showed superior photocatalytic activity for EE2 removal over P25 TiO2. The pseudo first-order rate constants for EE2 photocatalysis under UVA were 0.55 h−1 and 0.89 h−1 for TiO2 and Au-TiO2, respectively. E3 was effectively degraded by Au-TiO2 in the immersible LED strip reactor (0.13 h−1).
Soudagar, MEM, Kalam, MA, Sajid, MU, Afzal, A, Banapurmath, NR, Akram, N, Mane, SD & Saleel C, A 2020, 'Thermal analyses of minichannels and use of mathematical and numerical models', Numerical Heat Transfer, Part A: Applications, vol. 77, no. 5, pp. 497-537.
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Soudagar, MEM, Nik-Ghazali, N-N, Kalam, MA, Badruddin, IA, Banapurmath, NR, Bin Ali, MA, Kamangar, S, Cho, HM & Akram, N 2020, 'An investigation on the influence of aluminium oxide nano-additive and honge oil methyl ester on engine performance, combustion and emission characteristics', Renewable Energy, vol. 146, pp. 2291-2307.
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The potential use of aluminium oxide nanoparticles as nanofuel additives was investigated on honge oil methyl ester and diesel fuel blend. The nanofuel blends were prepared by dispersing aluminium oxide in varying quantities in a HOME(B20) (20% biodiesel+80% diesel). Sodium dodecyl sulfate (SDS), an anionic surfactant, was used for a stable dispersion of aluminium oxide nanoparticles in the fuel blends. HOME(B20) fuel with concentration levels of 20, 40, and 60 ppm of aluminium oxide nanoparticles (HOME20, HOME2040 and HOME2060) with varying ratios of SDS surfactants were prepared using ultrasonication technique. The investigated properties of diesel, honge oil biodiesel and nanofuel blends were in agreement with the ASTM D6751-15 standards. The dispersion and homogeneity were established and characterized by using the Ultraviolet–Visible (UV–Vis) spectrometry. The UV–Vis spectrometry results illustrated an increase in absorbance level with a relative increase in the concentration of surfactant. The highest absolute value of UV-absorbency was observed for a mass fraction of 1:4 (Al2O3 NPs to SDS ratio). The investigation was performed at a constant speed of 1500 rpm, and BP of 0 kW, 1.04 kW, 3.12 kW, 4.16 kW and 5.20 kW. The fuel HOME2040 demonstrated an overall improvement in the engine parameters, the brake thermal efficiency (BTE) enhanced by 10.57%, while there was a decline in brake specific fuel consumption (BSFC) by 11.65% and the engine exhaust emission: HC, CO, and smoke reduced by 26.72%, 48.43%, and 22.84%, while the NOx increased by 11.27%. Similarly, the addition of aluminium oxide nanoparticles in HOME(B20) fuel blend resulted in decent reduction in the combustion duration (CD), ignition delay period (ID), improvement in the peak pressure, and a marginal increase in heat release rate (HRR) and cylinder pressure at maximum loading conditions. Based on the experimental results, it is concluded that the aluminium oxide nanoparticles in HOME(B...
Srinivas, S, Gill, AQ & Roach, T 2020, 'Analytics-Enabled Adaptive Business Architecture Modeling.', Complex Syst. Informatics Model. Q., vol. 23, no. 23, pp. 23-43.
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Srivastava, A, Yetemen, O, Kumari, N & Saco, PM 2020, 'Influence of orographic precipitation on the co-evolution of landforms and vegetation'.
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<p>Topography plays an important role in controlling the amount and the spatial distribution of precipitation due to orographic lift mechanisms. Thus, it affects the existing climate and vegetation distribution. Recent landscape modelling efforts show how the orographic effects on precipitation result in the development of asymmetric topography. However, these modelling efforts do not include vegetation dynamics that inhibits sediment transport. Here, we use the CHILD landscape evolution model (LEM) coupled with a vegetation dynamics component that explicitly tracks above- and below-ground biomass. We ran the model under three scenarios. A spatially&#8209;uniform precipitation scenario, a scenario with increasing rainfall as a function of elevation, and another one that includes rain shadow effects in which leeward hillslopes receive less rainfall than windward ones. Preliminary results of the model show that competition between increased shear stress due to increased runoff and vegetation protection affects the shape of the catchment. Hillslope asymmetry between polar- and equator-facing hillslopes is enhanced (diminished) when rainfall coincides with a windward (leeward) side of the mountain range. It acts to push the divide (i.e., the boundary between leeward and windward flanks) and leads to basin reorganization through reach capture. Our findings suggest that there exists a strong coupling between climate and landform evolution processes, and that orographic precipitation can imprint its influence on landforms in semi-arid ecosystems. &#160;</p>
Stewart, MG & Mueller, J 2020, 'Terrorism risks, chasing ghosts and infrastructure resilience', Sustainable and Resilient Infrastructure, vol. 5, no. 1-2, pp. 78-89.
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Risk is the integration of threat, vulnerability and consequences, but threat is often based on worse-case thinking about the capability of terrorists to successfully plan and execute large-scale bombings. The paper looks at the nature of terrorists by exploring their capabilities and motivation, technical skills, and target selection. Key among this is the risk of progressive collapse of multi-storey buildings, and the track record of terrorists attacking targets in the West. In the past decade, most deaths from terrorists in the West have arisen from shooting attacks, vehicle impact, and mass transit bombings. In this case, there is little or no need to protect civilian buildings, bridges and other infrastructure from car or truck bombs unless there is a specific threat. Existing infrastructure has also proven to be highly resilient and robust against car and truck bombings. It is easy to overestimate the impacts of terrorist attacks. An improved understanding of the threat allows decision-makers to more effectively deploy resources to counter it, which includes appropriate design and assessment of civilian and military protective structures. A case study describes existing fatality risks from progressive collapse caused by a large vehicle bomb, and then assesses the costs and benefits of design (protective) measures mandated by the United States to mitigate against progressive collapse for new or leased federal government (civilian and defence) buildings.
Stewart, MG & Netherton, MD 2020, 'Statistical variability and fragility assessment of ballistic perforation of steel plates for 7.62 mm AP ammunition', Defence Technology, vol. 16, no. 3, pp. 503-513.
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The paper describes field test results of 7.62 × 51 mm M61 AP (armour piercing) ammunition fired into mild steel targets at an outdoor range. The targets varied from 10 mm to 32 mm in thickness. The tests recorded penetration depth, probability of perforation (i.e., complete penetration), muzzle and impact velocities, bullet mass, and plate yield strength and hardness. The measured penetration depth exhibited a variability of approximately ±12%. The paper then compared ballistic test results with predictive models of steel penetration depth and thickness to prevent perforation. Statistical parameters were derived for muzzle and impact velocity, bullet mass, plate thickness, plate hardness, and model error. A Monte-Carlo probabilistic analysis was then developed to estimate the probability of plate perforation of 7.62 mm M61 AP ammunition for a range of impact velocities, and for mild steels, and High Hardness Armour (HHA) plates. This perforation fragility analysis considered the random variability of impact velocity, bullet mass, plate thickness, plate hardness, and model error. Such a probabilistic analysis allows for reliability-based design, where, for example, the plate thickness with 95% reliability (i.e. only 1 in 20 shots will penetrate the wall) can be estimated knowing the probabilistic distribution of perforation. Hence, it was found that the plate thickness to ensure a low 5% probability of perforation needs to be 11–15% thicker than required to have a 50/50 chance of perforation for mild steel plates. Plates would need to be 20–30% thicker if probability of perforation is reduced to zero.
Stewart, MG, Netherton, MD & Baldacchino, H 2020, 'Observed airblast variability and model error from repeatable explosive field trials', International Journal of Protective Structures, vol. 11, no. 2, pp. 235-257.
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Explosive field trials have been conducted to measure the peak incident pressure, impulse and time of positive phase duration following the detonation of 15 different masses of the Plastic Explosive No #4. A novel aspect of these field trials was the repeatability of tests. Eight pressure gauges collected data during each blast, and at each scaled distance. In all, 4 blasts were conducted for each scaled distance (i.e. up to 32 measurements recorded for each scaled distance) – 60 blasts were fired in total. Consequently, this repeatability of testing allowed the mean and variance of blast pressure–time histories to be quantified, with a view to better characterise the variability of a blast itself and model error variability. This article describes the explosive field trials, and the statistical analysis of blast load variability and model error for peak incident pressure, impulse and time of positive phase duration. It was found that the mean model error is close to unity with a coefficient of variation of up to 0.15 for pressure and 0.21 for impulse. The lognormal probability distribution best fits the model error data. The probabilistic models derived from these tests can be used for a variety of structural engineering applications, such as calculating reliability-based design load or partial safety factors for explosive blast loading, and estimating the probability of damage and casualties for infrastructure subject to explosive blast loading. This is illustrated for a terrorist explosive scenario involving a spherical free-air burst, where the damage modes of interest are breaching and spalling of a concrete slab. It was found that the variability of charge mass, range and model error have a significant effect on reliability-based design.
Su, D, Vidal-Calleja, T & Miro, JV 2020, 'Asynchronous microphone arrays calibration and sound source tracking', Autonomous Robots, vol. 44, no. 2, pp. 183-204.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. In this paper, we proposed an optimisation method to solve the problem of sound source localisation and calibration of an asynchronous microphone array. This method is based on the graph-based formulation of the simultaneous localisation and mapping problem. In this formulation, a moving sound source is considered to be observed from a static microphone array. Traditional approaches for sound source localisation rely on the well-known geometrical information of the array and synchronous readings of the audio signals. Recent work relaxed these two requirements by estimating the temporal offset between pair of microphones based on the assumption that the clock timing of each microphone is exactly the same. This assumption requires the sound cards to be identically manufactured, which in practice is not possible to achieve. Hereby an approach is proposed to jointly estimate the array geometrical information, time offset and clock difference/drift rate of each microphone together with the location of a moving sound source. In addition, an observability analysis of the system is performed to investigate the most suitable configuration for sound source localisation. Simulation and experimental results are presented, which prove the effectiveness of the proposed methodology.
Su, QP, Zhao, ZW, Meng, L, Ding, M, Zhang, W, Li, Y, Liu, M, Li, R, Gao, Y-Q, Xie, XS & Sun, Y 2020, 'Superresolution imaging reveals spatiotemporal propagation of human replication foci mediated by CTCF-organized chromatin structures', Proceedings of the National Academy of Sciences, vol. 117, no. 26, pp. 15036-15046.
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Mammalian DNA replication is initiated at numerous replication origins, which are clustered into thousands of replication domains (RDs) across the genome. However, it remains unclear whether the replication origins within each RD are activated stochastically or preferentially near certain chromatin features. To understand how DNA replication in single human cells is regulated at the sub-RD level, we directly visualized and quantitatively characterized the spatiotemporal organization, morphology, and in situ epigenetic signatures of individual replication foci (RFi) across S-phase at superresolution using stochastic optical reconstruction microscopy. Importantly, we revealed a hierarchical radial pattern of RFi propagation dynamics that reverses directionality from early to late S-phase and is diminished upon caffeine treatment or CTCF knockdown. Together with simulation and bioinformatic analyses, our findings point to a “CTCF-organized REplication Propagation” (CoREP) model, which suggests a nonrandom selection mechanism for replication activation at the sub-RD level during early S-phase, mediated by CTCF-organized chromatin structures. Collectively, these findings offer critical insights into the key involvement of local epigenetic environment in coordinating DNA replication across the genome and have broad implications for our conceptualization of the role of multiscale chromatin architecture in regulating diverse cell nuclear dynamics in space and time.
Suarez-Rodriguez, C, Haider, N, He, Y & Dutkiewicz, E 2020, 'Network Optimisation in 5G Networks: A Radio Environment Map Approach.', IEEE Trans. Veh. Technol., vol. 69, no. 10, pp. 12043-12057.
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Subasinghage, K, Gunawardane, K & Kularatna, N 2020, 'Stability analysis and experimental validation of the supercapacitor‐assisted low‐dropout regulator', IET Power Electronics, vol. 13, no. 15, pp. 3213-3225.
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Sui, Y & Xue, J 2020, 'Value-Flow-Based Demand-Driven Pointer Analysis for C and C++', IEEE Transactions on Software Engineering, vol. 46, no. 8, pp. 812-835.
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IEEE We present SUPA, a value-flow-based demand-driven flow- and context-sensitive pointer analysis with strong updates for C and C++ programs. SUPA enables computing points-to information via value-flow refinement, in environments with small time and memory budgets. We formulate SUPA by solving a graph-reachability problem on an inter-procedural value-flow graph representing a program's def-use chains, which are pre-computed efficiently but over-approximately. To answer a client query (a request for a variable's points-to set), SUPA reasons about the flow of values along the pre-computed def-use chains sparsely (rather than across all program points), by performing only the work necessary for the query (rather than analyzing the whole program). In particular, strong updates are performed to filter out spurious def-use chains through value-flow refinement as long as the total budget is not exhausted.
Sui, Y, Cheng, X, Zhang, G & Wang, H 2020, 'Flow2Vec: value-flow-based precise code embedding', Proceedings of the ACM on Programming Languages, vol. 4, no. OOPSLA, pp. 1-27.
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Code embedding, as an emerging paradigm for source code analysis, has attracted much attention over the past few years. It aims to represent code semantics through distributed vector representations, which can be used to support a variety of program analysis tasks (e.g., code summarization and semantic labeling). However, existing code embedding approaches are intraprocedural, alias-unaware and ignoring the asymmetric transitivity of directed graphs abstracted from source code, thus they are still ineffective in preserving the structural information of code.This paper presents Flow2Vec, a new code embedding approach that precisely preserves interprocedural program dependence (a.k.a value-flows). By approximating the high-order proximity, i.e., the asymmetric transitivity of value-flows, Flow2Vec embeds control-flows and alias-aware data-flows of a program in a low-dimensional vector space. Our value-flow embedding is formulated as matrix multiplication to preserve context-sensitive transitivity through CFL reachability by filtering out infeasible value-flow paths. We have evaluated Flow2Vec using 32 popular open-source projects. Results from our experiments show that Flow2Vec successfully boosts the performance of two recent code embedding approaches codevec and codeseq for two client applications, i.e., code classification and code summarization. For code classification, Flow2Vec improves codevec with an average increase of 21.2%, 20.1% and 20.7% in precision, recall and F1, respectively. For code summarization, Flow2Vec outperforms codeseq by an average of 13.2%, 18.8% and 16.0% in precision, recall and F1, respectively.
Sukor, NR, Shamsuddin, AH, Mahlia, TMI & Mat Isa, MF 2020, 'Techno-Economic Analysis of CO2 Capture Technologies in Offshore Natural Gas Field: Implications to Carbon Capture and Storage in Malaysia', Processes, vol. 8, no. 3, pp. 350-350.
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Growing concern on global warming directly related to CO2 emissions is steering the implementation of carbon capture and storage (CCS). With Malaysia having an estimated 37 Tscfd (Trillion standard cubic feet) of natural gas remains undeveloped in CO2 containing natural gas fields, there is a need to assess the viability of CCS implementation. This study performs a techno-economic analysis for CCS at an offshore natural gas field in Malaysia. The framework includes a gas field model, revenue model, and cost model. A techno-economic spreadsheet consisting of Net Present Value (NPV), Payback Period (PBP), and Internal Rate of Return (IRR) is developed over the gas field’s production life of 15 years for four distinctive CO2 capture technologies, which are membrane, chemical absorption, physical absorption, and cryogenics. Results predict that physical absorption solvent (Selexol) as CO2 capture technology is most feasible with IRR of 15% and PBP of 7.94 years. The output from the techno-economic model and associated risks of the CCS project are quantified by employing sensitivity analysis (SA), which indicated that the project NPV is exceptionally sensitive to gas price. On this basis, the economic performance of the project is reliant on revenues from gas sales, which is dictated by gas market price uncertainties.
Sun, B, Cao, Y, Guo, Z, Yan, Z & Wen, S 2020, 'Quantized passification of delayed memristor-based neural networks via sliding model control', Journal of the Franklin Institute, vol. 357, no. 6, pp. 3741-3752.
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In this paper, quantized passification is investigated for memristive neural networks (MNNs) with time-varying delays via sliding model control. The controller is designed with quantized schemes to reduce the computational complexity via uniform quantization and logarithmic quantizer. By choosing suitable Lyapunov functional and using LMI toolbox, some specific conditions are obtained to make MNN passive. At last, we give an illustrative example to ensure the correctness of the theorem.
Sun, B, Cao, Y, Guo, Z, Yan, Z & Wen, S 2020, 'Synchronization of discrete-time recurrent neural networks with time-varying delays via quantized sliding mode control', Applied Mathematics and Computation, vol. 375, pp. 125093-125093.
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In this paper, we discuss synchronization of discrete-time recurrent neural networks (DRNNs) with time-varying delays via quantized sliding mode control. A feedback controller based on sliding mode control is firstly imported in the synchronization of DRNNs. The activation functional in our paper can be more relaxed than the other papers which should satisfy the Lipschitz conditions. For the sake of reducing the computational complexity and conservatism, we consider two quantized methods with uniform and logarithmic quantizer. We gain some specific conditions to ensure the synchronization of discrete-time system. Several examples are presented to support our theorem in the ending.
Sun, B, Wang, S, Cao, Y, Guo, Z, Huang, T & Wen, S 2020, 'Exponential synchronization of memristive neural networks with time-varying delays via quantized sliding-mode control', Neural Networks, vol. 126, pp. 163-169.
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In the paper, exponential synchronization issue is considered for memristive neural networks (MNNs) with time-varying delays via quantized sliding-mode algorithm. Quantized Sliding-mode controller is introduced to ensure the slave system can be exponentially synchronized with the host system via the super-twisting algorithm, which has been proved in the main results. Quantization function consists of uniform quantizer and logarithmic quantizer. Simulation results are given with comparisons between two quantizers in the end.
Sun, B, Wen, S, Wang, S, Huang, T, Chen, Y & Li, P 2020, 'Quantized synchronization of memristive neural networks with time-varying delays via super-twisting algorithm', Neurocomputing, vol. 380, pp. 133-140.
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In this paper, we investigate quantized synchronization control problem of memristive neural networks (MNNs) with time-varying delays via super-twisting algorithm. A feedback controller is introduced with quantized method. To enormously reduce the computational complexity of the controller under super-twisting algorithm, two quantized control schemes are proposed with uniform quantizer and logarithmic quantization. We obtain some sufficient conditions of specific control plans to guarantee that the driving MNNs can synchronize with the response MNNs. A neoteric Lyapunov functional is designed to analyze the synchronization problem. Finally, in this paper ending, some illustrative examples are given in support of our results.
Sun, F, Gómez‐García, R, Zhu, X, Zhu, H, Yang, Y & Tong, X 2020, 'Miniaturised millimetre‐wave BPF with broad stopband suppression in silicon–germanium technology', IET Microwaves, Antennas & Propagation, vol. 14, no. 4, pp. 308-313.
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On‐chip passive distributed‐element‐based bandpass filters (BPFs) usually provide a decent stopband suppression across a limited bandwidth. To solve this drawback without adversely affecting other performance metrics, a simple but effective miniaturised BPF design approach is presented in this work. The proposed integrated BPF topology uses a combination of a coupled‐inductor structure with a pair of metal–insulator–metal capacitors in a quasi‐lumped‐element realisation. To show the operational principles of this BPF approach, a simplified inductor–capacitor‐equivalent circuit model is used for its theoretical analysis. From this analytical framework as an initial design guideline, a quasi‐millimetre‐wave BPF is designed and implemented in a standard 0.13 µm bipolar complementary–metal–oxide semiconductor technology. The measured results show that the developed BPF device has a centre frequency of 28 GHz with a 3 dB fractional bandwidth of 21% and minimum in‐band power‐insertion‐loss level of 3.4 dB. The stopband suppression is higher than 25 dB beyond 45 GHz. The chip size, excluding the pads, is only 0.017 mm2 (0.06 × 0.284 mm2).
Sun, G, Tian, Y, Wang, R, Fang, J & Li, Q 2020, 'Parallelized multiobjective efficient global optimization algorithm and its applications', Structural and Multidisciplinary Optimization, vol. 61, no. 2, pp. 763-786.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. In engineering practice, most optimization problems have multiple objectives, which are usually in a form of expensive black-box functions. The multiobjective efficient global optimization (MOEGO) algorithms have been proposed recently to sequentially sample the design space, aiming to seek for optima with a minimum number of sampling points. With the advance in computing resources, it is wise to make optimization parallelizable to shorten the total design cycle further. In this study, two different parallelized multiobjective efficient global optimization algorithms were proposed on the basis of the Kriging modeling technique. With use of the multiobjective expectation improvement, the proposed algorithm is able to balance local exploitation and global exploration. To implement parallel computing, the “Kriging Believer” and “multiple good local optima” strategies were adopted here to develop new sample infill criteria for multiobjective optimization problems. The proposed algorithms were applied to five mathematical benchmark examples first, which demonstrated faster convergence and better accuracy with more uniform distribution of Pareto points, in comparison with the two other conventional algorithms. The best performed “Kriging Believer” strategy approach was then applied to two more sophisticated real-life engineering case studies on the tailor-rolled blank (TRB) structures for crashworthiness design. After optimization, the TRB hat-shaped tube achieved a 3% increase in energy absorption and a 10.7% reduction in mass, and the TRB B-pillar attained a 10.1% reduction in mass and a 12.8% decrease in intrusion, simultaneously. These benchmark and engineering examples demonstrated that the proposed methods are fairly promising for being an effective tool for a range of design problems.
Sun, G, Wang, L, Chen, D & Luo, Q 2020, 'Tensile performance of basalt fiber composites with open circular holes and straight notches', International Journal of Mechanical Sciences, vol. 176, pp. 105517-105517.
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© 2020 Elsevier Ltd Basalt fiber composites have attracted increasing attention in recent years due to their advantages over carbon fiber composites in many aspects such as lower cost, environmental friendliness, superior heat resistance and ductility. Notches in structural components are unavoidable in practical applications. In the present study, the effects of notch shape and size on the tensile properties of basalt fiber laminates were investigated by experiment, finite element analysis and theoretical calculation. Specimens were prepared using laminates reinforced by plain woven basalt or carbon fiber fabrics and machined with an open circular hole or straight notch. Standard tensile tests were conducted and recorded using digital image correlation, aiming to measure the full-field surface strain. Continuum damage mechanics based finite element models were developed to predict stress concentration factors and failure processes of notched specimens. The characteristic distances of the stress criterion models were calibrated by the experimental results of un-notched and notched specimens so that failure of basalt fiber laminates with circular and straight notches could be analytically predicted.
Sun, H-H, Ding, C, Zhu, H & Guo, YJ 2020, 'Dual-Polarized Multi-Resonance Antennas With Broad Bandwidths and Compact Sizes for Base Station Applications', IEEE Open Journal of Antennas and Propagation, vol. 1, pp. 11-19.
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In this paper, a novel design method for dual-polarized multi-resonance antennas is presented for base station applications. The radiator of the antenna is configured as cross-dipoles with four thin metal strips connected to the adjacent dipole arms. The attached strips create multiple current paths and introduce additional resonant points. As a result, the bandwidth of the antennas is broadened while maintaining a very compact size. Based on this working mechanism, two multi-resonance antennas are designed, fabricated, and tested. The antennas achieve bandwidths of 46.7% and 66.7% respectively, with excellent matching capabilities. The antennas also exhibit high port isolation levels and stable radiation performances. The promising wideband performances with compact physical sizes make the antennas highly suitable for the base station applications.
Sun, H-H, Jones, B, Guo, YJ & Lee, YH 2020, 'Suppression of Cross-Band Scattering in Interleaved Dual-Band Cellular Base-Station Antenna Arrays', IEEE Access, vol. 8, pp. 222486-222495.
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Sun, H-H, Zhu, H, Ding, C, Jones, B & Guo, YJ 2020, 'Scattering Suppression in a 4G and 5G Base Station Antenna Array Using Spiral Chokes', IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 10, pp. 1818-1822.
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© 2020 IEEE. This letter presents a novel distributed choking technique, the spiral choke, for scattering suppression in dual-band antenna arrays. The working principle and the scattering suppression capability of the choke are analyzed. The spiral chokes are implemented as low-band radiators in a colocated 4G and 5G dual-band array to suppress cross-band scattering while broadening the bandwidth of the choked element. The experimental results demonstrate that the cross-band scattering in the array is largely eliminated, and the realized dual-band array has very stable radiation performance in both well-matched bands.
Sun, X, Cao, J, Lei, G, Guo, Y & Zhu, J 2020, 'Speed Sensorless Control for Permanent Magnet Synchronous Motors Based on Finite Position Set', IEEE Transactions on Industrial Electronics, vol. 67, no. 7, pp. 6089-6100.
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© 1982-2012 IEEE. This article presents a novel method for the sensorless control of interior permanent-magnet synchronous motors. An iterative search strategy based on dichotomy is proposed to provide a finite number of rotor position angles with good accuracy. These position angles are used to calculate the back electromotive force (EMF) in d-axis. The optimal rotor position angle is the one that yields a back EMF minimizing the defined cost function. With the increase of the iterations, the accuracy of rotor position angle increases geometrically. To effectively extract the back EMF signal under the low-speed condition, the high-frequency signal injection method is used to realize the low-speed operation of the motor. A hybrid control strategy is adopted to achieve the smooth switching from the low-speed to high-speed. The performance of the proposed method has been validated experimentally and compared with that of the conventional phase locked loop under different conditions.
Sun, X, Diao, K, Lei, G, Guo, Y & Zhu, J 2020, 'Real-Time HIL Emulation for a Segmented-Rotor Switched Reluctance Motor Using a New Magnetic Equivalent Circuit', IEEE Transactions on Power Electronics, vol. 35, no. 4, pp. 3841-3849.
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Sun, X, Hu, C, Lei, G, Guo, Y & Zhu, J 2020, 'State Feedback Control for a PM Hub Motor Based on Gray Wolf Optimization Algorithm', IEEE Transactions on Power Electronics, vol. 35, no. 1, pp. 1136-1146.
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© 1986-2012 IEEE. This paper presents an optimal control strategy for a permanent-magnet synchronous hub motor (PMSHM) drive using the state feedback control method plus the gray wolf optimization (GWO) algorithm. First, the linearized PMSHM mathematical model is obtained by voltage feedforward compensation. Second, to acquire satisfactory dynamics of speed response and zero d-axis current, the discretized state-space model of the PMSHM is augmented with the integral of rotor speed error and integral of d-axis current error. Then, the GWO algorithm is employed to acquire the weighting matrices Q and R in linear quadratic regulator optimization process. Moreover, a penalty term is introduced to the fitness index to suppress overshoots effectively. Finally, comparisons among the GWO-based state feedback controller (SFC) with and without the penalty term, the conventional SFC, and the genetic algorithm enhanced proportional-integral controllers are conducted in both simulations and experiments. The comparison results show the superiority of the proposed SFC with the penalty term in fast response.
Sun, X, Hu, C, Lei, G, Yang, Z, Guo, Y & Zhu, J 2020, 'Speed Sensorless Control of SPMSM Drives for EVs With a Binary Search Algorithm-Based Phase-Locked Loop', IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 4968-4978.
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© 1967-2012 IEEE. This article presents a new method to extract accurate rotor position for the speed sensorless control of surface-mounted permanent-magnet synchronous motors (SPMSMs), based on the back electromotive force (EMF) information. The concept of finite control set-model predictive control is employed, and its cost function is related to the back EMF. An optimal voltage vector is selected from several given voltage vectors by comparing their fitness values. Moreover, the position space is divided into four sectors, and the fitness of each sector boundary is calculated and compared. The rotor position is first located in the sector surrounded by two boundaries that minimize the cost function. Then the selected sector is split into two parts, and the binary search algorithm is applied to reduce the sector area to improve the accuracy of position estimation. To overcome the drawback of the back EMF-based sensorless scheme, an I-f startup method is employed to accelerate the motor to the desired speed. An experiment has been carried out to compare the performance of the proposed method and the conventional phase-locked loop (PLL) in terms of steady-state and transient conditions.
Sun, X, Shi, Z, Cai, Y, Lei, G, Guo, Y & Zhu, J 2020, 'Driving-Cycle-Oriented Design Optimization of a Permanent Magnet Hub Motor Drive System for a Four-Wheel-Drive Electric Vehicle', IEEE Transactions on Transportation Electrification, vol. 6, no. 3, pp. 1115-1125.
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© 2015 IEEE. The electrical drive system is crucial to the drive performance and safety of electric vehicles (EVs). In contrast to the traditional two-wheel-driven EVs, the hub motor four-wheel-drive system can steer the vehicle by controlling the torque and speed of each wheel independently, yielding a very simple distributed drivetrain with high efficiency and reliability. This article presents a system-level design optimization method for a permanent magnet hub motor drive system for a campus patrol EV based on a practical driving cycle. An outer rotor permanent-magnet synchronous hub motor (PMSHM) and an improved model predicate current control are proposed for the drive system. Due to the lack of reducers, the direct-drive PMSHM needs to face more complex working conditions and design constraints. In the implementation, the motor design requirements are obtained through the collection of practical EV driving cycles on the campus. Based on these requirements, two models are proposed as the preliminary designs for the PMSHM. To improve their performance, an efficient multiobjective optimization method is employed to the motor considering different operational conditions. The finite-element model and thermal network model are employed to verify the performance of the optimized PMSHM. An optimal design scheme is selected by comparing the comprehensive performance of the two optimized motors. In addition, a duty-cycle model predictive current control is adopted to drive the motor. Finally, a prototype is developed and tested, and the experimental results are presented.
Sun, X, Wu, M, Yang, Z, Lei, G & Guo, Y 2020, 'High‐performance control for a permanent‐magnet linear synchronous generator using state feedback control scheme plus grey wolf optimisation', IET Electric Power Applications, vol. 14, no. 5, pp. 771-780.
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This study proposes an optimal control scheme for a permanent‐magnet linear synchronous generator (PMLSG) using the state feedback control (SFC) method plus the grey wolf optimisation (GWO) algorithm. First, A novel state‐space model of linear PMLSG is established in order to obtain desired dynamics and enough power when used for the smooth wave energy. Second, the GWO algorithm is adopted to acquire weighting matrices Q and R in the process of optimising linear quadratic regulator (LQR). What is more, a penalty term is brought into the fitness index to reduce the overstrike of output voltage and keep the rate of work more stable. Finally, optimal LQR‐based SFC with and without penalty term and proportional‐integral (PI) controllers are compared both in simulations and in experiments. Results clearly prove that the proposed optimal control strategy performs a better response when compared to other strategies.
Suraweera, N, Li, S, Johnson, M, Collings, IB, Hanly, SV, Ni, W & Hedley, M 2020, 'Environment-Assisted Passive WiFi Tracking With Self-Localizing Asynchronous Sniffers', IEEE Systems Journal, vol. 14, no. 4, pp. 4798-4809.
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Surawski, NC, Macdonald, LM, Baldock, JA, Sullivan, AL, Roxburgh, SH & Polglase, PJ 2020, 'Exploring how fire spread mode shapes the composition of pyrogenic carbon from burning forest litter fuels in a combustion wind tunnel', Science of The Total Environment, vol. 698, pp. 134306-134306.
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© 2019 In this study, solid state 13C nuclear magnetic resonance (NMR) spectroscopy was used to explore the carbon-containing functional groups present in pyrogenic carbon (PyC) produced during different fire spread modes to forest litter fuels from a dry sclerophyll eucalypt forest burnt in a combustion wind tunnel. A replicated experimental study was performed using three different fire spread modes: heading fires (i.e. fires which spread with the wind), flanking fires (i.e. fires which spread perpendicular to the wind) and backing fires (i.e. fires which spread against the wind). In addition to 13C NMR measurements of PyC, detailed fire behaviour measurements were recorded during experiments. Experiments showed that heading fires produced significantly more aryl carbon in ash samples than flanking fires. All other experimental comparisons for burnt fuel samples involving different fire spread modes were statistically insignificant. Principal component analysis (PCA) was used to explore the relationship between 13C NMR functional groups and fire behaviour observations. Results from PCA indicate that maximising the residence time of high temperature combustion and the combustion factor (i.e. the fraction of pre-fire biomass consumed by fire) could be a method for increasing the amount of aryl carbon in PyC. Maximising the amount of aryl carbon could be beneficial for the overall PyC balance from fire, since more recalcitrant carbon (e.g. carbon with a higher aryl carbon content) that is not emitted to the atmosphere has been shown to have longer residence times in environmental media such as soils or sediments.
Suryani, S, Sariani, S, Earnestly, F, Marganof, M, Rahmawati, R, Sevindrajuta, S, Mahlia, TMI & Fudholi, A 2020, 'A Comparative Study of Virgin Coconut Oil, Coconut Oil and Palm Oil in Terms of Their Active Ingredients', Processes, vol. 8, no. 4, pp. 402-402.
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This research aims to study the unique factors of virgin coconut oil (VCO) compared with coconut oil (i.e., coconut oil processed through heating the coconut milk and palm oil sold on the market). Its novelty is that it (VCO) contains lactic acid bacteria and bacteriocin. Lauric acid content was analyzed by the Chromatographic Gas method. Isolation of lactic acid bacteria (LAB) was conducted by the dilution method using MRSA + 0.5% CaCO3 media. Iodium number, peroxide, and %FFA were analyzed using a general method, and isolation bacteriocin by the deposition method using ammonium sulfate. In addition, macromolecular identification was conducted by 16S rRNA. VCO was distinguished by a higher content of lauric acid (C12:0) 41%–54.5% as compared with 0% coconut and 0, 1% palm oil, respectively. The VCO also contains LAB, namely Lactobacillus plantarum and Lactobacillus paracasei, and can inhibit the growth of pathogenic bacteria, such as Pseudomonas aeruginosa, Klebsiella, Staphylococcus aureus, S. epidermidis, Proteus, Escherichia coli, Listeria monocytogenes, Bacillus cereus, Salmonella typhosa and bacteriocin. Comparison with VCO is based on having a high content of lauric acid, 54%, and LAB content. The difference between VCO and coconut oil and palm oil is fatty acids. In VCO there are lauric acid and stearic acid, namely lauric acid VCO (A) 54.06%, VCO (B) 53.9% and VCO (C) 53.7%. The content of stearic acid VCO (A) is 12.03%, VCO (B) 12.01% and VCO (C) 11.9%. Coconut oil contains a little lauric acid, which is 2.81%, stearic acid 2.65% and palmitic acid 2.31%. Palm oil can be said to have very little lauric acid, namely in palm oil 1, 0.45%, and even in palm oil 2, 0%; in turn, palmitic acid palm oil 1 has 2.88% and palm oil 2 palmitic acid has 24.42%.
Sutrisno, J, Dharma, S, Silitonga, AS, Shamsuddin, AH, Sebayang, AH, Milano, J, Rahmawaty & Supriyanto 2020, 'Experimental assessment of the performance and exhaust emissions characteristic of a diesel with jatropa curcas-ceiba pentandra mixture biodiesel/bioethanol blends', Journal of Mechanical Engineering Research and Developments, vol. 43, no. 3, pp. 442-458.
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The depletion of world oil reserves currently positively impacts the development of biofuels such as biodiesel and bioethanol, as this fuel is a renewable and environmentally friendly alternative and is considered to have enormous potential to supply energy needs. The purpose of this research is to investigate the performance and exhaust emissions from single diesel direct injection diesel engine which is fueled by biodiesel-bioethanol-diesel. Biodiesel was obtained from a mixture of Jatropa curcas-Ceiba pentandra crude oil with each composition being 50% produced by degumming process, acid catalyzed esterification, and alkaline-catalyzed transesterification. Biodiesel-bioethanoldiesel fuel is mixed in several conditions ie B10BE5, B20BE8, B30BE10, B40BE13, and B50BE15. In general, engine performance for low blend of biodiesel and bioethanol is close to diesel, for some engine parameters such as engine torque, brake power, and brake thermal efficiency. The addition of biodiesel and bioethanol into diesel fuel also has implications for reduced carbon emissions and smoke opacity. Overall, based on test results it can be concluded that the biodiesel-bioethanol mixture in low concentration in diesel fuel qualifies as an alternative fuel in diesel engines.
Sutton, GJ, Liu, RP & Guo, YJ 2020, 'Coexistence Performance and Limits of Frame-Based Listen-Before-Talk', IEEE Transactions on Mobile Computing, vol. 19, no. 5, pp. 1084-1095.
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Suwaileh, W, Pathak, N, Shon, H & Hilal, N 2020, 'Forward osmosis membranes and processes: A comprehensive review of research trends and future outlook', Desalination, vol. 485, pp. 114455-114455.
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Syahir, AZ, Harith, MH, Zulkifli, NWM, Masjuki, HH, Kalam, MA, Yusoff, MNAM, Zulfattah, ZM & Ibrahim, TM 2020, 'Compatibility of Ionic Liquid With Glycerol Monooleate and Molybdenum Dithiocarbamate as Additives in Bio-Based Lubricant', Journal of Tribology, vol. 142, no. 6.
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AbstractThis study reports the tribological characteristics of trimethylolpropane trioleate (TMPTO) additivated with antifriction and antiwear additives, which are ionic liquid (IL), glycerol monooleate (GMO), and molybdenum dithiocarbamate (MoDTC). In addition, to obtain the ideal composition that results in the minimal coefficient of friction (COF), optimization tool was employed using response surface methodology (RSM) technique with the Box–Behnken design. The IL used in this study was a phosphorus-type IL, namely trihexyl(tetradecyl)phosphonium bis(2,4,4-trimethylpentyl) phosphinate, [P14,6,6,6][TMPP]. The resulting COF and worn surface morphology were investigated using high-frequency reciprocating rig (HFRR) tribotester and scanning electron microscope with energy-dispersive X-ray spectroscopy (SEM-EDX), respectively. From the experimental results, a second-order polynomial mathematical model was constructed and able to statistically predict the resulting COF. The optimized values that resulted in the lowest average COF of 0.0458 were as follows: 0.93 wt% IL, 1.49 wt% GMO, and 0.52 wt% MoDTC. The addition of IL into neat base oil managed to reduce the COF, while the combination of IL, GMO, and MoDTC at optimum concentration further reduced the average COF and wear as observed through SEM micrographs when compared with those of additive-free TMPTO, suggesting that GMO and MoDTC were compatible to be used with IL.
Syahir, AZ, Zulkifli, NWM, Masjuki, HH, Kalam, MA, Harith, MH, Yusoff, MNAM, Zulfattah, ZM & Jamshaid, M 2020, 'Tribological Improvement Using Ionic Liquids as Additives in Synthetic and Bio-Based Lubricants for Steel–Steel Contacts', Tribology Transactions, vol. 63, no. 2, pp. 235-250.
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Syed, MS, Mirakhorli, F, Marquis, C, Taylor, RA & Warkiani, ME 2020, 'Particle movement and fluid behavior visualization using an optically transparent 3D-printed micro-hydrocyclone', Biomicrofluidics, vol. 14, no. 6, pp. 064106-064106.
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A hydrocyclone is a macroscale separation device employed in various industries, with many advantages, including high-throughput and low operational costs. Translating these advantages to microscale has been a challenge due to the microscale fabrication limitations that can be surmounted using 3D printing technology. Additionally, it is difficult to simulate the performance of real 3D-printed micro-hydrocyclones because of turbulent eddies and the deviations from the design due to printing resolution. To address these issues, we propose a new experimental method for the direct observation of particle motion in 3D printed micro-hydrocyclones. To do so, wax 3D printing and soft lithography were used in combination to construct a transparent micro-hydrocyclone in a single block of polydimethylsiloxane. A high-speed camera and fluorescent particles were employed to obtain clear in situ images and to confirm the presence of the vortex core. To showcase the use of this method, we demonstrate that a well-designed device can achieve a 95% separation efficiency for a sample containing a mixture of (desired) stem cells and (undesired) microcarriers. Overall, we hope that the proposed method for the direct visualization of particle trajectories in micro-hydrocyclones will serve as a tool, which can be leveraged to accelerate the development of micro-hydrocyclones for biomedical applications.
Syifa, M, Kadavi, PR, Lee, C-W & Pradhan, B 2020, 'Landsat images and artificial intelligence techniques used to map volcanic ashfall and pyroclastic material following the eruption of Mount Agung, Indonesia', Arabian Journal of Geosciences, vol. 13, no. 3.
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Szemes, M, Melegh, Z, Bellamy, J, Greenhough, A, Kollareddy, M, Catchpoole, D & Malik, K 2020, 'A Wnt-BMP4 Signaling Axis Induces MSX and NOTCH Proteins and Promotes Growth Suppression and Differentiation in Neuroblastoma', Cells, vol. 9, no. 3, pp. 783-783.
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The Wnt and bone morphogenetic protein (BMP) signaling pathways are known to be crucial in the development of neural crest lineages, including the sympathetic nervous system. Surprisingly, their role in paediatric neuroblastoma, the prototypic tumor arising from this lineage, remains relatively uncharacterised. We previously demonstrated that Wnt/β-catenin signaling can have cell-type-specific effects on neuroblastoma phenotypes, including growth inhibition and differentiation, and that BMP4 mRNA and protein were induced by Wnt3a/Rspo2. In this study, we characterised the phenotypic effects of BMP4 on neuroblastoma cells, demonstrating convergent induction of MSX homeobox transcription factors by Wnt and BMP4 signaling and BMP4-induced growth suppression and differentiation. An immunohistochemical analysis of BMP4 expression in primary neuroblastomas confirms a striking absence of BMP4 in poorly differentiated tumors, in contrast to a high expression in ganglion cells. These results are consistent with a tumor suppressive role for BMP4 in neuroblastoma. RNA sequencing following BMP4 treatment revealed induction of Notch signaling, verified by increases of Notch3 and Hes1 proteins. Together, our data demonstrate, for the first time, Wnt-BMP-Notch signaling crosstalk associated with growth suppression of neuroblastoma.
Taddei, C, Zhou, B, Bixby, H, Carrillo-Larco, RM, Danaei, G, Jackson, RT, Farzadfar, F, Sophiea, MK, Di Cesare, M, Iurilli, MLC, Martinez, AR, Asghari, G, Dhana, K, Gulayin, P, Kakarmath, S, Santero, M, Voortman, T, Riley, LM, Cowan, MJ, Savin, S, Bennett, JE, Stevens, GA, Paciorek, CJ, Aekplakorn, W, Cifkova, R, Giampaoli, S, Kengne, AP, Khang, Y-H, Kuulasmaa, K, Laxmaiah, A, Margozzini, P, Mathur, P, Nordestgaard, BG, Zhao, D, Aadahl, M, Abarca-Gómez, L, Rahim, HA, Abu-Rmeileh, NM, Acosta-Cazares, B, Adams, RJ, Agdeppa, IA, Aghazadeh-Attari, J, Aguilar-Salinas, CA, Agyemang, C, Ahluwalia, TS, Ahmad, NA, Ahmadi, A, Ahmadi, N, Ahmed, SH, Ahrens, W, Ajlouni, K, Alarouj, M, AlBuhairan, F, AlDhukair, S, Ali, MM, Alkandari, A, Alkerwi, A, Aly, E, Amarapurkar, DN, Amouyel, P, Andersen, LB, Anderssen, SA, Anjana, RM, Ansari-Moghaddam, A, Aounallah-Skhiri, H, Araújo, J, Ariansen, I, Aris, T, Arku, RE, Arlappa, N, Aryal, KK, Aspelund, T, Assunção, MCF, Auvinen, J, Avdicová, M, Azevedo, A, Azizi, F, Azmin, M, Balakrishna, N, Bamoshmoosh, M, Banach, M, Bandosz, P, Banegas, JR, Barbagallo, CM, Barceló, A, Barkat, A, Bata, I, Batieha, AM, Batyrbek, A, Baur, LA, Beaglehole, R, Belavendra, A, Ben Romdhane, H, Benet, M, Benn, M, Berkinbayev, S, Bernabe-Ortiz, A, Bernotiene, G, Bettiol, H, Bhargava, SK, Bi, Y, Bienek, A, Bikbov, M, Bista, B, Bjerregaard, P, Bjertness, E, Bjertness, MB, Björkelund, C, Bloch, KV, Blokstra, A, Bo, S, Boehm, BO, Boggia, JG, Boissonnet, CP, Bonaccio, M, Bongard, V, Borchini, R, Borghs, H, Bovet, P, Brajkovich, I, Breckenkamp, J, Brenner, H, Brewster, LM, Bruno, G, Bugge, A, Busch, MA, de León, AC, Cacciottolo, J, Can, G, Cândido, APC, Capanzana, MV, Capuano, E, Capuano, V, Cardoso, VC, Carvalho, J, Casanueva, FF, Censi, L, Chadjigeorgiou, CA, Chamukuttan, S, Chaturvedi, N, Chen, C-J, Chen, F, Chen, S, Cheng, C-Y, Cheraghian, B, Chetrit, A, Chiou, S-T, Chirlaque, M-D, Cho, B, Cho, Y, Chudek, J, Claessens, F, Clarke, J, Clays, E, Concin, H, Confortin, SC, Cooper, C, Costanzo, S, Cottel, D, Cowell, C, Crujeiras, AB, Csilla, S, Cui, L, Cureau, FV, D’Arrigo, G, d’Orsi, E, Dallongeville, J, Damasceno, A, Dankner, R, Dantoft, TM, Dauchet, L, Davletov, K, De Backer, G, De Bacquer, D, de Gaetano, G, De Henauw, S, de Oliveira, PD, De Ridder, D, De Smedt, D, Deepa, M, Deev, AD, Dehghan, A, Delisle, H, Dennison, E & et al. 2020, 'Repositioning of the global epicentre of non-optimal cholesterol', Nature, vol. 582, no. 7810, pp. 73-77.
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AbstractHigh blood cholesterol is typically considered a feature of wealthy western countries1,2. However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health4,5. However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol—which is a marker of cardiovascular risk—changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million–4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and ...
Taghikhah, F, Voinov, A, Shukla, N & Filatova, T 2020, 'Exploring consumer behavior and policy options in organic food adoption: Insights from the Australian wine sector', Environmental Science & Policy, vol. 109, pp. 116-124.
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Taghizadeh, S, Hossain, MJ, Poursafar, N, Lu, J & Konstantinou, G 2020, 'A Multifunctional Single-Phase EV On-Board Charger With a New V2V Charging Assistance Capability', IEEE Access, vol. 8, pp. 116812-116823.
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© 2013 IEEE. This paper presents the design and implementation of a single-phase multifunctional electric-vehicle (EV) on-board charger with an advanced vehicle-to-vehicle (V2V) functionality for emergency roadside charging assistance situations. Using this function, an EV is able to charge from another EV in case of an emergency when the battery is flat and there is no access to a charging station. The designed EV charger can support the proposed V2V function with rated power and without the need for an additional portable charger. It can also provide conventional functions of vehicle-to-grid (V2G), grid-to-vehicle (G2V), the static synchronous compensators (STATCOM) and active power filter (APF) (i.e. reactive power support, and harmonics reduction). All the functions are addressed in the control part through the sharing of existing converters in an all-in-one system. The proposed EV charger is designed and simulated in MATLAB/Simulink, and a laboratory prototype is also implemented to validate its key functions.
Tahmassebi, A, Mohebali, B, Meyer‐Baese, A & Gandomi, AH 2020, 'Multiobjective genetic programming for reinforced concrete beam modeling', Applied AI Letters, vol. 1, no. 1.
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AbstractThis paper presents the application of multiobjective genetic programming (MOGP) in engineering issues. An evolutionary symbolic implementation was developed based on a case study on prediction of the shear strength of slender reinforced concrete beams without stirrups including 1942 set of published test results. In the implementation of the MOGP model, the nondominated sorting genetic algorithm II with adaptive regression by mixing algorithm with considering the optimization of mean‐square error as the fitness measure and the subtree complexity was used. The developed MOGP model was compared to previously developed genetic programming models, different building codes, and additional machine learning based approaches. It is clearly shown that the MOGP model outperformed the other algorithms applied on this database and can be a general solution on any engineering problems with the main advantage of prediction equations without assuming prior form of the relevance among the input predictor variables.
Tahmoorian, F & Khabbaz, H 2020, 'Performance comparison of a MSW settlement prediction model in Tehran landfill', Journal of Environmental Management, vol. 254, pp. 109809-109809.
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The municipal solid waste (MSW) landfills experience a large post-closure settlement over time. Waste settlement significantly impairs utilities, structures, and the other facilities constructed on top of a landfill. This study presents the settlement mechanisms and the methods of estimating MSW landfill settlements. Since the waste materials exhibit engineering properties which vary depending on many factors such as the location, time, climate, this study also presents the data related to the landfill characteristics, waste composition, waste moisture content, and other physical and chemical properties of waste. In addition, this paper discusses the findings of a settlement investigation conducted at a municipal solid waste landfill in Tehran. In this research, based on the collected field data and data obtained from the available literature, a technical management tool for MSW closed landfills has been developed using MATLAB, which aims to predict time dependent settlement under self-weight and surcharge loads in landfills considering various related parameters, leachate, gas generation, and moisture distribution, coefficients of compression, whilst it calculates different properties of wastes, and determines the landfill slope stability under various conditions. This user-friendly program captures the variation of the model parameters with time. The results of the verification process indicate that the results from the technical management tool have been in a very good agreement with the measured field settlement data, collected from Tehran landfill. Moreover, the results of sensitivity analysis of the model in regard to variation of input parameters indicate that there are two prominent characteristics, having significant impacts on the overall landfill settlement. These characteristics are the landfill height and the compressibility parameters. The outcomes of this study can improve the confidence for design and construction on MSW landfills. It ma...
Tai, P, Indraratna, B & Rujikiatkamjorn, C 2020, 'Consolidation Analysis of Soft Ground Improved by Stone Columns Incorporating Foundation Stiffness', International Journal of Geomechanics, vol. 20, no. 6, pp. 04020067-04020067.
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Takalkar, MA, Xu, M & Chaczko, Z 2020, 'Manifold feature integration for micro-expression recognition', Multimedia Systems, vol. 26, no. 5, pp. 535-551.
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Takashima, H, Maruya, H, Ishihara, K, Tashima, T, Shimazaki, K, Schell, AW, Tran, TT, Aharonovich, I & Takeuchi, S 2020, 'Determination of the Dipole Orientation of Single Defects in Hexagonal Boron Nitride', ACS Photonics, vol. 7, no. 8, pp. 2056-2063.
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© 2020 American Chemical Society. Dipole orientation in solid-state single photon emitters plays an important role in applications such as quantum information devices integrated with nanophotonic components. In various single photon emitters, hexagonal boron nitride (hBN) with point defects is one of the most promising candidates as a single photon emitter for high photostability, ultrahigh brightness, nonlinearity, and narrow emission line width. In applying hBN with a single point defect to those applications, three-dimensional determination of its dipole orientation is critically important. In this paper, we three-dimensionally determine the dipole orientation of single defects in hBN nanoflakes. By measuring the second-order correlation function and emission spectra, hBN nanoflakes with single defects were found from hBN nanoflakes placed on microscope coverslips. High-resolution emission intensity patterns were measured by exciting the defects in the hBNs with a focused radially polarized beam and azimuthally polarized beam. By comparing these patterns with theoretical calculations, we determined the polar angle and azimuthal angle of the dipole moment and found that they were oriented near the plane of the layers of the hBN nanoflakes on the microscope coverslip on which they were placed. This information is important to realize highly efficient quantum information devices in which the dipole orientation has to be precisely controlled.
Takodjou Wambo, JD, Pour, AB, Ganno, S, Asimow, PD, Zoheir, B, Salles, RDR, Nzenti, JP, Pradhan, B & Muslim, AM 2020, 'Identifying high potential zones of gold mineralization in a sub-tropical region using Landsat-8 and ASTER remote sensing data: A case study of the Ngoura-Colomines goldfield, eastern Cameroon', Ore Geology Reviews, vol. 122, pp. 103530-103530.
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© 2020 Elsevier B.V. Climatic conditions and vegetation constrain the use of optical satellite imagery as an exploration tool for hydrothermal ore mineralization in tropical and subtropical regions. In this investigation, Landsat-8 and ASTER satellite imagery were used to detect hydrothermal alteration zones associated with gold mineralization in the Ngoura-Colomines region, Eastern Cameroon. The study area contains several gold-bearing quartz veins associated with zones of pyritization, muscovite/sericite, iron oxides, and silicification. Principal Component Analysis (PCA), Independent Component Analysis (ICA), and specialized spectral band ratios were used to extract spectral information related to vegetation, iron oxide/hydroxide minerals, Al–OH, Fe-Mg–OH, carbonate group minerals, and silicification using Landsat-8 data at regional scale. Linear Spectral Unmixing (LSU) algorithm was implemented to ASTER VNIR + SWIR bands for detailed discrimination of hematite, jarosite, kaolinite, muscovite, chlorite and epidote at district scale. The Automated Spectral Hourglass (ASH) technique was employed to extract reference spectra directly from the ASTER bands for producing fraction images of end-members using the LSU. A comprehensive field survey was used to verify the remote sensing results. Petrographic study, X-ray diffraction analysis and reflectance spectroscopy indicated the presence of quartz, goethite and sericite, as well as the absorption features of Fe3+/Fe2+, Al–OH, OH/H2O and SiO2 in the alteration zones. Several hydrothermal alteration zones of iron oxide/hydroxide, clay, carbonate minerals and silicification zones were identified, which are spatially associated with known mining areas and gold occurrences in the study area. High potential prospects were also delineated, including the Ngoura-Colomines prospects and the newly discovered Yangamo-Ndatanga and Taparé-Tapondo prospects in the southwestern and southeastern parts of the study area. Co...
Tam, NT, Dung, DA, Hung, TH, Binh, HTT & Yu, S 2020, 'Exploiting relay nodes for maximizing wireless underground sensor network lifetime', Applied Intelligence, vol. 50, no. 12, pp. 4568-4585.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. A major challenge in wireless underground sensor networks is the signal attenuation originated from multi-environment transmission between underground sensor nodes and the above-ground base station. To overcome this issue, an efficient approach is deploying a set of relay nodes aboveground, thereby reducing transmission loss by shortening transmitting distance. However, this introduces several new challenges, including load balancing and transmission loss minimization. This paper tackles the problem of deploying relay nodes to reduce transmission loss under a load balancing constraint by proposing two approximation algorithms. The first algorithm is inspired by Beam Search, combined with a new selection scheme based on Boltzmann distribution. The second algorithm aims to further improve the solutions obtained by the former by reducing the transmission loss. We observe that we can find an optimal assignment between sensor nodes and a set of the chosen relay in polynomial time by reformulating the part of the problem as a bipartite matching problem with minimum cost. Experimental results indicate that the proposed methods perform better than the other existing ones in most of our test instances while reducing the execution time.
Tam, VWY, Butera, A, Le, KN & Li, W 2020, 'Utilising CO2 technologies for recycled aggregate concrete: A critical review', Construction and Building Materials, vol. 250, pp. 118903-118903.
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© 2020 Elsevier Ltd Employment of recycled aggregate within concrete provides great potential for the reduction of landfilling. Unfortunately, recycled aggregate exhibits a high porosity and water absorption and consequently produces a substandard material when compared to the mainstream virgin aggregate concrete. Recently, the injection of CO2 into cementitious materials has been studied, for both improving the overall quality of recycled aggregate concrete as well as permanently chemically converting CO2 into stone. CO2 treatment can permit recycled aggregate concrete to rival virgin aggregate concrete in phycial and mechanical properties. Currently, there are two primary methodologies for the sequestration of CO2 into concrete: (1) carbon-conditioning is the injection of CO2 into recycled aggregate; and (2) carbon-curing involves sequestering CO2 into new concrete's cement paste. Whilst both technologies permit recycled aggregate concrete for achieving great mechanical property and durability, carbon-conditioning provides a practical implementation. Carbon-conditioning permits a prompt and complete carbonation of recycled aggregate which enhances the final concrete's mechanical property and durability. This paper provides an insight into the available CO2 technologies for concrete improvement.
Tan, X, Hu, Z, Li, W, Zhou, S & Li, T 2020, 'Micromechanical Numerical Modelling on Compressive Failure of Recycled Concrete using Discrete Element Method (DEM)', Materials, vol. 13, no. 19, pp. 4329-4329.
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This paper investigates the failure processes of recycled aggregate concrete by a model test and numerical simulations. A micromechanical numerical modeling approach to simulate the progressive cracking behavior of the modeled recycled aggregate concrete, considering its actual meso-structures, is established based on the discrete element method (DEM). The determination procedure of contact microparameters is analyzed, and a series of microscopic contact parameters for different components of modeled recycled aggregate concrete (MRAC) is calibrated using nanoindentation test results. The complete stress–strain curves, cracking process, and failure pattern of the numerical model are verified by the experimental results, proving their accuracy and validation. The initiation, growth, interaction, coalescence of microcracks, and subsequent macroscopic failure of the MRAC specimen are captured through DEM numerical simulations and compared with digital image correlation (DIC) results. The typical cracking modes controlled by meso-structures of MRAC are concluded according to numerical observations. A parameter study indicates the dominant influence of the macroscopic mechanical behaviors from the shear strength of the interfacial transition zones (ITZs).
Tang, C, Jiao, Y, Shi, B, Liu, J, Xie, Z, Chen, X, Zhang, Q & Qiao, S 2020, 'Coordination Tunes Selectivity: Two‐Electron Oxygen Reduction on High‐Loading Molybdenum Single‐Atom Catalysts', Angewandte Chemie International Edition, vol. 59, no. 23, pp. 9171-9176.
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AbstractSingle‐atom catalysts (SACs) have great potential in electrocatalysis. Their performance can be rationally optimized by tailoring the metal atoms, adjacent coordinative dopants, and metal loading. However, doing so is still a great challenge because of the limited synthesis approach and insufficient understanding of the structure–property relationships. Herein, we report a new kind of Mo SAC with a unique O,S coordination and a high metal loading over 10 wt %. The isolation and local environment was identified by high‐angle annular dark‐field scanning transmission electron microscopy and extended X‐ray absorption fine structure. The SACs catalyze the oxygen reduction reaction (ORR) via a 2 e− pathway with a high H2O2 selectivity of over 95 % in 0.10 m KOH. The critical role of the Mo single atoms and the coordination structure was revealed by both electrochemical tests and theoretical calculations.
Tang, J, Liu, H & Yang, Y 2020, 'Compact Wide-Stopband Dual-Band Balanced Filter Using an Electromagnetically Coupled SIR Pair With Controllable Transmission Zeros and Bandwidths', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 11, pp. 2357-2361.
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© 2004-2012 IEEE. This brief presents a dual-band balanced filter with wide stopband, controllable transmission zeros and bandwidths, and compact size based on a newly designed electromagnetically coupled stepped impedance resonator (SIR) pair. The proposed SIR contains three different impedance lines, which can lead to more degrees of design freedom for resonant frequency control. Two electrical length ratios of the SIR can be individually tuned to obtain two desired differential-mode (DM) resonant frequencies for dual-band design, while keeping the first spurious frequency far away for wide-stopband design. Moreover, an inherent common-mode (CM) suppression can also be obtained without adding extra structures due to the discriminating CM resonant frequencies. The mechanism of transmission zeros (TZs) generation is also investigated by means of phases and admittance analysis. It reveals that four TZs around the two operating bands can be flexibly controlled via adjusting the ratio of magnetic and electric coupling coefficients of the SIR pair as well as the coupling space of the feeding lines, which have greatly enhanced the passband selectivities and the band-to-band isolation. Subsequently, the graphs of coupling coefficients and external quality factors for the filter design are extracted, showing sufficient degrees of design freedom for bandwidth control. The proposed dual-band balanced filter is finally fabricated and measured at 1.75 GHz and 3.64 GHz, respectively. Measured results are in good agreement with the simulated ones. Compared with the state-of-the-art works, the proposed circuit performs a deeper and wider stopband of 4.5{f}{1}{d} with 40dB attenuation (where {f}{1}{d} is the fundamental frequency), and more than 50-/45-dB in-band CM suppressions with compact size ( 0.14\lambda {g} \times 0.27{\lambda }{g} , where {\lambda }{g} is the guided wavelength of the fundamental frequency).
Tang, Q, Yang, J, Jia, W, He, X, Zhang, Q & Liu, H 2020, 'A GMS-Guided Approach for 2D Feature Correspondence Selection', IEEE Access, vol. 8, pp. 36919-36929.
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© 2013 IEEE. Feature correspondence selection, which aims to seek as many true matches (i.e., inliers) as possible from a given putative set while minimizing false matches (i.e., outliers), is crucial to many feature-matching based tasks in computer vision. It remains a challenging problem how to deal with putative sets with low inlier ratios. To address this problem, in this paper, we propose a novel correspondence selection strategy, which is guided by Grid-based Motion Statistics (GMS). We first adopt the GMS to generate a small correspondence set with a high inlier ratio. Then, an accurate geometric model is built using the above correspondence set. Finally, the built geometric model is used to filter the given putative correspondence set to obtain true correspondences. The experimental results on benchmark datasets demonstrate that our proposed approach outperforms the state-of-the-art approaches for putative sets with various inlier ratios, especially for cases with low inlier ratios.
Tang, RCO, Jang, J-H, Lan, T-H, Wu, J-C, Yan, W-M, Sangeetha, T, Wang, C-T, Ong, HC & Ong, ZC 2020, 'Review on design factors of microbial fuel cells using Buckingham's Pi Theorem', Renewable and Sustainable Energy Reviews, vol. 130, pp. 109878-109878.
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© 2020 Elsevier Ltd Microbial fuel cells (MFCs) have become a promising approach to generate cleaner and more sustainable electrical energy. Involvement of various disciplines had been contributing to enhance the performance of the MFCs. Factors affecting the performance such as chemical components, bacteria species, electrodes materials, flow interaction and electrical parts are being widely reviewed, however most of the research are highly field-specific without considering other important variables from different disciplines. In this study, Buckingham's Pi Theorem has been utilized to be implemented in the design pattern of MFCs. Several dominated variables of interest have also been pointed out including the design limitation. Modelling and application of Buckingham's Pi Theorem has been discussed as well which is useful for performance enhancement of MFCs and their application in wastewater treatment in the future.
Tang, Y, Liu, H, Li, Z, Meng, M & Zhang, J 2020, 'Tri‐component coupling transesterification for efficient no‐glycerol biodiesel production using methyl acetate as methyl reagent', Journal of Chemical Technology & Biotechnology, vol. 95, no. 4, pp. 1234-1242.
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AbstractBACKGROUNDConventional transesterification of vegetable oil with methanol for biodiesel production leads to the separation of glycerol and relatively low reaction efficiency. Methyl acetate is a more common solvent being less toxic and less soluble in water. In the presence of bases the transesterification of methyl acetate with glycerol can easily proceed at refluxing temperature.RESULTSA yield of fatty acid methyl ester (FAME) of 98.0% can be obtained with oil/methyl acetate/methanol molar ratio of 1:1:8 and 10% dosage of KCl/CaO at 65 °C after reaction for 1 h. Furthermore, results of water resistance experiments indicated that trace water gave a promoting effect on FAME yield. Recycling experiments were conducted for four cycles and a greater than 90% yield of FAME indicated the high stability of KCl/CaO.CONCLUSIONSEfficient biodiesel production with no glycerol byproduct has been developed using a tri‐component (canola oil, methyl acetate and methanol) coupling chemical reaction with calcium oxide‐supported chloride as catalyst. Various characterization techniques revealed that the unique catalytic activity of KCl/CaO was related to its high degree of crystallinity, relatively high surface basicity and large pore size. © 2019 Society of Chemical Industry
Tang, Y, Yang, Y, Liu, H, Li, Z, Zhang, J & Zhang, Z 2020, 'Efficient no‐glycerol biodiesel production using a novel biotemplated hierarchical porous‐structure CaO(O)', Journal of Chemical Technology & Biotechnology, vol. 95, no. 5, pp. 1467-1475.
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AbstractBACKGROUNDIn order to obtain an effective solid base with high surface area and hierarchical distribution of pore size, CaO(O) was synthesized via an exotemplating method, using radish as template and calcium acetate as precursor.RESULTSThe CaO(O) obtained by calcination of the impregnated biomorphic template demonstrated effective catalytic activity for the coupling transesterification of vegetable oil to produce biodiesel under mild reaction conditions using tri‐components (methanol, oil and methyl acetate) as resources. High yield of biodiesel, 98.6%, was obtained with a molar rapeseed oil/methyl acetate/methanol ratio of 1:1:8 under 65 °C at 2 h, which is greatly shorter than 6 h over commercial calcium oxide (CaO).CONCLUSIONVarious techniques including nitrogen physical adsorption, X‐ray diffraction (XRD), Fourier‐transform infrared (FTIR), thermogravimetric (TG), carbon dioxide‐chemical adsorption and morphology have been employed to characterize the samples. These results demonstrated that the synthesized CaO derived from plant template has a large surface area and various pore diameter distribution which cause hierarchical basic sites over the solid base. © 2020 Society of Chemical Industry
Tang, Y, Yang, Y, Liu, H, Yan, T & Zhang, Z 2020, 'Preparation of nano-CaO and catalyzing tri-component coupling transesterification to produce biodiesel', Inorganic and Nano-Metal Chemistry, vol. 50, no. 7, pp. 501-507.
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Tang, Z, Li, W, Tam, VWY & Luo, Z 2020, 'Investigation on dynamic mechanical properties of fly ash/slag-based geopolymeric recycled aggregate concrete', Composites Part B: Engineering, vol. 185, pp. 107776-107776.
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By harnessing the benefits from both construction and demolition waste recycling and geopolymer binders, geopolymeric recycled aggregate concrete (GRAC) can contribute to the green and eco-friendly construction material products. In this study, the compressive behavior of GRAC based on fly ash and slag was experimentally investigated under both quasi-static and dynamic loadings. Quasi-static compressive tests were performed by using a high-force servo-hydraulic test system, while dynamic compressive tests were carried out by using a Ø80-mm split Hopkinson pressure bar (SHPB) apparatus. The compressive properties of GRAC under dynamic loading, including stress-strain curves, energy absorption capability, and failure modes were obtained and compared with those under quasi-static loading. The results show that the compressive properties of GRAC exhibit a strong strain rate dependency. Although the recycled aggregate replacement decreases the quasi-static compressive strength, it exhibits a slight effect on the compressive strength at high strain rates. The dynamic increase factor (DIF) for compressive strength exhibits an significant increasing trend with the recycled aggregate replacement. On the other hand, the incorporation of slag increases the quasi-static compressive strength, dynamic compressive strength, and DIF. As for the energy absorption capacity, a minor enhancement is achieved with the recycled aggregate replacement, while a significant improvement is identified after the inclusion of slag. Empirical DIF formulae for compressive strength of GRAC are proposed, in which the DIF increases approximately linearly with the strain rate in a logarithmic manner.
Tang, Z, Li, W, Tam, VWY & Xue, C 2020, 'Advanced progress in recycling municipal and construction solid wastes for manufacturing sustainable construction materials', Resources, Conservation & Recycling: X, vol. 6, pp. 100036-100036.
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© 2020 The sharply increasing solid waste generation has raised the environmental concerns worldwide which currently have been escalated to a worrying level. Intending to eliminate the negative environmental impacts of solid waste and meanwhile promote sustainability on the energy- and resource-intensive construction and building sector, considerable efforts have been devoted to recycling solid waste for the possible use in sustainable construction material products. This paper reviews the existing studies on recycling municipal and construction solid waste for the manufacture of geopolymer composites. Special attention is paid to the predominate performance of these geopolymer composite products. The principal findings of this work reveal that municipal and construction solid waste could be successfully incorporated into geopolymer composites in the forms of precursor, aggregate, additive, reinforcement fiber, or filling material. Additionally, the results indicate that although the inclusion of such waste might depress some of the attributes of geopolymer composites, proper proportion design and suitable treatment technique could alleviate these detrimental effects and further smooth the recycling progress. Finally, a brief discussion is provided to identify the important needs in the future research and development for promoting the utilization of solid waste materials in the forthcoming sustainable geopolymer industry. In summary, this work offers guidance for the better ecological choice to municipal and construction solid waste through developing waste materials into highly environmental-friendly construction materials.
Tang, Z, Li, W, Tam, VWY & Yan, L 2020, 'Mechanical behaviors of CFRP-confined sustainable geopolymeric recycled aggregate concrete under both static and cyclic compressions', Composite Structures, vol. 252, pp. 112750-112750.
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© 2020 Elsevier Ltd Geopolymeric recycled aggregate concrete (GRAC) can greatly facilitate sustainability in the construction industry by the simultaneous utilization of solid waste-based recycled aggregate and eco-friendly binder–geopolymer. This study presents an experimental investigation on the mechanical behaviors of GRAC confined by carbon fiber-reinforced polymer (CFRP) jackets under both monotonic and cyclic compressive loading. A total of 24 CFRP-confined GRAC specimens were fabricated and tested, in which four aggregate replacement ratios (i.e., 0%, 25%, 50%, and 100%) and two thicknesses of CFRP jackets (i.e., 1 and 2 layers) were considered. The failure patterns, compressive stress-strain behavior, and axial-lateral strain responses of CFRP-confined GRAC were investigated and compared. The characteristics of stress-strain relationships were also discussed in terms of the peak stress, ultimate strain, residual modulus, plastic strain, reloading modulus, and stress deterioration ratio. Moreover, the related results were analyzed by comparing to the prediction ones of the existing models for FRP-confined concrete, to evaluate their applicability and accuracies for CFRP-confined GRAC. The outcomes will enrich the experimental database of CFRP-confined concrete and provide insights into the practical application of CFRP-confined GRAC.
Tang, Z, Li, W, Tam, VWY & Yan, L 2020, 'Mechanical performance of CFRP-confined sustainable geopolymeric recycled concrete under axial compression', Engineering Structures, vol. 224, pp. 111246-111246.
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© 2020 Elsevier Ltd Sustainable geopolymeric recycled aggregate concrete (RAC) by utilizing environmentally-friendly binder-geopolymer and constructional solid waste-recycled aggregate (RA) will facilitate the sustainability in concrete industry. This study investigated the compressive behavior of sustainable geopolymeric RAC confined by carbon fiber-reinforced polymer (CFRP) jackets. A total of 72 cylindrical fly ash/slag-based geopolymeric concrete specimens, including 48 CFRP-confined specimens and 24 unconfined specimens were fabricated and tested. The testing variables included: coarse aggregate type (i.e., natural aggregate and RA), thickness of CFRP jackets (i.e., 1, 2, and 3 layers) and (iii) slag content (i.e., 0, 10%, 20% and 30% of the total binder by mass). The results indicate that the CFRP confinement remarkably enhances the compressive strength and ultimate strain of geopolymeric concrete, and the enhancement is more pronounced with the increase of CFRP jacket thickness. Moreover, the RA replacement and the inclusion of slag have minor influences on the CFRP confinement performance for the compressive strength, but have obvious effects on the CFRP confinement performance for the ultimate axial strain. Based on the test results, empirical stress and strain models were proposed to predict the ultimate condition of the CFRP-confined geopolymeric concrete.
Tang, Z-E, Lim, S, Pang, Y-L, Shuit, S-H & Ong, H-C 2020, 'Utilisation of biomass wastes based activated carbon supported heterogeneous acid catalyst for biodiesel production', Renewable Energy, vol. 158, pp. 91-102.
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© 2020 Elsevier Ltd This study evaluated the utilisation of biomass wastes as catalyst supports by comparing the catalytic performance of papaya seed, empty fruit bunch (EFB) and corncob biomass waste derived carbon based acid catalysts applied for biodiesel production through esterification reaction of palm fatty acid distillate (PFAD) and methanol. Arylation of 4-benzenediazonium sulfonate synthesis method was able to sulfonate the catalyst support efficiently. The activated carbon (AC) synthesised possessed high porosity with surface area ranged between 639.68 and 972.66 m2/g. The effect of catalyst synthesising condition including carbonisation temperature (600–1000 °C), sulfonation time (0.5–2.5 h) and sulfanilic acid to AC weight ratio (3:1–13:1) towards the FAME yield and free fatty acid (FFA) conversion were evaluated. At the optimum catalyst synthesis conditions, corncob waste derived sulfonated AC catalyst exhibited the highest FAME yield and FFA conversion of 72.09% and 93.49%, respectively. Reusability study showed that corncob waste derived sulfonated AC catalyst was able to achieve relatively high FAME yield at the first two reaction cycles. The esterification reaction followed the irreversible pseudo-homogeneous reaction model. The high catalytic efficiency of the catalyst had shown its high potential to fit into the cost-effective and sustainable framework for biodiesel production.
Tanveer, M, Khanna, P, Prasad, M & Lin, CT 2020, 'Introduction to the Special Issue on Computational Intelligence for Biomedical Data and Imaging', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 1s, pp. 1-4.
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Tanveer, M, Richhariya, B, Khan, RU, Rashid, AH, Khanna, P, Prasad, M & Lin, CT 2020, 'Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 1s, pp. 1-35.
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Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.
Tao, Y, Chan, HF, Shi, B, Li, M & Leong, KW 2020, 'Light: A Magical Tool for Controlled Drug Delivery', Advanced Functional Materials, vol. 30, no. 49.
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AbstractLight is a particularly appealing tool for on‐demand drug delivery due to its noninvasive nature, ease of application, and exquisite temporal and spatial control. Great progress is achieved in the development of novel light‐driven drug delivery strategies with both breadth and depth. Light‐controlled drug delivery platforms can be generally categorized into three groups: photochemical, photothermal, and photoisomerization‐mediated therapies. Various advanced materials, such as metal nanoparticles, metal sulfides and oxides, metal–organic frameworks, carbon nanomaterials, upconversion nanoparticles, semiconductor nanoparticles, stimuli‐responsive micelles, polymer‐ and liposome‐based nanoparticles are applied for light‐stimulated drug delivery. In view of the increasing interest in on‐demand targeted drug delivery, the development of light‐responsive systems with a focus on recent advances, key limitations, and future directions is reviewed.
Tashtarian, F, Zhani, MF, Fatemipour, B & Yazdani, D 2020, 'CoDeC: A Cost-Effective and Delay-Aware SFC Deployment', IEEE Transactions on Network and Service Management, vol. 17, no. 2, pp. 793-806.
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Service Function Chain (SFC) provides an end-to-end service by processing traffic flow through a series of Virtual Network Functions (VNFs) in a specific order. Satisfying user's demands (e.g., end-to-end delay) on one hand and minimizing the cost of SFC deployment in terms of energy and resource on the other hand, introduces VNFs placement as a crucial issue that is receiving significant attention by researchers. To address this problem and boost the performance of SFC, different techniques such as Network Function (NF) distribution, NF parallelism and optimal resource allocation have been utilized. Applying these mechanisms imposes other costs which must be taken into account by network providers. In this paper, we introduce CoDeC as a Cost-effective and Delay-aware resource allocation approach. By having user defined end-to-end threshold and using aforementioned mechanisms, CoDeC tries to place the requested VNFs with the minimum cost of deployment, distribution, parallelism and energy. Therefore, we formulate the addressed problem in form of Mixed Integer linear Programming (MILP) model. We then show that the problem is NP-complete and suffers from high time complexity in large-scale scenarios. Thus, a heuristic algorithm is introduced to determine a near-optimal solution in a reasonable amount of time. Our simulation results show that CoDeC achieves better performance in term of cost and acceptance rate compared to using each mechanism individually.
Tavakoli, J, Diwan, AD & Tipper, JL 2020, 'Advanced Strategies for the Regeneration of Lumbar Disc Annulus Fibrosus', International Journal of Molecular Sciences, vol. 21, no. 14, pp. 4889-4889.
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Damage to the annulus fibrosus (AF), the outer region of the intervertebral disc (IVD), results in an undesirable condition that may accelerate IVD degeneration causing low back pain. Despite intense research interest, attempts to regenerate the IVD have failed so far and no effective strategy has translated into a successful clinical outcome. Of particular significance, the failure of strategies to repair the AF has been a major drawback in the regeneration of IVD and nucleus replacement. It is unlikely to secure regenerative mediators (cells, genes, and biomolecules) and artificial nucleus materials after injection with an unsealed AF, as IVD is exposed to significant load and large deformation during daily activities. The AF defects strongly change the mechanical properties of the IVD and activate catabolic routes that are responsible for accelerating IVD degeneration. Therefore, there is a strong need to develop effective therapeutic strategies to prevent or reconstruct AF damage to support operational IVD regenerative strategies and nucleus replacement. By the way of this review, repair and regenerative strategies for AF reconstruction, their current status, challenges ahead, and future outlooks were discussed.
Tavakoli, J, Diwan, AD & Tipper, JL 2020, 'Elastic fibers: The missing key to improve engineering concepts for reconstruction of the Nucleus Pulposus in the intervertebral disc', Acta Biomaterialia, vol. 113, pp. 407-416.
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Tavakoli, J, Diwan, AD & Tipper, JL 2020, 'The ultrastructural organization of elastic fibers at the interface of the nucleus and annulus of the intervertebral disk', Acta Biomaterialia, vol. 114, pp. 323-332.
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There has been no study to describe the ultrastructural organization of elastic fibers at the interface of the nucleus pulposus and annulus fibrosus of the intervertebral disk (IVD), a region called the transition zone (TZ). A previously developed digestion technique was optimized to eliminate cells and non-elastin ECM components except for the elastic fibers from the anterolateral (AL) and posterolateral (PL) regions of the TZ in ovine IVDs. Not previously reported, the current study identified a complex elastic fiber network across the TZ for both AL and PL regions. In the AL region, this network consisted of major thick elastic fibers (≈ 1 µm) that were interconnected with delicate (< 200 nm) elastic fibers. While the same ultrastructural organization was observed in the PL region, interestingly the size of the elastic fibers was smaller (< 100 nm) compared to those that were located in the AL region. Quantitative analysis of the elastic fibers revealed significant differences in the size (p < 0.001) and the orientation of elastic fibers (p = 0.001) between the AL and PL regions, with a higher orientation and larger size of elastic fibers observed in the AL region. The gradual elimination of cells and non-elastin extracellular matrix components identified that elastic fibers in the TZ region in combination with the extracellular matrix created a honeycomb structure that was more compact at the AF interface compared to that located close to the NP. Three different symmetrically organized angles of rotation (0⁰ and ±90⁰) were detected for the honeycomb structure at both interfaces, and the structure was significantly orientated at the TZ-AF compared to the TZ-NP interface (p = 0.003).
Tavakoli, J, Joseph, N, Chuah, C, Raston, CL & Tang, Y 2020, 'Vortex fluidic enabling and significantly boosting light intensity of graphene oxide with aggregation induced emission luminogen', Materials Chemistry Frontiers, vol. 4, no. 7, pp. 2126-2130.
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We have discovered a novel and cost-effective approach to produce GO/aggregation-induced emission demonstrating high fluorescent performance.
Tavakoli, J, Pye, S, Reza, AHMM, Xie, N, Qin, J, Raston, CL, Tang, BZ & Tang, Y 2020, 'Tuning aggregation-induced emission nanoparticle properties under thin film formation', Materials Chemistry Frontiers, vol. 4, no. 2, pp. 537-545.
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The preparation of AIE nanoparticles under thin film formation controls their size and the associated fluorescent intensity, with the smaller nanoparticles significantly increasing brightness.
Tavakoli, J, Raston, CL & Tang, Y 2020, 'Tuning Surface Morphology of Fluorescent Hydrogels Using a Vortex Fluidic Device', Molecules, vol. 25, no. 15, pp. 3445-3445.
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In recent decades, microfluidic techniques have been extensively used to advance hydrogel design and control the architectural features on the micro- and nanoscale. The major challenges with the microfluidic approach are clogging and limited architectural features: notably, the creation of the sphere, core-shell, and fibers. Implementation of batch production is almost impossible with the relatively lengthy time of production, which is another disadvantage. This minireview aims to introduce a new microfluidic platform, a vortex fluidic device (VFD), for one-step fabrication of hydrogels with different architectural features and properties. The application of a VFD in the fabrication of physically crosslinked hydrogels with different surface morphologies, the creation of fluorescent hydrogels with excellent photostability and fluorescence properties, and tuning of the structure–property relationship in hydrogels are discussed. We conceive, on the basis of this minireview, that future studies will provide new opportunities to develop hydrogel nanocomposites with superior properties for different biomedical and engineering applications.
Tavakoli, J, Raston, CL, Ma, Y & Tang, Y 2020, 'Vortex fluidic mediated one-step fabrication of polyvinyl alcohol hydrogel films with tunable surface morphologies and enhanced self-healing properties', Science China Materials, vol. 63, no. 7, pp. 1310-1317.
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Tavakoli, J, Wang, J, Chuah, C & Tang, Y 2020, 'Natural-based Hydrogels: A Journey from Simple to Smart Networks for Medical Examination', Current Medicinal Chemistry, vol. 27, no. 16, pp. 2704-2733.
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Natural hydrogels, due to their unique biological properties, have been used extensively for various medical and clinical examinations that are performed to investigate the signs of disease. Recently, complex-crosslinking strategies improved the mechanical properties and advanced approaches have resulted in the introduction of naturally derived hydrogels that exhibit high biocompatibility, with shape memory and self-healing characteristics. Moreover, the creation of self-assembled natural hydrogels under physiological conditions has provided the opportunity to engineer fine-tuning properties. To highlight recent studies of natural-based hydrogels and their applications for medical investigation, a critical review was undertaken using published papers from the Science Direct database. This review presents different natural-based hydrogels (natural, natural-synthetic hybrid and complex-crosslinked hydrogels), their historical evolution, and recent studies of medical examination applications. The application of natural-based hydrogels in the design and fabrication of biosensors, catheters and medical electrodes, detection of cancer, targeted delivery of imaging compounds (bioimaging) and fabrication of fluorescent bioprobes is summarised here. Without doubt, in future, more useful and practical concepts will be derived to identify natural-based hydrogels for a wide range of clinical examination applications.
Tawadros, P, Awadallah, M, Walker, P & Zhang, N 2020, 'Using a low-cost bluetooth torque sensor for vehicle jerk and transient torque measurement', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 234, no. 2-3, pp. 423-437.
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This paper presents the use and development of a specific wireless torque measurement system that is used to obtain the transient torque performance of vehicle transmissions. The torque sensor is strain-based, using surface-mounted strain gauges on a prop shaft. The gauges are connected to a compact printed circuit board, which is clamped to the shaft next to the strain gauges using a three-dimensional printed housing. The printed circuit board contains an amplifier, low-pass filter, analog-to-digital converter, microcontroller and bluetooth transceiver. The printed housing is impact resistant carbon-reinforced nylon and securely retains the printed circuit board and the battery powering the device. The transmitted torque data are received by a transceiver, which is interfaced to a PC through an RS-232 connection. NI LabVIEW is used to process, display and save data. The wireless torque sensor was installed to the Unit Under Test at the output shaft of the five-speed manual transmission. The Unit Under Test was installed on a dynamometer for verification purposes and the transient torque was recorded under various operational conditions. The transient output torque of the manual transmission is measured and compared with results obtained from simulations performed under similar operating conditions. The two sets of transient responses show a good correlation with each other and hence demonstrate that the torque sensor meets the major design specifications. The data obtained will be used to enhance the fidelity of the software model.
te West, NID, Day, RO, Hiley, B, White, C, Wright, M & Moore, KH 2020, 'Estriol serum levels in new and chronic users of vaginal estriol cream: A prospective observational study', Neurourology and Urodynamics, vol. 39, no. 4, pp. 1137-1144.
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AbstractAimsTo quantify estriol serum concentrations in “new” and “chronic users” of topical estriol cream using quantitative liquid chromatography tandem mass spectrometry.MethodsIn this singlecentre prospective observational study, postmenopausal women with urogynaecological complaints were enrolled: 40 had not used topical estriol previously (“new users”) and 50 had been applying estriol cream for more than 12 weeks (“chronic users”). In “new users,” serum estriol levels were measured at baseline and after 12 weeks use. Estriol cream 1 mg/g was used daily for 3 weeks, then twice weekly with applicator (group 1A) or digitally (group 1B) or three times per week digitally (group 1C). “Chronic users” applied the cream twice (n = 7) or three (n = 43) times per week. Serum samples were taken in the morning after using cream the previous night. The main outcome measures were estriol serum concentrations in “new” and “chronic users” of estriol cream.ResultsBaseline serum estriol concentrations were less than 5 pmol/L in all 40 “new users.” At 12 weeks, the 12‐hour serum estriol levels ranged from less than 5 to 494 pmol/L (median 22.8; Interquartile range [IQR] 9.2–108.5). Seven “new users” had levels more than 100 pmol/L. Most of the 50 “chronic users” also had 12‐hour levels less than 100 pmol/L (median 15.1 pmol/L [IQR 2.7–33.9]: three had levels more than 100 pmol/L.ConclusionsThis study reports serum estriol concentrations in a large number of “new” and “chronic users” of vaginal estriol cream, employing a novel highly sensitive and specific technique. Overall, the results are reassuring: 87% had 12‐hour estriol levels less than 100 pmol/L.
Tee, AE, Ciampa, OC, Wong, M, Fletcher, JI, Kamili, A, Chen, J, Ho, N, Sun, Y, Carter, DR, Cheung, BB, Marshall, GM, Liu, PY & Liu, T 2020, 'Combination therapy with the CDK7 inhibitor and the tyrosine kinase inhibitor exerts synergistic anticancer effects against MYCN‐amplified neuroblastoma', International Journal of Cancer, vol. 147, no. 7, pp. 1928-1938.
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Patients with neuroblastoma due to MYCN oncogene amplification and consequent N‐Myc oncoprotein overexpression have very poor prognosis. The cyclin‐dependent kinase 7 (CDK7)/super‐enhancer inhibitor THZ1 suppresses MYCN gene transcription, reduces neuroblastoma cell proliferation, but does not cause significant cell death. The protein kinase phosphatase 1 nuclear targeting subunit (PNUTS) has recently been shown to interact with c‐Myc protein and suppresses c‐Myc protein degradation. Here we screened the U.S. Food and Drug Administration‐Approved Oncology Drugs Set V from the National Cancer Institute, and identified tyrosine kinase inhibitors (TKIs), including ponatinib and lapatinib, as the Approved Oncology Drugs exerting the best synergistic anticancer effects with THZ1 in MYCN‐amplified neuroblastoma cells. Combination therapy with THZ1 and ponatinib or lapatinib synergistically induced neuroblastoma cell apoptosis, while having little effects in normal nonmalignant cells. Differential gene expression analysis identified PNUTS as one of the genes most synergistically reduced by the combination therapy. Reverse transcription polymerase chain reaction and immunoblot analyses confirmed that THZ1 and the TKIs synergistically downregulated PNUTS mRNA and protein expression and reduced N‐Myc protein but not N‐Myc mRNA expression. In addition, PNUTS knockdown resulted in decreased N‐Myc protein but not mRNA expression and decreased MYCN‐amplified neuroblastoma cell proliferation and survival. As CDK7 inhibitors are currently under clinical evaluation in patients, our data suggest the addition of the TKI ponatinib or lapatinib in CDK7 inhibitor clinical trials in patients.
Telikani, A, Gandomi, AH & Shahbahrami, A 2020, 'A survey of evolutionary computation for association rule mining', Information Sciences, vol. 524, pp. 318-352.
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© 2020 Association Rule Mining (ARM) is a significant task for discovering frequent patterns in data mining. It has achieved great success in a plethora of applications such as market basket, computer networks, recommendation systems, and healthcare. In the past few years, evolutionary computation-based ARM has emerged as one of the most popular research areas for addressing the high computation time of traditional ARM. Although numerous papers have been published, there is no comprehensive analysis of existing evolutionary ARM methodologies. In this paper, we review emerging research of evolutionary computation for ARM. We discuss the applications on evolutionary computations for different types of ARM approaches including numerical rules, fuzzy rules, high-utility itemsets, class association rules, and rare association rules. Evolutionary ARM algorithms were classified into four main groups in terms of the evolutionary approach, including evolution-based, swarm intelligence-based, physics-inspired, and hybrid approaches. Furthermore, we discuss the remaining challenges of evolutionary ARM and discuss its applications and future topics.
Teng, J, Kou, J, Yan, X, Zhang, S & Sheng, D 2020, 'Parameterization of soil freezing characteristic curve for unsaturated soils', Cold Regions Science and Technology, vol. 170, pp. 102928-102928.
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© 2019 The soil freezing characteristic curve (SFCC) describes the relationship between the temperature and unfrozen water content in a soil. The SFCC is indispensable in modelling the hydro-mechanical behaviour of frozen soils, but is less understood for the unsaturated soils. A series of SFCC tests of unsaturated silica sand, silt and red clay are preformed based on a newly developed nuclear magnetic resonance (NMR) apparatus, which can precisely control the sample temperature in the magnetic field. The experimental results show that the measured SFCC varies significantly for different initial water contents, and that a lower initial water content leads to a slower increase in unfrozen water content, proving that the SFCC is closely related to the initial unsaturated state. It is found that the thawing curve is better to represent the SFCC, in contrast the freezing curve is significantly affected by the supercooling phenomenon. A new parameterization of the SFCC is presented for unsaturated soils by combining the Clapeyron equation and the model for Soil Water Characteristic Curve (SWCC). A number of test results from the literature and this study are used to validate the new SFCC model. By inputting the parameters for the SWCC and initial state into the proposed model, the predicted SFCC can agree well with the measured results. The new model has a theoretical basis and simple form and is applicable to both saturated and unsaturated soils.
Teng, J, Liu, J, Zhang, S & Sheng, D 2020, 'Modelling frost heave in unsaturated coarse-grained soils', Acta Geotechnica, vol. 15, no. 11, pp. 3307-3320.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Coarse-grained soils were considered not susceptible to frost heave. However, substantial frost heave has been observed in unsaturated coarse fills in high-speed railway embankments. Recent experimental results in the literature show that vapour transfer has a considerable influence on the frost heaving of coarse-grained soil. However, vapour transfer has rarely been considered in modelling frost heave. This study presents a new frost heave model that considers vapour transfer and its contribution to ice formation. An updated computer program (PCHeave) is developed to account for the vapour transfer in unsaturated coarse-grained soils, where the rigid ice theory is applied to initiate ice lens formation in the frozen fringe. The results of the proposed model are compared with laboratory test results, which show reasonable agreement. The frost heave data monitored in 2013–2014 along the embankment of the Harbin–Dalian Passenger Dedicated Railway are also used to validate the proposed model. The prediction of the model agrees well with the measured results of frost heave and frost depth. This indicates that the proposed model can reasonably reflect the process of frost heave caused by vapour transfer in unsaturated coarse-grained soils.
Thakur, AK, Chellappan, DK, Dua, K, Mehta, M, Satija, S & Singh, I 2020, 'Patented therapeutic drug delivery strategies for targeting pulmonary diseases', Expert Opinion on Therapeutic Patents, vol. 30, no. 5, pp. 375-387.
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Introduction: Pulmonary route is one of the preferred routes for the administration of therapeutically active agents for systemic as well as localized delivery. Chronic obstructive pulmonary disease (COPD), bronchial asthma, pneumonia, pulmonary hypertension, bronchiolitis, lung cancer, and tuberculosis are the major chronic diseases associated with the pulmonary system. Knowledge about the affecting factors, namely, the etiology, pathophysiology, and the various barriers (mechanical, chemical, immunological, and behavioral) in pulmonary drug delivery is essential to develop an effective drug delivery system. Formulation strategies and mechanisms of particle deposition in the lungs also play an important role in designing a suitable delivery system.Areas covered: In the present paper, various drug delivery strategies, viz. nanoparticles, microparticles, liposomes, powders, and microemulsions have been discussed systematically, from a patent perspective.Expert opinion: Patent publications on formulation strategies have been instrumental in the evolution of new techniques and technologies for safe and effective treatment of pulmonary diseases. New delivery systems are required to be simple/reproducible/scalable/cost-effective scale for manufacturing ability and should be safe/effective/stable/controllable for meeting quality and regulatory compliance.
Thanh, HT, Li, J & Zhang, YX 2020, 'Numerical simulation of self-consolidating engineered cementitious composite flow with the V-funnel and U-box', Construction and Building Materials, vol. 236, pp. 117467-117467.
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Thiyagarajan, K, Kodagoda, S, Ranasinghe, R, Vitanage, D & Iori, G 2020, 'Robust Sensor Suite Combined With Predictive Analytics Enabled Anomaly Detection Model for Smart Monitoring of Concrete Sewer Pipe Surface Moisture Conditions', IEEE Sensors Journal, vol. 20, no. 15, pp. 8232-8243.
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Thomas, P, Chauviré, B, Flower-Donaldson, K, Aldridge, L, Smallwood, A & Liu, B 2020, 'FT-NIR and DSC characterisation of water in opal', Ceramics International, vol. 46, no. 18, pp. 29443-29450.
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© 2020 Elsevier Ltd and Techna Group S.r.l. Opal is a hydrous silica (Si02.nH2O) formed through a dissolution-precipitation process. The formation process incorporates water into the structure as bound silanol and molecular water. As the water is distributed in a range of states, multiple methods of characterisation are required to identify each state. This study reports the results of temperature dependent FT-NIR and DSC investigation on natural opal samples of the opal-A (amorphous) and opal-CT (poorly crystalline cristobalite with tridymitic stacking faults) types. Significant differences in the melting behaviour of crystallisable water as well as differences in the spectral characteristics of the non-crystallisable molecular water are observed. These differences are ascribed to the different microstructures of the opal types.
Thöns, S & Stewart, MG 2020, 'On the cost-efficiency, significance and effectiveness of terrorism risk reduction strategies for buildings', Structural Safety, vol. 85, pp. 101957-101957.
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We analyse the performance of risk reduction strategies for terrorist attacks with Improvised Explosive Devices (IEDs) for large governmental building structures in terms of cost-efficiency, significance and effectiveness accounting for life safety in conjunction with societal preferences and capabilities. The approach builds upon an extended Bayesian pre-posterior decision analysis and the principles of the marginal lifesaving costs based on the Life Quality Index (LQI). The decision scenario is formulated for a decision maker responsible for the safety of governmental or large commercial buildings and consequently the direct risks, the indirect risks due to fatalities and economical importance of the building beside the expected cost for the individual risk reduction strategies are modelled, aggregated and optimised. The considered risk reduction strategies encompass an explicit consideration and distinction of information and actions such as (i) threat surveillance may trigger the temporary evacuation of the building, (ii) the implementation of protection provisions provided by codes and guidelines, (iii) a detailed progressive collapse assessment and specific protection measures and (iv) the combination of protection and surveillance. All considered strategies are found to contribute to risk reduction and can be cost-efficient, especially for higher threat probabilities. The risk reduction strategies comply with societal macroeconomic and demographical characteristics and societal preferences according to the LQI. The progressive collapse assessment with targeted protection measures is found to be the most cost-efficient, significant and effective counter-terrorism strategy. This finding points to the necessity for a comprehensive utilisation of scientific methods and sophisticated engineering for progressive collapse assessment to determine targeted protection measures.
Tian, S, Indraratna, B, Tang, L, Qi, Y & Ling, X 2020, 'A semi-empirical elasto-plastic constitutive model for coarse-grained materials that incorporates the effects of freeze-thaw cycles', Transportation Geotechnics, vol. 24, pp. 100373-100373.
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© 2020 Elsevier Ltd A volume-shear coupling mechanism is imperative for developing high-speed railways in very cold regions. A series of consolidated drained static triaxial experiments were carried out to investigate the effect of freeze-thaw (F-T) cycles on the stress-strain features of coarse-grained materials (CGM) typically used at the bottom layer of subgrade for high-speed rail tracks in China. Mathematical expressions describing the effect of F-T cycles for residual stress state stress ratio, elastic shear modulus, and specific volume have been proposed. Laboratory observations enabled an empirical dilatancy equation to be incorporated in a constitutive model to capture the salient aspects of the monotonic deformation behaviour of CGM including the F-T effects. After comparing with experimental observations and validating through past independent studies, the proposed constitutive model could accurately predict the monotonic shear behaviour of the CGM exposed to F-T cycles.
Tijing, LD, Dizon, JRC, Ibrahim, I, Nisay, ARN, Shon, HK & Advincula, RC 2020, '3D printing for membrane separation, desalination and water treatment', Applied Materials Today, vol. 18, pp. 100486-100486.
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© 2019 Elsevier Ltd Additive manufacturing or commonly known as 3D printing is driving innovation in many industries and academic research including the water resource sector. The capability of 3D printing to fabricate complex objects in a fast and cost-effective manner makes it highly desirable over conventional manufacturing processes. Recent years have seen a rapid increase in research using 3D printing for membrane separation, desalination and water purification applications, potentially revolutionizing this field. This review focuses on recent advancements in 3D-printed materials and methods for water-related applications including developments in module spacers, novel filtration and desalination membranes, adsorbents, water remediation, solar steam generation materials, catalysis, etc. The emergence of new 3D printers with higher printing resolution, better efficiency, faster speed, and wider material applicability has garnered more interest and can potentially reshape research and development in this field. The promising potential, challenges and future prospects of 3D printing, additive manufacturing, and materials for water resource and treatment-related applications are all discussed in this review.
To, VHP, Nguyen, TV, Bustamante, H & Vigneswaran, S 2020, 'Effects of extracellular polymeric substance fractions on polyacrylamide demand and dewatering performance of digested sludges', Separation and Purification Technology, vol. 239, pp. 116557-116557.
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© 2020 Elsevier B.V. High polymer demand in sludge conditioning is an intractable aspect of the water industry. This study investigated the effects of extracellular polymeric substances (EPS) fractions on polyacrylamide demand for conditioning and dewatering performance. Specifically, it examined aerobically and anaerobically digested sludges from seven full-scale wastewater treatment plants (WWTPs). Our study successfully quantified the contributions of soluble EPS to polyacrylamide demand during conditioning and explained the role of tightly bound EPS (TB-EPS) in determining the digested sludges’ dewatering performance. Results show that the concentrations of soluble EPS in the sludges varied between 92 and 1148 mg/L. Experimental results also demonstrated that between 25% and 80% of polyacrylamides used for conditioning were wasted in “parasitic” reactions with soluble EPS. The residual cationic polyacrylamide left in solution, after the parasitic reactions, was substantial and varied between 35 and 254 mg/L. Despite this outcome, the zeta potential values of dewatered sludge cakes remained negative, i.e. between −24 and −35 mV. These indicated that the residual soluble cationic polyacrylamides would not have been absorbed on the negatively charged sludge particles. This explained the relatively poor performance of the dewatering stage in the treatment plants studied. Furthermore the results suggested the TB-EPS attached to the sludge particles would be responsible for the poor dewatering. We postulated that the TB-EPS would gelify and immobilize the water surrounding the sludge particles. Our study suggested that new and more effective polymers for conditioning are needed to both: (i) reduce polymer demand; and (ii) improve the dewatering performance.
Tofigh, F, Amiri, M, Shariati, N, Lipman, J & Abolhasan, M 2020, 'Crowd Estimation Using Electromagnetic Wave Power-Level Measurements: A Proof of Concept', IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 784-792.
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© 1967-2012 IEEE. Current crowd density estimation technologies that leverage IR depth perception, video and image processing or WiFi/BLE-based sniffing and probing have privacy and deployment issues. This paper presents a novel method for non-intrusive crowd density estimation that monitors variation in EM radiation within an environment. The human body's electrical and magnetic characteristics can be correlated with variations in available EM energy. This allows for the determination of the number of people within a room. Simulations conducted using Comsol to analyse and measure electromagnetic energy levels inside a room containing human bodies. Experimental analysis provides validation of the simulation results by showing $\text{0.8}\;\text{dBm}$ drop on the average level of EM energy per person.
Tofigh, F, Amiri, M, Shariati, N, Lipman, J & Abolhasan, M 2020, 'Polarization-Insensitive Metamaterial Absorber for Crowd Estimation Based on Electromagnetic Energy Measurements', IEEE Transactions on Antennas and Propagation, vol. 68, no. 3, pp. 1458-1467.
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© 2020 IEEE. Noninvasive crowd estimation has remained a challenging issue among researchers. Methods such as image analysis and Wi-Fi/Bluetooth probing can always be used to identify and track people. Lately, authors have introduced a noninvasive method for crowd estimation based on ambient RF energy measurements. In this article, a polarization-insensitive multilayer metamaterial absorber is introduced to measure the variation in the available RF energy levels for crowd estimation purposes. The proposed dual-band absorber is designed to absorb and transfer the maximum of the available Wi-Fi energy to a lumped element to enable proper and accurate measurements. To evaluate the design, the proposed structure is fabricated as an array, and its performance is tested, proving perfect absorption at the desired frequencies, 2.4 and 5 GHz.
Toghroli, A, Mehrabi, P, Shariati, M, Trung, NT, Jahandari, S & Rasekh, H 2020, 'Evaluating the use of recycled concrete aggregate and pozzolanic additives in fiber-reinforced pervious concrete with industrial and recycled fibers', Construction and Building Materials, vol. 252, pp. 118997-118997.
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© 2020 Elsevier Ltd The aim of this study is to investigate the effects of using recycled concrete aggregate (RCA) and pozzolanic materials as a partial replacement of natural coarse aggregate (NCA) and cement, respectively, on the mechanical and permeability properties of fiber-reinforced pervious concrete mixes. For this purpose, mixes were prepared with 25%, 50%, 75%, and 100% (by weight) RCA as coarse aggregate, and cement was partially replaced with 10% silica fume (SF) and 1%, 2%, and 3% nano-clay (NC). In order to enhance the mechanical strength of mixes, steel fiber (STF) and waste plastic fiber (WPF) were incorporated in the mixtures at a volume fraction of 1% and 2%. The experiments were carried out on a total number of 2310 samples casted from 110 mixes. Based on the test results, up to 25% increase in permeability and about 60% reduction in strength properties of mix incorporating 100% RCA were observed. The use of SF and NC led to enhancements in the strength properties because of micro-filling ability and pozzolanic reactivity. In general, the addition of fibers enhanced both compressive and flexural strengths up to 65% and 79%, respectively, over that of the unreinforced counterpart mix by incorporating 2% STF. WPF-reinforced mixes showed inferior performance compared to the STF-reinforced counterparts, due to the low quality and poor dispersion of WPF in mixes. It was found that, incorporating 100% RCA combined with 2% STF and 2% NC yields a pervious concrete suitable for structural applications.
Tohry, A, Chehreh Chelgani, S, Matin, SS & Noormohammadi, M 2020, 'Power-draw prediction by random forest based on operating parameters for an industrial ball mill', Advanced Powder Technology, vol. 31, no. 3, pp. 967-972.
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Tong, C-X, Burton, GJ, Zhang, S & Sheng, D 2020, 'Particle breakage of uniformly graded carbonate sands in dry/wet condition subjected to compression/shear tests', Acta Geotechnica, vol. 15, no. 9, pp. 2379-2394.
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Torghabeh, AK, Pradhan, B & Jahandari, A 2020, 'Assessment of geochemical and sedimentological characteristics of atmospheric dust in Shiraz, southwest Iran', Geoscience Frontiers, vol. 11, no. 3, pp. 783-792.
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© 2019 China University of Geosciences (Beijing) and Peking University Geogenic dust is commonly believed to be one of the most important environmental problems in the Middle East. The present study investigated the geochemical characteristics of atmospheric dust particles in Shiraz City (south of Iran). Atmospheric dust samples were collected through a dry collector method by using glass trays at 10 location sites in May 2018. Elemental composition was analysed through inductively coupled plasma optical emission spectrometry. Meteorological data showed that the dustiest days were usually in spring and summer, particularly in April. X-ray diffraction analysis of atmospheric dust samples indicated that the mineralogical composition of atmospheric dust was calcite + dolomite (24%)>palygorskite (18%)>quartz (14%)>muscovite (13%)>albite (11%)>kaolinite (7%)>gypsum (7%)>zircon = anatase (3%). The high occurrence of palygorskite (16%–23%) could serve as a tracer of the source areas of dust storms from the desert of Iraq and Saudi Arabia to the South of Iran. Scanning electron microscopy indicated that the sizes of the collected dust varied from 50 μm to 0.8 μm, but 10 μm was the predominant size. The atmospheric dust collected had prismatic trigonal–rhombohedral crystals and semi-rounded irregular shapes. Moreover, diatoms were detected in several samples, suggesting that emissions from dry-bed lakes, such as Hoor Al-Azim Wetland (located in the southwest of Iran), also contributed to the dust load. Backward trajectory simulations were performed at the date of sampling by using the NOAA HYSPLIT model. Results showed that the sources of atmospheric dust in the studied area were the eastern area of Iraq, eastern desert of Saudi Arabia, Kuwait and Khuzestan Province. The Ca/Al ratio of the collected samples (1.14) was different from the upper continental crust (UCC) value (UCC = 0.37), whereas Mg/Al (0.29), K/Al (0.22) and Ti/Al (0.07) ratios were close to the UC...
Toriello, M, Afsari, M, Shon, H & Tijing, L 2020, 'Progress on the Fabrication and Application of Electrospun Nanofiber Composites', Membranes, vol. 10, no. 9, pp. 204-204.
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Nanofibers are one of the most attractive materials in various applications due to their unique properties and promising characteristics for the next generation of materials in the fields of energy, environment, and health. Among the many fabrication methods, electrospinning is one of the most efficient technologies which has brought about remarkable progress in the fabrication of nanofibers with high surface area, high aspect ratio, and porosity features. However, neat nanofibers generally have low mechanical strength, thermal instability, and limited functionalities. Therefore, composite and modified structures of electrospun nanofibers have been developed to improve the advantages of nanofibers and overcome their drawbacks. The combination of electrospinning technology and high-quality nanomaterials via materials science advances as well as new modification techniques have led to the fabrication of composite and modified nanofibers with desired properties for different applications. In this review, we present the recent progress on the fabrication and applications of electrospun nanofiber composites to sketch a progress line for advancements in various categories. Firstly, the different methods for fabrication of composite and modified nanofibers have been investigated. Then, the current innovations of composite nanofibers in environmental, healthcare, and energy fields have been described, and the improvements in each field are explained in detail. The continued growth of composite and modified nanofiber technology reveals its versatile properties that offer alternatives for many of current industrial and domestic issues and applications.
Tran, AT, Ha, QP & Hunjet, R 2020, 'Reliability enhancement with dependable model predictive control', ISA Transactions, vol. 106, pp. 152-170.
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© 2020 ISA Operational Technology (OT) systems are merging towards a conjoint architecture with the advances in communication networks and emerging standards such as IEC/IEEE 60802 for industrial automation, automotive, power and energy and other areas. In this paper, we present a Dependable Control System (DepCS) with Model Predictive Control (MPC) algorithm that works in such architectures using multiple MPC controllers (of a feedback control loop) to enhance the operational reliability. We termed this as Dependable Model Predictive Control (DepMPC) system. The reliability enhancement of a DepMPC system is achievable thanks to the fault-tolerance of multiple MPC controllers and the tractable information flows with Time-Sensitive Networking (TSN). Here, our discussion was focused only on the logical connectivity and not the hardware architecture. The numerical simulations are studied with three multi-variable plants that have control constraints. In this study, we introduced a Replacement Controller (RC) to improve the control performance of the DepMPC system. The combination of both the Replacement Controller and Dependable Model Predictive Control (RC-DepMPC) system proves a promising solution for actual implementations.
Tran, T, Bliuc, D, O’Donoghue, S, Hansen, L, Abrahamsen, B, Bergh, JVD, Geel, TV, Geusens, P, Vestergaard, P, Nguyen, TV, Eisman, JA & Center, J 2020, 'OR13-03 Understanding Why Older People with Low Trauma Fractures Die Prematurely', Journal of the Endocrine Society, vol. 4, no. Supplement_1.
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Abstract There is increasing evidence that all proximal and not just hip fractures are associated with increased mortality risk. However, the cause of this increased mortality is unknown. We sought to determine the post-fracture trajectories of subsequent hospital admissions and mortality to develop an understanding of why patients with non-hip fractures die prematurely. This nationwide Danish population-based study included all individuals aged 50+ years who sustained an incident fragility fracture between 2001 and 2014. High-trauma fractures or individuals with fracture prior to 2001 were excluded. Fracture patients were matched 1:4 by sex, age and comorbidity status with non-fracture subjects alive at the time of fracture. Comorbidities included 33 unique medical conditions of the Charlson or Elixhauser comorbidity index. We modelled the contribution of specific fractures on the risk of subsequent admissions or death within the following 2 years. There were 212,498 women and 95,372 men with fracture followed by 30,677 and 19,519 deaths, respectively over 163,482 and 384,995 person-years of follow up. Mean age at fracture was 72± 11 for women and 75± 11 for men. Proximal fractures including hip, femur, pelvis, rib, clavicle and humerus had increased mortality compared with their matched non-fracture counterparts with HRs ranging from 1.5-4.0, while distal fractures such as ankle, forearm, hand or foot fractures had similar or lower mortality risk. Almost 75% of men and 60% of women had ≥1 comorbidity. For every additional comorbidity, risk of mortality increased for all fracture types. However, only for proximal fractures did the fracture itself independently increase mortality risk over and above co-morbidity status. The 2-yr post fracture admission and mortality patterns diffe...
Tran, T, Bliuc, D, Pham, HM, van Geel, T, Adachi, JD, Berger, C, van den Bergh, J, Eisman, JA, Geusens, P, Goltzman, D, Hanley, DA, Josse, RG, Kaiser, SM, Kovacs, CS, Langsetmo, L, Prior, JC, Nguyen, TV & Center, JR 2020, 'A Risk Assessment Tool for Predicting Fragility Fractures and Mortality in the Elderly', Journal of Bone and Mineral Research, vol. 35, no. 10, pp. 1923-1934.
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ABSTRACT Existing fracture risk assessment tools are not designed to predict fracture-associated consequences, possibly contributing to the current undermanagement of fragility fractures worldwide. We aimed to develop a risk assessment tool for predicting the conceptual risk of fragility fractures and its consequences. The study involved 8965 people aged ≥60 years from the Dubbo Osteoporosis Epidemiology Study and the Canadian Multicentre Osteoporosis Study. Incident fracture was identified from X-ray reports and questionnaires, and death was ascertained though contact with a family member or obituary review. We used a multistate model to quantify the effects of the predictors on the transition risks to an initial and subsequent incident fracture and mortality, accounting for their complex interrelationships, confounding effects, and death as a competing risk. There were 2364 initial fractures, 755 subsequent fractures, and 3300 deaths during a median follow-up of 13 years (interquartile range [IQR] 7–15). The prediction model included sex, age, bone mineral density, history of falls within 12 previous months, prior fracture after the age of 50 years, cardiovascular diseases, diabetes mellitus, chronic pulmonary diseases, hypertension, and cancer. The model accurately predicted fragility fractures up to 11 years of follow-up and post-fracture mortality up to 9 years, ranging from 7 years after hip fractures to 15 years after non-hip fractures. For example, a 70-year-old woman with a T-score of −1.5 and without other risk factors would have 10% chance of sustaining a fracture and an 8% risk of dying in 5 years. However, after an initial fracture, her risk of sustaining another fracture or dying doubles to 33%, ranging from 26% after a distal to 42% post hip fracture. A robust statistical technique was used to develop a prediction model for individualization of progression to fracture and its consequences, f...
Tran, VH, Phuntsho, S, Han, DS, Dorji, U, Zhang, X & Shon, HK 2020, 'Submerged module of outer selective hollow fiber membrane for effective fouling mitigation in osmotic membrane bioreactor for desalination', Desalination, vol. 496, pp. 114707-114707.
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© 2020 Elsevier B.V. This paper investigated the membrane fouling mitigation efficacy and performance of a home-made submerged module containing outer selective hollow fiber thin film composite forward osmosis (OSHF TFC FO) membrane in osmosis membrane bioreactor (OMBR) system treating municipal wastewater for desalination. Initial tests, optimization of draw solution flowrate and pumping mode for the submerged module were carried out before it was applied into the OMBR system. Overall, the OMBR system exhibited an initial water flux of approximately 6.3 LMH using 35 g/L NaCl as draw solution, and high removal efficiencies of bulk organic matter and nutrients. Moreover, membrane fouling was effectively mitigated with slow rate of flux decline during 33-day operation of the OMBR system. These results indicated that the submerged membrane module of OSHF TFC FO membrane has stable and reliable performances making it suitable for OMBR supplication without the need of air scouring to prevent membrane fouling.
Tran, X-T, Nguyen, N-S, Bui, D-H, Pham, M-T, Nguyen, H & Pham, C-K 2020, 'Reducing Bitrate and Increasing the Quality of Inter Frame by Avoiding Quantization Errors in Stationary Blocks', EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, vol. 7, no. 22, pp. 162795-162795.
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Tran, Y, Craig, A, Craig, R, Chai, R & Nguyen, H 2020, 'The influence of mental fatigue on brain activity: Evidence from a systematic review with meta‐analyses', Psychophysiology, vol. 57, no. 5.
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AbstractThe occurrence of mental fatigue during tasks like driving a vehicle increases risk of injury or death. Changes in electroencephalographic (EEG) activity associated with mental fatigue has been frequently studied and considered a promising biomarker of mental fatigue. This is despite differences in methodologies and outcomes in prior research. A systematic review with meta‐analyses was conducted to establish the influence of mental fatigue on EEG activity spectral bands, and to determine in which regions fatigue‐related EEG spectral changes are likely to occur. A high‐yield search strategy identified 21 studies meeting inclusion criteria for investigating the change in EEG spectral activity in non‐diseased adults engaged in mentally fatiguing tasks. A medium effect size (using Cohen's g) of 0.68 (95%CI: 0.24–1.13) was found for increase in overall EEG activity following mental fatigue. Further examination of individual EEG spectral bands and regions using network meta‐analyses indicated large increases in theta (g = 1.03; 95%CI: 0.79–1.60) and alpha bands (g = 0.85; 95%CI: 0.47–1.43), with small to moderate changes found in delta and beta bands. Central regions of the scalp showed largest change (g = 0.80; 95%CI: 0.46–1.21). Sub‐group analyses indicated large increases in theta activity in frontal, central and posterior sites (all g > 1), with moderate changes in alpha activity in central and posterior sites. Findings have implications for fatigue monitoring and countermeasures with support for change in theta activity in frontal, central and posterior sites as a robust biomarker of mental fatigue and change in alpha wave activity considered a second line biomarker to account for individual variability.
Trianni, A, Accordini, D & Cagno, E 2020, 'Identification and Categorization of Factors Affecting the Adoption of Energy Efficiency Measures within Compressed Air Systems', Energies, vol. 13, no. 19, pp. 5116-5116.
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Understanding the factors driving the implementation of energy efficiency measures in compressed air systems is crucial to improve industrial energy efficiency, given their low implementation rate. Starting from a thorough review of the literature, it is thus clear the need to support companies in the decision-making process by offering an innovative framework encompassing the most relevant factors to be considered when adopting energy efficiency measures in compressed air systems, inclusive of the impacts on the production resources and the operations of a company. The framework, designed following the perspective of the industrial decision-makers, has been validated, both theoretically and empirically, and preliminarily applied to a heterogeneous cluster of manufacturing industries. Results show that, beside operational, energetic, and economic factors, in particular contextual factors such as complexity, compatibility, and observability may highlight critical features of energy efficiency measures whose absence may change the outcome of a decision-making process. Further, greater awareness and knowledge over the important factors given by the implementation of the framework could play an important role in fostering the implementation of energy efficiency measures in compressed air systems. The paper concludes with further research avenues to further promote energy efficiency and sustainability oriented practices in the industrial sector.
Trinh, VT, Nguyen, TMP, Van, HT, Hoang, LP, Nguyen, TV, Ha, LT, Vu, XH, Pham, TT, Nguyen, TN, Quang, NV & Nguyen, XC 2020, 'Phosphate Adsorption by Silver Nanoparticles-Loaded Activated Carbon derived from Tea Residue', Scientific Reports, vol. 10, no. 1, p. 3634.
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AbstractThis study presents the removal of phosphate from aqueous solution using a new silver nanoparticles-loaded tea activated carbon (AgNPs-TAC) material. In order to reduce costs, the tea activated carbon was produced from tea residue. Batch adsorption experiments were conducted to evaluate the effects of impregnation ratio of AgNPs and TAC, pH solution, contact time, initial phosphate concentration and dose of AgNPs-AC on removing phosphate from aqueous solution. Results show that the best conditions for phosphate adsorption occurred at the impregnation ratio AgNPs/TAC of 3% w/w, pH 3, and contact time lasting 150 min. The maximum adsorption capacity of phosphate on AgNPs-TAC determined by the Langmuir model was 13.62 mg/g at an initial phosphate concentration of 30 mg/L. The adsorption isotherm of phosphate on AgNPs-TAC fits well with both the Langmuir and Sips models. The adsorption kinetics data were also described well by the pseudo-first-order and pseudo-second-order models with high correlation coefficients of 0.978 and 0.966, respectively. The adsorption process was controlled by chemisorption through complexes and ligand exchange mechanisms. This study suggests that AgNPs-TAC is a promising, low cost adsorbent for phosphate removal from aqueous solution.
Truong, MV, Nguyen, LN, Li, K, Fu, Q, Johir, MAH, Fontana, A & Nghiem, LD 2020, 'Biomethane production from anaerobic co-digestion and steel-making slag: A new waste-to-resource pathway', Science of The Total Environment, vol. 738, pp. 139764-139764.
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Truong, NT, Thi, HPN, Ninh, HD, Phung, XT, Van Tran, C, Nguyen, TT, Pham, TD, Dang, TD, Chang, SW, Rene, ER, Ngo, HH, Nguyen, DD & La, DD 2020, 'Facile fabrication of graphene@Fe-Ti binary oxide nanocomposite from ilmenite ore: An effective photocatalyst for dye degradation under visible light irradiation', Journal of Water Process Engineering, vol. 37, pp. 101474-101474.
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© 2020 Elsevier Ltd Photocatalysis is an effective treatment technique for the removal of toxic pollutants present in water and wastewater. In this study, graphene@Fe-Ti binary oxide composites was prepared using a hydrothermal method and characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, Fourier-transform infrared spectroscopy, and Brunauer-Emmett-Teller surface area analysis. The prepared composite exhibited even distribution of the Fe-Ti binary oxide on the surface of graphene, with an average diameter of 16.4 nm and a surface area of 133.7 m2/g. The optical property was evaluated and the band gap was calculated to be 2.867 eV using solid-state UV–vis spectroscopy and the [F(R)hν]1/2 plot. Lab-scale experiments were performed to evaluate the performance of graphene@Fe-Ti binary oxides to remove methyl blue (i.e. a dye) from wastewater. It was observed that the graphene loading had a significant effect on the photocatalytic activity of the composite and a composite with 20 % graphene showed the highest photocatalytic activity, with 100 % removal of the dye, after 20 min of irradiation time and a degradation rate constant of 0.213 min−1. Besides, the possible photocatalytic dye degradation mechanism using graphene@Fe-Ti binary oxide composite has also been proposed.
Ubaid, A, Hussain, F & Charles, J 2020, 'Modeling Shipment Spot Pricing in the Australian Container Shipping Industry: Case of ASIA-OCEANIA trade lane', Knowledge-Based Systems, vol. 210, pp. 106483-106483.
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Ubando, AT, Chen, W, Show, P & Ong, HC 2020, 'Kinetic and thermodynamic analysis of iron oxide reduction by graphite for CO2mitigation in chemical‐looping combustion', International Journal of Energy Research, vol. 44, no. 5, pp. 3865-3882.
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Ukritnukun, S, Koshy, P, Rawal, A, Castel, A & Sorrell, CC 2020, 'Predictive Model of Setting Times and Compressive Strengths for Low-Alkali, Ambient-Cured, Fly Ash/Slag-Based Geopolymers', Minerals, vol. 10, no. 10, pp. 920-920.
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The effects of curing temperature, blast furnace slag content, and Ms on the initial and final setting times, and compressive strengths of geopolymer paste and mortars are examined. The present work demonstrates that ambient-cured geopolymer pastes and mortars can be fabricated without requiring high alkalinity activators or thermal curing, provided that the ratios of Class F fly ash (40–90 wt%), blast furnace slag (10–60 wt%), and low alkalinity sodium silicate (Ms = 1.5, 1.7, 2.0) are appropriately balanced. Eighteen mix designs were assessed against the criteria for setting time and compressive strength according to ASTM C150 and AS 3972. Using these data, flexible and reproducible mix designs in terms of the fly ash/slag ratio and Ms were mapped and categorised. The optimal mix designs are 30–40 wt% slag with silicate modulus (Ms) = 1.5–1.7. These data were used to generate predictive models for initial and final setting times and for ultimate curing times and ultimate compressive strengths. These projected data indicate that compressive strengths >100 MPa can be achieved after ambient curing for >56 days of mixes of ≥40 wt% slag.
Ulapane, N, Thiyagarajan, K, Hunt, D & Valls Miro, J 2020, 'Quantifying the Relative Thickness of Conductive Ferromagnetic Materials Using Detector Coil-Based Pulsed Eddy Current Sensors', Journal of Visualized Experiments, vol. 155, no. 155.
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Thickness quantification of conductive ferromagnetic materials by means of non-destructive evaluation (NDE) is a crucial component of structural health monitoring of infrastructure, especially for assessing the condition of large diameter conductive ferromagnetic pipes found in the energy, water, oil, and gas sectors. Pulsed eddy current (PEC) sensing, especially detector coil-based PEC sensor architecture, has established itself over the years as an effective means for serving this purpose. Approaches for designing PEC sensors as well as processing signals have been presented in previous works. In recent years, the use of the decay rate of the detector coil-based time domain PEC signal for the purpose of thickness quantification has been studied. Such works have established that the decay rate-based method holds generality to the detector coil-based sensor architecture, with a degree of immunity to factors such as sensor shape and size, number of coil turns, and excitation current. Moreover, this method has shown its effectiveness in NDE of large pipes made of grey cast iron. Following such literature, the focus of this work is explicitly PEC sensor detector coil voltage decay rate-based conductive ferromagnetic material thickness quantification. However, the challenge faced by this method is the difficulty of calibration, especially when it comes to applications such as in situ pipe condition assessment since measuring electrical and magnetic properties of certain pipe materials or obtaining calibration samples is difficult in practice. Motivated by that challenge, in contrast to estimating actual thickness as done by some previous works, this work presents a protocol for using the decay rate-based method to quantify relative thickness (i.e., thickness of a particular location with respect to a maximum thickness), without the requirement for calibration.
Ulhaq, A, Born, J, Khan, A, Gomes, DPS, Chakraborty, S & Paul, M 2020, 'COVID-19 Control by Computer Vision Approaches: A Survey', IEEE Access, vol. 8, pp. 179437-179456.
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The COVID-19 pandemic has triggered an urgent call to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of artificial intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at test to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources, and an indication of future research directions. We want to make it possible for computer vision researchers to find existing and future research directions. This survey article presents a preliminary review of the literature on research community efforts against COVID-19 pandemic.
Ureta, FG, Pietroni, N & Zorin, D 2020, 'Reinforcement of General Shell Structures.', ACM Trans. Graph., vol. 39, pp. 153:1-153:1.
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Usman, M, Jan, MA, He, X & Nanda, P 2020, 'QASEC: A secured data communication scheme for mobile Ad-hoc networks', Future Generation Computer Systems, vol. 109, pp. 604-610.
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© 2018 Elsevier B.V. Mobile Adhoc NETworks (MANETs) are valuable for various applications due to an efficient, flexible, low-cost and dynamic infrastructure. In these networks, proper utilization of network resources is desirable to maintain Quality of Service (QoS). In multi-hop end-to-end communication, intermediate nodes may eavesdrop on data in transit. As a result, a secured and reliable data delivery from source to destination is required. In this paper, we propose a novel scheme, known as QASEC, to achieve better throughput by securing end-to-end communication in MANETs. The QoS is maintained through an optimal link selection from a queue of available transmission links. The end-to-end communication is secured by authentication. A simple secret-key based symmetric encryption is deployed for interacting nodes. Our proposed QASEC scheme prevents the malicious nodes from data exchange with legitimate intermediate nodes on any established path between the source and the destination. Experimental results show that QASEC performs better in terms of packet-loss rate, jitter and end-to-end delay. Furthermore, QASEC is efficient against various attacks and has a much better performance in terms of associated costs, such as key generation, encryption, and storage and communication.
Usman, M, Jan, MA, Jolfaei, A, Xu, M, He, X & Chen, J 2020, 'A Distributed and Anonymous Data Collection Framework Based on Multilevel Edge Computing Architecture', IEEE Transactions on Industrial Informatics, vol. 16, no. 9, pp. 6114-6123.
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Usman, M, Shi, Z, Ren, S, Ngo, HH, Luo, G & Zhang, S 2020, 'Hydrochar promoted anaerobic digestion of hydrothermal liquefaction wastewater: Focusing on the organic degradation and microbial community', Chemical Engineering Journal, vol. 399, pp. 125766-125766.
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Vahabi, S, Stewart, M, Kasim, A, Hancock, H, Norouzi, M, Maddox, J & Austin, D 2020, 'P1383 The effects of doxorubicin on left and right ventricular strain in patients with lymphoma: insights from a retrospective study', European Heart Journal - Cardiovascular Imaging, vol. 21, no. Supplement_1.
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Abstract Funding Acknowledgements South Tees Research and Development Fund (UK) Background Anthracyclines are a cornerstone in the management of lymphoma. However, their use is associated with cardiotoxicity. Speckle tracking echocardiography (STE) has been established as a valid measure of quantifying cardiac function. However, most studies to this date have focused predominantly on left ventricular (LV) global longitudinal strain (GLS) with only a limited number assessing the right ventricle (RV) and other LV strain parameters. Purpose Using 2D STE, we assessed the effects of anthracyclines on LV and RV strain parameters, focusing on LV endocardial (GLS), LV myocardial GLS (myoGLS), LV radial strain (GRS), RV endocardial (RV GLS), myocardial GLS (RV myoGLS), and RV free wall strain (RVFWS). Methods We retrospectively collected data on patients treated for lymphoma between 2015-2018. Two groups (G) were defined: those with a conventional drop in LV ejection fraction (EF), (G1, n = 11) and those without (G2, n = 24). Echocardiograms were performed pre-chemotherapy (T0), mid-treatment (T1), and post-chemotherapy (T2) and were analysed offline using vendor-independent software (TomTec 2D CPA). LV and RV strain analysis was performed in both groups. This study was ethically approved by Health Research Association (REC Reference 18/SS/0139). Results ...
Vahidi, E, Rodríguez, JF, Bayne, E & Saco, PM 2020, 'One Flood Is Not Enough: Pool‐Riffle Self‐Maintenance Under Time‐Varying Flows and Nonequilibrium Multifractional Sediment Transport', Water Resources Research, vol. 56, no. 8.
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AbstractThe interaction of sediment supply and hydrographs can affect, on a mesoscale, geomorphic features like pools and riffles, which are fundamental units of many gravel bed rivers. In the past decades, different hypotheses have been developed to characterize the hydrodynamics of pool‐riffle sequences; however, most of the previous studies considered equilibrium or near‐equilibrium sediment transport conditions. Here we investigate the stability of pools and riffles during a sequence of different hydrographs representative of a natural flow regime, without satisfying the equilibrium sediment transport condition. In the current study, the effects of bed geometry, sediment sorting and hydrograph duration, are explained and quantified. The results show that under nonequilibrium conditions, the reversal episodes are not always competent enough for complete self‐maintenance during a single flood. However, width variations and grain sorting effects prevented the pools to be completely filled up with the upstream sediment supply. Hydrograph duration had a significant role in the riffle bed geometry. Even though a single flood (irrespective of the magnitude) was not competent enough to restore the pool‐riffle feature, a sequence of floods progressively improved conditions for self‐maintenance. These findings can bring more insight into flow management strategies, in terms of the importance of multiple sequential floods for restoring rivers with high sediment supply.
Vakhshouri, B, Nejadi, S & Erkmen, E 2020, 'Advances in numerical analysis of creep effect in time-dependent deflection of light-weight concrete slabs', Mechanics of Advanced Materials and Structures, vol. 27, no. 18, pp. 1563-1570.
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© 2018, © 2018 Taylor & Francis Group, LLC. Using a wide range of creep models, the experimental results of long-term deflection of lightweight concrete slabs subjected to two levels of early-age loading are investigated. Different creep models give considerably different estimation of the experimental deflection of slabs. The included factors in each creep model to simulate the experimental creep behavior of the concrete, and loading level on the slabs are the main causes of different results. Among the investigated models, the BP1 and FIBMC-2010 models including the aggregate type and concrete density is shown to be in good agreement with the experimental data in both loading levels.
Van Huynh, N, Nguyen, DN, Thai Hoang, D, Dutkiewicz, E & Mueck, M 2020, 'Ambient Backscatter: A Novel Method to Defend Jamming Attacks for Wireless Networks', IEEE Wireless Communications Letters, vol. 9, no. 2, pp. 175-178.
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© 2012 IEEE. This letter introduces a novel idea to defend jamming attacks for wireless communications. In particular, when the jammer attacks the channel, the transmitter can leverage the jamming signals to transmit data by using ambient backscatter technique or harvest energy from the jamming signals to support its operation. To deal with the uncertainty of the jammer, we propose a reinforcement learning-based algorithm that allows the transmitter to obtain the optimal operation policy through real-time interaction processes with the attacker. The simulation results show the effectiveness of ambient backscatter in combating jammers, i.e., it enables the transmitter to transmit data even under the jamming attacks. We observe that the more power the jammer uses to attack the channel, the better performance the network can achieve.
Varjani, S, Joshi, R, Srivastava, VK, Ngo, HH & Guo, W 2020, 'Treatment of wastewater from petroleum industry: current practices and perspectives', Environmental Science and Pollution Research, vol. 27, no. 22, pp. 27172-27180.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Petroleum industry is one of the fastest growing industries, and it significantly contributes to economic growth in developing countries like India. The wastewater from a petroleum industry consist a wide variety of pollutants like petroleum hydrocarbons, mercaptans, oil and grease, phenol, ammonia, sulfide, and other organic compounds. All these compounds are present as very complex form in discharged water of petroleum industry, which are harmful for environment directly or indirectly. Some of the techniques used to treat oily waste/wastewater are membrane technology, photocatalytic degradation, advanced oxidation process, electrochemical catalysis, etc. In this review paper, we aim to discuss past and present scenario of using various treatment technologies for treatment of petroleum industry waste/wastewater. The treatment of petroleum industry wastewater involves physical, chemical, and biological processes. This review also provides scientific literature on knowledge gaps and future research directions to evaluate the effect(s) of various treatment technologies available.
Varjani, S, Pandey, A, Tyagi, RD, Ngo, HH & Larroche, C 2020, 'Preface', pp. xxi-xxii.
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Vasilescu, SA, Bazaz, SR, Jin, D, Shimoni, O & Warkiani, ME 2020, '3D printing enables the rapid prototyping of modular microfluidic devices for particle conjugation', Applied Materials Today, vol. 20, pp. 100726-100726.
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© 2020 Elsevier Ltd Antibody micro/nano-particle conjugates have proven to be essential tools in many diagnostic and nanomedicine applications. However, their production with homogenous coating and in a continuous fashion remains a tedious, labor-intensive, and costly process. In this regard, 3D micromixer-based microfluidic devices offer significant advantages over existing methods, where manipulating the flow in three dimensions increases fluid contact area and surface disruption, facilitating efficient mixing. While conventional softlithography is capable of fabricating simple 2D micromixers, complications arise when processing 3D structures. In this paper, we report the direct fabrication of a 3D complex microchannel design using additive manufacturing for the continuous conjugation of antibodies onto particle surfaces. This method benefits from a reduction in cost and time (from days to hours), simplified fabrication process, and limited post-processing. The flexibility of direct 3D printing allows quick and easy tailoring of design features to facilitate the production of micro and nanoparticles conjugated with functional antibodies in a continuous mixing process. We demonstrate that the produced antibody-functionalized particles retain their functionality by a firm and specific interaction with antigen presenting cells. By connecting 3D printed micromixers across the conjugation process, we illustrate the role of 3D printed microchannels as modularized components. The 3D printing method we report enables a broad spectrum of researchers to produce complex microfluidic geometries within a short time frame.
Vaughan, N & Gabrys, B 2020, 'Scoring and assessment in medical VR training simulators with dynamic time series classification', Engineering Applications of Artificial Intelligence, vol. 94, pp. 103760-103760.
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© 2020 Elsevier Ltd This research proposes and evaluates scoring and assessment methods for Virtual Reality (VR) training simulators. VR simulators capture detailed n-dimensional human motion data which is useful for performance analysis. Custom made medical haptic VR training simulators were developed and used to record data from 271 trainees of multiple clinical experience levels. DTW Multivariate Prototyping (DTW-MP) is proposed. VR data was classified as Novice, Intermediate or Expert. Accuracy of algorithms applied for time-series classification were: dynamic time warping 1-nearest neighbor (DTW-1NN) 60%, nearest centroid SoftDTW classification 77.5%, Deep Learning: ResNet 85%, FCN 75%, CNN 72.5% and MCDCNN 28.5%. Expert VR data recordings can be used for guidance of novices. Assessment feedback can help trainees to improve skills and consistency. Motion analysis can identify different techniques used by individuals. Mistakes can be detected dynamically in real-time, raising alarms to prevent injuries.
Vazquez, S, Acuna, P, Aguilera, RP, Pou, J, Leon, JI & Franquelo, LG 2020, 'DC-Link Voltage-Balancing Strategy Based on Optimal Switching Sequence Model Predictive Control for Single-Phase H-NPC Converters', IEEE Transactions on Industrial Electronics, vol. 67, no. 9, pp. 7410-7420.
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In this article, a model predictive control (MPC) strategy based on the optimal switching sequence (OSS) concept for a single-phase grid-connected H-bridge neutral-point-clamped (H-NPC) power converter is presented. The proposed OSS-MPC algorithm considers both the grid current tracking error and the dc-link capacitor voltage balance. Special emphasis is placed on the power converter control region in order to design suitable switching sequence candidates for this multiobjective control problem. Additionally, based on an analysis of the weighting factor effect over closed-loop performance, it is possible to demonstrate that this controller parameter is relatively easy to adjust. In fact, the weighting factor only affects the peak current during transients, with no effect over the steady-state performance. As a result, the proposed OSS-MPC provides a fast closed-loop dynamic to the H-NPC converter, which operates with a fixed switching frequency at all times. This predictive control strategy is experimentally validated in a 3.5-kVA laboratory setup.
Veerappan Kousik, NG, Natarajan, Y, Suresh, K, Patan, R & Gandomi, AH 2020, 'Improving Power and Resource Management in Heterogeneous Downlink OFDMA Networks', Information, vol. 11, no. 4, pp. 203-203.
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In the past decade, low power consumption schemes have undergone degraded communication performance, where they fail to maintain the trade-off between the resource and power consumption. In this paper, management of resource and power consumption on small cell orthogonal frequency-division multiple access (OFDMA) networks is enacted using the sleep mode selection method. The sleep mode selection method uses both power and resource management, where the former is responsible for a heterogeneous network, and the latter is managed using a deactivation algorithm. Further, to improve the communication performance during sleep mode selection, a semi-Markov sleep mode selection decision-making process is developed. Spectrum reuse maximization is achieved using a small cell deactivation strategy that potentially identifies and eliminates the sleep mode cells. The performance of this hybrid technique is evaluated and compared against benchmark techniques. The results demonstrate that the proposed hybrid performance model shows effective power and resource management with reduced computational cost compared with benchmark techniques.
Velasco, SÁ 2020, 'Ilegalizados en Ecuador, el país de la “ciudadanía universal”', Sociologias, vol. 22, no. 55, pp. 138-170.
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Resumen En base a un análisis etnográfico multisituado conducido en Ecuador entre 2015 y 2017, este artículo analiza cómo en el marco del mayor progresismo constitucional en materia migratoria, en el país de la “ciudadanía universal”, varios mecanismos legales y sociales fueron adoptados y terminaron confinando a migrantes y refugiados regionales y extracontinentales a encarnar situaciones de ilegalidad, posible deportación y desechabilidad. Se parte de una revisión teórica sobre el régimen de control fronterizo neoliberal global y sobre cómo la producción legal de la ilegalidad migrante es nodal en su funcionamiento, para después analizar por qué inmigrantes caribeños, africanos y de Medio Oriente escogieron a Ecuador como su destino, cuáles fueron los principales reveses e incongruencias en la política migratoria y cómo éstos impactaron en la cotidianeidad de esos inmigrantes hasta multiplicar sus salidas irregularizadas posteriores. El artículo constata que el giro progresista ecuatoriano no estuvo exento de mecanismos análogos al régimen de control fronterizo neoliberal global, hecho que ayuda a comprender el rol que el país andino cumple en la geopolítica de las migraciones contemporáneas: ser un espacio de producción de migrantes ilegalizados o mano de obra barata en ruta a EE.UU., rol que confirma su funcionalidad como un nodo conector dentro de un sistema mucho más amplio y complejo de control neoliberal de la movilidad.
Verhoeven, D, Musial, K, Palmer, S, Taylor, S, Abidi, S, Zemaityte, V & Simpson, L 2020, 'Controlling for openness in the male-dominated collaborative networks of the global film industry', PLOS ONE, vol. 15, no. 6, pp. e0234460-e0234460.
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Studies of gender inequality in film industries have noted the persistence of male domination in creative roles (usually defined as director, producer, writer) and the slow pace of reform. Typical policy remedies are premised on aggregate counts of women as a proportion of overall industry participation. Network science offers an alternative way of identifying and proposing change mechanisms, as it puts emphasis on relationships instead of individuals. Preliminary work on applying network analysis to understand inequality in the film industry has been undertaken. However, in this study we offer a comprehensive approach that enables us to not only understand what inequality in the film industry looks like through the lens of network science but also how we can attempt to address this issue. We offer a data-driven simulation framework that investigates various what-if scenarios when it comes to network evolution. We then assess each of these scenarios with respect to its potential to address gender inequality in the film industry. As suggested by previous studies, inequality is exacerbated when industry networks are most closed. We review evidence from three different national film industries on network relationships in creative teams and identify a high proportion of men who only work with other men. In response to this observation, we test several mechanisms through which industry structures may generate higher levels of openness. Our results reveal that the most critical factor for improving network openness is not simply the statistical improvement of the number of women in a network, nor the removal of men who do not work with women. The most likely behavioural changes to a network will involve the production of connections between women and powerful men.
Verma, R & Merigó, JM 2020, 'A New Decision Making Method Using Interval-Valued Intuitionistic Fuzzy Cosine Similarity Measure Based on the Weighted Reduced Intuitionistic Fuzzy Sets', Informatica, vol. 31, no. 2, pp. 399-433.
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In this paper, we develop a new flexible method for interval-valued intuitionistic fuzzy decision-making problems with cosine similarity measure. We first introduce the interval-valued intuitionistic fuzzy cosine similarity measure based on the notion of the weighted reduced intuitionistic fuzzy sets. With this cosine similarity measure, we are able to accommodate the attitudinal character of decision-makers in the similarity measuring process. We study some of its essential properties and propose the weighted interval-valued intuitionistic fuzzy cosine similarity measure.
Further, the work uses the idea of GOWA operator to develop the ordered weighted interval-valued intuitionistic fuzzy cosine similarity (OWIVIFCS) measure based on the weighted reduced intuitionistic fuzzy sets. The main advantage of the OWIVIFCS measure is that it provides a parameterized family of cosine similarity measures for interval-valued intuitionistic fuzzy sets and considers different scenarios depending on the attitude of the decision-makers. The measure is demonstrated to satisfy some essential properties, which prepare the ground for applications in different areas. In addition, we define the quasi-ordered weighted interval-valued intuitionistic fuzzy cosine similarity (quasi-OWIVIFCS) measure. It includes a wide range of particular cases such as OWIVIFCS measure, trigonometric-OWIVIFCS measure, exponential-OWIVIFCS measure, radical-OWIVIFCS measure. Finally, the study uses the OWIVIFCS measure to develop a new decision-making method to solve real-world decision problems with interval-valued intuitionistic fuzzy information. A real-life numerical example of contractor selection is also given to demonstrate the effectiveness of the developed approach in solving real-life problems.
Verma, R & Merigó, JM 2020, 'Multiple attribute group decision making based on 2-dimension linguistic intuitionistic fuzzy aggregation operators', Soft Computing, vol. 24, no. 22, pp. 17377-17400.
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Vettori, L, Sharma, P, Rnjak-Kovacina, J & Gentile, C 2020, '3D Bioprinting of Cardiovascular Tissues for In Vivo and In Vitro Applications Using Hybrid Hydrogels Containing Silk Fibroin: State of the Art and Challenges', Current Tissue Microenvironment Reports, vol. 1, no. 4, pp. 261-276.
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AbstractPurpose of Review3D bioprinting of cardiovascular tissues for in vitro and in vivo applications is currently investigated as a potential solution to better mimic the microenvironment typical of the human heart. However, optimal cell viability and tissue vascularization remain two of the main challenges in this regard. Silk fibroin (SF) as a natural biomaterial with unique features supports cell survival and tissue vascularization. This review aims to evaluate the potential of hydrogels containing SF in 3D bioprinting of cardiac tissue that better recapitulate the native cardiac microenvironment.Recent FindingsSF hydrogels spontaneously develop nanocrystals, which limit their use for 3D bioprinting applications. Nevertheless, the printability of SF is improved in hybrid hydrogels by mixing it with other natural polymers (such as alginate and gelatin). This is achieved by adding SF with other polymers or by crosslinking it by peroxidase catalysis (i.e., with alginate). Compared to only SF-based hydrogels, hybrid hydrogels provide a durable bioprinted construct with improved mechanical stability and biological properties. To date, studies using cardiac cells in bioprinted SF constructs are yet to be performed.SummaryMixing SF with other polymers in bioprinted hybrid hydrogels improves the printability and durability of 3D bioprinted tissues. Studies using these hydrogels with cardiac cells will be required to evaluate the biocompatibility of SF hybrid hydrogels and to establish their potential use for cardiovascular applications.
Vilayphone, V, Outram, JG, Collins, F, Millar, GJ & Altaee, A 2020, 'Process design of coal seam gas associated water treatment plants to facilitate beneficial reuse', Journal of Environmental Chemical Engineering, vol. 8, no. 5, pp. 104255-104255.
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Vo, HNP, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Chen, Z, Wang, XC, Chen, R & Zhang, X 2020, 'Microalgae for saline wastewater treatment: a critical review', Critical Reviews in Environmental Science and Technology, vol. 50, no. 12, pp. 1224-1265.
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© 2019, © 2019 Taylor & Francis Group, LLC. Saline wastewater contains numerous pollutants such as nutrients, heavy metals, micropollutants, and organic pollutants. This kind of wastewater needs to be treated prior to discharging. Compared to other technologies for saline wastewater treatment, the microalgae process is considered to be ‘green’ or environmentally friendly as it generates no secondary pollutants and creates profit. To elucidate the issue, this review investigated the following: (1) the nature of saline wastewater; (2) adaptation of microalgae in saline wastewater; (3) pollutants’ remediation by microalgae in saline wastewater; (4) comparisons with other technologies; and (5) future perspectives. Most importantly, during microalgae process, the saline wastewater is transformed from a waste into a source for biofuel and pigment production. This trend implies to heal the environment, cut remediation expenses and raise revenue.
Vo, HNP, Ngo, HH, Guo, W, Liu, Y, Woong Chang, S, Nguyen, DD, Zhang, X, Liang, H & Xue, S 2020, 'Selective carbon sources and salinities enhance enzymes and extracellular polymeric substances extrusion of Chlorella sp. for potential co-metabolism', Bioresource Technology, vol. 303, pp. 122877-122877.
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This study investigated the extracellular polymeric substance (EPS) and enzyme extrusion of Chlorella sp. using seven carbon sources and two salinities for potential pollutant co-metabolism. Results indicated that the levels of biomass, EPS and enzymes of microalgae cultured with glucose and saccharose outcompeted other carbon sources. For pigment production, glycine received the highest chlorophyll and carotene, up to 10 mg/L. The EPS reached 30 mg/L, having doubled the amount of protein than carbohydrate. For superoxide dismutase and peroxidase enzymes, the highest concentrations were beyond 60 U/ml and 6 nmol/d.ml, respectively. This amount could be potentially used for degrading 40% ciprofloxacin of concentration 2000 µg/L. When increasing salinity from 0.1% to 3.5%, the concentrations of pigment, EPS and enzymes rose 3 to 30 times. These results highlighted that certain carbon sources and salinities could induce Chlorella sp. to produce EPS and enzymes for pollutant co-metabolism and also for revenue-raising potential.
Vo, HNP, Ngo, HH, Guo, W, Nguyen, KH, Chang, SW, Nguyen, DD, Liu, Y, Liu, Y, Ding, A & Bui, XT 2020, 'Micropollutants cometabolism of microalgae for wastewater remediation: Effect of carbon sources to cometabolism and degradation products', Water Research, vol. 183, pp. 115974-115974.
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This study investigated the impacts of selective sole carbon source-induced micropollutants (MPs) cometabolism of Chlorella sp. by: (i) extracellular polymeric substances (EPS), superoxide dismutase and peroxidase enzyme production; (ii) MPs removal efficiency and cometabolism rate; (iii) MPs' potential degradation products identification; and (iv) degradation pathways and validation using the Eawag database to differentiate the cometabolism of Chlorella sp. with other microbes. Adding the sole carbon sources in the presence of MPs increased EPS and enzyme concentrations from 2 to 100-fold in comparison with only sole carbon sources. This confirmed that MPs cometabolism had occurred. The removal efficiencies of tetracycline, sulfamethoxazole, and bisphenol A ranged from 16-99%, 32-92%, and 58-99%, respectively. By increasing EPS and enzyme activity, the MPs concentrations accumulated in microalgae cells also fell 400-fold. The cometabolism process resulted in several degradation products of MPs. This study drew an insightful understanding of cometabolism for MPs remediation in wastewater. Based on the results, proper carbon sources for microalgae can be selected for practical applications to remediate MPs in wastewater while simultaneously recovering biomass for several industries and gaining revenue.
Volpin, F, Badeti, U, Wang, C, Jiang, J, Vogel, J, Freguia, S, Fam, D, Cho, J, Phuntsho, S & Shon, HK 2020, 'Urine Treatment on the International Space Station: Current Practice and Novel Approaches', Membranes, vol. 10, no. 11, pp. 327-327.
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A reliable, robust, and resilient water recovery system is of paramount importance on board the International Space Station (ISS). Such a system must be able to treat all sources of water, thereby reducing resupply costs and allowing for longer-term space missions. As such, technologies able to dewater urine in microgravity have been investigated by different space agencies. However, despite over 50 years of research and advancements on water extraction from human urine, the Urine Processing Assembly (UPA) and the Water Processor Assembly (WPA) now operating on the ISS still achieve suboptimal water recovery rates and require periodic consumables resupply. Additionally, urine brine from the treatment is collected for disposal and not yet reused. These factors, combined with the need for a life support system capable of tolerating even dormant periods of up to one year, make the research in this field ever more critical. As such, in the last decade, extensive research was conducted on the adaptation of existing or emerging technologies for the ISS context. In virtue of having a strong chemical resistance, small footprint, tuneable selectivity and versatility, novel membrane-based processes have been in focus for treating human urine. Their hybridisation with thermal and biological processes as well as the combination with new nanomaterials have been particularly investigated. This article critically reviews the UPA and WPA processes currently in operation on the ISS, summarising the research directions and needs, highlighted by major space agencies, necessary for allowing life support for missions outside the Low Earth Orbit (LEO). Additionally, it reviews the technologies recently proposed to improve the performance of the system as well as new concepts to allow for the valorisation of the nutrients in urine or the brine after urine dewatering.
Volpin, F, Jiang, J, El Saliby, I, Preire, M, Lim, S, Hasan Johir, MA, Cho, J, Han, DS, Phuntsho, S & Shon, HK 2020, 'Sanitation and dewatering of human urine via membrane bioreactor and membrane distillation and its reuse for fertigation', Journal of Cleaner Production, vol. 270, pp. 122390-122390.
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© 2020 Elsevier Ltd Source separation and recovery of human urine have often been proposed as an effective way to achieve a more sustainable waste-to-resource cycle. Its high density of available macronutrients (N–P–K) in urine makes it an ideal raw material for the production of fertiliser. However, to improve the safety and public acceptance of urine-based fertilisers, odour and pathogens must be removed. In this work, low-temperature DCMD was investigated a mean to produce a non-odorous high-concentration liquid fertiliser. The effectiveness of urine-fertiliser in hydroponically growing leafy vegetables was benchmarked with a commercial solution. Also, prior to the DCMD, urine was biologically oxidised through an MBR which removed over 95% of the DOC and converted almost 50% of the NH3 into NO3−. The results showed that, despite the high salinity and high LMW organics in human urine, MD was still able to achieve a final product with TDS concentration up to 280 g.L−1. A sharp flux decline was measured after 80% water recovery, but alkaline cleaning effectively removed the thick fouling layer and fully recovered the initial flux. When used to grow lettuce and Pak Choi hydroponically, the produced urine fertiliser achieved promising performances as the biomass from the aerial part of the plants was often similar to the one obtained with commercial fertilisers. Overall, this article investigates the whole urine-to-biomass cycle, from collection to treatment to plant growth tests.
Volpin, F, Woo, YC, Kim, H, Freguia, S, Jeong, N, Choi, J-S, Cho, J, Phuntsho, S & Shon, HK 2020, 'Energy recovery through reverse electrodialysis: Harnessing the salinity gradient from the flushing of human urine', Water Research, vol. 186, pp. 116320-116320.
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Urine dilution is often performed to avoid clogging or scaling of pipes, which occurs due to urine's Ca2+ and Mg2+ precipitating at the alkaline conditions created by ureolysis. The large salinity gradient between urine and flushing water is, theoretically, a source of potential energy which is currently unexploited. As such, this work explored the use of a compact reverse electrodialysis (RED) system to convert the chemical potential energy of urine dilution into electric energy. Urine' composition and ureolysis state as well as solution pumping costs were all taken into account. Despite having almost double its electric conductivity, real hydrolysed urine obtained net energy recoveries ENet of 0.053-0.039 kWh/m3, which is similar to energy recovered from real fresh urine. The reduced performances of hydrolysed urine were linked to its higher organic fouling potential and possible volatilisation of NH3 due to its high pH. However, the higher-than-expected performance achieved by fresh urine is possibly due to the fast diffusion of uncharged urea to the freshwater side. Real urine was also tested as a novel electrolyte solution and its performance compared with a conventional K4Fe(CN)6/K3Fe(CN)6 couple. While K4Fe(CN)6/K3Fe(CN)6 outperformed urine in terms of power densities and energy recoveries, net chemical reactions seemed to have occurred in urine when used as an electrolyte solution, leading to TOC, ammonia and urea removal of up to 13%, 6% and 4.4%, respectively. Finally, due to the migration of K+, NH4+ and PO43-, the low concentration solution could be utilised for fertigation. Overall, this process has the potential of providing off-grid urine treatment or energy production at a household or building level.
Vu, HNK, Ha, QP, Nguyen, DH, Nguyen, TTT, Nguyen, TT, Nguyen, TTH, Tran, ND & Ho, BQ 2020, 'Poor Air Quality and Its Association with Mortality in Ho Chi Minh City: Case Study', Atmosphere, vol. 11, no. 7, pp. 750-750.
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Along with its rapid urban development, Ho Chi Minh City (HCMC) in recent years has suffered a high concentration of air pollutants, especially fine particulate matters or PM2.5. A comprehensive study is required to evaluate the air quality conditions and their health impact in this city. Given the lack of adequate air quality monitoring data over a large area of the size of HCMC, an air quality modeling methodology is adopted to address the requirement. Here, by utilizing a corresponding emission inventory in combination with The Air Pollution Model-Chemical Transport Model (TAPM-CTM), the predicted concentration of air pollutants is first obtained for PM2.5, NOx, and SO2. Then by associating the pollutants exposed with the mortality rate from three causes, namely Ischemic Heart Disease (IHD), cardiopulmonary, and lung cancer, the impact of air pollution on human health is obtained for this purpose. Spatial distribution has shown a high amount of pollutants concentrated in the central city with a high density of combustion vehicles (motorcycles and automobiles). In addition, a significant amount of emissions can be observed from stevedoring and harbor activities, including ferries and cargo handling equipment located along the river. Other sources such as household activities also contribute to an even distribution of emission across the city. The results of air quality modeling showed that the annual average concentrations of NO2 were higher than the standard of Vietnam National Technical Regulation on Ambient Air Quality (QCVN 05: 2013 40 µg/m3) and World Health Organization (WHO) (40 µg/m3). The annual average concentrations of PM2.5 were 23 µg/m3 and were also much higher than the WHO (10 µg/m3) standard by about 2.3 times. In terms of public health impacts, PM2.5 was found to be responsible for about 1136 deaths, while the number of mortalities from exposure to NO2 and SO2 was 172 and 89 deaths, respectively. These figures demand some stri...
Vu, HP, Nguyen, LN, Lesage, G & Nghiem, LD 2020, 'Synergistic effect of dual flocculation between inorganic salts and chitosan on harvesting microalgae Chlorella vulgaris', Environmental Technology & Innovation, vol. 17, pp. 100622-100622.
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© 2020 Elsevier B.V. The flocculation efficiency of microalgae Chlorella vulgaris for subsequent harvesting was investigated using single flocculants of inorganic salts, synthetic polymer, chitosan and dual flocculants of inorganic salts and chitosan. Synthetic polymer (FlopamTM) could achieve over 90% optical density removal (OD680removal) at a low flocculant dose (20 to 40 mg polymer per litre of algal suspension) through the bridging mechanism and charge neutralisation. Inorganic salts (i.e. ferric chloride and aluminium sulphate) and chitosan individually resulted in low flocculation efficiency (<90%) despite high dose (i.e. 160 to 200 mg per litre of algal suspension). The dual flocculation combining ferric chloride or aluminium sulphate with chitosan induced synergistic effects, resulting in >80% flocculation efficiency, significantly higher than the sum of each individual flocculation. The improvement in flocculation efficiency was 57 and 24% respectively for ferric chloride/chitosan and aluminium sulphate/chitosan. Charge neutralisation of microalgal cells by ferric chloride or aluminium sulphate combined with bridging by chitosan produced the synergy.
Vu, HP, Nguyen, LN, Vu, MT, Johir, MAH, McLaughlan, R & Nghiem, LD 2020, 'A comprehensive review on the framework to valorise lignocellulosic biomass as biorefinery feedstocks', Science of The Total Environment, vol. 743, pp. 140630-140630.
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An effective pretreatment is the first step to enhance the digestibility of lignocellulosic biomass - a source of renewable, eco-friendly and energy-dense materials - for biofuel and biochemical productions. This review aims to provide a comprehensive assessment on the advantages and disadvantages of lignocellulosic pretreatment techniques, which have been studied at the lab-, pilot- and full-scale levels. Biological pretreatment is environmentally friendly but time consuming (i.e. 15-40 days). Chemical pretreatment is effective in breaking down lignocellulose and increasing sugar yield (e.g. 4 to 10-fold improvement) but entails chemical cost and expensive reactors. Whereas the combination of physical and chemical (i.e. physicochemical) pretreatment is energy intensive (e.g. energy production can only compensate 80% of the input energy) despite offering good process efficiency (i.e. > 100% increase in product yield). Demonstrations of pretreatment techniques (e.g. acid, alkaline, and hydrothermal) in pilot-scale have reported 50-80% hemicellulose solubilisation and enhanced sugar yields. The feasibility of these pilot and full-scale plants has been supported by government subsidies to encourage biofuel consumption (e.g. tax credits and mandates). Due to the variability in their mechanisms and characteristics, no superior pretreatment has been identified. The main challenge lies in the capability to achieve a positive energy balance and great economic viability with minimal environmental impacts i.e. the energy or product output significantly surpasses the energy and monetary input. Enhancement of the current pretreatment techno-economic efficiency (e.g. higher product yield, chemical recycling, and by-products conversion to increase environmental sustainability) and the integration of pretreatment methods to effectively treat a range of biomass will be the steppingstone for commercial lignocellulosic biorefineries.
Vu, HP, Nguyen, LN, Zdarta, J, Nga, TTV & Nghiem, LD 2020, 'Blue-Green Algae in Surface Water: Problems and Opportunities', Current Pollution Reports, vol. 6, no. 2, pp. 105-122.
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Vu, HT, Wilkinson, RH, Lech, M & Cheng, E 2020, 'A Hybrid Neural Network for Graph-Based Human Pose Estimation From 2D Images', IEEE Access, vol. 8, pp. 52830-52840.
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Vu, L, Nguyen, QU, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2020, 'Deep Transfer Learning for IoT Attack Detection', IEEE Access, vol. 8, pp. 107335-107344.
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The digital revolution has substantially changed our lives in which Internet-of-Things (IoT) plays a prominent role. The rapid development of IoT to most corners of life, however, leads to various emerging cybersecurity threats. Therefore, detecting and preventing potential attacks in IoT networks have recently attracted paramount interest from both academia and industry. Among various attack detection approaches, machine learning-based methods, especially deep learning, have demonstrated great potential thanks to their early detecting capability. However, these machine learning techniques only work well when a huge volume of data from IoT devices with label information can be collected. Nevertheless, the labeling process is usually time consuming and expensive, thus, it may not be able to adapt with quick evolving IoT attacks in reality. In this paper, we propose a novel deep transfer learning (DTL) method that allows to learn from data collected from multiple IoT devices in which not all of them are labeled. Specifically, we develop a DTL model based on two AutoEncoders (AEs). The first AE (AE 1 ) is trained on the source datasets (source domains) in the supervised mode using the label information and the second AE (AE 2 ) is trained on the target datasets (target domains) in an unsupervised manner without label information. The transfer learning process attempts to force the latent representation (the bottleneck layer) of AE 2 similarly to the latent representation of AE 1 . After that, the latent representation of AE 2 is used to detect attacks in the incoming samples in the target domain. We carry out intensive experiments on nine recent IoT datasets to evaluate the performance of the proposed model. The experimental results demonstrate that the proposed DTL model significantly improves the accuracy in detecting IoT attacks compared to the baseline deep learning technique and two recent DTL approaches.
Vu, MT, Vu, HP, Nguyen, LN, Semblante, GU, Johir, MAH & Nghiem, LD 2020, 'A hybrid anaerobic and microalgal membrane reactor for energy and microalgal biomass production from wastewater', Environmental Technology & Innovation, vol. 19, pp. 100834-100834.
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Vu, TH, Gowripalan, N, De Silva, P, Paradowska, A, Garbe, U, Kidd, P & Sirivivatnanon, V 2020, 'Assessing carbonation in one-part fly ash/slag geopolymer mortar: Change in pore characteristics using the state-of-the-art technique neutron tomography', Cement and Concrete Composites, vol. 114, pp. 103759-103759.
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© 2020 Elsevier Ltd Carbonation has long been recognised as a durability issue attributed to corrosion of steel reinforcement in geopolymer materials. The currently available information, however, is not sufficient to gain a deep understanding of this issue, particularly the facet of the carbonation impact on the pore structure of such materials. This paper, thus, assessed the influence of carbonation on porosity and pore size characteristics of one-part fly ash/slag geopolymer mortar, by using neutron tomography. The cutting-edge thermal neutron tomography used in this study provided the prowess of non-destructive 3D analysis of exploring different regions within geopolymers. The results obtained showed that carbonation in the investigated geopolymer mortars drew their porosity down approximately 30% and shifted pore size regions to smaller pore areas. Other evaluations such as changing pH, carbonation front depth and elemental mapping by scanning electron microscopy (SEM) with energy dispersive X-ray spectrometry (EDS) were also performed in this study, in order to supplement the findings of neutron tomography.
Wadhwa, R, Paudel, KR, Mehta, M, Shukla, SD, Sunkara, K, Prasher, P, Panth, N, Goyal, R, Chellappan, DK, Gupta, G, Hansbro, PM, Aljabali, AAA, Tambuwala, MM & Dua, K 2020, 'Beyond the Obvious: Smoking and Respiratory Infection Implications on Alzheimer's Disease', CNS & Neurological Disorders - Drug Targets, vol. 19, no. 9, pp. 698-708.
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Tobacco smoke is not only a leading cause for chronic obstructive pulmonary disease, cardiovascular disorders, and lung and oral cancers, but also causes neurological disorders such as Alzheimer ’s disease. Tobacco smoke consists of more than 4500 toxic chemicals, which form free radicals and can cross blood-brain barrier resulting in oxidative stress, an extracellular amyloid plaque from the aggregation of amyloid β (Aβ) peptide deposition in the brain. Further, respiratory infections such as Chlamydia pneumoniae, respiratory syncytial virus have also been involved in the induction and development of the disease. The necessary information collated on this review has been gathered from various literature published from 1995 to 2019. The review article sheds light on the role of smoking and respiratory infections in causing oxidative stress and neuroinflammation, resulting in Alzheimer's disease (AD). This review will be of interest to scientists and researchers from biological and medical science disciplines, including microbiology, pharmaceutical sciences and the translational researchers, etc. The increasing understanding of the relationship between chronic lung disease and neurological disease is two-fold. First, this would help to identify the risk factors and possible therapeutic interventions to reduce the development and progression of both diseases. Second, this would help to reduce the probable risk of development of AD in the population prone to chronic lung diseases.
Waheed, W, Deng, G & Liu, B 2020, 'Discrete Laplacian Operator and Its Applications in Signal Processing', IEEE Access, vol. 8, pp. 89692-89707.
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Wang, A, Wang, W, Chen, J, Mao, R, Pang, Y, Li, Y, Chen, W, Chen, D, Hao, D, Ni, B-J, Saunders, M & Jia, G 2020, 'Dominant Polar Surfaces of Colloidal II–VI Wurtzite Semiconductor Nanocrystals Enabled by Cation Exchange', The Journal of Physical Chemistry Letters, vol. 11, no. 13, pp. 4990-4997.
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Polar surfaces of ionic crystals are of growing technological importance, with implications for the efficiency of photocatalysts, gas sensors, and electronic devices. The creation of ionic nanocrystals with high percentages of polar surfaces is an option for improving their efficiency in the aforementioned applications but is hard to accomplish because they are less thermodynamically stable and prone to vanish during the growth process. Herein, we develop a strategy that is capable of producing polar surface-dominated II-VI semiconductor nanocrystals, including ZnS and CdS, from copper sulfide hexagonal nanoplates through cation exchange reactions. The obtained wurtzite ZnS hexagonal nanoplates have dominant {002} polar surfaces, occupying up to 97.8% of all surfaces. Density functional theory calculations reveal the polar surfaces can be stabilized by a charge transfer of 0.25 eV/formula from the anion-terminated surface to the cation-terminated surface, which also explains the presence of polar surfaces in the initial Cu1.75S hexagonal nanoplates with cation deficiency prior to cation exchange reactions. Experimental results showed that the HER activity could be boosted by the surface polarization of polar surface-dominated ZnS hexagonal nanoplates. We anticipate this strategy is general and could be used with other systems to prepare nanocrystals with dominant polar surfaces. Furthermore, the availability of colloidal semiconductor nanocrystals with dominant polar surfaces produced through this strategy opens a new avenue for improving their efficiency in catalysis, photocatalysis, gas sensing, and other applications.
Wang, B, Li, T, Yan, Z, Zhang, G & Lu, J 2020, 'DeepPIPE: A distribution-free uncertainty quantification approach for time series forecasting', Neurocomputing, vol. 397, pp. 11-19.
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Wang, B, Liu, H, Zhu, Y, Yan, L, Li, JJ & Zhao, B 2020, 'Risk Factors with Multilevel Evidence for Dislocation in Patients with Femoral Neck Fractures After Hip Hemiarthroplasty: A Systematic Review', Indian Journal of Orthopaedics, vol. 54, no. 6, pp. 795-804.
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Background
Hip hemiarthroplasty (HA) is a standard surgical procedure for elderly patients with displaced fracture of the femoral neck, where dislocation is a possible complication. This study is a systematic review on the risk factors of implant dislocation in patients with femoral neck fracture following hip hemiarthroplasty (HA), and evaluates the methodological quality of the included studies.
Methods
Studies on risk factor assessment of dislocation following hip HA were sourced from EMBASE, Ovid, PubMed and ScienceDirect databases. The quality of included studies was evaluated using an improved quality evaluation method combined with a best-evidence synthesis method.
Results
A total of 130,127 patients were involved in 17 observational studies included in this systematic review, with a dislocation rate that ranged between 0.76 and 12.2% (overall incidence was 4-5% by meta-analysis). According to the applied quality evaluation criteria, eight studies were considered to be of high quality, six to be of medium quality, and three to be of low quality. The posterolateral surgical approach was identified as the only risk factor supported by strong evidence, while patients with small acetabular coverage and low postoperative offset were identified as risk factors supported by moderate evidence, and 11 other risk factors were supported by limited evidence.
Conclusion
This systematic review provides some evidence in helping surgeons develop optimal prevention strategies for dislocation following hip HA during the perioperative period based on common risk factors identified in the literature. However, conclusive evidence supporting most of these risk factors is lacking and more methodologically rigorous studies are required to increase the confidence of recommendations.
Wang, B, Ni, B-J, Yuan, Z & Guo, J 2020, 'Unravelling kinetic and microbial responses of enriched nitrifying sludge under long-term exposure of cephalexin and sulfadiazine', Water Research, vol. 173, pp. 115592-115592.
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Wastewater treatment plants (WWTPs) have been identified as one of the reservoirs of antibiotics. Although nitrifying bacteria have been reported to be capable of degrading various antibiotics, there are very few studies investigating long-term effects of antibiotics on kinetic and microbial responses of nitrifying bacteria. In this study, cephalexin (CFX) and sulfadiazine (SDZ) were selected to assess chronic impacts on nitrifying sludge with stepwise increasing concentrations in two independent bioreactors. The results showed that CFX and SDZ at an initial concentration of 100 μg/L could be efficiently removed by enriched nitrifying sludge, as evidenced by removal efficiencies of more than 88% and 85%, respectively. Ammonia-oxidizing bacteria (AOB) made a major contribution to the biodegradation of CFX and SDZ via cometabolism, compared to limited contributions from heterotrophic bacteria and nitrite-oxidizing bacteria. Chronic exposure to CFX (≥30 μg/L) could stimulate ammonium oxidation activity in terms of a significant enhancement of ammonium oxidation rate (p < 0.01). In contrast, the ammonium oxidation activity was inhibited due to exposure to 30 μg/L SDZ (p < 0.01), then it recovered after long-term adaption under exposure to 50 and 100 μg/L SDZ. In addition, 16S rRNA gene amplicon sequencing revealed that the relative abundance of AOB decreased distinctly from 23.8% to 28.8% in the control phase (without CFX or SDZ) to 14.2% and 10.8% under exposure to 100 μg/L CFX and SDZ, respectively. However, the expression level of amoA gene was up-regulated to overcome this adverse impact and maintain a stable and efficient removal of both ammonium and antibiotics. The findings in this study shed a light on chronic effects of antibiotic exposure on kinetic and microbial responses of enriched nitrifying sludge in WWTPs.
Wang, C-T, Chen, Y-M, Tang, RCO, Garg, A, Ong, H-C & Yang, Y-C 2020, 'Dominated flow parameters applied in a recirculation microbial fuel cell', Process Biochemistry, vol. 99, pp. 236-245.
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© 2020 Elsevier Ltd Scaling up of microbial fuel cells is a challenge for practical applications in wastewater treatment. In addition, the flow control is an important aspect for the electrochemical reactions occurring at the electrodes are influenced by fluid motions. By using dimensionless parameter analysis fluid regimes can be investigated in different scales of reactors. In this study, four important dimensionless flow parameters such as Reynolds number, Péclet number, Schmidt number, and Sherwood number were used for systematic analysis of hydrodynamic effects and power performance of recirculation mode microbial fuel cells together with computational fluid dynamics method. Results showed that the higher value of Reynolds number enhanced the convective flow of anolyte due to the dominant inertial forces in the flow field. Therefore, Reynolds number of 1.6 × 101 were obtained high mass transfer coefficient of 4.76 × 10−7 m s-1 and thin diffusion layer thickness of 2.52 × 10-3 m. Maximum power density and limited current density of 2422.8 mW m-2 and 4736.4 mA m-2 were obtained respectively which were higher than Reynolds number of 0 by 1.61 and 1.69 times. These findings shall be useful for effective recirculation flow mode MFCs power production and have a great possibility for large scale applications.
Wang, C-T, Ong Tang, RC, Wu, M-W, Garg, A, Ubando, AT, Culaba, A, Ong, H-C & Chong, W-T 2020, 'Flow shear stress applied in self-buffered microbial fuel cells', Process Biochemistry, vol. 99, pp. 324-330.
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Wang, D, Wu, C, Zhang, Y, Xue, G & Xu, Y 2020, 'Study on seismic performance of a precast buckling-restrained composite shear wall system with three assembly arrangements', Bulletin of Earthquake Engineering, vol. 18, no. 10, pp. 4839-4872.
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© 2020, Springer Nature B.V. Precast buildings have attarcted worldwide attention because of their significant role in the realization of sustainable urbanization. In this study, a precast buckling-restrained composite shear wall (PBRSW) system is developed, which is assemblied by multiple composite shear wall modules on site. The PBRSW system with three assembly arrangements of the composite shear wall modules, vertical, horizontal and cross arrangements, are designed and explored comparatively to mitigate buckling phenomena and obtain beneficial mechanical behaviours with experiment and simulation methods. To bring insight the seismic performance of the developed system, traditional buckling-restrained shear wall (BRSW) system and steel plate shear wall (SPSW) system are further investigated. The results show that the PBRSW system achieves plumper hysteresis behaviors, higher force-bearing and energy-dissipation capacities, and better ductility performance than that of the other two systems. Buckling phenomena of the PBRSW system are restrined effectively, and its maximum out-of-plane displacement is only 1/18 and 1/15 of the SPSW and BRSW systems on average respectively. The PBRSW system with vertical arrangement of the composite shear wall modules shows the best mechanical behavior with the highest bearing capacity and energy dissipation among the three assembly arrangements. Experimental data coincides well with those from finite element model (FEM) analysis and therefore validates FEM.
Wang, F, Zheng, Q, Zhang, G, Wang, C, Cheng, F & Lin, G 2020, 'Preparation and Hydration Mechanism of Mine Cemented Paste Backfill Material for Secondary Smelting Water-Granulated Nickel Slag', Journal of New Materials for Electrochemical Systems, vol. 23, no. 1, pp. 51-59.
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Wang, F, Zhu, L, Liang, C, Li, J, Chang, X & Lu, K 2020, 'Robust optimal graph clustering', Neurocomputing, vol. 378, pp. 153-165.
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Most graph-based clustering methods separate the graph construction and clustering into two independent processes. The manually pre-constructed graph may not be suitable for the subsequent clustering. Moreover, as real world data generally contains noises and outliers, the similarity graph directly learned from them will be unreliable and further impair the subsequent clustering performance. To tackle the problems, in this paper, we propose a novel clustering framework where a robust graph is learned with noise removal, and simultaneously, with desirable clustering structure. To this end, we first learn a discriminative representation of data samples via sparse reconstruction. Then, a robust graph is automatically constructed with adaptive neighbors to each data sample. Simultaneously, a reasonable rank constraint is imposed on the Laplacian matrix of similarity graph to pursue the ideal clustering structure, where the number of connected components in the learned graph is exactly equal to the number of clusters. We finally derive an alternate optimization algorithm guaranteed with convergence to solve the formulated unified learning framework to achieve better prediction accuracy. Experiments on both synthetic and real datasets demonstrate the superior performance of the proposed method compared with several state-of-the-art clustering techniques.
Wang, G, Ji, J & Zhou, J 2020, 'Stochastic distribution synchronization and pinning control for complex heterogeneous dynamical networks', Asian Journal of Control, vol. 22, no. 4, pp. 1547-1564.
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AbstractThis paper investigates the stochastic synchronization and pinning control in the sense of probability distribution for a general model of complex heterogeneous dynamical networks subjected to stochastic disturbances. Some generic stochastic synchronization criteria are established for both cases of undirected and directed topology by using the ergodic theory on stochastic dynamical systems. Compared with most existing studies on the stochastic synchronization in the sense of mean square, it is demonstrated that the concept of stochastic distribution synchronization can well characterize the realistic structure and essential nature of complex practical stochastic systems. Subsequently, two representative examples of complex heterogeneous dynamical networks, namely coupled stochastic Duffing oscillators and coupled FitzHugh‐Nagumo neuron oscillators, are given to illustrate and numerically verify the theoretical results.
Wang, G, Li, Y, Sheng, L, Xing, Y, Liu, G, Yao, G, Ngo, HH, Li, Q, Wang, XC, Li, Y-Y & Chen, R 2020, 'A review on facilitating bio-wastes degradation and energy recovery efficiencies in anaerobic digestion systems with biochar amendment', Bioresource Technology, vol. 314, pp. 123777-123777.
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In this review, progress in the potential mechanisms of biochar amendment for AD performance promotion was summarized. As adsorbents, biochar was beneficial for alleviating microbial toxicity, accelerating refractory substances degradation, and upgrading biogas quality. The buffering capacity of biochar balanced pH decreasing caused by volatile fatty acids accumulation. Moreover, biochar regulated microbial metabolism by boosting activities, mediating electron transfer between syntrophic partners, and enriching functional microbes. Recent studies also suggested biochar as potential useful additives for membrane fouling alleviation in anaerobic membrane bioreactors (AnMBR). By analyzing the reported performances based on different operation models or substrate types, debatable issues and associated research gaps of understanding the real role of biochar in AD were critically discussed. Accordingly, Future perspectives of developing biochar-amended AD technology for real-world applications were elucidated. Lastly, with biochar-amended AD as a core process, a novel integrated scheme was proposed towards high-efficient energy-resource recovery from various bio-wastes.
Wang, G, Lu, J, Choi, K-S & Zhang, G 2020, 'A Transfer-Based Additive LS-SVM Classifier for Handling Missing Data', IEEE Transactions on Cybernetics, vol. 50, no. 2, pp. 739-752.
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IEEE The performance of a classifier might greatly deteriorate due to missing data. Many different techniques to handle this problem have been developed. In this paper, we solve the problem of missing data using a novel transfer learning perspective and show that when an additive least squares support vector machine (LS-SVM) is adopted, model transfer learning can be used to enhance the classification performance on incomplete training datasets. A novel transfer-based additive LS-SVM classifier is accordingly proposed. This method also simultaneously determines the influence of classification errors caused by each incomplete sample using a fast leave-one-out cross validation strategy, as an alternative way to clean the training data to further improve the data quality. The proposed method has been applied to seven public datasets. The experimental results indicate that the proposed method achieves at least comparable, if not better, performance than case deletion, mean imputation, and k-nearest neighbor imputation methods, followed by the standard LS-SVM and support vector machine classifiers. Moreover, a case study on a community healthcare dataset using the proposed method is presented in detail, which particularly highlights the contributions and benefits of the proposed method to this real-world application.
Wang, G, Teoh, JY-C, Lu, J & Choi, K-S 2020, 'Least squares support vector machines with fast leave-one-out AUC optimization on imbalanced prostate cancer data', International Journal of Machine Learning and Cybernetics, vol. 11, no. 8, pp. 1909-1922.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Quite often, the available pre-biopsy data for early prostate cancer detection are imbalanced. When the least squares support vector machines (LS-SVMs) are applied to such scenarios, it becomes naturally desirable for us to introduce the well-known AUC performance index into the LS-SVMs framework to avoid bias towards majority classes. However, this may result in high computational complexity for the minimal leave-one-out error. In this paper, by introducing the parameter λ, a generalized Area under the ROC curve (AUC) performance index RAUCLS is developed to theoretically guarantee that RAUCLS linearly depends on the classical AUC performance index RAUC. Based on both RAUCLS and the classical LS-SVM, a new AUC-based least squares support vector machine called AUC-LS-SVMs is proposed for directly and effectively classifying imbalanced prostate cancer data. The distinctive advantage of the proposed classifier AUC-LS-SVMs exists in that it can achieve the minimal leave-one-out error by quickly optimizing the parameter λ in RAUCLS using the proposed fast leave-one-out cross validation (LOOCV) strategy. The proposed classifier is first evaluated using generic public datasets. Further experiments are then conducted on a real-world prostate cancer dataset to demonstrate the efficacy of our proposed classifier for early prostate cancer detection.
Wang, G, Wang, D, Du, C, Li, K, Zhang, J, Liu, Z, Tao, Y, Wang, M, Cao, Z & Yan, X 2020, 'Seizure Prediction Using Directed Transfer Function and Convolution Neural Network on Intracranial EEG', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 12, pp. 2711-2720.
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Automatic seizure prediction promotes the development of closed-loop treatment system on intractable epilepsy. In this study, by considering the specific information exchange between EEG channels from the perspective of whole brain activities, the convolution neural network (CNN) and the directed transfer function (DTF) were merged to present a novel method for patient-specific seizure prediction. Firstly, the intracranial electroencephalogram (iEEG) signals were segmented and the information flow features of iEEG signals were calculated by using the DTF algorithm. Then, these features were reconstructed as the channel-frequency maps according to channel pairs and the frequency of information flow. Finally, these maps were fed into the CNN model and the outputs were post-processed by the moving average approach to predict the epileptic seizures. By the evaluation of cross-validation method, the proposed algorithm achieved the averaged sensitivity of 90.8%, the averaged false prediction rate of 0.08 per hour. Compared to the random predictor and other existing algorithms tested on the Freiburg EEG dataset, our proposed method achieved better performance for seizure prediction in all patients. These results demonstrated that the proposed algorithm could provide an robust seizure prediction solution by using deep learning to capture the brain network changes of iEEG signals from epileptic patients.
Wang, G, Zhao, X, Wu, H, Lovejoy, DB, Zheng, M, Lee, A, Fu, L, Miao, K, An, Y, Sayyadi, N, Ding, K, Chung, RS, Lu, Y, Li, J, Morsch, M & Shi, B 2020, 'A Robust Intrinsically Green Fluorescent Poly(Amidoamine) Dendrimer for Imaging and Traceable Central Nervous System Delivery in Zebrafish', Small, vol. 16, no. 39, pp. 1-9.
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AbstractIntrinsically fluorescent poly(amidoamine) dendrimers (IF‐PAMAM) are an emerging class of versatile nanoplatforms for in vitro tracking and bio‐imaging. However, limited tissue penetration of their fluorescence and interference due to auto‐fluorescence arising from biological tissues limit its application in vivo. Herein, a green IF‐PAMAM (FGP) dendrimer is reported and its biocompatibility, circulation, biodistribution and potential role for traceable central nervous system (CNS)‐targeted delivery in zebrafish is evaluated, exploring various routes of administration. Key features of FGP include visible light excitation (488 nm), high fluorescence signal intensity, superior photostability and low interference from tissue auto‐fluorescence. After intravenous injection, FGP shows excellent imaging and tracking performance in zebrafish. Further conjugating FGP with transferrin (FGP‐Tf) significantly increases its penetration through the blood–brain barrier (BBB) and prolongs its circulation in the blood stream. When administering through local intratissue microinjection, including intracranial and intrathecal injection in zebrafish, both FGP and FGP‐Tf exhibit excellent tissue diffusion and effective cellular uptake in the brain and spinal cord, respectively. This makes FGP/FGP‐Tf attractive for in vivo tracing when transporting to the CNS is desired. The work addresses some of the major shortcomings in IF‐PAMAM and provides a promising application of these probes in the development of drug delivery in the CNS.
Wang, H, Lian, D, Zhang, Y, Qin, L, He, X, Lin, Y & Lin, X 2020, 'Binarized Graph Neural Network.', CoRR, vol. abs/2004.11147.
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Recently, there have been some breakthroughs in graph analysis by applying the graph neural networks (GNNs) following a neighborhood aggregation scheme, which demonstrate outstanding performance in many tasks. However, we observe that the parameters of the network and the embedding of nodes are represented in real-valued matrices in existing GNN-based graph embedding approaches which may limit the efficiency and scalability of these models. It is well-known that binary vector is usually much more space and time efficient than the real-valued vector. This motivates us to develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters following the GNN-based paradigm. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding. Extensive experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space while matching the state-of-the-art performance.
Wang, H, Roche, CD & Gentile, C 2020, 'Omentum support for cardiac regeneration in ischaemic cardiomyopathy models: a systematic scoping review', European Journal of Cardio-Thoracic Surgery, vol. 58, no. 6, pp. 1118-1129.
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Abstract OBJECTIVES Preclinical in vivo studies using omental tissue as a biomaterial for myocardial regeneration are promising and have not previously been collated. We aimed to evaluate the effects of the omentum as a support for bioengineered tissue therapy for cardiac regeneration in vivo. METHODS A systematic scoping review was performed. Only English-language studies that used bioengineered cardio-regenerative tissue, omentum and ischaemic cardiomyopathy in vivo models were included. RESULTS We initially screened 1926 studies of which 17 were included in the final qualitative analysis. Among these, 11 were methodologically comparable and 6 were non-comparable. The use of the omentum improved the engraftment of bioengineered tissue by improving cell retention and reducing infarct size. Vascularization was also improved by the induction of angiogenesis in the transplanted tissue. Omentum-supported bioengineered grafts were associated with enhanced host reverse remodelling and improved haemodynamic measurements. CONCLUSIONS The omentum is a promising support for myocardial regenerative bioengineering in vivo. Future studies would benefit from more homogenous methodologies and reporting of outcomes to allow for direct comparison.
Wang, H, Tan, J & Wen, S 2020, 'Exponential Stability Analysis of Mixed Delayed Quaternion-Valued Neural Networks Via Decomposed Approach', IEEE Access, vol. 8, pp. 91501-91509.
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© 2013 IEEE. With the application of quaternion in technology, quaternion-valued neural networks (QVNNs) have attracted many scholars' attention in recent years. For the existing results, dynamical behavior is an important studying side. In this paper, we mainly research the existence, uniqueness and exponential stability criteria of solutions for the QVNNs with discrete time-varying delays and distributed delays by means of generalized 2-norm. In order to avoid the noncommutativity of quaternion multiplication, the QVDNN system is firstly decomposed into four real-number systems by Hamilton rules. Then, we obtain the sufficient criteria for the existence, uniqueness and exponential stability of solutions by special Lyapunov-type functional, Cauchy convergence principle and monotone function. Furthermore, several corollaries are derived from the main results. Finally, we give one numerical example and its simulated figures to illustrate the effectiveness of the obtained conclusion.
Wang, H, Wei, G, Wen, S & Huang, T 2020, 'Generalized norm for existence, uniqueness and stability of Hopfield neural networks with discrete and distributed delays', Neural Networks, vol. 128, pp. 288-293.
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In this paper, the existence, uniqueness and stability criteria of solutions for Hopfield neural networks with discrete and distributed delays (DDD HNNs) are investigated by the definitions of three kinds of generalized norm (ξ-norm). A general DDD HNN model is firstly introduced, where the discrete delays τpq(t) are asynchronous time-varying delays. Then, {ξ,1}-norm, {ξ,2}-norm and {ξ,∞}-norm are successively used to derive the existence, uniqueness and stability criteria of solutions for the DDD HNNs. In the proof of theorems, special functions and assumptions are given to deal with discrete and distributed delays. Furthermore, a corollary is concluded for the existence and stability criteria of solutions. The methods given in this paper can also be used to study the synchronization and μ-stability of different DDD NNs. Finally, two numerical examples and their simulation figures are given to illustrate the effectiveness of these results.
Wang, H, Yu, S, Zeadally, S, Rawat, DB & Gao, Y 2020, 'Introduction to the Special Section on Network Science for Internet of Things (IoT)', IEEE Transactions on Network Science and Engineering, vol. 7, no. 1, pp. 237-238.
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Wang, J, Li, H, Lu, H, Yang, H & Wang, C 2020, 'Integrating offline logistics and online system to recycle e-bicycle battery in China', Journal of Cleaner Production, vol. 247, pp. 119095-119095.
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© 2019 Elsevier Ltd E-bicycles are powered by batteries including lithium-ion, lead–acid, and others. The reuse of waste batteries shows promise for grid-scale storage. The New National Standard for e-bicycles is to be introduced in China, that might result in the country becoming the largest source of battery waste in the world. If the waste batteries are not recycled appropriately, it will cause significant heavy metal pollution, which will in turn, pose a serious threat to the ecological environment and human health. This paper discusses the current status of recycling of e-bicycle batteries in China and reviews the current recycling approaches. We developed a waste e-bicycle battery recycling system based on “Internet+” to solve the dilemma of recycling end-of-life batteries; this system has three subsystems: offline reverse logistics recovery system, online network recycling system, and traceability management system. In particular, the participation of consumers and government, reward-penalty mechanism, “Internet +” development, and other strategies are considered to improve recycling systems throughout life cycle of the products. The proposed recycling system can increase the waste battery recycling rate by 2.59% under the reward-penalty mechanism, and reduce carbon dioxide emissions by 58%, which is conducive to promoting sustainable development.
Wang, J, Niu, T, Lu, H, Yang, W & Du, P 2020, 'A Novel Framework of Reservoir Computing for Deterministic and Probabilistic Wind Power Forecasting', IEEE Transactions on Sustainable Energy, vol. 11, no. 1, pp. 337-349.
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Wang, J, Wang, Z, Zhong, Y, Zou, Y, Wang, C, Wu, H, Lee, A, Yang, W, Wang, X, Liu, Y, Zhang, D, Yan, J, Hao, M, Zheng, M, Chung, R, Bai, F & Shi, B 2020, 'Central metal-derived co-assembly of biomimetic GdTPP/ZnTPP porphyrin nanocomposites for enhanced dual-modal imaging-guided photodynamic therapy', Biomaterials, vol. 229, pp. 119576-119576.
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Wang, K, Ruppert, MG, Manzie, C, Nesic, D & Yong, YK 2020, 'Adaptive Scan for Atomic Force Microscopy Based on Online Optimization: Theory and Experiment', IEEE Transactions on Control Systems Technology, vol. 28, no. 3, pp. 869-883.
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Wang, K, Ruppert, MG, Manzie, C, Nesic, D & Yong, YK 2020, 'Scan Rate Adaptation for AFM Imaging Based on Performance Metric Optimization', IEEE/ASME Transactions on Mechatronics, vol. 25, no. 1, pp. 418-428.
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Wang, L, Niu, J & Yu, S 2020, 'SentiDiff: Combining Textual Information and Sentiment Diffusion Patterns for Twitter Sentiment Analysis', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 10, pp. 2026-2039.
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Twitter sentiment analysis has become a hot research topic in recent years. Most of existing solutions to Twitter sentiment analysis basically only consider textual information of Twitter messages, and struggle to perform well when facing short and ambiguous Twitter messages. Recent studies show that sentiment diffusion patterns on Twitter have close relationships with sentiment polarities of Twitter messages. Therefore, in this paper, we focus on how to fuse textual information of Twitter messages and sentiment diffusion patterns to obtain better performance of sentiment analysis on Twitter data. To this end, we first analyze sentiment diffusion by investigating a phenomenon called sentiment reversal, and find some interesting properties of sentiment reversals. Then, we consider the inter-relationships between textual information of Twitter messages and sentiment diffusion patterns, and propose an iterative algorithm called SentiDiff to predict sentiment polarities expressed in Twitter messages. To the best of our knowledge, this work is the first to utilize sentiment diffusion patterns to help improve Twitter sentiment analysis. Extensive experiments on real-world dataset demonstrate that compared with state-of-the-art textual information based sentiment analysis algorithms, our proposed algorithm yields PR-AUC improvements between 5.09 and 8.38 percent on Twitter sentiment classification tasks.
Wang, L, Yang, Y, Deng, L, Hong, W, Zhang, C & Li, S 2020, 'Terahertz Angle‐Multiplexed Metasurface for Multi‐Dimensional Multiplexing of Spatial and Frequency Domains', Advanced Theory and Simulations, vol. 3, no. 10, pp. 2000115-2000115.
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AbstractIncreasing information capacity is crucial for high‐capacity and high‐speed communication, especially for sub‐terahertz communication. Over the last decade, spatial multiplexing based on the orbital angular momentum (OAM) by adopting a multi‐mode OAM metasurface has attracted a lot of attention. However, current metasurface‐based OAM multiplexing methods suffer from complex and limited multiplexing channels. In this paper, a novel method to realize multi‐dimensional multiplexing combining OAM and frequency based on an angle‐multiplexed metasurface over a broadband terahertz region is proposed and investigated. A frequency‐independent phase profile formula of the angle‐multiplexed metasurface is derived. A reflective metasurface operating from 0.25 to 0.35 terahertz (THz) is designed based on this formula. For proof of concept, nine‐channel multiplexing is illustrated based on this novel method. The simulation results verify that nine‐channel off‐axis left‐hand circularly polarized beams are converted to nine orthogonal coaxial beams. Besides, according to the conventional method for OAM multiplexing, an angle‐multiplexed reflective metasurface working at 0.3 THz is designed for comparison. The simulation results show that only three‐channel multiplexing can be obtained by this model with nine‐channel incident waves. The proposed method has a great potential to enhance the transmission capacity of the communication system.
Wang, L, Yang, Y, Li, S, Deng, L, Hong, W, Zhang, C, Zhu, J & McGloin, D 2020, 'Terahertz Reconfigurable Metasurface for Dynamic Non-Diffractive Orbital Angular Momentum Beams using Vanadium Dioxide', IEEE Photonics Journal, vol. 12, no. 3, pp. 1-12.
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© 2009-2012 IEEE. Orbital angular momentum (OAM) generation based on metasurfaces has attracted tremendous interest due to its potential in capacity enhancement of high-speed wireless communication systems. Reconfigurability is one of the key desired characteristics for the design of future metasurfaces. In this paper, a metasurface taking advantage of vanadium dioxide (VO2) is proposed. The proposed design can generate a non-diffractive OAM beam and achieve the multiple reconfigurability of the topological charge, beam radius, beam deflection angle. The operation frequency can be adjusted by controlling the state of VO2 at terahertz (THz) region. Simulation results demonstrate that the designed metasurface can generate a non-diffractive OAM beam with tunable topological charge and beam radius, propagating along ±x or ±y directions with the controllable deflection angle between 6.74° and 44.77°, ranging from 0.69 THz to 0.79 THz.
Wang, M, Yan, Z, Wang, T, Cai, P, Gao, S, Zeng, Y, Wan, C, Wang, H, Pan, L, Yu, J, Pan, S, He, K, Lu, J & Chen, X 2020, 'Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors', Nature Electronics, vol. 3, no. 9, pp. 563-570.
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© 2020, The Author(s), under exclusive licence to Springer Nature Limited. Gesture recognition using machine-learning methods is valuable in the development of advanced cybernetics, robotics and healthcare systems, and typically relies on images or videos. To improve recognition accuracy, such visual data can be combined with data from other sensors, but this approach, which is termed data fusion, is limited by the quality of the sensor data and the incompatibility of the datasets. Here, we report a bioinspired data fusion architecture that can perform human gesture recognition by integrating visual data with somatosensory data from skin-like stretchable strain sensors made from single-walled carbon nanotubes. The learning architecture uses a convolutional neural network for visual processing and then implements a sparse neural network for sensor data fusion and recognition at the feature level. Our approach can achieve a recognition accuracy of 100% and maintain recognition accuracy in non-ideal conditions where images are noisy and under- or over-exposed. We also show that our architecture can be used for robot navigation via hand gestures, with an error of 1.7% under normal illumination and 3.3% in the dark.
Wang, M, Zhu, T, Zhang, T, Zhang, J, Yu, S & Zhou, W 2020, 'Security and privacy in 6G networks: New areas and new challenges', Digital Communications and Networks, vol. 6, no. 3, pp. 281-291.
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© 2020 Chongqing University of Posts and Telecommunications With the deployment of more and more 5g networks, the limitations of 5g networks have been found, which undoubtedly promotes the exploratory research of 6G networks as the next generation solutions. These investigations include the fundamental security and privacy problems associated with 6G technologies. Therefore, in order to consolidate and solidify this foundational research as a basis for future investigations, we have prepared a survey on the status quo of 6G security and privacy. The survey begins with a historical review of previous networking technologies and how they have informed the current trends in 6G networking. We then discuss four key aspects of 6G networks – real-time intelligent edge computing, distributed artificial intelligence, intelligent radio, and 3D intercoms – and some promising emerging technologies in each area, along with the relevant security and privacy issues. The survey concludes with a report on the potential use of 6G. Some of the references used in this paper along and further details of several points raised can be found at: security-privacyin5g-6g.github.io.
Wang, N, Ma, Z, Ding, C, Jia, H, Sui, G & Gao, X 2020, 'Characteristics of Dual‐Gate Graphene Thermoelectric Devices Based on Voltage Regulation', Energy Technology, vol. 8, no. 7, pp. 1901466-1901466.
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The bandgap, the carrier concentration, and the polarity in graphene can all be controlled by gate voltage, which provides a new opportunity for the study of the regulation of thermoelectric devices. Herein, a dual‐gate thermoelectric device model for graphene with top‐gate and back‐gate structures is proposed. Based on the influence of gate voltage on carrier concentration and the Fermi level, the relationship between the gate voltage and the channel resistance, the Seebeck coefficient, and the conductivity of dual‐gate graphene, thermoelectric devices are established according to the mechanism of carrier transport. The results demonstrate that the optimal voltage window of the Seebeck coefficient, conductivity, and power factor is obtained independently. Compared with the conventional graphene thermoelectric device without the top‐gate structure, the Seebeck coefficient and the power factor for the proposed dual‐gate structure are increased twofold and tenfold, respectively. Herein, a new approach is provided for high‐performance thermoelectric device designs with accurate regulation.
Wang, N, Ma, Z, Ding, C, Jia, H, Sui, G & Gao, X 2020, 'Characteristics of Dual‐Gate Graphene Thermoelectric Devices Based on Voltage Regulation', Energy Technology, vol. 8, no. 7.
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Wang, N, Shen, Z-H, Gao, C, Chen, M-M, Ding, C, Sui, G-R, Jia, H-Z & Gao, X-M 2020, 'Electrothermal Collaborative Cooling With Delayed Power Rail Switching Auxiliary Charging by Considering Energy Harvesting Mechanism for High-Power LEDs', IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 10, no. 9, pp. 1507-1514.
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© 2011-2012 IEEE. With the development of high-power light-emitting diodes (LEDs), the heat flux density of devices has continued to increase, which, in turn, requires the development of increasingly effective methods of heat dissipation to control the working temperature of LEDs. Due to their impressive performance in solid refrigeration and energy harvesting, thermoelectric devices such as thermoelectric coolers (TECs) and thermoelectric generators (TEGs) have been used to develop the methods of heat dissipation, which have been applied to power components and electronic devices. This article proposes a delayed electrothermal collaborative cooling system based on a TEC-TEG system that uses an auxiliary charging technique for energy harvesting. In the designed delay circuit, two power source rails containing a TEG and a charged capacitor are switched automatically to supply energy to a TEC according to the charge on the capacitor and the discharge time of the delay circuit used for energy transmission. The results of experiments show that using the proposed scheme, the electromotive force can be increased by 21.6%, from 0.37 to 0.45 V, in the TEG module compared with the collaborative electrothermal cooling system without auto-delayed power rail switching. The switching time cost of the proposed system was only 0.8 s, and it could continuously supply enough electromotive force to drive the TEC and the overall cooling system. The proposed electrothermal collaborative cooling system with delayed power rail switching and auxiliary charging can improve energy utilization and reduce device cost, which helps to efficiently manage heat dissipation in high-power LEDs.
Wang, Q, Huang, Y, Jia, W, He, X, Blumenstein, M, Lyu, S & Lu, Y 2020, 'FACLSTM: ConvLSTM with focused attention for scene text recognition', Science China Information Sciences, vol. 63, no. 2.
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© 2020, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature. Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM (FC-LSTM) has played a critical role. Owing to the limitation of FC-LSTM, existing methods have to convert 2-D feature maps into 1-D sequential feature vectors, resulting in severe damages of the valuable spatial and structural information of text images. In this paper, we argue that scene text recognition is essentially a spatiotemporal prediction problem for its 2-D image inputs, and propose a convolution LSTM (ConvLSTM)-based scene text recognizer, namely, FACLSTM, i.e., focused attention ConvLSTM, where the spatial correlation of pixels is fully leveraged when performing sequential prediction with LSTM. Particularly, the attention mechanism is properly incorporated into an efficient ConvLSTM structure via the convolutional operations and additional character center masks are generated to help focus attention on right feature areas. The experimental results on benchmark datasets IIIT5K, SVT and CUTE demonstrate that our proposed FACLSTM performs competitively on the regular, low-resolution and noisy text images, and outperforms the state-of-the-art approaches on the curved text images with large margins.
Wang, Q, Li, Q, Wu, D, Yu, Y, Tin-Loi, F, Ma, J & Gao, W 2020, 'Machine learning aided static structural reliability analysis for functionally graded frame structures', Applied Mathematical Modelling, vol. 78, pp. 792-815.
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© 2019 A novel machine learning aided structural reliability analysis for functionally graded frame structures against static loading is proposed. The uncertain system parameters, which include the material properties, dimensions of structural members, applied loads, as well as the degree of gradation of the functionally graded material (FGM), can be incorporated within a unified structural reliability analysis framework. A 3D finite element method (FEM) for static analysis of bar-type engineering structures involving FGM is presented. By extending the traditional support vector regression (SVR) method, a new kernel-based machine learning technique, namely the extended support vector regression (X-SVR), is proposed for modelling the underpinned relationship between the structural behaviours and the uncertain system inputs. The proposed structural reliability analysis inherits the advantages of the traditional sampling method (i.e., Monte-Carlo Simulation) on providing the information regarding the statistical characteristics (i.e., mean, standard deviations, probability density functions and cumulative distribution functions etc.) of any concerned structural outputs, but with significantly reduced computational efforts. Five numerical examples are investigated to illustrate the accuracy, applicability, and computational efficiency of the proposed computational scheme.
Wang, Q, Zhou, Y, Ding, W, Zhang, Z, Muhammad, K & Cao, Z 2020, 'Random Forest with Self-Paced Bootstrap Learning in Lung Cancer Prognosis', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 1s, pp. 1-12.
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Training gene expression data with supervised learning approaches can provide an alarm sign for early treatment of lung cancer to decrease death rates. However, the samples of gene features involve lots of noises in a realistic environment. In this study, we present a random forest with self-paced learning bootstrap for improvement of lung cancer classification and prognosis based on gene expression data. To be specific, we propose an ensemble learning with random forest approach to improving the model classification performance by selecting multi-classifiers. Then, we investigate the sampling strategy by gradually embedding from high- to low-quality samples by self-paced learning. The experimental results based on five public lung cancer datasets show that our proposed method could select significant genes exactly, which improves classification performance compared to that of existing approaches. We believe that our proposed method has the potential to assist doctors in gene selections and lung cancer prognosis.
Wang, S & Cao, L 2020, 'Inferring Implicit Rules by Learning Explicit and Hidden Item Dependency', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 3, pp. 935-946.
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© 2017 IEEE. Revealing complex relations between entities (e.g., items within or between transactions) is of great significance for business optimization, prediction, and decision making. Such relations include not only co-occurrence-based explicit relations but also nonco-occurrence-based implicit ones. Explicit relations have been substantially studied by rule mining-based approaches, including association rule mining and causal rule discovery. In contrast, implicit relations have received much less attention but could be more actionable. In this paper, we focus on the implicit relations between items which rarely or never co-occur while each of them co-occurs with other identical items (link items) with a high probability. A framework integrates both explicit and hidden item dependencies and a corresponding efficient algorithm IRRMiner captures such implicit relations with implicit rule inference. Experimental results show that IRRMiner not only infers implicit rules of various sizes consisting of both frequent and infrequent items effectively, it also runs at least four times faster than IARMiner, a typical indirect association rule mining algorithm which can only mine size-2 indirect association rules between frequent items. IRRMiner is applied to make recommendations and shows that the identified implicit rules can increase recommendation reliability.
Wang, S, Cao, Y, Huang, T, Chen, Y & Wen, S 2020, 'Event-triggered distributed control for synchronization of multiple memristive neural networks under cyber-physical attacks', Information Sciences, vol. 518, pp. 361-375.
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This paper investigates the synchronization of multiple memristive neural networks (MMNNs) under cyber-physical attacks through distributed event-triggered control. In the field of multi-agent dynamics, memristive neural network (MNN) is considered as a kind of switched systems because of its state-dependent parameters which can lead to the parameters mismatch during synchronization. This will increase the uncertainty of the system and affect the theoretical analysis. Also, neural network is considered as a typical nonlinear system. Therefore, the model studied in this paper is a nonlinear system with switching characteristics. In complex environments, MMNNs may receive mixed attacks, one of which is called cyber-physical attacks that may influence both communication links and MNN nodes to cause changes in topology and physical state. To tackle this issue, we construct a novel Lyapunov functional and use properties of M-matrix to get the criteria for synchronization of MMNNs under cyber-physical attacks. It is worth mentioning that the controllers in this paper are designed to be distributed under event-triggering conditions and Zeno behavior is also excluded. In addition, the algorithm of parameter selection is given to help designing the controllers. One example is given at the end of the paper to support our results.
Wang, S, Cao, Y, Huang, T, Chen, Y, Li, P & Wen, S 2020, 'Sliding mode control of neural networks via continuous or periodic sampling event-triggering algorithm', Neural Networks, vol. 121, pp. 140-147.
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This paper presents the theoretical results on sliding mode control (SMC) of neural networks via continuous or periodic sampling event-triggered algorithm. Firstly, SMC with continuous sampling event-triggered scheme is developed and the practical sliding mode can be achieved. In addition, there is a consistent positive lower bound for the time interval between two successive trigger events which implies that the Zeno phenomenon will not occur. Next, a more economical and realistic SMC technique is presented with periodic sampling event-triggered algorithm, which guarantees the robust stability of the augmented system. Finally, two illustrative examples are presented to substantiate the effectiveness of the derived theoretical results.
Wang, S, Cao, Y, Wen, S, Guo, Z, Huang, T & Chen, Y 2020, 'Projective Synchroniztion of Neural Networks via Continuous/Periodic Event-Based Sampling Algorithms', IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 2746-2754.
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This study concerns the projective synchronization problem of basic neural networks via continuous/periodic event-based sampling algorithms. Firstly, an event-Triggering control scheme is proposed via continuous sampling. In addition, there exists a consistent positive lower bound for the time interval between two successive trigger events, which implies that the Zeno phenomenon will not occur. Next, by designing an appropriate sampling period, a more practical event-Triggering scheme is proposed with periodic sampling, which can ensure the projective synchronization of the drive-response neural networks systems. Finally, several examples are elaborated to substantiate the theoretical results.
Wang, S, Guo, Z, Wen, S & Huang, T 2020, 'Global synchronization of coupled delayed memristive reaction–diffusion neural networks', Neural Networks, vol. 123, pp. 362-371.
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This paper focuses on the global exponential synchronization of multiple memristive reaction-diffusion neural networks (MRDNNs) with time delay. Due to introducing the influences of space as well as time on state variables and replacing resistors with memristors in circuit realization, the state-dependent partial differential mathematical model of MRDNN is more general and realistic than traditional neural network model. Based on Lyapunov functional theory, Divergence theorem and inequality techniques, global exponential synchronization criteria of coupled delayed MRDNNs are derived via directed and undirected nonlinear coupling. Finally, three numerical simulation examples are presented to verify the feasibility of our main results.
Wang, S, Guo, Z, Wen, S, Huang, T & Gong, S 2020, 'Finite/fixed-time synchronization of delayed memristive reaction-diffusion neural networks', Neurocomputing, vol. 375, pp. 1-8.
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This paper is concerned with the finite/fixed-time synchronization (FFTS) problems of two delayed memristive reaction–diffusion neural networks (MRDNNs). By designing appropriate state feedback controllers, utilizing the Lyapunov function method and inequality techniques, several sufficient criteria are derived to guarantee the FFTS of the drive-response MRDNNs. Taking into account both the influences of time and space, the model, described as a state-dependent switching system here, is more complex and closer to practical applications than those in the existing results. Finally, an example is presented to substantiate the effectiveness of the theoretical results.
Wang, S, Liu, C, Wang, Y, Lei, G, Guo, Y & Zhu, J 2020, 'Electromagnetic performance analysis of flux-switching permanent magnet tubular machine with hybrid cores', CES Transactions on Electrical Machines and Systems, vol. 4, no. 1, pp. 43-52.
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Wang, S, Ma, J, Liu, C, Wang, Y, Lei, G, Guo, Y & Zhu, J 2020, 'Design and performance analysis of a novel synchronous reluctance machine', International Journal of Applied Electromagnetics and Mechanics, vol. 63, no. 2, pp. 249-265.
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To improve output torque ability and reduce torque ripple in traditional synchronous reluctance motor (TSynRM), a new synchronous reluctance motor (NSynRM) is proposed in this paper. The rotor of NSynRM is composed of both grain-oriented silicon steel and non-oriented silicon steel. With the reasonable design of rotor structure, the torque of NSynRM has been improved and its torque ripple has been reduced greatly. Firstly, TSynRM and NSynRM are qualitatively compared by using the magnetic network method. Secondly, the main parameters of these two machines are optimized by using finite element method (FEM). Then the performance comparison between two optimized machines are carried out. Finally, the equivalent stress of these two machines at the maximum speed are analyzed. It can be seen that NSynRM can have 6.8% higher torque under rated load, 8% higher torque under maximum load, 17.5% wider constant torque operation region, and lower torque ripple compared with the TSynRM.
Wang, S, Pasi, G, Hu, L & Cao, L 2020, 'The Era of Intelligent Recommendation: Editorial on Intelligent Recommendation with Advanced AI and Learning', IEEE Intelligent Systems, vol. 35, no. 5, pp. 3-6.
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Wang, S, Wang, Y, Liu, C, Lei, G, Zhu, J & Guo, Y 2020, 'Detent Force Minimization of a Tubular Flux-Switching Permanent Magnet Motor Using Un-Equal Width Stator Slots Based on Taguchi Method', IEEE Transactions on Applied Superconductivity, vol. 30, no. 4, pp. 1-5.
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Wang, S, Zhong, J, Qiu, X & Burnett, I 2020, 'A note on using panel diffusers to improve sound field diffusivity in reverberation rooms below 100 Hz', Applied Acoustics, vol. 169, pp. 107471-107471.
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© 2020 Elsevier Ltd In many standard acoustic tests, the sound field in a reverberation room is required to be sufficiently diffuse. The standard deviation of sound pressure levels is commonly used to evaluate the spatial uniformity of a sound field; however, this paper shows that the standard deviation of squared sound pressures is a better indicator of sound field diffusivity at frequencies below 100 Hz where the sound field is very uneven. Fixed diffusers are recommended in ISO 354 to improve sound field diffusivity in reverberation rooms, and performing sound absorption measurements with an increasing number of diffusers is employed as a check on the diffusivity of the sound field above 500 Hz. This paper demonstrates that typical panel diffusers (as suggested in ISO 354) cannot increase the sound field diffusivity at low frequencies. While low frequency diffusivity can be improved with large panels at some frequencies, the diffusivity at other frequencies generally deteriorates. Experimental results in a reverberation room are presented to support the numerical simulation results and analyses.
Wang, W & CAO, L 2020, 'Negative Sequence Analysis', ACM Computing Surveys, vol. 52, no. 2, pp. 1-39.
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Negative sequential patterns (NSPs) produced by negative sequence analysis (NSA) capture more informative and actionable knowledge than classic positive sequential patterns (PSPs) due to involving both occurring and nonoccurring items, which appear in many applications. However, the research on NSA is still at an early stage, and NSP mining involves very high computational complexity and a very large search space, there is no widely accepted problem statement on NSP mining, and different settings on constraints and negative containment have been proposed in existing work. Among existing NSP mining algorithms, there are no general and systemic evaluation criteria available to assess them comprehensively. This article conducts a comprehensive technical review of existing NSA research. We explore and formalize a generic problem statement of NSA; investigate, compare, and consolidate the definitions of constraints and negative containment; and compare the working mechanisms and efficiency of existing NSP mining algorithms. The review is concluded by discussing new research opportunities in NSA.
Wang, W, Wu, C, Liu, Z, An, K & Zeng, J-J 2020, 'Experimental Investigation of the Hybrid FRP-UHPC-Steel Double-Skin Tubular Columns under Lateral Impact Loading', Journal of Composites for Construction, vol. 24, no. 5, pp. 04020041-04020041.
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© 2020 American Society of Civil Engineers. The lateral impact behavior of hybrid fiber-reinforced polymer (FRP)-ultrahigh-performance concrete (UHPC)-steel double-skin tubular columns (DSTCs) was experimentally investigated in this study. Seven specimens, which had an outer diameter of 168 mm and a length of 2,000 mm, were tested under lateral impact loading. Different parameters, including the axial force level, impact energy, concrete type, void ratio, FRP tube thickness, and the presence/absence of the FRP tube, were investigated. The dynamic responses, including global/local damage modes, lateral deflection-time histories, impact force-time histories, strain-time histories, and acceleration-time histories, were investigated. The test results prove that the hybrid UHPC DSTCs exhibit very ductile behavior under lateral impact loading. The hybrid UHPC DSTCs have a higher lateral impact resistance capacity as compared to the hybrid DSTCs infilled with normal-strength concrete. The lateral impact resistance capacity of hybrid UHPC DSTCs with an applied axial force of 200 kN can be improved to some extent compared with those without any axial force. The impact energy, the void ratio, the FRP tube thickness, and the presence/absence of the FRP tube can significantly affect the lateral impact behavior of hybrid UHPC DSTCs. Furthermore, the lateral impact behaviors of hybrid DSTCs, concrete-filled double-skin steel tubes (CFDSTs), and concrete-filled steel tubes (CFSTs) were compared and discussed based on the experimental results in this study as well as in other literature studies.
Wang, X, Gu, B, Ren, Y, Ye, W, Yu, S, Xiang, Y & Gao, L 2020, 'A Fog-Based Recommender System', IEEE Internet of Things Journal, vol. 7, no. 2, pp. 1048-1060.
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© 2014 IEEE. Fog computing is an emergent computing paradigm that extends the cloud paradigm. With the explosive growth of smart devices and mobile users, cloud computing no longer matches the requirements of the Internet of Things (IoT) era. Fog computing is a promising solution to satisfying these new requirements, such as low latency, uninterrupted service, and location awareness. As a typical new computing paradigm and network architecture, fog computing raises new challenges, such as privacy, data management, data analytics, information overload, and participatory sensing. In this article, we present a fog-based hybrid recommender system to address the issue of information overload in fog computing. Our proposed system not only abstracts useful information from the fog environment but can also be considered as an optimization tool due to its ability to provide suggestions to improve system performance. In particular, we demonstrate that the proposed system provides personalized and localized recommendations to users, and also advise the system itself to precache the content to optimize the storage capacity of the fog server.
Wang, X, Guo, Z, Hu, Z, Ngo, H, Liang, S & Zhang, J 2020, 'Adsorption of phenanthrene from aqueous solutions by biochar derived from an ammoniation-hydrothermal method', Science of The Total Environment, vol. 733, pp. 139267-139267.
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Wang, X, Yang, Y, Liu, H, Ren, J, Xu, S, Wang, S & Yu, S 2020, 'Efficient measurement of round-trip link delays in software-defined networks', Journal of Network and Computer Applications, vol. 150, pp. 102468-102468.
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© 2019 Elsevier Ltd Round-trip link delay is an important indicator for network performance optimization and troubleshooting. The Software-Defined Networking (SDN) paradigm, which provides flexible and centralized control capability, paves the way for efficient round-trip link delay measurement. In this paper, we study the round-trip link delay measurement problem in SDN networks. We propose an efficient measurement scheme, which infers round-trip link delays from end-to-end delay of some measurement paths implemented with few flow rules in each SDN switch. Furthermore, to reduce measurement cost and meet measurement constraint, we address the Monitor Placement and Link Assignment (MPLA) problem involved in the measurement scheme. Specifically, we formulate the MPLA problem as a Mixed Integer Linear Programming (MILP) problem, prove that it is NP-hard, and propose an efficient algorithm called MPLA Algorithm based on Biding Strategy (MPLAA-BS) to solve the problem. The extensive simulation results on real network topologies reveal that the proposed scheme can efficiently and accurately measure round-trip link delays in SDN networks, and the MPLAA-BS can find feasible and resource-efficient solutions for the MPLA problem.
Wang, Y, Cao, Y, Guo, Z & Wen, S 2020, 'Passivity and passification of memristive recurrent neural networks with multi-proportional delays and impulse', Applied Mathematics and Computation, vol. 369, pp. 124838-124838.
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The research direction of this paper is passivity and passification of memristive recurrent neural networks (MRNNs) with multi-proportional delays and impulse. Preparing for passive analysis, the model of MRNNs is transformed into the general recurrent neural networks (RNNs) model through the way of non-smooth analysis. Utilizing the proper Lyapunov–Krasovskii functions constructed in this paper and the common matrix inequalities technique, a novel condition is derived which is sufficient to make sure that system is passive. In addition, it relaxes the condition that the symmetric matrices are all required to be positive definite. The final results are presented by linear matrix inequalities (LMIs) and its verification is easy to be carried out by the LMI toolbox. And there are several numerical examples showing the effectiveness and correctness of the derived criteria.
Wang, Y, Cao, Y, Guo, Z, Huang, T & Wen, S 2020, 'Event-based sliding-mode synchronization of delayed memristive neural networks via continuous/periodic sampling algorithm', Applied Mathematics and Computation, vol. 383, pp. 125379-125379.
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This paper investigates the problem of event-based sliding-mode synchronization of memristive neural networks with delay through continuous/periodic sampling algorithm. Memristive neural networks are converted into the form of general neural networks by nonsmooth analysis. Then the controller is designed on the sliding surface selected and the trajectory of the system with this controller are analyzed in detail. Based on the continuous sampling, this paper further draws new results with the periodic sampling rule. Finally, some numerical examples are given to verify the correctness of the theoretical results.
Wang, Y, Liu, X, Liu, Y, Wang, D, Xu, Q, Li, X, Yang, Q, Wang, Q, Ni, B-J & Chen, H 2020, 'Enhancement of short-chain fatty acids production from microalgae by potassium ferrate addition: Feasibility, mechanisms and implications', Bioresource Technology, vol. 318, pp. 124266-124266.
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Anaerobic fermentation of microalgae was always hindered by its rigid cell wall structure. This paper reports a novel technique, i.e., adding potassium ferrate (K2FeO4) into microalgae fermentation systems to enhance short-chain fatty acids (SCFAs) production. The results showed that the maximum SCFAs production and acetic acid proportion were 732.6 mg COD/g VS and 54.6% at a dosage of 112.8 mg Fe(VI)/g VS, which were 168% and 208% of those in the control, respectively. Mechanism studies revealed that K2FeO4 effectively destroyed surface morphology and cell structure, and thus facilitated microalgae solubilization, providing a large number of biodegradable substrates for subsequent SCFA production. Although K2FeO4 inhibited all the microbial activities relevant to hydrolysis, acidification and methanogenesis processes to some degree, its inhibition to methanogens was much severer than that to other microbes. Illumina MiSeq sequencing analyses revealed that K2FeO4 addition increased the relative abundance (from 9.45% to 50.4%) of hydrolytic and SCFAs-forming bacteria.
Wang, Y, Sayyadi, N, Zheng, X, Woods, TA, Leif, RC, Shi, B, Graves, SW, Piper, JA & Lu, Y 2020, 'Time-resolved microfluidic flow cytometer for decoding luminescence lifetimes in the microsecond region', Lab on a Chip, vol. 20, no. 3, pp. 655-664.
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Time-resolved luminescence detection using long-lived probes with lifetimes in the microsecond region have shown great potential in ultrasensitive and multiplexed bioanalysis.
Wang, Y, Sun, H, Huang, S & Song, Y 2020, 'Description of stability for linear time‐invariant systems based on the first curvature', Mathematical Methods in the Applied Sciences, vol. 43, no. 2, pp. 486-511.
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This paper focuses on using the first curvature κ(t) of trajectory to describe the stability of linear time‐invariant system. We extend the results for two and three‐dimensional systems (Wang, Sun, Song et al, arXiv:1808.00290) to n‐dimensional systems. We prove that for a system , (a) if there exists a measurable set whose Lebesgue measure is greater than zero, such that or does not exist for any initial value in this set, then the zero solution of the system is stable; (b) if the matrix A is invertible, and there exists a measurable set whose Lebesgue measure is greater than zero, such that for any initial value in this set, then the zero solution of the system is asymptotically stable.
Wang, Y, Wang, D, Yi, N, Li, Y, Ni, B-J, Wang, Q, Wang, H & Li, X 2020, 'Insights into the toxicity of troclocarban to anaerobic digestion: Sludge characteristics and methane production', Journal of Hazardous Materials, vol. 385, pp. 121615-121615.
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© 2019 Elsevier B.V. Triclocarban (TCC), as the most typical antibacterial agent, is widely discovered in many ecological environment, especially in sludge. However, so far, no studies have reported the effect of TCC exposure on the properties of excess sludge. Therefore, in this study, TCC's toxicities to waste activated sludge (WAS) were analyzed by investigating the variation of physicochemical properties of sludge. It was found that TCC exposure has no effect on sludge pH, while it facilitated organic substances release from sludge, e.g. dissolved organic matter (DOM), protein and polysaccharide, which caused an increase of sludge reduction and changed the structure of functional groups and surface morphology of sludge. Moreover, we explored the effect of TCC on anaerobic digestion of WAS and found methane production was seriously inhibited by TCC. The related mechanism tests had illustrated that TCC exposure did not affect the hydrolysis process, but promoted the acidification and acetogenesis, and importantly inhibited the methanogenesis process. Methanogenic community was further evaluated and observed that the presence of TCC could vary the microbial community of methanogens with the abundance of aceticlastic methanogens increasing and hydrogenotrophic methanogens decreasing. These findings reached in this study would widen the understanding scope for TCC's toxicity to WAS.
Wang, Y, Wei, W, Wu, S-L & Ni, B-J 2020, 'Zerovalent Iron Effectively Enhances Medium-Chain Fatty Acids Production from Waste Activated Sludge through Improving Sludge Biodegradability and Electron Transfer Efficiency', Environmental Science & Technology, vol. 54, no. 17, pp. 10904-10915.
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A novel zerovalent iron (ZVI) technique to simultaneously improve the production of medium-chain fatty acids (MCFAs) from waste activated sludge (WAS) and enhance WAS degradation during anaerobic WAS fermentation was proposed in this study. Experimental results showed that the production and selectivity of MCFAs were effectively promoted when ZVI was added at 1-20 g/L. The maximum MCFAs production of 15.4 g COD (Chemical Oxygen Demand)/L and MCFAs selectivity of 71.7% were both achieved at 20 g/L ZVI, being 5.3 and 4.8 times that without ZVI (2.9 g COD/L and 14.9%). Additionally, ZVI also promoted WAS degradation, which increased from 0.61 to 0.96 g COD/g VS when ZVI increased from 0 to 20 g/L. The microbial community analysis revealed that the ZVI increased the populations of key anaerobes related to hydrolysis, acidification, and chain elongation. Correspondingly, the solubilization, hydrolysis, and acidification processes of WAS were revealed to be improved by ZVI, thereby providing more substrates (short-chain fatty acids (SCFAs)) for producing MCFAs. The mechanism studies showed that ZVI declined the oxidation-reduction potential (ORP), creating a more favorable environment for the anaerobic biological processes. More importantly, ZVI with strong conductivity could act as an electron shuttle, contributing to increasing electron transfer efficiency from electron donor to acceptor. This strategy provides a new paradigm of transforming waste sludge into assets by a low-cost waste to bring significant economic benefits to sludge disposal and wastewater treatment.
Wang, Y, Xia, J, Luo, Z, Yan, H, Sun, J & Lü, E 2020, 'Self-supporting topology optimization method for selective laser melting', Additive Manufacturing, vol. 36, pp. 101506-101506.
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© 2020 Elsevier B.V. The design of self-supporting structures is critical for the selective laser melting (SLM)-based 3D printing techniques. However, the control of the overhang feature conflicts with the mechanical performance of the structure. This paper proposes an approach to achieve the self-supporting structural design to facilitate the SLM process. Printable overhang heights of samples under various overhang angles are investigated through experimental tests, and the maximum overhang heights are mathematically related to the corresponding critical overhang angle. Subsequently, this relationship is incorporated into the topology optimization formulation to realize the optimized self-supporting structures. The SIMP (solid isotropic material with penalization) method is used to conduct topology optimization. An effective filtering strategy with the overhang restrictions is developed to eliminate the material parts that cannot be supported from below. A typical beam structure to maximize the stiffness is used as a numerical example to demonstrate the proposed method. The numerical results show that the restrictions with both the overhang angles and heights can generate optimized structures with better performance than those only with the overhang angle constraint. In addition, prototypes are used to validate the manufacturability of the topologically optimized designs.
Wang, Y, Zhang, C, Wang, S, Yu, PS, Bai, L, Cui, L & Xu, G 2020, 'Generative temporal link prediction via self-tokenized sequence modeling', World Wide Web, vol. 23, no. 4, pp. 2471-2488.
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Wang, Z, Yu, X, Lu, M, Wang, Q, Qian, C & Xu, F 2020, 'Single image portrait relighting via explicit multiple reflectance channel modeling', ACM Transactions on Graphics, vol. 39, no. 6, pp. 1-13.
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Portrait relighting aims to render a face image under different lighting conditions. Existing methods do not explicitly consider some challenging lighting effects such as specular and shadow, and thus may fail in handling extreme lighting conditions. In this paper, we propose a novel framework that explicitly models multiple reflectance channels for single image portrait relighting, including the facial albedo, geometry as well as two lighting effects, i.e. , specular and shadow. These channels are finally composed to generate the relit results via deep neural networks. Current datasets do not support learning such multiple reflectance channel modeling. Therefore, we present a large-scale dataset with the ground-truths of the channels, enabling us to train the deep neural networks in a supervised manner. Furthermore, we develop a novel module named Lighting guided Feature Modulation (LFM). In contrast to existing methods which simply incorporate the given lighting in the bottleneck of a network, LFM fuses the lighting by layer-wise feature modulation to deliver more convincing results. Extensive experiments demonstrate that our proposed method achieves better results and is able to generate challenging lighting effects.
Wang, Z, Zhang, W, Luo, Q, Zheng, G, Li, Q & Sun, G 2020, 'A novel failure criterion based upon forming limit curve for thermoplastic composites', Composites Part B: Engineering, vol. 202, pp. 108320-108320.
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© 2020 Elsevier Ltd This study aims to explore failure mechanism and failure criteria of carbon fiber reinforced polypropylene (CFRPP) by presenting a novel notch-shaped design of specimens with two different fiber orientations (0/90)4 and (+45/-45)4 subject to stamping process under room temperature. Two forming limit curves/diagrams (FLCs/FLDs) were established based upon the minor strain and major strain, as well as the developed equivalent fiber strain combined with a ratio of minor to major strain (SR), respectively. A novel notch-shaped design enables to minimize the influence of fiber orientation on failure modes and FLC of CFRPP effectively. Not only did the equivalent fiber strain based FLC reflect the failure mode in terms of a certain SR value, but also showed the failure strain on fiber bundles quantitatively. Finite element (FE) models involving the new failure criteria (FLC) were developed on the basis of the experimental results. It was found that such a new FE model is able to better predict the results than those with the conventional maximum strain or maximum stress failure criteria. The difference in failure behaviors among these three failure criteria (i.e. FLC, maximum strain and maximum stress) was compared. The history of failure evolution and potential damage status of typical specimens were further analyzed through the FE model. The results indicated that the new FLC failure criteria can be used for the failure assessment in CFRPP structures. This study is anticipated to provide a guideline for further investigation into failure mechanism and failure criteria of thermoplastic composites under different service conditions.
Waqas, S, Bilad, MR, Man, Z, Wibisono, Y, Jaafar, J, Indra Mahlia, TM, Khan, AL & Aslam, M 2020, 'Recent progress in integrated fixed-film activated sludge process for wastewater treatment: A review', Journal of Environmental Management, vol. 268, pp. 110718-110718.
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Integrated fixed-film activated sludge (IFAS) process is considered as one of the leading-edge processes that provides a sustainable solution for wastewater treatment. IFAS was introduced as an advancement of the moving bed biofilm reactor by integrating the attached and the suspended growth systems. IFAS offers advantages over the conventional activated sludge process such as reduced footprint, enhanced nutrient removal, complete nitrification, longer solids retention time and better removal of anthropogenic composites. IFAS has been recognized as an attractive option as stated from the results of many pilot and full scales studies. Generally, IFAS achieves >90% removals for combined chemical oxygen demand and ammonia, improves sludge settling properties and enhances operational stability. Recently developed IFAS reactors incorporate frameworks for either methane production, energy generation through algae, or microbial fuel cells. This review details the recent development in IFAS with the focus on the pilot and full-scale applications. The microbial community analyses of IFAS biofilm and floc are underlined along with the special emphasis on organics and nitrogen removals, as well as the future research perspectives.
Wei, J, Li, J & Wu, C 2020, 'Behaviour of hollow-core and steel wire mesh reinforced ultra-high performance concrete columns under lateral impact loading', International Journal of Impact Engineering, vol. 146, pp. 103726-103726.
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© 2020 Elsevier Ltd Adopting UHPC in practical construction is very expensive due to the high steel fibre content (>2.5 vol%) and passive flexure reinforcement. Aiming at balancing the performance and cost, two UHPC column designs (2000 × 168 × 168 mm) are proposed in the present study. Hollow-core components and steel wire mesh reinforced components were cast with UHPC that contained 1.5 vol% steel fibre, and the impact resistance of both structural types was studied. The test specimens included two hollow-core UHPC columns with square and circular hollow shapes, and two steel wire mesh reinforced UHPC columns with 6 and 10 layers wire mesh reinforcement. The impact scenario was modelled with a 411 kg drop hammer falling freely from 1.25 m height to the mid-span of the test specimen. The results demonstrated that all UHPC specimens remained a flexural response with minimal damage. The developed numerical model captured the impact force, structural deformation and damage with reasonable accuracy. With the validated model, the energy evolution, dynamic shear and moment distribution, residual axial capacity and damage level of post-impact columns were evaluated. The effects of hollow section shape and ratio, axial load level, and longitudinal reinforcement ratio for hollow-core UHPC columns and the effects of layers of steel wire mesh for steel wire mesh reinforced UHPC columns were investigated. Compared with other hollow-core UHPC columns under the impact velocities between 4.95 m/s – 6.64 m/s, UHPC columns with a circular hollow section and 15% hollow ratio was the most effective in balancing the cost and impact resistance. For steel wire mesh reinforced UHPC columns, the column with steel wire mesh strengthening in the whole section had better impact resistance than its counterpart that only had wire mesh reinforcement in the tensile zone.
Wei, W, Guo, W, Ngo, HH, Mannina, G, Wang, D, Chen, X, Liu, Y, Peng, L & Ni, B-J 2020, 'Enhanced high-quality biomethane production from anaerobic digestion of primary sludge by corn stover biochar', Bioresource Technology, vol. 306, pp. 123159-123159.
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This study conducted batch and continuous tests to reveal the feasibility of corn stover biochar on improving anaerobic digestion of primary sludge (PS). Dosing biochar (1.82, 2.55 and 3.06 g/g Total Solids (TS)) in digester improved methane content increasing from 67.5% to 81.3-87.3% and enhanced methane production by 8.6-17.8%. Model analysis indicated that biochar accelerated PS hydrolysis and enhanced methane potential of PS. The mechanistic studies showed that biochar enhanced process stability provided by strong buffering capacity and alleviated NH3 inhibition. In continuous test over 116 days, the volatile solids (VS) destruction in the biochar-dosed digester increased by 14.9%, resulting in a 14% reduction in the volume of digestate for disposal. Biochar changed microbial community in an expected direction for anaerobic digestion. This work suggests that biochar technology would apply to co-digestion of WAS and PS to maximize the energy recovery and sludge reduction from the two sludge streams.
Wei, W, Hao, Q, Chen, Z, Bao, T & Ni, B-J 2020, 'Polystyrene nanoplastics reshape the anaerobic granular sludge for recovering methane from wastewater', Water Research, vol. 182, pp. 116041-116041.
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Wastewater has been identified as an important carrier for nanoplastics, which could elicit unintended impacts on critical microbial processes. However, the long-term impacts of nanoplastics on anaerobic granular sludge (AGS) for methane recovery from wastewater and the mechanisms involved remains unclear. In this study, we investigated the long term exposure-response relationship between polystyrene nanoplastics (Nano-PS) and AGS. In continuous test over 120 days with 86 days' Nano-PS exposure, feeding wastewater with 10 μg/L of Nano-PS had no significant impacts on the AGS performance. In comparison, higher levels (i.e., 20 and 50 μg/L) of Nano-PS decreased methane production and chemical oxygen demand (COD) removal by 19.0-28.6% and 19.3-30.0%, respectively, along with volatile fatty acids (VFA) accumulation. More extracellular polymeric substance (EPS) was induced by 10 μg/L of Nano-PS as a response to protect microbes, but higher levels (i.e., 20 and 50 μg/L) of Nano-PS decreased EPS generation, causing a decline in granule size and cell viability. Fluorescence tagging found that a large number of Nano-PS agglomerated/accumulated on the outer layer of AGS and even transferred into deeper layers of AGS over exposure time, producing toxic effects to adherent microorganisms, e.g., Longilinea sp., Paludibacter sp. and Methanosaeta sp.. The oxidative stress induced by Nano-PS was revealed to be a key factor for reshaping the AGS, reflected by the increased reactive oxygen species (ROS) generation and lactate dehydrogenase (LDH) release. The sodium dodecyl sulfate (SDS) leached from Nano-PS was also demonstrated to restrain the activities of antioxidant enzymes, thereby further lessening resistance to oxidative stress induced by Nano-PS. This work improves our ability to predict the risks associated with this ubiquitous contaminant in the environment.
Wei, W, Liu, X, Wu, L, Wang, D, Bao, T & Ni, B-J 2020, 'Sludge Incineration Bottom Ash Enhances Anaerobic Digestion of Primary Sludge toward Highly Efficient Sludge Anaerobic Codigestion', ACS Sustainable Chemistry & Engineering, vol. 8, no. 7, pp. 3005-3012.
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Copyright © 2020 American Chemical Society. In wastewater treatment plants (WWTPs), two main sludge streams, i.e., primary sludge (PS) and waste activated sludge (WAS), are generally mixed for anaerobic codigestion. However, the methane production is usually restricted by the poor and slow biodegradability of PS, and an effective approach for its efficient codigestion with WAS is still lacking due to its highly different sludge properties from those of WAS. Herein, we reported a novel strategy through using the sludge incineration bottom ash to enhance the anaerobic digestion of PS and its codigestion with WAS. Biochemical methane potential (BMP) test results showed that ash additive at 0.6-1.2 g/g-dry matter (DM) significantly enhanced PS anaerobic digestion, identified by an up to 18.2% increase in specific methane production. This was accompanied by a significantly improved dewaterability in the digestate. The transformations of metabolic intermediates revealed that the ash additive accelerated the hydrolysis and acidogenesis processes, which were also supported by the increased hydrolysis rate (k) of PS determined through kinetic modeling. Ash additive was then experimentally demonstrated to be effective in enhancing the anaerobic codigestion of PS and WAS, with the increased volatile solids (VS) destruction being approximately 19.8%, representing a reduction of digestate volume by 12.6%. The novel strategy proposed in this study provides a new paradigm of an integrated sludge-control by sludge to bring significant economic benefits to wastewater treatment and sludge disposal.
Wei, W, Wu, L, Liu, X, Chen, Z, Hao, Q, Wang, D, Liu, Y, Peng, L & Ni, B-J 2020, 'How does synthetic musks affect methane production from the anaerobic digestion of waste activated sludge?', Science of The Total Environment, vol. 713, pp. 136594-136594.
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The increasing use of synthetic musks has led to a large amount of synthetic musks retaining in waste activated sludge (WAS) via wastewater treatment, thereby entering anaerobic digester. However, the potential effects of synthetic musks on WAS anaerobic digestion remain unknown. Herein, this study selected the dominant galaxolide (HHCB) in WAS as the typical synthetic musks and experimentally evaluated the long-term effects on WAS anaerobic digestion using continuous lab-scale anaerobic digesters as well as the mechanisms involved. The results demonstrated that the increased HHCB levels (i.e., 90, 150 and 200 mg/kg-dw) resulted in the decreased methane production, with the methane production at 200 mg/kg-dw being only 80.5 ± 0.1% of the control. Supporting the methane production data, volatile solids (VS) destruction decreased by 18.6 ± 0.9%, which increased 6.8% of volume waste sludge for transfer and disposal. Correspondingly, the microbial community was shifted in the direction against anaerobic digestion. By modeling based on biochemical methane potential tests and investigating the key stages involved in anaerobic digestion, it was found that although the HHCB showed little impacts on the solubilization, WAS hydrolysis-acidification steps was inhibited by HHCB with the decreased hydrolysis rate and methane production potential, thereby causing the deteriorated performance of WAS anaerobic digestion.
Weibel, J-B, Patten, T & Vincze, M 2020, 'Addressing the Sim2Real Gap in Robotic 3-D Object Classification', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 407-413.
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Wen, D, Liu, J, Fan, S, Zhang, Z & Wu, G 2020, 'Evaluation on the fitness and population projection ofNilaparvata lugensin response to elevated CO2using two-sex life table', International Journal of Pest Management, vol. 66, no. 4, pp. 368-377.
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Wen, D, Yang, B, Qin, L, Zhang, Y, Chang, L & Li, R 2020, 'Computing K-Cores in Large Uncertain Graphs: An Index-based Optimal Approach', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-1.
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Wen, S, Wei, H, Yan, Z, Guo, Z, Yang, Y, Huang, T & Chen, Y 2020, 'Memristor-Based Design of Sparse Compact Convolutional Neural Network', IEEE Transactions on Network Science and Engineering, vol. 7, no. 3, pp. 1431-1440.
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© 2013 IEEE. Memristor has been widely studied for hardware implementation of neural networks due to the advantages of nanometer size, low power consumption, fast switching speed and functional similarity to biological synapse. However, it is difficult to realize memristor-based deep neural networks for there exist a large number of network parameters in general structures such as LeNet, FCN, etc. To mitigate this problem, this paper aims to design a memristor-based sparse compact convolutional neural network (MSCCNN) to reduce the number of memristors. We firstly use an average pooling and 1× 1 convolutional layer to replace fully connected layers. Meanwhile, depthwise separation convolution is utilized to replace traditional convolution to further reduce the number of parameters. Furthermore, a network pruning method is adopted to remove the redundant memristor crossbars for depthwise separation convolutional layers. Therefore, a more compact network structure is obtained while the recognition accuracy remaining unchanged. Simulation results show that the designed model achieves superior accuracy rates while greatly reducing the scale of the hardware circuit. Compared with traditional designs of memristor-based CNN, our proposed model has smaller area and lower power consumption.
Westerhausen, MT, Trycz, AT, Stewart, C, Nonahal, M, Regan, B, Kianinia, M & Aharonovich, I 2020, 'Controlled Doping of GeV and SnV Color Centers in Diamond Using Chemical Vapor Deposition', ACS Applied Materials & Interfaces, vol. 12, no. 26, pp. 29700-29705.
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Group IV color centers in diamond (Si, Ge, Sn, and Pb) have recently emerged as promising candidates for realization of scalable quantum photonics. However, their synthesis in nanoscale diamond is still in its infancy. In this work we demonstrate controlled synthesis of selected group IV defects (Ge and Sn) into nanodiamonds and nanoscale single crystal diamond membranes by microwave plasma chemical vapor deposition. We take advantage of inorganic salts to prepare the chemical precursors that contain the required ions that are then incorporated into the growing diamond. Photoluminescence measurements confirm that the selected group IV emitters are present in the diamond without degrading its structural quality. Our results are important to expand the versatile synthesis of color centers in diamond.
White, SV, Franzen, TMO, Riseley, CJ, Wong, OI, Kapińska, AD, Hurley-Walker, N, Callingham, JR, Thorat, K, Wu, C, Hancock, P, Hunstead, RW, Seymour, N, Swan, J, Wayth, R, Morgan, J, Chhetri, R, Jackson, C, Weston, S, Bell, M, For, B-Q, Gaensler, BM, Johnston-Hollitt, M, Offringa, A & Staveley-Smith, L 2020, 'The GLEAM 4-Jy (G4Jy) Sample: I. Definition and the catalogue', Publications of the Astronomical Society of Australia, vol. 37.
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Abstract The Murchison Widefield Array (MWA) has observed the entire southern sky (Declination, $\delta< 30^{\circ}$ ) at low radio frequencies, over the range 72–231MHz. These observations constitute the GaLactic and Extragalactic All-sky MWA (GLEAM) Survey, and we use the extragalactic catalogue (EGC) (Galactic latitude, $|b| >10^{\circ}$ ) to define the GLEAM 4-Jy (G4Jy) Sample. This is a complete sample of the ‘brightest’ radio sources ( $S_{\textrm{151\,MHz}}>4\,\text{Jy}$ ), the majority of which are active galactic nuclei with powerful radio jets. Crucially, low-frequency observations allow the selection of such sources in an orientation-independent way (i.e. minimising the bias caused by Doppler boosting, inherent in high-frequency surveys). We then use higher-resolution radio images, and information at other wavelengths, to morphologically classify the brightest components in GLEAM. We also conduct cross-checks against the literature and perform internal matching, in order to improve sample completeness (which is estimated to be ...
White, SV, Franzen, TMO, Riseley, CJ, Wong, OI, Kapińska, AD, Hurley-Walker, N, Callingham, JR, Thorat, K, Wu, C, Hancock, P, Hunstead, RW, Seymour, N, Swan, J, Wayth, R, Morgan, J, Chhetri, R, Jackson, C, Weston, S, Bell, M, Gaensler, BM, Johnston–Hollitt, M, Offringa, A & Staveley–Smith, L 2020, 'The GLEAM 4-Jy (G4Jy) Sample: II. Host galaxy identification for individual sources', Publications of the Astronomical Society of Australia, vol. 37.
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Abstract The entire southern sky (Declination, $\delta< 30^{\circ}$ ) has been observed using the Murchison Widefield Array (MWA), which provides radio imaging of $\sim$ 2 arcmin resolution at low frequencies (72–231 MHz). This is the GaLactic and Extragalactic All-sky MWA (GLEAM) Survey, and we have previously used a combination of visual inspection, cross-checks against the literature, and internal matching to identify the ‘brightest’ radio-sources ( $S_{\mathrm{151\,MHz}}>4$ Jy) in the extragalactic catalogue (Galactic latitude, $|b| >10^{\circ}$ ). We refer to these 1 863 sources as the GLEAM 4-Jy (G4Jy) Sample, and use radio images (of The Journal of the Acoustical Society of America, vol. 147, no. 6, pp. 4202-4213.
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Long range ultrasonic testing of pipelines sends an ultrasonic wave along a pipe wall and then detects scattering from defects present. It is well known that scattering by pipe fixtures and fittings, such as a flange, can cause distortion and interfere with the ability to identify defects. This article develops a theoretical model to investigate scattering from a flange in a fluid-filled pipe with elastic walls. Mode matching is used as this is a computationally efficient way to examine long lengths of pipe and for enforcing the appropriate axial continuity conditions over area discontinuities. A recent article presented a mode matching approach for a similar problem, and it is demonstrated here that a re-casting of the equations is necessary to ensure all of the appropriate matching conditions are enforced. Mode matching predictions are also compared with an alternative point collocation approach in order to provide an independent benchmark. Excellent agreement between mode matching and point collocation is demonstrated, and reflection and transmission coefficients are generated in order to show the resonant behaviour of a flange and illustrate that its influence is significant and strongly frequency dependent.
Wilson, KJ, Alabd, R, Abolhasan, M, Safavi-Naeini, M & Franklin, DR 2020, 'Optimisation of monolithic nanocomposite and transparent ceramic scintillation detectors for positron emission tomography', Scientific Reports, vol. 10, no. 1.
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AbstractHigh-resolution arrays of discrete monocrystalline scintillators used for gamma photon coincidence detection in PET are costly and complex to fabricate, and exhibit intrinsically non-uniform sensitivity with respect to emission angle. Nanocomposites and transparent ceramics are two alternative classes of scintillator materials which can be formed into large monolithic structures, and which, when coupled to optical photodetector arrays, may offer a pathway to low cost, high-sensitivity, high-resolution PET. However, due to their high optical attenuation and scattering relative to monocrystalline scintillators, these materials exhibit an inherent trade-off between detection sensitivity and the number of scintillation photons which reach the optical photodetectors. In this work, a method for optimising scintillator thickness to maximise the probability of locating the point of interaction of 511 keV photons in a monolithic scintillator within a specified error bound is proposed and evaluated for five nanocomposite materials (LaBr3:Ce-polystyrene, Gd2O3-polyvinyl toluene, LaF3:Ce-polystyrene, LaF3:Ce-oleic acid and YAG:Ce-polystyrene) and four ceramics (GAGG:Ce, GLuGAG:Ce, GYGAG:Ce and LuAG:Pr). LaF3:Ce-polystyrene and GLuGAG:Ce were the best-performing nanocomposite and ceramic materials, respectively, with maximum sensitivities of 48.8% and 67.8% for 5 mm localisation accuracy with scintillator thicknesses of 42.6 mm and 27.5 mm, respectively.
Wong, S-W, Lin, J-Y, YangYang, Zhu, H, Chen, R-S, Zhu, L & He, Y 2020, 'Cavity Balanced and Unbalanced Diplexer Based on Triple-Mode Resonator', IEEE Transactions on Industrial Electronics, vol. 67, no. 6, pp. 4969-4979.
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© 1982-2012 IEEE. In this paper, a series of designs for cavity balanced and unbalanced diplexer are proposed. The balanced and unbalanced designs can be categorized into four groups - unbalanced-to-unbalanced, unbalanced-to-balanced, balanced-to-unbalanced (B2U), and balanced-to-balanced. First, two approaches to achieve out-of-phase characteristics of three fundamental modes, namely TE011, TE101, and TM110 in a single triple-mode resonator, are proposed for balun filter designs. Second, four types of unbalanced and balanced diplexers are presented by adopting these three fundamental modes, of which the Butterworth response applies with specific external quality and coupling coefficient. To the authors' best knowledge, full-metal cavity balun diplexer and balanced diplexer are not reported in the open literature. For proof of concept, the design of a B2U diplexer is fabricated and measured. Good matching between simulated and measured results shows the accuracy of the proposed design and methodology, which would be attractive in the high-power radio frequency (RF) front-end systems.
Wu, C, Fang, J, Zhang, Z, Entezari, A, Sun, G, Swain, MV & Li, Q 2020, 'Fracture modeling of brittle biomaterials by the phase-field method', Engineering Fracture Mechanics, vol. 224, pp. 106752-106752.
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© 2019 Elsevier Ltd Biomaterials have been extensively used in prosthetic applications for their proven biocompatibility and osseointegration characteristics. Nevertheless, one of the critical issues of some synthetic biomaterials is brittleness prone to experience fracture failure due to low tensile strength and low fracture toughness. This study aims to employ a recently-developed phase-field model to simulate the crack propagation in brittle biomaterials. Unlike discrete fracture modeling methods, the phase-field approach allows simulating crack path in a continuous manner, thereby avoiding remeshing that may not be trivial for complicated fracture surfaces and facilitate iterative procedure commonly required for structural optimization. The phase-field model is formulated to treat the fracture path as a localized region of diffusive damage that can be described in terms of a phase-field function, in which the discreteness in cracked materials is assumed to be smeared. In this study, three representative case studies from the biomedical context, namely a zirconia-based dental bridge (or namely fixed partial denture (FPD)), a ceramic tissue scaffold and an analog saw-bone femur, are employed as illustrative examples. The phase-field modeling results are compared with the in-house experimental tests, demonstrating the effectiveness of the phase-field technique for predicting brittle fracture failure in several typical biomedical case scenarios. The phase-field model provides a useful tool for the computational fracture analysis and design optimization of other brittle biomaterials.
Wu, C, Fang, J, Zhou, S, Zhang, Z, Sun, G, Steven, GP & Li, Q 2020, 'Level‐set topology optimization for maximizing fracture resistance of brittle materials using phase‐field fracture model', International Journal for Numerical Methods in Engineering, vol. 121, no. 13, pp. 2929-2945.
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SummaryFracture is one of the most common failure modes in brittle materials. It can drastically decrease material integrity and structural strength. To address this issue, we propose a level‐set (LS) based topology optimization procedure to optimize the distribution of reinforced inclusions within matrix materials subject to the volume constraint for maximizing structural resistance to fracture. A phase‐field fracture model is formulated herein to simulate crack initiation and propagation, in which a staggered algorithm is developed to solve such time‐dependent crack propagation problems. In line with diffusive damage of the phase‐field approach for fracture; topological derivatives, which provide gradient information for the topology optimization in a LS framework, are derived for fracture mechanics problems. A reaction‐diffusion equation is adopted to update the LS function within a finite element framework. This avoids the reinitialization by overcoming the limitation to time step with the Courant‐Friedrichs‐Lewy condition. In this article, three numerical examples, namely, a L‐shaped section, a rectangular slab with predefined cracks, and an all‐ceramic onlay dental bridge (namely, fixed partial denture), are presented to demonstrate the effectiveness of the proposed LS based topology optimization for enhancing fracture resistance of multimaterial composite structures in a phase‐field fracture context.
Wu, C, Zheng, K, Fang, J, Steven, GP & Li, Q 2020, 'Time-dependent topology optimization of bone plates considering bone remodeling', Computer Methods in Applied Mechanics and Engineering, vol. 359, pp. 112702-112702.
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© 2019 Elsevier B.V. Bone plates have been widely used for the treatment of bone defects and trauma. These fixation plates can stabilize or replace bone tissue to restore appropriate load-bearing functionality. Nevertheless, the use of bone plates may lead to the stress shielding, thereby weakening prosthetic bone substitutes (e.g. bone graft or scaffolds) due to significant change in the biomechanical environment after implantation. To address this issue, we propose a time-dependent topology optimization procedure for the design of bone plates by taking into account bone remodeling. A solid isotropic material penalization (SIMP) model is used to interpolate design variables. The objective is to maximize total bone density within a reconstruction area at the final stage of bone remodeling, subject to a volume constraint of the bone plate and maximum allowable compliance of the prosthetic system. The sensitivity of bone density at the final stage is derived with respect to the topological variables of the plate in a step-wise manner. To facilitate sensitivity analysis, a bone remodeling rule is formulated in two different ways to accommodate a C1continuity. A jaw reconstruction problem is exemplified in this study to demonstrate the effectiveness of the proposed approach. Through this specific case, the non-differentiability issue due to the lazy zone of a remodeling rule is smoothed; and the proposed approach is also compared with that of a time-independent design. The effects of volume fraction and compliance constraints are also investigated to gain further insights into the design of prosthetic substitutes. Together with additive manufacturing technology, the proposed time-dependent topology optimization procedure is expected to form a useful tool for the design of implantable devices ensuring favorable long-term treatment outcomes.
Wu, D, Lin, C-T, Huang, J & Zeng, Z 2020, 'On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression', IEEE Transactions on Fuzzy Systems, vol. 28, no. 10, pp. 2570-2580.
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Wu, K, Ni, W, Andrew Zhang, J, Liu, RP & Jay Guo, Y 2020, 'Refinement of Optimal Interpolation Factor for DFT Interpolated Frequency Estimator', IEEE Communications Letters, vol. 24, no. 4, pp. 782-786.
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© 1997-2012 IEEE. Frequency estimation is a fundamental problem in many areas. The previously proposed q-shift estimator (QSE), which interpolates the discrete Fourier transform (DFT) coefficients by a factor of q, enables the estimation accuracy to approach the Cramér-Rao lower bound (CRLB). However, it becomes less effective when the number of samples is small. In this letter, we provide an in-depth analysis to unveil the impact of q on the convergence of QSE, and derive the bounds of a refined region of q that ensures the convergence of QSE to the CRLB even with a small number of samples. Simulations validate our analysis, showing that the refined interpolation factor is able to reduce the estimation mean squared error of QSE by up to 13.14 dB when the sample number is as small as 8.
Wu, K, Ni, W, Zhang, JA, Liu, RP & Guo, J 2020, 'Secrecy Rate Analysis for Millimeter-Wave Lens Antenna Array Transmission', IEEE Communications Letters, vol. 24, no. 2, pp. 272-276.
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© 1997-2012 IEEE. Physical layer security is vital to millimeter-wave communications enabled by large-scale arrays, particularly the energy-efficient lens antenna arrays (LAAs). However, the broad application of LAAs can be hindered by the lack of a proper understanding of the secrecy performance. This letter derives an asymptotic closed-form expression for the secrecy rate of LAA, despite the critical challenges including the coupling of unknown lens beam responses. With the new secrecy rate analysis, the optimal power assignment for the legitimate transmission is achieved, leading to the maximization of LAA secrecy. This power assignment is unprecedentedly studied in LAA due to the previous absence of an analytical secrecy rate. Simulations validate the accuracy of the analysis over wide ranges of system parameters.
Wu, K, Zhang, JA, Huang, X, Guo, YJ & Jr, RWH 2020, 'Waveform Design and Accurate Channel Estimation for Frequency-Hopping MIMO Radar-Based Communications', IEEE Transactions on Communications, vol. PP, no. 99, pp. 1-1.
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Frequency-hopping (FH) MIMO radar-based dual-function radar communication(FH-MIMO DFRC) enables communication symbol rate to exceed radar pulserepetition frequency, which requires accurate estimations of timing offset andchannel parameters. The estimations, however, are challenging due to unknown,fast-changing hopping frequencies and the multiplicative coupling betweentiming offset and channel parameters. In this paper, we develop accuratemethods for a single-antenna communication receiver to estimate timing offsetand channel for FH-MIMO DFRC. First, we design a novel FH-MIMO radar waveform,which enables a communication receiver to estimate the hopping frequencysequence (HFS) used by radar, instead of acquiring it from radar. Importantly,the novel waveform incurs no degradation to radar ranging performance. Then,via capturing distinct HFS features, we develop two estimators for timingoffset and derive mean squared error lower bound of each estimator. Using thebounds, we design an HFS that renders both estimators applicable. Furthermore,we develop an accurate channel estimation method, reusing the single hop fortiming offset estimation. Validated by simulations, the accurate channelestimates attained by the proposed methods enable the communication performanceof DFRC to approach that achieved based on perfect timing and ideal knowledgeof channel.
Wu, L, Chen, X, Wei, W, Liu, Y, Wang, D & Ni, B-J 2020, 'A Critical Review on Nitrous Oxide Production by Ammonia-Oxidizing Archaea', Environmental Science & Technology, vol. 54, no. 15, pp. 9175-9190.
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The continuous increase of nitrous oxide (N2O) in the atmosphere has become a global concern because of its property as a potent greenhouse gas. Given the important role of ammonia-oxidizing archaea (AOA) in ammonia oxidation and their involvement in N2O production, a clear understanding of the knowledge on archaeal N2O production is necessary for global N2O mitigation. Compared to bacterial N2O production by ammonia-oxidizing bacteria (AOB), AOA-driven N2O production pathways are less-well elucidated. In this Critical Review, we synthesized the currently proposed AOA-driven N2O production pathways in combination with enzymology distinction, analyzed the role of AOA species involved in N2O production pathways, discussed the relative contribution of AOA to N2O production in both natural and anthropogenic environments, summarized the factors affecting archaeal N2O yield, and compared the distinctions among approaches used to differentiate ammonia oxidizer-associated N2O production. We, then, put forward perspectives for archaeal N2O production and future challenges to further improve our understanding of the production pathways, putative enzymes involved and potential approaches for identification in order to potentially achieve effective N2O mitigations.
Wu, L, Falque, R, Perez-Puchalt, V, Liu, L, Pietroni, N & Vidal-Calleja, TA 2020, 'Skeleton-Based Conditionally Independent Gaussian Process Implicit Surfaces for Fusion in Sparse to Dense 3D Reconstruction.', IEEE Robotics Autom. Lett., vol. 5, no. 2, pp. 1532-1539.
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© 2016 IEEE. 3D object reconstructions obtained from 2D or 3D cameras are typically noisy. Probabilistic algorithms are suitable for information fusion and can deal with noise robustly. Consequently, these algorithms can be useful for accurate surface reconstruction. This paper presents an approach to estimate a probabilistic representation of the implicit surface of 3D objects. One of the contributions of the paper is the pipeline for generating an accurate reconstruction, given a set of sparse points that are close to the surface and a dense noisy point cloud. A novel submapping method following the topology of the object is proposed to generate conditional independent Gaussian Process Implicit Surfaces. This allows inference and fusion mechanisms to be performed in parallel followed by information propagation through the submaps. Large datasets can efficiently be processed by the proposed pipeline producing not only a surface but also the uncertainty information of the reconstruction. We evaluate the performance of our algorithm using simulated and real datasets.
Wu, L, Peng, L, Wei, W, Wang, D & Ni, B-J 2020, 'Nitrous oxide production from wastewater treatment: The potential as energy resource rather than potent greenhouse gas', Journal of Hazardous Materials, vol. 387, pp. 121694-121694.
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Nitrous oxide (N2O), produced from wastewater treatment, is a potent greenhouse gas and has become a global concern in recent years. However, N2O has also been commonly used as a powerful oxidant for energy generation. As such, an increasing effort has been devoted to explore the energy potential of N2O from wastewater treatment processes recently. Nevertheless, the holistic knowledge on energy recovery from nitrogen in wastewater is still lacking for facilitating its further development. Striving for sustainable wastewater treatment, this review paper aimed to give the up-to-date status on several essential aspects regarding the N2O recovery as an energy resource rather than emission as a greenhouse gas, including energy production via N2O decomposition, main biotic N2O production sources, the potential bioprocesses used for N2O recovery, and the possible N2O harvesting strategies. We then put forward perspectives for N2O recovery and future challenges to improve our understanding of the energy generation, microbial processes involved and harvesting approaches in order to potentially achieve sustainable wastewater treatment via N2O recovery.
Wu, L, Xu, M, Qian, S & Cui, J 2020, 'Image to Modern Chinese Poetry Creation via a Constrained Topic-aware Model', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 2, pp. 1-21.
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Artificial creativity has attracted increasing research attention in the field of multimedia and artificial intelligence. Despite the promising work on poetry/painting/music generation, creating modern Chinese poetry from images, which can significantly enrich the functionality of photo-sharing platforms, has rarely been explored. Moreover, existing generation models cannot tackle three challenges in this task: (1) Maintaining semantic consistency between images and poems; (2) preventing topic drift in the generation; (3) avoidance of certain words appearing frequently. These three points are even common challenges in other sequence generation tasks. In this article, we propose a Constrained Topic-aware Model (CTAM) to create modern Chinese poetries from images regarding the challenges above. Without image-poetry paired dataset, we construct a visual semantic vector to embed visual contents via image captions. For the topic-drift problem, we propose a topic-aware poetry generation model. Additionally, we design an Anti-frequency Decoding (AFD) scheme to constrain high-frequency characters in the generation. Experimental results show that our model achieves promising performance and is effective in poetry’s readability and semantic consistency.
Wu, L, Xu, M, Wang, J & Perry, S 2020, 'Recall What You See Continually Using GridLSTM in Image Captioning', IEEE Transactions on Multimedia, vol. 22, no. 3, pp. 808-818.
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The goal of image captioning is to automatically describe an image with a sentence, and the task has attracted research attention from both the computer vision and natural-language processing research communities. The existing encoder–decoder model and its variants, which are the most popular models for image captioning, use the image features in three ways: first, they inject the encoded image features into the decoder only once at the initial step, which does not enable the rich image content to be explored sufficiently while gradually generating a text caption; second, they concatenate the encoded image features with text as extra inputs at every step, which introduces unnecessary noise; and, third, they using an attention mechanism, which increases the computational complexity due to the introduction of extra neural nets to identify the attention regions. Different from the existing methods, in this paper, we propose a novel network, Recall Network, for generating captions that are consistent with the images. The recall network selectively involves the visual features by using a GridLSTM and, thus, is able to recall image contents while generating each word. By importing the visual information as the latent memory along the depth dimension LSTM, the decoder is able to admit the visual features dynamically through the inherent LSTM structure without adding any extra neural nets or parameters. The Recall Network efficiently prevents the decoder from deviating from the original image content. To verify the efficiency of our model, we conducted exhaustive experiments on full and dense image captioning. The experimental results clearly demonstrate that our recall network outperforms the conventional encoder–decoder model by a large margin and that it performs comparably to the state-of-the-art methods.
Wu, M & Zhang, Y 2020, 'Exploring Genetic Basis for Diseases Through a Heterogeneous Bibliometric Network: Methodology and a Case Study', Technological Forecasting and Social Change, vol. 164.
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Literature-based knowledge (LBD) discovery is a practical approach to inferring the associations between diseases and genetic factors from unstructured biomedical data, i.e., the literature. However, most of the contemporary LBD methods are designed for specific cases and rely heavily on prior knowledge. In this paper, we propose an adaptable and transferable methodology that not only summarizes the genetic factors known to be associated with a queried disease but also predicts likely associations that have yet to be identified. The framework incorporates different biomedical entities in a heterogeneous co-occurrence network. Three centrality indicators, coupled with a novel measure based on intersection ratios, capture the importance and specificity of each factor to the disease under study. Undiscovered, but likely, associations are identified through a semantic similarity matrix generated by our Bioentity2Vec model and an innovative weighted link prediction algorithm. The final outputs are ranked lists of the most relevant known or potential biomedical associations. To both test and showcase the methodology, we conducted a case study on atrial fibrillation. The analysis yields specific insights into the key biomedical entities associated with this disease. Moreover, it demonstrates the kind of valuable decision support this framework can provide to medical researchers, policymakers and public health administrations.
WU, P, WU, C, LIU, Z & XU, S 2020, 'Numerical simulation of SHPB test of ultra-high performance fiber reinforced concrete with meso-scale model', SCIENTIA SINICA Physica, Mechanica & Astronomica, vol. 50, no. 2, pp. 024614-024614.
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Wu, P, Xiao, F, Huang, H, Sha, C & Yu, S 2020, 'Adaptive and Extensible Energy Supply Mechanism for UAVs-Aided Wireless-Powered Internet of Things', IEEE Internet of Things Journal, vol. 7, no. 9, pp. 9201-9213.
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This article studies multiple unmanned aerial vehicles (multi-UAVs)-enabled wireless-powered Internet of Things (IoT), where a group of UAVs is dispatched as mobile power sources to charge a set of ground IoT devices. Different from the conventional radio-frequency (RF) wireless power transfer (WPT) systems, magnetic resonance-coupled (MRC) WPT systems can guarantee high power transfer efficiency without the complete alignment, which is remarkable. In this article, we extend the charging range by the wired connection between the energy receiving systems and IoT devices. Due to the restriction of carriable energy on the UAVs, designing the shortest possible trajectory for each UAV is necessary. We formulate it as a multidepots multi-UAVs trajectory optimization problem, jointly with constraints of the UAV's energy capacity and the area of the target region, to maximize the resource utilization of UAVs. To tackle this nonconvex problem, we decompose it into two subproblems, i.e., hovering locations selection and multi-UAVs trajectory optimization. For the first subproblem, we propose two approximation algorithms to obtain the near-optimal solution in the sparse networks. Then, we adopt a heuristic algorithm, a memetic algorithm-based variable neighborhood search (MAVNS), to achieve the quasioptimal trajectory rapidly. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithms. New insights are investigated on the estimation of feasibility that whether the given UAVs with energy capacity constraint can fully charge ground IoT devices within open areas.
Wu, S, Wen, S, Zhou, Q & Qin, X 2020, 'Coordination of Store Brand Product’s Green Supply Chain Based on Negotiation', Sustainability, vol. 12, no. 9, pp. 3637-3637.
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The environmental input of a store brand product’s green supply chain plays an important role in improving the product brand image and expanding the product demand. According to the difference of the initial one-off environmental investment of the store brand product, it can be divided into three modes: direct OEM, retailer’s full participation and retailer’s partial participation. The research methods employed in this study include model establishment, numerical analysis and comparison under three entrustment modes based on retailers’ negotiation strength. In addition, sensitivity analysis was used to test the influence of parameter variations on the results. The research results show that: (i) the direct OEM mode is the best choice for retailers when the retailer is in a weak position, but it is not the best choice for the manufacturer. With the increase of the retailers’ negotiation strength, the profits of both sides will decline, causing the problem of double marginal profit decreasing; (ii) the retailer’s full participation mode is the best choice for the manufacturer when the retailer is in a strong position, but not the best choice for the retailer. It is not the best choice for both sides when the retailer is in a weak position; (iii) the greenness and total profit of the supply chain are no relative with the negotiation strength of the retailer under the partial participation mode, and the greenness and total profit of the supply chain are the same as the condition under the integrated control to achieve the best coordination effect.
Wu, S-L, Sun, J, Chen, X, Wei, W, Song, L, Dai, X & Ni, B-J 2020, 'Unveiling the mechanisms of medium-chain fatty acid production from waste activated sludge alkaline fermentation liquor through physiological, thermodynamic and metagenomic investigations', Water Research, vol. 169, pp. 115218-115218.
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Effective sludge treatment with bioenergy production is attracting increasing interests as large quantities of waste activated sludge (WAS) are produced during the wastewater treatment. In this study, a new biotechnical process for converting the WAS alkaline fermentation liquor (WASAFL) into valuable, easy-separated medium chain fatty acids (MCFAs) through chain elongation (CE) was investigated, which may provide a new insight into sludge treatment. In the process, ethanol was served as the electron donor (EDs) and WASAFL were main electron acceptors (EAs). The MCFAs productions were investigated under three different ED to EA ratios (i.e., 1:2, 1:1 and 2:1). The result showed that MCFAs production was increased from 2.88 ± 0.01 to 5.28 ± 0.18 g COD/L with the increase of ED to EA ratio. However, the highest MCFA selectivity was achieved at 72.9% when the ED to EA ratio was 1:1. The decrease in the selectivity at high ED:EA ratio is mainly due to the production of higher alcohol (i.e., n-butanol and n-hexanol). The thermodynamic analysis confirmed all CE processes for MCFAs production from WASAFL were exothermic reactions, with the spontaneity and energy release of the reactions increased with the ethanol level. The microbial community analysis showed that the relative abundances of Clostridium, Oscillibacter, Leptolinea and Exilispira were positively correlated with the MCFAs production. The metagenomic analysis suggested that both the reverse β-oxidization pathway and fatty acid biosynthesis pathway contributed to the CE process in the studied system. The functional enzymes were mainly associated within Clostridium, with Clostridium Kluyveri, Clostridium botulinum and Clostridium magnum being likely the key species responsible for the CE process.
Wu, S-L, Wei, W, Sun, J, Xu, Q, Dai, X & Ni, B-J 2020, 'Medium-Chain fatty acids and long-chain alcohols production from waste activated sludge via two-stage anaerobic fermentation', Water Research, vol. 186, pp. 116381-116381.
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Traditional bioenergy recovery in the form of short chain fatty acids (SCFAs) from waste activated sludge (WAS) is generally limited by economic unattractiveness and complexity of products separation. Herein, a novel biotechnology process of two-stage anaerobic fermentation for converting the WAS into high energy density, easy-separated medium chain fatty acids (MCFAs) and long-chain alcohols (LCAs) was evaluated. In this process, the WAS was first converted to WAS alkaline fermentation liquid (WASAFL), serving as electron acceptors (EAs) and inoculum, then adding ethanol as electron donor (ED) for chain elongation (CE). The co-production of MCFAs and LCAs during CE were studied under three different ED to EA ratios, i.e., 3:1, 4:1 and 5:1. Experimental results demonstrated that when the ratio of ED to EA increased from 3:1 to 5:1, the production of MCFA and LCAs respectively increased from 5.57 ± 0.17 and 2.58 ± 0.18 to7.67 ± 0.48 and 4.21 ± 0.19 g COD/L. A similar observation was made in the total product electron efficiency, increasing from 59.9% to 72.1%. However, the highest total product selectivity (i.e., 68.0%) and highest products production yield (i.e., 59.77%) were not achieved at the ED to EA ratio of 5:1 due to toxicity caused by higher accumulation of n-caproate. The kinetic analysis further confirmed that high ratio of ED to EA induced improvement in product maximum yield, production rate for both MCFAs and LCAs. Microbial community analysis indicated that Clostridium, Caproiciproducens, Acinetobacter, Exilispira, and Oscillibacter were clearly enriched in the CE reactor and had positive correlation with MCFAs and LCAs production.
Wu, T, Feng, Z, Wu, C, Lei, G, Guo, Y, Zhu, J & Wang, X 2020, 'Multiobjective Optimization of a Tubular Coreless LPMSM Based on Adaptive Multiobjective Black Hole Algorithm', IEEE Transactions on Industrial Electronics, vol. 67, no. 5, pp. 3901-3910.
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© 1982-2012 IEEE. In most multiobjective optimization problems of electrical machines, the weighted function method is used to convert them into single-objective optimization problems. This paper applies a kind of new multiobjective evolutionary algorithms (MOEAs), called adaptive multiobjective black hole (AMOBH) algorithms, to achieve effective multiobjective optimization of a tubular coreless linear permanent magnet synchronous motor (LPMSM). To reduce the computation cost of the MOEAs, a one-layer analytical model (AM) is presented for the tubular coreless LPMSM in this paper. The accuracy of the simplified one-layer AM is verified by comparisons with multilayer AM and finite element analysis (FEA) under different structure parameters. It is found that the simplified AM has good accuracy and can decrease the computation cost significantly. AMOBH algorithm is subsequently introduced. The optimal Pareto front with regard to thrust, copper loss, and permanent magnet volume are analyzed, and more diversified optimization results are provided. The final Pareto solution can be selected directly by practical physical values according to the application requirements. Finally, a prototype is fabricated for the selected design; its experimental results are provided and compared with those of the FEA results.
Wu, W, Li, B, Chen, L, Gao, J & Zhang, C 2020, 'A Review for Weighted MinHash Algorithms', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Data similarity (or distance) computation is a fundamental research topic which underpins many high-level applications based on similarity measures in machine learning and data mining. However, in large-scale real-world scenarios, the exact similarity computation has become daunting due to "3V" nature (volume, velocity and variety) of big data. In this case, the hashing techniques have been verified to efficiently conduct similarity estimation in terms of both theory and practice. Currently, MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore, weighted MinHash is generalized to estimate the generalized Jaccard similarity of weighted sets. This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms. In this review, we mainly categorize the weighted MinHash algorithms into quantization-based approaches, "active index"-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash ones to the real-valued weighted MinHash ones. Also, we have developed a Python toolbox for the algorithms, and released it in our github. We experimentally conduct a comprehensive study of the standard MinHash algorithm and the weighted MinHash ones in the similarity estimation error and the information retrieval task.
Wu, W, Xu, M, Liang, Q, Mei, L & Peng, Y 2020, 'Multi‐camera 3D ball tracking framework for sports video', IET Image Processing, vol. 14, no. 15, pp. 3751-3761.
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Accurate ball tracking in sports is vital for automatic sports analysis yet it is challenging mainly due to the small size and occlusions. This study proposes a novel multi‐camera 3D ball tracking (MBT) framework for sports video. The proposed framework consists of four parts: 2D ball detection, 2D ball tracking, 3D position fusion, and 3D ball tracking. In 2D aspect, the multi‐scale features are introduced to enhance the 2D ball detection, and the 2D ball tracking is also improved by exploring cross‐view information to handle the occlusion and timely updating tracking model with detection results to alleviate the problem of tracking drift. For 3D ball, a novel 3D position fusion method is proposed to optimise the ball position and the 3D ball tracking approach with improved Kalman filter is finally applied to ensure a smooth 3D ball trajectory. Moreover, compared to the existing products in commercial, the proposed framework does not require any special equipment and is thus low cost. Extensive experiments for 2D and 3D ball on a public dataset demonstrate that the proposed framework is robust to ball tracking in sports video, even in the presence of environmental interferences, substantial occlusions, and even calibration errors.
Wu, Y, Fang, J, Cheng, Z, He, Y & Li, W 2020, 'Crashworthiness of tailored-property multi-cell tubular structures under axial crushing and lateral bending', Thin-Walled Structures, vol. 149, pp. 106640-106640.
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© 2020 Elsevier Ltd Thin-walled structures have been widely used as energy-absorbing components, which can be probably subjected to multiple loading conditions in real life, such as axial crushing and lateral bending. Most of the existing literature solely focuses on the pure axial crushing or lateral bending. In this paper, a novel tailored-property multi-cell tubular structure is proposed, where the material's ultimate strength at the corner region is increased to accommodate both the axial crushing and lateral bending conditions. Finite element (FE) models were developed and validated through experimental results. The FE models were used to investigate the crashworthiness performances of the tailored-property multi-cell tubes under axial crushing and lateral bending. Under both axial crushing and lateral bending, it was found that the tailored-property multi-cell tubes exhibited noticeable advantages over the corresponding traditional tubes. The tailoring ratio and thickness had a significant influence on the crashworthiness performance of the tailored-property multi-cell tubes. Moreover, the well-designed tailored-property multi-cell tubes could exhibit the progressive deformation mode under axial crushing. Furthermore, a theoretical model for the tailored-property multi-cell tubes under axial crushing was developed based on the Superfolding Element (SFE) Method. The results showed that the theoretical solutions were in good agreement with the finite element analysis results. The findings of this paper have the potential for energy absorption applications under different loading conditions.
Wu, Z, Wang, R, Li, Q, Lian, X, Xu, G, Chen, E & Liu, X 2020, 'A Location Privacy-Preserving System Based on Query Range Cover-Up or Location-Based Services', IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5244-5254.
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© 1967-2012 IEEE. Location-based service (LBS) has been widely used in various fields of industry, and become a vital part of people's daily life. However, while providing great convenience for users, LBS results in a serious threat on users' location privacy, due to its more and more untrusted server-side. In this article, we propose a location privacy-preserving system for LBS by constructing 'cover-up ranges' to protect the query ranges associated with a location query sequence. Firstly, we present a client-based system framework for location privacy protection in LBS, which requires no compromise to the accuracy and usability of LBS. Secondly, based on the framework, we introduce a location privacy model to formulate the constraints that ideal cover-up ranges should satisfy, so as to improve the efficiency of location services and the security of location privacy. Finally, we describe an implementation algorithm to well meet the location privacy model. Both theoretical analysis and experimental evaluation demonstrate the effectiveness of our system, which can improve the security of users' location privacy on the untrusted server-side, without compromising the accuracy and usability of LBS.
Xia, B, Zhang, Y, Shi, B, Ran, J, Davey, K & Qiao, S 2020, 'Photocatalysts for Hydrogen Evolution Coupled with Production of Value‐Added Chemicals', Small Methods, vol. 4, no. 7.
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AbstractThe conversion of water into clean hydrogen fuel using renewable solar energy can potentially be used to address global energy and environmental issues. However, conventional photocatalytic H2 evolution from water splitting has low efficiency and poor stability. Hole scavengers are therefore added to boost separation efficiency of photoexcited electron–hole pairs and improve stability by consuming the strongly oxidative photoexcited holes. The drawbacks of this approach are increased cost and production of waste. Recently, researchers have reported the use of abundantly available hole scavengers, including biomass, biomass‐derived intermediates, plastic wastes, and a range of alcohols for H2 evolution, coupled with value‐added chemicals production using semiconductor‐based photocatalysts. It is timely, therefore, to comprehensively summarize the properties, performances, and mechanisms of these photocatalysts, and critically review recent advances, challenges, and opportunities in this emerging area. Herein, this paper: 1) outlines reaction mechanisms of photocatalysts for H2 evolution coupled with selective oxidation, C–H activation and C–C coupling, together with nonselective oxidation, using hole‐scavengers; 2) introduces equations to compute conversion/selectivity of selective oxidation; 3) summarizes and critically compares recently reported photocatalysts with particular emphasis on correlation between physicochemical characteristics and performances, together with photocatalytic mechanisms, and; 4) appraises current advances and challenges.
Xiang, Y, Basirun, C, Chou, J, Warkiani, ME, Török, P, Wang, Y, Gao, S & Kabakova, IV 2020, 'Background-free fibre optic Brillouin probe for remote mapping of micromechanics', Biomedical Optics Express, vol. 11, no. 11, pp. 6687-6687.
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Brillouin spectroscopy is a century-old technique that has recently receivedrenewed interest, as modern instrumentation has transformed it into a powerfulcontactless and label-free probe of micromechanical properties for biomedicalapplications. In particular, to fully harness the non-contact andnon-destructive nature of Brillouin imaging, there is strong motivation todevelop a fibre-integrated device and extend the technology into the domain ofin vivo and in situ operation, such as for medical diagnostics. This workpresents the first demonstration of a fibre optic Brillouin probe that iscapable of mapping the mechanical properties of a tissue-mimicking phantom.This is achieved through combination of miniaturised optical design, advancedhollow-core fibre fabrication and high-resolution 3D printing. The protypeprobe is compact, background-free and possesses the highest collectionefficiency to date, thus provides the foundation of a fibre-based Brillouindevice for remote in situ measurements in challenging and otherwisedifficult-to-reach environments, for biomedical, material science andindustrial applications.
Xiao, G, Zheng, Z, Jiang, B & Sui, Y 2020, 'An Empirical Study of Regression Bug Chains in Linux', IEEE Transactions on Reliability, vol. 69, no. 2, pp. 558-570.
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Regression bugs are a type of bugs that cause a feature of software that worked correctly but stop working after a certain software commit. This paper presents a systematic study of regression bug chains, an important but unexplored phenomenon of regression bugs. Our paper is based on the observation that a commit c1, which fixes a regression bug b1, may accidentally introduce another regression bug b2. Likewise, commit c2 repairing b2 may cause another regression bug b3, resulting in a bug chain, i.e., b1 → c1 → b2 → c2 → b3. We have conducted a large-scale study by collecting 1579 regression bugs and 2630 commits from 57 Linux versions (from 2.6.12 to 4.9). The relationships between regression bugs and commits are modeled as a directed bipartite network. Our major contributions and findings are fourfold: 1) a novel concept of regression bug chains and their formulation; 2) compared to an isolated regression bug, a bug on a regression bug chain is much more difficult to repair, costing 2.4× more fixing time, involving 1.3× more developers and 2.8× more comments; 3) 85.8% of bugs on the chains in Linux reside in Drivers, ACPI, Platform Specific/Hardware, and Power Management; and 4) 83% of the chains affect only a single Linux subsystem, while 68% of the chains propagate across Linux versions.
Xiao, J, Cao, J, Cheng, J, Zhong, S & Wen, S 2020, 'Novel methods to finite-time Mittag-Leffler synchronization problem of fractional-order quaternion-valued neural networks', Information Sciences, vol. 526, pp. 221-244.
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© 2020 Elsevier Inc. This paper proposes two methods to investigate the problem of finite-time Mittag-Leffler synchronization for the systems of fractional-order quaternion-valued neural networks (FQVNNs) with two kinds of activation functions, respectively. Generally, the first method mainly reflects in the new establishment of Lyapunov-Krasovskii functionals (LKFs) and the novel application of a new fractional-order derivative inequality which contains and exploits the wider coefficients with more values. Meanwhile, the second one is embodied in the comprehensive development of both the norm comparison rules and the generalized Gronwall-Bellman inequality with the help of Laplace transform of Mittag-Leffler function. Thanks to the above two methods, the flexible synchronization criteria are easily and separately obtained for the studied four systems of FQVNNs with general activation functions and linear threshold ones. Finally, two numerical simulations are given to demonstrate the feasibility and effectiveness of the newly proposed approaches.
Xiao, J, Wen, S, Yang, X & Zhong, S 2020, 'New approach to global Mittag-Leffler synchronization problem of fractional-order quaternion-valued BAM neural networks based on a new inequality', Neural Networks, vol. 122, pp. 320-337.
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In this paper, a novel kind of neural networks named fractional-order quaternion-valued bidirectional associative memory neural networks (FQVBAMNNs) is formulated. On one hand, applying Hamilton rules in quaternion multiplication which is essentially non-commutative, the system of FQVBAMNNs is separated into eight fractional-order real-valued systems. Meanwhile, the activation functions are considered to be quaternion-valued linear threshold ones which help to reduce the unnecessary computational complexity. On the other hand, based on fractional-order Lyapunov technology, a new fractional-order derivative inequality is established. Mainly by employing the new inequality technique, constructing three novel Lyapunov-Krasovskii functionals (LKFs) and designing simple linear controllers, the global Mittag-Leffler synchronization problems are investigated and the corresponding criteria are acquired for the system of FQVBAMNNs and its special cases such as fractional-order complex-valued BAM neural networks (FCVBAMNNs) and fractional-order real-valued BAM neural networks (FRVBAMNNs), respectively. Finally, two numerical examples are given to show the effectiveness and availability of the proposed results.
Xiao, J, Zeng, Z, Wu, A & Wen, S 2020, 'Fixed-time synchronization of delayed Cohen–Grossberg neural networks based on a novel sliding mode', Neural Networks, vol. 128, pp. 1-12.
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This paper has discussed fixed-time synchronization of discontinuous Cohen-Grossberg neural networks with time-varying delays and matched disturbances based on sliding mode control technology. First, a novel sliding-mode surface is established. And, the dynamics on the sliding-mode surface can be achieved in the fixed time by employing the Gudermannian function. Then, considering the effect of delay, two different control schemes are introduced to ensure the fixed time reachability of the sliding mode. In addition, some useful criteria are given out for fixed-time synchronization of neural networks, and the setting time is formulated in a straightforward way. Finally, some examples and simulations are presented to verify the validity of the proposed results.
Xiao, L, Dai, J, Lu, R, Li, S, Li, J & Wang, S 2020, 'Design and Comprehensive Analysis of a Noise-Tolerant ZNN Model With Limited-Time Convergence for Time-Dependent Nonlinear Minimization', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 12, pp. 5339-5348.
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Zeroing neural network (ZNN) is a powerful tool to address the mathematical and optimization problems broadly arisen in the science and engineering areas. The convergence and robustness are always co-pursued in ZNN. However, there exists no related work on the ZNN for time-dependent nonlinear minimization that achieves simultaneously limited-time convergence and inherently noise suppression. In this article, for the purpose of satisfying such two requirements, a limited-time robust neural network (LTRNN) is devised and presented to solve time-dependent nonlinear minimization under various external disturbances. Different from the previous ZNN model for this problem either with limited-time convergence or with noise suppression, the proposed LTRNN model simultaneously possesses such two characteristics. Besides, rigorous theoretical analyses are given to prove the superior performance of the LTRNN model when adopted to solve time-dependent nonlinear minimization under external disturbances. Comparative results also substantiate the effectiveness and advantages of LTRNN via solving a time-dependent nonlinear minimization problem.
Xiao, T, Halkon, B, Zhao, S & Qiu, X 2020, 'A remote acoustic sensing apparatus based on a laser Doppler vibrometer'.
Xiao, T, Qiu, X & Halkon, B 2020, 'Ultra-broadband local active noise control with remote acoustic sensing', Scientific Reports, vol. 10, no. 1, p. 20784.
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AbstractOne enduring challenge for controlling high frequency sound in local active noise control (ANC) systems is to obtain the acoustic signal at the specific location to be controlled. In some applications such as in ANC headrest systems, it is not practical to install error microphones in a person’s ears to provide the user a quiet or optimally acoustically controlled environment. Many virtual error sensing approaches have been proposed to estimate the acoustic signal remotely with the current state-of-the-art method using an array of four microphones and a head tracking system to yield sound reduction up to 1 kHz for a single sound source. In the work reported in this paper, a novel approach of incorporating remote acoustic sensing using a laser Doppler vibrometer into an ANC headrest system is investigated. In this “virtual ANC headphone” system, a lightweight retro-reflective membrane pick-up is mounted in each synthetic ear of a head and torso simulator to determine the sound in the ear in real-time with minimal invasiveness. The membrane design and the effects of its location on the system performance are explored, the noise spectra in the ears without and with ANC for a variety of relevant primary sound fields are reported, and the performance of the system during head movements is demonstrated. The test results show that at least 10 dB sound attenuation can be realised in the ears over an extended frequency range (from 500 Hz to 6 kHz) under a complex sound field and for several common types of synthesised environmental noise, even in the presence of head motion.
Xiao, T, Qiu, X & Halkon, B 2020, 'Ultra-broadband local active noise control with remote acoustic sensing.', Scientific reports, vol. 10, no. 1.
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One enduring challenge for controlling high frequency sound in local active noise control (ANC) systems is to obtain the acoustic signal at the specific location to be controlled. In some applications such as in ANC headrest systems, it is not practical to install error microphones in a person's ears to provide the user a quiet or optimally acoustically controlled environment. Many virtual error sensing approaches have been proposed to estimate the acoustic signal remotely with the current state-of-the-art method using an array of four microphones and a head tracking system to yield sound reduction up to 1 kHz for a single sound source. In the work reported in this paper, a novel approach of incorporating remote acoustic sensing using a laser Doppler vibrometer into an ANC headrest system is investigated. In this "virtual ANC headphone" system, a lightweight retro-reflective membrane pick-up is mounted in each synthetic ear of a head and torso simulator to determine the sound in the ear in real-time with minimal invasiveness. The membrane design and the effects of its location on the system performance are explored, the noise spectra in the ears without and with ANC for a variety of relevant primary sound fields are reported, and the performance of the system during head movements is demonstrated. The test results show that at least 10 dB sound attenuation can be realised in the ears over an extended frequency range (from 500 Hz to 6 kHz) under a complex sound field and for several common types of synthesised environmental noise, even in the presence of head motion.
Xiao, Y, Pei, Q, Yao, L & Wang, X 2020, 'RecRisk: An enhanced recommendation model with multi-facet risk control', Expert Systems with Applications, vol. 158, pp. 113561-113561.
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Xiao, Y, Pei, Q, Yao, L, Yu, S, Bai, L & Wang, X 2020, 'An enhanced probabilistic fairness-aware group recommendation by incorporating social activeness', Journal of Network and Computer Applications, vol. 156, pp. 102579-102579.
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Xiao, Y, Yao, L, Pei, Q, Wang, X, Yang, J & Sheng, QZ 2020, 'MGNN: Mutualistic Graph Neural Network for Joint Friend and Item Recommendation', IEEE Intelligent Systems, vol. 35, no. 5, pp. 7-17.
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IEEE Many social studies and practical cases suggest that people's consumption behaviors and social behaviors are not isolated but interrelated in social network services. However, most existing research either predicts users' consumption preferences or recommends friends to users without dealing with them simultaneously. We propose a holistic approach to predict users' preferences on friends and items jointly and thereby make better recommendations. To this end, we design a graph neural network that incorporates a mutualistic mechanism to model the mutual reinforcement relationship between users' consumption behaviors and social behaviors. Our experiments on the two-real world datasets demonstrate the effectiveness of our approach in both social recommendation and link prediction.
Xie, A & Li, S 2020, 'On constructing the largest and smallest uninorms on bounded lattices', Fuzzy Sets and Systems, vol. 386, pp. 95-104.
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© 2019 Elsevier B.V. Uninorms on the unit interval are a common extension of triangular norms (t-norms) and triangular conorms (t-conorms). As important aggregation operators, uninorms play a very important role in fuzzy logic and expert systems. Recently, several researchers have studied constructions of uninorms on more general bounded lattices. In particular, Çaylı (2019) gave two methods for constructing uninorms on a bounded lattice L with e∈L∖{0,1}, which is based on a t-norm Te on [0,e] and a t-conorms Se on [e,1] that satisfy strict boundary conditions. In this paper, we propose two new methods for constructing uninorms on bounded lattices. Our constructed uninorms are indeed the largest and the smallest among all uninorms on L that have the same restrictions Te and Se on [0,e] and, respectively, [e,1]. Moreover, our constructions does not require the boundary condition, and thus completely solved an open problem raised by Çaylı.
Xie, A, Hanif, S, Ouyang, J, Tang, Z, Kong, N, Kim, NY, Qi, B, Patel, D, Shi, B & Tao, W 2020, 'Stimuli-responsive prodrug-based cancer nanomedicine', EBioMedicine, vol. 56, pp. 102821-102821.
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Xie, F, Li, L, Sun, X, Hu, T, Song, K, Giesy, JP & Wang, Q 2020, 'A novel Mg(OH)2 binding layer-based DGT technique for measuring phosphorus in water and sediment', Environmental Science: Processes & Impacts, vol. 22, no. 2, pp. 340-349.
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Diffusive gradients in thin films (DGT) have gained wide attention for in situ measurement of reactive phosphorus species (PO4) in natural water, sediments and potentially soils.
Xie, H & Veitch, D 2020, 'Nested saturation control of multiple vector integrators and its application to motion control of UAVs', International Journal of Robust and Nonlinear Control, vol. 30, no. 1, pp. 246-265.
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SummaryThis paper presents two nested input saturation control schemes for a special class of multiple vector integrators with bounded additive disturbances. The considered systems originate from the motion control of rotary‐wing unmanned aerial vehicles (UAVs). The first scheme is based on a feedforward form, which requires state transformation and can be applied to stabilize arbitrary order vector integrator systems. The second scheme is constructed with original state variables using a new approach, applied here to double vector integrator systems. The capability of handling external disturbance using the two schemes is also analyzed. The two schemes are applied to design motion controllers for rotary‐wing UAVs and simulation results are provided to show the performances of two controllers.
Xie, J, Liu, C-H, Mo, Z, Huang, Y & Mok, W-C 2020, 'Near-field dynamics and plume dispersion after an on-road truck: Implication to remote sensing', Science of The Total Environment, vol. 748, pp. 141211-141211.
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Apart from the aerodynamic performance (efficiency and safety), the wake after an on-road vehicle substantially influences the tailpipe pollutant dispersion (environment). Remote sensing is the most practicable measures for large-scale emission control. Its reliability, however, is largely dictated by how well the complicated vehicular flows and instrumentation constraint are tackled. Specifically, the broad range of motion scales and the short sampling duration (less than 1 s) are the most prominent ones. Their impact on remote sensing has not been studied. Large-eddy simulation (LES) is thus employed in this paper to look into the dynamics and the plume dispersion after an on-road heavy-duty truck at speed U∞ so as to elucidate the transport mechanism, examine the sampling uncertainty and develop the remedial measures. A major recirculation of size comparable to the truck height h is induced collectively by the roof-level prevailing flows, side entrainment and underbody wall jet. The tailpipe is enclosed by dividing streamlines so the plume is carried back to the truck right after emission. The recirculation augments the pollutant mixing, resulting in a more homogeneous pollutant distribution together with a rather high fluctuating concentration (over 20% of the time-averaged concentrations). The plume ascends mildly before being purged out of the major recirculation to the far field by turbulence, leading to a huge reduction in pollutant concentration (an order of magnitude) outside the near wake. In the far-field, the plume is higher than the tailpipe and disperses in a conventional Gaussian distribution manner. Under this circumstance, a sampling duration for remote sensing longer than h/U∞ would be prone to underestimating the tailpipe emission.
Xie, J, Ma, J, Wu, L, Xu, M, Ni, W & Yan, Y-M 2020, 'Carbon nanotubes in-situ cross-linking the activated carbon electrode for high-performance capacitive deionization', Separation and Purification Technology, vol. 239, pp. 116593-116593.
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Xie, S, Li, X, Wang, C, Kulandaivelu, J & Jiang, G 2020, 'Enhanced anaerobic digestion of primary sludge with additives: Performance and mechanisms', Bioresource Technology, vol. 316, pp. 123970-123970.
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Anaerobic digestion of primary sludge with different additives, namely nano magnetite, graphite powder, activated carbon powder and NiCl2/CoCl2, were evaluated by biomethane potential tests, kinetics modelling and microbial community analysis. Specific methane yields increased from 136 mL/g VS for primary sludge to 146 mL/g VS, 151 mL/g VS, and 152 mL/g VS for the addition of nano magnetite, graphite powder, and activated carbon powder at optimal dosages, respectively. The first order hydrolysis constant kh increased from 0.488 d-1 to 0.526 d-1, 0.622 d-1, and 0.724 d-1, respectively. Microbial community analysis revealed that the abundance of key bacterial and archaeal populations was positively correlated with hydrolysis and methane production. The enhanced methane production with activated carbon powder was due to shifting methane formation pathway from acetoclastic to hydrogenotrophic methanogenesis. In contrast, nano magnetite and graphite powder additives enhanced the direct interspecies electron transfer evidenced by increased abundance of Methanosaeta and Methanolinea.
Xie, S, Xia, R, Chen, Z, Tian, J, Yan, L, Ren, M, Li, Z, Zhang, G, Xue, Q, Yip, H-L & Cao, Y 2020, 'Efficient monolithic perovskite/organic tandem solar cells and their efficiency potential', Nano Energy, vol. 78, pp. 105238-105238.
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Xie, W, Lei, J, Cui, Y, Li, Y & Du, Q 2020, 'Hyperspectral Pansharpening With Deep Priors', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 5, pp. 1529-1543.
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Xie, W, Lei, J, Yang, J, Li, Y, Du, Q & Li, Z 2020, 'Deep Latent Spectral Representation Learning-Based Hyperspectral Band Selection for Target Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 3, pp. 2015-2026.
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Xie, W, Li, Y, Lei, J, Yang, J, Chang, C-I & Li, Z 2020, 'Hyperspectral Band Selection for Spectral–Spatial Anomaly Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 5, pp. 3426-3436.
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Xie, W, Li, Y, Lei, J, Yang, J, Li, J, Jia, X & Li, Z 2020, 'Unsupervised spectral mapping and feature selection for hyperspectral anomaly detection', Neural Networks, vol. 132, pp. 144-154.
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Xie, W, Liu, B, Li, Y, Lei, J & Du, Q 2020, 'Autoencoder and Adversarial-Learning-Based Semisupervised Background Estimation for Hyperspectral Anomaly Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 8, pp. 5416-5427.
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Xie, W, Liu, B, Li, Y, Lei, J, Chang, C-I & He, G 2020, 'Spectral Adversarial Feature Learning for Anomaly Detection in Hyperspectral Imagery', IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 4, pp. 2352-2365.
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Xie, W, Yang, J, Lei, J, Li, Y, Du, Q & He, G 2020, 'SRUN: Spectral Regularized Unsupervised Networks for Hyperspectral Target Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 2, pp. 1463-1474.
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Xie, X, Wen, S, Yan, Z, Huang, T & Chen, Y 2020, 'Designing pulse-coupled neural networks with spike-synchronization-dependent plasticity rule: image segmentation and memristor circuit application', Neural Computing and Applications, vol. 32, no. 17, pp. 13441-13452.
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Pulse-coupled neural network (PCNN) is a powerful unsupervised learning model with many parameters to be determined empirically. In particular, the weight matrix is invariable in the iterative process, which is inconsistent with the actual biological system. Based on the existing research foundation of biology and neural network, we propose a spike-synchronization-dependent plasticity (SSDP) rule. In this paper, the mathematical model and algorithm of SSDP are presented. Furthermore, a novel memristor-based circuit model of SSDP is designed. Finally, experimental results demonstrate that SSDP has greatly improved the image processing capabilities of PCNN.
Xing, D, Liu, W, Li, JJ, Liu, L, Guo, A, Wang, B, Yu, H, Zhao, Y, Chen, Y, You, Z, Lyu, C, Li, W, Liu, A, Du, Y & Lin, J 2020, 'Engineering 3D functional tissue constructs using self-assembling cell-laden microniches', Acta Biomaterialia, vol. 114, pp. 170-182.
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Tissue engineering using traditional size fixed scaffolds and injectable biomaterials are faced with many limitations due to the difficulties of producing macroscopic functional tissues. In this study, 3D functional tissue constructs were developed by inducing self-assembly of microniches, which were cell-laden gelatin microcryogels. During self-assembly, the accumulation of extracellular matrix (ECM) components was found to strengthen cell-cell and cell-ECM interactions, leading to the construction of a 'native' microenvironment that better preserved cell viability and functions. MSCs grown in self-assembled constructs showed increased maintenance of stemness, reduced senescence and improved paracrine activity compared with cells grown in individual microniches without self-assembly. As an example of applying the self-assembled constructs in tissue regeneration, the constructs were used to induce in vivo articular cartilage repair and successfully regenerated hyaline-like cartilage tissue in the absence of other extrinsic factors. This unique approach of developing self-assembled 3D functional constructs holds great promise for the generation of tissue engineered organoids and repair of challenging tissue defects. STATEMENT OF SIGNIFICANCE: We developed 3D functional tissue constructs using a unique gelatin-based microscopic hydrogel (microcryogels). Mesenchymal stem cells (MSCs) were loaded into gelatin microcryogels to form microscopic cell-laden units (microniches), which were induced to undergo self-assembly using a specially designed 3D printed frame. Extracellular matrix accumulation among the microniches resulted in self-assembled macroscopic constructs with superior ability to maintain the phenotypic characteristics and stemness of MSCs, together with the suppression of senescence and enhanced paracrine function. As an example of application in tissue regeneration, the self-assembled constructs were shown to successfully repair articular cartil...
Xing, D, Liu, W, Wang, B, Li, JJ, Zhao, Y, Li, H, Liu, A, Du, Y & Lin, J 2020, 'Intra-articular Injection of Cell-laden 3D Microcryogels Empower Low-dose Cell Therapy for Osteoarthritis in a Rat Model', Cell Transplantation, vol. 29, pp. 096368972093214-096368972093214.
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Intra-articular injection of mesenchymal stem cells (MSCs) in an osteoarthritic joint can help slow down cartilage destruction. However, cell survival and the efficiency of repair are generally low due to mechanical damage during injection and a high rate of cell loss. We, thus, investigated an improved strategy for cell delivery to an osteoarthritic joint through the use of three-dimensional (3D) microcryogels. MSCs were seeded into 3D microcryogels. The viability and proliferation of MSCs in microcryogels were determined over 5 d, and the phenotype of MSCs was confirmed through trilineage differentiation tests and flow cytometry. In Sprague Dawley rats with induced osteoarthritis (OA) of the knee joint, a single injection was made with the following groups: saline control, low-dose free MSCs (1 × 105 cells), high-dose free MSCs (1 × 106 cells), and microcryogels + MSCs (1 × 105 cells). Cartilage degeneration was evaluated by macroscopic examination, micro-computed tomographic analysis, and histology. MSCs grown in microcryogels exhibited optimal viability and proliferation at 3 d with stable maintenance of phenotype in vitro. Microcryogels seeded with MSCs were, therefore, primed for 3 d before being used for in vivo experiments. At 4 and 8 wk, the microcryogels + MSCs and high-dose free MSC groups had significantly higher International Cartilage Repair Society macroscopic scores, histological evidence of more proteoglycan deposition and less cartilage loss accompanied by a lower Mankin score, and minimal radiographic evidence of osteoarthritic changes in the joint compared to the other two groups. In conclusion, intra-articular injection of cell-laden 3D microcryogels containing a low dose of MSCs can achieve similar effects as a high dose of free MSCs for OA in a rat model. Primed MSCs in 3D microcryogels can be considered as an improved delivery strategy for cell therapy in tre...
Xing, D, Wu, J, Wang, B, Liu, W, Liu, W, Zhao, Y, Wang, L, Li, JJ, Liu, A, Zhou, Q, Hao, J & Lin, J 2020, 'Intra‐articular delivery of umbilical cord‐derived mesenchymal stem cells temporarily retard the progression of osteoarthritis in a rat model', International Journal of Rheumatic Diseases, vol. 23, no. 6, pp. 778-787.
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AbstractAimMesenchymal stem cell (MSC)‐based therapy is being explored in treating osteoarthritis (OA). Human umbilical cord‐derived mesenchymal stem cells (hUC‐MSCs) are least reported. In this study, we investigated the effects of single intra‐articular injections of hUC‐MSCs on a rat OA model.MethodhUC‐MSCs were isolated from the Wharton's jelly of the human umbilical cord and identified. Eighteen Sprague‐Dawley rats were used for the OA model. All rats were divided into 3 groups: hyaluronic acid (HA)+MSCs (n = 6), HA (n = 6), and control group (n = 6). One by 106 hUC‐MSCs in 100 μL HA, 100 μL HA or 100 μL saline were injected into the knee joint 4 weeks post‐surgery as a single dose. Cartilage degeneration was evaluated at 6 and 12 weeks after treatment with macroscopic examination, micro‐computed tomography analysis, behavioral analysis, and histology.ResultsAt 6 weeks, the HA + MSCs group had a significantly better International Cartilage Repair Society score in the femoral condyle compared to the HA and control groups. Histological analysis also showed more proteoglycan and less cartilage loss, with lower modified Mankin score in the HA + MSCs group. However, at 12 weeks there were no significant differences between groups from macroscopic examination and histological analysis. Subchondral bone sclerosis of the medial femoral condyle and behavioral tests showed no significant differences between groups at 6 and 12 weeks.ConclusionThese findings indicate that single injection of hUC‐MSCs can have temporary effects on decelerating the progression of cartilage degeneration in OA rats, but may not inhibit OA progression in the long‐term.
Xiong, K, Yu, J, Hu, C, Wen, S & Jiang, H 2020, 'Finite-time synchronization of fully complex-valued networks with or without time-varying delays via intermittent control', Neurocomputing, vol. 413, pp. 173-184.
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© 2020 Elsevier B.V. This paper explores finite-time (F-T) synchronization for a type of fully complex-valued (C-V) networks with or without delays by proposing intermittent control schemes but without using the ordinary separation technique. Above all, the vector signum function in complex field is proposed as the generalization of real-valued counterpart, which is critical to the control design in complex field and the construction of Lyapunov functions. To realize F-T synchronization, several complex-valued intermittent controllers, only dependent of the information of controlled nodes, are designed on the addressed complex-valued networks, which are different from the design on the divided real-valued subsystems. In the theoretical analysis, some nontrivial Lyapunov functionals and functions are constructed by virtue of the proposed signum function and some inequalities are developed in complex domain to establish criteria of F-T synchronization. Finally, some numerical examples are performed to reveal the effectiveness of the derived results and the practicality of the proposed control strategies.
Xu, B, Ahmed, MB, Zhou, JL & Altaee, A 2020, 'Visible and UV photocatalysis of aqueous perfluorooctanoic acid by TiO2 and peroxymonosulfate: Process kinetics and mechanistic insights', Chemosphere, vol. 243, pp. 125366-125366.
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© 2019 Elsevier Ltd The global occurrence and adverse environmental impacts of perfluorooctanoic acid (PFOA) have attracted wide attention. This study focused on the PFOA photodegradation by using photocatalyst TiO2 with peroxymonosulfate (PMS) activation. Aqueous PFOA (50 mg L−1) at the pH 3 was treated by TiO2/PMS under 300 W visible light (400–770 nm) or 32 W UV light (254 nm and 185 nm). The addition of PMS induced a significant degradation of PFOA under powerful visible light compared with sole TiO2. Under visible light, 0.25 g L−1 TiO2 and 0.75 g L−1 PMS in the solution with the initial pH 3 provided optimum condition which achieved 100% PFOA removal within 8 h. Under UV light irradiation at 254 nm and 185 nm wavelength, TiO2/PMS presented excellent performance of almost 100% removal of PFOA within 1.5 h, attributed to the high UV absorbance by the photocatalyst. The intermediates analysis showed that PFOA was degraded from a long carbon chain PFOA to shorter chain intermediates in a stepwise manner. Furthermore, scavenger experiments indicated that SO4•–radicals from PMS and photogenerated holes from TiO2 played an essential role in degrading PFOA. The presence of organic compounds in real wastewater reduced the degradation efficacy of PFOA by 18–35% in visible/TiO2/PMS system. In general, TiO2/PMS could be an ideal and effective photocatalysis system for the degradation of PFOA from wastewater using either visible or UV light source.
Xu, B, Zhou, JL, Altaee, A, Ahmed, MB, Johir, MAH, Ren, J & Li, X 2020, 'Improved photocatalysis of perfluorooctanoic acid in water and wastewater by Ga2O3/UV system assisted by peroxymonosulfate', Chemosphere, vol. 239, pp. 124722-124722.
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© 2019 Elsevier Ltd Perfluorooctanoic acid (PFOA) has attracted considerable attention worldwide due to its widespread occurrence and environmental impacts. This research focused on the photocatalytic process for the treatment of PFOA in water and wastewater. Gallium oxide (Ga2O3) and peroxymonosulfate (PMS) were mixed directly in PFOA solution, which was irradiated under different light sources. The treatment system showed excellent performance that 100% PFOA was degraded within 90 min and 60 min under 254 nm and 185 nm UV irradiation, respectively. Moreover, the degradation efficacy was unaffected by initial PFOA concentration from 50 ng L−1 to 50 mg L−1. Acidic solution (pH 3) improved the degradation process. The quantum yield in the PMS/Ga2O3 system under UV light (254 nm) was estimated to be 0.009 mol E−1. Scavengers such as tert-butanol (t-BuOH), disodium ethylenediaminetetraacetate (EDTA-Na2) and benzoquinone (BQ) were added into PFOA solution to prove that sulfate radicals (SO4•–), superoxide radical (O2•–) and photogenerated electrons (e–) were the main active species with strong redox ability for PFOA degradation in PMS/Ga2O3/UV system. Combined with the intermediates analysis, PFOA was degraded stepwise from long chain compound to shorter chain intermediates. In addition, PFOA in real wastewater exhibited similar degradation efficiency, together with 75–85% TOC removal by Ga2O3/PMS under 254 nm UV irradiation. Therefore, Ga2O3/PMS system was highly effective for PFOA photodegradation under UV irradiation, which has potential to be applied for the perfluoroalkyl substances (PFAS) treatment in water and wastewater.
Xu, B-H, He, N, Jiang, Y-B, Zhou, Y-Z & Zhan, X-J 2020, 'Experimental study on the clogging effect of dredged fill surrounding the PVD under vacuum preloading', Geotextiles and Geomembranes, vol. 48, no. 5, pp. 614-624.
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Xu, C, Luo, L, Ding, Y, Zhao, G & Yu, S 2020, 'Personalized Location Privacy Protection for Location-Based Services in Vehicular Networks', IEEE Wireless Communications Letters, vol. 9, no. 10, pp. 1633-1637.
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© 2012 IEEE. With the development of vehicular network, location-based services (LBSs) provide increasing diversified services for drivers and passengers. When users enjoy the services, users' location needs to be constantly updated to service providers, which causes the location information to be speculated and attacked by attackers. However, existing schemes don't provide differentiated protection for users' different locations, which may lead to the leakage of location information. Therefore, we propose a location privacy protection method to satisfy users' personalized privacy needs with reasonable protection of their privacy. Firstly, we define a normalized decision matrix to describe the efficiency and privacy effects of a route, and establish a multi-attribute utility function to quantify the utility of different routes for route selection. Then, according to users' personalized privacy protection need, we allocate the privacy budget for each query location on the selected route based on the distance between it and his nearest sensitive location. Experimental results demonstrate that compared to existing methods, our scheme can meet the user's service requirements and achieve better service quality under the conditions of reasonable protection of their privacy.
Xu, C, Xiong, Z, Han, Z, Zhao, G & Yu, S 2020, 'Link Reliability-Based Adaptive Routing for Multilevel Vehicular Networks', IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 11771-11785.
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© 1967-2012 IEEE. In multilevel vehicular ad-hoc network (VANET) scenario, dynamic vehicles, complex node distribution and poor wireless channel environment deteriorate the reliability of routing protocols. However, for the key issues of relay selection, existing algorithms analyze the wireless link performance without considering the influence of dynamics and shadow fading on location from GPS, as well as channel condition and buffer queue, which would lead to inaccurate link characterization and maladaptive to network variation. In this paper, we establish a dynamic link reliability model to portray the link complexity of multilevel VANET scenario, and propose a link reliability-based adaptive routing algorithm (LRAR) to improve the transmission efficiency. Firstly, we propose a Kalman filter-based estimation approach to amend GPS original data for precise location of vehicles. Then, we define link reliability to quantify the wireless link performance, and establish a multilevel dynamic link model (MDLM) to evaluate it. Moreover, to accurately describe the complexity of wireless links, we integrate the corrected GPS data and characteristics of multilevel VANET including vehicle dynamics, distribution hierarchy and shadow fading into the modeling of link reliability. Considering the difference of link state among diverse vehicles, a maximum deviation algorithm is introduced to adaptively calculate the weight of each parameter in the modeling. Finally, we formulate the routing decision as a multi-attribute decision problem, and select the link with highest reliability as transmission path. Simulation results demonstrate that LRAR outperforms the existing routing algorithms in terms of average end-to-end delay and packet delivery ratio.
Xu, C, Xiong, Z, Kong, X, Zhao, G & Yu, S 2020, 'A Packet Reception Probability-Based Reliable Routing Protocol for 3D VANET', IEEE Wireless Communications Letters, vol. 9, no. 4, pp. 495-498.
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© 2012 IEEE. In three-dimensional (3D) vehicular ad-hoc network (VANET) scenario, dynamic vehicles, complex node distribution and severe path loss increase the probability of link interruption significantly, which deteriorates the packet reception probability sharply. However, reliability is a crucial issue as efficiency to improve the performance of routing protocol in 3D VANET. In this letter, for the dynamic and multi-level shadowing scenario, we propose a packet reception probability-based reliable routing (PPR) protocol to improve the transmission link reliability. Especially, we introduce a packet reception probability model to characterize the link reliability of 3D network, and the unique characteristics of 3D VANETs are integrated into this model. Then, we formulate the routing decision issue as a constrained multi-objective optimization problem, which attempts to find a link with the highest packet reception probability as the relay link. The simulation results demonstrate that PPR outperforms the existing protocols in the aspects of packet delivery ratio, end-to-end delay and throughput.
Xu, D-S, Xu, X-Y, Li, W & Fatahi, B 2020, 'Field experiments on laterally loaded piles for an offshore wind farm', Marine Structures, vol. 69, pp. 102684-102684.
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© 2019 Elsevier Ltd Pile foundations are widely used to support offshore wind turbines due to their cost effectiveness and rapid constructions. Offshore piles must be designed with enough capacity to withstand overturning moments caused by wind turbines and other environmental factors such as wave excitations and extreme winds. In this study, a full-scale field experimental test is undertaken to determine the pile behaviour under various lateral loading conditions. A distributed fiber optic sensing technology is used to measure strains along two instrumented piles. The bending moments and lateral deflections are calculated from distributed fiber optic sensors, and then analysed with the various p-y methods. Field measurements indicated that for two offshore piles ZK01 and ZK28 with diameter of 2 m and length of 71.5 m and 77.5 m, the maximum lateral movements under a given lateral load of 800 kN were 369.1 mm and 351.7 mm, respectively. The maximum bending moment occurred at 6.5 m and 5.5 m below seabed level with the corresponding depth of 12.15D and 11.95D for pile ZK01 and ZK28, respectively. The position of “zero crossing” of soil resistance for two instrumented piles is almost the same, even though the piles have different lengths. The lateral deflections and bending moments of the two instrumented piles are predicted by the API and hyperbolic method, which indicates that the hyperbolic method yields larger prediction errors than the API method. A modified p-y approach is then proposed for more reliable predictions when compared with field measurements.
Xu, G, Duong, TD, Li, Q, Liu, S & Wang, X 2020, 'Causality Learning: A New Perspective for Interpretable Machine Learning', IEEE Intelligent Informatics Bulletin, vol. 20, no. 1, pp. 27-33.
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Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc. In spite of their great performance in different sectors, researchers still concern about the mechanism under any machine learning (ML) techniques that are inherently blackbox and becoming more complex to achieve higher accuracy. Therefore, interpreting machine learning model is currently a mainstream topic in the research community. However, the traditional interpretable machine learning focuses on the association instead of the causality. This paper provides an overview of causal analysis with the fundamental background and key concepts, and then summarizes most recent causal approaches for interpretable machine learning. The evaluation techniques for assessing method quality, and open problems in causal interpretability are also discussed in this paper.
Xu, H, Xu, F, Theurer, T, Egloff, D, Liu, Z-W, Yu, N, Plenio, MB & Zhang, L 2020, 'Experimental Quantification of Coherence of a Tunable Quantum Detector', Physical Review Letters, vol. 125, no. 6, p. 060404.
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Quantum coherence is a fundamental resource that quantum technologies exploit to achieve performance beyond that of classical devices. A necessary prerequisite to achieve this advantage is the ability of measurement devices to detect coherence from the measurement statistics. Based on a recently developed resource theory of quantum operations, here we quantify experimentally the ability of a typical quantum-optical detector, the weak-field homodyne detector, to detect coherence. We derive an improved algorithm for quantum detector tomography and apply it to reconstruct the positive-operator-valued measures of the detector in different configurations. The reconstructed positive-operator-valued measures are then employed to evaluate how well the detector can detect coherence using two computable measures. As the first experimental investigation of quantum measurements from a resource theoretical perspective, our work sheds new light on the rigorous evaluation of the performance of a quantum measurement apparatus.
Xu, J-X, Zhang, XY, Li, H-Y, Yang, Y, Dutkiewicz, E & Xue, Q 2020, 'Ultracompact Multichannel Bandpass Filter Based on Trimode Dielectric-Loaded Cavities', IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 5, pp. 1668-1677.
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© 1963-2012 IEEE. In this article, a method for designing multichannel bandpass filters (BPFs) based on trimode dielectric-loaded cavities is presented. A large number of BPFs are integrated as one multichannel BPF with multiple inputs and multiple outputs, resulting in an ultracompact size. The cubic trimode dielectric-loaded cavities are utilized with the TE101, TE011, and TM110 modes resonating at the same frequency and orthogonal to each other. Feeding probes and coupling probes are properly arranged where the three modes in one cavity can be excited for different BPF channels without interference with each other. Consequently, excellent isolation among multiple channels can be obtained. The multichannel BPF is designed based on a 3-D structure, which can be easily extended to higher filter orders with a larger number of channels to satisfy different requirements in wireless systems. For demonstration, a 12-channel BPF is fabricated and measured, which exhibits good filtering responses of each channel and high isolation among channels. Significant size reduction is achieved compared to conventional multiple single-channel filters, which is potential in high-integration base station applications.
Xu, K, Wang, F, Wang, H, Wang, Y & Zhang, Y 2020, 'Mitigating the Impact of Data Sampling on Social Media Analysis and Mining', IEEE Transactions on Computational Social Systems, vol. 7, no. 2, pp. 546-555.
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The last decade has witnessed the explosive growth of online social media in users and contents. Due to the unprecedented scale and the cascading power of the underlying social networks, social media has created a new paradigm for sharing information, broadcasting breaking news, and reporting real-time events by any user from anywhere at any time. Many popular social media sites including Twitter provide streaming data services by standard APIs to the broad researcher and developer communities. Given the sheer data volume, rapid velocity, and feature variety of online social media, these sites often supply only a sampled set of streaming data, rather than the full data set to reduce the resource cost of computations, storage, and network bandwidth. In light of the substantial impact of sampling in Twitter data stream, this article explores a combination of spectral clustering, locality-sensitive hashing (LSH), latent Dirichlet allocation (LDA) topic modeling, and differential equation modeling to mitigate the impact of sampling on social media data analysis, in particular on detecting real-world events and predicting information diffusion. Our extensive experiments demonstrate that our proposed method is able to detect effectively the real-time emerging events and predict accurately the cascading pattern of these events from the 1% sampled Twitter data stream. To the best of our knowledge, this article is the first effort to introduce a systematic methodology to study and mitigate the impact of data sampling on social media analysis and mining.
Xu, K-D, Zhu, X, Yang, Y & Chen, Q 2020, 'A Broadband On-Chip Bandpass Filter Using Shunt Dual-Layer Meander-Line Resonators', IEEE Electron Device Letters, vol. 41, no. 11, pp. 1617-1620.
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Xu, Q, Du, M, Liu, X, Wang, D, Wu, Y, Li, Y, Yang, J, Fu, Q, He, D, Feng, C, Liu, Y, Wang, Q & Ni, B-J 2020, 'Calcium peroxide eliminates grease inhibition and promotes short-chain fatty acids production during anaerobic fermentation of food waste', Bioresource Technology, vol. 316, pp. 123947-123947.
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Deterioration of anaerobic fermentation can occur with the presence of grease in food waste, but little information on eliminating this deterioration is currently available. In this study, it was found that the presence of 10 g/L grease decreased SCFAs production from 16.97 to 13.32 g COD/L and prolonged the optimal fermentation time to 7 days, but could be respectively recovered to 39.10 g COD/L and 4 days with 0.02 mg/g VS (volatile solids) calcium peroxide addition. Mechanism investigations indicated that calcium peroxide facilitated biodegradable organics release and improved grease degradation, thereby providing enough nutrients and better growth environments to microbes for SCFAs-producing, which could be further supported by the elevated enzymes activities responding to hydrolysis and acidification process. Further investigations revealed that among the main derivates of calcium peroxide, OH- and Ca2+ played vital role in SCFAs production promotion, O2- and OH radicals were the main contributors to grease degradation.
Xu, Q, Huang, Q-S, Wei, W, Sun, J, Dai, X & Ni, B-J 2020, 'Improving the treatment of waste activated sludge using calcium peroxide', Water Research, vol. 187, pp. 116440-116440.
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The treatment and disposal of waste activated sludge (WAS) has become one of the major challenges for the wastewater treatment plants (WWTPs) due to large output, high treatment costs and enriched substantial emerging contaminants (ECs). Therefore, reducing sludge volume, recovering energy and resource from WAS, and removing ECs and decreasing environmental risk have gained increasing attentions. Calcium peroxide (CaO2), a versatile and safe peroxide, has been widely applied in terms of WAS treatment including sludge dewatering, anaerobic sludge digestion and anaerobic sludge fermentation due to its specific properties such as generating free radicals and alkali, etc., providing supports for sludge reduction, recycling, and risk mitigation. This review outlines comprehensively the recent progresses and breakthroughs of CaO2 in the fields of sludge treatment. In particular, the relevant mechanisms of CaO2 enhancing WAS dewaterability, methane production from anaerobic digestion, short-chain fatty acids (SCFA) and hydrogen production from anaerobic fermentation, and the removal of ECs in WAS and role of experiment parameters are systematically elucidated and discussed, respectively. Finally, the knowledge gaps and opportunities in CaO2-based sludge treatment technologies that need to be focused in the future are prospected. The review presented can supply a theoretical basis and technical reference for the application of CaO2 for improving the treatment of WAS.
Xu, Q, Liu, X, Yang, G, Wang, D, Wu, Y, Li, Y, Huang, X, Fu, Q, Wang, Q, Liu, Y, Li, X & Yang, Q 2020, 'Norfloxacin-induced effect on enhanced biological phosphorus removal from wastewater after long-term exposure', Journal of Hazardous Materials, vol. 392, pp. 122336-122336.
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In this study, long-term experiments were performed under synthetic wastewater conditions to evaluated the potential impacts of norfloxacin (NOR) (10, 100 and 500 μg/L) on enhanced biological phosphorus removal (EBPR). Experimental result showed that long-term exposure to 10 μg/L NOR induced negligible effects on phosphorus removal. The presence of 100 μg/L NOR slightly decreased phosphorus removal efficiency to 94.41 ± 1.59 %. However, when NOR level further increased to 500 μg/L, phosphorus removal efficiency was significantly decreased from 97.96 ± 0.8 5% (control) to 82.33 ± 3.07 %. The mechanism study revealed that the presence of 500 μg/L NOR inhibited anaerobic phosphorus release and acetate uptake as well as aerobic phosphorus uptake during long-term exposure. It was also found that 500 μg/L NOR exposure suppressed the activity of key enzymes related to phosphorus removal but promoted the transformations of intracellular polyhydroxyalkanoate and glycogen. Microbial analysis revealed that that the presence of 500 μg/L NOR reduced the abundances of polyphosphate accumulating organisms but increased glycogen accumulating organisms, as compared the control.
Xu, Q, Su, Z, Dai, M & Yu, S 2020, 'APIS: Privacy-Preserving Incentive for Sensing Task Allocation in Cloud and Edge-Cooperation Mobile Internet of Things With SDN', IEEE Internet of Things Journal, vol. 7, no. 7, pp. 5892-5905.
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© 2014 IEEE. The popularization of mobile devices connected to the network promotes the rise and development of the emerging mobile Internet of Things (MIoT). Crowdsensing is a promising mode to perceive data in MIoT, where the collection of sensing data is outsourced to the public crowd carrying mobile devices. However, this crowdsensing mode inevitably makes privacy compromised, due to the workers' sensitive information in the sensing data. As such, how to incentivize workers' participation with privacy preservation becomes a challenge. To tackle this problem, in this article, we propose an auction-based privacy-preserving incentive scheme (APIS) for sensing task allocation in MIoT. Specifically, integrating the idea of software-defined network (SDN), we first present a cloud and edge cooperation-based crowdsensing framework, where the cloud is designed as the controller to collect sensing results from the distributed edge nodes and each edge node outsources sensing tasks to participating workers. To motivate workers' participation, we devise a differential privacy-based auction mechanism, whereby each worker can utilize her privacy budget to control how much privacy can be leaked and decide the sensing precision by the sensing time. Moreover, to maximize the utility of the sensing platform, we design a greed-based algorithm to select the winning workers and determine payments to winners. Finally, we conduct extensive simulations to verify the effectiveness of APIS and demonstrate its superiority.
Xu, S, Wu, P & Wu, C 2020, 'Calibration of KCC concrete model for UHPC against low-velocity impact', International Journal of Impact Engineering, vol. 144, pp. 103648-103648.
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Xu, X, Liu, X, Yin, X, Wang, S, Qi, Q & Qi, L 2020, 'Privacy-aware offloading for training tasks of generative adversarial network in edge computing', Information Sciences, vol. 532, pp. 1-15.
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© 2020 Elsevier Inc. Currently, the generative adversarial network (GAN), with complex training processes in the physical machine (PM), has achieved great priority in image generation, audio conversion, image translation, etc. To improve the training efficiency of GAN, the edge computing paradigm is accepted as an alternative of the PMs to accommodate the training tasks, that is, the training tasks are migrated to the edge nodes (ENs) for hosting. However, it is still a key challenge to keep the overall network performance (i.e., load balance, transmission time) and privacy protection of training tasks at the same time. To address this challenge, a privacy-aware task offloading method, named POM, is developed accordingly in this paper. First, improving the strength pareto evolutionary algorithm (SPEA2) is fully investigated to obtain the offloading strategies for collaboratively improving the training performance and privacy preservation. Then, the most balanced offloading strategy is acquired for training GAN. Eventually, systematic experiments indicate that POM achieves an optimal performance efficiently among the other representative benchmark methods.
Xu, X, Lu, H & Lee, R 2020, 'Near Infrared Light Triggered Photo/Immuno-Therapy Toward Cancers', Frontiers in Bioengineering and Biotechnology, vol. 8.
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Nanomaterials-based phototherapies, mainly including photothermal therapy (PTT), photodynamic therapy (PDT) and photoimmunotherapy (PIT), present high efficacy, minimal invasion and negligible adverse effects in cancer treatment. The integrated phototherapeutic modalities can enhance the efficiency of cancer immunotherapy for clinical application transformation. The near-infrared (NIR) light source enables phototherapies with the high penetration depth in the biological tissues, less toxic to normal cells and tissues and a low dose of light irradiation. Mediated via the novel NIR-responsive nanomaterials, PTT and PDT are able to provoke cancer cells apoptosis from the generated heat and reactive oxygen species, respectively. The released cancer-specific antigens and membrane damage danger signals from the damaged cancer cells trigger immune responses, which would enhance the antitumor efficacy via a variety of immunotherapy. This review summarized the recent advances in NIR-triggered photo-/immune-therapeutic modalities and their synergistic mechanisms and applications toward cancers. Furthermore, the challenges, potential solutions and future directions of NIR-triggered photo-/immunotherapy were briefly discussed.
Xu, X, Veitch, D, Li, Y & Vucetic, B 2020, 'Minimum Cost Reconfigurable Network Template Design With Guaranteed QoS', IEEE Transactions on Communications, vol. 68, no. 2, pp. 1013-1024.
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© 2019 IEEE. Conventional networks are based on layered protocols with intensive cross-layer interactions and complex signal processing at every node, making it difficult to meet the ultra-low latency requirement of mission critical applications in future communication systems. In this paper, we address this issue by proposing the concept of network template, which allows data to flow through it at the transmission symbol level, with minimal node processing. This is achieved by carefully calibrating the inter-connecting links among the nodes and pre-calculating the routing/network coding actions for each node, according to a set of preconfigured flows. In this paper, we focus on the minimum cost network template design to minimize the connections within the template, while ensuring that all the pre-defined configurations are feasible with the guaranteed throughput, latency and reliability. We show that the minimum cost network template design problem is difficult to solve optimally in general. We thus propose an efficient greedy algorithm to find a close-to-optimal solution. Simulation results show that the construction cost of the templates obtained by the proposed algorithm is very close to a lower bound. Furthermore, the construction cost increases only slightly with the number of pre-defined configurations, which confirms the flexibility of the network template design.
Xu, X, Zhang, X, Khan, M, Dou, W, Xue, S & Yu, S 2020, 'A balanced virtual machine scheduling method for energy-performance trade-offs in cyber-physical cloud systems', Future Generation Computer Systems, vol. 105, pp. 789-799.
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© 2017 Elsevier B.V. The cloud computing scheme promises many salient features such as on-demand resource provisioning to users, and it therefore has drawn significant attention from the cyber-physical systems (CPS). An increasing number of CPS have been deployed in cloud platforms, and to accommodate numerous CPS applications, cloud datacenters often consist of a huge number of physical computation and storage nodes, and the number is still increasing. As a result, the electricity power consumption in cloud datacenters is considerable, currently accounting for about 1.3% of the worldwide electricity. How to reduce the energy consumption of datacenters is an economically beneficial but challenging problem. Optimizing virtual machine (VM) scheduling in datacenters by live VM migration is an appealing method to save energy consumption. However, it is still a challenge to conduct VM scheduling in an energy-efficient and performance-guaranteed manner, since VM migration can suffer from severe performance degradation while saving energy. In this paper, we propose a balanced VM scheduling method to achieve trade-offs between energy and performance in cyber-physical cloud systems. Specifically, the problem is formulated via a joint optimization model, and a balanced VM scheduling method is proposed accordingly to determine which VMs and where should be migrated, aiming at both reducing energy consumption and mitigating performance degradation. Both analytical and simulation results demonstrate the effectiveness and efficiency of our method.
Xu, Y, Peng, L, Liu, Y, Xie, G, Song, S & Ni, B-J 2020, 'Modelling melamine biodegradation in a membrane aerated biofilm reactor', Journal of Water Process Engineering, vol. 38, pp. 101626-101626.
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© 2020 Elsevier Ltd Membrane aerated biofilm reactor (MABR) system is excellent in developing slow growing microorganisms and treating micropollutants prior to entering the aquatic environment. In this work, a mathematical biofilm model was developed to assess melamine biodegradation under different conditions and to predict the profiles of melamine, nitrogen species and microbial biomass in the MABR system. Comtabolism linked to growth of ammonia oxidizing bacteria (AOB) or heterotrophic bacteria (HB) and their respective metabolism were involved in the model to contribute to melamine biodegradation. Results demonstrated the good predictive performance of the developed model in describing dynamic profiles of melamine, COD and nitrogen species in the MABR system. The relative contribution by AOB-induced cometabolism and metabolism by AOB and HB varied depending on the stratification of the biofilm system with AOB prevalent in the inner layer of the biofilm. Metabolism by AOB and HB played more important roles than AOB-induced cometabolism in melamine removal. Controlling optimal biofilm thickness in the suitable range (e.g., more than 750 μm) might realize better simultaneous removal of melamine and nitrogen. This work might provide further insight on efficient removal of melamine from wastewater.
Xuan, J, Lu, J & Zhang, G 2020, 'A Survey on Bayesian Nonparametric Learning', ACM Computing Surveys, vol. 52, no. 1, pp. 1-36.
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Bayesian (machine) learning has been playing a significant role in machine learning for a long time due to its particular ability to embrace uncertainty, encode prior knowledge, and endow interpretability. On the back of Bayesian learning’s great success, Bayesian nonparametric learning (BNL) has emerged as a force for further advances in this field due to its greater modelling flexibility and representation power. Instead of playing with the fixed-dimensional probabilistic distributions of Bayesian learning, BNL creates a new “game” with infinite-dimensional stochastic processes. BNL has long been recognised as a research subject in statistics, and, to date, several state-of-the-art pilot studies have demonstrated that BNL has a great deal of potential to solve real-world machine-learning tasks. However, despite these promising results, BNL has not created a huge wave in the machine-learning community. Esotericism may account for this. The books and surveys on BNL written by statisticians are overcomplicated and filled with tedious theories and proofs. Each is certainly meaningful but may scare away new researchers, especially those with computer science backgrounds. Hence, the aim of this article is to provide a plain-spoken, yet comprehensive, theoretical survey of BNL in terms that researchers in the machine-learning community can understand. It is hoped this survey will serve as a starting point for understanding and exploiting the benefits of BNL in our current scholarly endeavours. To achieve this goal, we have collated the extant studies in this field and aligned them with the steps of a standard BNL procedure—from selecting the appropriate stochastic processes through manipulation to executing the model inference algorithms. At each step, past efforts have been thoroughly summarised and discussed. In addition, we have reviewed the common methods for implementing BNL in various machine-learning tasks along with its diverse applications i...
Xuan, J, Luo, X, Lu, J & Zhang, G 2020, 'Web event evolution trend prediction based on its computational social context', World Wide Web, vol. 23, no. 3, pp. 1861-1886.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Predicting future trends of Web events can help significantly improve the quality of Web services, e.g., improving the user satisfaction of news websites. Existing approaches in this regard are based mainly on temporal patterns mined with the assumption that enough temporal data is available on hand. However, most Web events do not have a long lifecycle, but a burst property, which drastically reduces the performance of temporal patterns mining. Furthermore, these approaches overlook the influence of the social context surrounding the Web events. In this paper, we propose a novel method to predict future trends of Web events, based on their social contexts rather than temporal patterns. More specially, in the proposed method, a computational model for the social context is first built as a two-layer Association Linked Network considering its properties, such as the associative network property and the small world property. Then, the interaction between a Web event and the social context is simulated, based on the anchoring theory. Finally, an external force is defined and evaluated to quantify the influence of the social context on the evolution of Web events, which is used to predict future trends of Web events. Experiments show that the performance of the proposed method is better than that of the traditional time series-based approaches.
Xue, C, Li, W, Castel, A, Wang, K & Sheng, D 2020, 'Effect of incompatibility between healing agent and cement matrix on self-healing performance of intelligent cementitious composite', Smart Materials and Structures, vol. 29, no. 11, pp. 115020-115020.
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Abstract Encapsulation-based intelligent self-healing cementitious composite with a potential of crack self-healing and closure is promising to recovery concrete from damage and improve the durability and serviceability of infrastructures. The efficiency of self-healing concrete were investigated, but limited studies have been conducted on effect of incompatibility between the self-healing agent and cement matrix on the cracking behaviour and recovery efficiency of crack-healed concrete. In this study, a coupled experimental and numerical investigations were adopted to understand the cracking behaviours of crack-healed cementitious composites using traction–separation law by extended finite element method (XFEM). Firstly, experimental investigation was conducted to characterize the properties and parameters of cement matrix and healing agent-crack interface to calibrate the traction–separation law. Then, various parameters of healing agent, cement matrix, and their interface on the performance of crack-healed cementitious composite was numerically analysed. The results indicate that to achieve excellent self-healing performance, it is vital to consider the incompatibility between healing agent and cement matrix in the design of intelligent self-healing cementitious composites.
Xue, C, Li, W, Qu, F, Sun, Z & Shah, SP 2020, 'Self-healing efficiency and crack closure of smart cementitious composite with crystalline admixture and structural polyurethane', Construction and Building Materials, vol. 260, pp. 119955-119955.
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© 2020 Elsevier Ltd The crack closure and self-healing efficiency of smart self-healing cementitious composite can effectively reveal the mechanism of self-healing performance recovery. This study focused on effects of crack healing on crack closure and mechanical performance recovery of crack-healed cementitious composite, including flexural compressive behaviours. Meanwhile, several parameters were defined to quantify the efficiency of mechanical performance recovery efficiency for self-healing cementitious composite. Furthermore, the interfaces between self-healing products and crack surface were analyzed and compared to provide understanding insight to the self-healing recovery. It is found that the bonding interface dominated the flexural strength recovery, and therefore autonomous self-healing yielded the maximum self-healing efficiency. On the other hand, the stiffness damage recovery index under compression is found to be an effective parameter to evaluate the inner crack healing, which slightly depends on the bonding interface. The related results indicate that the development of smart self-healing cementitious composite should consider the bonding between self-healing product and crack surface to improve the self-healing recovery efficiency for engineering application.
Xue, C, Li, W, Wang, K, Sheng, D & Shah, SP 2020, 'Novel experimental and numerical investigations on bonding behaviour of crack interface in smart self-healing concrete', Smart Materials and Structures, vol. 29, no. 8, pp. 085004-085004.
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© 2020 IOP Publishing Ltd. There are crack interfaces between self-healing agent and cement matrix in smart encapsulation-based self-healing concrete, whose mechanical properties significantly affects the load capacity recovery of crack-healed concrete. In this study, both experimental and numerical investigations were conducted on the crack-healed concrete under uniaxial tension to investigate the interface bonding behaviours and the self-healing agent distribution on the crack surface. The results show that the bonding behaviour of the crack interface depends on the content of healing agent and mechanical properties of the crack surface. However, it is still difficult to accurately understand their effects on the bonding behaviour by experimental investigation due to the high brittleness of the crack interface and the discrepancy of self-healing concrete. Therefore, based on the experimental results, a novel numerical model of the interface between self-healing agent and cement matrix was developed to investigate effects of aggregates, pores and interface properties on the bonding behaviour of crack interface by the cohesive surface technique (CS). Parametric analysis was also performed on the bonding behaviours and a method was proposed for assessing the load capacity of crack-healed concrete. Based on the experimental and numerical investigations on the healing agent-concrete crack interface in the smart encapsulation-based self-healing concrete, this novel numericla methods can be used to assess the recovery efficiency and performance of smart self-healing concrete structure.
Xue, H, Luo, Z, Brown, T & Beier, S 2020, 'Design of Self-Expanding Auxetic Stents Using Topology Optimization', Frontiers in Bioengineering and Biotechnology, vol. 8, p. 736.
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Implanting stents is the most efficient and minimally invasive technique for treating coronary artery diseases, but the risks of stent thrombosis (ST) and in-stent restenosis (IRS) hamper the healing process. There have been a variety of stents in market but dominated by ad hoc design motifs. A systematic design method that can enhance deliverability, safety and efficacy is still in demand. Most existing designs are focused on patient and biological factors, while the mechanical failures related to stenting architectures, e.g., inadequate stent expansion, stent fracture, stent malapposition and foreshortening, are often underestimated. With regard to these issues, the self-expanding (SE) stents may perform better than balloon-expandable (BE) stents, but the SE stents are not popular in clinic practice due to poor deliverability, placement accuracy, and precise match of the stent size and shape to the vessel. This paper addresses the importance between stent structures and clinic outcomes in the treatment of coronary artery disease. First, a concurrent topological optimization method will be developed to systematically find the best material distribution within the design domain. An extended parametric level set method with shell elements is proposed in the topology optimization to ensure the accuracy and efficiency of computations. Second, the auxetic metamaterial with negative Poisson's ratio is introduced into the self-expanding stents. Auxetics can enhance mechanical properties of structures, e.g., fracture toughness, indentation and shear resistance and vibration energy absorption, which will help resolve the drawbacks due to the mechanical failures. Final, the optimized SE stent is numerically validated with the commercial software ANSYS and then prototyped using additive manufacturing techniques. Topological optimization gives a rare opportunity to exploiting the unique advantages of additive manufacturing. Hence, the topologically optimized auxet...
Xue, M, Shivakumara, P, Wu, X, Lu, T, Pal, U, Blumenstein, M & Lopresti, D 2020, 'Deep invariant texture features for water image classification', SN Applied Sciences, vol. 2, no. 12, p. 2068.
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Detecting potential issues in naturally captured images of water is a challenging task due to visual similarities between clean and polluted water, as well as causes posed by image acquisition with different camera angles and placements. This paper presents novel deep invariant texture features along with a deep network for detecting clean and polluted water images. The proposed method first divides an input image into H, S and V components to extract finer details. For each of the color spaces, the proposed approach generates two directional coherence images based on Eigen value analysis and gradient distribution, which results in enhanced images. Then the proposed method extracts scale invariant gradient orientations based on Gaussian first order derivative filters on different standard deviations to study texture of each smoothed image. To strengthen the above features, we explore the combination of Gabor-wavelet-binary pattern for extracting texture of the input water image. The proposed method integrates merits of aforementioned features and the features extracted by VGG16 deep learning model to obtain a single feature vector. Furthermore, the extracted feature is fed to a gradient boosting decision tree for water image detection. A variety of experimental results on a large dataset containing different types of clean and stagnant water images show that the proposed method outperforms the existing methods in terms of classification rate and accuracy.
Yadav, R, Zhang, W, Kaiwartya, O, Song, H & Yu, S 2020, 'Energy-Latency Tradeoff for Dynamic Computation Offloading in Vehicular Fog Computing', IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 14198-14211.
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Yadav, S, Ibrar, I, Altaee, A, Déon, S & Zhou, J 2020, 'Preparation of novel high permeability and antifouling polysulfone-vanillin membrane', Desalination, vol. 496, pp. 114759-114759.
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A novel high-performance nanofiltration membrane was fabricated by a simple and scalable route involving in situ cross-linking of hydrophilic, cheap, and environmentally friendly vanillin as antifouling agent with polysulfone (PSf) for salt rejection performance. Vanillin acts as a porogen, which induces a negative surface charge on the membrane surface due to the presence of polar functional groups like alcohol and aldehyde. The surface properties, including charge, morphology, and hydrophilicity, were investigated in detail using analytical instruments. The nanofiltration performance of the fabricated PSf-vanillin membranes was dependent on the percentage of vanillin added in the casting solution. The PSf-vanillin membrane antifouling tests were evaluated using 200 mg/L bovine serum albumin (BSA), and results showed 99% rejection with 88.55% flux recovery ratio. Performance studies were compared with commercially available TRISEP® UA60 nanofiltration membrane. PSf-vanillin membrane M2 showed higher MgSO4 (87.49%), NaCl (25.78%) rejection with excellent antifouling properties compared to commercial UA60 membrane. It is believed that charged membranes are the building blocks for the development of future generation desalination membranes possessing high permeability and selectivity index. The developed membranes have potential niche application in the pre-treatment of feed solution.
Yadav, S, Ibrar, I, Altaee, A, Samal, AK, Ghobadi, R & Zhou, J 2020, 'Feasibility of brackish water and landfill leachate treatment by GO/MoS2-PVA composite membranes', Science of The Total Environment, vol. 745, pp. 141088-141088.
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Two-dimensional (2D) based layered materials with tunable chemical functionalities and surface charge properties have emerged for on-demand applications including membrane technology. However, the process control, time and energy-efficient production of non-swelling graphene oxide (GO) with retaining physicochemical properties are still challenging. In this work, we have fabricated highly ordered GO membrane on cellulose acetate supporting membrane filters of 1.2 μm pore size using molybdenum disulphide (MoS2) as a nano-spacer and polyvinyl alcohol (PVA) as an adhesive for the first time with limited swelling. The fabricated membranes were used for NaCl rejection and removal of toxic heavy metal ions, and the radioactive element from landfill leachate water. The introduction of hydrophilic PVA, thickness control using a various amount of nanospacer and graphene oxide played a vital role in the transport mechanism, permeability, and selectivity index. The composition of PVA and MoS2 in the coating solution was optimized to tune the d-spacing of graphene oxide layers. The newly developed composite membranes have 89% rejection rate to NaCl and 3.96 L/m2h water flux at low operating pressures of 5 bar. Also, the prepared membranes have a high rejection of multivalent metal ions in landfill leachate. 86.5% to 99.8% rejection rate of multivalent metal ions in landfill leachate was observed for the M3 (GO (10): MoS2 (10): PVA (0.5)) membrane. The excellent rejection performance is ascribed to the combined impact of size exclusion, ion adsorption, electrostatic interaction and Gibbs-Donnan exclusion mechanism. The excellent stability and high rejection rate even after 216 h of operation make the fabricated membranes promising for use in practical water separation applications.
Yadav, S, Ibrar, I, Bakly, S, Khanafer, D, Altaee, A, Padmanaban, VC, Samal, AK & Hawari, AH 2020, 'Organic Fouling in Forward Osmosis: A Comprehensive Review', Water, vol. 12, no. 5, pp. 1505-1505.
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Organic fouling in the forward osmosis process is complex and influenced by different parameters in the forward osmosis such as type of feed and draw solution, operating conditions, and type of membrane. In this article, we reviewed organic fouling in the forward osmosis by focusing on wastewater treatment applications. Model organic foulants used in the forward osmosis literature were highlighted, which were followed by the characteristics of organic foulants when real wastewater was used as feed solution. The various physical and chemical cleaning protocols for the organic fouled membrane are also discussed. The study also highlighted the effective pre-treatment strategies that are effective in reducing the impact of organic fouling on the forward osmosis (FO) membrane. The efficiency of cleaning methods for the removal of organic fouling in the FO process was investigated, including recommendations on future cleaning technologies such as Ultraviolet and Ultrasound. Generally, a combination of physical and chemical cleaning is the best for restoring the water flux in the FO process.
Yadav, S, Saleem, H, Ibrar, I, Naji, O, Hawari, AA, Alanezi, AA, Zaidi, SJ, Altaee, A & Zhou, J 2020, 'Recent developments in forward osmosis membranes using carbon-based nanomaterials', Desalination, vol. 482, pp. 114375-114375.
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© 2020 Elsevier B.V. Contamination and industrial development are among the reasons for water quality deterioration beyond treatability by conventional processes. Unfortunately, conventional water and wastewater treatment technologies are not always capable of handling industrial wastewaters, and hence more advanced treatment technologies are required. The new trend of osmotically driven membrane technologies has demonstrated an exceptional efficiency for water purification and treatment including seawater desalination. Compared to pressure-driven membrane processes, forward osmosis (FO) technology, as a standalone process, is more energy-efficient, and less prone to membrane fouling than its predecessor reverse osmosis (RO) technology. However, forward osmosis suffers a severe concentration polarization that is acting on both sides of the membrane and results in a sharp decline in water flux. A thinner support layer has been recommended to lessen the concentration polarization impact in the FO process but a very thin support layer compromises the membrane mechanical strength. Recently, researchers have applied different carbon-based nanomaterials to enhance water flux, fouling propensity, and mechanical strength of the FO membrane. This work reviews advancement in the FO membrane fabrication using carbon nanomaterials to improve the membrane characteristics. Despite a large number of laboratory experiments, carbon-based nanomaterials in the FO membrane are still at the early-stage of laboratory investigation and no commercial products are available yet. The study also reviews the main challenges that limit the application of carbon-based nanomaterials for FO membranes.
Yan, B, Ma, J, Wu, D & Wriggers, P 2020, 'The analyses of dynamic response and reliability for failure-dependent stochastic micro-resonator with thermoelastic coupling effects', Applied Mathematical Modelling, vol. 77, pp. 1168-1187.
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© 2019 The Authors A crucial measure for the design of high-performance micro-resonators is to consider the randomness of structural parameters when analyzing the structural system reliability. In this work, the stochastic dynamic response analysis and subsequently, a dynamic reliability assessment of the random micro-resonators are originally presented, where the thermoelastic coupling effects are freshly incorporated in the models proposed. The dynamic characteristics equation of the deterministic micro-resonator is firstly established based on the finite element method. The random dynamic characteristics of the resonator are then solved by implementing the left and right eigenvectors and the block Lanczos algorithm, and the random temperature field and structural random dynamic stress are also tackled. Afterwards, the overall structural reliability is investigated with a comprehensive consideration of the strength failure and frequency resonance failure, in which the Copula function is used for describing the dynamic correlation between two failure modes. Finally, the feasibility and rationality of the method put forward are demonstrated via a practically motivated example.
Yan, B, Zhao, Q, Zhang, JA & Wang, Z 2020, 'Multi-Objective Sparse Reconstruction With Transfer Learning and Localized Regularization', IEEE Access, vol. 8, pp. 184920-184933.
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Multi-objective sparse reconstruction methods have shown strong potential in sparse reconstruction. However, most methods are computationally expensive due to the requirement of excessive functional evaluations. Most of these methods adopt arbitrary regularization values for iterative thresholding-based local search, which hardly produces high-precision solutions stably. In this article, we propose a multi-objective sparse reconstruction scheme with novel techniques of transfer learning and localized regularization. Firstly, we design a knowledge transfer operator to reuse the search experience from previously solved homogeneous or heterogeneous sparse reconstruction problems, which can significantly accelerate the convergence and improve the reconstruction quality. Secondly, we develop a localized regularization strategy for iterative thresholding-based local search, which uses systematically designed independent regularization values according to decomposed subproblems. The strategy can lead to improved reconstruction accuracy. Therefore, our proposed scheme is more computationally efficient and accurate, compared to existing multi-objective sparse reconstruction methods. This is validated by extensive experiments on simulated signals and benchmark problems.
Yan, C, Zheng, Q, Chang, X, Luo, M, Yeh, C-H & Hauptman, AG 2020, 'Semantics-Preserving Graph Propagation for Zero-Shot Object Detection', IEEE Transactions on Image Processing, vol. 29, pp. 8163-8176.
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Most existing object detection models are restricted to detecting objects from previously seen categories, an approach that tends to become infeasible for rare or novel concepts. Accordingly, in this paper, we explore object detection in the context of zero-shot learning, i.e., Zero-Shot Object Detection (ZSD), to concurrently recognize and localize objects from novel concepts. Existing ZSD algorithms are typically based on a simple mapping-transfer strategy that is susceptible to the domain shift problem. To resolve this problem, we propose a novel Semantics-Preserving Graph Propagation model for ZSD based on Graph Convolutional Networks (GCN). More specifically, we employ a graph construction module to flexibly build category graphs by incorporating diverse correlations between category nodes; this is followed by two semantics preserving modules that enhance both category and region representations through a multi-step graph propagation process. Compared to existing mapping-transfer based methods, both the semantic description and semantic structural knowledge exhibited in prior category graphs can be effectively leveraged to boost the generalization capability of the learned projection function via knowledge transfer, thereby providing a solution to the domain shift problem. Experiments on existing seen/unseen splits of three popular object detection datasets demonstrate that the proposed approach performs favorably against state-of-the-art ZSD methods.
Yan, J, Liu, H, Zhu, X, Men, K & Yeo, KS 2020, 'Ka-Band Marchand Balun with Edge- and Broadside-Coupled Hybrid Configuration', Electronics, vol. 9, no. 7, pp. 1116-1116.
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This article presents a novel Ka-band Marchand balun implemented in 0.13-μm SiGe bipolar complementary metal–oxide–semiconductor (BiCMOS) process. By combining both edge- and broadside-coupled structures, the new hybrid balun is able to increase the coupling and minimize the balun insertion loss. As compared with conventional edge-coupled or broadside-coupled structures, the proposed balun achieves the lowest insertion loss of 1.02 dB across a wide 1-dB bandwidth from 29.0 GHz to 46.0 GHz, with a core size of 270 μm × 280 μm.
Yan, X, Sun, J, Kenjiahan, A, Dai, X, Ni, B-J & Yuan, Z 2020, 'Rapid and strong biocidal effect of ferrate on sulfidogenic and methanogenic sewer biofilms', Water Research, vol. 169, pp. 115208-115208.
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For the control of sulfide and methane in sewers, it is favorable to reduce their production rather than to remove them after generation. In this study, we revealed rapid and strong biocidal effect of ferrate (Fe(VI)) on sulfidogenic and methanogenic sewer biofilms, leading to control of sulfide and methane production in sewer. The inactivation of the microorganisms in sewer biofilms by Fe(VI) could be accomplished within 15 min for a single dosing event and the biocidal effect could be enhanced by applying pulse dosing strategy. The microbiological analysis showed that the key functional genes involved in sulfide and methane production, i.e. dsrA and mcrA, in the viable cells after Fe(VI) dosing were decreased substantially by 84.2% and 86.6%, respectively. Significant drops were also observed in the relative abundances of viable sulfide reducing bacteria (SRB) and methanogenic archaea (MA). The direct dosing of Fe(VI) into a sewer reactor led to instant and complete suppression of sulfidogenic and methanogenic activities, and the recovery of the activities resembled the regrowth of residual SRB and MA. The results of this study suggested the feasibility for developing an efficient and cost-effective sulfide and methane control strategy using Fe(VI).
Yan, Y, Tan, M, Tsang, IW, Yang, Y, Shi, Q & Zhang, C 2020, 'Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis, and Case Study', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 2, pp. 288-301.
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© 1989-2012 IEEE. Matrix factorization has been widely applied to various applications. With the fast development of storage and internet technologies, we have been witnessing a rapid increase of data. In this paper, we propose new algorithms for matrix factorization with the emphasis on efficiency. In addition, most existing methods of matrix factorization only consider a general smooth least square loss. Differently, many real-world applications have distinctive characteristics. As a result, different losses should be used accordingly. Therefore, it is beneficial to design new matrix factorization algorithms that are able to deal with both smooth and non-smooth losses. To this end, one needs to analyze the characteristics of target data and use the most appropriate loss based on the analysis. We particularly study two representative cases of low-rank matrix recovery, i.e., collaborative filtering for recommendation and high dynamic range imaging. To solve these two problems, we respectively propose a stage-wise matrix factorization algorithm by exploiting manifold optimization techniques. From our theoretical analysis, they are both are provably guaranteed to converge to a stationary point. Extensive experiments on recommender systems and high dynamic range imaging demonstrate the satisfactory performance and efficiency of our proposed method on large-scale real data.
Yan, Z, Chen, J, Hu, R, Huang, T, Chen, Y & Wen, S 2020, 'Training memristor-based multilayer neuromorphic networks with SGD, momentum and adaptive learning rates', Neural Networks, vol. 128, pp. 142-149.
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Neural networks implemented with traditional hardware face inherent limitation of memory latency. Specifically, the processing units like GPUs, FPGAs, and customized ASICs, must wait for inputs to read from memory and outputs to write back. This motivates memristor-based neuromorphic computing in which the memory units (i.e., memristors) have computing capabilities. However, training a memristor-based neural network is difficult since memristors work differently from CMOS hardware. This paper proposes a new training approach that enables prevailing neural network training techniques to be applied for memristor-based neuromorphic networks. Particularly, we introduce momentum and adaptive learning rate to the circuit training, both of which are proven methods that significantly accelerate the convergence of neural network parameters. Furthermore, we show that this circuit can be used for neural networks with arbitrary numbers of layers, neurons, and parameters. Simulation results on four classification tasks demonstrate that the proposed circuit achieves both high accuracy and fast speed. Compared with the SGD-based training circuit, on the WBC data set, the training speed of our circuit is increased by 37.2% while the accuracy is only reduced by 0.77%. On the MNIST data set, the new circuit even leads to improved accuracy.
Yang, C, Ji, J & Li, S 2020, 'Stability analysis of chemotaxis dynamics in bacterial foraging optimization over multi-dimensional objective functions', Soft Computing, vol. 24, no. 5, pp. 3711-3725.
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Yang, D, Ni, W, Du, L, Liu, H & Wang, J 2020, 'Efficient Attributed Scatter Center Extraction Based on Image-Domain Sparse Representation', IEEE Transactions on Signal Processing, vol. 68, pp. 4368-4381.
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© 1991-2012 IEEE. As an efficient way to interpret the measurements of high-frequency synthetic aperture radar (SAR), an attributed scattering center (ASC) model provides concise and physically relevant features of complex targets. However, accurate extractions of ASCs have been heavily penalized by high memory requirements and computational complexity. We propose to convert SAR measurements to sparse representations in the image domain where the ASC model parameters can be estimated by using an orthogonal matching pursuit (OMP) algorithm or its Newtonlized variation. Two important new properties of the ASC model are unveiled in the image domain, namely, 'translatability' and 'additivity.' The properties can help save the dictionary of OMP from sampling the position and length parameters. The atoms of the dictionary become localized, thereby reducing the dictionary size and accelerating ASC extractions. Extensive experiments are conducted based on open-source XPATCH Backhoe data, measured MSTAR data, and synthetic backscatter data. The results show that the proposed approach is able to outperform existing image-domain algorithms in terms of accuracy and noise resistance, and outperform existing frequency-domain algorithms in terms of memory requirement and runtime.
Yang, G, Zhang, N, Yang, J, Fu, Q, Wang, Y, Wang, D, Tang, L, Xia, J, Liu, X, Li, X, Yang, Q, Liu, Y, Wang, Q & Ni, B-J 2020, 'Interaction between perfluorooctanoic acid and aerobic granular sludge', Water Research, vol. 169, pp. 115249-115249.
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The increasing use of perfluorooctanoic acid (PFOA) raises concerns about its potential toxicity to the environment. However, the interaction between PFOA and aerobic granular sludge has never been documented. This work therefore aims to provide such support through investigating the fate of PFOA at environmentally relevant levels in aerobic granular sludge systems and its impact on aerobic granular sludge. Experimental results showed that 32.0%∼36.4% of wastewater PFOA was removed by aerobic granular sludge in stable operation when PFOA concentration was ranged from 0.1 to 1.0 mg/L. Mass balance analyses and X-ray photoelectron spectroscopy survey scan revealed that the removal of PFOA was dominated by adsorption rather than biodegradation, and sorption kinetic analysis indicated that inhomogeneous multilayer adsorption was responsible for this removal. The adsorbed PFOA deteriorated the settleability of granular sludge and biological nitrogen and phosphorus removal significantly. Experimental results showed that 1.0 mg/L PFOA inhibited anaerobic phosphate release, aerobic phosphate uptake, nitrate reduction, and nitrite reduction processes by 60%, 50%, 13.1%, and 5.8%, respectively. It was observed that PFOA induced large amounts of filamentous villus growing on the surface and increased the extracellular polymeric substances of granular sludge. Fourier-transform infrared spectra and X-ray photoelectron spectroscopy spectrum showed that several function groups in extracellular polymeric substances such as hydroxyl groups, amides and polysaccharides were affected by PFOA. It was also found that PFOA inhibited the cyclic transformations of polyhydroxyalkanoates and glycogen. Microbial community analyses showed that PFOA decreased the abundances of Nitrosomonas, Nitrospira, Accumulibacter, and other function microbes such as Rhodospirillaceae, Thauera, and Azoarcus.
Yang, J, Liu, X, Liu, X, Xu, Q, Wang, W, Wang, D, Yang, G, Fu, Q, Kang, Z, Yang, Q, Liu, Y, Wang, Q & Ni, B-J 2020, 'Enhanced dark fermentative hydrogen production from waste activated sludge by combining potassium ferrate with alkaline pretreatment', Science of The Total Environment, vol. 707, pp. 136105-136105.
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© 2019 Alkaline pretreatment was demonstrated to be effective in the enhancement of hydrogen production. However, the sludge solubilization rate of alkaline pretreatment is still limited. This study reports a new strategy of K2FeO4 + pH 9.5 for sludge mesophilic anaerobic fermentation. Experimental results showed that the combination of K2FeO4/pH 9.5 pretreatment had a greater hydrogen yield than the individual K2FeO4 and pH 9.5. The maximum hydrogen yield was 19.2 mL per gram volatile suspended solids (VSS) under the optimal condition (0.02 g per gram total suspended solids K2FeO4 + pH 9.5). Kinetic analysis showed that the highest hydrogen production potential of 19.9 mL/g VSS was obtained in the combined reactor, which well fitted the first-order kinetic model (R2 = 0.9925). Besides, the fermentation type was mainly acetic and butyric in the combined reactor, which contributed to hydrogen production. Further analyses showed that the combined pretreatment reduced hydrogen sulfide yield, providing an environmentally friendly method to sludge treatment.
Yang, M & Sharma, D 2020, 'The Spatiality and Temporality of Electricity Reform: A Comparative and Critical Institutional Perspective', Energy Research & Social Science, vol. 60, pp. 101327-101327.
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Yang, Q, Lyu, M & Zhu, X 2020, 'Nonlinear Connection Stiffness Identification of Heritage Timber Buildings Using a Temperature-Driven Multi-Model Approach', International Journal of Structural Stability and Dynamics, vol. 20, no. 10, pp. 2042001-2042001.
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‘Que-Ti’ is an important component in typical Tibetan heritage timber buildings and it performs similar to corbel brackets connecting beam and column in modern structures. It transfers shear, compression and bending moment by slippage and deformation of components as well as limited joint rotation. A rigorous analytical model of ‘Que-Ti’ is needed for predicting the behavior of a timber structure under extreme loadings. Few researches have been done on this topic, particularly with the parameters describing the performances of this connection subjected to external loads. In this paper, a new temperature-driven multimodel approach is proposed to identify the stiffness parameters of a ‘Que-Ti’ connection in its operating environment. Models with nonlinear compression and rotational springs have been developed to take into account the change of mechanical behavior of the ‘Que-Ti’ affected by the temperature variation in typical heritage Tibetan buildings. The column–beam connection is modeled as two nonlinear rotational springs and one nonlinear compressive spring. Ambient temperature variation is treated as a force function in the input (temperature)–output (local mechanical strains) relationship, and stiffness identification is conducted iteratively via correlating the calculated strain responses with measured data. The nonlinear model of the joint is reproduced with a number of linear local models in different deformation scenarios that are corresponding to different temperature ranges. The stiffness parameters can be identified using a multimodel approach. Numerical results show that the method is effective and reliable to identify the nonlinear connection stiffness of the ‘Que-Ti’ accurately with the temperature change even with 10% noise in measurements. The monitoring data from a long-term monitoring system installed in a typical heritage Tibetan building is used to further verify the method. The experimental results show that the identifie...
Yang, S, Bai, L, Cui, L, Ming, Z, Wu, Y, Yu, S, Shen, H & Pan, Y 2020, 'An efficient pipeline processing scheme for programming Protocol-independent Packet Processors', Journal of Network and Computer Applications, vol. 171, pp. 102806-102806.
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© 2020 Elsevier Ltd OpenFlow is unable to provide customized flow tables, resulting in memory explosions and high switch retirement rates. This is the bottleneck for the development of SDN. Recently, P4 (Programming Protocol-independent Packet Processors) attracts much attentions from both academia and industry. It provides customized networking services by offering flow-level control. P4 can “produce” various forwarding tables according to packets. P4 increases the speed of custom ASICs. However, with the prevalence of P4, the multiple forwarding tables could explode when used in large scale networks. The explosion problem can slow down the lookup speed, which causes congestions and packet losses. In addition, the pipelined structure of forwarding tables brings additional processing delay. In this study, we will improve the lookup performance by optimizing the forwarding tables of P4. Intuitively, we will install the rules according to their popularity, i.e., the popular rules will appear earlier than others. Thus, the packets can hit the matched rule sooner. In this paper, we formalize the optimization problem, and prove that the problem is NP-hard. To solve the problem, we propose a heuristic algorithm called EPSP (Efficient Pipeline Processing Scheme for P4), which can largely reduce the lookup time while keeping the forwarding actions the same. Because running the optimization algorithm frequently brings additional processing burdens, wedesign an incremental update algorithm to alleviate this problem. To evaluate the proposed algorithms, we set up the simulation environments based on ns-3. The simulation results show that the algorithm greatly reduces both the lookup time and the number of memory accesses. The incremental algorithm largely reduces the processing burdens while the lookup time remains almost the same with the non-incremental algorithm. We also implemented a prototype using floodlight and mininet. The results show that our algorithm b...
Yang, S, Jiang, J, Pal, A, Yu, K, Chen, F & Yu, S 2020, 'Analysis and Insights for Myths Circulating on Twitter During the COVID-19 Pandemic', IEEE Open Journal of the Computer Society, vol. 1, pp. 209-219.
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The current COVID-19 pandemic and its uncertainty have given rise to various myths and rumours. These myths spread incredibly fast through social media, which has caused massive panic in society. In this paper, we comprehensively examined the prevailing myths related to COVID-19 in regard to the diffusion of myths, people's engagement with myths and people's subjective emotions to myths. First, we classified the myths into five categories: spread of infection, preventive measures, detection measures, treatment and miscellaneous. We collected the tweets about each category of myths from 1 January to 7 July in the year 2020. We found that the vast majority of the myth tweets were about the spread of the infection. Next, we fitted myths spreading with the SIR epidemic model and calculated the basic reproduction number R0 for each category of myths. We observed that the myths about the spread of infection and preventive measures propagated faster than other categories of myths, and more miscellaneous myths raised and quickly spread from later June 2020. We further analyzed people's emotions evoked by each category of myths and found that fear was the strongest emotion in all categories of myths and around 64% of the collected tweets expressed the emotion of fear. The study in this paper provides insights for authorities and governments to understand the myths during the eruption of the pandemic, and hence enable targeted and feasible measures to demystify the most concerned myths in due time.
Yang, T, Ding, C, Ziolkowski, RW & Guo, YJ 2020, 'A Controllable Plasmonic Resonance in a SiC-Loaded Single-Polarization Single-Mode Photonic Crystal Fiber Enables Its Application as a Compact LWIR Environmental Sensor', Materials, vol. 13, no. 18, pp. 3915-3915.
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Near-perfect resonant absorption is attained in a single-polarization single-mode photonic crystal fiber (SPSM PCF) within the long-wave infrared (LWIR) range from 10 to 11 μm. The basic PCF design is a triangular lattice-based cladding of circular air holes and a core region augmented with rectangular slots. A particular set of air holes surrounding the core is partially filled with SiC, which exhibits epsilon near-zero (ENZ) and epsilon negative (ENG) properties within the wavelength range of interest. By tuning the configuration to have the fields of the unwanted fundamental and all higher order modes significantly overlap with the very lossy ENG rings, while the wanted fundamental propagating mode is concentrated in the core, the SPSM outcome is realized. Moreover, a strong plasmonic resonance is attained by adjusting the radii of the resulting cylindrical core-shell structures. The cause of the resonance is carefully investigated and confirmed. The resonance wavelength is shown to finely shift, depending on the relative permittivity of any material introduced into the PCF’s air holes, e.g., by flowing a liquid or gas in them. The potential of this plasmonic-based PCF structure as a very sensitive, short length LWIR spectrometer is demonstrated with an environmental monitoring application.
Yang, T, Liu, Z, Yang, Y & Wu, C 2020, 'Experimental investigation on behavior of ultra-high performance concrete after high temperature', Tumu yu Huanjing Gongcheng Xuebao/Journal of Civil and Environmental Engineering, vol. 42, no. 3, pp. 115-126.
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The apparent characteristics, mass loss and mechanical properties of ultra-high performance concrete after exposure to high temperature were studied through the high temperature heating test and the cubic compressive strength test. The effects of steel fiber, polypropylene fiber, steel fiber and polypropylene fiber on cracking suppression of ultra-high performance concrete were compared. The effects of temperature, fiber type and content, aggregate (quartz sand and steel slag) on the strength of ultra-high performance concrete were investigated. The test results show that 1% steel fibers and 2% polypropylene fibers can effectively restrain high temperature explosion behavior, and the specimen remains intact after high temperature. Ultra-high performance concrete with steel slag aggregate and hybrid fiber has excellent high temperature mechanical properties, the residual strength of 67% can still be maintained after being exposed to high temperature at 1 000℃. With the increase of temperature, the cubic compressive strength of ultra-high performance concrete increases first and then decreases. High temperature enhances the compressive ductility of ultra-high performance concrete when the target temperature is more than 600℃.
Yang, T, Miro, JV, Lai, Q, Wang, Y & Xiong, R 2020, 'Cellular Decomposition for Nonrepetitive Coverage Task With Minimum Discontinuities', IEEE/ASME Transactions on Mechatronics, vol. 25, no. 4, pp. 1698-1708.
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A mechanism to derive nonrepetitive coverage path solutions with a proven minimal number of discontinuities is proposed in this work, with the aim to avoid unnecessary, costly end-effector lift-offs for manipulators. The problem is motivated by the automatic polishing of an object. Due to the nonbijective mapping between the workspace and the joint-space, a continuous coverage path in the workspace may easily be truncated in the joint-space, incurring undesirable end-effector lift-offs. Inversely, there may be multiple configuration choices to cover the same point of a coverage path through the solution of the inverse kinematics. The solution departs from the conventional local optimization of the coverage path shape in task space, or choosing appropriate but possibly disconnected configurations, to instead explicitly explore the least number of discontinuous motions through the analysis of the structure of valid configurations in joint-space. The two novel contributions of this article include proof that the least number of path discontinuities is predicated on the surrounding environment, independent from the choice of the actual coverage path; thus, has a minimum. In addition, an efficient finite cellular decomposition method to optimally divide the workspace into the minimum number of cells, each traversable without discontinuities by any arbitrary coverage path within. Extensive simulation examples and real-world results on a 5 DoF manipulator are presented to prove the validity of the proposed strategy in realistic settings.
Yang, Y, Hou, ZJ, Zhu, X, Che, W & Xue, Q 2020, 'A Millimeter-Wave Reconfigurable On-Chip Coupler With Tunable Power-Dividing Ratios in 0.13-$\mu$ m BiCMOS Technology', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 5, pp. 1516-1526.
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This paper presents a millimeter-wave (mm-wave) on-chip coupler with tunable power dividing ratios and constant phase response. Composed by two coupled lines, two capacitors and two series-connected varactors, the proposed tunable coupler offers broadband frequency responses for 5G applications. Theoretical analysis for wideband operation is provided. For demonstration, a millimeter-wave tunable coupler is implemented in a standard 0.13-\mu \text{m} SiGe (Bi) CMOS technology and measured through an on-wafer probing system. From 24 to 38 GHz, the proposed tunable coupler shows a power-dividing ratio ranged from 0 to 6.5 dB, while maintaining an in-band return loss of better than 10 dB and output isolation of 20 dB, simultaneously. The phase imbalance is better than ±2.5° with a measured insertion loss of 1.3 dB across the entire tuning range. To the authors' best knowledge, this is the first time that an on-chip coupler with tunable power-dividing ratios is reported operating at mm-wave bands for, particularly, 5G applications.
Yang, Y, Liu, Y, Fang, X, Miao, W, Chen, X, Sun, J, Ni, B-J & Mao, S 2020, 'Heterogeneous Electro-Fenton catalysis with HKUST-1-derived Cu@C decorated in 3D graphene network', Chemosphere, vol. 243, pp. 125423-125423.
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Transition metal and nanocarbon-based composites with high activity and stability draw great attention in electro-Fenton system for organic pollutants removal. In this study, HKUST-1-derived Cu@C nanoparticles embedded within three-dimensional reduced graphene oxide (rGO) network (denoted as 3DG/Cu@C) is synthesized through a simple strategy. The prepared catalyst shows ordered 3D porous carbon structure and Cu@C NPs are uniformly dispersed in the matrix. The 3DG/Cu@C is used as heterogeneous electro-Fenton (hetero-EF) catalyst and shows outstanding performance in various persistent organic pollutants removal. High concentration Rhodamine B (RhB) (40 mg L-1) can achieve a complete decolorization within 150 min with 25 mg L-1 3DG/Cu@C catalyst, which is one of the lowest catalyst dosages in hetero-EF for RhB removal. More importantly, the 3DG/Cu@C achieves high RhB mineralization efficiency of 81.5% and exhibits high catalytic performance in a wide pH window from 3 to 9. The 3DG/Cu@C also remains high efficiency after five successive reaction cycles. The working mechanism study shows that RhB is mainly oxidized by •OH and O2•- radicals through hetero-EF and anodic oxidation processes. The high stability and outstanding performance of 3DG/Cu@C provide new insights in organic pollutants removal by hetero-EF process with transition metal and nanocarbon-based catalysts.
Yang, Y, Xing, D, Wang, Y, Jia, H, Li, B & Li, JJ 2020, 'A long non-coding RNA, HOTAIR, promotes cartilage degradation in osteoarthritis by inhibiting WIF-1 expression and activating Wnt pathway', BMC Molecular and Cell Biology, vol. 21, no. 1, pp. 53-11.
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Abstract Background Long noncoding RNAs (lncRNAs) are recently found to be critical regulators of the epigenome. However, our knowledge of their role in osteoarthritis (OA) development is limited. This study investigates the mechanism by which HOTAIR, a key lncRNA with elevated expression in OA, affects OA disease progression. Results HOTAIR expression was greatly elevated in osteoarthritic compared to normal chondrocytes. Silencing and over-expression of HOTAIR in SW1353 cells respectively reduced and increased the expression of genes associated with cartilage degradation in OA. Investigation of molecular pathways revealed that HOTAIR acted directly on Wnt inhibitory factor 1 (WIF-1) by increasing histone H3K27 trimethylation in the WIF-1 promoter, leading to WIF-1 repression that favours activation of the Wnt/β-catenin pathway. Conclusions Activation of Wnt/β-catenin signalling by HOTAIR through WIF-1 repression in osteoarthritic chondrocytes increases catabolic gene expression and promotes cartilage degradation. This is the first study to demonstrate a direct link between HOTAIR, WIF-1 and OA progression, which may be useful for future investigations into disease biomarkers or therapeutic targets.
Yang, Y, Zang, Y, Hu, Y, Wang, XC & Ngo, HH 2020, 'Upflow anaerobic dynamic membrane bioreactor (AnDMBR) for wastewater treatment at room temperature and short HRTs: Process characteristics and practical applicability', Chemical Engineering Journal, vol. 383, pp. 123186-123186.
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© 2019 Elsevier B.V. An upflow anaerobic dynamic membrane bioreactor (AnDMBR) was set up for real domestic wastewater treatment at room temperature (20–25 °C) and short hydraulic retention time (HRT = 8 h, 4 h, 2 h, and 1 h). Following continuous operation for 93 days with stepwise decreased HRT, stable average chemical oxygen demand (COD) removal was achieved (between 77.3% and 70.6%) when HRT was reduced from 8 h to 4 h, then 2 h with flux varying from 22.5 to 90 L/m2·h. At these three HRTs, the rate of increase in trans-membrane pressure (TMP) was 0.4, 0.38, and 0.57 kPa/d, and average methane (CH4) production was 0.12, 0.10, and 0.08 L/g CODremoved, respectively. Furthermore, decreasing the HRT to 1 h resulted in less COD being removed (60.4%) and lower CH4 production (0.05 L/g CODremoved) as well as a faster rate of TMP increase (2.11 kPa/d). Various analytical methods were applied to characterize the morphology and composition of the dynamic membrane (DM) layers. Organic components analysis revealed that, with reduced HRT, there were apparent increases in soluble microbial products in the liquid phase and accumulation of tryptophan protein-like substances and aromatic protein-like substances in the DM layer, especially when the HRT was shortened to 1 h. Whilst the upflow AnDMBR proved applicable to wastewater treatment at room temperature with short HRTs, 2 h could be the HRT limit for maintaining stable operation.
Yao, M, Tijing, LD, Naidu, G, Kim, S-H, Matsuyama, H, Fane, AG & Shon, HK 2020, 'A review of membrane wettability for the treatment of saline water deploying membrane distillation', Desalination, vol. 479, pp. 114312-114312.
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© 2020 Elsevier B.V. Membrane distillation (MD) is an alternative membrane technology that offers the capacity to treat highly saline water including industrial wastewater, seawater, brine water from other processes, and oil-gas field produced water. However, conventional hydrophobic membranes suffer fast wetting and severe fouling especially when low surface tension chemicals exist in saline water, which compromises the performance of MD. Recent advances in material science and nanomaterials research have led to the incorporation of special wetting properties on membrane surfaces. Membranes with special wettability can achieve high resistance against membrane fouling and wetting, as well as overcome the trade-off between membrane permeability and selectivity. This review summarizes the progress and recent development of studies on MD membranes with special wettability. Firstly, the fundamental concepts pertaining to membrane surface wettability including insights of their benefits and potential issues are highlighted in this review. Secondly, fabrication methods and applications of membranes utilizing various special wettability are discussed in detail, along with their challenges. Finally, this review concludes the advantages of membranes with special wettability and demonstrates potential solutions to the above-mentioned challenges for future research in high saline wastewater treatment.
Yao, Y, Shen, F, Xie, G, Liu, L, Zhu, F, Zhang, J & Shen, HT 2020, 'Exploiting Web Images for Multi-Output Classification: From Category to Subcategories', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 7, pp. 1-13.
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Yao, Y, Zhang, J, Shen, F, Liu, L, Zhu, F, Zhang, D & Shen, HT 2020, 'Towards Automatic Construction of Diverse, High-Quality Image Datasets', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 6, pp. 1199-1211.
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© 1989-2012 IEEE. The availability of labeled image datasets has been shown critical for high-level image understanding, which continuously drives the progress of feature designing and models developing. However, constructing labeled image datasets is laborious and monotonous. To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries. We aim at collecting diverse and accurate images for given queries from the Web. Specifically, we formulate noisy textual queries removing and noisy images filtering as a multi-view and multi-instance learning problem separately. Our proposed approach not only improves the accuracy but also enhances the diversity of the selected images. To verify the effectiveness of our proposed approach, we construct an image dataset with 100 categories. The experiments show significant performance gains by using the generated data of our approach on several tasks, such as image classification, cross-dataset generalization, and object detection. The proposed method also consistently outperforms existing weakly supervised and web-supervised approaches.
Yao, Z & Li, W 2020, 'Microstructure and thermal analysis of APS nano PYSZ coated aluminum alloy piston', Journal of Alloys and Compounds, vol. 812, pp. 152162-152162.
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© 2019 Elsevier B.V. Aluminium alloys in internal combustion (IC) engines may suffer from heat damage. Such heat damage can be mitigated using thermal barrier coatings (TBCs). In this study, the TBC Nano yttria partially stabilized zirconia (PYSZ) is applied as an aggregated powder to an aluminum alloy piston using an atmospheric plasma spray (APS) method. The preparation and application of the Nano PYSZ aggregated powder are critical to its effectiveness as a TBC. IC engine bench experiments were undertaken to provide a baseline against which the effectiveness of the TBC could be judged. The microstructure of the Nano PYSZ aggregated powder and thermal barrier coatings were examined using three instruments: scanning electron microscopy (SEM), field emission scanning electron microscopy (FESEM) and X-ray powder diffraction (XRD). Results from this study show that the Nano PYSZ ceramic TBCs, applied to the aluminum alloy piston using a plasma spraying technique, (a) has a high quality Nano-structure, (b) can effectively resist the thermal shock of high temperature gas in the cylinder and (c) maintains both stable macro characteristics and micro structure during the working cycle of the IC engine. The thermal insulation properties of TBCs were also examined. The thermal analyses describe the distribution of temperature across both the piston and the aluminum alloy substrate. Results desmonstrate the effectiveness of the TBCs in reducing the temperature of the aluminium alloy substrate at the top of piston. One benefit is that the piston can operate effectively at higher temperatures. Specifically, as the thickness of ceramic coating increased from 0.1 mm to 1.4 mm, the maximum temperature of the pistons coated with the TBCs increased from 399 °C to 665 °C. The maximum temperature of the aluminum alloy substrates simultaneously decreased from 336 °C to 241 °C. This study clearly demonstrates the excellent thermal insulation properties of the TBCs and shows...
Yau, YH, Wong, CM, Ong, HC & Chin, WM 2020, 'Application of the bin weather data for building energy analysis in the tropics', Energy Efficiency, vol. 13, no. 5, pp. 935-953.
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Yazdani, D, Omidvar, MN, Branke, J, Nguyen, TT & Yao, X 2020, 'Scaling Up Dynamic Optimization Problems: A Divide-and-Conquer Approach', IEEE Transactions on Evolutionary Computation, vol. 24, no. 1, pp. 1-15.
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Scalability is a crucial aspect of designing efficient algorithms. Despite their prevalence, large-scale dynamic optimization problems are not well studied in the literature. This paper is concerned with designing benchmarks and frameworks for the study of large-scale dynamic optimization problems. We start by a formal analysis of the moving peaks benchmark (MPB) and show its nonseparable nature irrespective of its number of peaks. We then propose a composite MPB suite with exploitable modularity covering a wide range of scalable partially separable functions suitable for the study of large-scale dynamic optimization problems. The benchmark exhibits modularity, heterogeneity, and imbalance features to resemble real-world problems. To deal with the intricacies of large-scale dynamic optimization problems, we propose a decomposition-based coevolutionary framework which breaks a large-scale dynamic optimization problem into a set of lower-dimensional components. A novel aspect of the framework is its efficient bi-level resource allocation mechanism which controls the budget assignment to components and the populations responsible for tracking multiple moving optima. Based on a comprehensive empirical study on a wide range of large-scale dynamic optimization problems with up to 200-D, we show the crucial role of problem decomposition and resource allocation in dealing with these problems. The experimental results clearly show the superiority of the proposed framework over three other approaches in solving large-scale dynamic optimization problems.
Ye, D, Zhu, T, Zhou, W & Yu, PS 2020, 'Differentially Private Malicious Agent Avoidance in Multiagent Advising Learning', IEEE Transactions on Cybernetics, vol. 50, no. 10, pp. 4214-4227.
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Agent advising is one of the key approaches to improve agent learning performance by enabling agents to ask for advice between each other. Existing agent advising approaches have two limitations. The first limitation is that all the agents in a system are assumed to be friendly and cooperative. However, in the real world, malicious agents may exist and provide false advice to hinder the learning performance of other agents. The second limitation is that the analysis of communication overhead in these approaches is either overlooked or simplified. However, in communication-constrained environments, communication overhead has to be carefully considered. To overcome the two limitations, this paper proposes a novel differentially private agent advising approach. Our approach employs the Laplace mechanism to add noise on the rewards used by student agents to select teacher agents. By using the differential privacy technique, the proposed approach can reduce the impact of malicious agents without identifying them. Also, by adopting the privacy budget concept, the proposed approach can naturally control communication overhead. The experimental results demonstrate the effectiveness of the proposed approach.
Ye, K, Ji, J & Han, S 2020, 'Semi-active noise control for a hermetic digital scroll compressor', Journal of Low Frequency Noise, Vibration and Active Control, vol. 39, no. 4, pp. 1204-1215.
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Hermetic digital scroll compressor has been widely used as a small-scale organic Rankine cycle application in the heating, ventilation, and air conditioning systems. A clunking noise issue is recently found in an air conditioning outdoor unit, and the main cause of the noise is experimentally identified to be the impact of the scrolls in the compressor unit during the switching process. The semi-active control methods are thus designed to greatly reduce the noise level by using additional valves to adjust the pressure changing rate within the modulation chamber. The response time for the impact of the scrolls can then be controlled by the added valves. The additional release valve with a smaller diameter pipe parallel to the main valve is tested firstly for its performance. Slower flow rate is produced and the pipe can extend the response time and decrease the speed of the impact process by reducing the pressure changing rate. The use of a discharge valve is also tested for controlling the pressure changing rate inside the chamber. The discharge valve with an opposite effect to the release valve is found useful for solving the noise issue. Both noise and vibration results confirm that the impact noise in the frequency range of interest can be reduced by using the proposed semi-active control methods.
Ye, K, Ji, JC & Brown, T 2020, 'Design of a quasi-zero stiffness isolation system for supporting different loads', Journal of Sound and Vibration, vol. 471, pp. 115198-115198.
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© 2020 Elsevier Ltd The quasi-zero stiffness (QZS) vibration isolation system using negative stiffness structure can generally increase the workable frequency range and improve the isolation performance, in comparison with a linear vibration isolator. However, most of the QZS isolation systems are sensitive to the loads applied for achieving effective isolation. A QZS system designed for a certain load supported cannot provide an effective vibration isolation for another load, as the designed QZS region is not suitable for the new load and thus it no longer demonstrates the anticipated isolation performance. This paper presents an optimized structure for the QZS system to adaptively respond to different loads based on a cam-roller mechanism. Innovation of the present design is the capacity of supporting multi-load levels to isolate the vibrations in low frequency range. Frictional force occurring on the cam-roller contact is considered in the modelling to represent practical application situations. Both static and dynamic responses are theoretically studied for the QZS characteristic and isolation performance. A prototype of the proposed QZS structure is designed, fabricated and tested to verify its isolation performance. Experimental results demonstrate an excellent agreement with the theoretical results, which promotes the implementation of the proposed design into engineering applications.
Ye, S-Q, Mao, X-Y, Ding, H, Ji, J-C & Chen, L-Q 2020, 'Nonlinear vibrations of a slightly curved beam with nonlinear boundary conditions', International Journal of Mechanical Sciences, vol. 168, pp. 105294-105294.
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© 2019 The existing studies of nonlinear vibration of elastic structures are usually focused on straight structures with homogeneous linear boundaries. Differently, this paper investigates the nonlinear transverse vibrations of a slightly curved beam with nonlinear boundary conditions. By using the generalized Hamilton's principle, the governing equation with geometric nonlinearity is obtained for the dynamics of the curved beam. A method of dealing with nonlinear boundaries is proposed, which is considered as a nonlinear concentrated force at the boundary. The normal modes and natural frequencies of the curved beam are determined using two different hypothetical modes based on the derived system. The harmonic balance method in combination with the pseudo arc-length method is employed to obtain the primary resonance response and 1/2 super-harmonic resonance response of the slightly curved beam. It is found that the initial curvature plays a significant role in the characteristics of the nonlinear vibrations of the curved beam. With an increase of the initial curvature, the nonlinear characteristics of softening and hardening types can coexist in the steady-state amplitude-frequency response. Moreover, the results show that the initial curvature can induce 1/2 super-harmonic resonance. Furthermore, it is also found that the nonlinear boundary has a significant influence on the nonlinear vibration of the curved structure. Therefore, the obtained results provide useful information for further studying the nonlinear vibrations of the curved beam with nonlinear and time-dependent boundary conditions.
Ye, X, Wang, S, Li, Q, Zhang, S & Sheng, D 2020, 'Negative Effect of Installation on Performance of a Compaction-Grouted Soil Nail in Poorly Graded Stockton Beach Sand', Journal of Geotechnical and Geoenvironmental Engineering, vol. 146, no. 8, pp. 04020061-04020061.
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© 2020 American Society of Civil Engineers. In this study, a latex membrane with a diameter of 50 mm and thickness of 0.5 mm is used to encase an injection hole. The gap between the membrane and the nail rod is fixed to achieve compaction grouting and to prevent fracturing and permeating; hence, a regular grout bulb is easily formed and locked into the soil matrix to provide a pullout force for a compaction-grouted soil nail. For this type of soil nail, two series of physical model tests for an embedded soil nail and a soil nail with a predrilled hole (the soil sample was moistened and could sustain the hole without collapsing during the placement of the nail rod) were conducted to study the influence of the installation methods on the performance of a compaction-grouted soil nail. The results of the two series of tests were compared, and some conclusions were drawn: First, the aforementioned installation methods for a soil nail had little impact on the mass of injected grout, whereas the shape of the cured grout bulb showed some differences based on the type of soil response. Second, compared with that of an embedded soil nail, the pullout force of a postplaced soil nail remarkably decreased because the hole drilled for installation led to a gap between the soil nail and the surrounding soil. In addition, the loss rate correlated with the grouting pressure (i.e., the diameter of the grout bulb). Third, because of the lower soil densification, dilation, and squeeze effect, a slower growth rate (with increasing grouting pressure) of the pullout force (i.e., resistance) was found for the postplaced soil nail relative to that of the embedded soil nail, during which the efficiency of the increasing pullout force decreased.
Ye, Y, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, X, Zhang, J & Liang, S 2020, 'Nutrient recovery from wastewater: From technology to economy', Bioresource Technology Reports, vol. 11, pp. 100425-100425.
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© 2020 Elsevier Ltd The recovery of fertilizer-used nutrients from wastewater is a sustainable approach for wastewater management and helping social sustainability. This is especially the case given the strict discharge requirements and shortages existing in nutrients supply. Recognizing that wastewater is a very useful resource and the value of recycled nutrients has made researchers consider the recovery of nutrients from wastewater. This review described the current technologies used to recover nutrients in wastewater treatment and their mechanisms, including chemical methods, biological technologies, membrane systems and advanced membrane systems. Also, an economic analysis of these nutrient recovery systems was discussed and compared them in terms of positive and negative aspects. The economic feasibility of recovered nutrients was investigated. Finally, future perspectives expects some possible research directions regarding recovery system which can be more economically accessible for wastewater treatment, in which the osmotic membrane bioreactors (OMBR) and bioelectrochemical systems (BES)-based hybrid systems are highly recommended.
Ye, Y, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, X, Zhang, S, Luo, G & Liu, Y 2020, 'Impacts of hydraulic retention time on a continuous flow mode dual-chamber microbial fuel cell for recovering nutrients from municipal wastewater', Science of The Total Environment, vol. 734, pp. 139220-139220.
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Yin, H, Wang, Y, Ding, X, Tang, L, Huang, S & Xiong, R 2020, '3D LiDAR-Based Global Localization Using Siamese Neural Network', IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 4, pp. 1380-1392.
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Yin, S, Wen, G, Ji, J & Xu, H 2020, 'Novel two-parameter dynamics of impact oscillators near degenerate grazing points', International Journal of Non-Linear Mechanics, vol. 120, pp. 103403-103403.
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© 2020 Elsevier Ltd Following the previous work on the degenerate grazing bifurcations of impact oscillators, this paper aims to explore novel two-parameter dynamics near the degenerate grazing points using GPU parallel computing technology. By using the technology, a further understanding of the near-grazing dynamics can be developed for impact oscillators. Three main indicators, i.e., the largest Lyapunov exponent, number of excitation periods and number of impacts, are calculated for each grid of the two-parameter plane chosen. Based on these indicators, the dynamic response in the vicinity of degenerate grazing points can be characterized and more dynamic behaviors than the published results can be discovered. Phenomena of coexisting attractors and chaotic transitions including crisis are also discussed. The single and two degree-of-freedom impact oscillators are selected as illustrative examples to demonstrate the results.
Yin, W, Liu, L & Rui, X 2020, 'Analysis, Modeling and Control of a Hybrid Drive Wind Turbine With Hydrogen Energy Storage System', IEEE Access, vol. 8, pp. 114795-114806.
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Hybrid drive wind power generation system, equipped with the speed regulating differential mechanism (SRDM), is able to be friendly connected to power grid without the need of partly- or fully-rated converters. The novel transmission schemes can promote not only the output power quality but also the low voltage ride through (LVRT) capability of the existing wind turbines (WTs). For the purpose of further improving the grid-connected operating performances of SRDM-based WT, this paper aims to develop a hybrid power production unit, in which the hydrogen storage system (HSS), comprising an electrolyzer, a hydrogen fuel cell and a supercapacitor, is integrated into SRDM-based WT. The basic architecture and numerical modelling methods of key subsystems in SRDM-based WT as well as in HSS are analyzed. After determining the eight different operating modes, a power supervision approach is synthesized for the proposed SRDM-based WT with HSS, by which the power flow management between energy sources and storage elements can be realized. Case studies are carried out in the presence of different randomly varying wind speeds and grid voltage faults. The satisfactory operating performances of the proposed wind-hydrogen hybrid system in terms of maximizing wind energy utilization, suppressing output power fluctuation and improving system continuous operating stability are verified.
Ying, J, Han, Z, Shen, L & Li, W 2020, 'Influence of Parent Concrete Properties on Compressive Strength and Chloride Diffusion Coefficient of Concrete with Strengthened Recycled Aggregates', Materials, vol. 13, no. 20, pp. 4631-4631.
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Parent concrete coming from a wide range of sources can result in considerable differences in the properties of recycled coarse aggregate (RCA). In this study, the RCAs were obtained by crushing the parent concrete with water-to-cement ratios (W/Cparent) of 0.4, 0.5 and 0.6, respectively, and were strengthened by carbonation and nano-silica slurry wrapping methods. It was found that when W/Cparen was 0.3, 0.4 and 0.5, respectively, compared with the mortar in the untreated RCA, the capillary porosity of the mortar in the carbonated RCA decreased by 19%, 16% and 30%, respectively; the compressive strength of concrete containing the carbonated RCA increased by 13%, 11% and 13%, respectively; the chloride diffusion coefficient of RAC (DRAC) containing the nano-SiO2 slurry-treated RCA decreased by 17%, 16% and 11%; and that of RAC containing the carbonated RCA decreased by 21%, 25% and 26%, respectively. Regardless of being strengthened or not, both DRAC and porosity of old mortar in RCAs increased with increasing W/Cparent. For different types of RCAs, DRAC increased obviously with increasing water absorption of RCA. Finally, a theoretical model of DRAC considering the water absorption of RCA was established and verified by experiments, which can be used to predict the DRAC under the influence of different factors, especially the water absorption of RCA.
Ying, X, Wang, Y, Li, W, Liu, Z & Ding, G 2020, 'Group Layout Pattern and Outdoor Wind Environment of Enclosed Office Buildings in Hangzhou', Energies, vol. 13, no. 2, pp. 406-406.
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This paper presents a study of the effects of wind-induced airflow through the urban built layout pattern using statistical analysis. This study investigates the association between typically enclosed office building layout patterns and the wind environment. First of all, this study establishes an ideal site model of 200 m × 200 m and obtains four typical multi-story enclosed office building group layouts, namely the multi-yard parallel opening, the multi-yard returning shape opening, the overall courtyard parallel opening, and the overall courtyard returning shape opening. Then, the natural ventilation performance of different building morphologies is further evaluated via the computational fluid dynamics (CFD) simulation software Phoenics. This study compares wind speed distribution at an outdoor pedestrian height (1.5 m). Finally, the natural ventilation performance corresponding to the four layout forms is obtained, which showed that the outdoor wind environment of the multi-yard type is more comfortable than the overall courtyard type, and the degree of enclosure of the building group is related to the advantages and disadvantages of the outdoor wind environment. The quantitative relevance between building layout and wind environment is examined, according to which the results of an ameliorated layout proposal are presented and assessed by Phoenics. This research could provide a method to create a livable urban wind environment.
Yoo, C, Lensgraf, S, Fitch, R, Clemon, LM & Mettu, RR 2020, 'Toward Optimal FDM Toolpath Planning with Monte Carlo Tree Search.', CoRR, vol. abs/2002.01631, pp. 4037-4043.
Youssry, A, Paz-Silva, GA & Ferrie, C 2020, 'Beyond Quantum Noise Spectroscopy: modelling and mitigating noise with quantum feature engineering', npj Quantum Inf, vol. 6, p. 95.
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The ability to use quantum technology to achieve useful tasks, be theyscientific or industry related, boils down to precise quantum control. Ingeneral it is difficult to assess a proposed solution due to the difficultiesin characterising the quantum system or device. These arise because of theimpossibility to characterise certain components in situ, and are exacerbatedby noise induced by the environment and active controls. Here we present ageneral purpose characterisation and control solution making use of a noveldeep learning framework composed of quantum features. We provide the framework,sample data sets, trained models, and their performance metrics. In addition,we demonstrate how the trained model can be used to extract conventionalindicators, such as noise power spectra.
Yu, G, Wang, X, Yu, K, Ni, W, Zhang, JA & Liu, RP 2020, 'Survey: Sharding in Blockchains', IEEE Access, vol. 8, pp. 14155-14181.
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© 2013 IEEE. The Blockchain technology, featured with its decentralized tamper-resistance based on a Peer-to-Peer network, has been widely applied in financial applications, and even further been extended to industrial applications. However, the weak scalability of traditional Blockchain technology severely affects the wide adoption due to the well-known trillema of decentralization-security-scalability in Blockchains. In regards to this issue, a number of solutions have been proposed, targeting to boost the scalability while preserving the decentralization and security. They range from modifying the on-chain data structure and consensus algorithms to adding the off-chain technologies. Therein, one of the most practical methods to achieve horizontal scalability along with the increasing network size is sharding, by partitioning network into multiple shards so that the overhead of duplicating communication, storage, and computation in each full node can be avoided. This paper presents a survey focusing on sharding in Blockchains in a systematic and comprehensive way. We provide detailed comparison and quantitative evaluation of major sharding mechanisms, along with our insights analyzing the features and restrictions of the existing solutions. We also provide theoretical upper-bound of the throughput for each considered sharding mechanism. The remaining challenges and future research directions are also reviewed.
Yu, G, Zha, X, Wang, X, Ni, W, Yu, K, Yu, P, Zhang, JA, Liu, RP & Guo, YJ 2020, 'Enabling Attribute Revocation for Fine-Grained Access Control in Blockchain-IoT Systems', IEEE Transactions on Engineering Management, vol. 67, no. 4, pp. 1213-1230.
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© 1988-2012 IEEE. The attribute-based encryption (ABE) has drawn a lot of attention for fine-grained access control in blockchains, especially in blockchain-enabled tampering-resistant Internet-of-Things (IoT) systems. However, its adoption has been severely hindered by the incompatibility between the immutability of typical blockchains and the attribute updates/revocations of ABE. In this article, we propose a new blockchain-based IoT system, which is compatible with the ABE technique, and fine-grained access control is implemented with the attribute update enabled by integrating Chameleon Hash algorithms into the blockchains. We design and implement a new verification scheme over a multilayer blockchain architecture to guarantee the tamper resistance against malicious and abusive tampering. The system can provide an update-oriented access control, where historical on-chain data can only be accessible to new members and inaccessible to the revoked members. This is distinctively different from existing solutions, which are threatened by data leakage toward the revoked members. We also provide analysis and simulations showing that our system outperforms other solutions in terms of overhead, searching complexity, security, and compatibility.
Yu, G, Zha, X, Wang, X, Ni, W, Yu, K, Zhang, JA & Liu, RP 2020, 'A Unified Analytical model for proof-of-X schemes', Computers & Security, vol. 96, pp. 101934-101934.
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© 2020 Nakamoto protocol, practically solving the Byzantine Generals Problem, can support a variety of proof-based consensus engines, referred to as Proof-of-X (PoX) in permissionless Blockchains. However, there has been to date in lack of a general approach for each miner to evaluate its steady-state profit against the competitors. This paper presents a Markov model which captures explicitly the weighted resource distribution of PoX schemes in large-scale networks and unifies the analysis of different PoX schemes. The new model leads to the development of three new unified metrics for the evaluation, namely, Resource Sensitivity, System Convergence, and Resource Fairness, accounting for security, stability, and fairness, respectively. The generality and applicability of our model are validated by simulation results, revealing that among typically non-Fairness-oriented PoX schemes (such as Proof-of-Work (PoW) and Proof-of-Stake (PoS)), the strongly restricted coinage-based PoS with a Pareto-distributed resource can offer the best performance on Resource Sensitivity, while Proof-of-Publication (PoP) with normal-distributed resource performs the best on System Convergence. Our simulations also reveal the important role of carefully designed Resource Fairness parameter in balancing Resource Sensitivity and System Convergence and improving the performance compared with other non-Fairness-oriented PoX schemes.
Yu, H, Lu, J & Zhang, G 2020, 'An Online Robust Support Vector Regression for Data Streams', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-1.
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Yu, H, Lu, J & Zhang, G 2020, 'Online Topology Learning by a Gaussian Membership-Based Self-Organizing Incremental Neural Network', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 10, pp. 3947-3961.
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Yu, H, Lu, W, Han, Y, Liu, D & Zhang, M 2020, 'Heterogeneous Dimensionality Reduction for Efficient Motion Planning in High-Dimensional Spaces', IEEE Access, vol. 8, pp. 42619-42632.
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© 2013 IEEE. Increasing the dimensionality of the configuration space quickly makes trajectory planning computationally intractable. This paper presents an efficient motion planning approach that exploits the heterogeneous low-dimensional structures of a given planning problem. These heterogeneous structures are obtained via a Dirichlet process (DP) mixture model and together cover the entire configuration space, resulting in more dimensionality reduction than single-structure approaches from the existing literature. Then, a unified low-dimensional trajectory optimization problem is formulated based on the obtained heterogeneous structures and a proposed transversality condition which is further solved via SQP in our implementation. The positive results demonstrate the feasibility and efficiency of our trajectory planning approach on an autonomous underwater vehicle (AUV) and a high-dimensional intervention autonomous underwater vehicle (I-AUV) in cluttered 3D environments.
Yu, H, Tuan, HD, Duong, TQ, Fang, Y & Hanzo, L 2020, 'Improper Gaussian Signaling for Integrated Data and Energy Networking', IEEE Transactions on Communications, vol. 68, no. 6, pp. 3922-3934.
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© 1972-2012 IEEE. The paper considers the problem of beamforming design for a multi-cell network of downlink users, who either harvest energy or decode information or do both by receiving signals from the multi-antenna base station (BS) within a time slot and over the same frequency band. Our previous contributions have showed that the time-fraction based energy and information transmission, under which first the energy is transferred within the initial fraction of time and then the information is transferred within the remaining fraction, is the most efficient design alternative both in terms of its practical implementation and network performance. However, at the time of writing, both energy and information beamforming has only been implemented for proper Gaussian signaling (PGS), which has limited the network's throughput. Although the network throughput could be improved in some specific scenarios by using non-orthogonal multi-access (NOMA), this may compromise the user secrecy. In order to circumvent the above implementations, we conceive improper Gaussian signaling (IGS) for information beamforming, which enables the network to substantially improve its throughput in any scenario without jeopardizing the user secrecy despite its low-complexity signal processing at the user end. A simpler subclass of IGS is also considered, which also outperforms NOMA PGS and works under any arbitrary scenario.
Yu, H, Tuan, HD, Duong, TQ, Poor, HV & Fang, Y 2020, 'Optimization for Signal Transmission and Reception in a Macrocell of Heterogeneous Uplinks and Downlinks', IEEE Transactions on Communications, vol. 68, no. 11, pp. 7054-7067.
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© 1972-2012 IEEE. Internet-of-things (IoT) applications continue to drive advancements in serving as many heterogeneous low-latency downlinks and uplinks as possible within a constrained communication bandwidth. Full-duplexing (FD) transceivers have been introduced to implement simultaneous signal transmission and reception (STR) over the entire available frequency band. However, both inter-link interference and FD loop-interference are hardly suppressed to a necessary level for the effectiveness of FD-based STR even for microcells. This paper proposes an alternative STR technique per one time-slot for macrocells, where a fraction of a time-slot is used for downlinks and the remaining complementary fraction of the time-slot is used for uplinks. Thus, STR over the entire available bandwidth can be implemented in a way with no loop interference. Furthermore, another approach of using a fraction of the available bandwidth for downlinks and the remaining complementary fraction of the bandwidth for uplinks over the whole time-slot is also proposed. The problem of both downlink and uplink beamforming to maximize the energy efficiency of such heterogeneous networks subject to the quality-of-service in terms of downlink and uplink throughput is examined for all three possible STRs. Numerical results demonstrate the advantages of the time-fraction-wise STR and bandwidth-fraction-wise STR over the FD-based STR, where the time-fraction-wise STR is not only the best in serving the same numbers of downlinks and uplinks but also is capable of serving many more downlinks and uplinks with a higher energy efficiency.
Yu, H, Tuan, HD, Nasir, AA, Duong, TQ & Hanzo, L 2020, 'Improper Gaussian Signaling for Computationally Tractable Energy and Information Beamforming', IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 13990-13995.
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© 1967-2012 IEEE. The transmit time-switching (transmit-TS) approach, under which the energy and information are transferred over different fractions of a time slot has proved its supremacy over the power splitting (PS) approach of simultaneous wireless information and power transfer, where PS splits the power of the received signal for energy harvesting and information decoding. For integrating data and energy transfer, this paper develops new classes of beamforming that are suitable for improper Gaussian signaling which is capable of network throughput improvements while maintaining high computational efficiency in its design.
Yu, H, Tuan, HD, Nasir, AA, Duong, TQ & Poor, HV 2020, 'Joint Design of Reconfigurable Intelligent Surfaces and Transmit Beamforming Under Proper and Improper Gaussian Signaling', IEEE Journal on Selected Areas in Communications, vol. 38, no. 11, pp. 2589-2603.
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© 1983-2012 IEEE. This paper considers a network consisting of a multiple antenna array access point serving multiple single antenna downlink users with the assistance of a reconfigurable intelligent surface (RIS). The reflecting coefficients of the RIS can be programmed to ensure that the signals reflected from the RIS elements add coherently at the users. The joint design of these programmable reflecting coefficients and transmit beamforming to maximize the users' worst rate is addressed. Under either proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS), the design poses a very computationally challenging nonconvex problem. Based on their exactly penalized optimization reformulation, which incorporates the computationally intractable unit-modulus constraints on the reflecting coefficients into the optimization objectives, new iterative algorithms of low computational complexity, which converge at least to a locally optimal solution, are developed. The provided simulations show not only the benefit of using the RIS, but also the advantage of IGS over PGS in delivering higher rates to users.
Yu, H, Ye, L, Guo, Y & Su, S 2020, 'An Innovative 9-Parameter Magnetic Calibration Method Using Local Magnetic Inclination and Calibrated Acceleration Value', IEEE Sensors Journal, vol. 20, no. 19, pp. 11275-11282.
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© 2001-2012 IEEE. In this paper, an innovative algorithm for Tri-Axial Magnetometers calibration based on the magnetic inclination is proposed. The proposed 'Inclination based Calibration method' uses the fact that the angle between the local gravity and magnetic field is invariant hence overcoming the limitations of most existing in-field calibration methods which require nonlinear optimization. This calibration algorithm is formulated as the solution to a linear least square problem. A commonly used 9-parameter model and its associated 12-observation Icosahedron experimental scheme were developed to evaluate its applicability to calibrated Tri-Axial Magnetometers based on measured acceleration and magnetic data. The results show that the algorithm can provide effective calibration results for the magnetic field in both simulation and experiments. In addition, the influence of accelerometers data applied in this algorithm is investigated by simulation and experiment to demonstrate the importance of accelerometers data accuracy. The acceleration value after effective calibration is demonstrated to make an improvement in the estimation results.
Yu, H, Zhang, T & Jia, W 2020, 'Shared subspace least squares multi-label linear discriminant analysis', Applied Intelligence, vol. 50, no. 3, pp. 939-950.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Multi-label linear discriminant analysis (MLDA) has been explored for multi-label dimension reduction. However, MLDA involves dense matrices eigen-decomposition which is known to be computationally expensive for large-scale problems. In this paper, we show that the formulation of MLDA can be equivalently casted as a least squares problem so as to significantly reduce the computation burden and scale to the data collections with higher dimension. Further, it is also found that appealing regularization techniques can be incorporated into the least-squares model to boost generalization accuracy. Experimental results on several popular multi-label benchmarks not only verify the established equivalence relationship, but also demonstrate the effectiveness and efficiency of our proposed algorithms.
Yu, H, Zhang, Y, Ye, L, Alqudah, HM, Guo, K, Argha, A, Celler, BG, Song, R & Su, S 2020, 'Nonparametric Model Prediction for Intelligent Regulation of Human Cardiorespiratory System to Prescribed Exercise Medicine', IEEE Access, vol. 8, pp. 224621-224630.
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Yu, J, Ji, J, Miao, Z & Zhou, J 2020, 'Region-based flocking control for networked robotic systems with communication delays', European Journal of Control, vol. 52, pp. 78-86.
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© 2019 European Control Association Based on a self-tuning adaptive control gain technique, this paper proposes a novel adaptive controller to implement the region-based flocking control for the networked robotic systems with communication delays. It is shown that under the proposed control strategy, all the robots can always reach into the objective region, realize velocity matching and ensure collision avoidance, if the network topology graph is connected under certain initial position conditions. Some simulation results are provided to illustrate the effectiveness and robustness of the proposed novel controller.
Yu, JW, Zhang, XH, Ji, JC, Tian, JY & Zhou, J 2020, 'Region-Reaching Control of a Flexible-Joint Manipulator', Journal of Dynamic Systems, Measurement, and Control, vol. 142, no. 11.
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Abstract This paper addresses the region-reaching control problem for a flexible-joint robotic manipulator which is formulated by Lagrangian dynamics. An adaptive control scheme is proposed for the manipulator system having two constrained regions which are constructed by selecting appropriate objective functions. The two joints of the flexible-joint manipulator can be, respectively, confined in different regions, and this gives more flexibility than the traditional fixed-point tracking control. By performing a straightforward Lyapunov stability analysis, a simple control algorithm is established to provide a solution for the region-reaching control problem. Finally, numerical simulations are given to validate the theoretical results.
Yu, KL, Chen, W-H, Sheen, H-K, Chang, J-S, Lin, C-S, Ong, HC, Show, PL & Ling, TC 2020, 'Bioethanol production from acid pretreated microalgal hydrolysate using microwave-assisted heating wet torrefaction', Fuel, vol. 279, pp. 118435-118435.
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© 2020 Elsevier Ltd This study focused on the bioethanol production from the co-production of solid biochar and liquid hydrolysate under microwave-assisted heating wet torrefaction towards a sustainable green technology. The two indigenous microalgal biomass undergone dilute acid pretreatment using wet torrefaction to produce microalgal hydrolysates and biochar at operating conditions of 160–170 °C with holding times of 5–10 min. The hydrolysates were utilized for fermentation with the yeast Saccharomyces cerevisiae at 29 °C in a dark condition at a non-agitation state for 120 h. The concentrations of total reducing sugar, reducing sugar by-product, and bioethanol in the hydrolysates were determined. The carbohydrate-rich microalga C. vulgaris ESP-31 showed a good performance in bioethanol production. Microalgal hydrolysate obtained after the pretreatment consisted of a total reducing sugar with the highest concentration of 98.11 g/L. The formation of by-product 5-hydroxymethyl-2-furaldehyde (5-HMF), which might act as the fermentation inhibitor that led to the low ethanol yield, was also analyzed. The highest ethanol yield achieved was 7.61% with a maximum experimental conversion probability of 95.22%. This study has demonstrated the feasible bioethanol production from microalgal hydrolysate through microwave-assisted heating wet torrefaction using dilute acids and the optimization of bioethanol production can be carried out for better performance in the future study.
Yu, N 2020, 'Multipartite Entanglement Certification, With or Without Tomography', IEEE Transactions on Information Theory, vol. 66, no. 10, pp. 6369-6377.
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© 1963-2012 IEEE. Certifying multipartite entanglement is a fundamental task. Since n-qubit state is parameterized by 4n-1 real numbers, it is interesting to design a measurement setup that detects multipartite entanglement with as little effort as possible, and at a minimum without fully revealing the whole information of the state, the so-called 'tomography'. In this paper, we study the relationship between multipartite entanglement certification and tomography, with the constraint that only single-copy measurements are allowed. We show that by using nonadaptive single-copy measurements, universal entanglement detection, among all states, can not be accomplished without full state tomography. Moreover, we show that almost all multipartite correlations, including the genuine entanglement and the entanglement depth, require full state tomography to detect in this measurement setting. We also observe that universal entanglement detection, among pure states, can be accomplished using much fewer measurements than full state tomography even using only local measurements.
Yu, X, Porikli, F, Fernando, B & Hartley, R 2020, 'Hallucinating Unaligned Face Images by Multiscale Transformative Discriminative Networks', International Journal of Computer Vision, vol. 128, no. 2, pp. 500-526.
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Yu, X, Shiri, F, Ghanem, B & Porikli, F 2020, 'Can We See More? Joint Frontalization and Hallucination of Unaligned Tiny Faces', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 9, pp. 2148-2164.
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Yu, Y, Gao, W, Castel, A, Liu, A, Chen, X & Liu, M 2020, 'Assessing external sulfate attack on thin-shell artificial reef structures under uncertainty', Ocean Engineering, vol. 207, pp. 107397-107397.
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© 2020 Elsevier Ltd Thin-shell artificial reef (AR) structures with spatial internal volumes have demonstrated superior stock recruitment ability and material efficiency than many gravity-based reef blocks, and cementitious materials, given the easy-to-tailor nature, remains the most popular in reef constructions to date. However, under constant seawater immersion, external sulfate attack (ESA) introduces a major and uncertain reliability concern to this type of AR system, due to the inherent material randomness. This study is concerned with developing a novel stochastic modelling framework for assessing the ESA under material uncertainty. In this paper, the main difficulty associated with the stochastic ESA modelling is identified for the first time, and a novel machine learning aided chemophysical modelling approach is proposed. The performance of the developed framework is carefully examined through the analyses on two types of cementitious materials under ESA.
Yu, Y, Royel, S, Li, Y, Li, J, Yousefi, AM, Gu, X, Li, S & Li, H 2020, 'Dynamic modelling and control of shear-mode rotational MR damper for mitigating hazard vibration of building structures', Smart Materials and Structures, vol. 29, no. 11, pp. 114006-114006.
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Abstract Magneto-rheological (MR) materials and their devices are being rapidly developed and have drawn surge of interest for the potential application in vibration control. Among them, a novel shear-mode rotational MR damper (SM-RMRD) with adaptive variable stiffness and damping was developed for adaptive structural control in real-time against different types of earthquakes. To make use of this innovative device perfectly, a robust and reliable model should be developed to simulate the nonlinear and hysteretic behaviours for the application in adaptive control. Accordingly, this research initially presents a new phenomenological model to describe the force response of the SM-RMRD. Then, model parameters are estimated based on experimental data of force, displacement and velocity, which were directly or indirectly obtained from the device under different loading protocols. The field dependence of each model parameter is also investigated so that a general model with current-related parameters is acquired for designing the control strategy. Using the current-dependent model of SM-RMRD, a semi-active controller is developed and implemented to the SM-RMRD to produce the feedback control for the structures in real-time. Finally, the effectiveness of proposed control method is appraised by a numerical study, in which an SM-RMRDs-incorporated three-storey building model with different control strategies are subjected to various scaled benchmark earthquakes. The comparison result verifies the excellent capacity of the proposed controller based on the developed phenomenological model in terms of reducing the storey acceleration and inter-storey drift.
Yu, Y, Subhani, M, Hoshyar, AN, Li, J & Li, H 2020, 'Automated Health Condition Diagnosis of in situ Wood Utility Poles Using an Intelligent Non-Destructive Evaluation (NDE) Framework', International Journal of Structural Stability and Dynamics, vol. 20, no. 10, pp. 2042002-2042002.
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Wood utility poles are widely applied in power transmission and telecommunication systems in Australia. Because of a variety of external influence factors, such as fungi, termite and environmental conditions, failure of poles due to the wood degradation with time is of common occurrence with high degree uncertainty. The pole failure may result in serious consequences including both economic and public safety. Therefore, accurately and timely identifying the health condition of the utility poles is of great significance for economic and safe operation of electricity and communication networks. In this paper, a novel non-destructive evaluation (NDE) framework with advanced signal processing and artificial intelligence (AI) techniques is developed to diagnose the condition of utility pole in field. To begin with, the guided waves (GWs) generated within the pole is measured using multi-sensing technique, avoiding difficult interpretation of various wave modes which cannot be detected by only one sensor. Then, empirical mode decomposition (EMD) and principal component analysis (PCA) are employed to extract and select damage-sensitive features from the captured GW signals. Additionally, the up-to-date machine learning (ML) techniques are adopted to diagnose the health condition of the pole based on selected signal patterns. Eventually, the performance of the developed NDE framework is evaluated using the field testing data from 15 new and 24 decommissioned utility poles at the pole yard in Sydney.
Yu, Y, Wu, D & Gao, W 2020, 'Stochastic chemo-physical-mechanical degradation analysis on hydrated cement under acidic environments', Applied Mathematical Modelling, vol. 78, pp. 75-97.
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© 2019 Elsevier Inc. The accumulation of material degradation under contact with aggressive aqueous environments could lead to reduced structural reliability. In terms of hydrated cementitious materials, such interactions often result in the chemo-physical-mechanical (CPM) degradation, which represents a multiphysics process of high non-linearity and complexity. By further considering the inevitable uncertainties associated with both the materials and the serving conditions, solving such a process requires novel probabilistic approaches. This paper presents a stochastic chemo-physical-mechanical (SCPM) degradation analysis on the hydrated cement under acidic environment. The SCPM analysis consists of modelling the stochastic chemophysical degradation by finite element method, and assessing the mechanical deterioration through analytical micromechanics. The proposed modelling framework couples the conventional Monte Carlo Simulation with a novel support vector regression algorithm. The present method is able to not only address the detailed degradation mechanisms, but also ensure low computational costs for an accurate SCPM degradation assessment.
Yuan, B, Zhao, H, Lin, C, Zou, D, Yang, LT, Jin, H, He, L & Yu, S 2020, 'Minimizing Financial Cost of DDoS Attack Defense in Clouds With Fine-Grained Resource Management', IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 2541-2554.
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As the cloud systems gain in popularity, they suffer from cyber attacks. One of the most notorious cyber attacks is Distributed Denial of Service (DDoS) attack, which aims to drain the system resources so that the system becomes unresponsive to the genuine users. DDoS attack and defense essentially revolve around resource competition. Many efforts have been made from the perspective of resource investment and management. However, these defending schemes assume that the resources available to defend the attacks are unlimited without taking the financial cost into account. Such coarse-grained defense strategies could cause the problem of resource overprovisioning, which would incur unwanted extra costs to the defender. To tackle this issue, we systematically investigate the problem and propose a birth-death-based fine-grained resource management mechanism, which can both scale in/out and scale down/up. That is, the proposed mechanism adaptively selects the optimal resource leasing mode for cloud service customers so that they can defeat the DDoS attack with minimal financial cost. Extensive analyses and empirical data-based experiments are conducted. The results show both the effectiveness and efficiency of the proposed approach. Comparing to existing work, our proposal can averagely save 53.58% (up to 93.75%) of the cost for the attack defense.
Yuan, B, Zou, D, Jin, H, Yu, S & Yang, LT 2020, 'HostWatcher: Protecting hosts in cloud data centers through software-defined networking', Future Generation Computer Systems, vol. 105, pp. 964-972.
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© 2017 Elsevier B.V. Cloud has become a dominant computing platform, and cloud data centers have been widely deployed all over the world. Naturally, cloud data centers become the targets of cyber attacks due to the feature of publicity. In addition, the price of renting resources from cloud constantly gets cheaper and cheaper. Therefore, attackers can rent hosts from cloud data centers to initiate attacks with rather low cost. As a result, hosts in a cloud center could be either victims or attackers. However, most existing researches only treat the hosts as the targets or the sources of attacks, either protecting the hosts from being attacked or identifying the malicious hosts, which is insufficient to protect the cloud data centers comprehensively. In this paper, we hire the novel techniques of SDN to protect the cloud data centers in both directions. Aiming at mitigating DDoS attacks, we propose HostWatcher, a system that watches and protects every host in cloud data center. HostWatcher leverages the advantages of SDN techniques and distributed processing. Caching and round-robin-resending scheme is introduced to the proposed system. Our goal is to protect the hosts comprehensively with QoS guarantee. The extensive experiments show that HostWatcher can effectively mitigate the DDoS attacks that target the hosts. Meanwhile, HostWatcher can also significantly limit the packet rate of hosts that are controlled by attackers. Also, the comprehensive evaluations show that the overheads of our system are trivial, and that our system is practical to implement and deploy in the cloud data centers.
Yuan, C, Tao, X, Ni, W, Li, N, Jamalipour, A & Liu, RP 2020, 'Joint Power Allocation and Beamforming for Overlaid Secrecy Transmissions in MIMO-OFDM Channels', IEEE Transactions on Vehicular Technology, vol. 69, no. 9, pp. 10019-10032.
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Yuan, J, Mostaan, A, Yang, Y, Siwakoti, YP & Blaabjerg, F 2020, 'A Modified Y-Source DC–DC Converter With High Voltage-Gains and Low Switch Stresses', IEEE Transactions on Power Electronics, vol. 35, no. 8, pp. 7716-7720.
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Yuan, W, Wu, N, Zhang, A, Huang, X, Li, Y & Hanzo, L 2020, 'Iterative Receiver Design for FTN Signaling Aided Sparse Code Multiple Access', IEEE Transactions on Wireless Communications, vol. 19, no. 2, pp. 915-928.
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© 2002-2012 IEEE. The sparse code multiple access (SCMA) is a promising candidate for bandwidth-efficient next generation wireless communications, since it can support more users than the number of resource elements. On the same note, faster-than-Nyquist (FTN) signaling can also be used to improve the spectral efficiency. Hence in this paper, we consider a combined uplink FTN-SCMA system in which the data symbols corresponding to a user are further packed using FTN signaling. As a result, a higher spectral efficiency is achieved at the cost of introducing intentional inter-symbol interference (ISI). To perform joint channel estimation and detection, we design a low complexity iterative receiver based on the factor graph framework. In addition, to reduce the signaling overhead and transmission latency of our SCMA system, we intrinsically amalgamate it with grant-free scheme. Consequently, the active and inactive users should be distinguished. To address this problem, we extend the aforementioned receiver and develop a new algorithm for jointly estimating the channel state information, detecting the user activity and for performs data detection. In order to further reduce the complexity, an energy minimization based approximation is employed for restricting the user state to Gaussian. Finally, a hybrid message passing algorithm is conceived. Our Simulation results show that the FTN-SCMA system relying on the proposed receiver design has a higher throughput than conventional SCMA scheme at a negligible performance loss.
Yuan, X, Chen, K, Zhao, W, Hu, S, Yu, F, Diao, X, Chen, X & Hu, S 2020, 'Open-label, single-centre, cluster-randomised controlled trial to Evaluate the Potential Impact of Computerisedantimicrobial stewardship (EPIC) on the antimicrobial use after cardiovascular surgeries: EPIC trial study original protocol', BMJ Open, vol. 10, no. 11, pp. e039717-e039717.
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IntroductionInappropriate antimicrobial use increases the prevalence of antimicrobial-resistant bacteria. Surgeons are reluctant to implement recommendations of guidelines in clinical practice. Antimicrobial stewardship (AMS) is effective in antimicrobial management, but it remains labour intensive. The computerised decision support system (CDSS) has been identified as an effective way to enable key elements of AMS in clinical settings. However, insufficient evidence is available to evaluate the efficacy of computerised AMS in surgical settings.Methods and analysisThe Evaluate of the Potential Impact of Computerised AMS trial is an open-label, single-centre, two-arm, cluster-randomised, controlled trial, which aims to determine whether a multicomponent CDSS intervention reduces overall antimicrobial use after cardiovascular surgeries compared with usual clinical care in a specialty hospital with a big volume of cardiovascular surgeries. Eighteen cardiovascular surgical teams will be randomised 1:1 to either the intervention or the control arm. The intervention will consist of (1) re-evaluation alerts and decision support for the duration of antimicrobial treatment decision, (2) re-evaluation alerts and decision support for the choice of antimicrobial, (3) quality control audit and feedback. The primary outcome will be the overall systemic antimicrobial use measured in days of therapy (DOT) per admission and DOT per 1000 patient-days over the whole intervention period (6 months). Secondary outcomes include a series of indices to evaluate antimicrobial use, microbial resistance, perioperative infection outcomes, patient safety, resource consumption, and user compliance and satisfaction.Ethics and disseminationThe Ethics Committee in Fuwai Hospital approved this study (2020-1329). The resul...
Yuan, X, Feng, Z, Ni, W, Liu, RP, Zhang, JA & Xu, W 2020, 'Secrecy Performance of Terrestrial Radio Links Under Collaborative Aerial Eavesdropping', IEEE Transactions on Information Forensics and Security, vol. 15, pp. 604-619.
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© 2005-2012 IEEE. Motivated to understand the increasingly severe threat of unmanned aerial vehicles (UAVs) to the confidentiality of terrestrial radio links, this paper analyzes the ergodic and ϵ-outage secrecy capacities of the links in the presence of multiple cooperative aerial eavesdroppers flying autonomously in three-dimensional (3D) spaces and exploiting selection combining (SC) or maximal ratio combining (MRC). The 'cut-off' density of the eavesdroppers under which the secrecy capacities vanish is identified. By decoupling the analysis of the random trajectories from the random channel fading, closed-form approximations with almost sure convergence to the secrecy capacities are devised. The analysis is extended to study the impact of the oscillator phase noises and finite memories of the aerial eavesdroppers on the secrecy performance of the ground link. Validated by simulations, the cut-off density only depends on the range of the link in the case of SC eavesdropping, while it depends on the flight region of the eavesdroppers in the case of MRC eavesdropping.
Yuan, X, Feng, Z, Ni, W, Wei, Z, Liu, RP & Xu, C 2020, 'Connectivity of UAV Swarms in 3D Spherical Spaces Under (Un)Intentional Ground Interference', IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 8792-8804.
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© 1967-2012 IEEE. This paper analyzes the wireless connectivity of an Unmanned Aerial Vehicle (UAV) swarm in the presence of (un)intentional external interference from the ground. Different from existing studies, the swarm UAVs fly independently around a given three-dimensional (3D) location and are all within a 3D spherical space. Closed-form bounds are delivered for the average outage probability of a UAV from its nearest neighbor in the swarm, and the density of the swarm, which allows the swarm to operate uninterruptedly in the presence of the interference. Our analysis involves closed-form approximations of the instantaneous outage probability of a UAV from its nearest neighbor by using the first-order Marcum Q-function and the zero-Th order modified Bessel function of the first kind. The analysis also involves applying Jensen's inequality to the instantaneous outage probability to bound the average outage probability and the density of the swarm. Corroborated by simulations, our analysis is accurate, and useful to evaluate the impact of external interference on the connectivity of UAV swarms. Interesting insights are shed on the connectivity and coverage of the UAV swarm.
Yuan, X, Feng, Z, Zhang, JA, Ni, W, Liu, RP, Wei, Z & Xu, C 2020, 'Waveform Optimization for MIMO Joint Communication and Radio Sensing Systems with Training Overhead'.
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In this paper, we study optimal waveform design to maximize mutualinformation (MI) for a joint communication and (radio) sensing (JCAS, a.k.a.,radar-communication) multi-input multi-output (MIMO) downlink system. Weconsider a typical packet-based signal structure which includes training anddata symbols. We first derive the conditional MI for both sensing andcommunication under correlated channels by considering the training overheadand channel estimation error (CEE). Then, we derive a lower bound for thechannel estimation error and optimize the power allocation between the trainingand data symbols to minimize the CEE. Based on the optimal power allocation, weprovide optimal waveform design methods for three scenarios, includingmaximizing MI for communication only and for sensing only, and maximizing aweighted sum MI for both communication and sensing. We also present extensivesimulation results that provide insights on waveform design and validate theeffectiveness of the proposed designs.
Yuan, X, Li, B, Yang, Y, Wang, H, Sun, H, Song, Y & Wang, W 2020, 'Surgical results and pathological analysis of cardiac fibroma in the adolescent and the adult', Journal of Cardiac Surgery, vol. 35, no. 8, pp. 1912-1919.
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Yuntai, Y, Hua, Y, Nengxin, F, Yongbao, Z & Xin, Y 2020, 'Penehyclidine Hydrochloride Premedication Is Not Associated with Increased Incidence of Post-Operative Cognitive Dysfunction or Delirium:A Systemic Review and Meta-Analysis', Chinese Medical Sciences Journal, vol. 35, no. 2, pp. 121-134.
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Yuntai, Y, Xin, Y & Ken, S 2020, 'Acute Myocardial Infarction After Tranexamic Acid: Review of Published Case Reports', Chinese Medical Sciences Journal, vol. 35, no. 1, pp. 65-70.
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Zang, X, Xue, Y, Ni, W, Li, C, Hu, L, Zhang, A, Yang, Z & Yan, Y-M 2020, 'Enhanced Electrosorption Ability of Carbon Nanocages as an Advanced Electrode Material for Capacitive Deionization', ACS Applied Materials & Interfaces, vol. 12, no. 2, pp. 2180-2190.
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Zang, Y, Yang, Y, Hu, Y, Ngo, HH, Wang, XC & Li, Y-Y 2020, 'Zero-valent iron enhanced anaerobic digestion of pre-concentrated domestic wastewater for bioenergy recovery: Characteristics and mechanisms', Bioresource Technology, vol. 310, pp. 123441-123441.
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Pre-concentrated domestic wastewater (PDWW) rich in organic matters can be a suitable substrate for anaerobic digestion (AD) towards holistic resource and bioenergy recovery. Micron zero-valent iron (ZVI) was applied in designed batch experiments during anaerobic treatment of PDWW to verify its roles in performance enhancement and associated mechanisms. In the selected range of food to microorganism (F/M) ratio, 0.5 gCOD/gMLVSS was most appropriate as biomethane production potential (BMP) of 0.275 L CH4/gCOD was obtained. The optimal ZVI dosage at fixed F/M of 0.5 was 6 g/L, further enhancing the BMP by 15.2%. Furthermore, ZVI improved the hydrolysis process (producing more soluble organics) and regulated acidification process (affecting volatile fatty acids distribution). No obvious impact on acetoclastic and hydrogenotrophic methanogenesis processes was noted with ZVI addition. ZVI based AD of the PDWW is promising for promoting the practical application of advanced domestic wastewater treatment strategy (pre-concentration plus anaerobic digestion).
Zdańkowski, P, Trusiak, M, McGloin, D & Swedlow, JR 2020, 'Numerically Enhanced Stimulated Emission Depletion Microscopy with Adaptive Optics for Deep-Tissue Super-Resolved Imaging', ACS Nano, vol. 14, no. 1, pp. 394-405.
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Copyright © 2019 American Chemical Society. In stimulated emission depletion (STED) nanoscopy, the major origin of decreased signal-to-noise ratio within images can be attributed to sample photobleaching and strong optical aberrations. This is due to STED utilizing a high-power depletion laser (increasing the risk of photodamage), while the depletion beam is very sensitive to sample-induced aberrations. Here, we demonstrate a custom-built STED microscope with automated aberration correction that is capable of 3D super-resolution imaging through thick, highly aberrating tissue. We introduce and investigate a state of the art image denoising method by block-matching and collaborative 3D filtering (BM3D) to numerically enhance fine object details otherwise mixed with noise and further enhance the image quality. Numerical denoising provides an increase in the final effective resolution of the STED imaging of 31% using the well established Fourier ring correlation metric. Results achieved through the combination of aberration correction and tailored image processing are experimentally validated through super-resolved 3D imaging of axons in differentiated induced pluripotent stem cells growing under an 80 μm thick layer of tissue with lateral and axial resolution of 204 and 310 nm, respectively.
Zeng, J, Lv, T, Lin, Z, Liu, RP, Mei, J, Ni, W & Guo, YJ 2020, 'Achieving Ultrareliable and Low-Latency Communications in IoT by FD-SCMA', IEEE Internet of Things Journal, vol. 7, no. 1, pp. 363-378.
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To enable ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT), a sparse-code multiple-access (SCMA)-enhanced full-duplex (FD) scheme (FD-SCMA) is proposed in this article. FD-SCMA can support short-packet transmissions of several SCMA users in the uplink (UL) and downlink (DL) simultaneously by an FD next generation node B (gNB). First, the gNB and UL users can generate and superpose signals according to the preconfigured SCMA codebooks, and simultaneously transmit the signals via occupied subcarriers in a joint SCMA pattern. The receivers at the gNB and DL users can demodulate and decode the signals with multiuser detection (MUD). With the imperfect self-interference suppression (SIS) of FD considered, the effective signal-to-noise ratio (SNR) of FD-SCMA at the gNB and DL users is formulated. The error probability of FD-SCMA in the UL and DL is also derived under a given transmission latency constraint of short-packet transmissions. In the stationary flat-fading channel, it is proved that FD-SCMA can achieve better reliability than the existing FD and SCMA schemes. In the time-invariant frequency-selective fading channel, the upper bounds for error probability of the UL and DL users in FD-SCMA are derived, respectively. Through the theoretical calculation and Monte Carlo simulation, it is verified that the superiority of FD-SCMA in supporting ultrareliable and low-latency short-packet transmissions in IoT.
Zeng, J, Lv, T, Liu, RP, Su, X, Guo, YJ & Beaulieu, NC 2020, 'Enabling Ultrareliable and Low-Latency Communications Under Shadow Fading by Massive MU-MIMO', IEEE Internet of Things Journal, vol. 7, no. 1, pp. 234-246.
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© 2014 IEEE. It is challenging to satisfy the critical requirements of ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT) under severe channel fading. The emerging massive multiuser multiple-input-multiple-output (MU-MIMO) concept is applied in IoT networks under shadow fading, enabling URLLC with pilot-assisted channel estimation (PACE) and zero-forcing (ZF) detection. Assuming users are uniformly and randomly deployed under log-normal shadow fading, the probability density function (pdf) of postprocessing signal-to-noise ratios (SNRs) is derived for the uplink (UL) of massive MU-MIMO with perfect channel state information (CSI) and imperfect CSI obtained by PACE. Then, finite blocklength (FBL) information theory is utilized to derive the error probability of accessing users with a given latency, thereby evaluating the reliability of massive MU-MIMO for short-packet transmissions. Further, the length of pilots to minimize the error probability can be decided by the golden section search method (GSSM), which can converge rapidly. Numerical results verify that massive MU-MIMO can support a large number of UL URLLC users even when users are randomly deployed under shadow fading.
Zeweldi, HG, Bendoy, AP, Park, MJ, Shon, HK, Kim, H-S, Johnson, EM, Kim, H, Lee, S-P, Chung, W-J & Nisola, GM 2020, 'Tetrabutylammonium 2,4,6-trimethylbenzenesulfonate as an effective and regenerable thermo-responsive ionic liquid drawing agent in forward osmosis for seawater desalination', Desalination, vol. 495, pp. 114635-114635.
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© 2020 Elsevier B.V. Efficient drawing agents are essential in forward osmosis (FO) for clean water production. Monomeric thermo-responsive ionic liquid (IL) [N4444]2,4,6-MeBnSO3 was thoroughly investigated as a drawing agent in FO. The IL can be safely employed due to its thermal stability and low cytotoxicity. It has a van't Hoff factor i = 1.21, with sufficient ionic strength to generate osmotic pressure ~ 58.92 bars (2 M). FO operations especially under PRO mode demonstrate that 2 M [N4444]2,4,6-MeBnSO3 can induce competitive water flux Jv ~ 12.3 L m−2 h−1 with remarkably low reverse solute flux Js < 0.006 mol m−2 h−1 and specific reverse solute flux Js/Jv ~ 4.5 × 10−4 mol L−1. Using 0.6 M NaCl as feed demonstrates its consistency in desalinating seawater (Jv ~ 3.72 L m−2 h−1, Js ~ 0.004 mol m−2 h−1, and Js/Jv ~ 0.91 × 10−3 mol L−1). After FO, [N4444]2,4,6-MeBnSO3 can be effectively retrieved (~98%) through thermal precipitation at 60 °C, above its cloud point temperature (57 °C). Meanwhile, >99% of the remaining 2% can be recovered through reverse osmosis or membrane distillation to produce water effluents with non-toxic IL concentrations (≪100 mg L−1). Results indicate that thermo-responsive [N4444]2,4,6-MeBnSO3 is a promising alternative reusable drawing agent in FO process.
Zhan, Y, Guo, Y, Zhu, J, Li, L, Yang, B & Liang, B 2020, 'A review on mitigation technologies of low frequency current ripple injected into fuel cell and a case study', International Journal of Hydrogen Energy, vol. 45, no. 46, pp. 25167-25190.
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Zhand, S, Razmjou, A, Azadi, S, Bazaz, SR, Shrestha, J, Jahromi, MAF & Warkiani, ME 2020, 'Metal–Organic Framework-Enhanced ELISA Platform for Ultrasensitive Detection of PD-L1', ACS Applied Bio Materials, vol. 3, no. 7, pp. 4148-4158.
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© 2020 American Chemical Society. The programmed cell death ligand 1 (PD-L1) protein has emerged as a predictive cancer biomarker and sensitivity to immune checkpoint blockade-based cancer immunotherapies. Current technologies for the detection of protein-based biomarkers, including the enzyme-linked immunosorbent assay (ELISA), have limitations such as low sensitivity and limit of detection (LOD) in addition to degradation of antibodies in exposure to environmental changes such as temperature and pH. To address these issues, we have proposed a metal-organic framework (MOF)-based ELISA for the detection of the PD-L1. A protective coating based on Zeolitic Imidazolate Framework 8 (ZIF-8) MOF thin film and polydopamine-polyethylenimine (PDA-PEI) was introduced on an ELISA plate for the improvement of antibody immobilization. Sensitivity and LOD of the resulting platform were compared with a conventional ELISA kit, and the bioactivity of the antibody in the proposed immunoassay was investigated in response to various pH and temperature values. The LOD and sensitivity of the MOF-based PD-L1 ELISA were 225 and 15.12 times higher, respectively, compared with those of the commercial ELISA kit. The antibody@ZIF-8/PDA-PEI was stable up to 55 °C and the pH range 5-10. The proposed platform can provide sensitive detection for target proteins, in addition to being resistant to elevated temperature and pH. The proposed MOF-based ELISA has significant potential for the clinical and diagnostic studies.
Zhang, D, Yao, L, Chen, K, Wang, S, Chang, X & Liu, Y 2020, 'Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition', IEEE Transactions on Cybernetics, vol. 50, no. 7, pp. 3033-3044.
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Brain-computer interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG)-based BCI is one of the promising solutions due to its convenient and portable instruments. Despite the extensive research of EEG in recent years, it is still challenging to interpret EEG signals effectively due to its nature of noise and difficulties in capturing the inconspicuous relations between EEG signals and specific brain activities. Most existing works either only consider EEG as chain-like sequences while neglecting complex dependencies between adjacent signals or requiring complex preprocessing. In this paper, we introduce two deep learning-based frameworks with novel spatio-temporal preserving representations of raw EEG streams to precisely identify human intentions. The two frameworks consist of both convolutional and recurrent neural networks effectively exploring the preserved spatial and temporal information in either a cascade or a parallel manner. Extensive experiments on a large scale movement intention EEG dataset (108 subjects, 3 145 160 EEG records) have demonstrated that the proposed frameworks achieve high accuracy of 98.3% and outperform a set of state-of-the-art and baseline models. The developed models are further evaluated with a real-world brain typing BCI and achieve a recognition accuracy of 93% over five instruction intentions suggesting good generalization over different kinds of intentions and BCI systems.
Zhang, D, Yin, J, Zhu, X & Zhang, C 2020, 'Network Representation Learning: A Survey', IEEE Transactions on Big Data, vol. 6, no. 1, pp. 3-28.
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Zhang, F, Li, C, Zhang, Y, Qin, L & Zhang, W 2020, 'Finding Critical Users in Social Communities: The Collapsed Core and Truss Problems.', IEEE Trans. Knowl. Data Eng., vol. 32, no. 1, pp. 78-91.
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© 1989-2012 IEEE. In social networks, the leave of critical users may significantly break network engagement, i.e., lead a large number of other users to drop out. A popular model to measure social network engagement is kk-core, the maximal subgraph in which every vertex has at least kk neighbors. To identify critical users, we propose the collapsed kk-core problem: given a graph GG, a positive integer kk and a budget bb, we aim to find bb vertices in GG such that the deletion of the bb vertices leads to the smallest kk-core. We prove the problem is NP-hard and inapproximate. An efficient algorithm is proposed, which significantly reduces the number of candidate vertices. We also study the user leave towards the model of kk-truss which further considers tie strength by conducting additional computation w.r.t. kk-core. We prove the corresponding collapsed kk-truss problem is also NP-hard and inapproximate. An efficient algorithm is proposed to solve the problem. The advantages and disadvantages of the two proposed models are experimentally compared. Comprehensive experiments on nine real-life social networks demonstrate the effectiveness and efficiency of our proposed methods.
Zhang, G, Li, Y, Yu, Y, Wang, H & Wang, J 2020, 'Modeling the non-linear rheological behavior of magnetorheological gel using a computationally efficient model', Smart Materials and Structures, vol. 29, no. 10, pp. 105021-105021.
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Abstract Magnetorheological (MR) gel is a novel generation of smart MR material, which has the inherent hysteretic properties and strain stiffening behaviors that are dependent on applied excitation, i.e. magnetic field. The main challenge for the application of the MR gel is the accurate reproduction of the above characteristics by a computationally efficient model that can predict the dynamic stress-strain/rate responses. In this work, parametric modeling on the non-linear rheological behavior of MR gel is conducted. Firstly, a composite MR gel sample was developed by dispersing carbon iron particles into the polyurethane matrix. The dynamic stress-strain/rate responses of the MR gel are obtained using a commercial rheometer with strain-controlled mode under harmonic excitation with frequencies of 0.1 Hz, 5 Hz and 15 Hz and current levels of 1 A and 2 A at a fixed amplitude of 10%. Following a mini-review on the available mathematical models, the experimental data is utilized to fit into the models to find the best candidate utilizing a genetic algorithm. Then, a statistical analysis is conducted to evaluate the model’s performance. The non-symmetrical Bouc–Wen model outperforms all other models in reproducing the non-linear behavior of MR gel. Finally, the parameter sensitivity analysis is employed to simplify the non-symmetrical Bouc–Wen model and then the parameter generalization is conducted and verified for the modified non-symmetrical Bouc–Wen model.
Zhang, H & Xu, M 2020, 'Improving the generalization performance of deep networks by dual pattern learning with adversarial adaptation', Knowledge-Based Systems, vol. 200, pp. 106016-106016.
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Zhang, H, Huang, X, Zhang, JA & Guo, YJ 2020, 'Dual Pulse Shaping Transmission and Equalization for High-Speed Wideband Wireless Communication Systems', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 7, pp. 2372-2382.
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© 2004-2012 IEEE. Analog-to-digital and digital-to-analog conversion devices for signals with very large bandwidth are not always available due to technical or cost issues. This limits the realization of very high data-rate digital communication systems. In this paper, we propose a dual pulse shaping (DPS) transmission scheme, which can achieve full Nyquist rate transmission with only a half of the sampling rate for each of the two data streams. Two classes of ideal complementary Nyquist pulses are formulated assuming raised-cosine (RC) pulse shaping. The condition for cross-symbol interference (CSI) free transmission is derived and validated for the proposed pulses. Structures of the DPS transmitter and receiver are described and low-complexity equalization techniques tailored to DPS are proposed. With DPS, a millimeter wave system with commercially available and affordable data conversion devices is exemplified for achieving high-speed low-cost wireless communications. Simulation results with two sets of practical dual spectral shaping pulses are provided. The results verify the effectiveness of the proposed scheme, with comparison to the benchmark conventional Nyquist pulse shaping system.
Zhang, H, Liang, X, Gao, Z & Zhu, X 2020, 'Seismic performance analysis of a large-scale single-layer lattice dome with a hybrid three-directional seismic isolation system', Engineering Structures, vol. 214, pp. 110627-110627.
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© 2020 Elsevier Ltd A large number of studies and applications have been carried out on horizontal seismic isolation systems, and their effectiveness has been indicated. For long-span spatial structures, vertical seismic load plays an important role. However, vertical seismic isolation technology has not been extensively investigated. In this paper, a hybrid bearing with three-directional seismic isolation effects is proposed, in which the triple friction pendulum component and the viscous damping component are combined in series. Compared with other seismic isolation systems, the advantages of this hybrid seismic isolation system are that it can not only greatly lengthen the structural periods but also dissipate the seismic energy in all three directions. A hybrid numerical modeling method for this hybrid seismic isolation bearing is also developed. The seismic performance of a welded large-scale single-layer lattice dome with this hybrid seismic isolation system subjected to near-field ground motions is analyzed. The results show that the important dynamic demands in the dome are significantly suppressed compared with the base-fixed dome. The seismic isolation effects are evaluated in all three directions, and the effectiveness of the hybrid isolation system is verified. Finally, a comparative study is performed, and the mechanical parameters of this hybrid bearing are discussed. It is found that the damping energy dissipation in the seismic isolation bearings is not the most important factor in reducing structural dynamic demands. The proposed seismic isolation system and its numerical modeling method provide an attractive and effective alternative for the design of long-span spatial structures with hybrid seismic isolation systems.
Zhang, H, Zhu, X & Yao, S 2020, 'Nonlinear dynamic analysis method for large-scale single-layer lattice domes with uncertain-but-bounded parameters', Engineering Structures, vol. 203, pp. 109780-109780.
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© 2019 Elsevier Ltd Currently, nonlinear dynamic analysis for large-scale single-layer domes is commonly performed using deterministic numerical methods. However, in practical engineering cases, complex large-scale single-layer domes have many uncertain parameters that cannot be considered using deterministic methods. Therefore, there is a growing awareness that classical deterministic methods need to be extended towards the introduction of the uncertain aspects in dynamic analysis, and a non-deterministic analysis method for large-scale spatial structures is required. In this paper, a new method is presented by introducing uncertainties into the nonlinear dynamic analysis for large-scale single-layer lattice domes. The method accounts for uncertainties in material properties, structural imperfections, loads, and damping with bounds. The focus is on the treatment of uncertainty in damping and the adopted geometric shape, which is different from that of conventional approaches. Finite element dynamic analyses for sample structures with multiple sources of uncertainty subjected to dynamic loads are performed. Results show that the variability of the variables with an associated uncertainty imposes significant negative effects on the dynamic properties, dynamic demands, and safety of a dome. Uncertain damping in a structure plays the most important role in determining structural performance. The numerical results reveal the differences between conventional analysis methods with deterministic parameters used in previous practical applications and the uncertain analysis method. Finally, a parametric study is performed, and the impacts of sample size on statistical dynamic demands, single uncertain source on structural failure, and single uncertain source on damping coefficients are discussed.
Zhang, H-W, Kok, VC, Chuang, S-C, Tseng, C-H, Lin, C-T, Li, T-C, Sung, F-C, Wen, CP, Hsiung, CA & Hsu, CY 2020, 'Long-Term Exposure to Ambient Hydrocarbons Increases Dementia Risk in People Aged 50 Years and above in Taiwan', Current Alzheimer Research, vol. 16, no. 14, pp. 1276-1289.
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Background:Alzheimer’s disease, the most common cause of dementia among the elderly, isa progressive and irreversible neurodegenerative disease. Exposure to air pollutants is known to haveadverse effects on human health, however, little is known about hydrocarbons in the air that can triggera dementia event.Objective:We aimed to investigate whether long-term exposure to airborne hydrocarbons increases therisk of developing dementia.Method:The present cohort study included 178,085 people aged 50 years and older in Taiwan. Coxproportional hazards regression analysis was used to fit the multiple pollutant models for two targetedpollutants, including total hydrocarbons and non-methane hydrocarbons, and estimated the risk of dementia.Results:Before controlling for multiple pollutants, hazard ratios with 95% confidence intervals for theoverall population were 7.63 (7.28-7.99, p <0.001) at a 0.51-ppm increases in total hydrocarbons, and2.94 (2.82-3.05, p <0.001) at a 0.32-ppm increases in non-methane hydrocarbons. The highest adjustedhazard ratios for different multiple-pollutant models of each targeted pollutant were statistically significant(p <0.001) for all patients: 11.52 (10.86-12.24) for total hydrocarbons and 9.73 (9.18-10.32) fornon-methane hydrocarbons.Conclusion:Our findings suggest that total hydrocarbons and non-methane hydrocarbons may be contributingto dementia development.
Zhang, J, Zhang, H, Bo, L-L, Li, H-R, Xu, S & Yuan, D-Q 2020, 'Subspace transform induced robust similarity measure for facial images', Frontiers of Information Technology & Electronic Engineering, vol. 21, no. 9, pp. 1334-1345.
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Zhang, JA, Rahman, ML, Wu, K, Huang, X, Guo, YJ, Chen, S & Yuan, J 2020, 'Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey'.
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Mobile network is evolving from a communication-only network towards one withjoint communication and radar/radio sensing (JCAS) capabilities, that we callperceptive mobile network (PMN). In PMNs, JCAS integrates sensing intocommunications, sharing a majority of system modules and the same transmittedsignals. The PMN is expected to provide a ubiquitous radio sensing platform andenable a vast number of novel smart applications, whilst providingnon-compromised communications. In this paper, we present a broad picture ofthe motivation, methodologies, challenges, and research opportunities ofrealizing PMN, by providing a comprehensive survey for systems and technologiesdeveloped mainly in the last ten years. Beginning by reviewing the work oncoexisting communication and radar systems, we highlight their limits onaddressing the interference problem, and then introduce the JCAS technology. Wethen set up JCAS in the mobile network context and envisage its potentialapplications. We continue to provide a brief review of three types of JCASsystems, with particular attention to their differences in design philosophy.We then introduce a framework of PMN, including the system platform andinfrastructure, three types of sensing operations, and signals usable forsensing. Subsequently, we discuss required system modifications to enablesensing on current communication-only infrastructure. Within the context ofPMN, we review stimulating research problems and potential solutions, organizedunder nine topics: performance bounds, waveform optimization, antenna arraydesign, clutter suppression, sensing parameter estimation, resolution ofsensing ambiguity, pattern analysis, networked sensing under cellular topology,and sensing-assisted communications. We conclude the paper by listing key openresearch problems for the aforementioned topics and sharing some lessons thatwe have learned.
Zhang, K, Hsieh, M-H, Liu, L & Tao, D 2020, 'Quantum Gram-Schmidt Processes and Their Application to Efficient State Read-out for Quantum Algorithms', Physical Review Research, vol. 3, p. 04395.
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Many quantum algorithms that claim speed-up over their classical counterpartsonly generate quantum states as solutions instead of their final classicaldescription. The additional step to decode quantum states into classicalvectors normally will destroy the quantum advantage in most scenarios becauseall existing tomographic methods require runtime that is polynomial withrespect to the state dimension. In this work, we present an efficient read-outprotocol that yields the classical vector form of the generated state, so itwill achieve the end-to-end advantage for those quantum algorithms. Ourprotocol suits the case that the output state lies in the row space of theinput matrix, of rank $r$, that is stored in the quantum random access memory.The quantum resources for decoding the state in $\ell^2$ norm with $\epsilon$error require $\poly(r,1/\epsilon)$ copies of the output state and $\poly(r,\kappa^r,1/\epsilon)$ queries to the input oracles, where $\kappa$ is thecondition number of the input matrix. With our read-out protocol, we completelycharacterise the end-to-end resources for quantum linear equation solvers andquantum singular value decomposition. One of our technical tools is anefficient quantum algorithm for performing the Gram-Schmidt orthonormalprocedure, which we believe, will be of independent interest.
Zhang, K, Hsieh, M-H, Liu, L & Tao, D 2020, 'Toward Trainability of Quantum Neural Networks'.
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Quantum Neural Networks (QNNs) have been recently proposed as generalizationsof classical neural networks to achieve the quantum speed-up. Despite thepotential to outperform classical models, serious bottlenecks exist fortraining QNNs; namely, QNNs with random structures have poor trainability dueto the vanishing gradient with rate exponential to the input qubit number. Thevanishing gradient could seriously influence the applications of large-sizeQNNs. In this work, we provide a viable solution with theoretical guarantees.Specifically, we prove that QNNs with tree tensor and step controlledarchitectures have gradients that vanish at most polynomially with the qubitnumber. We numerically demonstrate QNNs with tree tensor and step controlledstructures for the application of binary classification. Simulations showfaster convergent rates and better accuracy compared to QNNs with randomstructures.
Zhang, L, Chang, X, Liu, J, Luo, M, Prakash, M & Hauptmann, AG 2020, 'Few-shot activity recognition with cross-modal memory network', Pattern Recognition, vol. 108, pp. 107348-107348.
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Deep learning based action recognition methods require large amount of labelled training data. However, labelling large-scale video data is time consuming and tedious. In this paper, we consider a more challenging few-shot action recognition problem where the training samples are few and rare. To solve this problem, memory network has been designed to use an external memory to remember the experience learned in training and then apply it to few-shot prediction during testing. However, existing memory-based methods just update the visual information with fixed label embeddings in the memory, which cannot adapt well to novel activities during testing. To alleviate the issue, we propose a novel end-to-end cross-modal memory network for few-shot activity recognition. Specifically, the proposed memory architecture stores the dynamic visual and textual semantics for some high-level attributes related to human activities. And the learned memory can provide effective multi-modal information for new activity recognition in the testing stage. Extensive experimental results on two video datasets, including HMDB51 and UCF101, indicate that our method could achieve significant improvements over other previous methods.
Zhang, L, Chen, Y, Ma, C, Liu, L, Pan, J, Li, B, Wu, X & Wang, Q 2020, 'Improving heavy metals removal, dewaterability and pathogen removal of waste activated sludge using enhanced chemical leaching', Journal of Cleaner Production, vol. 271, pp. 122512-122512.
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© 2020 In order to enhance heavy metals removal, two enhanced chemical leaching techniques were examined comparatively using NaClO and NaNO2 with the addition of FeCl3. The phosphorus release, dewaterability and pathogen removal of treated sludge were also examined after chemical leaching. The results showed that the heavy metals solubilization, improvement of dewaterability and pathogen removal were simultaneously achieved. Compared with NaClO treatment system, the better solubilization rates of Zn and Ni were observed in the NaNO2 treatment system. The improvement of Cu, Zn and Ni removal can be attributed to the disruption of the organically bound metal fraction based on metal distribution and EEM analysis. The TP loss caused by chemical leaching in this study was in the range of 47 %–54%. The treated sludge of the two systems could both meet Class A biosolids standards (US EPA) for land application. These results provided an alternative chemical leaching method for simultaneous improvement of sludge properties.
Zhang, L, Luo, M, Liu, J, Chang, X, Yang, Y & Hauptmann, AG 2020, 'Deep Top-$k$ Ranking for Image–Sentence Matching', IEEE Transactions on Multimedia, vol. 22, no. 3, pp. 775-785.
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© 1999-2012 IEEE. Image-sentence matching is a challenging task for the heterogeneity-gap between different modalities. Ranking-based methods have achieved excellent performance in this task in past decades. Given an image query, these methods typically assume that the correct matched image-sentence pair must rank before all other mismatched ones. However, this assumption may be too strict and prone to the overfitting problem, especially when some sentences in a massive database are similar and confusable with one another. In this paper, we relax the traditional ranking loss and propose a novel deep multi-modal network with a top-k ranking loss to mitigate the data ambiguity problem. With this strategy, query results will not be penalized unless the index of ground truth is outside the range of top-k query results. Considering the non-smoothness and non-convexity of the initial top-k ranking loss, we exploit a tight convex upper bound to approximate the loss and then utilize the traditional back-propagation algorithm to optimize the deep multi-modal network. Finally, we apply the method on three benchmark datasets, namely, Flickr8k, Flickr30k, and MSCOCO. Empirical results on metrics R@K (K = 1, 5, 10) show that our method achieves comparable performance in comparison to state-of-the-art methods.
Zhang, M & Wu, RMX 2020, 'Empirical research methods adopted in China's e-commerce research: review, discussion, and recommendations', China Circulation Economy (in Mandarin).
Zhang, P, Li, H, Ha, QP, Yin, Z-Y & Chen, R-P 2020, 'Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses', Advanced Engineering Informatics, vol. 45, pp. 101097-101097.
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Zhang, P, Xu, J, Wu, Q, Huang, Y & Zhang, J 2020, 'Top-Push Constrained Modality-Adaptive Dictionary Learning for Cross-Modality Person Re-Identification', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 12, pp. 4554-4566.
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Zhang, Q, Li, L, Chen, H, Zhang, G, Zhu, S, Kong, R, Chen, H, Wang, G & Sun, B 2020, 'Soluble urokinase plasminogen activator receptor associates with higher risk, advanced disease severity as well as inflammation, and might serve as a prognostic biomarker of severe acute pancreatitis', Journal of Clinical Laboratory Analysis, vol. 34, no. 3.
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AbstractBackgroundThis study aimed to explore the potential of soluble urokinase plasminogen activator receptor (suPAR) as a biomarker for severe acute pancreatitis (SAP) risk prediction and disease management in SAP patients.MethodsTotally 225 acute pancreatitis (AP) patients (including 75 SAP, 75 moderate‐severe acute pancreatitis [MSAP], and 75 mild acute pancreatitis [MAP] patients) were recruited based on the Atlanta classification, and their serum samples were obtained within 24 hours after admission. Meanwhile, 75 health controls (HCs) were recruited with their serum samples collected at the enrollment. The serum suPAR was then detected using enzyme‐linked immunosorbent assay.ResultsThe suPAR level was increased in SAP patients compared with MSAP patients (P = .023), MAP patients (P < .001), and HCs (P < .001). Receiver operating characteristic (ROC) curve presented that suPAR could not only differentiate SAP patients from HCs (AUC: 0.920, 95%CI: 0.875‐0.965) but also differentiate SAP patients from MSAP (AUC: 0.684, 95%CI: 0.600‐0.769) and MAP patients (AUC: 0.855, 95%CI: 0.797‐0.912). In SAP patients, suPAR was positively correlated with Ranson score (P < .001), acute physiology and chronic healthcare evaluation II score (P = .001), sequential organ failure assessment score (P < .001), and C‐reaction protein (P = .002). Further ROC curve exhibited that suPAR (AUC: 0.806, 95%CI: 0.663‐0.949) was of good value in predicting increased inhospital mortality of SAP patients.ConclusionSoluble urokinase...
Zhang, R, Wu, L, Yang, Y, Wu, W, Chen, Y & Xu, M 2020, 'Multi-camera multi-player tracking with deep player identification in sports video', Pattern Recognition, vol. 102, pp. 107260-107260.
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© 2020 Identity switches caused by inter-object interactions remain a critical problem for multi-player tracking in real-world sports video analysis. Existing approaches utilizing the appearance model is difficult to associate detections and preserve identities due to the similar appearance of players in the same team. Instead of the appearance model, we propose a distinguishable deep representation for player identity in this paper. A robust multi-player tracker incorporating with deep player identification is further developed to produce identity-coherent trajectories. The framework consists of three parts: (1) the core component, a Deep Player Identification (DeepPlayer) model that provides an adequate discriminative feature through the coarse-to-fine jersey number recognition and the pose-guided partial feature embedding; (2) an Individual Probability Occupancy Map (IPOM) model for players 3D localization with ID; and (3) a K-Shortest Path with ID (KSP-ID) model that links nodes in the flow graph by a proposed player ID correlation coefficient. With the distinguishable identity, the performance of tracking is improved. Experiment results illustrate that our framework handles the identity switches effectively, and outperforms state-of-the-art trackers on the sports video benchmarks.
Zhang, S, Lin, M, Zou, X, Su, S, Zhang, W, Zhang, X & Guo, Z 2020, 'LSTM-based air quality predicted model for large cities in China', Nature Environment and Pollution Technology, vol. 19, no. 1, pp. 229-236.
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In this paper, the LSTM model is used to predict the PM2.5 concentrations in five representative Chinese cities with the GDP exceeding 1 trillion Yuan, including Beijing, Chengdu, Shanghai, Shenzhen and Wuhan. The PM2.5 concentration data in 2015-2017 are selected for training, and the results are optimized to achieve an efficient solution by adjusting the parameters. Based on the optimized solution, a test is carried out to predict the PM2.5 concentration in 2018, and the results are compared with the real value obtained from the monitoring centre. According to the comparison results, the correlation coefficient of Wuhan and Chengdu is 0.86724 and 0.80070, which are the highest in these five cities. While the correlation coefficient of Shenzhen and Shanghai, are 0.78225, 0.72147, Beijing, as the capital city of China achieved the lowest correlation coefficient which is 0.64118. The LSTM-based predictive model has relatively good reliability and transferability. More effective predictive results can be achieved by implementing deep learning to analyse PM2.5 concentration.
Zhang, S, Ly, QV, Nghiem, LD, Wang, J, Li, J & Hu, Y 2020, 'Optimization and organic fouling behavior of zwitterion-modified thin-film composite polyamide membrane for water reclamation: A comprehensive study', Journal of Membrane Science, vol. 596, pp. 117748-117748.
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© 2019 Membrane fouling can hinder the widespread application of thin film composite (TFC) reverse osmosis (RO) for water treatment. This study evaluated a novel zwitterion-grafted TFC RO as a mean to address organic fouling for water reclamation. The membrane exhibited the best permeability at the grafting condition of 45 °C in 1 h. This modified membrane consistently possessed improved antifouling ability irrespective of organic foulants. Among individual foulants, surfactant Dodecyl Trimethyl Ammonium Chloride (DTAC) posed the worst fouling potential due to its low molecular weight and positive charge, whereas fouling induced by other substances were relatively analogous and minor. In the mixture of DTAC and proteins, the former played a key role in governing the membrane fouling. While, their interplay affected membrane fouling, the fouling extent varied upon the membrane materials. The extended Derijaguin, Landau, Verwey and Overbeek (xDLVO) theory was unable to fully describe the interactions between surfactant foulants and the membrane materials. The complementary use of quartz crystal microbalance with dissipation (QCM-D), otherwise, concurred the fouling potential and gave the plausible interpretation for fouling mechanisms by providing insightful information of foulant layer on the polyamide-coated sensor. This study provided critical insights of organic foulants’ behavior on TFC RO membrane and offered the promising industrial implication of the novel membrane.
Zhang, S, Yan, H, Teng, J & Sheng, D 2020, 'A mathematical model of tortuosity in soil considering particle arrangement', Vadose Zone Journal, vol. 19, no. 1.
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AbstractTortuosity is an important parameter for studying the permeability of soil. Existing studies of soil tortuosity are usually of empirical nature and attempt to relate tortuosity to soil porosity alone. By assuming a laminar flow through the pores of two‐dimensional square solid particles, we present a mathematical model for calculating soil tortuosity under different particle arrangements. The effect of the randomness of the particle arrangement on the tortuosity is evaluated, which generates the variation range of the tortuosity. The proposed model provides the upper and lower bounds of the tortuosity, while existing empirical models tend to fall within these bounds. The consistency between the proposed model and the numerical calculation provides a validity for the proposed model.
Zhang, T, Zhu, T, Xiong, P, Huo, H, Tari, Z & Zhou, W 2020, 'Correlated Differential Privacy: Feature Selection in Machine Learning', IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 2115-2124.
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© 2005-2012 IEEE. Privacy preserving in machine learning is a crucial issue in industry informatics since data used for training in industries usually contain sensitive information. Existing differentially private machine learning algorithms have not considered the impact of data correlation, which may lead to more privacy leakage than expected in industrial applications. For example, data collected for traffic monitoring may contain some correlated records due to temporal correlation or user correlation. To fill this gap, in this article, we propose a correlation reduction scheme with differentially private feature selection considering the issue of privacy loss when data have correlation in machine learning tasks. The proposed scheme involves five steps with the goal of managing the extent of data correlation, preserving the privacy, and supporting accuracy in the prediction results. In this way, the impact of data correlation is relieved with the proposed feature selection scheme, and moreover the privacy issue of data correlation in learning is guaranteed. The proposed method can be widely used in machine learning algorithms, which provide services in industrial areas. Experiments show that the proposed scheme can produce better prediction results with machine learning tasks and fewer mean square errors for data queries compared to existing schemes.
Zhang, W, Liu, T, Ueland, M, Forbes, SL, Wang, RX & Su, SW 2020, 'Design of an efficient electronic nose system for odour analysis and assessment', Measurement, vol. 165, pp. 108089-108089.
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© 2020 Elsevier Ltd This paper presents an efficient electronic nose (e-nose) system, named “NOS.E”, for odour analysis and assessment. In addition to the reliable hardware and software designs, an airflow intake system is implemented to ensure the precise odour analysis procedure in the NOS.E system. Additionally, a particular control logic was introduced to improve the test efficiency of the NOS.E by reducing operation time. Furthermore, the fault detection and alarming design can generate a high-reliability performance by constantly monitoring its working status. To evaluate the performance of the NOS.E, three types of alcohols were tested by the NOS.E and compared to data collected by comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). The results indicate that the NOS.E can successfully distinguish three different alcohols with high efficiency and low cost and has the potential to be a universal odour analysis platform implemented in various applications.
Zhang, W, Xiong, J, Gui, L, Liu, B, Qiu, M & Shi, Z 2020, 'Distributed Caching Mechanism for Popular Services Distribution in Converged Overlay Networks', IEEE Transactions on Broadcasting, vol. 66, no. 1, pp. 66-77.
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IEEE With the proliferation of portable devices, the exponential growth of the global mobile traffic brings great challenges to the traditional communication networks and the traditional wireless communication technologies. In this context, converged networks and cache-based data offloading have drawn more and more attention based on the strong correlation of services. This paper proposes a novel popular services pushing and caching scheme by using converged overlay networks. The most popular services are pushed by terrestrial broadcasting networks. And they are cached in n router-nodes with limited cache sizes. Each router-node only interconnects with its neighbor nodes. Users are served through the router's WiFi link. If the services requested are cached in the routers, the user can be immediately responded; otherwise, the requests can be responded through the link from cellular stations to the router. In the proposed scheme, the cache size of the router, the maximum number of requests each router can serve, and the whole-time delay are limited. Three node-selecting and dynamic programming algorithms are adopted to maximize the equivalent throughput. Analytical and numerical results demonstrate that the proposed scheme is very effective.
Zhang, X, Hu, W, Pei, L, Zhao, S, Zhang, C & Wang, Z 2020, 'In(NO3)3 catalyzed curing reaction of benzoxazine', High Performance Polymers, vol. 32, no. 6, pp. 702-709.
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Benzoxazine is a new kind of thermoset resin with excellent properties, but it suffers from high curing temperature and low char yield in the presence of catalyst without halogen. In(NO3)3 was herein used for the first time to efficiently catalyze the curing reaction of benzoxazine and to elevate the char yield at 800°C. The reaction of benzoxazine was catalyzed by In(NO3)3 after stirring at 35°C for 300 min, and the initial curing temperature decreased to 151°C. Polybenzoxazine/In(NO3)3 showed higher thermal stability and char yield at 800°C (increased by 7.5%) compared with those of polybenzoxazine. The possible pathway of coordination bonding between In3+ and benzoxazine was proposed. In the cross-linking process, two different structures, that is, the N, O-acetal bridge structure and arylamine Mannich bridge structure formed at 35°C, both existed, which ultimately affected the thermal stability of the cured product.
Zhang, X, Li, W, Tang, Z, Wang, X & Sheng, D 2020, 'Sustainable regenerated binding materials (RBM) utilizing industrial solid wastes for soil and aggregate stabilization', Journal of Cleaner Production, vol. 275, pp. 122991-122991.
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© 2020 Elsevier Ltd This study presents an experimental investigation on a sustainable regenerated binding material (RBM), which is derived from several industrial solid wastes. Initially, the hydration process, mechanical behaviors, and microstructural characteristics of the RBM were investigated. Subsequently, the feasibility of RBM for the stabilization of macadam, expansive soil, and weathered sand was evaluated. The results reveal that in comparison with the ordinary Portland cement (OPC), the RBM exhibits a slightly faster hydration rate at the initial stage and comparable mechanical performance. For the stabilized macadam, the one stabilized by the RBM exhibits better unconfined compressive strength, scouring resistance and freeze-thaw resistance than the counterpart stabilized by OPC. Furthermore, the RBM can significantly improve the performance index of the expansive soil and weathered sand, and this enhancement is more significant as the RBM content increasing. Additionally, the RBM has been successfully applied in practical engineering, manifesting the promising application potential of the RBM. Overall, the excellent performance of RBM as an alternative stabilizer of the subgrade soil and aggregates can promote the application of the RBM low-carbon pavement construction in the future.
Zhang, X, Song, Z, Hao Ngo, H, Guo, W, Zhang, Z, Liu, Y, Zhang, D & Long, Z 2020, 'Impacts of typical pharmaceuticals and personal care products on the performance and microbial community of a sponge-based moving bed biofilm reactor', Bioresource Technology, vol. 295, pp. 122298-122298.
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Four lab-scale moving bed biofilm reactors (MBBRs) were built to treat simulated wastewater containing typical pharmaceuticals and personal care products (PPCPs). The efficiency in removing different PPCPs at different concentrations (1, 2 and 5 mg/L) and their effects on the performance of MBBRs were investigated. Results showed that the average removal efficiencies of sulfadiazine, ibuprofen and carbamazepine were 61.1 ± 8.8%, 74.9 ± 8.8% and 28.3 ± 7.4%, respectively. Compared to the reactor without PPCPs, the total nitrogen (TN) removal efficiency of the reactors containing sulfadiazine, ibuprofen and carbamazepine declined by 21%, 30% and 42%, respectively. Based on the microbial community analysis, increasing the PPCPs concentration within a certain range (<2 mg/L) could stimulate microbial growth and increase microbial diversity yet the diversity reduced when the concentration (5 mg/L) exceeded the tolerance of microorganisms. Furthermore the presence and degradation of different PPCPs resulted in a different kind of microbial community structure in the MBBRs.
Zhang, X, Zhang, Y, Ngo, HH, Guo, W, Wen, H, Zhang, D, Li, C & Qi, L 2020, 'Characterization and sulfonamide antibiotics adsorption capacity of spent coffee grounds based biochar and hydrochar', Science of The Total Environment, vol. 716, pp. 137015-137015.
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A large amount of spent coffee grounds is produced as a processing waste each year during making the coffee beverage. Sulfonamide antibiotics (SAs) are frequently detected in the environment and cause pollution problems. In this study, biochar (BC) and hydrochar (HC) were derived from spent coffee grounds through pyrolysis and hydrothermal carbonization, respectively. Their characteristics and sulfonamide antibiotics adsorption were investigated and compared with reference to adsorption capacity, adsorption isotherm and kinetics. Results showed BC possessed more carbonization and less oxygen-containing functional groups than HC when checked by Elemental Analysis, X-ray diffraction, X-ray photoelectron spectrometry and Fourier transform infrared. These groups affected the adsorption of sulfonamide antibiotics and adsorption mechanism. The maximum adsorption capacities of BC for sulfadiazine (SDZ) and sulfamethoxazole (SMX) were 121.5 μg/g and 130.1 μg/g at 25 °C with the initial antibiotic concentration of 500 μg/L, respectively. Meanwhile the maximum adsorption capacities of HC were 82.2 μg/g and 85.7 μg/g, respectively. Moreover, the adsorption mechanism for SAs adsorbed onto BC may be dominated by π-π electron donor-acceptor interactions, yet the SAs adsorption to HC may be attributed to hydrogen bonds. Further analysis of the adsorption isotherms and kinetics, found that physical and chemical interactions were involved in the SAs adsorption onto BC and HC. Overall, results suggested that: firstly, pyrolysis was an effective thermochemical conversion of spent coffee grounds; and secondly, BC was the more promising adsorbent for removing sulfonamide antibiotics.
Zhang, X, Zhao, Z, Zheng, Y & Li, J 2020, 'Prediction of Taxi Destinations Using a Novel Data Embedding Method and Ensemble Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 1, pp. 68-78.
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Zhang, Y, Cai, X, Fry, CV, Wu, M & Wagner, C 2020, 'Topic Evolution, Disruption and Resilience in Early COVID-19 Research', Scientometrics, vol. 126, no. 5, pp. 1-29.
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The COVID-19 pandemic presented a challenge to the global research community as scientists rushed to find solutions to the devastating crisis. Drawing expectations from resilience theory, this paper explores how the trajectory of and research community around the coronavirus research was affected by the COVID-19 pandemic. Characterizing epistemic clusters and pathways of knowledge through extracting terms featured in articles in early COVID-19 research, combined with evolutionary pathways and statistical analysis, the results reveal that the pandemic disrupted existing lines of coronavirus research to a large degree. While some communities of coronavirus research are similar pre- and during COVID-19, topics themselves change significantly and there is less cohesion amongst early COVID-19 research compared to that before the pandemic. We find that some lines of research revert to basic research pursued almost a decade earlier, whilst others pursue brand new trajectories. The epidemiology topic is the most resilient among the many subjects related to COVID-19 research. Chinese researchers in particular appear to be driving more novel research approaches in the early months of the pandemic. The findings raise questions about whether shifts are advantageous for global scientific progress, and whether the research community will return to the original equilibrium or reorganize into a different knowledge configuration.
Zhang, Y, Feng, W, Shi, G, Jiang, F, Chowdhury, M & Ling, SH 2020, 'UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm', Sensors, vol. 20, no. 8, pp. 2307-2307.
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To solve the real-time complex mission-planning problem for Multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in the dynamic environments, this paper addresses a new approach by effectively adapting the Consensus-Based Bundle Algorithms (CBBA) under the constraints of task timing, limited UAV resources, diverse types of tasks, dynamic addition of tasks, and real-time requirements. We introduce the dynamic task generation mechanism, which satisfied the task timing constraints. The tasks that require the cooperation of multiple UAVs are simplified into multiple sub-tasks to perform by a single UAV independently. We also introduce the asynchronous task allocation mechanism. This mechanism reduces the computational complexity of the algorithm and the communication time between UAVs. The partial task redistribution mechanism has been adopted for achieving the dynamic task allocation. The real-time performance of the algorithm is assured on the premise of optimal results. The feasibility and real-time performance of the algorithm are validated by conducting dynamic simulation experiments.
Zhang, Y, Ji, J & Ma, B 2020, 'Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network', Measurement, vol. 156, pp. 107619-107619.
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© 2020 Elsevier Ltd In order to denoise the raw signal and fuse multiple sources of information for the fault diagnosis of reciprocating compressor, this paper proposes a novel convolutional deep belief network-based method and employs a novel framework fusing multi-source information to improve the performance of fault diagnosis. Firstly, signals from different sensors of the RC are input into an auto-denoising network, namely, ensemble empirical model decomposition-convolutional deep belief network, to denoise the signal and to extract more robust features by the unsupervised learning. Secondly, the extracted features of each source are input into multiple Gaussian process classifiers which are adopted as the members of probabilistic committee machine (PCM) to calculate the probabilities that each fault occurs. Finally, these probabilities are combined with an optimized weight to make a committee decision on fault type. The proposed method combines the information from multiple sources and enhances the robustness of fault diagnosis. Data from an industrial plant were collected to verify the proposed method. The obtained results demonstrate that the proposed method can effectively diagnose the RC faults with the accuracy rate of up to 91.89%. Furthermore, a comparison of the proposed method with the other methods illustrates the superiority of the proposed method for the diagnosis of RC faults.
Zhang, Y, Ji, J & Ma, B 2020, 'Reciprocating compressor fault diagnosis using an optimized convolutional deep belief network', Journal of Vibration and Control, vol. 26, no. 17-18, pp. 1538-1548.
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This article proposes an optimized convolutional deep belief network for fault diagnosis of reciprocating compressors. Sparse filtering is first used to compress raw signal into compact time series by refining the most representative information and to reduce the computational burden. Then, the proposed convolutional deep belief network is adopted to learn the unsupervised features of the compressed signal without the need of feature extraction by human effort. To improve the generalization ability of the network, an optimized probabilistic pooling out is proposed in this article to replace the standard one in the pooling layer of the convolutional deep belief network. Finally, the unsupervised features calculated by the optimized convolutional deep belief network are fed as the input of the softmax regression classifier for fault identification. Four types of vibration signals reflecting different operating conditions are collected from the industry to validate the effectiveness of the proposed method. The obtained results demonstrate that the proposed convolutional deep belief network method can achieve a higher classification accuracy rate of up to 91% for fault diagnosis than the traditional methods and accomplish the fault diagnosis of reciprocating compressor effectively.
Zhang, Y, Sui, Y, Pan, S, Zheng, Z, Ning, B, Tsang, I & Zhou, W 2020, 'Familial Clustering for Weakly-Labeled Android Malware Using Hybrid Representation Learning', IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3401-3414.
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IEEE Labeling malware or malware clustering is important for identifying new security threats, triaging and building reference datasets. The state-of-the-art Android malware clustering approaches rely heavily on the raw labels from commercial AntiVirus (AV) vendors, which causes misclustering for a substantial number of weakly-labeled malware due to the inconsistent, incomplete and overly generic labels reported by these closed-source AV engines, whose capabilities vary greatly and whose internal mechanisms are opaque (i.e., intermediate detection results are unavailable for clustering). The raw labels are thus often used as the only important source of information for clustering. To address the limitations of the existing approaches, this paper presents ANDRE, a new ANDroid Hybrid REpresentation Learning approach to clustering weakly-labeled Android malware by preserving heterogeneous information from multiple sources (including the results of static code analysis, the metainformation of an app, and the raw-labels of the AV vendors) to jointly learn a hybrid representation for accurate clustering. The learned representation is then fed into our outlieraware clustering to partition the weakly-labeled malware into known and unknown families. The malware whose malicious behaviours are close to those of the existing families on the network, are further classified using a three-layer Deep Neural Network (DNN). The unknown malware are clustered using a standard density-based clustering algorithm. We have evaluated our approach using 5,416 ground-truth malware from Drebin and 9,000 malware from VIRUSSHARE (uploaded between Mar. 2017 and Feb. 2018), consisting of 3324 weakly-labeled malware. The evaluation shows that ANDRE effectively clusters weaklylabeled malware which cannot be clustered by the state-of-theart approaches, while achieving comparable accuracy with those approaches for clustering ground-truth samples.
Zhang, Y, Tsang, I, Yin, H, Yang, G, Lian, D & Li, J 2020, 'Deep Pairwise Hashing for Cold-start Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 7, pp. 1-1.
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Recommendation efficiency and data sparsity problems have been regarded as two challenges of improving performance for online recommendation. Most of the previous related work focus on improving recommendation accuracy instead of efficiency. In this paper, we propose a Deep Pairwise Hashing (DPH) to map users and items to binary vectors in Hamming space, where a user's preference for an item can be efficiently calculated by Hamming distance, which significantly improves the efficiency of online recommendation. To alleviate data sparsity and cold-start problems, the user-item interactive information and item content information are unified to learn effective representations of items and users. Specifically, we first pre-train robust item representation from item content data by a Denoising Auto-encoder instead of other deterministic deep learning frameworks; then we finetune the entire framework by adding a pairwise loss objective with discrete constraints; moreover, DPH aims to minimize a pairwise ranking loss that is consistent with the ultimate goal of recommendation. Finally, we adopt the alternating optimization method to optimize the proposed model with discrete constraints. Extensive experiments on three different datasets show that DPH can significantly advance the state-of-the-art frameworks regarding data sparsity and item cold-start recommendation.
Zhang, Y, Tsang, IW & Duan, L 2020, 'Collaborative Generative Hashing for Marketing and Fast Cold-Start Recommendation', IEEE Intelligent Systems, vol. 35, no. 5, pp. 84-95.
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© 2001-2011 IEEE. Cold-start has being a critical issue in recommender systems with the explosion of data in e-commerce. Most existing studies proposed to alleviate the cold-start problem are also known as hybrid recommender systems that learn representations of users and items by combining user-item interactive and user/item content information. However, previous hybrid methods regularly suffered poor efficiency bottlenecking in online recommendations with large-scale items, because they were designed to project users and items into continuous latent space where the online recommendation is expensive. To this end, we propose a collaborative generated hashing (CGH) framework to improve the efficiency by denoting users and items as binary codes, then fast hashing search techniques can be used to speed up the online recommendation. In addition, the proposed CGH can generate potential users or items for marketing application where the generative network is designed with the principle of minimum description length, which is used to learn compact and informative binary codes. Extensive experiments on two public datasets show the advantages for recommendations in various settings over competing baselines and analyze its feasibility in marketing application.
Zhang, Y, Wang, M, Saberi, M & Chang, E 2020, 'Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges', Scientometrics, vol. 125, no. 3, pp. 2637-2666.
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© 2020, Akadémiai Kiadó, Budapest, Hungary. The ever growing volume of academic articles stresses the need for a new generation of knowledge management method to intelligently reuse the academic knowledge and facilitate the development of scientific research. Knowledge fusion (KF) serves a key element of such method addressing those needs, and breakthrough progress has taken place in the field of KF. This brings a great opportunity for the academic community to expedite the process of literature review and automatically retrieve the required knowledge from academic publications. Therefore, a survey reviewing the KF studies in terms of the related technologies and applications for valuable insights to reuse academic knowledge, which is missing from the state-of-the-art literature, is in need. Motivated to bridge this gap, this paper conducts a systematic survey reviewing the existing studies on KF, meanwhile discussing the opportunities and challenges of applying KF through academic articles. To this end, we revisit the definitions of knowledge and KF in the context of academic articles, and summarise the fusion patterns and their usage in existing applications. Furthermore, we review the techniques and applications of KF, especially those with academic articles as sources of knowledge. Finally, we discuss the challenges and future directions in order to bring new insights to researchers and practitioners to deepen their understanding of knowledge fusion and to develop versatile functions.
Zhang, Y, Wei, T, Tran, TT, Lu, KT, Zhang, Z, Price, JR, Aharonovich, I & Zheng, R 2020, '[U(H2O)2]{[(UO2)10O10(OH)2][(UO4)(H2O)2]}: A Mixed-Valence Uranium Oxide Hydrate Framework', Inorganic Chemistry, vol. 59, no. 17, pp. 12166-12175.
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A uranium oxide hydrate framework, [U(H2O)2]{[(UO2)10O10(OH)2][(UO4)(H2O)2]} (UOF1), was synthesized hydrothermally using schoepite as a uranium precursor. The crystal strucutre of UOF1 was revealed with synchrotron single-crystal X-ray diffraction and confirmed with transmission electron miscroscopy. The typical uranyl oxide hydroxide layers similar to those in β-U3O8 are further connected via double-pentagonal-bipyramidal uranium polyhedra to form a three-dimensional (3D) framework structure with tetravalent uranium species inside the channels. The presence of mixed-valence uranium was investigated with a combination of X-ray absorption near-edge structure and diffuse reflectance spectroscopy. Apart from the major hexavalent uranium, evidence for tetravalent uranium was also found, consistent with the bond valence sum calculations. The successful preparation of UOF1 as the first pure uranium oxide hydrate framework sheds light on the structural understanding of the alteration of UO2+x as either a mineral or spent nuclear fuel.
Zhang, Y, Wu, M, Lin, H, Tipper, S, Grosser, M, Zhang, G & Lu, J 2020, 'Framework of Computational Intelligence-Enhanced Knowledge Base Construction: Methodology and A Case of Gene-Related Cardiovascular Disease', International Journal of Computational Intelligence Systems, vol. 13, no. 1, pp. 1109-1109.
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Knowledge base construction (KBC) aims to populate knowledge bases with high-quality information from unstructured data but how to effectively conduct KBC from scientific documents with limited preknowledge is still elusive. This paper proposes a KBC framework by applying computational intelligent techniques through the integration of intelligent bibliometrics—e.g., co-occurrence analysis is used for profiling research topics/domains and identifying key players, and recommending potential collaborators based on the incorporation of a link prediction approach; an approach of scientific evolutionary pathways is exploited to trace the evolution of research topics; and a search engine incorporating with fuzzy logics, word embedding, and genetic algorithm is developed for knowledge searching and ranking. Aiming to examine and demonstrate the reliability of the proposed framework, a case of gene-related cardiovascular diseases is selected, and a knowledge base is constructed, with the validation of domain experts.
Zhang, Y-T, Wei, W, Huang, Q-S, Wang, C, Wang, Y & Ni, B-J 2020, 'Insights into the microbial response of anaerobic granular sludge during long-term exposure to polyethylene terephthalate microplastics', Water Research, vol. 179, pp. 115898-115898.
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The negative effects of ubiquitous microplastics on wastewater treatment have attracted increasing attention. However, the potential impacts of microplastics on anaerobic granular sludge (AGS) remain unknown. To fill this knowledge gap, this paper investigated the response of AGS to the exposure of model microplastics (polyethylene terephthalate (PET-MPs)) and provided insights into the mechanisms involved. The 84 days' long-term exposure experiments demonstrated that PET-MPs, at relatively low level (15 MP L-1) did not affect AGS performance during anaerobic wastewater treatment, while 75-300 MP L-1 of PET-MPs caused the decreases of COD removal efficiency and methane yields by 17.4-30.4% and 17.2-28.4%, accompanied with the 119.4-227.8% increase in short-chain fatty acid (SCFA) accumulation and particle breakage. Extracellular polymeric substances (EPS) analysis showed that dosage-dependent tolerance of AGS to PET-MPs was attributed to the induced EPS producing protection role, but PET-MPs at higher concentrations (75-300 MP L-1) suppressed EPS generation. Correspondingly, microbial community analysis revealed that the populations of key acidogens (e.g., Levilinea sp.) and methanogens (e.g., Methanosaeta sp.) decreased after long-term exposure to PET-MPs. Assessment of the toxicity of PET-MPs revealed that the leached di-n-butyl phthalate (DBP) and the induced reactive oxygen species (ROS) by PET-MPs were causing toxicity towards AGS, confirmed by the increases in cell mortality and lactate dehydrogenase (LDH) release. These results provide novel insights into the ecological risk assessment of microplastics in anaerobic wastewater treatment system.
Zhang, Y-T, Wei, W, Sun, J, Xu, Q & Ni, B-J 2020, 'Long-Term Effects of Polyvinyl Chloride Microplastics on Anaerobic Granular Sludge for Recovering Methane from Wastewater', Environmental Science & Technology, vol. 54, no. 15, pp. 9662-9671.
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Polyvinyl chloride microplastics (PVC-MPs) are emerging contaminants affecting biological wastewater treatment processes. However, most of the previous studies mainly focused on their short-term impacts on floc sludge, with little work being conducted to explore their potential effects on more complex anaerobic granular sludge (AGS), which has been widely used for high-strength organic wastewater treatment. In this paper, the long-term effects of PVC-MPs on AGS were investigated via continuous feeding tests that are representative of real wastewater treatment processes. The results of a continuous 264 days test showed that the prolonged exposure of PVC-MPs at 15-150 MPs·L-1 significantly (p = 7.86 × 10-37, 3.44 × 10-43, and 5.29 × 10-46) inhibited the COD removal efficiency of AGS by 13.2%-35.5%, accompanied by a 11.0%-32.3% decreased production of methane and 40.3%-272.7% increased accumulation of short-chain fatty acids (SCFAs). In addition, the PVC-MPs exposure suppressed the secretion of extracellular polymeric substances (EPS), causing AGS and the inside microorganisms to lose the protection of EPS, thereby resulting in granule breakage and decreased cells viability. Aligning with the deteriorated performance, the long-term exposure of PVC-MPs reduced the total microbial populations and the relative abundances of key methanogens and acidogens. A toxicity mechanism assessment revealed that the negative impacts induced by PVC-MPs are mainly attributed to the toxic leachate and excess oxidative stress.
Zhang, Z, Cheng, Z, Li, H, Ke, H & Guo, YJ 2020, 'A Broadband Doherty Power Amplifier With Hybrid Class-EFJ Mode', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 12, pp. 4270-4280.
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This paper proposes a method that employs novel hybrid continuous class-EFJ power amplifiers (PAs) as carrier PA to design a broadband high-efficiency Doherty power amplifier (DPA). Bandwidth characteristic of the proposed DPA is analyzed in detail. By proper selection of related parameter values, up to 78% fabrication bandwidth can be obtained. Post-harmonic tuning network is applied to improve the bandwidth and enhance the efficiency. Then, a closed design process is presented to design broadband DPA based on derived theories. For validation, a broadband DPA operating in 1.2-2.8 GHz is designed and fabricated. Measurements illustrate that the DPA can deliver saturated output power between 43.7 dBm and 44.1 dBm in 1.2-2.8 GHz, and the saturated drain efficiency from 60.5% to 74.2 % is achieved. Moreover, drain efficiency is 48.1%-57.6% at the 6 dB power back-off. Compared with conventional DPAs, the proposed DPA exhibits superior performance of bandwidth characteristics and power back-off efficiency over a wide bandwidth.
Zhang, Z, He, N, He, B, Xu, B & Jiang, Y 2020, 'New method to measure structure stress based on distributed optical fiber technology', Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, vol. 41, no. 9, pp. 45-55.
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Stress and deformation monitoring is the significant content for evaluating the structure working characteristics and safety. On the basis of the research results of existing distributed optical fiber sensing technology, this paper optimizes the strain and deformation fitting algorithm is optimized. Aiming at the two cases of concentrated force and multi-point force a distributed structural stress measurement method is troposed based on optical fiber sensing technology. The bending moment and shear force calculation program is written. Through the indoor test of square steel and H-steel beam, the force magnitude and position fitting of the measurement method is studied. The study results show that the fitting values of the measurement method agree well with the theoretical values in terms of bending moment and shear force. The maximum average relative error of shear position fitting in square steel test is only 2.75%, and average relative error of shear force fitting at most stress points is less than 6%, The larger the strain of the stress point is, the higher the fitting accuracy of shear force position and shear force magnitude can be. At the same time, the measurement method can match the accuracy upgrade of the optical fiber measurement equipment. With the improvement of the accuracy specification, more details of the data changes can be captured and the distributed measurement of more stress points can be realized. The measurement method was applied to an example of foundation pit excavation with SMW retaining structures, which can accurately reflect the stress changes of H-steel pile in the excavation process, and has certain engineering practicability.
Zhao, E & Wu, C 2020, 'Unified egg ellipse critical threshold estimation for the deformation behavior of ultrahigh arch dams', Engineering Structures, vol. 214, pp. 110598-110598.
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© 2020 Elsevier Ltd This paper presents an innovative critical threshold estimation approach using unified egg-shaped ellipsoid modelling to study the deformation behavior of ultrahigh arch dams. First, the deformation variation law is regarded as a direct indicator of the overall stability and potential damage of ultrahigh arch dams based on comprehensively comparison with the results of theoretical calculations, experimental tests, numerical simulations and monitoring data. Subsequently, a novel geometric center of an irregular deformation plane constituted by all the deflection curves is proposed according to the measured distribution characteristics of the deformation spatial fields of the Xiaowan and Jinping I arch dams. Furthermore, unified egg-shaped ellipse equations are proposed to systematically identify the deformation critical attributes of Jinping I dam. Eventually, based on the peaks over threshold model, critical indexes are estimated considering the abnormal probabilities. The proposed methods are applied to Xiaowan dam as well. Results demonstrate that unified ellipsoid modelling can uniformly describe the abnormal features of the deformation behaviors of different ultrahigh arch dams, thereby the universal structural evolution characteristics to be understood in a wider range during their long-term operations.
Zhao, E, Wu, C, Wang, S, Hu, J & Wang, W 2020, 'Seepage dissolution effect prediction on aging deformation of concrete dams by coupled chemo-mechanical model', Construction and Building Materials, vol. 237, pp. 117603-117603.
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© 2019 Elsevier Ltd Concrete dams undergo a seepage dissolution process because of huge reservoir water pressure and concrete permeability, and such dissolution weakens the mechanical properties of concrete and produces a certain amount of irreversible aging deformation. This study proposes a coupled chemo-mechanical model to predict the seepage dissolution effect on aging deformation of concrete dams. The interactions between elastic–plastic, chemical damage and mechanical damage are jointly explored combining with on-site inspection of long-term service performance of a concrete gravity dam firstly. Then an aging model of the seepage dissolution damage is digitized, and a novel model on chemo-mechanical coupled effect is put forward by introducing an overall chemo-mechanical damage scalar. The validity of the model is proved by a case study on the gravity dam through finite element simulation on its seepage dissolution and comparison with monitoring data. Finally, these methods are applied into an ultra-high arch dam to quantitatively calculate the irreversible aging deformation with the increase of the seepage dissolution degree. And the annual maximum aging deformation of the arch dam increases 0.65 mm after 100 years. The results indicate that the proposed model can effectively predict the aging deformation caused by the seepage dissolution during long-term operation of concrete dams.
Zhao, F, Ji, JC, Ye, K & Luo, Q 2020, 'Increase of quasi-zero stiffness region using two pairs of oblique springs', Mechanical Systems and Signal Processing, vol. 144, pp. 106975-106975.
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© 2020 Elsevier Ltd Quasi-zero stiffness (QZS) nonlinear isolation systems have demonstrated better performance than their linear counterparts. However, their optimal performance is achieved only in a small displacement range around the static equilibrium position. Based on the QZS system with one pair of oblique springs, this paper proposes a new limb-like QZS system with two pairs of oblique springs to enlarge the QZS range and thus improve its isolation performance. Two pairs of oblique springs are configured to provide the dynamic stiffness opposite to the vertical spring for generating QZS characteristics. In comparison with the corresponding QZS system with one pair of oblique springs, the proposed QZS system with two pairs of oblique springs can achieve a lower dynamic stiffness in a much wider region around the static equilibrium position. Based on the theoretical analysis, a prototype is designed and fabricated to physically realize the QZS isolation system. Experimental results are found to be in good agreement with the theoretical predictions which also confirm the proposed QZS system has better isolation performance than the corresponding QZS system with one pair of oblique springs. The proposed model can be adopted for isolating low frequency vibrations in practical applications.
Zhao, J, Wang, W, Sun, Q, Huo, H, Sun, G, Gao, X & Zhu, C 2020, 'CSELM‐QE: A Composite Semi‐supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction', Chinese Journal of Electronics, vol. 29, no. 6, pp. 1016-1024.
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Wireless local area network (WLAN) fingerprint-based localization has become the most attractive and popular approach for indoor localization. However, the primary concern for its practical implementation is the laborious manual effort of calibrating sufficient location-labeled fingerprints. The Semi-supervised extreme learning machine (SELM) performs well in reducing calibration effort. Traditional SELM methods only use Received signal strength (RSS) information to construct the neighbor graph and ignores location information, which helps recognizing prior information for manifold alignments. We propose Composite SELM (CSELM) method by using both RSS signals and location information to construct composite graph. Besides, the issue of unlabeled RSS data quality has not been solved. We propose a novel approach called Composite semi-supervised extreme learning machine with unlabeled RSS Quality estimation (CSELM-QE) that takes into account the quality of unlabeled RSS data and combines the composite neighbor graph, which considers location information in the semi-supervised extreme learning machine. Experimental results show that the CSELM-QE could construct a precise localization model, reduce the calibration effort for radio map construction and improve localization accuracy. Our quality estimation method can be applied to other methods that need to retain high quality unlabeled Received signal strength data to improve model accuracy.
Zhao, J, Wang, W, Zhang, Z, Sun, Q, Huo, H, Qu, L & Zheng, S 2020, 'TrustTF: A tensor factorization model using user trust and implicit feedback for context-aware recommender systems', Knowledge-Based Systems, vol. 209, pp. 106434-106434.
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© 2020 Elsevier B.V. In recent years, context information has been widely used in recommender systems. Tensor factorization is an effective method to process high-dimensional information. However, data sparsity is more serious in tensor factorization, and it is difficult to build a more accurate recommender system only based on user–item–context interaction information. Making full use of user's social information and implicit feedback can alleviate this problem. In this paper, we propose a new tensor factorization model named TrustTF, which mainly works as follows: (1) Using user's social trust information and implicit feedback to extend the bias tensor factorization (BiasTF), effectively alleviate data sparsity problem and improve the recommendation accuracy; (2) Dividing user's trust relationship into unilateral trust and mutual trust, which makes better use of user's social information. To our knowledge, this is the first work to consider the effects of both user trust and implicit feedback on the basis of the BiasTF model. The experimental results in two real-world data sets demonstrate that the TrustTF proposed in this paper can achieve higher accuracy than BiasTF and other social recommendation methods.
Zhao, J, Zhang, H & Zhang, JA 2020, 'Gaussian kernel adaptive filters with adaptive kernel bandwidth', Signal Processing, vol. 166, pp. 107270-107270.
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© 2019 Gaussian kernel adaptive filters (GKAFs) have been successfully applied in functional approximation. The kernel bandwidth for GKAFs not only impacts on the smoothness of function approximation and the locality of training samples, but also affects the convergence rate and testing accuracy. However, in practice, it is hard to predesign an optimal one. In this paper, for practical applications, we propose a novel framework for kernel bandwidth adaptation in sparsification case. In this framework, we consider the latest K kernel bandwidths as free parameters, and sequentially update them using a gradient decent method to minimize the instantaneous squared error. Furthermore, we apply the proposed method to the quantized kernel least mean square (QKLMS) algorithm, and conduct convergence analysis for the algorithm. Extensive simulation results are provided and validate the superiority of our method compared to some state-of-the-art algorithms.
Zhao, J, Zhang, H & Zhang, JA 2020, 'Generalized maximum correntropy algorithm with affine projection for robust filtering under impulsive-noise environments', Signal Processing, vol. 172, pp. 107524-107524.
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© 2020 Combining affine projection (AP) with the generalized maximum correntropy (GMC) criterion, we propose a new family of AP-type filtering algorithms, called as APGMC, for system identification under impulsive-noise environments. By optimizing GMC of the a posterior error vector with a ℓ2-norm constraint on the filter weight vector, APGMC avoids the computation of the inversion of the input data matrix. Simulation results validate that APGMC achieves better filtering accuracy and faster convergence rate, compared to state-of-the-art algorithms.
Zhao, J, Zhang, H, Wang, G & Zhang, JA 2020, 'Projected Kernel Least Mean $p$ -Power Algorithm: Convergence Analyses and Modifications', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 10, pp. 3498-3511.
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© 2004-2012 IEEE. Sparsified kernel adaptive filters (SKAFs) is an attractive filtering solution with low memory and computational complexity. Most of existing SKAFs are based on the mean square error (MSE) criterion under Gaussian noise assumption for its simplicity and convenience. When the assumption deviates largely from the underlying truth, the performance of these methods could degrade significantly. In this paper, we propose a novel SKAF, named as projected kernel least mean $p$ -power algorithm (PKLMP), based on the mean $p$ -power error (MPE) criterion and vector projection (VP) method. We provide convergence analyses in terms of the stead-state MSE, based on a Taylor expansion method, and derive the lower and upper bounds for the steady-state excess MSE. We also conduct mean convergence analysis for PKLMP, and derive convergence conditions. To exploit the information in the desired outputs, we further derive a modified PKLMP by smoothing the desired signal. Finally, a simple and effective online variable kernel centers strategy is proposed to improve the filtering performance of the proposed KAFs. Simulation results under a static function estimation, a chaotic time-series prediction, and two real-world time-series predictions are conducted and validate the effectiveness of the proposed PKLMP algorithms.
Zhao, J, Zhao, L, Huang, S & Wang, Y 2020, '2D Laser SLAM With General Features Represented by Implicit Functions', IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4329-4336.
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© 2016 IEEE. The main contribution of this letter is the problem formulation and algorithm framework for 2D laser SLAM with general features represented by implicit functions. Since 2D laser data reflect the distances from the robot to the boundary of objects in the environment, it is natural to use the boundary of the general objects/features within the 2D environment to describe the features. Implicit functions can be used to represent almost arbitrary shapes from simple (e.g. circle, ellipse, line) to complex (e.g. a cross-section of a bunny model), thus it is worth studying implicit-expressed feature in 2D laser SLAM. In this letter, we clearly formulate the SLAM problem with implicit functions as features, with rigorously computed observation covariance matrix to be used in the SLAM objective function and propose a solution framework. Furthermore, we use ellipses and lines as examples to compare the proposed SLAM method with the traditional pre-fit method (represent the feature using its parameters and pre-fit the laser scan to get the fitted parameter as virtual observations). Simulation and experimental results show that our proposed method has a better performance compared with the pre-fit method and other methods, demonstrating the potential of this new SLAM formulation and method.
Zhao, M, Zhang, C, Zhang, J, Porikli, F, Ni, B & Zhang, W 2020, 'Scale-Aware Crowd Counting via Depth-Embedded Convolutional Neural Networks', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 10, pp. 3651-3662.
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© 1991-2012 IEEE. Scale variation of pedestrians in a crowd image presents a significant challenge for vision-based people counting systems. Such variations are mainly caused by perspective-related distortions due to the camera pose relative to the ground plane. Following the density-based counting paradigm, we postulate that generating density values adaptive to object scales plays a critical role in the accuracy of the final counting results. Motivated by this, we distill the underlying information from depth cues to obtain scale-aware representations that can respond to object scales considering the fact that the scale is inversely proportional to the object depth. Specifically, we propose a depth embedding module as add-ons into existing networks. This module exploits essential depth cues to spatially re-calibrate the magnitude of the original features. In this way, the objects, although in the same class, will attain distinct representations according to their scales, which directly benefits the estimation of scale-aware density values. We conduct a comprehensive analysis of the effects of the depth embedding module and validate that exploiting depth cues to perceive object scale variations in convolutional neural networks improves crowd counting performances. Our experiments demonstrate the effectiveness of the proposed approach on four popular benchmark datasets.
Zhao, S, Dou, P, Song, J, Nghiem, LD, Li, X-M & He, T 2020, 'Direct preparation of dialysate from tap water via osmotic dilution', Journal of Membrane Science, vol. 598, pp. 117659-117659.
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© 2019 Elsevier B.V. Preparation of dialysate for hemodialysis (or dialysis) requires dilution of the dialysis concentrate with purified water. Present practice contains two steps: first to purify water, and then water is transported to clinic to mix with the dialysate concentrate before treatment. As a new forward osmosis dialysis hybrid process, based on osmotic dilution, is evaluated for decentralized health care systems. A commercial cellulose triacetate (CTA) and a tailor-made thin film composite (TFC) polyamide FO membranes were examined. The rejection of salts in tap water by the FO membranes was investigated, and the real rejections of various ions as a function of permeate flux were well described by using a irreversible thermodynamic model. The hollow fiber TFC FO membrane showed higher water flux and lower reverse salt flux than the CTA membrane in diluting process. Both steric hindrance and electrostatic repulsion explained the rejection behavior of the membranes to the ions. Higher rejections of anions were obtained than cations, which was attributed to the anions selection characteristics of the membranes. No obvious foulings or scalings were observed in a relatively long time osmotic dilution process over 5 repeated cycles. The stable, high efficient osmotic dilution process in hemodialysis holds a strong promise in reducing the consumption of purified water or even eliminating the water purification step. This work provides a potentially new platform hemodialysis which can be portable and implementable away from major hospitals and major clinics.
Zhao, S, Niu, F & Qiu, X 2020, 'Effects of geometric properties of a static pressure tube on its frequency response', The Journal of the Acoustical Society of America, vol. 148, no. 3, pp. 1289-1295.
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Static pressure tubes are widely used to measure the static pressure in turbulent flows. Existing work focuses on the alteration of the static pressure tubes to the flow field. This paper investigates the effects of the geometric properties of a static pressure tube on the frequency response. A theoretical formulation is developed to describe the relationship between the sound pressure inside and outside the tube. The numerical simulation results show that the peaks in the frequency response move to lower frequencies when the tube diameter, tube length, and orifice depth increase and when the orifice diameter decreases. Experiments with a 3D-printed static pressure tube were conducted to verify the analytical results. The proposed model can be used to optimize the static pressure tube in the design stage or to correct the measurement results afterwards instead of cumbersome experimental calibration.
Zhao, S, Pei, L, Li, H, Zhang, X, Hu, W, Zhao, G & Wang, Z 2020, 'Enhanced comprehensive properties of polybenzoxazine via tailored hydrogen-bonds', Polymer, vol. 201, pp. 122647-122647.
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Zhao, X, Guo, J, Nie, F, Chen, L, Li, Z & Zhang, H 2020, 'Joint Principal Component and Discriminant Analysis for Dimensionality Reduction', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 2, pp. 433-444.
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Linear discriminant analysis (LDA) is the most widely used supervised dimensionality reduction approach. After removing the null space of the total scatter matrix St via principal component analysis (PCA), the LDA algorithm can avoid the small sample size problem. Most existing supervised dimensionality reduction methods extract the principal component of data first, and then conduct LDA on it. However, 'most variance' is very often the most important, but not always in PCA. Thus, this two-step strategy may not be able to obtain the most discriminant information for classification tasks. Different from traditional approaches which conduct PCA and LDA in sequence, we propose a novel method referred to as joint principal component and discriminant analysis (JPCDA) for dimensionality reduction. Using this method, we are able to not only avoid the small sample size problem but also extract discriminant information for classification tasks. An iterative optimization algorithm is proposed to solve the method. To validate the efficacy of the proposed method, we perform extensive experiments on several benchmark data sets in comparison with some state-of-the-art dimensionality reduction methods. A large number of experimental results illustrate that the proposed method has quite promising classification performance.
Zhao, Y, Cheng, J, Zhan, P & Peng, X 2020, 'ECG Classification Using Deep CNN Improved by Wavelet Transform', Computers, Materials & Continua, vol. 64, no. 3, pp. 1615-1628.
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© 2020 Tech Science Press. All rights reserved. Atrial fibrillation is the most common persistent form of arrhythmia. A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function, and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise. A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes, and finally the softmax classifier is used to classify them. This paper applies this method of the ECG data set provided by the 2017 PhysioNet/CINC challenge. After cross validation, this method can obtain 87.1% accuracy and the F1 score is 86.46%. Compared with the existing classification method, our proposed algorithm has higher accuracy and generalization ability for ECG signal data classification.
Zhao, Y, Luo, Q, Wu, J, Sui, C, Tong, L, He, X & Wang, C 2020, 'Mechanical properties of helically twisted carbyne fibers', International Journal of Mechanical Sciences, vol. 186, pp. 105823-105823.
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© 2020 Elsevier Ltd Carbyne composed of sp-hybridized carbon atoms is perfectly one-dimensional material, showing superior mechanical properties as a promising building material for nanodevices. Such nanomaterials as carbon nanotube ropes with hierarchical helical structures hold a promise for potential applications. Here, a bottom-up theoretical model is established to investigate the mechanical properties of this kind of novel nanomaterials. The effect of helical structures is revealed by comparing the mechanical properties of carbyne ropes. The dependence of the mechanical properties of materials on the initial helical angles and fiber numbers at different structural levels are examined. Carbyne ropes are found with higher deformation ability and elastic property which can be easily tuned via their microstructural parameters. This work provides inspirations for optimal design of advanced nanomaterials with helical structures.
Zhao, Z, Zhang, X, Chen, F, Fang, L & Li, J 2020, 'Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features', BMC Genomics, vol. 21, no. 1, p. 627.
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AbstractBackgroundDNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem.ResultsThe feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene.ConclusionsThe results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical...
Zhe, T, Huang, L, Wu, Q, Zhang, J, Pei, C & Li, L 2020, 'Inter-Vehicle Distance Estimation Method Based on Monocular Vision Using 3D Detection', IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 4907-4919.
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© 1967-2012 IEEE. Most autonomous vehicles build their perception systems on expensive sensors, such as LIDAR, RADAR, and high-precision Global Positioning System (GPS). However, cameras can provide richer sensing at a considerably lower cost, this makes them a more appealing alternative. A driving assistance system (DAS) based on monocular vision has gradually become a research hotspot, and inter-vehicle distance estimation based on monocular vision is an important technology in DAS. There are still constrains in the existing methods for estimating the inter-vehicle distance based on monocular vision, such as low accuracy when distance is larger, unstable accuracy for different types vehicles, and significantly poor performance on distance estimation for severely occluded vehicles. To improve the accuracy and robustness of ranging results, this study proposes a monocular vision end-to-end inter-vehicle distance estimation method based on 3D detection. The actual area of the rare view of the vehicle and the corresponding projection area in the image are obtained by 3D detection method. An area-distance geometric model is then established on the basis of the camera projection principle to recover distance. Our method shows its potential in complex traffic scenarios by testing the test set data provided on the real-world computer vision benchmark, KITTI. The experimental results have superior performance than the existing published methods. Moreover, the accuracy of occluded vehicle ranging results can reach approximately 98%, while the accuracy deviation between vehicles with different visual angles is less than 2%.
Zheng, C, Zhang, Q, Long, G, Zhang, C, Young, SD & Wang, W 2020, 'Measuring Time-Sensitive and Topic-Specific Influence in Social Networks With LSTM and Self-Attention', IEEE Access, vol. 8, pp. 82481-82492.
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Zheng, D, Zhang, H, Zhang, JA, Zheng, W & Su, SW 2020, 'Stability of asynchronous switched systems with sequence-based average dwell time approaches', Journal of the Franklin Institute, vol. 357, no. 4, pp. 2149-2166.
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© 2019 The Franklin Institute This paper studies the stability problem of asynchronous switched systems and proposes novel sequence-based average dwell time approaches. Both continuous-time and discrete-time systems are considered. The proposed approaches exploit the switching sequences of subsystems which were seldom utilized in the literature. More specifically, our approaches exploit the differences between different switching sequences, including the maximal asynchronous switching time, the energy changing degree at switching times, and the increasing speed of energy functions in asynchronous time intervals. As a result, the proposed approaches can reduce the threshold value of average dwell time significantly. We also propose an approach to counterbalance the increasing of energy functions in asynchronous time intervals by prolonging the preceding rather than subsequent subsystem. Numerical results demonstrate that the proposed approaches can improve the performance significantly in comparison with a well-known method.
Zheng, D, Zhang, JA, Zhang, H, Zheng, WX & Su, SW 2020, 'Consensus of Second-Order Multi-Agent Systems Without a Spanning Tree: A Sequence-Based Topology-Dependent Method', IEEE Access, vol. 8, pp. 162209-162217.
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Zheng, J, He, X, Li, Y, Zhao, B, Ye, F, Gao, C, Li, M, Li, X & E, S 2020, 'Viscoelastic and Magnetically Aligned Flaky Fe-Based Magnetorheological Elastomer Film for Wide-Bandwidth Electromagnetic Wave Absorption', Industrial & Engineering Chemistry Research, vol. 59, no. 8, pp. 3425-3437.
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Zheng, J, Ji, J, Yin, S & Tong, V-C 2020, 'Fatigue life analysis of double-row tapered roller bearing in a modern wind turbine under oscillating external load and speed', Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 234, no. 15, pp. 3116-3130.
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Fatigue life analysis of roller bearing is usually performed for bearings under constant rotating speed and invariant loading conditions. For the bearings used in offshore floating direct-drive wind turbines, they often experience oscillating motions with varying loading patterns, for which the standard fatigue life analysis is not valid due to the presence of fluctuating loads. This paper presents the fatigue life analysis of a double-row tapered roller bearing under oscillating external load and speed conditions, which is used to support the main shaft of a large modern direct-drive wind turbine. First, a comprehensive quasi-static model of the double-row tapered roller bearing is developed for determining the internal load distribution of rollers. The contact pressure of rollers is then studied using an iterative scheme based on the elastic contact model. After that, the formulation of basic rating life of the double-row tapered roller bearing with oscillating external load and speed is given to calculate the fatigue life. Numerical simulations are carried out to investigate the effects of the oscillating load and speed, angular misalignment, and internal clearance on the fatigue life of the bearing.
Zheng, J, Ji, J, Yin, S & Tong, V-C 2020, 'Internal loads and contact pressure distributions on the main shaft bearing in a modern gearless wind turbine', Tribology International, vol. 141, pp. 105960-105960.
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© 2019 Elsevier Ltd The double-row tapered roller bearing (TRB) widely used to support the main shaft in a modern gearless wind turbine is one of the main components and its faults can lead to the malfunctions and downtime of wind turbines. Over the past decades, some numerical approaches have been proposed for calculating the contact force and pressure distribution of double-row TRBs. Nevertheless, most of the existing studies did not take the angular misalignment between inner and outer rings and the frictional force between the rollers and raceways into account. This paper presents a comprehensive quasi-static model to investigate the internal load and contact pressure distribution in a double-row TRB by considering the angular misalignment, the combined external loads and frictional force. It is found that a small misalignment angle between inner and outer rings can result in a significant change in the magnitude and distribution of the contact force and pressure. The double-row TRB with crowned roller profile exhibits a substantial improvement in contact pressure distribution by eliminating the occurrence of pressure concentration. Moreover, the peak contact pressure can be significantly reduced on the roller with the crowned profile, even if in the case of misaligned bearing. Comparisons of the simulated contact loads and pressure distributions demonstrate the necessity of considering angular misalignment and frictional force in the modelling of large size and heavily loaded double-row TRB.
Zheng, J, Li, J & Zheng, Y 2020, 'Guest Editorial for the 29th International Conference on Genome Informatics (GIW 2018)', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 17, no. 3, pp. 726-727.
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Zheng, P, Su, QP, Jin, D, Yu, Y & Huang, X-F 2020, 'Prevention of Neurite Spine Loss Induced by Dopamine D2 Receptor Overactivation in Striatal Neurons', Frontiers in Neuroscience, vol. 14.
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Psychosis has been considered a disorder of impaired neuronal connectivity. Evidence for excessive formation of dopamine D2 receptor (D2R) - disrupted in schizophrenia 1 (DISC1) complexes has led to a new perspective on molecular mechanisms involved in psychotic symptoms. Here, we investigated how excessive D2R-DISC1 complex formation induced by D2R agonist quinpirole affects neurite growth and dendritic spines in striatal neurons. Fluorescence resonance energy transfer (FRET), stochastic optical reconstruction microscopy (STORM), and cell penetrating-peptide delivery were used to study the cultured striatal neurons from mouse pups. Using these striatal neurons, our study showed that: (1) D2R interacted with DISC1 in dendritic spines, neurites and soma of cultured striatal neurons; (2) D2R and DISC1 complex accumulated in clusters in dendritic spines of striatal neurons and the number of the complex were reduced after application of TAT-D2pep; (3) uncoupling D2R-DISC1 complexes by TAT-D2pep protected neuronal morphology and dendritic spines; and (4) TAT-D2pep prevented neurite and dendritic spine loss, which was associated with restoration of expression levels of synaptophysin and PSD-95. In addition, we found that Neuropeptide Y (NPY) and GSK3β were involved in the protective effects of TAT-D2pep on the neurite spines of striatal spiny projection neurons. Thus, our results may offer a new strategy for precisely treating neurite spine deficits associated with schizophrenia.
Zheng, Y, Hu, R, Fung, S-F, Yu, C, Long, G, Guo, T & Pan, S 2020, 'Clustering social audiences in business information networks', Pattern Recognition, vol. 100, pp. 107126-107126.
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© 2019 Elsevier Ltd Business information networks involve diverse users and rich content and have emerged as important platforms for enabling business intelligence and business decision making. A key step in an organizations business intelligence process is to cluster users with similar interests into social audiences and discover the roles they play within a business network. In this article, we propose a novel machine-learning approach, called CBIN, that co-clusters business information networks to discover and understand these audiences. The CBIN framework is based on co-factorization. The audience clusters are discovered from a combination of network structures and rich contextual information, such as node interactions and node-content correlations. Since what defines an audience cluster is data-driven, plus they often overlap, pre-determining the number of clusters is usually very difficult. Therefore, we have based CBIN on an overlapping clustering paradigm with a hold-out strategy to discover the optimal number of clusters given the underlying data. Experiments validate an outstanding performance by CBIN compared to other state-of-the-art algorithms on 13 real-world enterprise datasets.
Zhou, C, Fu, A, Yu, S, Yang, W, Wang, H & Zhang, Y 2020, 'Privacy-Preserving Federated Learning in Fog Computing', IEEE Internet of Things Journal, vol. 7, no. 11, pp. 10782-10793.
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Federated learning can combine a large number of scattered user groups and train models collaboratively without uploading data sets, so as to avoid the server collecting user sensitive data. However, the model of federated learning will expose the training set information of users, and the uneven amount of data owned by users in multiple users' scenarios will lead to the inefficiency of training. In this article, we propose a privacy-preserving federated learning scheme in fog computing. Acting as a participant, each fog node is enabled to collect Internet-of-Things (IoT) device data and complete the learning task in our scheme. Such design effectively improves the low training efficiency and model accuracy caused by the uneven distribution of data and the large gap of computing power. We enable IoT device data to satisfy -differential privacy to resist data attacks and leverage the combination of blinding and Paillier homomorphic encryption against model attacks, which realize the security aggregation of model parameters. In addition, we formally verified our scheme can not only guarantee both data security and model security but completely resist collusion attacks launched by multiple malicious entities. Our experiments based on the Fashion-MNIST data set prove that our scheme is highly efficient in practice.
Zhou, F, Li, Z, Fan, X, Wang, Y, Sowmya, A & Chen, F 2020, 'Efficient inference for nonparametric hawkes processes using auxiliary latent variables', Journal of Machine Learning Research, vol. 21, pp. 1-31.
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The expressive ability of classic Hawkes processes is limited due to the parametric assumption on the baseline intensity and triggering kernel. Therefore, it is desirable to perform inference in a data-driven, nonparametric approach. Many recent works have proposed nonparametric Hawkes process models based on Gaussian processes (GP). However, the likelihood is non-conjugate to the prior resulting in a complicated and time-consuming inference procedure. To address the problem, we present the sigmoid Gaussian Hawkes process model in this paper: the baseline intensity and triggering kernel are both modeled as the sigmoid transformation of random trajectories drawn from a GP. By introducing auxiliary latent random variables (branching structure, Pólya-Gamma random variables and latent marked Poisson processes), the likelihood is converted to two decoupled components with a Gaussian form which allows for an efficient conjugate analytical inference. Using the augmented likelihood, we derive an efficient Gibbs sampling algorithm to sample from the posterior; an efficient expectation-maximization (EM) algorithm to obtain the maximum a posteriori (MAP) estimate and furthermore an efficient mean-field variational inference algorithm to approximate the posterior. To further accelerate the inference, a sparse GP approximation is introduced to reduce complexity. We demonstrate the performance of our three algorithms on both simulated and real data. The experiments show that our proposed inference algorithms can recover well the underlying prompting characteristics efficiently.
Zhou, F, Li, Z, Fan, X, Wang, Y, Sowmya, A & Chen, F 2020, 'Fast multi-resolution segmentation for nonstationary Hawkes process using cumulants', International Journal of Data Science and Analytics, vol. 10, no. 4, pp. 321-330.
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© 2020, Springer Nature Switzerland AG. The stationarity is assumed in the vanilla Hawkes process, which reduces the model complexity but introduces a strong assumption. In this paper, we propose a fast multi-resolution segmentation algorithm to capture the time-varying characteristics of the nonstationary Hawkes process. The proposed algorithm is based on the first- and second-order cumulants. Except for the computation efficiency, the algorithm can provide a hierarchical view of the segmentation at different resolutions. We extensively investigate the impact of hyperparameters on the performance of this algorithm. To ease the choice of hyperparameter, we propose a refined Gaussian process-based segmentation algorithm, which is proved to be a robust method. The proposed algorithm is applied to a real vehicle collision dataset, and the outcome shows some interesting hierarchical dynamic time-varying characteristics.
Zhou, I, Lipman, J, Abolhasan, M, Shariati, N & Lamb, DW 2020, 'Frost Monitoring Cyber–Physical System: A Survey on Prediction and Active Protection Methods', IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6514-6527.
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Zhou, J, Asteris, PG, Armaghani, DJ & Pham, BT 2020, 'Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models', Soil Dynamics and Earthquake Engineering, vol. 139, pp. 106390-106390.
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Zhou, J, Koopialipoor, M, Li, E & Armaghani, DJ 2020, 'Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system', Bulletin of Engineering Geology and the Environment, vol. 79, no. 8, pp. 4265-4279.
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Zhou, J, Koopialipoor, M, Murlidhar, BR, Fatemi, SA, Tahir, MM, Jahed Armaghani, D & Li, C 2020, 'Use of Intelligent Methods to Design Effective Pattern Parameters of Mine Blasting to Minimize Flyrock Distance', Natural Resources Research, vol. 29, no. 2, pp. 625-639.
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Zhou, J, Luo, S & Chen, F 2020, 'Effects of personality traits on user trust in human–machine collaborations', Journal on Multimodal User Interfaces, vol. 14, no. 4, pp. 387-400.
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Data analytics-driven solutions are widely used in various intelligent systems, where humans and machines make decisions collaboratively based on predictions. Human factors such as personality and trust have significant effects on such human–machine collaborations. This paper investigates effects of personality traits on user trust in human–machine collaborations under uncertainty and cognitive load conditions. A user study of 42 subjects in a repeated factorial design experiment found that uncertainty presentation led to increased trust but only under low cognitive load conditions when users had sufficient cognitive resources to process the information. Presentation of uncertainty under high load conditions led to a decrease in trust. When further drilling down into personality trait groups of users, overall, users with low Openness showed the highest trust. Furthermore, under the low cognitive load condition, it was found that the trust was enhanced under ambiguity uncertainty with low Agreeableness, low Neuroticism, high Extraversion, high Conscientiousness, and high Openness. Under the high cognitive load condition, high Neuroticism and low Extraversion benefitted the trust without the uncertainty presentation. The results demonstrated that different personality traits affected trust differently under uncertainty and cognitive load conditions. A framework of user trust feedback loop was set up to incorporate the study results into human–machine collaborations for the meaningful participatory design.
Zhou, J, Zogan, H, Yang, S, Jameel, S, Xu, G & Chen, F 2020, 'Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia', IEEE Transactions on Computational Social Systems, pp. 1-10.
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The recent COVID-19 pandemic has caused unprecedented impact across theglobe. We have also witnessed millions of people with increased mental healthissues, such as depression, stress, worry, fear, disgust, sadness, and anxiety,which have become one of the major public health concerns during this severehealth crisis. For instance, depression is one of the most common mental healthissues according to the findings made by the World Health Organisation (WHO).Depression can cause serious emotional, behavioural and physical healthproblems with significant consequences, both personal and social costsincluded. This paper studies community depression dynamics due to COVID-19pandemic through user-generated content on Twitter. A new approach based onmulti-modal features from tweets and Term Frequency-Inverse Document Frequency(TF-IDF) is proposed to build depression classification models. Multi-modalfeatures capture depression cues from emotion, topic and domain-specificperspectives. We study the problem using recently scraped tweets from Twitterusers emanating from the state of New South Wales in Australia. Our novelclassification model is capable of extracting depression polarities which maybe affected by COVID-19 and related events during the COVID-19 period. Theresults found that people became more depressed after the outbreak of COVID-19.The measures implemented by the government such as the state lockdown alsoincreased depression levels. Further analysis in the Local Government Area(LGA) level found that the community depression level was different acrossdifferent LGAs. Such granular level analysis of depression dynamics not onlycan help authorities such as governmental departments to take correspondingactions more objectively in specific regions if necessary but also allows usersto perceive the dynamics of depression over the time.
Zhou, L, Ying, S, Yu, N & Ying, M 2020, 'Strassen's theorem for quantum couplings', THEORETICAL COMPUTER SCIENCE, vol. 802, pp. 67-76.
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Zhou, L, Ying, S, Yu, N & Ying, M 2020, 'Strassen's theorem for quantum couplings.', Theor. Comput. Sci., vol. 802, pp. 67-76.
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© 2019 Elsevier B.V. Strassen's theorem for probabilistic couplings is a fundamental theorem in probability theory that can be used to bound the probability of an event in a distribution by the probability of an event in another distribution coupled with the first. It has been widely applied in computer science for analysis of random algorithms, machine learning and verification of security and privacy protocols. We extend the coupling techniques in probability theory to quantum systems. A quantum generalisation of the notion of lifting, a coupling under certain constraints, is introduced. Several interesting examples and basic properties of quantum couplings and liftings are presented. Finally, a quantum extension of Strassen's theorem is established.
Zhou, R, Chang, X, Shi, L, Shen, Y-D, Yang, Y & Nie, F 2020, 'Person Reidentification via Multi-Feature Fusion With Adaptive Graph Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 5, pp. 1592-1601.
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The goal of person reidentification (Re-ID) is to identify a given pedestrian from a network of nonoverlapping surveillance cameras. Most existing works follow the supervised learning paradigm which requires pairwise labeled training data for each pair of cameras. However, this limits their scalability to real-world applications where abundant unlabeled data are available. To address this issue, we propose a multi-feature fusion with adaptive graph learning model for unsupervised Re-ID. Our model aims to negotiate comprehensive assessment on the consistent graph structure of pedestrians with the help of special information of feature descriptors. Specifically, we incorporate multi-feature dictionary learning and adaptive multi-feature graph learning into a unified learning model such that the learned dictionaries are discriminative and the subsequent graph structure learning is accurate. An alternating optimization algorithm with proved convergence is developed to solve the final optimization objective. Extensive experiments on four benchmark data sets demonstrate the superiority and effectiveness of the proposed method.
Zhou, X, Gururajan, R, Li, Y, Venkataraman, R, Tao, X, Bargshady, G, Barua, PD & Kondalsamy-Chennakesavan, S 2020, 'A survey on text classification and its applications', Web Intelligence, vol. 18, no. 3, pp. 205-216.
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Text classification (a.k.a text categorisation) is an effective and efficient technology for information organisation and management. With the explosion of information resources on the Web and corporate intranets continues to increase, it has being become more and more important and has attracted wide attention from many different research fields. In the literature, many feature selection methods and classification algorithms have been proposed. It also has important applications in the real world. However, the dramatic increase in the availability of massive text data from various sources is creating a number of issues and challenges for text classification such as scalability issues. The purpose of this report is to give an overview of existing text classification technologies for building more reliable text classification applications, to propose a research direction for addressing the challenging problems in text mining.
Zhou, X, Jin, W, Wang, Q, Guo, S, Tu, R, Han, S-F, Chen, C, Xie, G, Qu, F & Wang, Q 2020, 'Enhancement of productivity of Chlorella pyrenoidosa lipids for biodiesel using co-culture with ammonia-oxidizing bacteria in municipal wastewater', Renewable Energy, vol. 151, pp. 598-603.
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© 2019 Elsevier Ltd As one of the most promising renewable energy, microalgal biodiesel has been widely studied worldwide. However, the low-efficiency of conventional microalgae cultivation procedures restrict the development of microalgae biodiesel production. Microalgal-bacterial symbiosis could both enhance the growth of algal-bacterial culture and promote the removal and conversion of wastewater nutrients. In this study, three strains of high-efficient heterotrophic ammonia-oxidizing bacteria JN1, FN3, and FN5 were screened from municipal wastewater treatment system with over 80% degradation rates of 50 mg/L ammonia-nitrogen (NH3–N) in 24 h. Among them, FN5, belonging to Kluyvera sp., had the optimum effect on enhancing growth of oil-rich microalga Chlorella pyrenoidosa. In stationary phase, the biomass and lipid content of Chlorella pyrenoidosa was14.8% and 13.6% higher than the blank control tests without FN5. In contrast, JN1 and FN3 failed to enhance the growth of Chlorella pyrenoidosa. After the cultivation of Chlorella pyrenoidosa-FN5 consortia in municipal wastewater, the degradation rate of NH3–N was up to 91% while the content of microalgae biomass and lipid attained 0.35 g/L and 39.0%. The Saturated fatty acids (SFAs), Monounsaturated fatty acids (MUFAs), and Polyunsaturated fatty acids (PUFAs) were 43.9, 37.1 and 19.0%, respectively, which had the potential for biodiesel production after pretreatment.
Zhou, X, Li, S & Feng, Y 2020, 'Quantum Circuit Transformation Based on Simulated Annealing and Heuristic Search', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 12, pp. 4683-4694.
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IEEE Quantum algorithm design usually assumes access to a perfect quantum computer with ideal properties like full connectivity, noise-freedom and arbitrarily long coherence time. In Noisy Intermediate-Scale Quantum (NISQ) devices, however, the number of qubits is highly limited and quantum operation error and qubit coherence are not negligible. Besides, the connectivity of physical qubits in a quantum processing unit (QPU) is also strictly constrained. Thereby, additional operations like SWAP gates have to be inserted to satisfy this constraint while preserving the functionality of the original circuit. This process is known as quantum circuit transformation. Adding additional gates will increase both the size and depth of a quantum circuit and therefore cause further decay of the performance of a quantum circuit. Thus it is crucial to minimize the number of added gates. In this paper, we propose an efficient method to solve this problem. We first choose by using simulated annealing an initial mapping which fits well with the input circuit and then, with the help of a heuristic cost function, stepwise apply the best selected SWAP gates until all quantum gates in the circuit can be executed. Our algorithm runs in time polynomial in all parameters including the size and the qubit number of the input circuit, and the qubit number in the QPU. Its space complexity is quadratic to the number of edges in the QPU. Experimental results on extensive realistic circuits confirm that the proposed method is efficient and the number of added gates of our algorithm is, on average, only 57% of that of state-of-the-art algorithms on IBM Q20 (Tokyo), the most recent IBM quantum device.
Zhou, Y, Zhu, F, Liu, Y, Zheng, M, Wang, Y, Zhang, D, Anraku, Y, Zou, Y, Li, J, Wu, H, Pang, X, Tao, W, Shimoni, O, Bush, AI, Xue, X & Shi, B 2020, 'Blood-brain barrier–penetrating siRNA nanomedicine for Alzheimer’s disease therapy', Science Advances, vol. 6, no. 41.
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Glycosylated “triple-interaction” stabilized siRNA nanomedicine ameliorated AD neuropathology by targeting BACE1.
Zhou, Z, Ni, W, Ji, Z, Liu, S, Han, X, Li, X & Mao, J 2020, 'Development of a Rapid Method for Determination of Main Higher Alcohols in Fermented Alcoholic Beverages Based on Dispersive Liquid-Liquid Microextraction and Gas Chromatography-Mass Spectrometry', Food Analytical Methods, vol. 13, no. 3, pp. 591-600.
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Zhu, C, Cao, L & Yin, J 2020, 'Unsupervised Heterogeneous Coupling Learning for Categorical Representation', IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, vol. PP, no. 99, pp. 1-1.
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Complex categorical data is often hierarchically coupled with heterogeneousrelationships between attributes and attribute values and the couplings betweenobjects. Such value-to-object couplings are heterogeneous with complementaryand inconsistent interactions and distributions. Limited research exists onunlabeled categorical data representations, ignores the heterogeneous andhierarchical couplings, underestimates data characteristics and complexities,and overuses redundant information, etc. The deep representation learning ofunlabeled categorical data is challenging, overseeing such value-to-objectcouplings, complementarity and inconsistency, and requiring large data,disentanglement, and high computational power. This work introduces a shallowbut powerful UNsupervised heTerogeneous couplIng lEarning (UNTIE) approach forrepresenting coupled categorical data by untying the interactions betweencouplings and revealing heterogeneous distributions embedded in each type ofcouplings. UNTIE is efficiently optimized w.r.t. a kernel k-means objectivefunction for unsupervised representation learning of heterogeneous andhierarchical value-to-object couplings. Theoretical analysis shows that UNTIEcan represent categorical data with maximal separability while effectivelyrepresent heterogeneous couplings and disclose their roles in categorical data.The UNTIE-learned representations make significant performance improvementagainst the state-of-the-art categorical representations and deeprepresentation models on 25 categorical data sets with diversifiedcharacteristics.
Zhu, D, Indraratna, B, Poulos, H & Rujikiatkamjorn, C 2020, 'Field study of pile – prefabricated vertical drain (PVD) interaction in soft clay', Canadian Geotechnical Journal, vol. 57, no. 3, pp. 377-390.
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Piles and prefabricated vertical drains (PVDs) are two well-established inclusions used by geotechnical practitioners when dealing with soft compressible foundations. Induced movements in highly compressible soil can adversely influence the pile response by inducing additional movements and stresses in the piles. Especially, undesirable soil–pile interaction often leads to the development of excess pore-water pressure during pile installation and negative skin friction caused by the settlement of compressible soil surrounding the piles. Additional drainage by PVDs prior to the installation of a pile could reduce excess pore-water pressure, lateral soil movement, and negative skin friction on the pile. In this paper, full-scale field testing on two trial embankments built on soft soil is reported and the relative behaviour of these two embankments is compared and discussed. Soft soil underneath both embankments was consolidated before one pile was installed at the centre of each embankment. The pore-water pressure, lateral soil movement, surface settlement, and associated strain at the pile shaft were recorded. The pile capacity was tested immediately and 3 h after pile installation. The monitoring and testing results indicated that preconsolidation with PVDs before piling can effectively reduce the excess pore-water pressure, lateral soil movement, and downdrag on the pile.
Zhu, F, Lu, J, Lin, A & Zhang, G 2020, 'A Pareto-smoothing method for causal inference using generalized Pareto distribution', Neurocomputing, vol. 378, pp. 142-152.
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© 2019 Elsevier B.V. Causal inference aims to estimate the treatment effect of an intervention on the target outcome variable and has received great attention across fields ranging from economics and statistics to machine learning. Observational causal inference is challenging because the pre-treatment variables may influence both the treatment and the outcome, resulting in confounding bias. The classic inverse propensity weighting (IPW) estimator is theoretically able to eliminate the confounding bias. However, in observational studies, the propensity scores used in the IPW estimator must be estimated from finite observational data and may be subject to extreme values, leading to the problem of highly variable importance weights, which consequently makes the estimated causal effect unstable or even misleading. In this paper, by reframing the IPW estimator in the importance sampling framework, we propose a Pareto-smoothing method to tackle this problem. The generalized Pareto distribution (GPD) from extreme value theory is used to fit the upper tail of the estimated importance weights and to replace them using the order statistics of the fitted GPD. To validate the performance of the new method, we conducted extensive experiments on simulated and semi-simulated datasets. Compared with two existing methods for importance weight stabilization, i.e., weight truncation and self-normalization, the proposed method generally achieves better performance in settings with a small sample size and high-dimensional covariates. Its application on a real-world heath dataset indicates its utility in estimating causal effects for program evaluation.
Zhu, H, Hu, W, Zhao, S, Zhang, X, Pei, L, Zhao, G & Wang, Z 2020, 'Flexible and thermally stable superhydrophobic surface with excellent anti-corrosion behavior', Journal of Materials Science, vol. 55, no. 5, pp. 2215-2225.
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Zhu, H, Qin, P-Y & Guo, YJ 2020, 'Single-Ended-to-Balanced Power Divider With Extended Common-Mode Suppression and Its Application to Differential $2\times4$ Butler Matrices', IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 4, pp. 1510-1519.
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Zhu, H, Zhu, X, Yang, Y & Sun, Y 2020, 'Design of Miniaturized On-Chip Bandpass Filters Using Inverting-Coupled Inductors in (Bi)-CMOS Technology', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 2, pp. 647-657.
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© 2004-2012 IEEE. In this work, a new type of miniaturized on-chip resonator using coupled-inductor structure is presented. The impact on resonances of the structure due to the use of non- inverting- and inverting-coupled configuration is extensively investigated. It has been found that using the inverting-coupled structure, a stronger resonance can be generated, which is ideally suitable for device miniaturization. To fully understand the working mechanism of the resonator and use it effectively for bandpass filter (BPF) design, simplified LC equivalent-circuit models and detailed theoretical analysis are provided. To further demonstrate the proposed concept is useful in practice, not only a 1st-order BPF, but also another two 2nd-order BPFs are designed and fabricated in a standard 0.13-μm (Bi)-CMOS technology. All of them are designed to have a centre frequency around 15 GHz. Their physical dimensions are 0.13 × 0.25 mm2, 0.26 × 0.25 mm2, 0.24 × 0.22 mm2, respectively. Good agreements between simulation and measurement have been obtained, which verify that the presented design approach is suitable for miniaturized on-chip passive design.
Zhu, J, Yang, Y, Chu, C, Li, S, Liao, S & Xue, Q 2020, 'Low-Profile Wideband and High-Gain LTCC Patch Antenna Array for 60 GHz Applications', IEEE Transactions on Antennas and Propagation, vol. 68, no. 4, pp. 3237-3242.
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© 1963-2012 IEEE. This communication presents a low-profile wideband and high-gain patch antenna array using low-temperature cofired ceramics (LTCCs) fabrication technique for 60 GHz applications. A coupled feeding scheme is adopted so that the antenna achieves wide impedance bandwidth that covers the entire 60 GHz license-free band with a height of only 0.384 mm (four tape layers). The shorting via connecting the upper patch and ground achieves the function of a virtual ac ground plane of differential feeding. This enables the antenna element to achieve good radiation performances, including stable gain (variation less than 1 dB) and a symmetrical beam with low cross-polarization over the entire frequency band. The performances of the antenna element are comparable to that of the differential-driven patch antenna while the complex differential feeding network is not required. Furthermore, the proposed antenna element with simple and easy-to-integrate geometry is successfully extended to a $4 \times 4$ antenna array using a substrate integrated waveguide (SIW)-based feeding network. The measured results show that the impedance bandwidth of the array covers the 60 GHz license-free band. The maximum gain can reach 16.7 dBi and the radiation performances are very stable over the operating frequency band.
Zhu, J, Yang, Y, McGloin, D, Rajasekharan Unnithan, R, Li, S, Liao, S & Xue, Q 2020, '3-D Printed Planar Dielectric Linear-to-Circular Polarization Conversion and Beam-Shaping Lenses Using Coding Polarizer', IEEE Transactions on Antennas and Propagation, vol. 68, no. 6, pp. 4332-4343.
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© 1963-2012 IEEE. This article presents a new linear-to-circular polarization conversion coding unit, on which two new kinds of beam-shaping lenses are proposed. First, under periodic boundary conditions, a linear-to-circular polarization conversion coding unit is introduced, which introduces the necessary phase delay by adjusting its geometrical parameters. The phase delay ranges from 0° to 360° and is discretized into 3 bit coding units corresponding to specific delays. Second, by properly arranging the coding units, a high-gain circularly polarized (CP) lens is proposed. The lens achieves linear-to-circular polarization conversion and beam collimation in the transmission mode simultaneously with a planar configuration, which is different from counterparts that place a lens atop of a polarizer. Furthermore, the coding units are used to form Wollaston-prism-like and Rochon-prism-like planar CP beam-shaping lenses, which split the beams with different polarizations into right-and left-handed components. These beams can be controlled independently. Prototypes working at 30 GHz band are designed, fabricated, and measured to verify the idea.
Zhu, L & Yang, Y 2020, 'Label Independent Memory for Semi-Supervised Few-shot Video Classification', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 1, pp. 1-1.
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In this paper, we propose to leverage freely available unlabeled video data to facilitate few-shot video classification. In this semi-supervised few-shot video classification task, millions of unlabeled data are available for each episode during training. These videos can be extremely imbalanced, while they have profound visual and motion dynamics. To tackle the semi-supervised few-shot video classification problem, we make the following contributions. First, we propose a label independent memory (LIM) to cache label related features, which enables a similarity search over a large set of videos. LIM produces a class prototype for few-shot training. This prototype is an aggregated embedding for each class, which is more robust to noisy video features. Second, we integrate a multi-modality compound memory network to capture both RGB and flow information. We propose to store the RGB and flow representation in two separate memory networks, but they are jointly optimized via a unified loss. In this way, mutual communications between the two modalities are leveraged to achieve better classification performance. Third, we conduct extensive experiments on the few-shot Kinetics-100, Something-Something-100 datasets, which validates the effectiveness of leveraging the accessible unlabeled data for few-shot classification.
Zhu, LF, Ke, LL, Xiang, Y & Zhu, XQ 2020, 'Free vibration and damage identification of cracked functionally graded plates', Composite Structures, vol. 250, pp. 112517-112517.
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© 2020 Elsevier Ltd This paper investigates the free vibration and crack identification of functionally graded material (FGM) plates with a through-width edge crack. The material properties of the FGM plates change continuously with the power law distribution along the plate thickness direction. The crack in an FGM plate is simulated as a massless rotational spring and the plate is separated into two sub-plates at the crack location connected by the line spring. The stress intensity factor (SIF) in the FGM strip is calculated to determine the stiffness of the spring. The governing equations of cracked FGM plates are derived from the Mindlin plate theory and solved by the differential quadrature (DQ) method to obtain modal parameters. The vibrational mode of a cracked FGM plate is analyzed by utilizing continuous wavelet transform (CWT). A novel damage index (DI) is developed based on calculated wavelet coefficients to localize the crack in FGM plates. This method can localize the crack accurately and reduce the edge effect even with the measurement noise.
Zhu, L-F, Ke, L-L, Xiang, Y, Zhu, X-Q & Wang, Y-S 2020, 'Vibrational power flow analysis of cracked functionally graded beams', Thin-Walled Structures, vol. 150, pp. 106626-106626.
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© 2020 Elsevier Ltd In this paper, the vibrational power flow of a cracked beam made of functionally graded materials (FGMs) is investigated. The Young's modulus and mass density change exponentially along the thickness direction of the beam. The cracked FGM beam is divided into two sub-beams at the crack section which are connected by a massless rotational spring. Based on the Timoshenko beam theory, the governing equations of the cracked FGM beam are derived by using the neutral plane as the reference plane. The dynamic response of the FGM beam subjected to a harmonic concentrated transverse force is solved by the wave propagation approach. The input power flow and the transmitted power flow are obtained. The effect of the crack location and depth and the Young's modulus ratio on the input power flow and the transmitted power flow is studied in detail. A new damage index (DI) for the crack identification of FGM beams is proposed by applying continuous wavelet transform (CWT) to the transmitted power flow distribution along the beam longitudinal direction. The peak of DI indicates the crack location in FGM beams with small crack depth.
Zhu, Q, Qiu, X, Coleman, P & Burnett, I 2020, 'A comparison between two modal domain methods for personal audio reproduction', The Journal of the Acoustical Society of America, vol. 147, no. 1, pp. 161-173.
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Personal audio provides private and personalized listening experiences by generating sound zones in a shared space with minimal interference between zones. One challenge of the design is to achieve the best performance with a limited number of microphones and loudspeakers. In this paper, two modal domain methods for personal audio reproduction are compared. One is the spatial harmonic decomposition (SHD) based method and the other is the singular value decomposition (SVD) based method. It is demonstrated that the SVD based method provides a more efficient modal domain decomposition than the SHD method for 2.5 dimensional personal audio design. Simulation results show that the SVD based method outperforms the SHD one by up to 10 dB in terms of acoustic contrast and up to 17 dB in terms of reproduction error for a compact arc array with five loudspeakers, while requiring fewer microphones around the zone boundaries. The SVD based method retains the inherent efficiency of optimizing in a modal domain while avoiding the inherent geometric limitations of using SHD basis functions. Thus, this approach is advantageous for applications with flexible system geometries and a small number of loudspeakers and microphones.
Zhu, QH, Shen, JW & Ji, JC 2020, 'Internal signal stochastic resonance of a two-component gene regulatory network under Lévy noise', Nonlinear Dynamics, vol. 100, no. 1, pp. 863-876.
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© 2020, Springer Nature B.V. Noises are ubiquitous in nature and can often induce some curious phenomena. In this paper, we investigate the internal signal stochastic resonance (ISSR) phenomenon of a two-component gene regulatory network under the excitation of Lévy noise. Our results reveal that the Lévy noise can induce the periodic oscillation of the protein concentration when the control parameter is close to its bifurcation points. Furthermore, we consider the noise-induced periodic signal as the periodic excitation and study the ISSR phenomenon under the cooperation of the nonlinear system, noise-induced periodic signal and random noise. And we found that there may be a connection between the ISSR phenomenon and the bifurcation mechanism. Besides, we also investigate the effects of different noise parameters on the ISSR phenomenon. The simulation results indicate that there is an optimal interval of the stability index α which can induce the ISSR phenomenon, and the skewness parameter β has a negative correlation with the ISSR phenomenon. Our results may provide a pathway to uncover the positive functional mechanism of noises in complex gene regulatory networks.
Zhu, Y, Li, JJ, Reng, J, Wang, S, Zhang, R & Wang, B 2020, 'Global trends of Pseudomonas aeruginosa biofilm research in the past two decades: A bibliometric study', MicrobiologyOpen, vol. 9, no. 6, pp. 1102-1112.
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AbstractPseudomonas aeruginosa biofilm formation is a primary cause of chronic infections. This has been a highly active area of research over the past two decades due to causing high mortality risks in immunocompromised patients. This study evaluates global trends in the dynamic and rapidly evolving field of P. aeruginosa biofilm research through bibliometric and visualized analyses. Publications from 1994 to 2018 on P. aeruginosa biofilm research were retrieved from Web of Science, Scopus, and PubMed, and their bibliometric data were systematically studied. The VOSviewer software was used to conduct global analyses of bibliographic coupling, coauthorship, cocitation, and co‐occurrence. A total of 9,527 publications were included in this study. The overall number of publications and research interest in the field displayed a strongly rising trend. The USA made the greatest contributions to the field, with the highest h‐index and number of citations compared with other countries, while Denmark had the highest average citation per publication. The Journal of Bacteriology had the highest number of publications in the field, while the University of Copenhagen was the institution with the highest contribution influence. Co‐occurrence network maps revealed that the most prominent topics in P. aeruginosa biofilm research were mechanistic studies, in vitro/in vivo studies, and biofilm formation studies. Pseudomonas aeruginosa biofilms constitute a dynamic research area in microbiology with increasing global research interest. Future studies will likely focus on investigating the mechanisms of biofilm formation to solve infection‐associated clinical problems.
Zhu, Y, Lu, H, Qiu, P, Shi, K, Chambua, J & Niu, Z 2020, 'Heterogeneous teaching evaluation network based offline course recommendation with graph learning and tensor factorization', Neurocomputing, vol. 415, pp. 84-95.
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Zhu, Y, Ouyang, L, Zhong, H, Liu, J, Wang, H, Shao, H, Huang, Z & Zhu, M 2020, 'Closing the Loop for Hydrogen Storage: Facile Regeneration of NaBH4 from its Hydrolytic Product', Angewandte Chemie International Edition, vol. 59, no. 22, pp. 8623-8629.
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AbstractSodium borohydride (NaBH4) is among the most studied hydrogen storage materials because it is able to deliver high‐purity H2 at room temperature with controllable kinetics via hydrolysis; however, its regeneration from the hydrolytic product has been challenging. Now, a facile method is reported to regenerate NaBH4 with high yield and low costs. The hydrolytic product NaBO2 in aqueous solution reacts with CO2, forming Na2B4O7⋅10 H2O and Na2CO3, both of which are ball‐milled with Mg under ambient conditions to form NaBH4 in high yield (close to 80 %). Compared with previous studies, this approach avoids expensive reducing agents such as MgH2, bypasses the energy‐intensive dehydration procedure to remove water from Na2B4O7⋅10 H2O, and does not require high‐pressure H2 gas, therefore leading to much reduced costs. This method is expected to effectively close the loop of NaBH4 regeneration and hydrolysis, enabling a wide deployment of NaBH4 for hydrogen storage.
Zhu, Y, Ouyang, L, Zhong, H, Liu, J, Wang, H, Shao, H, Huang, Z & Zhu, M 2020, 'Closing the Loop for Hydrogen Storage: Facile Regeneration of NaBH4 from its Hydrolytic Product', Angewandte Chemie, vol. 132, no. 22, pp. 8701-8707.
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AbstractSodium borohydride (NaBH4) is among the most studied hydrogen storage materials because it is able to deliver high‐purity H2 at room temperature with controllable kinetics via hydrolysis; however, its regeneration from the hydrolytic product has been challenging. Now, a facile method is reported to regenerate NaBH4 with high yield and low costs. The hydrolytic product NaBO2 in aqueous solution reacts with CO2, forming Na2B4O7⋅10 H2O and Na2CO3, both of which are ball‐milled with Mg under ambient conditions to form NaBH4 in high yield (close to 80 %). Compared with previous studies, this approach avoids expensive reducing agents such as MgH2, bypasses the energy‐intensive dehydration procedure to remove water from Na2B4O7⋅10 H2O, and does not require high‐pressure H2 gas, therefore leading to much reduced costs. This method is expected to effectively close the loop of NaBH4 regeneration and hydrolysis, enabling a wide deployment of NaBH4 for hydrogen storage.
Zhu, Y, Ouyang, L, Zhong, H, Liu, J, Wang, H, Shao, H, Huang, Z & Zhu, M 2020, 'Efficient Synthesis of Sodium Borohydride: Balancing Reducing Agents with Intrinsic Hydrogen Source in Hydrated Borax', ACS Sustainable Chemistry & Engineering, vol. 8, no. 35, pp. 13449-13458.
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© 2020 American Chemical Society. Sodium borohydride (NaBH4) has been identified as one of the most promising hydrogen storage materials; however, it is still challenging to produce NaBH4 with low cost and high efficiency, which are largely determined by the sources of boron and hydrogen and reducing agents used. Herein, we report an economical method to produce NaBH4 by ball milling hydrated borax (Na2B4O7·10H2O and/or Na2B4O7·5H2O) with different reducing agents such as MgH2, Mg, and NaH under ambient conditions. The direct use of natural hydrated borax avoids the dehydration process (at 600 °C) and consequently reduces cost and improves overall energy efficiency. A high yield of 93.1% can be achieved for a short ball mill duration (3.5 h) for Na2B4O7·5H2O-NaH-MgH2 system. In this system, H2 is generated in situ which subsequently reacts with Mg forming MgH2. Low cost Mg is therefore employed to replace the majority of MgH2, leading to an attractive yield of 78.6%. To further reduce the cost of raw materials and improve the utilization of hydrogen source in the hydrated borax, Na2B4O7·10H2O is used to partially substitute for Na2B4O7·5H2O, leading to a complete replacement of MgH2. Compared with literature results, the optimized recipe features low cost and high efficiency since it utilizes hydrogen from the hydrated water in natural borax and avoids high temperatures. Our finding is expected to facilitate applications of NaBH4 for hydrogen storage.
Zhu, Y, Zhang, S, Li, Y, Lu, H, Shi, K & Niu, Z 2020, 'Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace', Geoscience Data Journal, vol. 7, no. 1, pp. 61-79.
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AbstractCrowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing‐driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary. Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing‐based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological service. It was also evident that current researches are contributing towards a systemic and intelligent knowledge service to establish a better bridge among academic, industrial and individual community.
Zhuang, L-L, Li, M & Hao Ngo, H 2020, 'Non-suspended microalgae cultivation for wastewater refinery and biomass production', Bioresource Technology, vol. 308, pp. 123320-123320.
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Zhuang, Y, Chi, H, Huang, Y, Teng, Q, He, B, Chen, W & Qian, Y 2020, 'Investigation of water spray evolution process of port water injection and its effect on engine performance', Fuel, vol. 282, pp. 118839-118839.
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© 2020 Elsevier Ltd In this study, a 1.5L turbocharged gasoline direct injection (GDI) engine was modified by installing a port water injection (PWI) system adjacent to the intake valve to simulate the “quasi-direct” water injection. Experiments was performed at 1500 rpm wide throttle open (WOT) condition to investigate the effect of PWI on knock suppression, and 4850 rpm WOT condition to test the removal of fuel enrichment through PWI. Then, numerical simulation was conducted to investigate the water spray evolution process and subsequent influence on mixture formation. The experimental results showed that PWI could effectively suppress knock and decrease combustion temperature. Therefore, at 4850 rpm WOT condition, the engine was able to operate at a stoichiometric air/fuel ratio with moderate advancement of spark timing. The combined effect finally resulted in nearly 6% thermal efficiency improvement. At 1500 rpm WOT, 3.8% efficiency gain was achieved solely due to knock mitigation. Nitrogen oxides (NOx), soot and hydrocarbon (HC) emissions also showed a decreasing trend with the increase of water injection amount. The simulation results indicated that about 80% of total injected water collided on the inner surface of the intake port which became the major source of water vapor. The portion of water vaporized in the air is small. Sufficient time was important for intake port water film evaporation. PWI also resulted in in-cylinder wall wetting. The in-cylinder water wall wetting in 4850 rpm was sober than that at 1500 rpm due to stronger intake air motion and higher cylinder temperature. Although port water injection imposes limited impacts on the whole in-cylinder equivalent ratio, it can induce part fuel-rich zones inside the combustion chamber.
Zhuang, Y, Sun, Y, Huang, Y, Teng, Q, He, B, Chen, W & Qian, Y 2020, 'Investigation of water injection benefits on downsized boosted direct injection spark ignition engine', Fuel, vol. 264, pp. 116765-116765.
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© 2019 Elsevier Ltd Engine downsizing and boosting are key technologies to achieve the increasingly stringent emissions standards for spark ignition (SI) engines. However, knock is a major obstacle inhibiting further downsizing of SI engines. Water injection is a promising technology that has regained attention recently to solve the knock problem. In this paper, a 1.5L turbocharged gasoline direct injection (GDI) engine was modified by installing a water port injection (WPI) system on the intake manifold. The WPI system was modified from a GDI system and deionized water was pressured to 50 bar in a water tank by compressed nitrogen. The effect of WPI on engine combustion and emissions performance were experimentally investigated under different water/gasoline volume percentages and WPI timings. The results show that WPI has great potential in suppressing engine knock. At original engine setting (without adjustment of spark timing), all the combustion indexes related to knock are decreased by WPI, including maximum in-cylinder pressure (Pmax) and maximum pressure rise rate (Rmax). The flame kernel formation process (CA0-5), initiation combustion duration (CA0-10), early combustion duration (CA0-50) and major combustion duration (CA0-90) are deteriorated, resulting in decreased indicated mean effective pressure (IMEP) and thermal efficiency. By properly advancing spark timing, the combustion process can be improved, allowing the engine to achieve higher Pmax and better combustion phases without occurrence of knock. It is also found that the water/gasoline volume percentage should be kept within a proper range (30% in this study) because over WPI can lead to deterioration of combustion and pollutant emissions. WPI can effectively reduce the production of NO and CO emissions, while HC emissions are increased with the rise of water/gasoline volume percentage.
Ziaei, M, Panahi, M, Fanaei, MA, Rafiee, A & Khalilpour, KR 2020, 'Maximizing the profitability of integrated Fischer-Tropsch GTL process with ammonia and urea synthesis using response surface methodology', Journal of CO2 Utilization, vol. 35, pp. 14-27.
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© 2019 Elsevier Ltd. The integration of a natural gas to liquids (GTL) process with ammonia and urea synthesis units was conducted to utilize the emitted CO2 of the GTL process for the urea synthesis. The feedstocks of the ammonia synthesis unit including hydrogen and nitrogen were provided by a polymer electrolyte membrane (PEM) electrolyzer and air separation unit (ASU) of the GTL process, respectively. The required power for the PEM modules was assumed to be supplied by the surplus generated power of the GTL process. To enhance the overall carbon efficiency and profitability of the three processes, the emitted CO2 from the GTL process was utilized in the urea synthesis unit. Multi-objective optimization approach was conducted to determine the optimal values of carbon efficiency and wax production rate of the GTL process. Objective functions were calculated by response surface methodology with second-order polynomial regression. The degrees of freedom were defined as follows: Unpurged ratio of recycled tail gas from Fischer-Tropsch (FT) reactor, recycle ratio of the GTL tail gas to the FT reactor, CO2 removal percentage from the GTL process synthesis gas (syngas) section, steam to carbon ratio to pre-reformer, molar flow of feed to the ammonia synthesis unit, and CO2 intake to the urea unit. The presented integration results in the production of about 434,000»tonnes/year urea in addition to the FT-derived products. 13.71% (37»tonnes/h) of the produced CO2 in the GTL process is utilized in the urea production unit and the profitability of the integrated process is enhanced by 8%.
Zou, Y, Gong, S, Xu, J, Cheng, W, Hoang, DT & Niyato, D 2020, 'Wireless Powered Intelligent Reflecting Surfaces for Enhancing Wireless Communications', IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 12369-12373.
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© 1967-2012 IEEE. Recently, the intelligent reflecting surface (IRS) has become a promising technology for energy-, and spectrum-efficient communications by reconfiguring the radio environment. In this paper, we consider multiple-input single-output (MISO) transmissions from a multi-antenna access point (AP) to a receiver, assisted by a practical IRS with a power budget constraint. The IRS can work in energy harvesting, and signal reflecting phases. It firstly harvests RF energy from the AP's signal beamforming, and then uses it to sustain its operations in the signal reflecting phase. We aim to characterize the maximum capacity by optimizing the AP's transmit beamforming, the IRS's time allocation in two operational phases, and the IRS's passive beamforming to enhance the information rate. To solve the non-convex maximization problem, we exploit its structural properties, and decompose it into two sub-problems in two phases. The IRS's phase shift optimization in the reflecting phase follows a conventional passive beamforming problem to maximize the received signal power. In the energy harvesting phase, the IRS's time allocation, and the AP's transmit beamforming are jointly optimized using monotonic optimization. Simulation results verify the effectiveness of the proposed algorithm.
Zou, Y, Sun, X, Wang, Y, Yan, C, Liu, Y, Li, J, Zhang, D, Zheng, M, Chung, RS & Shi, B 2020, 'Single siRNA Nanocapsules for Effective siRNA Brain Delivery and Glioblastoma Treatment', Advanced Materials, vol. 32, no. 24.
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AbstractSmall interfering RNA (siRNA) has been considered as a highly promising therapeutic agent for human cancer treatment including glioblastoma (GBM), which is a fatal disease without effective therapy methods. However, siRNA‐based GBM therapy is seriously hampered by a number of challenges in siRNA brain delivery including poor stability, short blood circulation, low blood–brain barrier (BBB) penetration, and tumor accumulation, as well as inefficient siRNA intracellular release. Herein, an Angiopep‐2 (Ang) functionalized intracellular‐environment‐responsive siRNA nanocapsule (Ang‐NCss(siRNA)) is successfully developed as a safe and efficient RNAi agent to boost siRNA‐based GBM therapy. The experimental results demonstrate that the developed Ang‐NCss(siRNA) displays long circulation in plasma, efficient BBB penetration capability, and GBM accumulation and retention, as well as responsive intracellular siRNA release due to the unique design of small size (25 nm) with polymeric shell for siRNA protection, Ang functionalization for BBB crossing and GBM targeting, and disulfide bond as a linker for intracellular‐environment‐responsive siRNA release. Such superior properties of Ang‐NCss(siRNA) result in outstanding growth inhibition of orthotopic U87MG xenografts without causing adverse effects, achieving remarkably improved survival benefits. The developed siRNA nanocapsules provide a new strategy for RNAi therapy of GBM and beyond.
Zuo, X, Ye, W, Yang, Y, Zheng, R, Vidal‐Calleja, T, Huang, G & Liu, Y 2020, 'Multimodal localization: Stereo over LiDAR map', Journal of Field Robotics, vol. 37, no. 6, pp. 1003-1026.
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AbstractIn this paper, we present a real‐time high‐precision visual localization system for an autonomous vehicle which employs only low‐cost stereo cameras to localize the vehicle with a priori map built using a more expensive 3D LiDAR sensor. To this end, we construct two different visual maps: a sparse feature visual map for visual odometry (VO) based motion tracking, and a semidense visual map for registration with the prior LiDAR map. To register two point clouds sourced from different modalities (i.e., cameras and LiDAR), we leverage probabilistic weighted normal distributions transformation (ProW‐NDT), by particularly taking into account the uncertainty of source point clouds. The registration results are then fused via pose graph optimization to correct the VO drift. Moreover, surfels extracted from the prior LiDAR map are used to refine the sparse 3D visual features that will further improve VO‐based motion estimation. The proposed system has been tested extensively in both simulated and real‐world experiments, showing that robust, high‐precision, real‐time localization can be achieved.
Zuo, Y, Fang, Y, Yang, Y, Shang, X & Wu, Q 2020, 'Depth Map Enhancement by Revisiting Multi-Scale Intensity Guidance Within Coarse-to-Fine Stages', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 12, pp. 4676-4687.
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IEEE Being different from the most methods of guided depth map enhancement based on deep convolutional neural network which focus on increasing the depth of networks, this paper is to improve the effectiveness of intensity guidance when the network goes deep. Overall, the proposed network upsamples the low-resolution depth maps from coarse to fine. Within each refinement stage of certain-scale depth features, the current-scale and all coarse-scales of the guidance features are revisited by dense connection. Therefore, the multi-scale guidance is efficiently maintained as the propagation of features. Furthermore, the proposed network maintains the intensity features in the high-resolution domain from which the multi-scale guidance is directly extracted. This design further improves the quality of intensity guidance. In addition, the shallow depth features upsampled via transposed convolution layer are directly transferred to the final depth features for reconstruction, which is called global residual learning in feature domain. Similarly, the global residual learning in pixel domain learns the difference between the depth ground truth and the coarsely upsampled depth map. Also, the local residual learning is to maintain the low frequency within each refinement stage and progressively recover the high frequency. The proposed method is tested for noise-free and noisy cases which compares against 16 state-of-the-art methods. Our experimental results show the improved performances based on the qualitative and quantitative evaluations.
Zuo, Y, Wu, Q, Fang, Y, An, P, Huang, L & Chen, Z 2020, 'Multi-Scale Frequency Reconstruction for Guided Depth Map Super-Resolution via Deep Residual Network', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 2, pp. 297-306.
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© 1991-2012 IEEE. The depth maps obtained by the consumer-level sensors are always noisy in the low-resolution (LR) domain. Existing methods for the guided depth super-resolution, which are based on the pre-defined local and global models, perform well in general cases (e.g., joint bilateral filter and Markov random field). However, such model-based methods may fail to describe the potential relationship between RGB-D image pairs. To solve this problem, this paper proposes a data-driven approach based on the deep convolutional neural network with global and local residual learning. It progressively upsamples the LR depth map guided by the high-resolution intensity image in multiple scales. A global residual learning is adopted to learn the difference between the ground truth and the coarsely upsampled depth map, and the local residual learning is introduced in each scale-dependent reconstruction sub-network. This scheme can restore the depth structure from coarse to fine via multi-scale frequency synthesis. In addition, batch normalization layers are used to improve the performance of depth map denoising. Our method is evaluated in noise-free and noisy cases. A comprehensive comparison against 17 state-of-the-art methods is carried out. The experimental results show that the proposed method has faster convergence speed as well as improved performances based on the qualitative and quantitative evaluations.
Zurita, G, Merigó, JM, Lobos-Ossandón, V & Mulet-Forteza, C 2020, 'Bibliometrics in computer science: An institution ranking', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5441-5453.
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Zurita, G, Shukla, AK, Pino, JA, Merigó, JM, Lobos-Ossandón, V & Muhuri, PK 2020, 'A bibliometric overview of the Journal of Network and Computer Applications between 1997 and 2019', Journal of Network and Computer Applications, vol. 165, pp. 102695-102695.
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Сілі, ІІ & Азархов, ОЮ 2020, 'THE PARAMETERS OF THE PEST MANAGEMENT ANTENNA SYSTEM RESEARCH AND CALCULATION', Proceedings of the Tavria State Agrotechnological University, vol. 20, no. 2, pp. 241-249.
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Abbas, R, Westling, FA, Skinner, C, Hanus-Smith, M, Harris, A & Kirchner, N 1970, 'BuiltView: Integrating LiDAR and BIM for Real-Time Quality Control of Construction Projects', Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot, pp. 233-239.
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Global spending on rework in the trillion dollar construction industry is estimated at $570bn of direct costs and $440bn of indirect costs. The cost of rework is on the rise, and the main cause is rooted in quality deviations from construction design. Real-time or even near real-time remote progress monitoring and quality control has the potential to prevent a large portion of defects and enables quick detection and handling of errors that often go undetected for too long. Current procedure still relies on human visual inspections that are costly, in terms of time and human resources, and is subject to human error. Moreover, existing tools and services are limited in the size of data they can handle, as Li-DAR data can be gigabits in size with hundreds of millions of unstructured points. The technology often fails at being robust to occlusions and other noise elements commonly found on construction sites. More sophisticated algorithms exist to perform different parts of the required analysis; however, they require a high level of expertise to implement and tailor to different use cases. In this paper, we present BuiltView, an automated progressive assurance platform that allows users to validate and document construction activities on a daily basis. BuiltView can significantly r educe r ework by accurately and efficiently identifying BIM components and detecting discrepancies between the as-designed data and the as-built data, particularly in terms of geometric compliance. BuiltView is a powerful tool that processes billions of LiDAR points in a very efficient manner. Leveraging sophisticated analytical algorithms, BuiltView calculates accurate dimensions and generates comprehensive user-friendly reports to facilitate communication across the business. We show that BuiltView is a valuable tool for automation of quality assurance and quality control, progress tracking and documentation. Using the tool, builders can significantly reduce costly rework, inc...
Abbasnejad, B, McGloin, D & Clemon, L 1970, 'A Flexible Hair-Like Laser Induced Graphitic Sensor for Low Flow Rate Sensing Applications', Volume 5: Biomedical and Biotechnology, ASME 2020 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers.
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Abstract Direct low flow sensing is of interest to many applications in medical and biochemical industries. Low flow rate measurement is still challenging, and conventional flow sensors such as hot films, hot wires and Pitot probes are not capable of measuring very low flow rates accurately. In some applications that require flow measurement in a small diameter tubing (e.g. intravenous (IV) infusion), using such sensors also becomes mechanically impractical. Herein, a flexible laser-induced graphitic (LIG) piezoresistive flow sensor has been fabricated in a cost-effective single processing step. The capability of the LIG sensor in very low flow rate measurement has been investigated by embedding the sensor within an intravenous (IV) line. The embedded LIG hair-like sensor was tested at ambient temperature within the IV line at flow rates ranging from 0 m/s to 0.3 m/s (IV infusion free-flow rate). The LIG hair-like sensor presented in this study detects live flow rates of IV infusions with a threshold detection limit as low as 0.02 m/s. Moreover, the deformation of the LIG hair-like sensor that lead to resistance change in response to various flow rates is simulated using COMSOL Multiphysics.
Abdo, P & Reyes-Cubas, A 1970, 'Simulation of Ventilation Flow Through a Room Fitted With a Windcatcher Incorporating Phase Change Materials', Volume 3: Computational Fluid Dynamics; Micro and Nano Fluid Dynamics, ASME 2020 Fluids Engineering Division Summer Meeting collocated with the ASME 2020 Heat Transfer Summer Conference and the ASME 2020 18th International Conference on Nanochannels, Microchannels, and Minichannels, American Society of Mechanical Engineers, virtual, pp. 1-7.
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Abstract Natural ventilation is the process of supplying and removing air through an indoor space by natural means. Windcatcher has been used over centuries for providing natural ventilation using wind power, it is an effective passive method to provide healthy and comfortable indoor environment by decreasing moisture content in the air and reducing pollutants concentration effectively. The windcatcher’s function is based on the wind and on the stack effect resulting from temperature differences. Materials that change phase at certain temperature are frequently referred to as Phase Change Materials (PCMs). PCMs change from solid to liquid and vice versa. PCMs could be used in passive cooling systems and they are directly related to building energy efficiency. In this study air flow through a two-dimensional room fitted with a windcatcher and incorporated with phase change materials (PCMs) is simulated. The temperature change in the room implementing PCM is analyzed to monitor the PCMs’ performance. To achieve this, Ansys Fluent is used to simulate the temperature changes inside the room as well as the melting process of PCM. PCM is placed at the right and left walls of the room and at its bottom. Two cases have been considered (with and without PCM) and the average temperatures at three locations have been compared for an inlet velocity of 1 m/s and an inlet temperature of 302 K. The average temperature at 1.2 m high inside the room with PCM dropped by about 1–2 °C compared to that without PCM.
Abdollahi, M, Gao, X, Mei, Y, Ghosh, S & Li, J 1970, 'Ontology-Guided Data Augmentation for Medical Document Classification', Artificial Intelligence in Medicine, International Conference on Artificial Intelligence in Medicine, Springer International Publishing, USA, pp. 78-88.
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Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification.
Accordini, D, Ferrari, N, Trianni, A, Cagno, E & Gambaro, F 1970, 'Understanding the impacts on the industrial operations from the adoption of energy efficiency measures: lessons learnt from Italian case studies', Eceee Industrial Summer Study Proceedings, eceee Industrial Summer Study, ECEEE, Virtual, pp. 191-198.
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Energy efficiency is a key driver to decarbonize industry, improving its sustainability and competitiveness. Nevertheless, the adoption of well-known energy efficiency measures (EEMs) is in many cases hindered by the lack of information about them. Unfortunately, EEMs are usually assessed with a simplistic energy and cost effectiveness analysis, neglecting however other characteristics that should be carefully encompassed, since they can deeply affect the EEMs performance during their implementation and service phases. Among others, the impact EEMs could have on surrounding production activities plays a critical role, especially when embedded in the core business of a company. So far, too little literature has highlighted such impacts, mainly referring to the existence of the so-called non-energy benefits, while research linking the impacts to the key performance indicators of industrial operations is still scarce. Therefore, the present study is intended as a preliminary exploration giving contribution to this discussion, trying to highlight intrinsic features of the EEMs and connect them with their potential impacts in terms of performance indicators once implemented. Results show the need to create a framework linking, e.g., pure production and operations-related information to raw material consumptions and emissions, in order to provide an extensive and integrated vision of the impacts of EEMs adoption. The conceptual framework, to be further developed as an assessment tool in support of decision-makers and energy managers, could represent a valuable support for policymakers and technology suppliers in highlighting the real implications of adopting EEMs.
Adak, C, Chaudhuri, BB, Lin, C-T & Blumenstein, M 1970, 'Why Not? Tell us the Reason for Writer Dissimilarity', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-7.
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© 2020 IEEE. Writer verification has drawn significant attention over the past few decades due to its extensive applications in forensics and biometrics. In traditional writer verification, handwriting similarity/dissimilarity analysis is mostly performed by extracting two feature vectors from two respective handwritten samples, followed by comparing them in relation to their similarity. In the state-of-the-art writer verification approaches, a distance metric is usually employed in terms of the similarity between two handwritten samples. If the distance between two handwritten samples is greater than a given threshold, then the samples are assumed to be written by two different writers, otherwise, they are considered to be due to the same writer. In this paper, for the very first time, we propose a model that generates English sentences to explain reasons for writer dissimilarity/similarity. First, our proposed model obtains features from handwritten images by employing a convolutional neural network, verifies the writer using a Siamese architecture, and generates English words using a recurrent neural network. Finally, these two networks are merged using an affine transformation to produce an explanatory sentence in support of writer similarity/dissimilarity. We evaluated our model on a handwritten numeral database of 100 writers and obtained promising results.
Adinolf, S, Wyeth, P, Brown, R & Harman, J 1970, 'My Little Robot: User Preferences in Game Agent Customization', Proceedings of the Annual Symposium on Computer-Human Interaction in Play, CHI PLAY '20: The Annual Symposium on Computer-Human Interaction in Play, ACM, pp. 461-471.
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Adinolf, S, Wyeth, P, Brown, R & Simpson, L 1970, 'Near and Dear: Designing Relatable VR Agents for Training Games', 32nd Australian Conference on Human-Computer Interaction, OzCHI '20: 32nd Australian Conference on Human-Computer-Interaction, ACM, pp. 413-425.
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Afzal, MU & Esselle, KP 1970, 'Recent Advances in Near-Field Meta-Steering', 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, pp. 1745-1746.
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Afzal, MU, Esselle, KP & Lalbakhsh, A 1970, 'A System-Level Overview of Near-Field Meta-Steering', 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), IEEE, pp. 1-4.
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© 2020 URSI. The paper provides a system-level overview of Near-Field Meta-Steering (NFMS) technology. The NFMS is upcoming antenna beam-steering method that uses the physical rotation of pair of thin metasurfaces that are placed in very close proximity to a high-gain feeding base antenna. This method neither uses any active radio frequency (RF) components nor physical tilting of any antenna part. It is for these reasons that this method yield antenna systems that superior to traditional electronically scanned phased array and mechanically rotated beamsteering antennas. The antenna systems can be developed for a range of applications including inflight connectivity, low-cost satellite terminal antennas to provide connectivity at remote places, and high-power micro- and millimetre-wave applications. The dynamic phase transformation that is achieved by the rotation of two metasurfaces, in a proof-of-concept prototype reported in 2017, indicate that an antenna beam can be scanned in a conical region having an apex angle of 102°.
Agarwal, A, Chivukula, AS, Bhuyan, MH, Jan, T, Narayan, B & Prasad, M 1970, 'Identification and Classification of Cyberbullying Posts: A Recurrent Neural Network Approach Using Under-Sampling and Class Weighting', Communications in Computer and Information Science, International Conference on Neural Information Processing, Springer International Publishing, Online, pp. 113-120.
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© 2020, Springer Nature Switzerland AG. With the number of users of social media and web platforms increasing day-by-day in recent years, cyberbullying has become a ubiquitous problem on the internet. Controlling and moderating these social media platforms manually for online abuse and cyberbullying has become a very challenging task. This paper proposes a Recurrent Neural Network (RNN) based approach for the identification and classification of cyberbullying posts. In highly imbalanced input data, a Tomek Links approach does under-sampling to reduce the data imbalance and remove ambiguities in class labelling. Further, the proposed classification model uses Max-Pooling in combination with Bi-directional Long Short-Term Memory (LSTM) network and attention layers. The proposed model is evaluated using Wikipedia datasets to establish the effectiveness of identifying and classifying cyberbullying posts. The extensive experimental results show that our approach performs well in comparison to competing approaches in terms of precision, recall, with F1 score as 0.89, 0.86 and 0.88, respectively.
Ahmad, NZ, Zuhairi, MF, Dao, H & Yafi, E 1970, 'DNS Server Caching and Forwarding with Load Balance', 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM), IEEE.
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Ahmed, F, Afzal, MU, Hayatt, T & Esselle, KP 1970, 'Low-Cost All-Metal Resonant-Cavity Antenna for High Power Applications', 2020 4th Australian Microwave Symposium (AMS), 2020 4th Australian Microwave Symposium (AMS), IEEE, Sydney, Australia, pp. 1-2.
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Low-cost all-metal partially reflecting surface (AM PRS) based resonant-cavity antennas are presented in this paper. The AM PRSs are made by introducing square-shaped slots in a thin metallic sheet having moderate to high reflectivity ranging from -4.12 dB to -1.27 dB. An RCA designed using highly reflecting AM PRS has maximum directivity of 16.54 dBi and low 3 dB directivity bandwidth of 9.17% whereas those designed using less reflective AM PRSs have high directivity bandwidth but smaller peak directivity within the operating band.
Ahmed, F, Hayat, T, Afzal, MU, Lalbakhsh, A & Esselle, KP 1970, 'Dielectric-Free Cells for Low-Cost Near-Field Phase Shifting Metasurfaces', 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, Montreal, QC, Canada, pp. 741-742.
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© 2020 IEEE. This paper presents a unique dielectric-free cell configuration that is useful as a building block to design low-cost transparent phase-gradient metasurfaces required for antenna beam steering using both far-field and near-field methods. Each cell is made of a few thin metal sheets each having an asterisk-shaped slot. A phase variation of 360 degrees is possible with four metal layers, with less than 2 dB drop in amplitude. This is much greater than what is possible with the conventional cross-slot which lacks such a high degree of freedom.
Ahmed, SB, Naz, S, Razzak, I & Prasad, M 1970, 'Unconstrained Arabic Scene Text Analysis using Concurrent Invariant Points', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-6.
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© 2020 IEEE. Text in natural scene image portrays rich semantic information that plays an important role in content analysis. However, apart from Arabic text in documents, the text in natural scene images exhibit much higher diversity and variability, especially in uncontrolled circumstances. In this paper, a hybrid feature extraction approach is presented to detect extremal region of Arabic scene text. The binary image and image mask are considered as a variant of input image and look for concurrent extremal regions in both images. After determination of conjoined extremal points, the scale invariant technique is applied to consider those invariant points which are common in both images based on their coordinate positions. To evaluate the performance, a multidimensional long short term memory (LSTM) network is adapted and obtained 94.21% accuracy for word recognition on unconstrained Arabic scene text recognition (ASTR) dataset.
Akter, N, Perry, S, Fletcher, J, Simunovic, M & Roy, M 1970, 'Automated Artifacts and Noise Removal from Optical Coherence Tomography Images Using Deep Learning Technique', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 2536-2542.
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Alam, SL & Gill, AQ 1970, 'A social engagement framework for the government ecosystem: Insights from australian government facebook pages', International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive: Blending the Local and the Global, International Conference on Information Systems, AISEL, India.
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Government agencies are using social media in an ad-hoc manner for bi-directional broadcast style communication, rather than systematic and deep engagement through open participation for co-creating value. However, capabilities and practices of participation for value creation is less understood for an increasingly networked government ecosystem. This calls for the need of a structured social engagement framework for government agencies. Thus, based on an empirical analysis of over 68 federal government Facebook pages, this paper presents insights on online engagement and levels of maturity among Australian federal government Facebook pages. Informed through engagement research and social architecture lens, we propose an empirically bounded government Facebook engagement framework (GFEF) that has implications and recommendations for agency benchmarking and social engagement capability building.
Alanazi, F & Gay, V 1970, 'e-Health for Diabetes Self-Management in Saudi Arabia: Barriers and Solutions', International Business Information Management Association, International Business Information Management Association, Granada, Spain.
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The need to give support to diabetes patients through e-health becomes necessary in recent times. This study aims to determine how e-health applications can benefit diabetes self-management in Saudi Arabia through a systematic literature review. Barriers and solutions e-health for self-management of diabetes is the core focus of this study. Google Scholar, JSTOR, PubMed and Research Gate using articles published. Mobile-enabled and e-health applications were consistently found promising in most of the papers. The search yielded 40 usable papers, described then categorised according to the topic and direction of findings. Some barriers standing found in the study are integrating patient records at the national level, training people operating and maintaining the system, etc. The study found that the societies' cultural, religious and social practices significantly affect the effective use of e-health in the study area and everyone including parents need to be educated.
Al-Hadhrami, Y & Hussain, FK 1970, 'A Machine Learning Architecture Towards Detecting Denial of Service Attack in IoT', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 417-429.
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© 2020, Springer Nature Switzerland AG. Internet of thing is part of our everyday life nowadays. Where millions of devices contented to the internet to collect and share data. Although IoT devices are evolving quickly to the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators have been also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. With the rapid development of the Internet of Things in different areas, IoT is facing difficulty in securing overall availability of the network due to its heterogeneous nature. There are many types of vulnerability in IoT that can be mitigated with further research, however, in this paper, we have concentrated on distributed denial of Service attack (DDoS) on IoT. In this paper, we propose a machine learning architecture to detect DDoS attacks in IoT networks. The architecture collects IoT network traffic and analyzes the traffic through passing to machine learning model for attack detection. We propose the use of real-time data collection tool to dynamically monitor the network.
Al-Hadhrami, Y, Al-Hadhrami, N & Hussain, FK 1970, 'Data Exportation Framework for IoT Simulation Based Devices', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 212-222.
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© Springer Nature Switzerland AG 2020. Internet of things (IoT) is part of everyday life nowadays. Millions of devices are connected to the internet to collect and share data. Although IoT devices are evolving quickly in the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators are also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. A need for an IoT simulation tool is necessary for development purposes and testing before deployments. One of the widely used tools among IoT researchers is the open-source tool Cooja simulator. Cooja has limitations—one is the lack of a way to export collected data as a data set for further processing. Therefore, this study introduces an extension tool to present and export the data into different forms.
Ali, SMN, Hossain, MJ, Sharma, V & Kashif, M 1970, 'Thermal Control Compensation of Induction Motor Drive in Electrified Powertrain', 2020 IEEE Conference on Technologies for Sustainability (SusTech), 2020 IEEE Conference on Technologies for Sustainability (SusTech), IEEE, Santa Ana, CA, USA, pp. 1-6.
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The increase in operating as well as environmental temperature of an electric vehicle (EV) motor drive causes significant variations in its performance parameters such as rotor, stator resistance and mutual inductance. This variation results in the overall performance degradation of electrified powertrain in the form of excessive fuel (battery) consumption and inability to meet the desired terminal characteristics such as speed, torque and flux. To mitigate this issue, a robust linear parameter varying (LPV) control incorporated with linear matrix inequalities (LMIs) is presented in this work that ensures L2 gain bound and closed-loop control system stability. A comparison of sliding mode control (SMC) is made with the proposed controller to validate its robustness. In order to verify the efficacy of LPV controller, its performance is tested against a New European Driving Cycle (NEDC) in MATLAB Simulink environment. The nonlinear simulation results gurantee the excellence of LPV performance.
Almansor, EH & Hussain, FK 1970, 'Modeling the Chatbot Quality of Services (CQoS) Using Word Embedding to Intelligently Detect Inappropriate Responses', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 60-70.
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© 2020, Springer Nature Switzerland AG. The rapid growth of intelligent chatbots as conversational agents with the assistance of artificial intelligence has recently attracted much research attention. The major role of a chatbot is to generate appropriate responses to the user, however sometimes the chatbot fails to understand the user’s meaning. Therefore, detecting inappropriate responses from a chatbot is a critical issue. Several studies based on annotated datasets have investigated the problem of inappropriate responses from a chatbots perspective without considering the user’s perspective. Understanding the context of the conversation is an important point in determining whether a response is appropriate or inappropriate. Sentiment analysis is a natural language processing task that supports mining in user behavior. Therefore, we propose an intelligent framework that combines automated sentiment scoring and a word embedding model to detect the quality of chatbot responses considering the end-user’s point of view. We find our model achieves higher quality results than logistic regression.
Almansor, EH & Hussain, FK 1970, 'Survey on Intelligent Chatbots: State-of-the-Art and Future Research Directions', Complex, Intelligent, and Software Intensive Systems, International Conference on Complex, Intelligent, and Software Intensive Systems, Springer International Publishing, Sydney, pp. 534-543.
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Human-computer interaction (HCI) is an area of interest which plays a major role in understanding the interaction between humans and machines. Dialogue systems or conversational systems including chatbots, voice control interfaces and personal assistants are examples of HCI application that have been developed to interact with users using natural language. Chatbots can help customers find useful information for their needs. Thus, numerous organizations are using chatbots to automate their customer service. Thus, the needs for using artificial intelligence has been increasing due to the needs of automated services. However, devolving smart bots that can respond at the human level is challenging. In this paper, we survey the state-of-art chatbot approaches from based on the ability to generate appropriate responses perspective. After summarizing the review from this aspect, we identify the research issues and challenges in chatbots. The findings of this research will highlight directions for future work.
Almansor, EH, Al-Ani, A & Hussain, FK 1970, 'Transferring Informal Text in Arabic as Low Resource Languages: State-of-the-Art and Future Research Directions', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 176-187.
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© 2020, Springer Nature Switzerland AG. Rapid growth in internet technology lead to increase the usage of social media platforms which make communication between users easier. Through the communication users used their daily languages which considered as non-standard language. The non-slandered text contains lots of noise, such as abbreviations, slang which used more in English languages and dialect words which are widely used in Arabic language. These texts face challenging using any natural language processing tools. Therefore, these texts need to be treated and transferred to be similar to their standard form. According to that the normalization and translation approach have been used to transfer the informal text. However, using these approach need large label or parallel datasets. While high resource languages such as English have enough parallel datasets, low resource languages such as Arabic is lack of enough parallel dataset. Therefore, in this paper we focus on the Arabic and Arabic dialects as a low resource language in the era of transferring non-stander text using normalization and translation approach.
Almeida, R, Cunha, I, Teixeira, R, Veitch, D & Diot, C 1970, 'Classification of Load Balancing in the Internet', IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, IEEE, Toronto, ON, Canada.
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Recent advances in programmable data planes, software-defined networking, and the adoption of IPv6 support novel, more complex load balancing strategies. We introduce the Multipath Classification Algorithm (MCA), a probing algorithm that extends traceroute to identify and classify load balancing in Internet routes. MCA extends existing formalism and techniques to consider that load balancers may use arbitrary combinations of bits in the packet header for load balancing. We propose optimizations to reduce probing cost that are applicable to MCA and existing load balancing measurement techniques. Through large-scale measurement campaigns, we characterize and study the evolution of load balancing on the IPv4 and IPv6 Internet with multiple transport protocols. Our results show that load balancing is more prevalent and that load balancing strategies are more mature than previous characterizations have found.
Almusallam, M & Chandran, D 1970, 'Current and prospective views of the adoption of BISs in large companies and SMEs', 26th Americas Conference on Information Systems, AMCIS 2020, United states.
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Business intelligence systems (BISs) have attracted the attention of decision makers, as these have a significant impact on forecasts of current and prospective views of the decision process in business operations. This impact will be realized only when BIS is used and spread widely. The literature suggests that more than 70% of BIS projects in organizations fail to achieve their expected returns and benefits. Therefore, BIS adoption has grown over the last decade. However, no prior studies have comprehensively discussed and compared BIS adoption determinants in both large companies and SMEs. Therefore, by using a systematic literature review (SLR), this study has presented comprehensive knowledge about the current domain of BISs' adoption in large companies and SMEs. This paper also makes recommendations for future research. For the purpose of this study, a total of 76 studies published between 2009 and 2019 were selected.
Alnefaie, A, Gupta, D, Bhuyan, MH, Razzak, I, Gupta, P & Prasad, M 1970, 'End-to-End Analysis for Text Detection and Recognition in Natural Scene Images', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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© 2020 IEEE. Right from the very beginning, the text has vital importance in human life. As compared to the vision-based applications, preference is always given to the precise and productive information embodied in the text. Considering the importance of text, recognition, and detection of text is also equally important in human life. This paper presents a deep analysis of recent development on scene text and compare their performance and bring into light the real modern applications. Future potential directions of scene text detection and recognition are also discussed.
Alqahtani, A, Hawryszkiewycz, I & Erfani, E 1970, 'Analysing Citizens' Inputs in Public Online Open Innovation Platforms', 26th Americas Conference on Information Systems, AMCIS 2020, Americas Conference on Information Systems, AISEL, USA.
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Online open innovation platforms are being used widely in the public sector of many countries. Citizens are the main users of these types of platforms which allow citizens to share and post their ideas online. The citizens' values that can be derived from the content of public open innovation platforms are not clear in the literature as previous studies were limited to studying open innovation platforms in the private sector. This study will explore the content of two public online open innovation platforms, specifically citizens' interests which are called “values”. The ideas of around 2580 citizens from open innovation platforms in Saudi Arabia and Australia will be analysed. By using thematic analysis and a non-linear coding process, themes will be generated. These themes are categories of citizens' values. Finally, citizens' values will be represented as a framework of the content of citizen inputs in public online open innovation platforms.
Alqaisi, R, Le, TM & Khabbaz, H 1970, 'Applications of Recycled Sustainable Materials and By-Products in Soil Stabilization', International Congress and Exhibition "Sustainable Civil Infrastructures”, Springer International Publishing, Egypt, pp. 91-117.
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Alrashed, BA & Hussain, W 1970, 'Managing SLA Violation in the cloud using Fuzzy re-SchdNeg Decision Model', 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, pp. 136-141.
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Alsahafi, YA, Gay, V & Khwaji, AA 1970, 'The Acceptance of National Electronic Health Records in Saudi Arabia: Healthcare Consumers’ Perspectives', ACIS 2020 Proceedings - 31st Australasian Conference on Information Systems, ACIS, AISEL, New Zeeland.
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This study aims to investigate factors impacting healthcare consumers’ acceptance of National Electronic Health Records (NEHRs) in Saudi Arabia. The study incorporated perceived security concerns and trust factors into the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A questionnaire survey was distributed among Saudi citizens to gain their perceptions, and 794 valid responses were collected. Structural Equation Modeling (SEM) was used to analyse the collected data. Both the measurement model and structural model proved a good fit to the research data. All research hypotheses were supported at the significance level of p < 0.001 except the impact of social influence, which was significant at the level of p < 0.005. The proposed model explained 56% of the variance in behavioural intention, implying the presence of additional factors that are not yet identified. A better understanding of these influential factors could prompt policymakers to effectively plan for and enhance the acceptance and use of NEHRs.
Alsawwaf, M, Chaczko, Z & Kulbacki, M 1970, 'In Your Face: Person Identification Through Ratios of Distances Between Facial Features', Intelligent Information and Database Systems, Asian Conference on Intelligent Information and Database Systems, Springer International Publishing, Phuket, Thailand, pp. 527-536.
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© 2020, Springer Nature Switzerland AG. These days identification of a person is an integral part of many computer-based solutions. It is a key characteristic for access control, customized services, and a proof of identity. Over the last couple of decades, many new techniques were introduced for how to identify human faces. The purpose of this paper is to introduce yet another innovative approach for face recognition. The human face consists of multiple features that when considered together produces a unique signature that identifies a single person. Building upon this premise, we are studying the identification of faces by producing ratios from the distances between the different features on the face and their locations in an explainable algorithm with the possibility of future inclusion of multiple spectrum and 3D images for data processing and analysis.
Al-Shehri, MA, Guo, Y & Lei, G 1970, 'A Systematic Review of Reliability Studies of Grid-Connected Renewable Energy Microgrids', 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), IEEE, Istanbul, Turkey, pp. 1-6.
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This paper has carried out a comprehensive review of the reliability analysis of microgrid. Survey papers on grid-connected microgrids have reported. In addition, the most applicable indices used in the reliability analyses of microgrids have been investigated. Different techniques used for coupling microgrids have been reported. In the same vein, a survey of different models used for comprehensive reliability analysis of microgrids is also reported. In a similar passion, the most frequent indices used in the reliability evaluations of microgrid have been defined. Different microgrids existing globally have also been presented in the paper. Finally, some future research topics in the area of this research have been identified.
Al-Shehri, MA, Guo, Y & Lei, G 1970, 'Grid-Connected Renewable Energy Microgrids: A Systematic Review', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 1297-1306.
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A comprehensive review of the literature for the optimum design of microgrid is presented in this paper. This is aim at realistic evaluation of the current status, some existing research problems, and developed a future research topic in the area. Presently, the penetration of microgrid is increasing, ranging from developed to underdeveloped nations. Depending on the application, microgrids could be installed for specific applications, such as community-based and experimental microgrids. Examples of these situations are also highlighted in the paper.
Al-Shehri, MA, Guo, Y & Lei, G 1970, 'Statistical Fitting of Wind Speed Data for Determination of Wind Power Potentials in Saudi Arabia', 2020 International Conference on Decision Aid Sciences and Application (DASA), 2020 International Conference on Decision Aid Sciences and Application (DASA), IEEE, pp. 1029-1034.
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Al-Soeidat, M, Khawaldeh, H, Lu, DD-C & Zhu, J 1970, 'A Novel High Step-up Three-Port Bidirectional DC/DC Converter for PV-Battery Integrated System', 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE, New Orleans, LA, USA, pp. 3352-3357.
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In this paper, a new high step-up non-isolated three- port DC-DC converter (HS-NITPC) is proposed. The converter is designed to integrate a solar panel with battery storage in order to boost its voltage, reduce the effect of solar energy intermittency and enhance solar power performance under unpredictable load demand. The converter combines three converters to form one integrated converter by sharing some components. Thus, the converter has high power density and fewer components compared to the traditional DC-DC converters. The coupled inductor is used to achieve a high output regulated voltage, transfer energy among the ports and facilitate maximum power point tracking for the solar panel. A hardware prototype was built and tested to verify the proposed circuit for 180 W input power. The proposed converter is suitable for stand-alone or grid- connected solar system. Moreover, it could be used in the electric vehicle where the regenerative braking is used.
Alyami, A, Pileggi, SF & Hawryszkiewycz, I 1970, 'The impact of new technologies on learning: A literature review on mobile collaborative learning', Proceedings of the 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020, Pacific Asia Conference on Information Systems, AISEL, Dubai, UAE.
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Mobile Collaborative Learning (MCL) has recently caught the attention of the research community because of its potential impact on improving learners' effectiveness and performance. Nevertheless, to be really effective, the potential of MCL-based solutions is still largely unexplored, as well as further research is needed to develop improved learning environments. This literature review aims to discuss the possible impact of MCL, looking at a number of typical parameters, such as user satisfaction, perceived ease-of-use, perceived usefulness, impact on the affective and cognitive domain, and perceived enjoyment. The literature review has pointed out that, by adopting the MCL approach, learners improve their motivation, as well as their cognitive skills. It points out a positive impact also at an affective level. Additionally, we found that MCL currently targets education at any level, including university and school level. Last but not least, it seems to positively affect also learning outcomes, namely learners' performance. On the negative side, the study found a lack of research attention on possible difficulties, constraints, and barriers. Finally, learning environments are expected to become more and more sophisticated in the next future (e.g. by using augmented reality), and mobile technology is expected to play an even more relevant role.
Amirbagheri, K, Merigó, JM & Yang, J-B 1970, 'A Bibliometric Analysis of Leading Countries in Supply Chain Management Research', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 182-192.
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Supply chain management as a newly comer discipline has attracted many attentions of the scholars to do an investigation based on its prominent level of importance for the economy and its influence on the management of the organizations. So, the key point is to understand the trends among the countries throughout the time to have a powerful insight about this issue. To this end, this work does a comprehensive analysis from 1990 to 2017. The purpose of this study is to analyze the leading countries and understand thoroughly their trends during the time. The work has dedicated to three sections. In the first one the countries have studied globally to give a comprehensive overview to academics. Next, the performance of the countries is studied in three periods to understand better the changes of each during the time. Finally, some individual journals and groups of journals are also investigated. The results show that the USA is the leader of the countries while China has experienced an enormous growth and it is predictable that with this trend can reach to the top of the list.
Amiri, M, Tofigh, F, Shariati, N, Lipman, J & Abolhasan, M 1970, 'Ultra Wideband Dual Polarization Metamaterial Absorber for 5G frequency spectrum', 2020 14th European Conference on Antennas and Propagation (EuCAP), 2020 14th European Conference on Antennas and Propagation (EuCAP), IEEE, Copenhagen, Denmark, pp. 1-5.
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Implementing 5G technology contributes to improve communication quality and facilitate several interesting applications in daily life such as Internet of things. Despite outstanding features of 5G, the amount of ambient electromagnetic waves will be increased significantly in the environment, which may be undesired. Ultra-wideband metamaterial perfect absorber is a promising solution to collect these undesired signals. Using lumped elements in absorber structure to increase the absorption bandwidth leads to design and fabrication process complexity. In this paper, a low profile polarization angle selective metamaterial absorber has been designed to absorb signals in the frequency range of 21.79 GHz to 53.23 GHz with more than 90% efficiency. The relative absorption bandwidth of the final structure is 83.81%. Moreover, the final structure is reasonably insensitive facing different incident angle up to 40 degree.
An, B, Huang, S, Chen, Z, Lu, Z, Lu, W & Zhang, Y 1970, 'A 16bit 1MS/s High-Bit Sampling SAR ADC with Improved Binary-Weighted Capacitive Array', 2020 IEEE 5th International Conference on Integrated Circuits and Microsystems (ICICM), 2020 IEEE 5th International Conference on Integrated Circuits and Microsystems (ICICM), IEEE, pp. 267-271.
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Ansari, M, Zhu, H, Shariati, N & Guo, YJ 1970, 'Mm-wave Multi-Beam Antenna Array Based on Miniaturized Butler Matrix for 5G Applications', 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, pp. 1619-1620.
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Anwar, MJ & Gill, AQ 1970, 'Developing an Integrated ISO 27701 and GDPR based Information Privacy Compliance Requirements Model', ACIS 2020 Proceedings - 31st Australasian Conference on Information Systems, Australasian Conference on Information Systems 2020, Wellington, New Zealand.
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The protection of information assets requires interdisciplinary approach and cross-functional capabilities. In recent times, information security and privacy compliance continue to be a complicated task due to increasing regulatory restrictions, changing legislations and public awareness. The newly published information security and privacy standard ISO/IEC 27701:2019 provides support for organisations looking to put in place systems to support compliance with global data privacy requirements. However, there is little known about how does this standard map to other regulatory requirements in different jurisdictions specifically the globally relevant General Data Protection Regulation (GDPR). Hence, this research aims to answer an important research question: whether and how the ISO/IEC 27701:2019 framework represents an opportunity for the GDPR compliance? This research provides a review and mapping of ISO/IEC 27701:2019 and GDPR by using an integrated requirement engineering model as a kernel theory. The results of this research will assist organisations contemplating to meet their compliance needs. It will also help academics and practitioners interested in integrating the ISO/IEC 27701:2019 and GDPR for developing relevant compliance frameworks and tools.
Aseeri, M & Kang, K 1970, 'Technological and human factors for supporting big data analytics in Saudi Arabian higher education', 26th Americas Conference on Information Systems, AMCIS 2020.
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Big data analytics is an emerging technology that is widely utilized across industries and is increasingly discussed among researchers. This study investigates the effects of Technological and Human Factors for Big Data Analytics (THFFBDA) on technological improvements of big data analytics (BDA) towards improved decision making by top management in Saudis' Higher Education. This paper seeks to enhance our understanding of how these components impact on the implementation of big data analytics to improve decision making by top management in Saudi Arabian Higher Institutions. This study draws on the Sociotechnical theory to define and investigate THFFBDA, and the Delone & McLean Information System success model to highlight the technological improvements in BDA. This research paper concludes with propositions on the potential effects of THFFBDA on decision making among universities' top management and proposes a mixed-methods approach to study the above phenomenon.
Au, W, Sakaue, T & Liu, D 1970, 'A Model for Optimising the Size of Climbing Robots for Navigating Truss Structures', 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Las Vegas, NV, USA, pp. 3754-3760.
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Truss structures can be found in many buildings and civil infrastructure such as bridges and towers. But as these architectures age, their maintenance is required to keep them structurally sound. A legged robotic solution capable of climbing these structures for maintenance is sought, but determining the size and shape of such a robot to maximise structure coverage is a challenging task. This paper proposes a model in which the size of a multi-legged robot is optimised for coverage in a truss structure. A detailed representation of a truss structure is presented, which forms the novel framework for constraint modelling. With this framework, the overall truss structure coverage is modelled, given a robot's size and its climbing performance constraints. This is set up as an optimisation problem, such that its solution represents the optimum size of the robot that satisfies all constraints. Three case studies of practical climbing applications are conducted to verify the model. By intuitive analysis of the model's output data, the results show that the model accurately applies these constraints in a variety of truss structures.
Aung, TWW, Huo, H & Sui, Y 1970, 'A Literature Review of Automatic Traceability Links Recovery for Software Change Impact Analysis', Proceedings of the 28th International Conference on Program Comprehension, ICPC '20: 28th International Conference on Program Comprehension, ACM, pp. 14-24.
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In large-scale software development projects, change impact analysis (CIA) plays an important role in controlling software designevolution. Identifying and accessing the effects of software changesusing traceability links between various software artifacts is a common practice during the software development cycle. Recently,research in automated traceability-link recovery has received broadattention in the software maintenance community to reduce themanual maintenance cost of trace links by developers. In this study,we conducted a systematic literature review related to automatictraceability link recovery approaches with a focus on CIA. We identified 33 relevant studies and investigated the following aspects ofCIA: traceability approaches, CIA sets, degrees of evaluation, tracedirection and methods for recovering traceability link between artifacts of different types. Our review indicated that few traceabilitystudies focused on designing and testing impact analysis sets, presumably due to the scarcity of datasets. Based on the findings, weurge further industrial case studies. Finally, we suggest developingtraceability tools to support fully automatic traceability approaches,such as machine learning and deep learning.
Azizivahed, A, Arefi, A, Ghavidel, S, Shafie-khah, M, Li, L, Zhang, J & Catalao, J 1970, 'Energy Management Strategy in Dynamic Distribution Network Reconfiguration considering Renewable Energy Resources and Storage', 2020 IEEE Power & Energy Society General Meeting (PESGM), 2020 IEEE Power & Energy Society General Meeting (PESGM), IEEE, pp. 1-1.
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Azizivahed, A, Karandeh, R, Cecchi, V, Naderi, E, Li, L & Zhang, J 1970, 'Multi-Area Dynamic Economic Dispatch Considering Water Consumption Minimization, Wind Generation, and Energy Storage System', 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), IEEE, Washington, DC, USA, pp. 1-5.
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This paper introduces a novel, practical model for Multi-Area Dynamic Economic Dispatch (MADED) problem whose primary targets are to minimize both operational cost and water consumption. To make the proposed study more practical, several additional requirements that are inevitable in forthcoming power systems are taken into account including wind power penetration, pumped storage systems, and different interconnected areas. Such a complicated problem needs to be solved by a potent multi-objective optimization tool. In this regard, one of the relatively new optimization approaches, Whale Optimization Algorithm (WOA), is implemented to solve the proposed problem. The obtained optimal solutions on a 40-unit power network illustrate the effectiveness of the proposed approach and reveal a substantial improvement in reducing the operational cost, i.e., more than $30 million is reduced annually.
Azra, A, Gide, E, Wu, M, Karim, S & Sandu, R 1970, 'Artificial Intelligence Enhancing Learning and Satisfaction in Higher Education', CQU Scholarship of Tertiary Teaching Online Conference, Central Queensland University, Australia.
Azzam, R, Taha, T, Huang, S & Zweiri, Y 1970, 'A Deep Learning Framework for Robust Semantic SLAM', 2020 Advances in Science and Engineering Technology International Conferences (ASET), 2020 Advances in Science and Engineering Technology International Conferences (ASET), IEEE, Dubai, United Arab Emirates, pp. 1-7.
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Semantic simultaneous localization and mapping (SLAM) is susceptible to several sources of noise that hinder the accuracy of its trajectory and map estimates. Such sources include inaccurate landmark pose estimation and sensor limitations. In this paper, a novel deep learning based approach is proposed to improve the accuracy of semantic SLAM by reducing the trajectory estimation error. A deep neural network consisting of various non-linear activation functions is structured and pre-trained by means of an unsupervised greedy layer-wise pre-training technique. The network is then fine-tuned using the adaptive moment estimation (Adam) optimizer. The training datasets were collected using several simulated and realtime experiments and are composed of two parts, the estimated trajectory and the corresponding ground truth. Ground truth trajectories were obtained using a motion capture system in realtime experiments. The effectiveness of the proposed approach was shown through simulated experiments, real-time experiments, and a sequence from the Technical University of Munich (TUM) RGB-D dataset. The performance of the deep neural network (DNN) was tested with different pre-training techniques and the proposed unsupervised greedy layer-wise pre-training technique proved to perform the best across training, validation, and testing datasets in terms of reducing the mean absolute trajectory error (ATE).
Baba, AA, Hashmi, RM, Esselle, KP & Attygalle, M 1970, 'A Wideband Wide-Angle Beam-Steering System for Millimeter-wave Applications', 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, pp. 259-260.
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This paper presents the performance of an optimized wideband beam-steering antenna system for mm-wave applications. The antenna is passive and can provide continuous two-dimensional beam scanning up to \pm \mathbf{40}^{\mathbf{o}} away from broadside direction in elevation and any direction in azimuth. It uses a pair of near-field dielectric phase transformers, inspired from optical prisms, introducing a pre-determined phase gradient to the incoming wave front from a source antenna, and redirecting the radiated beam to specified direction with in a conical region with an apex angle of 80°. A prototype of the antenna system along with the mounting structure has been fabricated and tested.
Bai, L, Yao, L, Li, C, Wang, X & Wang, C 1970, 'Adaptive graph convolutional recurrent network for traffic forecasting', Advances in Neural Information Processing Systems, 34th Conference on Neural Information Processing Systems, online.
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Modeling complex spatial and temporal correlations in the correlated time series data is indispensable for understanding the traffic dynamics and predicting the future status of an evolving traffic system. Recent works focus on designing complicated graph neural network architectures to capture shared patterns with the help of pre-defined graphs. In this paper, we argue that learning node-specific patterns is essential for traffic forecasting while the pre-defined graph is avoidable. To this end, we propose two adaptive modules for enhancing Graph Convolutional Network (GCN) with new capabilities: 1) a Node Adaptive Parameter Learning (NAPL) module to capture node-specific patterns; 2) a Data Adaptive Graph Generation (DAGG) module to infer the inter-dependencies among different traffic series automatically. We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks. Our experiments 1 on two real-world traffic datasets show AGCRN outperforms state-of-the-art by a significant margin without pre-defined graphs about spatial connections.
Bai, L, Yao, L, Wang, X, Kanhere, SS & Xiao, Y 1970, 'Prototype Similarity Learning for Activity Recognition', Lecture Notes in Computer Science, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, online, pp. 649-661.
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Human Activity Recognition (HAR) plays an irreplaceable role in various applications such as security, gaming, and assisted living. Recent studies introduce deep learning to mitigate the manual feature extraction (i.e., data representation) efforts and achieve high accuracy. However, there are still challenges in learning accurate representations for sensory data due to the weakness of representation modules and the subject variances. We propose a scheme called Distance-based HAR from Ensembled spatial-temporal Representations (DHARER) to address above challenges. The idea behind DHARER is straightforward—the same activities should have similar representations. We first learn representations of the input sensory segments and latent prototype representations of each class, using a Convolution Neural Network (CNN)-based dual-stream representation module; then the learned representations are projected to activity types by measuring their similarity to the learned prototypes. We have conducted extensive experiments under a strict subject-independent setting on three large-scale datasets to evaluate the proposed scheme, and our experimental results demonstrate superior performance of DHARER to several state-of-the-art methods.
Ball, JE & Choi, KS 1970, 'Complexity in catchment modelling systems and its impact on predictive reliability', 30th Hydrology and Water Resources Symposium, HWRS 2006, Hydrology and Water Resources Symposium, Conference Design Pty Ltd, Launceston, Tasmania, Australia, pp. 247-252.
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In general, the calibration process involves minimisation of the deviation between recorded information and the simulation predictions through repeated adjustment of control parameters. Implementation of this process requires temporal and spatial information at an adequate resolution to achieve robust predictions from a catchment modelling system. Unfortunately, the available information usually is not adequate for this purpose and it becomes necessary to either use catchment average values or to use other techniques to infer the necessary information. Developments in information technology and the availability of digital information have facilitated the later approach (see, for example, Choi and Ball, 1999). Using the Centennial Park Catchment in Sydney, Australia as a test catchment, inference models for estimation of the control parameters necessary to implement the Stormwater Management Model were developed. A number of alternative inference models were developed to assess the influence of inference model complexity and structure on the calibration of the catchment modelling system. These inference models varied from the assumption of a spatially invariant value (catchment average) to spatially variable with each subcatchment having its own unique values. Furthermore, the influence of different measures of deviation between the monitored information and simulation predictions were considered. Presented herein will be the results of these investigations into the complexity and structure of models used in the calibration process.
Bandara, M & Rabhi, FA 1970, 'Knowledge-Driven Framework for Designing Visual Analytics Applications', 2020 24th International Conference Information Visualisation (IV), 2020 24th International Conference Information Visualisation (IV), IEEE, pp. 515-520.
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Bandara, N, Gunawardane, K & Kularatna, N 1970, 'Exprimental verification of Supercapacitor Assisted Sub Module Inverter (SCASMI) Technique', 2020 2nd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), 2020 2nd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), IEEE, pp. 176-181.
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Bandara, N, Gunawardane, K & Kularatna, N 1970, 'Supercapacitor based RC loop loss circumvention technique to improve the efficiency of photovoltaic inverters', 2020 IEEE International Conference on Industrial Technology (ICIT), 2020 IEEE International Conference on Industrial Technology (ICIT), IEEE, pp. 1151-1156.
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Banerjee, S, Ling, SH, Lyu, J, Su, S & Zheng, Y-P 1970, 'Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network', 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, IEEE, United States, pp. 2039-2042.
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Scoliosis is a 3D spinal deformation where the spine takes a lateral curvature, which generates an angle in a coronal plane. For periodic detection of scoliosis, safe and economic imaging modality is needed as continuous exposure to radiative imaging may cause cancer. 3D ultrasound imaging is a cost-effective and radiation-free imaging modality which gives volume projection image. Identification of mid-spine line using manual, semi-automatic and automatic methods have been published. Still, there are some difficulties like variations in human measurement, slow processing of data associated with them. In this paper, we propose an unsupervised ground truth generation and automatic spine curvature segmentation using U- Net. This approach of the application of Convolutional Neural Network on ultrasound spine image, to perform automatic detection of scoliosis, is a novel one.
Bano, M, Zowghi, D, Ferrari, A & Spoletini, P 1970, 'Inspectors Academy : Pedagogical Design for Requirements Inspection Training.', RE, IEEE International Requirements Engineering Conference, IEEE, Zurich, pp. 215-226.
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© 2020 IEEE. The core aim of requirements inspection is to ensure the high quality of already elicited requirements in the Software Requirements Specification. Teaching requirements inspection to novices is challenging, as inspecting requirements needs several skills as well as knowledge of the product and process that is hard to achieve in a classroom environment. Published studies about pedagogical design specifically for teaching requirements inspection are scarce. Our objective is to present the design and evaluation of a postgraduate course for requirements inspection training. We conducted an empirical study with 138 postgraduate students, teamed up in 34 groups to conduct requirements inspection. We performed qualitative analysis on the data collected from students' reflection reports to assess the effects of the pedagogical design in terms of benefits and challenges. We also quantitatively analyze the correlation between the students' performance in conducting inspections and their ability of writing specifications. From the analysis of students' reflections, several themes emerged such as their difficulty of working with limited information, but also revealed the benefits of learning teamwork and writing good requirements. This qualitative analysis also provides recommendations for improving the related activities. The results revealed a moderate positive correlation between the performance in writing specification and inspection.
Barbar, M, Sui, Y & Chen, S 1970, 'Flow-sensitive type-based heap cloning', Leibniz International Proceedings in Informatics, LIPIcs.
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By respecting program control-flow, flow-sensitive pointer analysis promises more precise results than its flow-insensitive counterpart. However, existing heap abstractions for C and C++ flow-sensitive pointer analyses model the heap by creating a single abstract heap object for each memory allocation. Two runtime heap objects which originate from the same allocation site are imprecisely modelled using one abstract object, which makes them share the same imprecise points-to sets and thus reduces the benefit of analysing heap objects flow-sensitively. On the other hand, equipping flow-sensitive analysis with context-sensitivity, whereby an abstract heap object would be created (cloned) per calling context, can yield a more precise heap model, but at the cost of uncontrollable analysis overhead when analysing larger programs. This paper presents TypeClone, a new type-based heap model for flow-sensitive analysis. Our key insight is to differentiate concrete heap objects lazily using type information at use sites within the program control-flow (e.g., when accessed via pointer dereferencing) for programs which conform to the strict aliasing rules set out by the C and C++ standards. The novelty of TypeClone lies in its lazy heap cloning: an untyped abstract heap object created at an allocation site is killed and replaced with a new object (i.e. a clone), uniquely identified by the type information at its use site, for flow-sensitive points-to propagation. Thus, heap cloning can be performed within a flow-sensitive analysis without the need for context-sensitivity. Moreover, TypeClone supports new kinds of strong updates for flow-sensitive analysis where heap objects are filtered out from imprecise points-to relations at object use sites according to the strict aliasing rules. Our method is neither strictly superior nor inferior to context-sensitive heap cloning, but rather, represents a new dimension that achieves a sweet spot between precision and efficienc...
Barthe, G, Hsu, J, Ying, M, Yu, N & Zhou, L 2020, 'Relational proofs for quantum programs', Proceedings of the ACM on Programming Languages, Association for Computing Machinery (ACM), pp. 1-29.
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Barzegarkhoo, R, Siwakoti, YP & Blaabjerg, F 1970, 'Model Predictive Control of a Five-level Active Boost Neutral Point Clamped (5L-ABNPC) Inverter for Transformerless Grid-Connected PV Applications', 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), IEEE, pp. 209-213.
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Barzegarkhoo, R, Siwakoti, YP, Long, T & Blaabjerg, F 1970, 'Five-Level Grid-Tied Inverter Employing Switched-Capacitor Cell with Common-Grounded Feature', 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE, USA, pp. 3298-3303.
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An advanced topology of single-phase five-level transformerless grid-connected inverter is presented in this paper, which offers a common ground between the input source and the null of the grid. This alleviated the concern of variable common-mode voltage and the leakage current problem, which makes the inverter suitable for grid-tied photovoltaic (PV)-based applications. The proposed topology is operated by a series-parallel switching conversion of a switched-capacitor (SC) cell. It consists of a single dc source, two power diodes/capacitors alongside six power switches. Using the SC technique, a single-stage two times voltage boosting inverter with a self-voltage balancing of the capacitors is achieved. By employing the SC cell, five distinct voltage levels are also made at the inverter's output, so a small L-Type filter can be utilized. The control/modulation of the proposed inverter is also on the basis of a new peak current controller (PCC) technique. The circuit description along with the proposed PCC operation is discussed and a brief comparative study besides the experimental results are given at the end to demonstrate the feasibility of the proposed topology.
Bastwadkar, M, McGregor, C & Balaji, S 1970, 'A Cloud Based Big Data Health-Analytics-as-a-Service Framework to Support Low Resource Setting Neonatal Intensive Care Unit', Proceedings of the 4th International Conference on Medical and Health Informatics, ICMHI 2020: 2020 4th International Conference on Medical and Health Informatics, ACM, Kamakura City, Japan, pp. 30-36.
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© 2020 ACM. Critical care patients are monitored by a range of medical devices collecting high frequency data. New computing frameworks and platforms are being proposed to review and analyze the data in detail. The application of these approaches in a low resource setting is challenged by the approaches used for data acquisition. Software as a Service (SaaS) is a form of cloud computing where a cloud-based software application enables the storage, analysis and visualization of data within the cloud. A subset of SaaS is Health Analytics as a Service (HAaaS), which provides software to support health analytics in the cloud. The objective of this study is to design, implement, and demonstrate an extendable big-data compatible HAaaS framework that offers both real-time and retrospective analysis where data acquisition is not tightly coupled. A data warehousing framework is presented to facilitate analysis within a low resource setting. The framework has been instantiated in the Artemis platform within the context of the Belgaum Children Hospital (BCH) case study. Initial end-to-end testing with the Nellcor monitor (bedside monitor at BCH), which was not connected to any human, was completed. This testing confirms the functionality of the new Artemis cloud instance to receive data from test device using an alternate data acquisition approach.
Bauer, D, Patten, T & Vincze, M 1970, 'Physical Plausibility of 6D Pose Estimates in Scenes of Static Rigid Objects', Springer International Publishing, pp. 648-662.
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Bawden, R, Di Nunzio, GM, Grozea, C, Unanue, IJ, Yepes, AJ, Mah, N, Martinez, D, Névéol, A, Neves, M, Oronoz, M, de Viñaspre, OP, Piccardi, M, Roller, R, Siu, A, Thomas, P, Vezzani, F, Navarro, MV, Wiemann, D & Yeganova, L 1970, 'Findings of the WMT 2020 Biomedical Translation Shared Task: Basque, Italian and Russian as New Additional Languages', 5th Conference on Machine Translation, WMT 2020 - Proceedings, Fifth Conference in Machine Translation (WMT 2020), The Association for Computational Linguistics, Online, pp. 660-687.
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Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities. In the fifth edition of the WMT Biomedical Task, we addressed a total of eight language pairs. Five language pairs were previously addressed in past editions of the shared task, namely, English/German, English/French, English/Spanish, English/Portuguese, and English/Chinese. Three additional languages pairs were also introduced this year: English/Russian, English/Italian, and English/Basque. The task addressed the evaluation of both scientific abstracts (all language pairs) and terminologies (English/Basque only). We received submissions from a total of 20 teams. For recurring language pairs, we observed an improvement in the translations in terms of automatic scores and qualitative evaluations, compared to previous years.
Begum, M, Li, L & Zhu, J 1970, 'Distributed Secondary Control of Energy Storage Units for SoC balancing in AC Microgrid', 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), IEEE, Washington, DC, USA, pp. 1-5.
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This paper introduces a new distributed secondary control (DSC) method for distributed energy storage units (DESUs) in an islanded alternative current (AC) microgrid (MG). Dynamics of distributed storages for the extended time duration are not taken into account in the traditional hierarchical control of MG. Thus, it is challenging to control the DESUs with various levels of stored energy represented by the state of charge (SoC). The storage units can utilise their full power capacity after converging to a common SoC to mitigate the generation and demand variations in the MG. SoC depletion of DESUs with lower initial SoC occurs faster than those with higher initial SoC by using the traditional P-f droop control and then their capacities are no longer accessible. Furthermore, applying the droop control to match the SoC of DESUs causes the deviation of frequency and voltage from their reference values. However, restoration of the MG frequency using the conventional DSCs disrupts the SoC-balancing. The designed DSC can achieve simultaneous frequency/voltage regulation, power sharing and SoC-balancing as well as removing the centralized communication. The proposed method is evaluated in the established Matlab/Simulink model and the results validate the effectiveness of the proposed method.
Bei, X, Chen, S, Guan, J, Qiao, Y & Sun, X 1970, 'From independent sets and vertex colorings to isotropic spaces and isotropic decompositions: Another bridge between graphs and alternating matrix spaces', Leibniz International Proceedings in Informatics, LIPIcs.
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In the 1970’s, Lovász built a bridge between graphs and alternating matrix spaces, in the context of perfect matchings (FCT 1979). A similar connection between bipartite graphs and matrix spaces plays a key role in the recent resolutions of the non-commutative rank problem (Garg-Gurvits-Oliveira-Wigderson, FOCS 2016; Ivanyos-Qiao-Subrahmanyam, ITCS 2017). In this paper, we lay the foundation for another bridge between graphs and alternating matrix spaces, in the context of independent sets and vertex colorings. The corresponding structures in alternating matrix spaces are isotropic spaces and isotropic decompositions, both useful structures in group theory and manifold theory. We first show that the maximum independent set problem and the vertex c-coloring problem reduce to the maximum isotropic space problem and the isotropic c-decomposition problem, respectively. Next, we show that several topics and results about independent sets and vertex colorings have natural correspondences for isotropic spaces and decompositions. These include algorithmic problems, such as the maximum independent set problem for bipartite graphs, and exact exponential-time algorithms for the chromatic number, as well as mathematical questions, such as the number of maximal independent sets, and the relation between the maximum degree and the chromatic number. These connections lead to new interactions between graph theory and algebra. Some results have concrete applications to group theory and manifold theory, and we initiate a variant of these structures in the context of quantum information theory. Finally, we propose several open questions for further exploration.
Bem, NFSD, Ruppert, MG, Yong, YK & Fleming, AJ 1970, 'Integrated force and displacement sensing in active microcantilevers for off-resonance tapping mode atomic force microscopy', 2020 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), 2020 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), IEEE, pp. 1-6.
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Bernhardt, N, Koshelev, K, White, S, Meng, KWC, Froch, JE, Tran, TT, Kim, S, Choi, D-Y, Kivshar, Y & Solntsev, AS 1970, 'Second-Harmonic Generation from WS2 Monolayers Enhanced by BIC Resonances', Conference on Lasers and Electro-Optics, CLEO: Science and Innovations, Optica Publishing Group, pp. SW3N.6-SW3N.6.
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Through the utilization of resonant dielectric metasurfaces governed by bound states in the continuum, we demonstrate a strong increase in the second-harmonic generation in WS2 monolayers by a factor exceeding 700
Best, G & Hollinger, GA 1970, 'Decentralised Self-Organising Maps for Multi-Robot Information Gathering', 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 4790-4797.
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Best, G, Cliff, OM, Patten, T, Mettu, RR & Fitch, R 1970, 'Decentralised Monte Carlo Tree Search for Active Perception', Workshop on the Algorithmic Foundations of Robotics (WAFR), Workshop on the Algorithmic Foundations of Robotics (WAFR), Springer International Publishing, San Francisco, USA, pp. 864-879.
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We propose a decentralised variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimise its own individual action space by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of these search trees, which are used to update the locally-stored joint distributions using an optimisation approach inspired by variational methods. Our method admits any objective function defined over robot actions, assumes intermittent communication, and is anytime. We extend the analysis of the standard MCTS for our algorithm and characterise asymptotic convergence under reasonable assumptions. We evaluate the practical performance of our method for generalised team orienteering and active object recognition using real data, and show that it compares favourably to centralised MCTS even with severely degraded communication. These examples support the relevance of our algorithm for real-world active perception with multi-robot systems.
Betti, F, Ramponi, G & Piccardi, M 1970, 'Controlled Text Generation with Adversarial Learning', INLG 2020 - 13th International Conference on Natural Language Generation, Proceedings, 13th International Conference on Natural Language Generation (INLG 2020), The Association for Computational Linguistics, Dublin, Ireland, pp. 29-34.
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In recent years, generative adversarial networks (GANs) have started to attain promising results also in natural language generation. However, the existing models have paid limited attention to the semantic coherence of the generated sentences. For this reason, in this paper we propose a novel network - the Controlled TExt generation Relational Memory GAN (CTERM-GAN) - that uses an external input to influence the coherence of sentence generation. The network is composed of three main components: a generator based on a Relational Memory conditioned on the external input; a syntactic discriminator which learns to discriminate between real and generated sentences; and a semantic discriminator which assesses the coherence with the external conditioning. Our experiments on six probing datasets have showed that the model has been able to achieve interesting results, retaining or improving the syntactic quality of the generated sentences while significantly improving their semantic coherence with the given input.
Beydoun, G, Suryanto, H, Guan, C, Guan, A & Sugumaran, V 1970, 'Knowledge Graphs in Support of Credit Risk Assessment', ACIS 2020 Proceedings - 31st Australasian Conference on Information Systems.
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An ontology is a formal and reusable knowledge structure that pertains to a specific domain of expertise. Building an ontology can be difficult. Consistency and completeness within the boundaries of the domain of expertise is required. Knowledge graphs are less complex to build. They remove the burden of specifying boundaries for the domain and reduce completeness and consistency requirements. They have been successful in facilitating knowledge reuse and maintenance. Adding knowledge continuously, in small localised chunks, is easier than the holistic engineering required for ontologies. In this paper, we exploit this to use knowledge graphs in combination with ontologies for transfer learning in machine learning. Through the use of knowledge graphs, data is extracted and transformed from one domain to another where data is lacking. This synthesized data is then used to support machine learning overcoming the lack of data. This approach is illustrated to support transfer learning in lending risk assessment. The approach provides a template for supporting data driven innovation as a finance company explores new markets and designs new products.
Biddle, R, Joshi, A, Liu, S, Paris, C & Xu, G 1970, 'Leveraging Sentiment Distributions to Distinguish Figurative From Literal Health Reports on Twitter', Proceedings of The Web Conference 2020, WWW '20: The Web Conference 2020, ACM, pp. 1217-1227.
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© 2020 ACM. Harnessing data from social media to monitor health events is a promising avenue for public health surveillance. A key step is the detection of reports of a disease (referred to as ĝ€?health mention classification') amongst tweets that mention disease words. Prior work shows that figurative usage of disease words may prove to be challenging for health mention classification. Since the experience of a disease is associated with a negative sentiment, we present a method that utilises sentiment information to improve health mention classification. Specifically, our classifier for health mention classification combines pre-trained contextual word representations with sentiment distributions of words in the tweet. For our experiments, we extend a benchmark dataset of tweets for health mention classification, adding over 14k manually annotated tweets across diseases. We also additionally annotate each tweet with a label that indicates if the disease words are used in a figurative sense. Our classifier outperforms current SOTA approaches in detecting both health-related and figurative tweets that mention disease words. We also show that tweets containing disease words are mentioned figuratively more often than in a health-related context, proving to be challenging for classifiers targeting health-related tweets.
Bossalini, C, Raffe, W & Andres Garcia, J 1970, 'Generative Audio and Real-Time Soundtrack Synthesis in Gaming Environments', 32nd Australian Conference on Human-Computer Interaction, OzCHI '20: 32nd Australian Conference on Human-Computer-Interaction, ACM, Australia, pp. 281-292.
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An important yet oft-overlooked front in the scope of interactive media, audio technologies have remained relatively stagnant compared to groundbreaking advancements made in fields such as visual fidelity and virtual reality. This paper explores the use of generative audio within a gaming environment, examining how dynamically-rendered audio can modify the creative pipeline, offer greater flexibility for audio designers, and improve the overall immersion of games and interactive media. A prototype generative audio engine is created, allowing for various musical parameters like tempo and pitch to be changed at runtime. Additionally, bidirectional linking between gameplay and music is explored, allowing player inputs to influence the soundtrack and the soundtrack to trigger or quantize player inputs. The final result, while somewhat limited in scope, demonstrates the potential of partially generative soundtracks to provide greater variety and freedom for audio engineers.
Bown, O, Fraietta, A, Loke, L & Ferguson, S 1970, 'Creative Coding and Interaction Design for Media Multiplicities', Proceedings of the Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction, TEI '20: Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction, ACM, pp. 877-880.
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Media multiplicities are media artworks that employ multiple networked digital devices to create holistic aesthetic effects. Examples include the networked light artworks of Squidsoup, the Spaxels drone-mounted light performances, DrawBots, Siftables and many others. In multiplicitous media artworks, each individual device is a programmable node connected to other nodes via a network connection, and may combine any number of sensors and actuators. A number of development technologies support artists and designers to configure and create media multiplicities, but this domain offers new challenges for creative practitioners. This workshop aims to bring together experts in creative coding and interaction design to discuss and conceptualise frameworks for the practice of media multiplicities. Open challenges include: speed of setup; ease of hardware configuration; speed of code deployment; ability to model and simulate works in VR; network connectivity and stability; and understanding network, computation and power constraints.
Brian Lee, KM, Martens, W, Khatkar, J, Fitch, R & Mettu, R 1970, 'Efficient Updates for Data Association with Mixtures of Gaussian Processes', 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 335-341.
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© 2020 IEEE. Gaussian processes (GPs) enable a probabilistic approach to important estimation and classification tasks that arise in robotics applications. Meanwhile, most GP-based methods are often prohibitively slow, thereby posing a substantial barrier to practical applications. Existing sparse methods to speed up GPs seek to either make the model more sparse, or find ways to more efficiently manage a large covariance matrix. In this paper, we present an orthogonal approach that memoises (i.e. reuses) previous computations in GP inference. We demonstrate that a substantial speedup can be achieved by incorporating memoisation into applications in which GPs must be updated frequently. Moreover, we derive a novel online update scheme for sparse GPs that can be used in conjunction with our memoisation approach for a synergistic improvement in performance. Across three robotic vision applications, we demonstrate between 40-100% speed-up over the standard method for inference in GP mixtures.
Brooksbank, PA, Li, Y, Qiao, Y & Wilson, JB 1970, 'Improved algorithms for alternating matrix space isometry: From theory to practice', Leibniz International Proceedings in Informatics, LIPIcs.
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Motivated by testing isomorphism of p-groups, we study the alternating matrix space isometry problem (AltMatSpIso), which asks to decide whether two m-dimensional subspaces of n × n alternating (skew-symmetric if the field is not of characteristic 2) matrices are the same up to a change of basis. Over a finite field Fp with some prime p 6= 2, solving AltMatSpIso in time pO(n+m) is equivalent to testing isomorphism of p-groups of class 2 and exponent p in time polynomial in the group order. The latter problem has long been considered a bottleneck case for the group isomorphism problem. Recently, Li and Qiao presented an average-case algorithm for AltMatSpIso in time pO(n) when n and m are linearly related (FOCS’17). In this paper, we present an average-case algorithm for AltMatSpIso in time pO(n+m). Besides removing the restriction on the relation between n and m, our algorithm is considerably simpler, and the average-case analysis is stronger. We then implement our algorithm, with suitable modifications, in Magma. Our experiments indicate that it improves significantly over default (brute-force) algorithms for this problem.
Brown, TA & Rayner, DG 1970, 'Motivation of first-year engineering students: A design and build project's contribution', SEFI 48th Annual Conference Engaging Engineering Education, Proceedings, SEFI Annual Conference, SEFI, Enschede, The Netherlands - Online, pp. 678-688.
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Hands-on 'design and build' projects have been advocated and reported to be essential components of practice-based learning for engineers for many years. The focus of most studies on the benefits of such design and build projects has been on technical/design skills. This paper is a reflection of practice in which the coordinator and teacher of a first-year engineering subject sought to find out how students' experience of a 'design and build' project might relate to their motivation and confidence to succeed as student engineers. Students in a first-year Introduction to Mechanical and Mechatronic Engineering subject (250-300 students in Autumn, approximately 100 students in Spring) participated in a 'design and build' project. The students worked in groups of 4-5 to design and build a small functional prototype wind-powered vehicle. After completing the subject, students completed an anonymous and voluntary online survey. The survey gathered some demographic information, asked several Likert-scale agree-disagree questions and encouraged students to write short explanations of why they agreed or disagreed and to describe their experiences. Student responses were evaluated and interpreted from expectancy-value theory of motivation and self-determination theory contexts. Students largely agreed that their participation in the design and build project had a positive impact on their confidence and expectation to succeed and on their perceived value of their studies. These results indicate that well-designed and supported design and build projects can have an important role to play in student motivation and successful transition to university.
Bryant, L, Hemsley, B, Bailey, B, Bluff, A, Nguyen, V, Stubbs, P, Barnett, D, Jacobs, C, Lucas, C & Power, E 1970, 'Opportunities for the Implementation of Immersive Virtual Reality in Rehabilitation', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Wailea, Maui, HI, pp. 3567-3576.
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Virtual reality (VR) technologies are emerging as novel platforms for physical and cognitive interventions, though applications in communication rehabilitation are scarce. Consultation with end-users on implementation of VR in clinical contexts is a vital first step to investigating the feasibility VR in communication rehabilitation. The aim of this study was to determine the views of professionals with expertise in health, rehabilitation, and VR technology, on the populations that might benefit from VR-based rehabilitation, and potential barriers and facilitators to their use of VR. Thematic content analysis of one interdisciplinary focus group and one in-depth interview identified two content themes relating to the use of VR in rehabilitation, and four themes related to the use of VR to maximize its clinical benefit and uptake. Consideration of these results in the development of VR programs in rehabilitation might lead to better acceptance and implementation of VR for improved health and participation outcomes.
Buchan, J, Zowghi, D & Bano, M 1970, 'Applying Distributed Cognition Theory to Agile Requirements Engineering.', REFSQ, International Working Conference on Requirements Engineering: Foundation for Software Quality, Springer, Pisa, Italy, pp. 186-202.
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© 2020, Springer Nature Switzerland AG. [Context & Motivation] Agile Requirements Engineering (ARE) is a collaborative, team-based process based on frequent elicitation, elaboration, estimation and prioritization of the user requirements, typically represented as user stories. While it is claimed that this Agile approach and the associated RE activities are effective, there is sparse empirical evidence and limited theoretical foundation to explain this efficacy. [Question/problem] We aim to understand and explain aspects of the ARE process by focusing on a cognitive perspective. We appropriate ideas and techniques from Distributed Cognition (DC) theory to analyze the cognitive roles of people, artefacts and the physical work environment in a successful collaborative ARE activity, namely requirement prioritization. [Principal idea/results] This paper presents a field study of two early requirements related meetings in an Agile product development project. Observation data, field notes and transcripts were collected and qualitatively analyzed. We have used DiCoT, a framework for systematically applying DC as a methodological contribution, to analyze the ARE process and explain its efficacy from a cognitive perspective. The analysis identified three main areas of cognitive effort in the ARE process as well as the significant information flows and artefacts. Analysis of these have identified that the use of physical user story cards, specific facilitator skills, and development of shared understanding of the user stories, were all key to the effectiveness of the ARE activity observed. [Contribution] The deeper understanding of cognition involved in ARE provides an empirically evidenced explanation, based on DC theory, of why this way of collaboratively prioritizing requirements was effective. Our result provides a basis for designing other ARE activities.
Buruk, OO, Özcan, O, Baykal, GE, Göksun, T, Acar, S, Akduman, G, Baytaş, MA, Beşevli, C, Best, J, Coşkun, A, Genç, HU, Kocaballi, AB, Laato, S, Mota, C, Papangelis, K, Raftopoulos, M, Ramchurn, R, Sádaba, J, Thibault, M, Wolff, A & Yildiz, M 1970, 'Children in 2077: Designing Children's Technologies in the Age of Transhumanism', Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-14.
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Cagno, E, Accordini, D, Trianni, A, Gambaro, F & Ferrari, N 1970, 'Real Adoption of Industrial Energy Efficiency Measures: Need for Empirical Evidence and an Adoption Framework', International Conference on Applied Energy, International Conference on Applied Energy, Virtual.
Cai, GQ, Zhou, AN & Sheng, D 1970, 'Predicting the dependency of a permeability function on initial density for unsaturated soils', Unsaturated Soils: Research and Applications - Proceedings of the 6th International Conference on Unsaturated Soils, UNSAT 2014, 6th International Conference on Unsaturated Soils (UNSAT), CRC Press, Sydney, AUSTRALIA, pp. 1091-1097.
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This paper presents a simple approach to quantify the effect of initial density on the relative coefficient of permeability for unsaturated soils. This approach is derived on two bases: an incremental relationship between the degree of saturation and the initial void ratio;- predicting the permeability function for unsaturated soils by use of the water retention curve. For a given soil, only one additional parameter is required, which can conveniently be calibrated by the conventional water retention curve tests. The relative coefficient of permeability for the same soil at different initial densities can be predicted by the proposed approach. The proposed approach has been validated by experimental data from the literature where both the water retention curves and the coefficients of permeability under different initial densities were measured. © 2014 Taylor & Francis Group.
Cai, L, Lin, D, Zhang, J & Yu, S 1970, 'Dynamic Sample Selection for Federated Learning with Heterogeneous Data in Fog Computing', ICC 2020 - 2020 IEEE International Conference on Communications (ICC), ICC 2020 - 2020 IEEE International Conference on Communications (ICC), IEEE, Dublin, Ireland, pp. 1-6.
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Federated learning is a state-of-the-art technology used in the fog computing, which allows distributed learning to train cross-device data while achieving efficient performance. Many current works have optimized the federated learning algorithm in homogeneous networks. However, in the actual application scenario of distributed learning, data is independently generated by each device, and this non-homologous data has different distribution characteristics. Therefore, the data used by each device for local learning is unbalanced and non-IID, and the heterogeneity of data affects the performance of federated learning and slows down the convergence. In this paper, we present a dynamic sample selection optimization algorithm, FedSS, to tackle heterogeneous data in federated learning. FedSS dynamically selects the training sample size during the gradient iteration based on the locally available data size, to settle the expensive evaluations of the local objective function with a massive amount of dataset. We theoretically analyze the convergence and present the complexity estimates of our framework when learning large data from unbalanced distribution. Our experimental results show that the use of dynamic sampling methods can effectively improve the convergence speed with heterogeneous data, and keep computational costs low while achieving the desired accuracy.
Cao, Y, Chen, X, Yao, L, Wang, X & Zhang, WE 1970, 'Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems', Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval, ACM, pp. 1669-1672.
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Adversarial attacks pose significant challenges for detecting adversarial attacks at an early stage. We propose attack-agnostic detection on reinforcement learning-based interactive recommendation systems. We first craft adversarial examples to show their diverse distributions and then augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our black-box detector trained with one crafting method has the generalization ability over several crafting methods.
Cao, Y, Lv, T & Ni, W 1970, 'Intelligent Reflecting Surface Aided Multi-User mmWave Communications for Coverage Enhancement', 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, IEEE, London, UK, pp. 1-6.
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Intelligent reflecting surface (IRS) is envisioned as a promising solution for controlling radio propagation environments in future wireless systems. In this paper, we propose a distributed intelligent reflecting surface (IRS) assisted multi-user millimeter wave (mmWave) system, where IRSs are exploited to enhance the mmWave signal coverage when direct links between base station and users are unavailable. First, a joint active and passive beamforming problem is established for weighted sum-rate maximization. Then, an alternating iterative algorithm with closed-form expressions is proposed to tackle the challenging non-convex problem, thereby decoupling the active and passive beamforming variables. Moreover, we design a constraint relaxation technique to address the unit modulus constraints pertaining to the IRS. Numerical results demonstrate that the distributed IRS can potentially enhance the communication performance of existing wireless systems.
Carmichael, M, Khonasty, R, Wilkinson, S & Schork, T 1970, 'The wallbot: A low-cost robot for green wall inspection', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Brisbane, Australia, pp. 1-7.
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The benefits of urban green infrastructure, such as attenuating the urban heat island effect and improving air quality, are widely accepted. Regardless, the uptake of green walls (i.e. vertical gardens) is low due to the high costs relating to maintenance and OH&S. These barriers to adoption may be mitigated by using robotics to inspect and maintain green walls. In this work we present the Wallbot, a robotic system to inspect, monitor and aid in the maintenance of green walls. In its current form the system comprises of affordable off-the-shelf components to keep the system cost low. Preliminary development of the system, results of initial tests and findings are presented. The system offers the chance to reduce OH&S issues and maintenance costs associated with green walls.
Carmichael, MG, Khonasty, R, Aldini, S & Liu, D 1970, 'Human Preferences in Using Damping to Manage Singularities During Physical Human-Robot Collaboration', 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Paris, France, pp. 10184-10190.
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When a robot manipulator approaches a kinematic singular configuration, control strategies need to be employed to ensure safe and robust operation. If this manipulator is being controlled by a human through physical human-robot collaboration, the choice of strategy for handling singularities can have a significant effect on the feelings and impressions of the user. To date the preferences of humans during physical human-robot collaboration regarding strategies for managing kinematic singularities have yet to be thoroughly explored.This work presents an empirical study of a damping-based strategy for handling singularities with regard to the preferences of the human operator. Two different parameters, damping rate and damping asymmetry, are tested using a double-blind A/B pairwise comparison testing protocol. Participants included two cohorts made up of the general public (n=51) and people working within a robotic research centre (n=18). In total 105 individual trials were performed. Results indicate a preference for a faster, asymmetric damping behavior that slows motions towards singularities whilst allowing for faster motions away.
Cetindamar, D, Shdifat, B & Erfani, S 1970, 'Assessing Big Data Analytics Capability and Sustainability in Supply Chains', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii, USA, pp. 208-217.
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Chaczko, Z, Kulbacki, M, Gudzbeler, G, Alsawwaf, M, Thai-Chyzhykau, I & Wajs-Chaczko, P 1970, 'Exploration of Explainable AI in Context of Human-Machine Interface for the Assistive Driving System', Intelligent Information and Database Systems, Asian Conference on Intelligent Information and Database Systems, Springer International Publishing, Phuket, Thailand, pp. 507-516.
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This paper presents the application and issues related to explainable AI in context of a driving assistive system. One of the key functions of the assistive system is to signal potential risks or hazards to the driver in order to allow for prompt actions and timely attention to possible problems occurring on the road. The decision making of an AI component needs to be explainable in order to minimise the time it takes for a driver to decide on whether any action is necessary to avoid the risk of collision or crash. In the explored cases, the autonomous system does not act as a “replacement” for the human driver, instead, its role is to assist the driver to respond to challenging driving situations, possibly difficult manoeuvres or complex road scenarios. The proposed solution validates the XAI approach for the design of a safety and security system that is able to identify and highlight potential risk in autonomous vehicles.
Chandran, D & Aljohani, N 1970, 'The role of cultural factors on mobile health adoption: The case of Saudi Arabia', 26th Americas Conference on Information Systems, AMCIS 2020, 2020 Americas Conference on Information Systems, Salt Lake City, Utah.
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The adoption of mobile health (m-health) applications (apps) is not as widespread as might be expected in Saudi Arabia. Even though healthcare providers have recently launched a few m-health apps, the use of such technologies in Saudi society is limited, with no explicit evidence of actual causes. Moreover, the role of cultural factors has not been involved or examined in the case of adopting m-health apps in Saudi Arabia. This study will investigate the role of cultural as well as technological factors on patients' intentions to adopt m-health apps in Saudi Arabia. The proposed model and factors identified will be tested to understand patients' perceptions of m-health apps. The results would be beneficial to increase the adoption rates of m-health in Saudi Arabia.
Chang, L, Feng, X, Lin, X, Qin, L, Zhang, W & Ouyang, D 1970, 'Speeding Up GED Verification for Graph Similarity Search.', ICDE, 2020 IEEE 36th International Conference on Data Engineering, IEEE, Dallas, TX, USA, pp. 793-804.
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Graph similarity search retrieves from a database all graphs whose edit distance (GED) to a query graph is within a threshold. As GED computation is NP-hard, the existing works adopt the filtering-and-verification paradigm to reduce the number of GED verifications, and they mainly focus on designing filtering techniques while using the now out-dated algorithm A*GED for verification. In this paper, we aim to speed up GED verification, which is orthogonal to the index structures used in the filtering phase. We propose a best-first search algorithm AStar+-LSa which improves A*GED by (1) reducing memory consumption, (2) tightening lower bound estimation, and (3) improving the time complexity for lower bound computation. We formally show that AStar+-LSa has a lower space and time complexity than A*GED. We further modify AStar+-LSa into a depth-first search algorithm to contrast these two search paradigms, and we extend our algorithms for exact GED computation. We conduct extensive empirical studies on real graph datasets, and show that our algorithm AStar+-LSa outperforms the state-of-the-art algorithms by several orders of magnitude for both GED verification and GED computation.
Chang, X, Liu, W, Huang, PY, Li, C, Zhu, F, Han, M, Li, M, Ma, M, Hu, S, Kang, G, Liang, J, Gui, L, Yu, L, Qian, Y, Wen, J & Hauptmann, A 1970, 'MMVG-inf-etrol@TRECVID 2019: Activities in extended video', 2019 TREC Video Retrieval Evaluation, TRECVID 2019.
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We propose a video analysis system detecting activities in surveillance scenarios which wins Trecvid Activities in Extended Video (ActEV1) challenge 2019. For detecting and localizing surveillance events in videos, Argus employs a spatial-temporal activity proposal generation module facilitating object detection and tracking, followed by a sequential classification module to spatially and temporally localize persons and objects involved in the activity. We detail the design challenges and provide our insights and solutions in developing the state-of-the-art surveillance video analysis system.
Charles, M, Yu, HS & Sheng, D 1970, 'Finite element analysis of pressuremeter tests using critical state soil models', NUMERICAL MODELS IN GEOMECHANICS - NUMOG VII, 7th International Symposium on Numerical Models in Geomechanics (NUMOG), CRC Press, GRAZ, AUSTRIA, pp. 645-650.
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Chaturvedi, K & Vishwakarma, DK 1970, 'Face Recognition in an Unconstrained Environment using ConvNet', Proceedings of the 2020 2nd International Conference on Big Data Engineering and Technology, BDET 2020: 2020 2nd International Conference on Big Data Engineering and Technology, ACM, pp. 67-71.
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Chemalamarri, VD, Braun, R & Abolhasan, M 1970, 'Constraint-Based Rerouting mechanism to address Congestion in Software Defined Networks', 2020 30th International Telecommunication Networks and Applications Conference (ITNAC), 2020 30th International Telecommunication Networks and Applications Conference (ITNAC), IEEE, Melbourne, VIC, Australia, pp. 1-6.
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In this paper, we propose a traffic rerouting mechanism to address congestion in Software-Defined networks. We employ back-tracking and constraint propagation techniques to find alternate paths to reroute multiple active flows simultaneously. Cost function is based on standard deviation of link-loads. We then compare traffic distribution and link utilisation with and without rerouting active flows. We measure and compare network performance using parameters such as total rate of transfer, jitter, and packet loss with that of Shortest Path First with no rerouting. Our proposed solution produces lower jitter, packet drops, and higher transfer rate. We finally conclude the paper by making observations and discussing the scope of the future work.
Chen, H, Guo, S, Xue, Y, Sui, Y, Zhang, C, Li, Y, Wang, H & Liu, Y 1970, 'MUZZ: Thread-aware grey-box fuzzing for effective bug hunting in multithreaded programs', Proceedings of the 29th USENIX Security Symposium, 29th USENIX Security Symposium, Online, pp. 2325-2342.
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Grey-box fuzz testing has revealed thousands of vulnerabilities in real-world software owing to its lightweight instrumentation, fast coverage feedback, and dynamic adjusting strategies. However, directly applying grey-box fuzzing to input-dependent multithreaded programs can be extremely inefficient. In practice, multithreading-relevant bugs are usually buried in the sophisticated program flows. Meanwhile, existing grey-box fuzzing techniques do not stress thread-interleavings that affect execution states in multithreaded programs. Therefore, mainstream grey-box fuzzers cannot adequately test problematic segments in multithreaded software, although they might obtain high code coverage statistics. To this end, we propose MUZZ, a new grey-box fuzzing technique that hunts for bugs in multithreaded programs. MUZZ owns three novel thread-aware instrumentations, namely coverage-oriented instrumentation, thread-context instrumentation, and schedule-intervention instrumentation. During fuzzing, these instrumentations engender runtime feedback to accentuate execution states caused by thread interleavings. By leveraging such feedback in the dynamic seed selection and execution strategies, MUZZ preserves more valuable seeds that expose bugs under a multithreading context. We evaluate MUZZ on twelve real-world multithreaded programs. Experiments show that MUZZ outperforms AFL in both multithreading-relevant seed generation and concurrency-vulnerability detection. Further, by replaying the target programs against the generated seeds, MUZZ also reveals more concurrency-bugs (e.g., data-races, thread-leaks) than AFL. In total, MUZZ detected eight new concurrency-vulnerabilities and nineteen new concurrency-bugs. At the time of writing, four reported issues have received CVE IDs.
Chen, H, Yin, H, Sun, X, Chen, T, Gabrys, B & Musial, K 1970, 'Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction', Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 1503-1511.
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Cross-platform account matching plays a significant role in social networkanalytics, and is beneficial for a wide range of applications. However,existing methods either heavily rely on high-quality user generated content(including user profiles) or suffer from data insufficiency problem if onlyfocusing on network topology, which brings researchers into an insolubledilemma of model selection. In this paper, to address this problem, we proposea novel framework that considers multi-level graph convolutions on both localnetwork structure and hypergraph structure in a unified manner. The proposedmethod overcomes data insufficiency problem of existing work and does notnecessarily rely on user demographic information. Moreover, to adapt theproposed method to be capable of handling large-scale social networks, wepropose a two-phase space reconciliation mechanism to align the embeddingspaces in both network partitioning based parallel training and accountmatching across different social networks. Extensive experiments have beenconducted on two large-scale real-life social networks. The experimentalresults demonstrate that the proposed method outperforms the state-of-the-artmodels with a big margin.
Chen, S-K, Chen, C-S, Wang, Y-K & Lin, C-T 1970, 'An SSVEP Stimuli Design using Real-time Camera View with Object Recognition', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, Canberra Australia, pp. 562-567.
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© 2020 IEEE. Most SSVEP-based stimuli BCIs are pre-defined using the white blocks. This kind of scenario lead less flexibility in the real life. To represent the flickers with the location, types and configurations of the objects in real world, this paper proposes an SSVEP-based BCI using real-time camera view with object recognition algorithm to provide intuitive BCI for users. A deep learning-based object recognition algorithm is used to calculate the location of the objects on the online camera view from a depth camera. After the bounding box of the objects is estimated, the location of the SSVEP flickers are designed to overlap on the object locations. An overlapping FFT and SVM is used to recognize the EEG signals into corresponding classes. In experimental results, the classification rate for camera view scenario is more than 94.1%. The results show that proposed SSVEP stimuli design is available to create an intuitive and reliable human machine interaction. The proposed results can be used for the users who have motor disabilities to further used to interact with assistive devices, such as: robotic arm and wheelchairs.
Chen, S-L, Ziolkowski, RW, Guo, YJ & Liu, Y 1970, 'Single-Feed, Highly-Directive, Higher-Order-Mode Cavity Antenna and Its Beam Tilting Realization', 2020 IEEE Asia-Pacific Microwave Conference (APMC), 2020 IEEE Asia-Pacific Microwave Conference (APMC 2020), IEEE, Hong Kong, Hong Kong, pp. 10-12.
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Fast-speed, mast-capacity, and low-cost communications are highly desired for future wireless systems. Single-feed overmoded slot-based rectangular cavity antennas are developed to meet this demand. A TE(10)(11)(0) mode is excited in the cavity with a rectangular waveguide. A total of 110 slots appropriately etched in its top surface yields a system that radiates its beam into the broadside direction with a gain of 26.6 dBi. An engineered phased patch surface is then introduced tofacilitate tilted-beam pattern for high-order-mode cavity antennas. The realized cavity antenna augmented with an appropriately-shaped phased patch surface attained a tilted beam at 30° with respect to the broadside direction. An antenna prototype was fabricated, and measured results agree well with the simulated ones.
Chen, T, Zhang, J, Xie, G-S, Yao, Y, Huang, X & Tang, Z 1970, 'Classification Constrained Discriminator For Domain Adaptive Semantic Segmentation', 2020 IEEE International Conference on Multimedia and Expo (ICME), 2020 IEEE International Conference on Multimedia and Expo (ICME), IEEE, London, United Kingdom, pp. 1-6.
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© 2020 IEEE. Unsupervised domain adaptation for semantic segmentation aims to transfer knowledge from label-rich synthetic datasets to real-world images without any annotation. The traditional adversarial learning methods for domain adaptation learn to extract domain-invariant feature representations by aligning the feature distributions of both domains. However, these methods suffer from an imbalance in adversarial training and feature distortion. In this work, we propose a classification constrained discriminator to alleviate these problems. Specifically, we first propose to balance the adversarial training by eliminating any pooling layers or strided convolutions in the discriminator. Then, we propose to constrain the discriminator with an auxiliary classification loss to help the feature generator extract the domain-invariant features that are useful for segmentation rather than just ambiguous features to fool the domain discriminator. Extensive experiments demonstrate the superiority of our proposed approach. The source code and models have been made available at https://github.com/NUSTMachine-Intelligence-Laboratory/ccd.
Chen, X, Huang, C, Yao, L, Wang, X, liu, W & Zhang, W 1970, 'Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK, pp. 1-8.
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Chen, X, Lai, L, Qin, L & Lin, X 1970, 'StructSim: Querying Structural Node Similarity at Billion Scale.', ICDE, 2020 IEEE 36th International Conference on Data Engineering, IEEE, Dallas, TX, USA, pp. 1950-1953.
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Structural node similarity is widely used in analyzing complex networks. As one of the structural node similarity metrics, role similarity has the good merit of indicating automorphism (isomorphism). Existing algorithms to compute role similarity (e.g., RoleSim and NED) suffer from severe performance bottlenecks, and thus cannot handle large real-world graphs. In this paper, we propose a new framework StructSim to compute nodes' role similarity. Under this framework, we prove that StructSim is guaranteed to be an admissible role similarity metric based on the maximum matching. While maximum matching is too costly to scale, we then devise the BinCount matching to speed up the computation. BinCount-based StructSim admits a precomputed index to query one single pair in O(k log D) time, where k is a small user-defined parameter and D is the maximum node degree. Extensive empirical studies show that StructSim is significantly faster than existing works for computing structural node similarities on the real-world graphs, with comparable effectiveness.
Chen, X, Lai, L, Qin, L, Lin, X & Liu, B 1970, 'A Framework to Quantify Approximate Simulation on Graph Data.', CoRR, IEEE, pp. 1308-1319.
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Simulation and its variants (e.g., bisimulation and degree-preserving simulation) are useful in a wide spectrum of applications. However, all simulation variants are coarse 'yes-or-no'indicators that simply confirm or refute whether one node simulates another, which limits the scope and power of their utility. Therefore, it is meaningful to develop a fractional χ-simulation measure to quantify the degree to which one node simulates another by the simulation variant χ. To this end, we first present several properties necessary for a fractional χ-simulation measure. Then, we present FSimχ, a general fractional χ-simulation computation framework that can be configured to quantify the extent of all χ-simulations. Comprehensive experiments and real-world case studies show the measure to be effective and the computation framework to be efficient.
Chen, X, Lu, Z, Ni, W, Wang, X, Zhang, S & Xu, S 1970, 'Joint Resource Allocation and Load Management for Cooling-Aware Mobile-Edge Computing', ICC 2020 - 2020 IEEE International Conference on Communications (ICC), ICC 2020 - 2020 IEEE International Conference on Communications (ICC), IEEE, Dublin, Ireland, pp. 1-6.
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In this paper, we jointly design resource allocation and load management in a mobile-edge computing (MEC) system with wireless power transfer (WPT), to minimize the total energy consumption of the BS, while meeting computation latency requirements. For the first time, the cooling energy, which is non-negligible, is considered to minimize the energy consumption of the MEC system. By orchestrating the alternative optimization technique, Lagrange duality method and subgradient method, we decompose the original optimization problem and obtain the optimal solution in a semi-closed form. Extensive numerical tests corroborate the merits of the proposed algorithm over existing benchmarks in terms of energy saving.
Chen, Y, Dong, G, Hao, Y, Zhang, Z, Peng, H & Yu, S 1970, 'An Open Identity Authentication Scheme Based on Blockchain', Algorithms and Architectures for Parallel Processing, International Conference on Algorithms and Architectures for Parallel Processing, Springer International Publishing, Australia, pp. 421-438.
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With the development of Public Key Infrastructure (PKI), there implements lots of identity management systems in enterprises, hospitals, government departments, etc. These systems based on PKI are typically centralized systems. Each of them has their own certificate authority (CA) as trust anchor and is designed according their own understanding, thus formalizing lots of trust domains isolated from each other and there is no unified business standards with regard to trust delivery of an identity system to another, which caused a lot of inconveniences to users who have cross-domain requirements, for example, repeatedly register same physical identity in different domains, hard to prove the validity of an attestation issued by a domain to another. Present PKI systems choose solutions such as Trust list, Bridge CA or Cross-authentication of CAs to break trust isolation, but practice shows that they all have obvious defects under existing PKI structure. We propose an open identity authentication structure based on blockchain and design 3 protocols including: Physical identity registration protocol, virtual identity binding protocol and Attribution attestation protocol. The tests and security analysis show that the scheme has better practice value compared to traditional ones.
Chen, Z, Yuan, L, Lin, X, Qin, L & Yang, J 1970, 'Efficient Maximal Balanced Clique Enumeration in Signed Networks.', WWW, 29th Web Conference (WWW), ACM / IW3C2, Taipei, TAIWAN, pp. 339-349.
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Clique is one of the most fundamental models for cohesive subgraph mining in network analysis. Existing clique model mainly focuses on unsigned networks. In real world, however, many applications are modeled as signed networks with positive and negative edges. As the signed networks hold their own properties different from the unsigned networks, the existing clique model is inapplicable for the signed networks. Motivated by this, we propose the balanced clique model that considers the most fundamental and dominant theory, structural balance theory, for signed networks, and study the maximal balanced clique enumeration problem which computes all the maximal balanced cliques in a given signed network. We show that the maximal balanced clique enumeration problem is NP-Hard. A straightforward solution for the maximal balanced clique enumeration problem is to treat the signed network as two unsigned networks and leverage the off-the-shelf techniques for unsigned networks. However, such a solution is inefficient for large signed networks. To address this problem, in this paper, we first propose a new maximal balanced clique enumeration algorithm by exploiting the unique properties of signed networks. Based on the new proposed algorithm, we devise two optimization strategies to further improve the efficiency of the enumeration. We conduct extensive experiments on large real and synthetic datasets. The experimental results demonstrate the efficiency, effectiveness and scalability of our proposed algorithms.
Chen, Z, Zhu, X & Zhong, L 1970, 'Multi-port Power Combining Grid Array Antenna on Fan-out Wafer Level Packaging', 2020 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), 2020 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), IEEE, pp. 1-3.
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© 2020 IEEE. This paper presents multi-port power combining grid array antenna on fan-out wafer level packaging (FOWLP). The size of the package is 9 x 9 x 0.35 mm3. The microstrip grid array antenna is chosen as the multi-port antenna radiator. The antenna shows high design flexibility. The feeding port are set symmetrically on the cross points of the short sides and long sides and are fed differentially. The distance of the ports are determined by the chip size. A four port microstrip grid array antenna is integrated with a chip of 3×3×0.1 mm3. The simulation results show the maximal peak realized gain is 14.75 dBi and the -10 dB input impedance bandwidth is 13.9 GHz.
Cheng, D, Wang, X, Zhang, Y & Zhang, L 1970, 'Risk Guarantee Prediction in Networked-Loans', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization, pp. 4483-4489.
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The guaranteed loan is a debt obligation promise that if one corporation gets trapped in risks, its guarantors will back the loan. When more and more companies involve, they subsequently form complex networks. Detecting and predicting risk guarantee in these networked-loans is important for the loan issuer. Therefore, in this paper, we propose a dynamic graph-based attention neural network for risk guarantee relationship prediction (DGANN). In particular, each guarantee is represented as an edge in dynamic loan networks, while companies are denoted as nodes. We present an attention-based graph neural network to encode the edges that preserve the financial status as well as network structures. The experimental result shows that DGANN could significantly improve the risk prediction accuracy in both the precision and recall compared with state-of-the-art baselines. We also conduct empirical studies to uncover the risk guarantee patterns from the learned attentional network features. The result provides an alternative way for loan risk management, which may inspire more work in the future.
Cheng, D, Yang, F, Wang, X, Zhang, Y & Zhang, L 1970, 'Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments', Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval, ACM, pp. 2221-2230.
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© 2020 ACM. Event representative learning aims to embed news events into continuous space vectors for capturing syntactic and semantic information from text corpus, which is benefit to event-driven quantitative investments. However, the financial market reaction of events is also influenced by the lead-lag effect, which is driven by internal relationships. Therefore, in this paper, we present a knowledge graph-based event embedding framework for quantitative investments. In particular, we first extract structured events from raw texts, and construct the knowledge graph with the mentioned entities and relations simultaneously. Then, we leverage a joint model to merge the knowledge graph information into the objective function of an event embedding learning model. The learned representations are fed as inputs of downstream quantitative trading methods. Extensive experiments on real-world dataset demonstrate the effectiveness of the event embeddings learned from financial news and knowledge graphs. We also deploy the framework for quantitative algorithm trading. The accumulated portfolio return contributed by our method significantly outperforms other baselines.
Cheng, Q, Lin, Z, Zhang, JA, Nguyen, D, Huang, X, Kekirigoda, A & Hui, K-P 1970, 'Multi-user MIMO with Jamming Suppression for Spectrum-Efficient Tactical Communications', 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, Adelaide, Australia, pp. 1-6.
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© 2020 IEEE. Being spectrum-efficient and robust to adversarial interference caused by jammers are critical to tactical wireless systems. Leveraging multiple-input multiple-output (MIMO) techniques, this paper investigates the realization of spectrum-efficient multi-user MIMO communications in the presence of high-power jammers. Unlike most existing work that only exploits the MIMO degree of freedom to nullify the jamming signal, we also aim to improve the spectral efficiency of the system with the MIMO spatial multiplexing capability. To that end, we first design a combiner at the receiver spanning the null space of the jamming channels, which can completely remove the jamming signals and optimize the communication reception. We further propose two methods for the design of precoders at the transmitter to mitigate multi-user interference. Simulation results are presented to verify the effectiveness of the proposed schemes in radio-frequency contested environments.
Cheng, T, Dah-ChuanLu, D & Siwakoti, YP 1970, 'Electro-Thermal Average Modeling of a Boost Converter Considering Device Self-heating', 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE, New Orleans, LA, USA, pp. 2854-2859.
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Due to the ongoing pursuit of high power density power supplies, thermal management becomes one of the most critical aspects to consider during the design phase for their stable and efficient operation. There are some reported electro-thermal models (ETMs) of power semiconductors and passive components, which help to estimate their electrical performance and temperature simultaneously. However, with the increase of the switching frequency for higher power density design, the simulation time increases accordingly. This can be effectively solved by adopting an averaged model of the converter. As the conventional average model is neither frequency nor temperature dependent with which are the two key parameters in ETM, modification is needed. Therefore, a modified electro-thermal average model (ETAM) of a boost converter considering self-heating phenomenon of all devices is proposed in this paper. This is achieved by a) adding additional behavior models to calculate the device losses; b) replacing the fixed resistance of each component with a temperature dependent one; c) using variable inductor and capacitor instead of a fixed value counterpart to obtain an accurate electrical model and precise losses estimation, and d) forming electrical and temperature feedback loops for each component. The advantages of the proposed research work are fast simulation speed, fairly accurate temperature prediction, and ease of implementation. Both the simulation and experimental results are given and compared to verify the proposed solution.
Cheng, X, Zhong, Y, Harandi, M, Dai, Y, Chang, X, Drummond, T, Li, H & Ge, Z 1970, 'Hierarchical neural architecture search for deep stereo matching', Advances in Neural Information Processing Systems.
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To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classification and semantic segmentation. The underlying idea for the NAS algorithm is straightforward, namely, to enable the network the ability to choose among a set of operations (e.g.,convolution with different filter sizes), one is able to find an optimal architecture that is better adapted to the problem at hand. However, so far the success of NAS has not been enjoyed by low-level geometric vision tasks such as stereo matching. This is partly due to the fact that state-of-the-art deep stereo matching networks, designed by humans, are already sheer in size. Directly applying the NAS to such massive structures is computationally prohibitive based on the currently available mainstream computing resources. In this paper, we propose the first end-to-end hierarchical NAS framework for deep stereo matching by incorporating task-specific human knowledge into the neural architecture search framework. Specifically, following the gold standard pipeline for deep stereo matching (ie., feature extraction – feature volume construction and dense matching), we optimize the architectures of the entire pipeline jointly. Extensive experiments show that our searched network outperforms all state-of-the-art deep stereo matching architectures and is ranked at the top 1 accuracy on KITTI stereo 2012, 2015 and Middlebury benchmarks, as well as the top 1 on SceneFlow dataset with a substantial improvement on the size of the network and the speed of inference. The code is available at LEAStereo.
Chin Derix, E & Leong, TW 1970, 'Tactics for Designing Probes to Explore Parents’ Differing Perspectives on Family Technology Use', Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI '20: Shaping Experiences, Shaping Society, ACM, pp. 1-11.
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Experiences of technology use in everyday family life can be complex. In particular, tensions can arise when parents have differing perspectives on their family's technology use. This paper describes design tactics we used to create a probe collection that successfully supported explorations of these differing perspectives, and to uncover the tensions involved whilst remaining sensitive to any existing conflict. The tactics created opportunities for conversation between parents and to shift their individual perspectives. These tactics helped to raise the awareness sets of parents' had of each other's perspectives on their family's technology use. Unexpected insights emerged that even surprised our participants, when they were asked to invert their point of view to imagine how their technologies might experience domestic life. Furthermore, deeper insights emerged when participants' responses to individual probes were viewed together, as a collection.
Chowdhury, PN, Shivakumara, P, Raghavendra, R, Pal, U, Lu, T & Blumenstein, M 1970, 'A New U-Net Based License Plate Enhancement Model in Night and Day Images', Pattern Recognition, ACPR, Springer International Publishing, Auckland, New Zealand, pp. 749-763.
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A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. This paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. This process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection.
Clemon, L 1970, 'Directed Graphical Model for Real-Time Process Monitoring in Additive Manufacturing', Volume 2A: Advanced Manufacturing, ASME 2020 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers.
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Abstract An important challenge for additive manufacturing and 3D printing processes is accurate and repeatable deposition quality. Current approaches are unable to handle variable process parameters and input material quality. Accurately controlling material properties requires predicting material state changes. This work proposes a model using statistical learning techniques in conjunction with iterative material study to identify and compute the sources of defects and local material properties. The model makes use of the element-by-element fabrication and time-series material changes of additive manufacturing. The deposition of a part is segmented into volume elements, called voxels. Each deposited voxel is treated as an independent sample of the process parameter effects. The time series of deposition is treated as a Markov Chain, with the control parameters and measurable emissions as known quantities. The state of the material is a hidden variable. The hidden variable is approximated using material models and post-fabrication testing results to train the distribution embedded in the Markov Chain. The results indicated that a physics-based material state transition matrix in conjunction with final material properties and time-series of physical emissions can give insight into process variability and control errors. These results have wide ranging implications as a computationally efficient means of iterative process improvement for additive manufacturing, designing new control strategies, and revealing the real-time state of voxels as they are deposited. This approach moves closer to a predictive model that includes current information on the state of the process to update the prediction.
Cotton, D, Traish, J & Chaczko, Z 1970, 'Coevolutionary Deep Reinforcement Learning', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, Australia, pp. 2600-2607.
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The ability to learn without instruction is a powerful enabler for learning systems. A mechanism for this, selfplay, allows reinforcement learning to develop high performing policies without large datasets or expert knowledge. Despite these benefits, self-play is known to be less sample efficient and suffer unstable learning dynamics. This is in part due to a nonstationary learning problem where an agent's actions influence their opponents and as a consequence the training data they receive. In this paper we demonstrate that competitive pressures can be utilised to improve self-play. This paper leverages coevolution, an evolutionary inspired process in which individuals are compelled to innovate and adapt, to optimise the training of a population of reinforcement learning agents. We demonstrate that our algorithm improves the final performance of a Rainbow DQN trained in the game Connect Four, achieving a 15% higher win percentage over the next leading self-play algorithm. Furthermore, our algorithm exhibits more stable training with less variation in evaluation performance.
Da Rocha, CG & Koskela, L 1970, 'Why is product modularity underdeveloped in construction?', IGLC 28 - 28th Annual Conference of the International Group for Lean Construction 2020, Annual Conference of the International Group for Lean Construction, International Group for Lean Construction, Berkeley, California, pp. 685-696.
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Product modularity (a term often associated with off-site construction/prefabrication) has been discussed in construction for a few decades. In spite of that, its understanding in this new context is still emergent. This paper sets out to explore why that is the case. The paper builds on both (i) recent investigations of this concept in construction, including empirical studies which are critically analysed here, and (ii) seminal works on the definition of product modularity in manufacturing. An important insight is that product modularity can benefit traditional construction (by adopting a space-oriented perspective), and thus should not be considered applicable only to off-site construction. Conversely, off-site construction does not ensure per se the adoption of product modularity (even though the terms might be sometimes perceived as closely related). Based on the analysis of literature and empirical cases, three limitations in the understanding and application of product modularity in construction are: (i) unclear boundaries between modules (namely, which components pertain to which module), (ii) invariant modules (namely, the components forming a module do not change depending on the combination in which it is used), and (iii) interfaces as synonymous with surfaces (despite the fact that an interface might entail more than one surface and vice-versa).
Dangol, S, Shrestha, R, Sirivivatnanon, V, Kidd, P & Perry, B 1970, 'Application of Geopolymer Concrete for Precast Components: a review', Concrete 2019, Concrete 2019, Sydney.
Daniel, S, Prpic, K & Cebon, P 1970, 'Evaluation of a program for the development of peer mentors', Annual Conference of the Australasian Association for Engineering Education, Sydney.
Darwish, A, Halkon, B, Oberst, S, Fitch, R & Rothberg, S 1970, 'CORRECTION OF LASER DOPPLER VIBROMETER MEASUREMENTS AFFECTED BY SENSOR HEAD VIBRATION USING TIME DOMAIN TECHNIQUES', Proceedings of the XI International Conference on Structural Dynamics, XI International Conference on Structural Dynamics, EASD, Athens, pp. 4842-4850.
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Despite widespread use in a variety of areas, in-field applications of laser Doppler vibrometers (LDVs) are still somewhat limited due to their inherent sensitivity to vibration of the instrument sensor head itself. Earlier work, briefly reviewed herein, has shown it to be possible
to subtract the instrument vibration via a number of means, however, it has been difficult up to now to truly compare the performance of these. This is compounded by the constraint that a frequency domain based approach only holds for stationary vibration signals while, particularly for in-field applications, an approach that is also applicable to transient signals is necessary.
This paper therefore describes the development of a novel time domain post-processing based approach for vibrating LDV measurement correction and compares it with the frequency domain counterpart. Results show that, while both techniques offer significant improvements in the corrected LDV signal when compared to a reference accelerometer measurement, the time domain based correction outperforms the frequency domain based method by a factor of eight
Das, A, Suwanwiwat, H, Pal, U & Blumenstein, M 1970, 'ICFHR 2020 Competition on Short answer ASsessment and Thai student SIGnature and Name COMponents Recognition and Verification (SASIGCOM 2020)', 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE, Dortmund, Germany, pp. 222-227.
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This paper describes the results of the competition on Short answer ASsessment and Thai student SIGnature and Name COMponents Recognition and Verification (SASIGCOM 2020) in conjunction with the 17th International Conference on Frontiers in Handwriting Recognition (ICFHR 2020). The competition was aimed to automate the evaluation process short answer-based examination and record the development and gain attention to such system. The proposed competition contains three elements which are short answer assessment (recognition and marking the answers to short-answer questions derived from examination papers), student name components (first and last names) and signature verification and recognition. Signatures and name components data were collected from 100 volunteers. For the Thai signature dataset, there are 30 genuine signatures, 12 skilled and 12 simple forgeries for each writer. With Thai name components dataset, there are 30 genuine and 12 skilfully forged name components for each writer. There are 104 exam papers in the short answer assessment dataset, 52 of which were written with cursive handwriting; the rest of 52 papers were written with printed handwriting. The exam papers contain ten questions, and the answers to the questions were designed to be a few words per question. Three teams from distinguished labs submitted their systems. For short answer assessment, word spotting task was also performed. This paper analysed the results produced by their algorithms using a performance measure and defines a way forward for this subject of research. Both the datasets, along with some of the accompanying ground truth/baseline mask will be made freely available for research purposes via the TC10/TC11.
Das, D, Hossain, MJ, Mishra, S & Singh, B 1970, 'Robust Power Sharing between Dual Active Bridges to Improve a DC Microgrid's Stability', 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), IEEE, pp. 1-6.
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Dawson, N, Rizoiu, M-A, Johnston, B & Williams, M-A 1970, 'Predicting Skill Shortages in Labor Markets: A Machine Learning Approach', Workshop on Human-in-the-Loop Methods and Future of Work in BigData (HMData'20), 2020.
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Skill shortages are a drain on society. They hamper economic opportunitiesfor individuals, slow growth for firms, and impede labor productivity inaggregate. Therefore, the ability to understand and predict skill shortages inadvance is critical for policy-makers and educators to help alleviate theiradverse effects. This research implements a high-performing Machine Learningapproach to predict occupational skill shortages. In addition, we demonstratemethods to analyze the underlying skill demands of occupations in shortage andthe most important features for predicting skill shortages. For this work, wecompile a unique dataset of both Labor Demand and Labor Supply occupationaldata in Australia from 2012 to 2018. This includes data from 7.7 million jobadvertisements (ads) and 20 official labor force measures. We use these data asexplanatory variables and leverage the XGBoost classifier to predict yearlyskills shortage classifications for 132 standardized occupations. The models weconstruct achieve macro-F1 average performance scores of up to 83 per cent. Ourresults show that job ads data and employment statistics were the highestperforming feature sets for predicting year-to-year skills shortage changes foroccupations. We also find that features such as 'Hours Worked', years of'Education', years of 'Experience', and median 'Salary' are highly importantfeatures for predicting occupational skill shortages. This research provides arobust data-driven approach for predicting and analyzing skill shortages, whichcan assist policy-makers, educators, and businesses to prepare for the futureof work.
de Campos Souza, PV, Wang, Y-K & Lughofer, E 1970, 'Knowledge extraction about patients surviving breast cancer treatment through an autonomous fuzzy neural network', 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Glasgow, UK, pp. 1-8.
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Cancer treatment is extremely aggressive and, in addition to causing considerable discomfort, can lead to death. Therefore, identifying aspects related to treatment assertiveness may be efficient for reducing the mortality rate of cancer patients. This paper seeks to identify the prognosis of cancer treatment survival through hybrid techniques based on the autonomous fuzzification process and artificial neural networks. The public dataset on cancer mortality is the source for conducting treatment assertiveness rating tests. The hybrid model had its results compared to other models present in the pattern classification literature with superior accuracy and identification of people likely to survive treatment (90.46%), and the fuzzy rules obtained with the execution of the model corroborate the high assertiveness of the model, even surpassing state of the art for the theme.
Derix, EC & Leong, TW 1970, 'Probes to Explore the Individual Perspectives on Technology Use that exist within Sets of Parents', Proceedings of the 2020 ACM Designing Interactive Systems Conference, DIS '20: Designing Interactive Systems Conference 2020, ACM, pp. 519-531.
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© 2020 ACM. Research reveals that family experiences of technology use in everyday life can be complex and messy, often associated with tension and conflict. This complexity can be intensified when sets of parents have differing individual perspectives on their family's technology use. Exploring these different perspectives, requires an approach that not only considers parents not only as individuals, but also as part of a set. To challenge matters further, parents may not be fully aware of their own attitudes and assumptions relating to technology, let alone of each other's. Parents may also be embarrassed to share details about family conflicts. This methods paper presents a probe study that successfully helped us to explore the individual perspectives on family technology use that exist within sets of parents. It provides an example of an approach to using probes that can reveal the hidden experiences of multiple individuals within a social context. In this way, it contributes an understanding of how we might interrogate the complexities of co-experience.
Deuse, J, Stankiewicz, L, Zwinkau, R & Weichert, F 1970, 'Automatic Generation of Methods-Time Measurement Analyses for Assembly Tasks from Motion Capture Data Using Convolutional Neuronal Networks - A Proof of Concept', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 141-150.
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© Springer Nature Switzerland AG 2020. This paper describes the research hypothesis that motion data can be utilized to derive MTM analyses. As a first step, manual assembly tasks are recorded with motion capture systems to generate motion data. These motion data are used as a training data set for an end-to-end deep learning architecture for motion classification. The result of this classification is the assignment of data sequences to corresponding basic motions of MTM-1. The paper also describes the prerequisites for an automatic generation of MTM analyses by considering an adaptation of the original MTM methodology to fit for an automatic approach, the acquisition of motion capture data and the automatic annotation of motion data.
Diao, K, Sun, X, Lei, G, Guo, Y & Zhu, J 1970, 'Application-Oriented System Level Optimization Method for Switched Reluctance Motor Drive Systems', 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), IEEE, Nanjing, China, pp. 472-477.
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This paper presents a novel application-oriented system level optimization method for switched reluctance motor (SRM) drive systems. First, the multiobjective optimization model is defined according to the design requirements. Then, all the parameters of the motor and controller are divided into three subspaces according to the sensitivity results on the defined objectives. Finally, the optimization of each subspace is performed sequentially by using the approximate models and advanced genetic algorithm, and the best solution can be selected from the Pareto optimal solutions. To verify the effectiveness of the proposed method, an SRM drive system with a segmented-rotor SRM and the angle position control method is investigated. This is a high-dimensional system level optimization problem with ten parameters. The computational cost can be greatly reduced without the sacrifice of accuracy. From the discussion, it can be found that the proposed multiobjective system level optimization method can achieve high efficiency and low torque ripple. Besides, it provides alternative solutions for applications with different output power demands.
Ding, C, Sun, H, Zhu, H & Guo, YJ 1970, 'Achieving Wider Impedance Bandwidth Using FullWavelength Dipoles', 2020 14th European Conference on Antennas and Propagation (EuCAP), 2020 14th European Conference on Antennas and Propagation (EuCAP), IEEE, Copenhagen, Denmark, pp. 1-5.
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© 2020 EurAAP. This paper investigates the use of full-wavelength dipoles (FWD) to achieve wider bandwidth than halfwavelength dipoles (HWD). Two dual-polarized antennas are built based on FWDs for base station applications as examples. The first antenna is an isolated cross-dipole employing two FWDs with simple configuration. It is able to cover the lower band for cellular communication from 698 to 960 MHz. The second antenna has four FWDs arranged in a square loop array form and tightly coupled with each other. The employed full-wavelength dipoles are bent upward to maintain a small aperture size, so that the realized element still fits in traditional base station antenna (BSA) array. The antenna can be matched across the band from 1.65 to 3.7 GHz, which can cover both the 3G/4G band from 1.7 to 2.7 GHz and the 5G (sub-6 GHz) band from 3.3 to 3.6 GHz simultaneously. By comparing the attained antennas with comparable antennas based on HWDs, it demonstrates a fact that, when fed properly, FWDs exhibit wider bandwidth than HWDs, and the available methods to improve the bandwidth of HWDs can also be used on FWDs.
Do, T-TN, Singh, AK, Cortes, CAT & Lin, C-T 1970, 'Estimating the cognitive load in physical spatial navigation', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, Canberra Australia, pp. 568-575.
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Dong, Y, Fauth, A, Huang, M, Chen, Y & Liang, J 1970, 'PansyTree: Merging Multiple Hierarchies', 2020 IEEE Pacific Visualization Symposium (PacificVis), 2020 IEEE Pacific Visualization Symposium (PacificVis), IEEE, Tianjin, China, pp. 131-135.
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Hierarchical structures are very common in the real world for recording all kinds of relational data generated in our daily life and business procedures. A very popular visualization method for displaying such data structures is called "Tree". So far, there are a variety of Tree visualization methods that have been proposed and most of them can only visualize one hierarchical dataset at a time. Hence, it raises the difficulty of comparison between two or more hierarchical datasets.In this paper, we proposed Pansy Tree which used a tree metaphor to visualize merged hierarchies. We design a unique icon named pansy to represent each merged node in the structure. Each Pansy is encoded by three colors mapping data items from three different datasets in the same hierarchical position (or tree node). The petals and sepal on Pansy are designed for showing each attribute's values and hierarchical information. We also redefine the links in force layout encoded by width and animation to better convey hierarchical information. We further apply Pansy Tree into CNCEE datasets and demonstrate two use cases to verify its effectiveness.The main contribution of this work is to merge three datasets into one tree that makes it much easier to explore and compare the structures, data items and data attributes with visual tools.
Dourish, P, Lawrence, C, Leong, TW & Wadley, G 1970, 'On Being Iterated: The Affective Demands of Design Participation', Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-11.
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© 2020 ACM. Iteration is a central feature of most HCI design methods, creating as it does opportunities for engagements with stakeholder groups. But what does iteration demand of those groups? Under what conditions do iterative engagements arise, and with what stakes? Building on experiences with Aboriginal Australian communities, and drawing on feminist and decolonial thinking, we examine the nature of iteration for HCI and how it frames encounters between design and use, with a focus on the affective dimension of engagement in iterative design processes.
Du, A, Pang, S, Huang, X, Zhang, J & Wu, Q 1970, 'Exploring Long-Short-Term Context For Point Cloud Semantic Segmentation', 2020 IEEE International Conference on Image Processing (ICIP), 2020 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 2755-2759.
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Du, H, Yu, X & Zheng, L 1970, 'Learning Object Relation Graph and Tentative Policy for Visual Navigation', Springer International Publishing, pp. 19-34.
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Du, X, Xiao, G & Sui, Y 1970, 'Fault Triggers in the TensorFlow Framework: An Experience Report', 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), IEEE, Coimbra, Portugal, pp. 1-12.
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©2020 IEEE. TensorFlow is one of the most popular machine learning frameworks for developing machine learning algorithms. Because of the popularity and large-scale use of TensorFlow, even a single bug may lead to severe consequences and impact a large number of users. With a growing number of safetycritical systems built upon TensorFlow, its reliability is becoming increasingly important. An essential step to ensure TensorFlow's reliability is to understand the characteristics of bugs that occurred in TensorFlow. This paper presents the first comprehensive empirical study on fault triggering conditions in TensorFlow. 2,285 bug reports from TensorFlow's GitHub repository are collected. A bug classification is performed based on fault triggering conditions, followed by the frequency distribution of different types of bugs and the evolution features of varying bug types over time. Then the relationships between bug types and fixing time are also investigated. In addition, the root causes of Bohrbugs and Mandelbugs are studied. Five root causes are discovered. Furthermore, the analysis of regression bugs in TensorFlow is conducted. We have revealed 10 important findings based on our empirical results. There are 8 implications based on these findings are provided for developers and users.
Eager, D & Hayati, H 1970, 'Understanding greyhound race track risk factors', GRV On-Track Veterinarians Conference, GRV On-Track Veterinarians Conference, Melbourne, Australia.
Eiffert, S, Wendel, A, Colborne-Veel, P, Leong, N, Gardenier, J & Presien, NK 1970, 'Toolbox spotter: A computer vision system for realworld situational awareness in heavy industries', Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot, pp. 813-820.
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The majority of fatalities and traumatic injuries in heavy industries involve mobile plant and vehicles, often resulting from a lapse of attention or communication. Existing approaches to hazard identification i nclude t he u se o f human spotters, passive reversing cameras, non-differentiating proximity sensors and tag based systems. These approaches either suffer from problems of worker attention or require the use of additional devices on all workers and obstacles. Whilst computer vision detection systems have previously been deployed in structured applications such as manufacturing and on-road vehicles, there does not yet exist a robust and portable solution for use in unstructured environments like construction that effectively communicates risks to relevant workers. To address these limitations, our solution, the Toolbox Spotter (TBS), acts to improve worker safety and reduce preventable incidents by employing an embedded robotic perception and distributed HMI alert system to augment both detection and communication of hazards in safety critical environments. In this paper we outline the TBS safety system and evaluate it's performance based on data from real world implementations, demonstrating the suitability of the Toolbox Spotter for applications in heavy industries. ,.
EL-HAWAT, O, FATAHI, B & MOSAVI, AA 1970, 'IMPACTS OF TRANSVERSE EARTHQUAKES ON SEISMIC RESPONSE OF BRIDGES WITH ROCKING FOUNDATIONS AND VARIOUS SHEAR KEYS', WIT Transactions on The Built Environment, SUSI 2020, WIT Press, Online, pp. 125-137.
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Rocking foundations are proven to be an effective base isolation technique that improves the seismic performance of bridges and minimises the damage at the piers during large earthquakes. However, due to the foundations ability to uplift, the subsequent reduction of the pier’s stiffness leads to larger column drifts and deck displacements. This not only attracts larger stresses to the transverse direction of the deck, but also at the abutment which, if not carefully considered, can lead to severe damages. Therefore, this study will investigate the seismic response of bridges with rocking pile foundations subjected to transverse earthquake excitations and compare it to the response of conventional fixed base bridges. Two separate shear key performance levels are investigated for each bridge: (1) non-linear shear keys that break off; and (2) shear keys that remain rigid. 3D numerical models of the bridges are developed using finite element software with consideration of soil-structure interaction. Moreover, non-linear time history analyses are performed on the bridges using four ground-motion records, where their dynamic response are then compared. Results show that the conventional bridges collapsed due to the development of plastic hinging at the piers. However, the bridges with the rocking pile foundations experienced significant deck displacements which caused flexural plastic hinging of the deck and the subsequent collapse of the bridge. Moreover, when the shear keys failed, the deck experienced large displacements at the abutment which caused the bearing to rupture and displace permanently with the risk of unseating and span failure. Bridges with this foundation system will require additional design provisions to prevent such failures from occurring.
Erfani, E, Abedin, B, Luckett, T, Lawrence, C & Hanna, ASH 1970, 'A Culturally and Language Appropriate Smartphone-based Support Intervention for Enhancinng the Psychological Well-being of Indigenous Australian People with cancer.', ECIS.
Eslahi, H, Hamilton, TJ & Khandelwal, S 1970, 'Frequency Behaviour of FeFET-Based Ultra-Low-Power Coupled Oscillator Neurons', 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 2020 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, pp. 1-4.
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Esselle, KP 1970, 'A Brief Overview of Antenna Technologies for Communications-On- The-Move Satellite Communication Mobile Terminals', 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, Montreal, QC, Canada, pp. 1637-1638.
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This paper serves as the introductory first presentation to the 2020 IEEE AP-S Industry-Focused Special Session on “Beam-Steerable Antenna Systems with High Gains for Mobile Satellite Terminals”. It presents a brief overview of established and emerging antenna technologies for Communication-On- The-Move (COTM) mobile satellite communication terminals and known or expected advantages and limitations of each technology.
Fan, Y, Bao, J, Wu, K & Li, H 1970, 'Ghost Image Due to mmWave Radar Interference: Experiment, Mitigation and Leverage', 2020 IEEE International Conference on Communications Workshops (ICC Workshops), 2020 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, pp. 1-6.
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© 2020 IEEE. Millimeter wave (mmWave) radar is becoming a major instrument for the ranging, Doppler and imaging of environment in various applications such as autonomous driving and unmanned aerial vehicles. As substantially more radar devices are employed in applications, the interference among different radar transceivers becomes a severe problem, which may bring substantial damages to the applications of radar. One of these impacts is the ghost image caused by the interference, which results in a fake target. In this paper, experiments are carried out to demonstrate the existence of ghost image, based on 77GHz mmWave automobile radar. Detailed analysis is carried out based on the experimental measurements for disclosing the mechanism of interference and the properties of the corresponding ghost image. The prominent features of ghost image include statistically narrow frequency spread, abnormal Doppler estimation and possible negative distance. Based on the these features, systematic approaches are introduced to mitigate the interference induced ghost images. Meanwhile, schemes are proposed to leverage the ghost images for localization and communications, instead of merely removing them.
Farahmandian, S & Hoang, DB 1970, 'A Policy-based Interaction Protocol between Software Defined Security Controller and Virtual Security Functions', 2020 4th Cyber Security in Networking Conference (CSNet), 2020 4th Cyber Security in Networking Conference (CSNet), IEEE, Lausanne, Switzerland, pp. 1-8.
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Cloud, Software-Defined Networking (SDN), and Network Function Virtualization (NFV) technologies have introduced a new era of cybersecurity threats and challenges. To protect cloud infrastructure, in our earlier work, we proposed Software Defined Security Service (SDS2) to tackle security challenges centered around a new policy-based interaction model. The security architecture consists of three main components: a Security Controller, Virtual Security Functions (VSF), and a Sec-Manage Protocol. However, the security architecture requires an agile and specific protocol to transfer interaction parameters and security messages between its components where OpenFlow considers mainly as network routing protocol. So, The Sec-Manage protocol has been designed specifically for obtaining policy-based interaction parameters among cloud entities between the security controller and its VSFs. This paper focuses on the design and the implementation of the Sec-Manage protocol and demonstrates its use in setting, monitoring, and conveying relevant policy-based interaction security parameters.
Farasat, M & Yang, Y 1970, 'Investigation of Radome Enclosed Antenna with Tilted Angles of 10° and 20° for Airborne Applications', 2020 4th Australian Microwave Symposium (AMS), 2020 4th Australian Microwave Symposium (AMS), IEEE, Sydney, Australia, pp. 1-2.
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Electromagnetic windows, called as Radome, are used to shield the antenna system. Antenna being a part of every microwave system needs more protection, particularly for applications where environmental effects are worse. Radome acts as a supporting structure for critical terrestrial antenna systems, airborne radar systems, and submarine antenna systems. This work discusses the design considerations, and particularly, tilted angles of the radome and their impact on antenna performance. Antenna performance is simulated with radome at different angles. Radome power transmission characteristics are simulated over antenna scan angle range.
Farhood, H, Perry, S, Cheng, E & Kim, J 1970, '3D point cloud reconstruction from a single 4D light field image', Optics, Photonics and Digital Technologies for Imaging Applications VI, Optics, Photonics and Digital Technologies for Imaging Applications VI, SPIE.
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© 2020 SPIE Obtaining accurate and noise-free three-dimensional (3D) reconstructions from real world scenes has grown in importance in recent decades. In this paper, we propose a novel strategy for the reconstruction of a 3D point cloud of an object from a single 4D light field (LF) image based on the transformation of point-plane correspondences. Considering a 4D LF image as an input, we first estimate the depth map using point correspondences between sub-aperture images. We then apply histogram equalization and histogram stretching to enhance the separation between depth planes. The main aim of this step is to increase the distance between adjacent depth layers and to enhance the depth map. We then detect edge contours of the original image using fast canny edge detection, and combine linearly the result with that of the previous steps. Following this combination, by transforming the point-plane correspondence, we can obtain the 3D structure of the point cloud. The proposed method avoids feature extraction, segmentation and the extraction of occlusion masks required by other methods, and due to this, our method can reliably mitigate noise. We tested our method with synthetic and real world image databases. To verify the accuracy of our method, we compared our results with two different state-of-the-art algorithms. In this way, we used the LOD (Level of Detail) to compare the number of points needed to describe an object. The results showed that our method had the highest level of detail compared to other existing methods.
Farina, E, Loglio, A, Tosetti, G, Vigano, M, Gentile, C, Perbellini, R, Borghi, M, Facchetti, F, Lunghi, G, Rumi, M, Primignani, M & Lampertico, P 1970, 'LONG-TERM TREATMENT WITH TENOFOVIR OR ENTECAVIR COULD SPARE ENDOSCOPIC SURVEILLANCE OF ESOPHAGEAL VARICES IN HBV COMPENSATED CIRRHOTICS: A 10-YEAR STUDY', HEPATOLOGY, Liver Meeting of the American-Association-for-the-Study-of-Liver-Diseases (AASLD), WILEY, ELECTR NETWORK, pp. 465A-466A.
Feng, Q, Liao, S, Yang, Y, Che, W & Xue, Q 1970, 'Differentially Fed Package-Substrate Distributed Antenna for 5G Millimeter-Wave Application', 2020 International Conference on Microwave and Millimeter Wave Technology (ICMMT), 2020 International Conference on Microwave and Millimeter Wave Technology (ICMMT), IEEE, pp. 1-3.
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Flores Terrazas, V, Sedehi, O, Katafygiotis, LS & Papadimitriou, C 1970, 'MONITORING FATIGUE DAMAGE ACCUMULATION OF WIND TURBINE TOWERS USING LIMITED NUMBER OF OUTPUT-ONLY VIBRATION MEASUREMENTS', Proceedings of the XI International Conference on Structural Dynamics, XI International Conference on Structural Dynamics, EASD, pp. 1178-1188.
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Fatigue monitoring and remaining fatigue life estimation of structures using output- only vibration measurements has recently garnered increasing attention, producing advances in theoretical, numerical and experimental studies of this phenomenon. The methodology presented in this paper combines methods for estimating stress time histories at the entire body of the structure with fatigue damage accumulation techniques for multiaxial stress state. A novel sequential Bayesian method is employed to estimate both input and state in the modal space and to reconstruct the full-field time-history response in the physical space using output-only vibration measurements. Stress and strain time histories at the finite element level are obtained by using a linear relationship with nodal displacements. Estimated stresses are then used to find the critical plane where the maximum fatigue damage is expected and the shear stress time histories are resolved on this plane. Shear stress cycles are counted by means of the Rainflow Counting Method, and a Modified Wöhler Curve Method is applied to estimate the fatigue damage, whereby normal and shear stress effects are accounted for. This procedure is capable of tackling inherent complexities found in real world applications, such as the multiaxiality of the applied loads and of the resulting stress state. A finite element model of a wind turbine tower was constructed based on reference specifications available from the National Renewable Energy Laboratory and used to illustrate the method presented herein. The results obtained demonstrate the applicability of the methodology as an efficient way to monitor fatigue damage accumulation in the entire body of a steel structure.
Fraietta, A, Bown, O & Ferguson, S 1970, 'Transparent Communication Within Multiplicities', 2020 27th Conference of Open Innovations Association (FRUCT), 2020 27th Conference of Open Innovations Association (FRUCT), IEEE, Trento, Italy, pp. 61-72.
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The Internet of Musical Things is an emerging field of research that intersects the Internet of Things, humancomputer interaction, ubiquitous music, artificial intelligence, gaming, virtual reality and participatory art through device multiplicity. This paper introduces a paradigm whereby data points and variable parameters can be strategically mapped or bound using aliases, data types and scoping as an alternative to flat address-structured mapping. The ability to send and/or access complex data types as complete entities rather than lists of parameters promotes data abstraction and encapsulation, allowing greater flexibility through modular architecture as underlying data structures can change during the lifestyle or evolution of a computer based composition. Additionally, the facility to define data accessibility, and the ability to reuse human readable names based on a variable's scope is a common feature of most programming languages. This paradigm has been extended in that scoping a variable can be dynamically bound or addressed to specific objects, class types, devices or globally on an entire network. We describe the evolution of this paradigm through its development via various project requirements.
Gautam, S, Lu, Y, Xiao, W, Lu, DD-C & Golsorkhi, MS 1970, 'Comparative Study of Phase Lead Compensator based In-loop Filtering Method in Single-Phase PLL', IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society, IEEE, pp. 4947-4954.
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Gentil, CL, Tschopp, F, Alzugaray, I, Vidal-Calleja, T, Siegwart, R & Nieto, J 1970, 'IDOL: A Framework for IMU-DVS Odometry using Lines', 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Las Vegas, NV, USA, pp. 5863-5870.
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In this paper, we introduce IDOL, an optimization-based framework for IMU-DVSOdometry using Lines. Event cameras, also called Dynamic Vision Sensors (DVSs),generate highly asynchronous streams of events triggered upon illuminationchanges for each individual pixel. This novel paradigm presents advantages inlow illumination conditions and high-speed motions. Nonetheless, thisunconventional sensing modality brings new challenges to perform scenereconstruction or motion estimation. The proposed method offers to leverage acontinuous-time representation of the inertial readings to associate each eventwith timely accurate inertial data. The method's front-end extracts eventclusters that belong to line segments in the environment whereas the back-endestimates the system's trajectory alongside the lines' 3D position byminimizing point-to-line distances between individual events and the lines'projection in the image space. A novel attraction/repulsion mechanism ispresented to accurately estimate the lines' extremities, avoiding theirexplicit detection in the event data. The proposed method is benchmarkedagainst a state-of-the-art frame-based visual-inertial odometry framework usingpublic datasets. The results show that IDOL performs at the same order ofmagnitude on most datasets and even shows better orientation estimates. Thesefindings can have a great impact on new algorithms for DVS.
Ghanbarikarekani, M, Zeibots, M, Qu, X & Arab, A 1970, 'Minimizing the stop time of private vehicles at intersections with LRT signal priority systems', Transportation Research Procedia, Elsevier BV, pp. 939-945.
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© 2020 The Authors. Published by Elsevier B.V. There are some strategies suggested to improve the performance of intersections and increase the demand for public vehicles by prioritizing them. To this end, several methods have been used such as Transit Signal Priority (TSP) system for Light Rail transit (LRT). LRT signal priority is a timing strategy that gives priority to LRTs at signalized intersections through changing the sequence of phases, extending green time and reducing red time at LRT's phase. In this paper, we propose a model to improve LRT signal priority systems. The developed model minimizes the green extension and red reduction of LRT's phase by estimating an optimal speed for LRTs reaching the stop line. Consequently, the priority of LRTs would be maintained while the performance of private vehicles would be improved by decreasing their stop time.
Ghantous, GB & Gill, A 1970, 'The DevOps Reference Architecture Evaluation : A Design Science Research Case Study', 2020 IEEE International Conference on Smart Internet of Things (SmartIoT), 2020 IEEE International Conference on Smart Internet of Things (SmartIoT), IEEE, Beijing, China, pp. 295-299.
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There is a growing interest to adopt vendor-driven DevOps tools in organizations. However, it is not clear which tools to use in a reference architecture which enables the deployment of the emerging IoT applications to multi-cloud environments. A research-based and vendor-neutral DevOps reference architecture (DRA) framework has been developed to address this critical challenge. The DRA framework can be utilized to architect and implement the DevOps environment that enables automation and continuous integration of software applications deployment to multi-cloud. This paper confers and discusses the evaluation outcomes of the DRA framework at the DigiSAS research Lab. The evaluation outcomes present practical evidence about the applicability of the DRA framework. The evaluation results also indicate that the DRA framework provides general knowledge-base to researchers and practitioners about the adoption DevOps approach in reference architecture design for deploying IoT-applications to multi-cloud environments.
Ghosh, S, Gaona, D, Siwakoti, Y & Long, T 1970, 'Synchronous Combined Cuk-SEPIC Converter for Single Phase Transformerless Solar Inverter', 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE, New Orleans, LA, USA, pp. 3225-3231.
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In this paper, a bidirectional power electronic converter based on the synchronous combined Cuk-SEPIC converter has been presented for single-phase transformerless solar power generation systems. The proposed topology completely eliminates the common-mode voltage, hence the common-mode current. The proposed converter generates a bipolar output with respect to the common ground; hence a simple half-bridge can be used for grid integration. The proposed synchronous operation allows significant reduction of the DC bus capacitance required for active power decoupling compared to the previously reported asynchronous operation. The converter operation, design, along with the simulation results are presented for a 1 kW prototype at 230 V, 50 Hz. Experimental results of the converter at full load with lagging, leading, and unity power factor conditions are reported as well.
Gill, AQ, Beydoun, G, Niazi, M & Khan, HU 1970, 'Adaptive Architecture and Principles for Securing the IoT Systems.', IMIS, International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Springer, Lodz, Poland, pp. 173-182.
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© Springer Nature Switzerland AG 2021. There is an increasing interest in IoT-enabled smart digital systems. However, it is important to address their security concerns. This paper aims to address this need and proposes an adaptive architecture driven approach to securing IoT systems. The paper proposes IoT security principles and a foundational adaptive architecture framework. These two combined provide a guide to design and embed the security across various layers of an IoT system. This will ensure that the important aspects of the IoT security are not accidentally missed, and thus provides a holistic end to end adaptive architecture driven approach for IoT security. This paper covers the interaction, human, digital technology, physical facility and environment architecture layers and principles related to IoT security as opposed to focusing only on the IoT devices. Thus, it demonstrates and concludes that the IoT security is much more than IoT device, network and perimeter security.
Glass, J & McGregor, C 1970, 'Towards Player Health Analytics in Overwatch', 2020 IEEE 8th International Conference on Serious Games and Applications for Health (SeGAH), 2020 IEEE 8th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, Vancouver, BC, Canada, pp. 1-5.
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© 2020 IEEE. Overwatch is a competitive, team-based first-person shooter game, with a professional eSports league supporting competitive play. Player mental health has been an issue in eSports, and in Overwatch multiple players have quit playing professionally and cited mental health concerns. Player physiology during gameplay presents an opportunity to understand stressors during gameplay that may affect individual performance and health. This paper presents the collection of physiological data from Overwatch players and overlays it with data from the video game. This method, demonstrated in a pilot study could be used to learn more about how in game events affect player mental health, and lead to the development of resilience building approaches for eSports athletes.
Goldfinch, T, Vulic, J, Leigh, E & Willey, K 1970, 'Student perceptions of complexity in engineering education', SEFI 47th Annual Conference: Varietas Delectat... Complexity is the New Normality, Proceedings, pp. 1576-1584.
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The complex and socially connected nature of modern engineering practice is well documented, motivating new approaches to engineering education across the globe. The challenge now is to bring about change at scale to traditional curricula [1]. The University of Sydney has implemented a core program designed to improve students' learning and preparation for professional practice. The program seeks to help students develop an appreciation of complexity in engineering practice and illustrate its interdisciplinary, connected nature. The program serves a cohort of ~800 commencing students annually and is delivered within the bounds of a traditional program structured in units of study. Standardised student satisfaction survey results have been below or well below faculty average, indicating that on this measure, a majority of students are not satisfied with their learning experience. To better understand why, student comments on these surveys were analysed through the lens of the Cynefin framework, a sense-making tool that provides a useful characterisation of complexity experienced in professional engineering [2, 3]. Analysis suggest students may be aligned along a continuum between two positions in regard to the perceived degree of complexity in the learning experience: Comfortable with complexity - Those who recognise and adopt strategies needed to succeed in complex projects; and, Resistance to complexity - Those who see the learning design as unsupportive and unnecessarily ambiguous. The results highlight issues around student perspectives of what 'learning' is, as well as structural issues existing within standardised student satisfaction surveys, each of which pose potential barriers to curriculum reform.
Golzan, M, Gheisari, S, Shariflou, S, Phu, J, Kennedy, PJ, Agar, A & Kalloniatis, M 1970, 'A combined convolutional and recurrent neural network applied to fundus videos markedly enhances glaucoma detection', INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, Annual Meeting of the Association-for-Research-in-Vision-and-Ophthalmology (ARVO), ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ELECTR NETWORK.
Gong, Y, Li, Z, Zhang, J, Liu, W & Yi, J 1970, 'Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development', Proceedings of the AAAI Conference on Artificial Intelligence, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), Association for the Advancement of Artificial Intelligence (AAAI), New York USA, pp. 4020-4027.
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Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For example, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results.
Gong, Y, Li, Z, Zhang, J, Liu, W, Chen, B & Dong, X 1970, 'A Spatial Missing Value Imputation Method for Multi-view Urban Statistical Data', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization, pp. 1310-1316.
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Large volumes of urban statistical data with multiple views imply rich knowledge about the development degree of cities. These data present crucial statistics which play an irreplaceable role in the regional analysis and urban computing. In reality, however, the statistical data divided into fine-grained regions usually suffer from missing data problems. Those missing values hide the useful information that may result in a distorted data analysis. Thus, in this paper, we propose a spatial missing data imputation method for multi-view urban statistical data. To address this problem, we exploit an improved spatial multi-kernel clustering method to guide the imputation process cooperating with an adaptive-weight non-negative matrix factorization strategy. Intensive experiments are conducted with other state-of-the-art approaches on six real-world urban statistical datasets. The results not only show the superiority of our method against other comparative methods on different datasets, but also represent a strong generalizability of our model.
Gromov, A, Maslennikov, A, Dawson, N, Musial, K & Kitto, K 1970, 'Curriculum profile: modelling the gaps between curriculum and the job market', Proceedings of the 13th International Conference on Educational Data Mining, EDM 2020, Ifrane, Morocco (Fully Virtual Conference), pp. 610-614.
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This study uses skill-based curriculum analytics to mine the curriculum of an entire university. A curriculum profile is constructed, providing insights about university curriculum design and the match between one institution’s curriculum and the job market for a cluster of data-intensive fields. Automating the delivery of diagnostic information like this would enable institutions to ensure that their professionally-oriented degrees meet the needs of industry, so helping to improve learner outcomes and graduate employability.
Grover, H, Verma, A, Bhatti, TS & Hossain, MJ 1970, 'Frequency Regulation Scheme Based on Virtual Synchronous Generator for an Isolated Microgrid', 2020 International Conference on Power, Instrumentation, Control and Computing (PICC), 2020 International Conference on Power, Instrumentation, Control and Computing (PICC), IEEE, pp. 1-6.
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Gu, C, Xiong, J, Shi, Z & Liu, B 1970, 'Video Cooperative Caching in High-Speed Train by using Differential Evolution Algorithm', 2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, pp. 1-5.
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Gu, X & Cao, Z 1970, 'An EEG Majority Vote Based BCI Classification System for Discrimination of Hand Motor Attempts in Stroke Patients', Communications in Computer and Information Science, Springer International Publishing, pp. 46-53.
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Stroke patients have symptoms of cerebral functional disturbance that could aggressively impair patient’s physical mobility, such as hand impairments. Although rehabilitation training from external devices is beneficial for hand movement recovery, for initiating motor function restoration purposes, there are still valuable research merits for identifying the side of hands in motion. In this preliminary study, we used an electroencephalogram (EEG) dataset from 8 stroke patients, with each subject conducting 40 EEG trials of left motor attempts and 40 EEG trials of right motor attempts. Then, we proposed a majority vote based EEG classification system for identifying the side in motion. In specific, we extracted 1–50 Hz power spectral features as input for a series of well-known classification models. The predicted labels from these classification models were compared and a majority vote based method was applied, which determined the finalised predicted label. Our experiment results showed that our proposed EEG classification system achieved $$99.83 \pm 0.42 \% $$ accuracy, $$ 99.98 \pm 0.13\% $$ precision, $$ 99.66 \pm 0.84 \% $$ recall, and $$ 99.83 \pm 0.43\% $$ f-score, which outperformed the performance of single well-known classification models. Our findings suggest that the superior performance of our proposed majority vote based EEG classification system has the potential for stroke patients’ hand rehabilitation.
Gui, L, Xiao, F, Zhou, Y, Shu, F & Yu, S 1970, 'Performance analysis of indoor localization based on channel state information ranging model', Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, Mobihoc '20: The Twenty-first ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, ACM, USA - Online, pp. 191-200.
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Due to robustness against multi-path effect, channel state information (CSI) of Orthogonal Frequency Division Multiplexing (OFDM) systems is supposed to provide accurate distance measurement for indoor localization. However, we find that the original CSI ranging model is biased, so the model cannot be used to directly derive Cramer-Rao lower bound (CRLB) of positioning error for CSI-ranging based localization scheme. In this paper we first analyze the estimation bias of the original CSI ranging model according to indoor wireless channel model. Then we propose a negative power summation ranging model which can be used as an unbiased ranging model for both Line-Of-Sight (LOS) and Non-LOS scenarios. Subsequently, based on the proposed model, we derive both the CRLB of ranging error and the CRLB of positioning error for CSI-ranging localization scheme. Through simulation we validate the bias of the original ranging model and the approximately zero bias of our proposed ranging model. Through comprehensive experiments in different indoor scenarios, localization errors by different ranging models are compared to the CRLB, meanwhile our proposed ranging model is demonstrated to have better ranging and localization accuracy than the original ranging model.
Guo, K & Guo, Y 1970, 'Design and Analysis of a Linear Rotary Permanent Magnet Machine with E-Type Stator Structure', 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE, Tianjin, China, pp. 1-2.
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In order to meet the requirement of ocean energy power generation, a novel linear rotary permanent magnet machine (LRPMM) is proposed with E-type stator structure and interlaced poles. The adjacent interlaced PM poles in the circumferential direction are staggered half pole pitch in the axial direction. The optimization design of LRPMM is analyzed by analytical calculation method and 3-D finite element method (FEM), and the best optimization variable values are achieved. Compared with the results of the traditional topology analyzed by 3-D FEM, the electromagnetic characteristics are increased and the amplitudes of the cogging torque and detent force are reduced. An energy storage system of LRPMM is built, which can improve the effective utilization of wave energy and tidal energy.
Guo, K & Guo, Y 1970, 'Optimization Design of Parallel Double Stator and Outer Mover Linear Rotary Permanent Magnet Machine Used for Drilling Robot', 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE, Tianjin, China, pp. 1-2.
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In order to meet the requirement of the drilling robot, a parallel double stator and outer mover linear rotary permanent magnet machine is proposed, which combines an outer rotor vernier machine (ORVM) and a cylindrical outer rotor linear vernier machine (CORLVM) by using a special support mechanism. The electromagnetic and mechanical structure optimization design is analyzed by 3-D finite element method (FEM) in the paper, including the design of ORVM and CORLVM, the stress calculation of support mechanism and the loss calculation. Then the optimized values of structure parameters are obtained. Compared with the results of the initial topology analyzed by 3-D FEM, the torque and thrust are improved and the amplitudes of the cogging torque and detent force are reduced. The combined structure design has the advantages of two different motors, which can provide a reference for the research of this type of motor.
Guo, W & Beydoun, G 1970, 'Preface', ACM International Conference Proceeding Series, p. vii.
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Habib Khan, MN, Siwakoti, Y, Li, L, Khan, SA & Blaabjerg, F 1970, 'Leakage Current Analysis of The HB-ZVSCR Transformerless Inverter for Grid-Tied Photovoltaic Application', 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), IEEE, Nanjing, China, pp. 611-616.
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This paper presents an analyses of the leakage current (icm) of a mid-point voltage clamping H-bridge zero voltage switch controlled rectifier (HB-ZVSCR) transformerless inverter for grid-connected photovoltaic (PV) application. The circuit is constructed to reduce the ground current through two freewheeling switches, and four bridge diodes. The leakage current (icm) flows through the parasitic capacitor (CPV ), which is generated between the Photovoltaic (PV) panel, and ground. This paper shows the icm characteristic in different condition for diverse CPV values. Further, the PLECS software simulation is shown with hardware implemented waveform for 1 kW rated power. Moreover, the measured icm values are presented in a tabular form at different parasitic capacitors and switching frequencies.
Habib Khan, MN, Siwakoti, YP, Scott, MJ, Ul Hasan, S, Shaffer, B, Li, L, Khan, SA & Blaabjerg, F 1970, 'A Common Ground-type Single-Phase Dual Mode Five-Level Switched-Capacitor Transformerless Inverter', 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE, New Orleans, LA, USA, pp. 436-441.
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© 2020 IEEE. This paper presents a novel dual mode five-level common ground type (5L-DM-CGT) transformerless inverter topology with a wide input range (200 V - 400 V). It consists of eight switches, one diode, two capacitors, and an LC filter at the output. The topology eliminates common mode (CM) leakage current by connecting the negative terminal of the photovoltaic (PV) directly to the neutral point of the grid, which bypasses the PV array's stray capacitance. Depending on the magnitude of the input voltage, the converter can operate in buck or boost mode to produce the same AC voltage output. The analysis shows the advantages of dual mode inverter for various industrial applications. MATLAB Simulink simulations and experimental results verify the concept of the proposed topology and control method.
Hadgraft, RG, Francis, B, Fitch, R, Halkon, B & Brown, T 1970, 'Renewing mechanical and mechatronics programs using studios', SEFI 47th Annual Conference: Varietas Delectat... Complexity is the New Normality, Proceedings, SEFI Annual Conference, SEFI, Budapest, Hungary, pp. 511-522.
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In a world of rapid change, engineering programs need to adapt to be relevant. This paper addresses the renewal processes for mechanical and mechatronics engineering programs at a large university of technology. The paper sits within a wider curriculum change movement, including all engineering and IT programs at this university. Several meetings have been held over the last 3 years with both industry panels and with academic staff and students to understand the nature of the problem. Using a design-thinking approach, we have explored: global trends, the nature of engineering work and projects, the capabilities required by engineers, and the kinds of capabilities that graduates need to operate confidently in this new world of work. There is a clear need for graduates to be more operational as they move from study to work. Consequently, a major focus on experiential learning is emerging as the key delivery vehicle for new kinds of graduates including projects, studios, and internships. These forms of learning are supported by ready access to online materials as required. A central thread is personalisation of the student learning experience through learning contracts and portfolios. There has been constant demand for change in engineering education for at least the last 20 years. Making change happen, however, is another matter. We are in the fortunate position at this university to have high level support from the Chancellery and the Dean to move our engineering programs to be more relevant to the future. This paper describes the process for engaging our academics, students and industry supporters in that process and will be of interest to many who are grappling with similar transitions.
Halkon, B, Cheong, I, Visser, G, Walker, P & Oberst, S 1970, 'An experimental assessment of torsional and package vibration in an industrial engine-compressor system', 12th International Conference on Vibrations in Rotating Machinery, Vibrations in Rotating Machinery, CRC Press, Liverpool, pp. 625-639.
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An experimental field vibration measurement campaign was conducted on an engine-compressor system. Torsional vibrations were measured using both a strain-gauge based technique at the engine-compressor coupling and a rotational laser vibrometer at the torsional vibration damper. Package vibration measurements were simultaneously captured using a number of accelerometers mounted at various locations on the engine and compressor casings. Findings from the study include the observation that the coupling/damper dominant order 1.5 torsional vibration level was higher at idle (c14.1 Hz) than at full speed (c19.1 Hz) and that this is likely the result of the coincidence of the first torsional natural frequency (c19-20 Hz); vibration remained within limits. The package vibration observed was in general within limits and displayed the expected behaviour when shaft speeds coincided with structural resonances. Increasing of system load was observed to result in package vibration level increase in the engine but reduction in the compressor and this is suspected to be as a result of the effect of increased damping. Induced cylinder misfire scenarios were shown to lead to higher vibration levels. To the authors’ knowledge, this is the first time that angular displacement, vibratory torque and package vibration have been simultaneously measured, analysed and reported in an industrial context/scenario. It is hoped that this contribution might, therefore, serve as a practical guide to vibration engineers that wish to embark on similar campaigns.
Hall, N, Peng, J, Parnell, J & Wassermann, J 1970, 'Investigation of uniform and non-uniform traffic distribution on road traffic noise prediction for multi-lane roadways', Acoustics 2019, Sound Decisions: Moving Forward with Acoustics - Proceedings of the Annual Conference of the Australian Acoustical Society.
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Road traffic noise prediction is routinely undertaken as part of environmental impact assessments to assist government planning authorities to understand the potential noise impact that could arise from proposed road infrastructure development projects. Typically, road traffic noise models developed in New South Wales detail all lanes of a roadway and assume uniform distribution of traffic volume and vehicle mix across the lanes of each carriageway. In this work, the effects of uniform and non-uniform traffic distribution on road traffic noise prediction for multi-lane roadways are investigated. Models with all lanes detailed are compared to simplified two-lane models for a range of receiver setback distances and shielding arrangements.
Han, B, Niu, G, Yu, X, Yao, Q, Xu, M, Tsang, IW & Sugiyama, M 1970, 'SIGUA: Forgetting may make learning with noisy labels more robust', 37th International Conference on Machine Learning, ICML 2020, pp. 3964-3974.
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Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end. Although equipped with corrections for noisy labels, many learning methods in this area still suffer overfitting due to undesired memorization. In this paper, to relieve this issue, we propose stochastic integrated gradient underweighted ascent (SIGUA): in a minibatch, we adopt gradient descent on good data as usual, and learning-rate-reduced gradient ascent on bad data; the proposal is a versatile approach where data goodness or badness is w.r.t. desired or undesired memorization given a base learning method. Technically, SIGUA pulls optimization back for generalization when their goals conflict with each other; philosophically, SIGUA shows forgetting undesired memorization can reinforce desired memorization. Experiments demonstrate that SIGUA successfully robustifies two typical base learning methods, so that their performance is often significantly improved.
Han, M, Wang, Y, Chang, X & Qiao, Y 1970, 'Mining Inter-Video Proposal Relations for Video Object Detection', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 431-446.
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Recent studies have shown that, context aggregating information from proposals in different frames can clearly enhance the performance of video object detection. However, these approaches mainly exploit the intra-proposal relation within single video, while ignoring the intra-proposal relation among different videos, which can provide important discriminative cues for recognizing confusing objects. To address the limitation, we propose a novel Inter-Video Proposal Relation module. Based on a concise multi-level triplet selection scheme, this module can learn effective object representations via modeling relations of hard proposals among different videos. Moreover, we design a Hierarchical Video Relation Network (HVR-Net), by integrating intra-video and inter-video proposal relations in a hierarchical fashion. This design can progressively exploit both intra and inter contexts to boost video object detection. We examine our method on the large-scale video object detection benchmark, i.e., ImageNet VID, where HVR-Net achieves the SOTA results. Codes and models are available at https://github.com/youthHan/HVRNet.
Hassan, M, Liu, D & Chen, X 1970, 'Squircular-CPP: A Smooth Coverage Path Planning Algorithm based on Squircular Fitting and Spiral Path', 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, Boston, MA, USA, pp. 1075-1081.
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Coverage path planning (CPP) is essential for applications such as robotic floor cleaning and high-pressure cleaning of surfaces. Smooth CPP algorithms have several benefits including smoother motion of the robot and the reduction of aggressive accelerations and decelerations resulting from sharp turns. In this paper, a novel smooth CPP algorithm is presented which is named Squircular-CPP. This algorithm proposes a squircular shape, which is an intermediate shape between the circle and the square, to fit a target area. Squircular-CPP can also fit a shape between the ellipse and the rectangle. The shape fitting is simple, fast, and analytical and doesn't require a preselection of the shape (i.e., square, circle, ellipse or rectangle). It enables and complements the creation of a smooth spiral path within the fitted shape. Several case studies are presented to demonstrate the effectiveness of the algorithm and to compare it against the popular boustrophedon-based coverage approach and the Deformable Spiral CPP (DSCPP) algorithm.
Hassan, M, Mustafic, D & Liu, D 1970, 'Dec-PPCPP: A Decentralized Predator–Prey-based Approach to Adaptive Coverage Path Planning Amid Moving Obstacles', 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Las Vegas, NV, USA, pp. 11732-11739.
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Hassan, W, Hasan, R, Lu, DD-C & Xiao, W 1970, 'Design and Development of High Step-up DC-DC Converter to Realize High Efficiency and Reduced Voltage Stress', 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE, pp. 2098-2103.
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Hauge, E, Bui, A, Rajalingam, J, Karunatilake, N, Hunt, D, Vitanage, D, Dissanayake, G & Valls Miro, J 1970, 'Robotic Pipe Scanning: Intelligent Internal Toolkit for Critical Water Mains'', Ozwater’20 Papers, OzWater'20 Australia's International Water Conference and Exhibition, Australian Water Association, Adelaide, pp. 1-7.
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Sydney Water manages a complex water network that includes 5,000 km of large-diameter critical pipelines. To maintain customer satisfaction and minimise loss of water it is essential to manage leaks, breaks through implementing an effective and efficient preventive maintenance and renewal program. Sydney Water in collaboration with the University of Technology Sydney’s Centre for Autonomous Systems (UTS CAS) has developed two world-leading robotic condition assessment tools. These travel inside a dewatered pipe, providing a full 360° wall thickness scan up to 500m in length. Sydney Water has successfully deployed the tools during main failures. It also expects to apply the technology in planned maintenance inspection interventions.
Hayat, T, Afzal, MU, Ahmed, F, Lalbakhsh, A & Esselle, KP 1970, '3D Printable Lightweight Porous Superstrate for Improved Radiation Performance of Antenna', 2020 4th Australian Microwave Symposium (AMS), 2020 4th Australian Microwave Symposium (AMS), IEEE, Sydney, Australia, pp. 1-2.
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© 2020 IEEE. The paper presents a 3D printable porous superstrate (PS) to enhance directive radiation performance of low to medium gain antennas. The PS design process is based on the theory of near-field phase correction. Transmission phase through PS is locally varied by changing sizes of perforations in different sections of the PS. The PS is designed for a resonant cavity antenna (RCA) using acrylonitrile butadiene styrene (ABS) filament. With PS the RCA aperture phase is relatively planar and its directivity in boresight direction is increased by 7.2 dB (14.8 dB to 22 dB) along with 8.2 dB reduction in side-lobe levels (SLL) and 31% increase in aperture efficiency.
Hayat, T, U.Afzal, M, Lalbakhsh, A, Ahmed, F & P.Esselle, K 1970, 'Comparative Analysis of Highly Transmitting Phase Correcting Structures for Electromagnetic Bandgap Resonator Antenna', 2020 International Workshop on Antenna Technology (iWAT), 2020 International Workshop on Antenna Technology (iWAT), IEEE, Bucharest, Romania, pp. 1-4.
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A comparative analysis of two phase correcting structures (PCSs) is presented for an electromagnetic-bandgap resonator antenna (ERA). PCSs are made out of two distinct high and low permittivity materials i.e. Rogers O3010 and polylactic acid (PLA), respectively. Design and performance analysis is based on superstrate height profile, side-lobe levels, antenna directivity, aperture efficiency, prototyping technique and cost. Insertion loss for both superstrates is greater than 0.1 dB, assuring the maximum transmission of the antenna's radiations through the PCSs. The presented study is based on full wave analysis used to integrate sections of superstrate with custom phase-delays, to attain nearly uniform phase at the output, resulting in improved radiation performance of antenna. The peak directivity of the ERA loaded with Rogers O3010 PCS has increased by 7.3 dB, which is 1.2 dB higher than that of PLA PCS. In addition, the height of the PCS made of Rogers is 71.3% smaller than the PLA PCS. However, the former will involve fabrication complexities related to machining compared to the latter which can be additively manufactured in single step.
He, N & Ferguson, S 1970, 'Music Social Tags Representation in Dimensional Emotion Models', 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), IEEE, pp. 819-826.
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HE, NA & Ferguson, S 1970, 'Multi-view Neural Networks for Raw Audio-based Music Emotion Recognition', 2020 IEEE International Symposium on Multimedia (ISM), 2020 IEEE International Symposium on Multimedia (ISM), IEEE, Naples, Italy, pp. 168-172.
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In Music Emotion Recognition (MER) research, most existing research uses human engineered audio features as learning model inputs, which require domain knowledge and much effort for feature extraction. We propose a novel end-to-end deep learning approach to address music emotion recognition as a regression problem, using the raw audio signal as input. We adopt multi-view convolutional neural networks as feature extractors to learn feature representations automatically. Then the extracted feature vectors are merged and fed into two layers of Bidirectional Long Short-Term Memory to capture temporal context sufficiently. In this way, our model is capable of recognizing dynamic music emotion without requiring too much workload on domain knowledge learning and audio feature processing. Combined with data augmentation strategies, the experimental results show that our model outperforms the state-of-the-art baseline with a significant margin in terms of R2 score (approximately 16%) on the Emotion in Music Database.
He, Y, Ding, Y, Liu, P, Zhu, L, Zhang, H & Yang, Y 1970, 'Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Seattle, WA, USA, pp. 2006-2015.
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© 2020 IEEE. Filter pruning has been widely applied to neural network compression and acceleration. Existing methods usually utilize pre-defined pruning criteria, such as Lp-norm, to prune unimportant filters. There are two major limitations to these methods. First, existing methods fail to consider the variety of filter distribution across layers. To extract features of the coarse level to the fine level, the filters of different layers have various distributions. Therefore, it is not suitable to utilize the same pruning criteria to different functional layers. Second, prevailing layer-by-layer pruning methods process each layer independently and sequentially, failing to consider that all the layers in the network collaboratively make the final prediction. In this paper, we propose Learning Filter Pruning Criteria (LFPC) to solve the above problems. Specifically, we develop a differentiable pruning criteria sampler. This sampler is learnable and optimized by the validation loss of the pruned network obtained from the sampled criteria. In this way, we could adaptively select the appropriate pruning criteria for different functional layers. Besides, when evaluating the sampled criteria, LFPC comprehensively consider the contribution of all the layers at the same time. Experiments validate our approach on three image classification benchmarks. Notably, on ILSVRC-2012, our LFPC reduces more than 60% FLOPs on ResNet-50 with only 0.83% top-5 accuracy loss.
Hemsley, B, Balandin, S, Dann, S, Gay, V, Josserand, E, Leong, T, Palmer, S & Skellern, K 1970, 'A device looking for a purpose and user-centred co-design: 3D food printing not yet delivering on expectations of benefit for people with swallowing disability.', SYMPOSIUM FOR SPACE NUTRITION AND FOOD ENGINEERING, SYMPOSIUM FOR SPACE NUTRITION AND FOOD ENGINEERING, Wuxi, China.
Heon Lee, JJ, Yoo, C, Anstee, S & Fitch, R 1970, 'Hierarchical Planning in Time-Dependent Flow Fields for Marine Robots', 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Paris, France (Virtual), pp. 885-891.
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We present an efficient approach for finding shortest paths in flow fields that vary as a sequence of flow predictions over time. This approach is applicable to motion planning for slow marine robots that are subject to dynamic ocean currents. Although the problem is NP-hard in general form, we incorporate recent results from the theory of finding shortest paths in time-dependent graphs to construct a polynomial-time algorithm that finds continuous trajectories in time-dependent flow fields. The algorithm has a hierarchical structure where a graph is constructed with time-varying edge costs that are derived from sets of continuous trajectories in the underlying flow field. We show that the continuous algorithm retains the time complexity and path quality properties of the discrete graph solution, and demonstrate its application to surface and underwater vehicles including a traversal along the East Australian Current with an autonomous marine vehicle. Results show that the algorithm performs efficiently in practice and can find paths that adapt to changing ocean currents. These results are significant to marine robotics because they allow for efficient use of time-varying ocean predictions for motion planning.
Hesamian, MH, Jia, W, He, X & Kennedy, P 1970, 'Region proposal network for lung nodule detection and segmentation', CEUR Workshop Proceedings, International Workshop on Knowledge Discovery in Healthcare Data, Aachen University, Santiago de Compostela, Spain & Virtually, pp. 48-52.
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Lung nodule detection and segmentation play a critical role in detecting and determining the stage of lung cancer. This paper proposes a two-stage segmentation method which is capable of improving the accuracy of detecting and segmentation of lung nodules from 2D CT images. The first stage of our approach proposes multiple regions, potentially containing the tumour, and the second stage performs the pixel-level segmentation from the resultant regions. Moreover, we propose an adaptive weighting loss to effectively address the issue of class imbalance in lung CT image segmentation. We evaluate our proposed solution on a widely adopted benchmark dataset of LIDC. We have achieved a promising result of 92.78% for average DCS that puts our method among the top lung nodule segmentation methods.
Hossain, A, Tipper, JL & Wei, D 1970, 'Analysis of a Multi-Material Bone Plate and its Effect on Interfragmentary Strain for Bone Remodeling Processes', Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation, ASME 2020 15th International Manufacturing Science and Engineering Conference, American Society of Mechanical Engineers.
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Abstract The success of bone repair using an internal fracture fixation technique is critically dependent on the stability and biological process between the fragmented bones. However, the currently used bone plates mainly focus on stability rather than biology of healing, which subsequently (a) results in significant stress-shielding effects and (b) prevents stress from transferring from the bone plate to the bone during the healing process. This study proposes a novel design of a bone plate for the fixation of long fractured bones, which can mitigate these disadvantages to strike a balance between stability and biology. The new multi-material design adopts stainless steel (SS316L) and magnesium alloy (AZ31B) of three thicknesses such as SS316L (1mm)-AZ31B (2mm), SS316L (1.5mm)-AZ31B (1.5mm), and SS316L (2mm)-AZ31B (1mm). The mechanical properties (bending stiffness and moment) of the bone plates were evaluated according to the ASTM: F382-17 standard. Static corrosion tests were conducted in Hank’s Balanced Salt Solution (HBSS) at 37.5 °C. Compared with those of the original (non-corroded) bone plates, the maximum load-carrying capacities of the corroded bone plates decreased from 670 N to 495 N, 891 N to 518 N, and 928 N to 709 N in the case of SS316L (1mm)-AZ31B (2mm), SS316L (1.5mm)-AZ31B (1.5mm), and SS316L (2mm)-AZ31B(1mm), respectively. Digital image correlation was utilized to evaluate the inter-fragmentary strain (IFS) in the physical model of fractured bone plates. The IFS increased from 0.526 to 0.815, 0.484 to 0.784, and 0.455 to 0.533 in the case of SS316L (1mm)-AZ31B (2mm), SS316L (1.5mm)-AZ31B (1.5mm), and SS316L (2mm)-AZ31B (1mm), respectively, when a load of 200 N was applied. An optimized design of the bone plate of SS316L and AZ31B for granulation tissue formation based on Perren’s theory and IFS was successfully proposed.
Hu, R, Pan, S, Long, G, Lu, Q, Zhu, L & Jiang, J 1970, 'Going Deep: Graph Convolutional Ladder-Shape Networks', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 2838-2845.
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Neighborhood aggregation algorithms like spectral graph convolutional networks (GCNs) formulate graph convolutions as a symmetric Laplacian smoothing operation to aggregate the feature information of one node with that of its neighbors. While they have achieved great success in semi-supervised node classification on graphs, current approaches suffer from the over-smoothing problem when the depth of the neural networks increases, which always leads to a noticeable degradation of performance. To solve this problem, we present graph convolutional ladder-shape networks (GCLN), a novel graph neural network architecture that transmits messages from shallow layers to deeper layers to overcome the over-smoothing problem and dramatically extend the scale of the neural networks with improved performance. We have validated the effectiveness of proposed GCLN at a node-wise level with a semi-supervised task (node classification) and an unsupervised task (node clustering), and at a graph-wise level with graph classification by applying a differentiable pooling operation. The proposed GCLN outperforms original GCNs, deep GCNs and other state-of-the-art GCN-based models for all three tasks, which were designed from various perspectives on six real-world benchmark data sets.
Huang Wei, Xu Richard Yi Da, Du Weitao, Zeng Yutian & Zhao Yunce 1970, 'Mean Field Theory for Deep Dropout Networks: Digging up Gradient Backpropagation Deeply', Frontiers in Artificial Intelligence and Applications, IOS Press, pp. 1215-1222.
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In recent years, the mean field theory has been applied to the study of neural networks and has achieved a great deal of success. The theory has been applied to various neural network structures, including CNNs, RNNs, Residual networks, and Batch normalization. Inevitably, recent work has also covered the use of dropout. The mean field theory shows that the existence of depth scales that limit the maximum depth of signal propagation and gradient backpropagation. However, the gradient backpropagation is derived under the gradient independence assumption that weights used during feed forward are drawn independently from the ones used in backpropagation. This is not how neural networks are trained in a real setting. Instead, the same weights used in a feed-forward step needs to be carried over to its corresponding backpropagation. Using this realistic condition, we perform theoretical computation on linear dropout networks and a series of experiments on dropout networks with different activation functions. Our empirical results show an interesting phenomenon that the length gradients can backpropagate for a single input and a pair of inputs are governed by the same depth scale. Besides, we study the relationship between variance and mean of statistical metrics of the gradient and shown an emergence of universality. Finally, we investigate the maximum trainable length for deep dropout networks through a series of experiments using MNIST and CIFAR10 and provide a more precise empirical formula that describes the trainable length than original work.
Huang, C, Jiang, S, Li, Y, Zhang, Z, Traish, J, Deng, C, Ferguson, S & Da Xu, RY 1970, 'End-to-end Dynamic Matching Network for Multi-view Multi-person 3D Pose Estimation', Computer Vision – ECCV 2020, European Conference on Computer Vision, Springer International Publishing, Glasgow, UK, pp. 477-493.
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As an important computer vision task, 3d human pose estimation in a multi-camera, multi-person setting has received widespread attention and many interesting applications have been derived from it. Traditional approaches use a 3d pictorial structure model to handle this task. However, these models suffer from high computation costs and result in low accuracy in joint detection. Recently, especially since the introduction of Deep Neural Networks, one popular approach is to build a pipeline that involves three separate steps: (1) 2d skeleton detection in each camera view, (2) identification of matched 2d skeletons and (3) estimation of the 3d poses. Many existing works operate by feeding the 2d images and camera parameters through the three modules in a cascade fashion. However, all three operations can be highly correlated. For example, the 3d generation results may affect the results of detection in step 1, as does the matching algorithm in step 2. To address this phenomenon, we propose a novel end-to-end training scheme that brings the three separate modules into a single model. However, one outstanding problem of doing so is that the matching algorithm in step 2 appears to disjoint the pipeline. Therefore, we take our inspiration from the recent success in Capsule Networks, in which its Dynamic Routing step is also disjointed, but plays a crucial role in deciding how gradients are flowed from the upper to the lower layers. Similarly, a dynamic matching module in our work also decides the paths in which gradients flow from step 3 to step 1. Furthermore, as a large number of cameras are present, the existing matching algorithm either fails to deliver a robust performance or can be very inefficient. Thus, we additionally propose a novel matching algorithm that can match 2d poses from multiple views efficiently. The algorithm is robust and able to deal with situations of incomplete and false 2d detection as well.
Huang, H, Long, G, Shen, T, Jiang, J & Zhang, C 1970, 'RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion', Proceedings of the 28th International Conference on Computational Linguistics, Proceedings of the 28th International Conference on Computational Linguistics, International Committee on Computational Linguistics, pp. 556-567.
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Many graph embedding approaches have been proposed for knowledge graph completion via link prediction. Among those, translating embedding approaches enjoy the advantages of lightweight structure, high efficiency and great interpretability. Especially when extended to complex vector space, they show the capability in handling various relation patterns including symmetry, antisymmetry, inversion and composition. However, previous translating embedding approaches defined in complex vector space suffer from two main issues: 1) representing and modeling capacities of the model are limited by the translation function with rigorous multiplication of two complex numbers; and 2) embedding ambiguity caused by one-to-many relations is not explicitly alleviated. In this paper, we propose a relation-adaptive translation function built upon a novel weighted product in complex space, where the weights are learnable, relation-specific and independent to embedding size. The translation function only requires eight more scalar parameters each relation, but improves expressive power and alleviates embedding ambiguity problem. Based on the function, we then present our Relation-adaptive translating Embedding (RatE) approach to score each graph triple. Moreover, a novel negative sampling method is proposed to utilize both prior knowledge and self-adversarial learning for effective optimization. Experiments verify RatE achieves state-of-the-art performance on four link prediction benchmarks.
Huang, H, Savkin, AV & Ni, W 1970, 'A Method for Covert Video Surveillance of a Car or a Pedestrian by an Autonomous Aerial Drone via Trajectory Planning', 2020 6th International Conference on Control, Automation and Robotics (ICCAR), 2020 6th International Conference on Control, Automation and Robotics (ICCAR), IEEE, Singapore, pp. 446-449.
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This paper considers the application of covert video surveillance of a ground mobile target by an autonomous aerial drone. We design a metric to measure the covertness of the drone. Then, we formulate a multi-objective drone trajectory planning problem, which maximizes the drone disguising performance and minimizes its energy consumption. We furthermore propose a forward dynamic programming method to solve the problem online and conduct simulations to verify its effectiveness.
Huang, H, Savkin, AV & Ni, W 1970, 'Decentralized Covert and Collaborative Radio Surveillance on a Group of Mobile Ground Nodes by a UAV Swarm', 2020 IEEE 18th International Conference on Industrial Informatics (INDIN), 2020 IEEE 18th International Conference on Industrial Informatics (INDIN), IEEE, pp. 307-310.
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Huang, P-Y, Chang, X, Hauptmann, A & Hovy, E 1970, 'Forward and Backward Multimodal NMT for Improved Monolingual and Multilingual Cross-Modal Retrieval', Proceedings of the 2020 International Conference on Multimedia Retrieval, ICMR '20: International Conference on Multimedia Retrieval, ACM, pp. 53-62.
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We explore methods to enrich the diversity of captions associated with pictures for learning improved visual-semantic embeddings (VSE) in cross-modal retrieval. In the spirit of 'A picture is worth a thousand words', it would take dozens of sentences to parallel each picture's content adequately. But in fact, real-world multimodal datasets tend to provide only a few (typically, five) descriptions per image. For cross-modal retrieval, the resulting lack of diversity and coverage prevents systems from capturing the fine-grained inter-modal dependencies and intra-modal diversities in the shared VSE space. Using the fact that the encoder-decoder architectures in neural machine translation (NMT) have the capacity to enrich both monolingual and multilingual textual diversity, we propose a novel framework leveraging multimodal neural machine translation (MMT) to perform forward and backward translations based on salient visual objects to generate additional text-image pairs which enables training improved monolingual cross-modal retrieval (English-Image) and multilingual cross-modal retrieval (English-Image and German-Image) models. Experimental results show that the proposed framework can substantially and consistently improve the performance of state-of-the-art models on multiple datasets. The results also suggest that the models with multilingual VSE outperform the models with monolingual VSE.
Huang, PY, Hu, J, Chang, X & Hauptmann, A 1970, 'Unsupervised multimodal neural machine translation with pseudo visual pivoting', Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 8226-8237.
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Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only. However, it is still challenging to associate source-target sentences in the latent space. As people speak different languages biologically share similar visual systems, the potential of achieving better alignment through visual content is promising yet under-explored in unsupervised multimodal MT (MMT). In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT. Our model employs multimodal back-translation and features pseudo visual pivoting in which we learn a shared multilingual visual-semantic embedding space and incorporate visually-pivoted captioning as additional weak supervision. The experimental results on the widely used Multi30K dataset show that the proposed model significantly improves over the state-of-the-art methods and generalizes well when images are not available at the testing time.
Huang, X, Mei, G & Zhang, J 1970, 'Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences', Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp. 11363-11371.
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We present a fast feature-metric point cloud registration framework, whichenforces the optimisation of registration by minimising a feature-metricprojection error without correspondences. The advantage of the feature-metricprojection error is robust to noise, outliers and density difference incontrast to the geometric projection error. Besides, minimising thefeature-metric projection error does not need to search the correspondences sothat the optimisation speed is fast. The principle behind the proposed methodis that the feature difference is smallest if point clouds are aligned verywell. We train the proposed method in a semi-supervised or unsupervisedapproach, which requires limited or no registration label data. Experimentsdemonstrate our method obtains higher accuracy and robustness than thestate-of-the-art methods. Besides, experimental results show that the proposedmethod can handle significant noise and density difference, and solve bothsame-source and cross-source point cloud registration.
Huang, Z, Wang, L-W, Leung, FHF, Banerjee, S, Yang, D, Lee, T, Lyu, J, Ling, SH & Zheng, Y-P 1970, 'Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise', 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE International Conference on Systems, Man and Cybernetics, IEEE, Toronto, ON, Canada, pp. 1566-1571.
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3D ultrasound imaging shows great promise for scoliosis diagnosis thanks toits low-costing, radiation-free and real-time characteristics. The key toaccessing scoliosis by ultrasound imaging is to accurately segment the bonearea and measure the scoliosis degree based on the symmetry of the bonefeatures. The ultrasound images tend to contain many speckles and regularocclusion noise which is difficult, tedious and time-consuming for experts tofind out the bony feature. In this paper, we propose a robust bone featuresegmentation method based on the U-net structure for ultrasound spine VolumeProjection Imaging (VPI) images. The proposed segmentation method introduces atotal variance loss to reduce the sensitivity of the model to small-scale andregular occlusion noise. The proposed approach improves 2.3% of Dice score and1% of AUC score as compared with the u-net model and shows high robustness tospeckle and regular occlusion noise.
Hughes, M, Garcia, J, Wilcox, F, Sazdov, R, Johnston, A & Bluff, A 1970, 'Immerse: Game Engines for Audio-Visual Art in the Future of Ubiquitous Mixed Reality', ICLI 2020 : International Conference on Live Interfaces, Trondheim.
Huynh, BP 1970, 'A LES Study of Ventilation Flow Through a 3-D Room Fitted With Solar Chimney', Volume 3: Computational Fluid Dynamics; Micro and Nano Fluid Dynamics, ASME 2020 Fluids Engineering Division Summer Meeting collocated with the ASME 2020 Heat Transfer Summer Conference and the ASME 2020 18th International Conference on Nanochannels, Microchannels, and Minichannels, American Society of Mechanical Engineers.
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Abstract Solar chimney (thermal chimney) is a device which absorbs solar radiation to heat the air. The heated air, becoming buoyant, rises through the chimney’s passage and induces further air currents. When fitted to a building, solar chimney can thus induce fresh outside air to flow through the building for ventilation. Because only natural means (solar radiation here) are involved to cause the air flow, solar chimney is considered a natural-ventilation device. This work investigates computationally natural ventilation induced by a roof-mounted solar chimney through a real-sized 3-dimensional room, using a commercial CFD (Computational Fluid Dynamics) software package which employs the Finite Volume Method. A LES (Large-Eddy Simulations) formulation with Smagorinsky SGS (Sub-Grid Scale) model is used. All fluid properties are assumed to be constant and corresponding to those of air at 300K (27°C, constant ambient temperature) and standard pressure at sea level (101.3kPa); but Boussinesq approximation (wherein temperature change affects only the fluid density pertaining to buoyancy force) is also assumed. Comparison is made with computational results obtained from a RANS (Reynolds-Averaged Navier-Stokes) formulation. Agreement between LES and RANS results indicate the trustworthiness of CFD methods used.
Huynh, NV, Hoang, DT, Nguyen, DN, Dutkiewicz, E & Mueck, M 1970, 'Defeating Smart and Reactive Jammers with Unlimited Power', 2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Seoul, pp. 1-6.
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Huynh, NV, Nguyen, DN, Hoang, DT, Dutkiewicz, E, Mueck, M & Srikanteswara, S 1970, 'Defeating Jamming Attacks with Ambient Backscatter Communications', 2020 International Conference on Computing, Networking and Communications (ICNC), 2020 International Conference on Computing, Networking and Communications (ICNC), IEEE, Hawaii, pp. 405-409.
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Ibrahim, IA & Hossain, MJ 1970, 'LSTM Neural Network Model for Ultra-short-term Distribution Zone Substation Peak Demand Prediction', 2020 IEEE Power & Energy Society General Meeting (PESGM), 2020 IEEE Power & Energy Society General Meeting (PESGM), IEEE, Quebec, Canada, pp. 1-5.
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The accurate prediction of the distribution load demand data is the corner stone of future planning of the power system networks and energy management strategies and policies. This paper presents a long short-term memory (LSTM) neural networks model to predict the distribution zone substation peak demand data in New South Wales state, Australia for 14 years and based on 15-minute intervals. The obtained results are compared with those obtained by feed-forward neural networks (FFNNs) and recurrent neural networks (RNNs) models. Three statistical performance evaluation, namely, the root-mean-square error (RMSE), mean bias error (MBE) and mean absolute percentage error (MAPE) are used to verify the effectiveness of the proposed model. The RMSE, MBE and MAPE of the LSTM neural network model are 1.2556%, 1.2201% and 2.2250%, respectively. In addition, the computational time is 12.3309 second which is faster than FFNNs and RNNs models. The results show the effectiveness of the proposed model over the aforementioned models in terms of accuracy and computational speed.
Ikram, MA, Sharma, N, Raza, M & Hussain, FK 1970, 'Dynamic Ranking System of Cloud SaaS Based on Consumer Preferences - Find SaaS M2NFCP', Advances in Intelligent Systems and Computing, International Conference on Advanced Information Networking and Applications, Springer International Publishing, Japan, pp. 1000-1010.
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Software as a Service (SaaS) is a type of software application that runs and operates over a cloud computing infrastructure. SaaS has grown more dramatically compared to other cloud services delivery models (i.e. PaaS and IaaS) in terms of the number of available services. This rapid growth in SaaS brings a lot of challenges for consumers in selecting the optimum services. The aim of this article is to propose a ranking system for SaaS based on consumer’s preferences called Find SaaS M2NFCP. The proposed ranking system is based on measuring the shortest distance to the minimum and maximum of the selected consumer’s non-functional preferences. In addition, linguistic terms are taken into account to weight the most important non-functional preferences. The proposed system is evaluated against traditional SaaS ranking systems using data collected from online CRM SaaS and achieved improved results.
Ilsar, A, Hughes, M & Johnston, A 1970, 'Nime or mime: A sound-first approach to developing an audio-visual gestural instrument', Proceedings of the International Conference on New Interfaces for Musical Expression, pp. 315-320.
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This paper outlines the development process of an audio-visual gestural instrument—the AirSticks—and elaborates on the role ‘miming’ has played in the formation of new mappings for the instrument. The AirSticks, although fully-functioning, were used as props in live performances in order to evaluate potential mapping strategies that were later implemented for real. This use of mime when designing Digital Musical Instruments (DMIs) can help overcome choice paralysis, break from established habits, and liberate creators to realise more meaningful parameter mappings. Bringing this process into an interactive performance environment acknowledges the audience as stakeholders in the design of these instruments, and also leads us to reflect upon the beliefs and assumptions made by an audience when engaging with the performance of such ‘magical’ devices. This paper establishes two opposing strategies to parameter mapping, ‘movement-first’ mapping, and the less conventional ‘sound-first’ mapping that incorporates mime. We discuss the performance ‘One Five Nine’, its transformation from a partial mime into a fully interactive presentation, and the influence this process has had on the outcome of the performance and the AirSticks as a whole.
Indraratna, B, Ngo, T, Rujikiatkamjorn, C & Ferreira, F 1970, 'Advancement of Rail Ballast Testing Methodologies and Design Implications', Geo-Congress 2020, Geo-Congress 2020, American Society of Civil Engineers, Minneapolis, MN, pp. 355-363.
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© 2020 American Society of Civil Engineers. Given the limited capacity of available railway tracks in Australia, to sustain increasingly faster and heavier trains, the development of innovative and sustainable ballasted tracks is essential for Australian transport infrastructure. Upon repeated train loading, ballast aggregates become degraded and fouled owing to the intrusion of external fines either from the subbase or surface, which decreases track drainage potentially leading to track instability. This paper reviews some advancements in testing methodologies and design implications of ballasted tracks stabilized with artificial inclusions, including geocomposites, energy absorbing rubber mats, and end-of-life tires. Measured test data shows that the use of these waste rubber products and geosynthetics provides an appropriate solution for mitigating unacceptable track degradation and for improving sustainable track alignment, apart from reducing the thickness of the ballast layer. Field monitoring data from fully instrumented tracks constructed at Singleton, Australia, is presented and discussed. The outcomes of this study contribute to a better understanding of the performance of reinforced ballasted tracks, which will be imperative for the development of more efficient and cost-effective track designs with enhanced safety and passenger comfort.
Inibhunu, C & McGregor, C 1970, 'Edge Computing with Big Data Cloud Architecture: A Case Study in Smart Building', 2020 IEEE International Conference on Big Data (Big Data), 2020 IEEE International Conference on Big Data (Big Data), IEEE.
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Inkermann, D, Gürtler, M & Seegrün, A 1970, 'RECAP – A FRAMEWORK TO SUPPORT STRUCTURED REFLECTION IN ENGINEERING PROJECTS', Proceedings of the Design Society: DESIGN Conference, 16th International Design Conference - DESIGN 2020, Cambridge University Press (CUP), Dubrovnik, Croatia - Online, pp. 597-606.
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AbstractReflection is understood as an integral part of designing and design processes. Despite the high relevance and an ongoing discussion about agile engineering, we found that reflection is rarley established in industrial practice. There is a need for an approach structuring the wide range of levels, stakeholders, objects and timing of reflections. The introduced RECAP framework is an important step towards a guideline (heuristic) for reflection in engineering projects. Based on the four dimensions objectives, stakeholders, objects, and processes it supports structured planning of reflection.
Inwumoh, J, Baguley, C & Gunawardane, K 1970, 'Intelligent Fault Localization for Meshed HVDC Transmission Systems', 2020 Australasian Universities Power Engineering Conference, AUPEC 2020 - Proceedings.
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Half-bridge Modular Multilevel Converters (HB-MMCs) enable the transmission of renewable energy from remote places to load centres with high levels of efficiency. Further, and relative to other MMC topologies, the HB-MMC is low in cost to implement. However, in cases where the severity of the fault increases beyond a given threshold, HB-MMCs could become blocked, which may lead to a grid collapse before the fault can be isolated. Therefore, an intelligent system is proposed to locate and isolate the exact fault path using Quadratic Support Vector Machine (QSVM) and Squared Exponential Gaussian Process Regression (seGPR) algorithms. This allows for timely fault clearance and, for meshed systems, identification of alternative power flow paths to achieve fault ride-through. Thus, the continuous operation of the grid under a fault condition can be assured. Converter simulation, data analysis and fault estimation are presented using MATLAB/Simulink to show the effectiveness of the proposed system.
Irshad, UB, Rafique, S, Hossain, MJ & Mukhopadhyay, SC 1970, 'Anti-Islanding Method for Houses Equipped with Electric Vehicles and Photovoltaic System', 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES), 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES), IEEE, pp. 397-401.
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© 2020 IEEE. Integration of electric vehicles (EVs) are exponentially increasing in the global market and by enabling vehicle-to-grid (V2G) EVs can inject power back into the grid. However, in an event of unintentional islanding, injecting power into the grid may causes potential safety threats to people, equipment, and power system. This paper proposes an adaptive reactive power mismatch method to detect islanding events. When islanding occurs, the proposed method drifts the system frequency away from the nominal value. Then the islanding event is detected based on frequency variations. Results show that the proposed method effectively detects islanding event within 0.801 milliseconds and have negligible non-detection zone.
Irshad, UB, Rafique, S, Hossain, MJ & Mukhopadhyay, SC 1970, 'Novel Sizing Method of Energy Storage System Considering Intermittent Usage of EVs in a Constrained Grid', 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES), 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES), IEEE, pp. 219-223.
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© 2020 IEEE. Charging of electric vehicles (EVs) significantly impact the reliability of the power system. A constrained power grid is a feasible solution to maintain the reliability of the power system. However, in a constrained power grid, it is challenging for the parking lot operator to balance the additional load. The fast and high-power density of batteries makes them a conceivable option for this task if adequately sized. A sizing algorithm is proposed to compute the battery capacity for parking lots while considering the intermittent usage of EVs in a constrained grid. Charging profile of EVs is constructed by considering travel pattern, charging need and driver's behaviour of EVs. The proposed sizing algorithm avoided over/under-sizing of the battery energy storage system and fulfilled the EV charging demand in the parking lot. The accuracy of the proposed battery sizing algorithm is shown by simulation results, characterized by real data of household travel survey and parking occupancy data.
Islam, MR, Liu, S, Razzak, I, Kabir, MA, Wang, X, Tilocca, P & Xu, G 1970, 'MHIVis: Visual Analytics for Exploring Mental Illness of Policyholders in Life Insurance Industry', 2020 7th International Conference on Behavioural and Social Computing (BESC), 2020 7th International Conference on Behavioural and Social Computing (BESC), IEEE, Bournemouth, United Kingdom, pp. 1-4.
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Stakeholders such as insurance managers (IMs) in the insurance industry are committed to yet lack the timely and actionable information for alleviating policyholder's mental health concerns and the industry's mental health climate. Existing research has revealed that depression, anxiety, stress, etc., can provide deeper insights into policyholders' mental health states. However, such data remain unexplored for supporting stakeholders and government goals. In this paper, we design an interactive visualization system to provide deeper insight into policyholder's mental health states. Our study has three implications: (i) insurance data are potentially useful for understanding policyholders' mental health; (ii) a dashboard-like visual representation is helpful for the decision-making of Stakeholders; and (iii) some insight into the mental health of Australians have been deduced. Finally, we evaluate the utility of our visualization system by comparing it's features with the existing dashboards.
Iyer, S, Sowmya, A, Blair, A, White, C, Dawes, L & Moses, D 1970, 'A Novel Approach to Vertebral Compression Fracture Detection Using Imitation Learning and Patch Based Convolutional Neural Network', 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), IEEE, pp. 726-730.
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Jauregi Unanue, I, Esmaili, N, Haffari, G & Piccardi, M 1970, 'Leveraging Discourse Rewards for Document-Level Neural Machine Translation', Proceedings of the 28th International Conference on Computational Linguistics, Proceedings of the 28th International Conference on Computational Linguistics, International Committee on Computational Linguistics, Barcelona, Spain, pp. 4467-4482.
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Jayasuriya, C, Indraratna, B, Rujikiatkamjorn, C & Navaratnarajah, SK 1970, 'Application of Elastic Inclusions to Improve the Performance of Ballasted Track', Geo-Congress 2020, Geo-Congress 2020, American Society of Civil Engineers, pp. 364-373.
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© 2020 American Society of Civil Engineers. Ballast is the most common foundation material of railways and as such is subjected to deformation and degradation from the large cyclic and impact loads generated by heavy, fast moving trains. These inevitable effects hamper the safety and efficiency of tracks and increase the track maintenance frequency. One of several promising approaches to mitigate these problems is stabilizing ballasted track with rubber mats (under sleeper pads -USP and under ballast mats -UBM), to absorb energy and reduce particle breakage, track stability, longevity, and safety. This paper analyses the current knowledge of using rubber elements in ballasted track acquired through large scale laboratory testing carried out at the University of Wollongong (UOW). This investigation reveals that indicate that the damping characteristics of rubber mats reduce the deformation and degradation of ballast. The results shows that USPs are better at reducing vertical permanent deformation while UBMs are better at reducing lateral deformation.
Jayasuriya, M, Arukgoda, J, Ranasinghe, R & Dissanayake, G 1970, 'Localising PMDs through CNN Based Perception of Urban Streets.', ICRA, IEEE International Conference on Robotics and Automation, IEEE, Paris, France, pp. 6454-6460.
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The main contribution of this paper is a novel Extended Kalman Filter (EKF) based localisation scheme that fuses two complementary approaches to outdoor vision based localisation. This EKF is aided by a front end consisting of two Convolutional Neural Networks (CNNs) that provide the necessary perceptual information from camera images. The first approach involves a CNN based extraction of information corresponding to artefacts such as curbs, lane markings, and manhole covers to localise on a vector distance transform representation of a binary image of these ground surface boundaries. The second approach involves a CNN based detection of common environmental landmarks such as tree trunks and light poles, which are represented as point features on a sparse map. Utilising CNNs to obtain higher level information about the environment enables this framework to avoid the typical pitfalls of common vision based approaches that use low level hand crafted features for localisation. The EKF framework makes it possible to deal with false positives and missed detections that are inevitable in a practical CNN, to produce a location estimate together with its associated uncertainty. Experiments using a Personal Mobility Device (PMD) driven in typical suburban streets are presented to demonstrate the effectiveness of the proposed localiser.
Jayasuriya, M, Arukgoda, J, Ranasinghe, R & Dissanayake, G 1970, 'Towards Adapting Autonomous Vehicle Technology for the Improvement of Personal Mobility Devices', 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), IEEE, Sydney, Australia, pp. 1-7.
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Personal Mobility Devices (PMDs) incorporated with autonomy, have great potential in becoming an essential building block of smart transportation infrastructures of the future. However, autonomous vehicle technologies currently employ large and expensive sensors / computers and resource intensive algorithms, which are not suitable for low cost, small form factor PMDs. In this paper, a mobility scooter is retrofitted with a low cost sensing and computing package with the aim of achieving autonomous driving capability. As a first step, a novel, real time, low cost and resource efficient vision only localisation framework based on Convolutional Neural Network (CNN) oriented feature extraction and extended Kalman filter oriented state estimation is presented. Real world experiments in a suburban environment are presented to demonstrate the effectiveness of the proposed localisation framework.
Jayasuriya, M, Ranasinghe, R & Dissanayake, G 1970, 'Active Perception for Outdoor Localisation with an Omnidirectional Camera.', IROS, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Las Vegas, NV, USA, pp. 4567-4574.
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This paper presents a novel localisation framework based on an omnidirectional camera, targeted at outdoor urban environments. Bearing only information to persistent and easily observable high-level semantic landmarks (such as lamp-posts, street-signs and trees) are perceived using a Convolutional Neural Network (CNN). The framework utilises an information theoretic strategy to decide the best viewpoint to serve as an input to the CNN instead of the full 360° coverage offered by an omnidirectional camera, in order to leverage the advantage of having a higher field of view without compromising on performance. Environmental landmark observations are supplemented with observations to ground surface boundaries corresponding to high-level features such as manhole covers, pavement edges and lane markings extracted from a second CNN. Localisation is carried out in an Extended Kalman Filter (EKF) framework using a sparse 2D map of the environmental landmarks and Vector Distance Transform (VDT) based representation of the ground surface boundaries. This is in contrast to traditional vision only localisation systems that have to carry out Visual Odometry (VO) or Simultaneous Localisation and Mapping (SLAM), since low level features (such as SIFT, SURF, ORB) do not persist over long time frames due to radical appearance changes (illumination, occlusions etc) and dynamic objects. As the proposed framework relies on highlevel persistent semantic features of the environment, it offers an opportunity to carry out localisation on a prebuilt map, which is significantly more resource efficient and robust. Experiments using a Personal Mobility Device (PMD) driven in a representative urban environment are presented to demonstrate and evaluate the effectiveness of the proposed localiser against relevant state of the art techniques.
Jena, R & Pradhan, B 1970, 'Earthquake Risk Assessment Using Integrated Influence Diagram–AHP Approach', IOP Conference Series: Earth and Environmental Science, International Conference and Exhibition on Geospatial & Remote Sensing, IOP Publishing, Kuala Lumpur, Malaysia, pp. 012078-012078.
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Abstract Indonesia is located at the joint situation of four major world tectonic plates in the Pacific Ring of Fire. Mostly, the coastal regions of Indonesia are highly prone to several natural hazards, such as tsunamis, earthquakes, and volcanic activity. The major earthquake incident in the country was the 2004 earthquake in Aceh, whereas a major volcanic eruption was the Mount Merapi volcanic eruption in 2010. With the present advancement of knowledge regarding the existing hazards, we acknowledge the importance of vulnerability and risk in monitoring and mitigating earthquake hazards. However, to date, a specific effort is unavailable for assessing the risk of earthquake hazards that will cover the city-level in Indonesia. Moreover, a comprehensive profile for risk assessment has yet to be created for small-scale urban areas. Few studies have been organized in Indonesia on city-scale risk assessment. Therefore, we attempt to fill this gap by calculating the risk percentage of Banda Aceh City by determining its conditioning factors and analyzing its variations spatially. We used an influence diagram approach and considered all the factors that affect the risk in Banda Aceh. Results show that only the central parts and some parts in the surrounding areas are under high risk compared with other locations. We validated the results using inventory earthquake events and the results of previously published articles.
Jena, R & Pradhan, B 1970, 'Earthquake Social Vulnerability Assessment Using Entropy Method', IOP Conference Series: Earth and Environmental Science, 10th Institution-of-Geospatial-and-Remote-Sensing-Malaysia(IGRSM) International Conference and Exhibition on Geospatial and Remote Sensing (IGRSM), IOP Publishing, ELECTR NETWORK, pp. 012079-012079.
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Abstract Earthquake is the most devastating event in the current time. Given the probability of highly dangerous future events, risk estimation should be given focus by using the limited and freely available data to predict future vulnerable scenarios of an area that observe the involved uncertainty in the analysis. However, vulnerability assessments should be prospective and based on expected scientifically acceptable events. Therefore, we applied a valuable weight calculation approach called entropy to produce a social vulnerability map for a particular city. We used the population data, including educated and non-educated people and household information, to develop the earthquake social vulnerability map. We used entropy to evaluate the actual weight and produce a good quality map because of some difficulty in the fuzzy synthetic evaluation method for factor weight calculation and relationship ignorance among layers. Results showed that approximately 6% of the population is under very high vulnerability and around 14% are under high vulnerability areas in Banda Aceh City. The developed model is accurate by considering the inventory earthquake vulnerability map. The applied method was favorable, and the process provided good evaluation results, which was reasonable for earthquake hazard, vulnerability, and risk assessment.
Jena, R & Pradhan, B 1970, 'Seismic vulnerability assessment for buildings typology using DEMATEL approach', IOP Conference Series: Earth and Environmental Science, 10th Institution-of-Geospatial-and-Remote-Sensing-Malaysia(IGRSM) International Conference and Exhibition on Geospatial and Remote Sensing (IGRSM), IOP Publishing, ELECTR NETWORK, pp. 012063-012063.
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Abstract During the last two decades, the severity of high magnitude earthquakes rose to a vast extent. A large amount of damage due to such devastating events reflects poor construction planning. Before the 2004 event in Indonesia, we assume poor construction planning with indigent seismic resistance in the Northern Sumatra. However, this event affected the modern buildings in Aceh province. Therefore, authors have categories all the building types into a catalogue. The typologies considered are hierarchical, construction material, structural irregularities, structural system, building height, and maintenance quality. We applied the Decision Making Trial and Evaluation Laboratory (DEMATEL) method to prepare the vulnerability map using the typology of the building. In addition, the results show that the prepared approach is effective and useful for seismic vulnerability assessment.
Jha, M, Richards, D, Porte, M & Atif, A 1970, 'Work-in-Progress—Virtual Agents in Teaching: A Study of Human Aspects', 2020 6th International Conference of the Immersive Learning Research Network (iLRN), 2020 6th International Conference of the Immersive Learning Research Network (iLRN), IEEE, Online, pp. 259-262.
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Students require human intelligence and social interaction in the form of academic assistance at different times of their study period. Their desire to get and find academic assistance varies and is dependent on many factors such as attendance mode, personal situation, semester timetables, and assessment due dates. Providing students with access to this expertise when it is needed and to large numbers of students is problematic. Virtual Agents (VAs) seek to provide a technology-enabled social element to encourage and provide timely support to aid students’ learning. We have implemented 4 unit-specific VIRtual Teaching Assistants (VIRTAs) across 2 universities to provide support to answer student’s questions about various aspects of the unit. In this paper, we present the usage patterns of students to show how many questions were asked by students and at what point of time in the semester the questions were asked addressing the desire to find assistance when required from VIRTA.
Jia, X, Sedehi, O, Papadimitriou, C & Katafygiotis, LS 1970, 'COMPUTATIONALLY EFFICIENT HIERARCHICAL BAYESIAN MODELING FRAMEWORK FOR LEARNING EMBEDDED MODEL UNCERTAINTIES', Proceedings of the XI International Conference on Structural Dynamics, XI International Conference on Structural Dynamics, EASD, pp. 3886-3893.
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A hierarchical Bayesian modeling (HBM) framework has recently been developed for estimating the uncertainties in the parameters of physics-based models of systems, as well as propagating these uncertainties to estimate the uncertainty in output quantities of interest. According to the framework, uncertainties due to model error are embedded into the model parameters by assigning a parameterized probability distribution and inferring the hyper-parameters of this distribution using multiple sets of experimental data. Herein the framework is extended to properly account for the uncertainty in the prediction error model. The error term is modeled by a Normal distribution with hyper parameters to be estimated by the multiple sets of data. This generalization allow making consistent uncertainty propagation for response quantities of interest. New asymptotic approximations for estimating the uncertainties in the hyper-parameters, as well as propagating these uncertainties to model parameters and observed and unobserved output quantities of interest are developed. The proposed framework provide realistic account of model uncertainties that are insensitive to large number of data sets, avoiding severe underestimation of uncertainty arising from conventional Bayesian learning techniques. Problems drawn from structural dynamics applications are used to demonstrate the effectiveness of the proposed framework.
Jian, S, Hu, L, Cao, L & Lu, K 1970, 'Representation Learning with Multiple Lipschitz-Constrained Alignments on Partially-Labeled Cross-Domain Data', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 4320-4327.
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The cross-domain representation learning plays an important role in tasks including domain adaptation and transfer learning. However, existing cross-domain representation learning focuses on building one shared space and ignores the unlabeled data in the source domain, which cannot effectively capture the distribution and structure heterogeneities in cross-domain data. To address this challenge, we propose a new cross-domain representation learning approach: MUltiple Lipschitz-constrained AligNments (MULAN) on partially-labeled cross-domain data. MULAN produces two representation spaces: a common representation space to incorporate knowledge from the source domain and a complementary representation space to complement the common representation with target local topological information by Lipschitz-constrained representation transformation. MULAN utilizes both unlabeled and labeled data in the source and target domains to address distribution heterogeneity by Lipschitz-constrained adversarial distribution alignment and structure heterogeneity by cluster assumption-based class alignment while keeping the target local topological information in complementary representation by self alignment. Moreover, MULAN is effectively equipped with a customized learning process and an iterative parameter updating process. MULAN shows its superior performance on partially-labeled semi-supervised domain adaptation and few-shot domain adaptation and outperforms the state-of-the-art visual domain adaptation models by up to 12.1%.
Jiang, L, Chang, X, Mao, Z, Armagan, A, Lan, Z, Li, X, Yu, SI, Yang, Y, Meng, D, Duygulu-Sahin, P & Hauptmann, A 1970, 'CMU-informedia @ TRECViD 2014 semantic indexing', 2014 TREC Video Retrieval Evaluation, TRECVID 2014.
Jiao, Y, Wang, Y, Fu, B, Tan, Q, Chen, L, Wang, M, Huang, S & Xiong, R 1970, 'Globally optimal consensus maximization for robust visual inertial localization in point and line map', 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 4631-4638.
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Jin, M, Xiong, J, Liu, B & Xiao, L 1970, 'On Channel Classification by Using DTMB Signal', 2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, Paris, France, pp. 1-6.
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This paper proposes machine learning algorithms to classify channels by using Digital Terrestrial Multimedia Broadcast (DTMB) signal. Channel state information (CSI) usually reflects the environment where a receiver is in. In this paper, the DTMB signal is adopted to extract the CSI features, including cross-correlation of the PN sequence in frame header and the baseband DTMB signal and the high order cumulants (HOCs) of the DTMB signal. Machine Learning algorithms, K-nearest neighbor (KNN), supported vector machine (SVM), Random Forest and Neural Network with one hidden layer, are employed respectively to classify and recognize ten typical broadcasting channel models. Simulations illustrate that the accuracy of the scheme based on the PN correlation features outweighs the HOCs features; and the adopted classification algorithms all show good performance in terms of accuracy; moreover, KNN has the lowest complexity compared to the other three. The accuracy of KNN based on PN correlation is over 95% even when the SNR is below -5dB if the correlation gains of two neighbour frame headers are combined.
Jose, S, Kochandra, R & Daniel, S 1970, 'INSTRUCTIONAL VIDEOS, CONCEPTUAL UNDERSTANDING AND SELF-EFFICACY IN THE TIME OF COVID-19', https://openjournals.library.sydney.edu.au/index.php/IISME/index, The Australian Conference on Science and Mathematics Education, Online.
Kacprzyk, J, Merigó, JM, Nurmi, H & Zadrożny, S 1970, 'Multi-agent Systems and Voting: How Similar Are Voting Procedures', Springer International Publishing, pp. 172-184.
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Kalantar, B, Ueda, N, Al-Najjar, HAH, Saeidi, V, Gibril, MBA & Halin, AA 1970, 'A COMPARISON BETWEEN THREE CONDITIONING FACTORS DATASET FOR LANDSLIDE PREDICTION IN THE SAJADROOD CATCHMENT OF IRAN', ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH, pp. 625-632.
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Abstract. This study investigates the effectiveness of three datasets for the prediction of landslides in the Sajadrood catchment (Babol County, Mazandaran Province, Iran). The three datasets (D1, D2 and D3) are constructed based on fourteen conditioning factors (CFs) obtained from Digital Elevation Model (DEM) derivatives, topography maps, land use maps and geological maps. Precisely, D1 consists of all 14 CFs namely altitude, slope, aspect, topographic wetness index (TWI), terrain roughness index (TRI), distance to fault, distance to stream, distance to road, total curvature, profile curvatures, plan curvature, land use, steam power index (SPI) and geology. D2, on the other hand, is a subset of D1, consisting of eight CFs. This reduction was achieved by exploiting the Variance Inflation Factor, Gini Importance Indices and Chi-Square factor optimization methods. Dataset D3 includes only selected factors derived from the DEM. Three supervised classification algorithms were trained for landslide prediction namely the Support Vector Machine (SVM), Logistic Regression (LR), and Artificial Neural Network (ANN). Experimental results indicate that D2 performed the best for landslide prediction with the SVM producing the best overall accuracy at 82.81%, followed by LR (81.71%) and ANN (80.18%). Extensive investigations on the results of factor optimization analysis indicate that the CFs distance to road, altitude, and geology were significant contributors to the prediction results. Land use map, slope, total-, plan-, and profile curvature and TRI, on the other hand, were deemed redundant. The analysis also revealed that sole reliance on Gini Indices could lead to inefficient optimization.
Kamal, A & Miro, JV 1970, 'Monocular end-to-end vehicle pose estimation for car manufacturing', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation (ACRA) 2020, Australian Robotics and Automation Association (ARAA), Brisbane, QLD, Australia.
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An efficient vehicle pose estimation method from a monocular camera to assist paint defect identification in a car manufacturing setting is presented in this paper. Inspired by promising results reported by the self-driving car community with end-to-end schemes, a cascaded deep neural network is proposed for rapid estimation of both translation and rotation of a moving vehicle along a production line, achieving pose estimate average errors below 1.0cm in translation and 0.009◦ in rotation on a ground-truth synthetic database. Notably compelling for the purpose of potential deployment in real factory settings is the ability to infer poses within 1 second. Comprehensive experiments are presented to determine the most accurate camera configuration, and comparisons to traditional two-stage iterative image processing and pose optimisation methods are also provided to demonstrate the network’s superior performance in provided accurate vehicle pose estimates in real-time.
Katic, M & Trianni, A 1970, 'Energy Efficiency Measures and Production Resources: Towards an Integrative Classification Framework for Decision Makers', 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE, Singapore, Singapore, pp. 225-229.
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Energy Efficiency Measures and Production Resources: Towards an Integrative Classification Framework for Decision MakersThe adoption of energy efficiency measures (EEMs) is a significant area of concern for today's industrial organisations. Whilst literature on this subject has soared in recent decades, there remains a gap in understanding the extent of their impact on an organisation's operations as well as the manner, and whether, they are adopted in the first place. This paper provides a preliminary attempt at addressing these concerns by investing attention into the notion of production resources as a mechanism through which a deeper appreciation of EEM impact on operations could be provided through the development of a generalised decision-making framework. We end this paper with conclusions and areas for further work.
Katuwandeniya, K, Miro, JV & Dantanarayana, L 1970, 'End-to-End Joint Intention Estimation for Shared Control Personal Mobility Navigation', 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, Shenzhen, PEOPLES R CHINA, pp. 1-6.
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Advancements in technology propose a future where systems work collaboratively sharing the same workspace as humans. Navigation is one such crucial aspect of daily life where collaborative technologies can offer major assistance. Ageing population dictates a likely increase in personal mobility devices (PMDs), whilst autonomous cars are bringing intelligent vehicles to the road today. However, in such scenarios the expected assistance can only be given if the device is aware of its user's intention, so that controls can be applied in a tightly collaborative manner. Moreover, they should be robust to different environments, users and mobile platforms. A user driven navigation framework is proposed in this work to complement end-to-end sensing-only solutions to estimate controls as joint intention from vehicle states and user inputs. The solution is proven to be an improvement over similar strategies that rely on exteroceptive data and omit inputs from the driving agent. Furthermore, the developed framework is proven capable of transferring the learning into different environments and mobility platforms using a small amount of training data. Data from the autonomous driving community (Udacity dataset) and other obtained in-house with an instrumented power wheelchair are given to demonstrate the validity of the proposed approach.
Keshavarz, R & Shariati, N 1970, 'Low Profile Metamaterial Band-Pass Filter Loaded with 4-Turn Complementary Spiral Resonator for WPT Applications', 2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS), IEEE, UK, pp. 1-4.
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In this paper, a very compact $(0.03\lambda_{\mathrm{g}}\times 0.18\lambda_{\mathrm{g}})$ and low insertion loss (<0.4 dB) metamaterial band-pass filter (MBPF) at the center frequency of f0=730 MHz is proposed, based on the rectangular-shape 4-turn complementary spiral resonators (4-CSR). The proposed MBPF consists of an interdigital capacitor as a series capacitance in the top layer, leading to improve the stopband performance in the pass-band range of 700760 MHz, which makes it suitable for wireless power transfer (WPT) systems by rejecting unwanted signals. In order to validate the performance of the proposed technique, the MBPF is fabricated on the RO-4003 substrate and great agreement is achieved between simulated and measured results. The stop-band attenuations of greater than 52 dB and 20 dB are obtained around the 0.8×fcl(lower cutoff frequency) and 1.2×fcu(upper cutoff frequency), respectively.
Keshavarz, R, Miyanaga, Y, Yamamoto, M, Hikage, T & Shariati, N 1970, 'Metamaterial-Inspired Quad-Band Notch Filter for LTE Band Receivers and WPT Applications', 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), IEEE.
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Khan, MNH, Siwakoti, YP, Li, L, Khan, SA & Blaabjerg, F 1970, 'Model Predictive Control of Seven-Level Single-Phase Boost Inverter without weighting factor for Grid-Tied Photovoltaic Applications', 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE, New Orleans, LA, USA, pp. 3238-3243.
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This paper presents a switched-capacitor integrated 7-level boost inverter for single-phase photovoltaic (PV) applications and its associated control scheme. It consists of three switched capacitors and eleven active switching elements. A boost converter at the front side helps to maintain the capacitor voltage balance during the operation modes. This topology does not require any control scheme to balance the switched capacitors at the DC-bus due to its inherent voltage balancing capability. Thus, it reduces the control complexity. The proposed structure has the capability to integrate low and varying voltage sources such as PV and consequently, reduces the number of components, required input voltage, and control complexity. Finite control set model predictive control (FCS-MPC) algorithm is used to control the proposed inverter. Thermal analysis of the proposed topology is presented for loss calculation of each power devices. Finally, detailed analysis followed by simulation and measurement results is presented at the end.
Khan, S & Hussain, FK 1970, 'A SOA Based SLA Negotiation and Formulation Architecture for Personalized Service Delivery in SDN', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 108-119.
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© Springer Nature Switzerland AG 2020. Supporting end-to-end personalized Quality of Services (QoS) delivery in existing network architecture is an ongoing issue. Software Defined Networking (SDN) model has emerged in response to the limitations of traditional network. Integrating Software Defined Network (SDN) architecture with Service Oriented Architecture (SOA) brings new concept for future service oriented delivery in SDN services. Researchers from both academic and industry are working to resolve the QoS limitations of service delivery, however; most of the proposed solutions are application oriented and unable to provide a reliable personalized QoS delivery in future service oriented SDN. This research propose a reliable Service Level Agreement (SLA) oriented Service Negotiation framework that would be able to provide reputation based personalized service delivery and assist in QoS management in SDN for informed decision making. Moreover, potential benefits of the proposed framework are also discussed in this paper in social, scientific and business aspects.
Khan, S & Hussain, FK 1970, 'Evaluation of SLA Negotiation for Personalized SDN Service Delivery', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 579-590.
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© 2020, Springer Nature Switzerland AG. Ensuring the quality of services (QoS) is crucial in a service-oriented business model. A service level agreement (SLA) is an important agreement between a consumer and a provider and is a key element in ensuring QoS. Service negotiation occurs in an initial stage of the SLA where service requirements are agreed upon to avoid conflict situations. Guaranteeing QoS is one of the key challenges in software defined networking (SDN). Several intelligent solutions have been proposed, however most of them are application focused and are unable to provide personalized and reliable QoS delivery in SDN. This paper presents a reputation data-driven SLA negotiation framework that provides personalized and reliable service delivery in SDN and assists in QoS management for informed decision making. In addition, a fuzzy inference system (FIS) is used to implement the framework and the results are discussed in this paper.
Khan, S, Solano-Paez, P, Suwal, T, Al-Karmi, S, Lu, M, Ho, B, Fouladi, M, Leary, S, Levy, JMM, Lassaletta, A, Rivas, E, Reddy, A, Gillespie, GY, Gupta, N, Yalon-Oren, M, Amariglio, L, Nakamura, H, Wu, K-S, Wong, T-T, Ra, Y-S, La Spina, M, Emanuele, PV, Massimi, L, Buccoliero, AM, Hansford, JR, Grundy, RG, Adamek, D, Fangusaro, J, Scharnhorst, D, Johnston, D, Lafay-Cousin, L, Camelo-Piragua, S, Kabbara, N, Gajjar, A, Boutarbouch, M, Gil da Costa, MJ, Hanson, D, Wood, P, Al-Hussaini, M, Amayiri, N, Wang, Y, Catchpoole, D, Michaud, J, Bendel, AE, Ellezam, B, Gerber, N, Plant, A, Jeffery, R, Dunham, C, Moertel, C, Walter, A, Ziegler, D, Dodgshun, A, Gottardo, N, Demir, A, Ramanujachar, R, Raabe, E, Mary, S, Dirks, P, Taylor, M, Eugene, H, Lindsey, H, Tihan, T, Mette, J, Dahl, C, Low, S, Smith, A, Hazrati, L-N, Kresak, J, Gino, S, Tan, E, Morales, A, Santa-Maria, V, Hawkins, C, Bartels, U, Stephens, D, Nobusawa, S, Dufour, C, Bourdeaut, F, Andre, N, Bouffet, E & Huang, A 1970, 'TITLE: DEFINING THE CLINICAL AND PROGNOSTIC LANDSCAPE OF EMBRYONAL TUMORS WITH MULTI-LAYERED ROSETTES (ETMRS), A RARE BRAIN TUMOR REGISTRY (RBTC) STUDY', NEURO-ONCOLOGY, OXFORD UNIV PRESS INC, pp. 328-328.
Khan, SA, Noman Habib Khan, M, Guo, Y, Siwakoti, YP & Zhu, J 1970, 'A novel five-level switched capacitor type inverter topology for grid-tied photovoltaic application', 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE, New Orleans, LA, USA, pp. 442-447.
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This paper presents a novel five-level inverter topology and associated control scheme. The proposed structure consists of a capacitor and eight active switching elements. It requires only one dc source and is capable of generating five voltage levels with double voltage boosting gain. On the other hand, it does not require any control scheme to balance the capacitor in the DC-bus due to inherent voltage balancing capability. As a result, the control complexity reduces a lot. Brief analysis followed by simulation and measurement results of a proposed 5-level inverter using the finite control set model predictive control (FCS-MPC) algorithm is presented. Detail of the analysis with more measurement result and comparison will be presented in the final paper.
Khan, SK, Naseem, U, Sattar, A, Waheed, N, Mir, A, Qazi, A & Ismail, M 1970, 'UAV-aided 5G Network in Suburban, Urban, Dense Urban, and High-rise Urban Environments', 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA), 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA), IEEE.
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Khoa, TV, Saputra, YM, Hoang, DT, Trung, NL, Nguyen, D, Ha, NV & Dutkiewicz, E 1970, 'Collaborative Learning Model for Cyberattack Detection Systems in IoT Industry 4.0', 2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Seoul.
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Khosoussi, K, Sukhatme, GS, Huang, S & Dissanayake, G 1970, 'Designing Sparse Reliable Pose-Graph SLAM: A Graph-Theoretic Approach', International Workshop on the Algorithmic Foundations of Robotics, International Workshop on the Algorithmic Foundations of Robotics, pp. 17-32.
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In this paper, we aim to design sparse D-optimal (determinantoptimal) pose-graph SLAM problems through the synthesis of sparse graphs with the maximum weighted number of spanning trees. Characterizing graphs with the maximum number of spanning trees is an open problem in general. To tackle this problem, several new theoretical results are established in this paper, including the monotone log-submodularity of the weighted number of spanning trees. By exploiting these structures, we design a complementary pair of near-optimal efficient approximation algorithms with provable guarantees. Our theoretical results are validated using random graphs and a publicly available pose-graph SLAM dataset.
Khuat, TT, Chen, F & Gabrys, B 1970, 'An improved online learning algorithm for general fuzzy min-max neural network', Proceedings of the International Joint Conference on Neural Networks, 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK, pp. 1-9.
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This paper proposes an improved version of the current online learningalgorithm for a general fuzzy min-max neural network (GFMM) to tackle existingissues concerning expansion and contraction steps as well as the way of dealingwith unseen data located on decision boundaries. These drawbacks lower itsclassification performance, so an improved algorithm is proposed in this studyto address the above limitations. The proposed approach does not use thecontraction process for overlapping hyperboxes, which is more likely toincrease the error rate as shown in the literature. The empirical resultsindicated the improvement in the classification accuracy and stability of theproposed method compared to the original version and other fuzzy min-maxclassifiers. In order to reduce the sensitivity to the training samplespresentation order of this new on-line learning algorithm, a simple ensemblemethod is also proposed.
Kieu, T-B, Pham, SB, Phan, X-H & Piccardi, M 1970, 'A Submodular Approach for Reference Recommendation', Communications in Computer and Information Science, International Conference of the Pacific Association for Computational Linguistics, Springer Singapore, Hanoi, Vietnam, pp. 3-14.
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© 2020, Springer Nature Singapore Pte Ltd. Choosing appropriate references for a given topic is an important, yet challenging task. The pool of potential candidates is typically very large, in the order of tens of thousands, and growing by the day. For this reason, this paper proposes an approach for automatically providing a reference list for a given manuscript. The approach is based on an original submodular inference function which balances relevance, coverage and diversity in the reference list. Experiments are carried out using an ACL corpus as a source for the references and evaluated by MAP, MRR and precision-recall. The results show the remarkable comparative performance of the proposed approach.
Kim, J, Bhambhani, Y, Byun, H & Johansen, TA 1970, 'Cascaded nonlinear attitude observer and simultaneous localisation and mapping', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Brisbane, pp. 1-6.
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This paper presents a novel integration of the nonlinear observer theory and simultaneous localisation and mapping for aerial navigation applications. This extends the previous work by the authors in which a nonlinear observer was applied to the attitude estimation and integrated navigation problem. The key novelty of this work is in the feedback correction mechanism from the linear SLAM estimator to the nonlinear observer, which enables the attitude correction from the feature position measurements. We utilise the relationship between the acceleration error and the attitude error, and the pseudo-inverse of a skew-symmetric matrix for the attitude feedback. Lyapunov-based stability analysis is provided for a simplified model without considering the gyroscope bias. Flight dataset is used to confirm the method. Thanks to the robustness of the nonlinear observer and the optimal linear estimator, the vehicle pose and map features are estimated effectively.
Kim, J, Byun, H, Guivant, J & Johansen, TA 1970, 'Compressed Pseudo-SLAM: Pseudorange integrated generalised compressed SLAM', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Brisbane.
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This paper addresses the fusion of the pseudorange/pseudorange rate observations from global navigation satellite system (GNSS), and the inertial-visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles (UAVs). This work extends the previous work on a simulation-based study [Kim et al.(2017)], and evaluates the method to a flight dataset collected from a fixed-wing UAV platform. We propose to use the generalised compressed filter which can effectively accumulate the information gain acquired from a local map, and update the global map in a much lower rate. The fusion filter also models and estimates the receiver clock and drift, which is crucial to integrate the pseudorange and pseudorange rate measurements. Evaluation results will show that the horizontal navigation error is effectively constrained even with 1 satellite vehicle and 1 landmark observations, thanks to the direct fusion of pseudorange and vision data.
Kiss, SH, To, KYC, Yoo, C, Fitch, R & Alempijevic, A 1970, 'Minimally Invasive Social Navigation', Australasian Conference on Robotics and Automation 2019, Australasian Conference on Robotics and Automation, ARAA, Adelaide, Australia, pp. 1-7.
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Integrating mobile robots into human society involves the fundamental problemof navigation in crowds. This problem has been studied by considering thebehaviour of humans at the level of individuals, but this representation limitsthe computational efficiency of motion planning algorithms. We explore the ideaof representing a crowd as a flow field, and propose a formal definition ofpath quality based on the concept of invasiveness; a robot should attempt tonavigate in a way that is minimally invasive to humans in its environment. Wedevelop an algorithmic framework for path planning based on this definition andpresent experimental results that indicate its effectiveness. These resultsopen new algorithmic questions motivated by the flow field representation ofcrowds and are a necessary step on the path to end-to-end implementations.
Kitto, K, Sarathy, N, Gromov, A, Liu, M, Musial, K & Buckingham Shum, S 1970, 'Towards skills-based curriculum analytics', Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, LAK '20: 10th International Conference on Learning Analytics and Knowledge, ACM, ELECTR NETWORK, pp. 171-180.
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Kocaballi, AB, Coiera, E & Berkovsky, S 1970, 'Revisiting Habitability in Conversational Systems', Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-8.
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Kocaballi, AB, Quiroz, JC, Laranjo, L, Rezazadegan, D, Kocielnik, R, Clark, L, Liao, QV, Park, SY, Moore, RJ & Miner, A 1970, 'Conversational Agents for Health and Wellbeing', Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20: CHI Conference on Human Factors in Computing Systems, ACM.
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Koli, NY, Afzal, MU, Esselle, KP & Islam, MZ 1970, 'Comparison Between Fully and Partially Filled Dielectric Materials on the Waveguide of Circularly Polarised Radial Line Slot Array Antennas', 2020 International Workshop on Antenna Technology (iWAT), 2020 International Workshop on Antenna Technology (iWAT), IEEE, Bucharest, Romania, pp. 1-3.
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This paper presents an investigation on the waveguide of circularly polarised radial line slot array (RLSA) antennas to improve gain and radiation bandwidth. Two circularly polarised (CP) RLSA antennas were designed with two different waveguide configurations. In the first configuration the waveguide is fully filled with dielectric materials and in the second configuration the waveguide is partially filled with dielectric materials and rest of the waveguide is filled with air. Numerical results of these two CP-RLSA antennas with two different waveguide configurations are presented and compared. Significant improvements have been made in the 3-dB directivity bandwidth and aperture efficiency of the antenna having waveguide partially filled with dielectric material. The 3-dB directivity bandwidth was measured 6.2% and aperture efficiency increased to 55.5%. The CP-RLSA antenna has also achieved a peak directivity of 31.7 dBic and a gain of 31.2 dBic as compared to the directivity 30.1 dBic and gain 29.5 dBic, respectively achieved with the CP-RLSA antenna having waveguide fully filled with dielectric material.
Koli, NY, Afzal, MU, Esselle, KP, Hashmi, RM & Islam, MZ 1970, 'A Beam Squinted Linearly Polarised Radial Line Slot Array Antenna with Improved Return Loss Bandwidth', 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, pp. 411-412.
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Koli, NY, Afzal, MU, Esselle, KP, Hashmi, RM & Islam, MZ 1970, 'A Low-profile and Efficient Front-End Antenna for Point-to-Point Wireless Communication Links', 2020 14th European Conference on Antennas and Propagation (EuCAP), 2020 14th European Conference on Antennas and Propagation (EuCAP), IEEE, Copenhagen, Denmark, pp. 1-4.
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This paper investigates the design and performance of an efficient, medium-gain, front-end antenna of the type of radial line slot array (RLSA), for wireless communication systems. The antenna consists of two conducting metal plates forming a radial waveguide. The top plate is composed of six rings of radiating slots in a spiral pattern. A single coaxial connector is used to feed the electromagnetic energy from the bottom of the radial waveguide. The antenna has a radius of 0.15 m and operating at a frequency of 12 GHz. It was simulated using Computer Simulation Technology (CST) Microwave Studio 2019 and the results show that the antenna has an acceptable level of impedance matching in the frequency range from 11 GHz to 13 GHz, with a peak directivity of 25.6 dBi and a peak realized gain of 25 dBic at 12 GHz. Its radiation efficiency is 96% and a total efficiency is 85.3% at 12 GHz.
Koli, NY, Afzal, MU, Esselle, KP, Hashmi, RM, Islam, MZ & Shrestha, S 1970, 'A Double Layer Circularly Polarised Radial Line Slot Array Antenna with Uniform Aperture Illumination', 2020 4th Australian Microwave Symposium (AMS), 2020 4th Australian Microwave Symposium (AMS), IEEE, Sydney, Australia, pp. 1-2.
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In this paper, we have designed and investigated a double layer circularly polarised radial line slot array (RLSA) antenna for satellite communication. The antenna is composed of twofold radial waveguide with slots acting as radiating elements on its surface. The radiating slots are arranged in a spiral pattern on the antenna aperture. Every slot has a particular length and position. The slots are oriented in a way to intercept the radial currents on the upper waveguide. The slot lengths were varied to achieve a uniform aperture distribution. The electromagnetic power is fed from center of the lower waveguide. Numerical results show that the antenna is well matched within the operating frequency range. The far-field results indicate a peak directivity of 27 dBi at 20 GHz with a good pattern quality and lower side lobe level of -27.2 dB.
Kong, Q, Ram, R & Rizoiu, M-A 1970, 'Evently: Modeling and Analyzing Reshare Cascades with Hawkes Processes', Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021.
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Modeling online discourse dynamics is a core activity in understanding thespread of information, both offline and online, and emergent online behavior.There is currently a disconnect between the practitioners of online socialmedia analysis -- usually social, political and communication scientists -- andthe accessibility to tools capable of examining online discussions of users.Here we present evently, a tool for modeling online reshare cascades, andparticularly retweet cascades, using self-exciting processes. It provides acomprehensive set of functionalities for processing raw data from Twitterpublic APIs, modeling the temporal dynamics of processed retweet cascades andcharacterizing online users with a wide range of diffusion measures. This toolis designed for researchers with a wide range of computer expertise, and itincludes tutorials and detailed documentation. We illustrate the usage ofevently with an end-to-end analysis of online user behavior on a topicaldataset relating to COVID-19. We show that, by characterizing users solelybased on how their content spreads online, we can disentangle influential usersand online bots.
Kong, Q, Rizoiu, M-A & Xie, L 1970, 'Describing and Predicting Online Items with Reshare Cascades via Dual Mixture Self-exciting Processes', International Conference on Information and Knowledge Management, Proceedings, pp. 645-654.
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It is well-known that online behavior is long-tailed, with most cascadedactions being short and a few being very long. A prominent drawback ingenerative models for online events is the inability to describe unpopularitems well. This work addresses these shortcomings by proposing dual mixtureself-exciting processes to jointly learn from groups of cascades. We firststart from the observation that maximum likelihood estimates for contentvirality and influence decay are separable in a Hawkes process. Next, ourproposed model, which leverages a Borel mixture model and a kernel mixturemodel, jointly models the unfolding of a heterogeneous set of cascades. Whenapplied to cascades of the same online items, the model directly characterizestheir spread dynamics and supplies interpretable quantities, such as contentvirality and content influence decay, as well as methods for predicting thefinal content popularities. On two retweet cascade datasets -- one relating toYouTube videos and the second relating to controversial news articles -- weshow that our models capture the differences between online items at thegranularity of items, publishers and categories. In particular, we are able todistinguish between far-right, conspiracy, controversial and reputable onlinenews articles based on how they diffuse through social media, achieving an F1score of 0.945. On holdout datasets, we show that the dual mixture modelprovides, for reshare diffusion cascades especially unpopular ones, bettergeneralization performance and, for online items, accurate item popularitypredictions.
Kovacevic-Opacic, L & Marjanovic, O 1970, 'Digital Platforms and Strategic Agility in Extreme Contexts', Pre-AMCIS European Journal of Information Systems (EJIS) Workskop - Special Issue on Digital-enabled Strategic Agility, Utah, Salt Lake City (Virtual).
Kovacevic-Opacic, L & Marjanovic, O 1970, 'The co-evolution of digital platform strategy and platform architecture', 26th Americas Conference on Information Systems, AMCIS 2020, Americas Conference on Information Systems, Virtual.
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The pervasiveness of digital technologies has spurred the rise of different types of digital platforms. While recent studies have provided insights into ecosystem strategies of digital platforms, strategy research focusing on internal digital platforms within organizations remains limited. The architecture of digital platforms is vital to our understanding of their nature and evolution. Strategy and architecture of digital platforms in tandem can provide valuable insights into this process. Employing a research case study and the theories of co-evolution and punctuated equilibrium, this study investigates how digital platform strategy (conceptualized as an ongoing process) and platform architecture co-evolve in an organization over time. The expected outcome is a theoretical model of co-evolution of digital platform strategy and architecture, consisting of generative mechanisms (e.g. patterns) of co-evolution. The theoretical model is expected to lead to a set of guidelines for practitioners interested in strategizing internal digital platforms.
Kovaleva, M, Bulger, D & Esselle, KP 1970, 'Towards Demand-Driven Optimization Algorithms in Electromagnetic Engineering (Invited Paper)', 2020 IEEE International Conference on Computational Electromagnetics (ICCEM), 2020 IEEE International Conference on Computational Electromagnetics (ICCEM), IEEE, Singapore, pp. 95-96.
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With the increasing popularity of optimization algorithms in electromagnetic engineering, it is clear that mixed-variable design problems prevail. This paper shows that the Cross-Entropy (CE) optimization method is intrinsically versatile to handle these and other types of problems. We provide implementation details of two antenna examples optimized by the CE method to demonstrate its elegance and efficiency.
Kulasinghe, A, Kapeleris, J, Kenny, L, Hughes, B, Warkiani, M, Vela, I, Thiery, J-P, O'Byrne, K & Punyadeera, C 1970, 'Abstract 3384: Characterization of the tumor microenvironment and liquid biopsy in head and neck and non-small cell lung cancer', Cancer Research, American Association for Cancer Research (AACR), pp. 3384-3384.
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Abstract Metastasis in cancer patients is reflected by measurable levels of circulating tumor cells (CTCs) in the blood of cancer patients. CTCs represent cancer cells from the primary and metastatic sites, thereby providing a comprehensive representation of the tumor burden of an individual patient. Recent advancements have shown that PD-1/PD-L1 immune checkpoint therapies have durable responses in a number of solid tumor types. Our study was designed to use multiple CTC enrichment platforms for the capture of CTCs and novel culture formulations for the ex vivo expansion of CTCs. Head and Neck cancer (n=350) and lung cancer (n=150) patients were recruited to investigate the prognostic role of CTCs. In parallel, a subset of HNC tumors were profiled using the NanoString GeoMx Digital Spatial Profiling (DSP) technology using a 44-plex antibody cocktail. We evaluated multiple CTC isolation technologies (CellSearch, filtration, CD45 depletion, Spiral, Straight and novel microfluidic chip technology) using matched patient samples which showed that epitope-independent CTC isolation captured a greater proportion of CTCs. Molecular alterations present in the primary tissue were confirmed in the CTCs by 3D-DNA FISH (EGFR-amplification, ALK-translocations). In HNC, the presence of CTC clusters associated with the development of distant metastatic disease (P=0.0313). HNC CTC-positive patients had shorter progression free survival (Hazard ratio [HR]: 4.946; 95% [CI]:1.571-15.57; P=0.0063) and PD-L1-positive CTCs were found to be significantly associated with worse outcome ([HR]:5.159; 95% [CI]:1.011-26.33; P=0.0485). In a proof of principle study, we were able to demonstrate for the first time, short-term patient derived CTC cultures outside the patient's body and exome sequencing of CTCs cultures confirmed the presence of mutational signatures consistent with The Cancer Genome Atlas (T...
Kulasinghe, A, Kapeleris, J, Kenny, L, Warkiani, M, Vela, I, Thiery, JP, O'Byrne, K & Punyadeera, C 1970, 'Isolation, characterization, and expansion of circulating tumor cells in head and neck cancers', CLINICAL CANCER RESEARCH, AACR-AHNS Head and Neck Conference - Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research, AMER ASSOC CANCER RESEARCH, TX, Austin, pp. 31-31.
Kulasinghe, A, O’Leary, C, Ladwa, R, Warkiani, M & O’byrne, K 1970, '43 Highly multiplexed digital spatial profiling of the tumor microenvironment of non-small-cell lung cancer (NSCLC)', Regular and young investigator award abstracts, 35th Anniversary Annual Meeting (SITC 2020), BMJ Publishing Group Ltd, pp. A26.1-A26.
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Kundu, S, Shivakumara, P, Grouver, A, Pal, U, Lu, T & Blumenstein, M 1970, 'A New Forged Handwriting Detection Method Based on Fourier Spectral Density and Variation', Pattern Recognition, Asian Conference on Pattern Recognition, Springer International Publishing, Auckland, New Zealand, pp. 136-150.
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Use of handwriting words for person identification in contrast to biometric features is gaining importance in the field of forensic applications. As a result, forging handwriting is a part of crime applications and hence is challenging for the researchers. This paper presents a new work for detecting forged handwriting words because width and amplitude of spectral distributions have the ability to exhibit unique properties for forged handwriting words compared to blurred, noisy and normal handwriting words. The proposed method studies spectral density and variation of input handwriting images through clustering of high and low frequency coefficients. The extracted features, which are invariant to rotation and scaling, are passed to a neural network classifier for the classification for forged handwriting words from other types of handwriting words (like blurred, noisy and normal handwriting words). Experimental results on our own dataset, which consists of four handwriting word classes, and two benchmark datasets, namely, caption and scene text classification and forged IMEI number dataset, show that the proposed method outperforms the existing methods in terms of classification rate.
Kusumo, F, Shamsuddin, AH, Ahmad, AR, Dharma, S, Milano, J, Silitonga, AS, Fazril, I, Marzuki, H, Akhiar, A, Sebayang, R & Tambunan, BH 1970, 'Production of biodiesel from Jatropha curcas mixed with waste cooking oil assisted by ultrasound', IOP Conference Series: Earth and Environmental Science, IOP Publishing, pp. 012082-012082.
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Abstract Jatropha curcas oil has a high amount of free fatty acid, while waste cooking oil has a low amount of free fatty acid content. The purpose of this study was to investigate the production of biodiesel from a mixture of Jatropha curcas and waste cooking. The highest biodiesel yields from the mixture of Jatropha curcas and waste cooking was obtained at 99.3%. The result shows that the physicochemical properties of mixed Jatropha curcas and waste cooking oil methyl ester met the standard requirements laid in ASTM D6751 and EN 14214.
Kutay, C, Szapiro, D, Garcia, J & Raffe, W 1970, 'Learning on country: A game-based approach towards preserving an Australian aboriginal language', ICCE 2020 - 28th International Conference on Computers in Education, Proceedings, International Conference of Innovation in Media and Visual Design, Atlantis Press, Tangerang, Indonesia, pp. 540-545.
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Nginya naaa-da banga-mari dalang wingaru-dane. Ngyina diya-ma murri dalan-wa dalang-ra1. This paper presents the design of a prototype 360 degree, interactive, Indigenous language learning game to support the reclamation of Indigenous languages through immersion in community oral traditions expressed through visual and audio effects and the choreography of the characters within the game. The project is underpinned by a foundational acknowledgement that Aboriginal culture is held within the country specific to the language and embodied in that country's landscape. Learning within the game is based around themes of country, weather, local environment and kinship. Animation and design principles were applied from an embodied communication perspective to increase engagement and to reinforce language learning principles, with Indigenous animation and design students bringing an Indigenous perspective to the gestural and design content of the game.
Kyosuke, S, Keiji, W, Pham, NH & Tomoyuki, M 1970, 'Experimental Verification of a Boost Integrated Three-Phase Inverter using Current Unfolding under a Grid-Connected Operation', Annual Meeting of the IEEJ, IEEJ, Japan, pp. ROMBUNNO.4-026-ROMBUNNO.4-026.
Laccone, F, Paolo, C, Pietroni, N, Maurizio, F & Malomo, L 1970, 'Automated design and analysis of reinforced and post-tensioned glass shells', Challenging Glass 7 Conference on Architectural and Structural Applications of Glass, Challenging Glass 7 Conference on Architectural and Structural Applications of Glass, TU Delft Open, Ghent University, pp. 1-17.
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Shells made of structural glass are beautiful objects from both the aesthetics and the engineering point of view. However, they pose two significant challenges. The first one is to assure adequate safety and redundancy concerning possible global collapse. Being single-layered, in a shell made of structural glass, the brittle cracking of a single pane can lead to a sudden propagation of failure, up to instability. The second one is to guarantee cheap replacing possibilities for potentially collapsed components. This research explores a novel concept to address both requirements, where glass is both post-tensioned and reinforced and develops the research on TVT post-tensioned glass beams. Following the Fail-Safe Design (FSD) principles, a steel reinforcement relieves glass deficiencies (i.e. brittleness and low tensile strength). Following the Damage Avoidance Design (DAD) principles, glass segmentation and post-tensioning avoid the propagation of cracks. Up to now, glass-steel systems were limited to mono-dimensional elements (such as beams and columns) or simple bi-dimensional elements (arches, domes, barrel vaults). Instead, massive structures are usually realized as grid shells, where glass is used as simple cladding. This research investigates piecewise triangulated glass shells to enable the creation of 3D free-form glass-steel systems, where glass is load-bearing material. Hence, laminated glass panels are mechanically coupled with a filigree steel truss, whose elements are placed at the edges of the panel and act as an unbonded reinforcement. In a performance-based perspective, these steel trusses can be sized to bear at least the weight of all panels in the occurrence of simultaneous cracks (worst-case scenario). The panels are post-tensioned using a set of edge-aligned cables that add beneficial compressive stress on glass to prevent crack initiation. The cable placement and accompanying pre-loads are derived with an optimization strategy that m...
Lama, S & Pradhan, S 1970, 'ICT in Sustainable Tourism: A Systematic Review', ACIS 2020 Proceedings - 31st Australasian Conference on Information Systems, Australasian Conference on Information Systems, Wellington, New Zealand.
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The emergence of sustainable tourism has been seamlessly replacing many facets of traditional tourism. ICT is regarded as an ideal partner to sustainable tourism as it can proficiently disseminate information and services. Several research studies have been conducted to study this synergy. This systematic review aims to investigate the emerging ICT discourse in sustainable tourism using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). Relevant articles were searched in the most common databases. Out of 357 articles retrieved, 41 articles were selected for the final analysis based on inclusion-exclusion criteria. MS Excel and Zotero applications were used. It has been observed that most commonly researched topics in ICT in sustainable tourism include GIS, web applications, gaming, Augmented Reality, IoTs and social media. This review identifies a need for a larger body of research focusing on ICT use in sustainable tourism and supports its advancement by identifying future directions.
Lammers, T, Sick, N & Kandlbinder, P 1970, 'Management Consulting Techniques in Engineering Education – The Case of Operations Engineering', https://www.aaee2020.com.au/wp-content/uploads/2020/11/AAEE2020_paper_158.pdf, Australasian Association of Engineering Education, AAEE, Sydney, pp. 1-9.
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There is an increasing need for engineering students to learn how to market themselves as professionals in globalised and competitive environments. When it comes to postgraduate education, students seek to build on their existing work experience and develop theirmanagement and leadership skills to complement their engineering knowledge. The collaborative nature of engineering work suggests that increased engagement in university engineering education will only come from the introduction of activities connected to professional practice.A solution to the challenge of learning in interdisciplinary, socio-technical subjects within engineering can be found by looking at the collaborative professional practice of interdisciplinary professions such as management consulting. In this article, we explore how an activity that has proven successful in engaging corporate stakeholders in a management consulting context, can be adapted in a management subject aimed at engineering students. Based on requirements identified from literature and student feedback, we tailor and implement a shift-and-share activity in the postgraduate subject Operations Engineering, highlighting the potential for consulting activities to contribute to the students’ learning experience. We also measure its success by evaluating the students’ work output and asking the students about the extent to which the activity contributed to achieving those requirements in a short survey. Our study confirms that management consulting techniques can be a source for inspiration when it comes to new activities and lays the foundation for the transfer of other activities in line with case-specific requirements. Our paper highlights a practical case example of how consulting techniques can be appropriately adapted from a corporate to an academic learning environment in a way that aligns with both institutional and individual learning goals. In this context, the paper provides guidance on how to successfull...
Le Gentil, C, Vayugundla, M, Giubilato, R, Sturzl, W, Vidal-Calleja, T & Triebel, R 1970, 'Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments', 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Las Vegas, NV, USA, pp. 1895-1902.
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The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. As a result, the ambiguity of the visual appearance makes state-of-the-art visual place recognition approaches less effective than in urban or man-made environments. This paper presents a method to solve the loop closure problem using only spatial information. The key idea is to use a novel continuous and probabilistic representations of terrain elevation maps. Given 3D point clouds of the environment, the proposed approach exploits Gaussian Process (GP) regression with linear operators to generate continuous gradient maps of the terrain elevation information. Traditional image registration techniques are then used to search for potential matches. Loop closures are verified by leveraging both the spatial characteristic of the elevation maps (SE (2) registration) and the probabilistic nature of the GP representation. A submap-based localization and mapping framework is used to demonstrate the validity of the proposed approach. The performance of this pipeline is evaluated and benchmarked using real data from a rover that is equipped with a stereo camera and navigates in challenging, unstructured planetary-like environments in Morocco and on Mt. Etna.
Le, AT, Tran, LC, Huang, X, Ritz, C, Dutkiewicz, E, Bouzerdoum, A & Franklin, DR 1970, 'Hybrid TOA/AOA Localization with 1D Angle Estimation in UAV-assisted WSN.', ICSPCS, 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, pp. 1-6.
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Unmanned aerial vehicles (UAVs) are considered as a great solution for a flexible and rapid deployment of wireless sensor networks (WSN) in emergency scenarios. Hybrid time-of-arrival (TOA) and angle-of-arrival (AOA) localization is widely used to estimate agents’ positions in WSN. Conventional TOA/AOA localization methods normally require both elevation and azimuth AOA estimations to estimate agents’ positions, leading to complicated L-shape antenna arrays and power-thirsty two-dimensional signal processing at the agents. We propose a hybrid TOA/1AOA localization approach which only requires elevation AOA estimations to combine with TOA measurements. A weighted least square algorithm is proposed to solve the non-linear problem. The performance of the proposed method is compared with that of the conventional approach under various scenarios. Simulation results show that, by adjusting different parameters such as transmit power, signal bandwidth, and the number of anchors, the proposed method outperforms the conventional counterpart while significantly reduces the complexity of the agents.
Le, DT, Sutjipto, S, Lai, Y & Paul, G 1970, 'Intuitive Virtual Reality based Control of a Real-world Mobile Manipulator', 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, Shenzhen, China, pp. 767-772.
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This paper presents an integration of Virtual Reality (VR) interfaces with the control system of a real-world mobile manipulator, ultimately facilitating a natural and intuitive method for human-robot interaction. VR’s ability to track movements in 3D space and translate performed motions provide an intuitive platform for users to explore and interact with the virtual environment. Coupled with intuitive controls, such as grabbing and pointing, the VR platform provides a compelling advantage that can be used to solve limitations of traditional remote robot teleoperation methods.This paper summarises the system implemented, which includes a simulation of the robot in Unity3d, as well as analyses critical results of accuracy and performance, from experiments with users of various experience levels. The method used for measuring accuracy with a simulated robot presented a utilitarian validation for contrasting the difference between 2D and VR 3D interfaces. Users’ performance and experience under various levels of control latency, which is a crucial factor in remote online robot control, were also measured.
Le, HX, Nguyen, L & Thiyagarajan, K 1970, 'A Dynamic Surface Controller based on Adaptive Neural Network for Dual Arm Robots', 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE.
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Le, K, To, A, Leighton, B, Hassan, M & Liu, D 1970, 'The SPIR: An Autonomous Underwater Robot for Bridge Pile Cleaning and Condition Assessment', 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 1725-1731.
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Le, TM & Khabbaz, H 1970, 'Predicting consolidation coefficient of soft clay by time-displacement-velocity methods', 16th Asian Regional Conference on Soil Mechanics and Geotechnical Engineering, ARC 2019, Asian Regional Conference on Soil Mechanics and Geotechnical Engineering, AGSSEA, Taiwan, pp. 1-4.
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The coefficient of consolidation is a parameter, governing the rate at which saturated clay undergoes consolidation when subjected to an increase in pressure. The rate and amount of compression in clay varies with the rate that excess pore water pressure is dissipated; and hence depends on clay permeability. Over many years, various methods have been proposed to determine the coefficient of consolidation, cv, which is an indication of the rate of foundation settlement on soft ground. However, defining this parameter is often problematic and greatly relies on graphical techniques, which are subject to some uncertainties. This paper initially presents an overview of many well-established methods to determine the vertical coefficient of consolidation from the incremental loading consolidation tests. An array of consolidation tests was conducted on fully-saturated and undisturbed clay samples retrieved by an oil-operated sampler, collected at various depths from a site in Nakdong river delta, Busan, South Korea. The test results on these soft sensitive clay samples were employed to predict the settlement rate of Busan clay. To establish the relationship of time-displacement-velocity, a total of 3 method groups from 10 common procedures were classified and compared together. Detailed discussion on the results of this study is also provided.
Le, TM & Khabbaz, H 2020, 'Predicting consolidation coefficient of soft clay by time-displacement-velocity methods', 16th Asian Regional Conference on Soil Mechanics and Geotechnical Engineering, ARC 2019.
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Copyright © Soil Mechanics and Geotechnical Engineering, ARC 2019.All rights reserved. The coefficient of consolidation is a parameter, governing the rate at which saturated clay undergoes consolidation when subjected to an increase in pressure. The rate and amount of compression in clay varies with the rate that excess pore water pressure is dissipated; and hence depends on clay permeability. Over many years, various methods have been proposed to determine the coefficient of consolidation, cv, which is an indication of the rate of foundation settlement on soft ground. However, defining this parameter is often problematic and greatly relies on graphical techniques, which are subject to some uncertainties. This paper initially presents an overview of many well-established methods to determine the vertical coefficient of consolidation from the incremental loading consolidation tests. An array of consolidation tests was conducted on fully-saturated and undisturbed clay samples retrieved by an oil-operated sampler, collected at various depths from a site in Nakdong river delta, Busan, South Korea. The test results on these soft sensitive clay samples were employed to predict the settlement rate of Busan clay. To establish the relationship of time-displacement-velocity, a total of 3 method groups from 10 common procedures were classified and compared together. Detailed discussion on the results of this study is also provided.
Lee, C, Best, G & Hollinger, GA 1970, 'Optimal Deployment of Multiple Passenger Robots using Sequential Stochastic Assignment', RSS Workshop on Heterogeneous Multi-Robot Task Allocation and Coordination.
Li, C, Peng, J, Yuan, L, Wang, G, Liang, X, Lin, L & Chang, X 1970, 'Block-Wisely Supervised Neural Architecture Search With Knowledge Distillation', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, ELECTR NETWORK, pp. 1986-1995.
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Neural Architecture Search (NAS), aiming at automatically designing network architectures by machines, is expected to bring about a new revolution in machine learning. Despite these high expectation, the effectiveness and efficiency of existing NAS solutions are unclear, with some recent works going so far as to suggest that many existing NAS solutions are no better than random architecture selection. The ineffectiveness of NAS solutions may be attributed to inaccurate architecture evaluation. Specifically, to speed up NAS, recent works have proposed under-training different candidate architectures in a large search space concurrently by using shared network parameters; however, this has resulted in incorrect architecture ratings and furthered the ineffectiveness of NAS. In this work, we propose to modularize the large search space of NAS into blocks to ensure that the potential candidate architectures are fully trained; this reduces the representation shift caused by the shared parameters and leads to the correct rating of the candidates. Thanks to the blockwise search, we can also evaluate all of the candidate architectures within each block. Moreover, we find that the knowledge of a network model lies not only in the network parameters but also in the network architecture. Therefore, we propose to distill the neural architecture (DNA) knowledge from a teacher model to supervise our block-wise architecture search, which significantly improves the effectiveness of NAS. Remarkably, the performance of our searched architectures has exceeded the teacher model, demonstrating the practicability of our method. Finally, our method achieves a state-of-the-art 78.4% top-1 accuracy on ImageNet in a mobile setting. All of our searched models along with the evaluation code are available at https://github.com/changlin31/DNA.
Li, D, Opazo, CR, Yu, X & Li, H 1970, 'Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison', 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 1448-1458.
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Li, D, Xu, C, Yu, X, Zhang, K, Swift, B, Suominen, H & Li, H 1970, 'TSPNet: Hierarchical feature learning via temporal semantic pyramid for sign language translation', Advances in Neural Information Processing Systems.
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Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences. Sign videos consist of continuous sequences of sign gestures with no clear boundaries in between. Existing SLT models usually represent sign visual features in a frame-wise manner so as to avoid needing to explicitly segmenting the videos into isolated signs. However, these methods neglect the temporal information of signs and lead to substantial ambiguity in translation. In this paper, we explore the temporal semantic structures of sign videos to learn more discriminative features. To this end, we first present a novel sign video segment representation which takes into account multiple temporal granularities, thus alleviating the need for accurate video segmentation. Taking advantage of the proposed segment representation, we develop a novel hierarchical sign video feature learning method via a temporal semantic pyramid network, called TSPNet. Specifically, TSPNet introduces an inter-scale attention to evaluate and enhance local semantic consistency of sign segments and an intra-scale attention to resolve semantic ambiguity by using non-local video context. Experiments show that our TSPNet outperforms the state-of-the-art with significant improvements on the BLEU score (from 9.58 to 13.41) and ROUGE score (from 31.80 to 34.96) on the largest commonly-used SLT dataset. Our implementation is available at https://github.com/verashira/TSPNet.
Li, D, Yu, X, Xu, C, Petersson, L & Li, H 1970, 'Transferring Cross-Domain Knowledge for Video Sign Language Recognition', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 6204-6213.
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Li, K, Lu, J, Zuo, H & Zhang, G 1970, 'Multi-Source Domain Adaptation with Distribution Fusion and Relationship Extraction', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-6.
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© 2020 IEEE. Transfer learning is gaining increasing attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a similar domain. While many existing transfer learning methods deal with single source and single target problem without considering the fact that a target domain maybe similar to multiple source domains, this work proposes a multi-source domain adaptation method based on a deep neural network. Our method contains common feature extraction, specific predictor learning and target predictor estimation. Common feature extraction explores the relationship between source domains and target domain by distribution fusion and guarantees the strength of similar source domains during training, something which has not been well considered in existing works. Specific predictor learning trains source tasks with cross-domain distribution constraint and cross-domain predictor constraint to enhance the performance of single source. Target predictor estimation employs relationship extraction and selective strategy to improve the performance of the target task and to avoid negative transfer. Experiments on real-world visual datasets show the performance of the proposed method is superior to other deep learning baselines.
Li, K, Ni, W, Tovar, E & Guizani, M 1970, 'Deep Reinforcement Learning for Real-Time Trajectory Planning in UAV Networks', 2020 International Wireless Communications and Mobile Computing (IWCMC), 2020 International Wireless Communications and Mobile Computing (IWCMC), IEEE, Limassol, Cyprus, pp. 958-963.
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In Unmanned Aerial Vehicle (UAV)-enabled wireless powered sensor networks, a UAV can be employed to charge the ground sensors remotely via Wireless Power Transfer (WPT) and collect the sensory data. This paper focuses on trajectory planning of the UAV for aerial data collection and WPT to minimize buffer overflow at the ground sensors and unsuccessful transmission due to lossy airborne channels. Consider network states of battery levels and buffer lengths of the ground sensors, channel conditions, and location of the UAV. A flight trajectory planning optimization is formulated as a Partial Observable Markov Decision Process (POMDP), where the UAV has partial observation of the network states. In practice, the UAV-enabled sensor network contains a large number of network states and actions in POMDP while the up-to-date knowledge of the network states is not available at the UAV. To address these issues, we propose an onboard deep reinforcement learning algorithm to optimize the realtime trajectory planning of the UAV given outdated knowledge on the network states.
Li, K, Ni, W, Tovar, E & Jamalipour, A 1970, 'Deep Q-Learning based Resource Management in UAV-assisted Wireless Powered IoT Networks', ICC 2020 - 2020 IEEE International Conference on Communications (ICC), ICC 2020 - 2020 IEEE International Conference on Communications (ICC), IEEE, Dublin, Ireland, pp. 1-6.
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In Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Internet of Things (IoT), the UAV is employed to charge the IoT nodes remotely via Wireless Power Transfer (WPT) and collect their data. A key challenge of resource management for WPT and data collection is preventing battery drainage and butter overflow of the ground IoT nodes in the presence of highly dynamic airborne channels. In this paper, we consider the resource management problem in practical scenarios, where the UAV has no a-prior information on battery levels and data queue lengths of the nodes. We formulate the resource management of UAV-assisted WPT and data collection as Markov Decision Process (MDP), where the states consist of battery levels and data queue lengths of the IoT nodes, channel qualities, and positions of the UAV. A deep Q-learning based resource management is proposed to minimize the overall data packet loss of the IoT nodes, by optimally deciding the IoT node for data collection and power transfer, and the associated modulation scheme of the IoT node.
Li, L & Kang, K 1970, 'Analyzing shopping behavior of the middle-aged users in tiktok live streaming platform', 26th Americas Conference on Information Systems, AMCIS 2020.
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With the popularity of live streaming platforms, an increasing number of middle-aged online users begin to spend plenty of time and money on these platforms. However, in China, most of the middle-aged users lack live shopping experience and detailed legal knowledge, which results that they are easier to build trust with live anchors. In light of this, this study analyses the online shopping behavior of middle-aged users and research the features of live streaming platform TikTok. Based on the analysis, the research model related to the trust-building between Chinese middle-aged online users and live anchors will be established.
Li, M, Chen, S, Zhang, Y & Tsang, I 1970, 'Graph cross networks with vertex infomax pooling', Advances in Neural Information Processing Systems.
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We propose a novel graph cross network (GXN) to achieve comprehensive feature learning from multiple scales of a graph. Based on trainable hierarchical representations of a graph, GXN enables the interchange of intermediate features across scales to promote information flow. Two key ingredients of GXN include a novel vertex infomax pooling (VIPool), which creates multiscale graphs in a trainable manner, and a novel feature-crossing layer, enabling feature interchange across scales. The proposed VIPool selects the most informative subset of vertices based on the neural estimation of mutual information between vertex features and neighborhood features. The intuition behind is that a vertex is informative when it can maximally reflect its neighboring information. The proposed feature-crossing layer fuses intermediate features between two scales for mutual enhancement by improving information flow and enriching multiscale features at hidden layers. The cross shape of feature-crossing layer distinguishes GXN from many other multiscale architectures. Experimental results show that the proposed GXN improves the classification accuracy by 2.12% and 1.15% on average for graph classification and vertex classification, respectively. Based on the same network, the proposed VIPool consistently outperforms other graph-pooling methods.
Li, M, Yang, Y, Zhang, Y, Iacopi, F, Ram, S & Nulman, J 1970, 'A Fully Integrated Conductive and Dielectric Additive Manufacturing Technology for Microwave Circuits and Antennas', 2020 50TH EUROPEAN MICROWAVE CONFERENCE (EUMC), 15th European Microwave Integrated Circuits Conference (EuMIC) / 50th European Microwave Conference (EuMC), IEEE, ELECTR NETWORK, pp. 392-395.
Li, M, Yang, Y, Zhang, Y, Iacopi, F, Ram, S & Nulman, J 1970, 'A Fully Integrated Conductive and Dielectric Additive Manufacturing Technology for Microwave Circuits and Antennas', 2020 50TH EUROPEAN MICROWAVE CONFERENCE (EUMC), 15th European Microwave Integrated Circuits Conference (EuMIC) / 50th European Microwave Conference (EuMC), IEEE, ELECTR NETWORK.
Li, M, Zhang, Y, Sun, Y, Wang, W, Tsang, IW & Lin, X 1970, 'I/O Efficient Approximate Nearest Neighbour Search based on Learned Functions', 2020 IEEE 36th International Conference on Data Engineering (ICDE), 2020 IEEE 36th International Conference on Data Engineering (ICDE), IEEE, Dallas, TX, USA, pp. 289-300.
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© 2020 IEEE. Approximate nearest neighbour search (ANNS) in high dimensional space is a fundamental problem in many applications, such as multimedia database, computer vision and information retrieval. Among many solutions, data-sensitive hashing-based methods are effective to this problem, yet few of them are designed for external storage scenarios and hence do not optimized for I/O efficiency during the query processing. In this paper, we introduce a novel data-sensitive indexing and query processing framework for ANNS with an emphasis on optimizing the I/O efficiency, especially, the sequential I/Os. The proposed index consists of several lists of point IDs, ordered by values that are obtained by learned hashing (i.e., mapping) functions on each corresponding data point. The functions are learned from the data and approximately preserve the order in the high-dimensional space. We consider two instantiations of the functions (linear and non-linear), both learned from the data with novel objective functions. We also develop an I/O efficient ANNS framework based on the index. Comprehensive experiments on six benchmark datasets show that our proposed methods with learned index structure perform much better than the state-of-the-art external memory-based ANNS methods in terms of I/O efficiency and accuracy.
Li, P, Dong, X, Yu, X & Yang, Y 1970, 'When Humans Meet Machines: Towards Efficient Segmentation Networks', 31st British Machine Vision Conference, BMVC 2020, The 31st British Machine Vision Virtual Conference, Virtual.
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In this paper, we investigate how to achieve a high-performance yet lightweight segmentation network for real-time applications. By analyzing three typical segmentation networks, we observe that the segmentation backbones and heads are often imbalanced which restricts network efficiency. Thus, we develop a lightweight context fusion (LCF) module and a lightweight global enhancement (LGE) module to construct our lightweight segmentation head. Specifically, LCF fuses multi-resolution features to capture image details and LGE is designed to enhance feature representations. In this manner, our lightweight head facilities network efficiency and significantly reduces network parameters. Furthermore, we design a Multi-Resolution Macro Segmentation structure (MRMS) to incorporate human knowledge into our network architecture composition. Given the resource-aware constraint (e.g., latency time), we optimize our network with network architecture search while considering the relationships among atomic operators, network depth and feature resolution in segmentation tasks. Since MRMS embeds the segmentation-specific knowledge, it also provides a better architecture search space. Our Human-Machine collaboratively designed Segmentation network (HMSeg) achieves better performance and faster inference speed. Experiments demonstrate that our network achieves 71.4% mean intersection over union (mIOU) on Cityscapes dataset with only 0.7M parameters at 172.4 FPS on NVIDIA GTX1080Ti.
Li, S, Liao, S, Yang, Y, Che, W & Xue, Q 1970, 'A Low-Profile Sequential Rotation-Fed Circularly Polarized Annular Aperture Antenna Array for Earth Coverage Applications', 2020 IEEE MTT-S International Wireless Symposium (IWS), 2020 IEEE MTT-S International Wireless Symposium (IWS), IEEE, pp. 1-3.
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Li, S, Wu, Y, Liu, Y, Wang, D, Wen, M, Tao, Y, Sui, Y & Liu, Y 1970, 'An Exploratory Study of Bugs in Extended Reality Applications on the Web', 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), IEEE, Coimbra, Portugal, pp. 172-183.
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© 2020 IEEE Computer Society. All rights reserved. Extended Reality (XR) technologies are becoming increasingly popular in recent years. To help developers deploy XR applications on the Web, W3C released the WebXR Device API in 2019, which enable users to interact with browsers using XR devices. Given the convenience brought byWebXR, a growing number of WebXR projects have been deployed in practice. However, many WebXR applications are insufficiently tested before being released. They suffer from various bugs that can degrade user experience or cause undesirable consequences. Yet, the community has limited understanding towards the bugs in the WebXR ecosystem, which impedes the advance of techniques for assuring the reliability of WebXR applications. To bridge this gap, we conducted the first empirical study of WebXR bugs. We collected 368 real bugs from 33 WebXR projects hosted on GitHub. Via a seven-round manual analysis of these bugs, we built a taxonomy of WebXR bugs according to their symptoms and root causes. Furthermore, to understand the uniqueness of WebXR bugs, we compared them with bugs in conventional JavaScript programs and web applications. We believe that our findings can inspire future researches on relevant topics and we released our bug dataset to facilitate follow-up studies.
Li, W, Cai, H, Sui, Y & Manz, D 1970, 'PCA: memory leak detection using partial call-path analysis', Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ACM, pp. 1621-1625.
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Li, W, Qiao, M, Qin, L, Zhang, Y, Chang, L & Lin, X 1970, 'Scaling Up Distance Labeling on Graphs with Core-Periphery Properties.', SIGMOD Conference, SIGMOD/PODS '20: International Conference on Management of Data, ACM, ELECTR NETWORK, pp. 1367-1381.
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© 2020 Association for Computing Machinery. In indexing a graph for distance queries, distance labeling is a common practice; in particular, 2-hop labeling which guarantees the exactness of the query results is widely adopted. When it comes to a massive real graph with a relatively large treewidth such as social networks and web graphs, however, 2-hop labeling can hardly be constructed due to the oversized index. This paper discloses the theoretical relationships between the graph treewidth and 2-hop labeling's index size and query time. To scale up distance labeling, this paper proposes Core-Tree (CT) Index to facilitate a critical and effective trade-off between the index size and query time. The reduced index size enables CT-Index to handle massive graphs that no existing approaches can process while the cost in the query time is negligible: the query time is below 0.4 milliseconds on all tested graphs including one graph with 5.5 billion edges.
Li, XL, Tse, CK & Lu, DD-C 1970, 'Single-Inductor Multi-Input Multi-Output DC-DC Converter with High Flexibility and Simple Control', 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 2020 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, pp. 1-5.
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Li, Y, Fan, X, Chen, L, Li, B, Yu, Z & Sisson, SA 1970, 'Recurrent Dirichlet Belief Networks for interpretable Dynamic Relational Data Modelling', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization, pp. 2470-2476.
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The Dirichlet Belief Network~(DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data. The proposed Recurrent-DBN has the following merits: (1) it infers interpretable and organised hierarchical latent structures for objects within and across time steps; (2) it enables recurrent long-term temporal dependence modelling, which outperforms the one-order Markov descriptions in most of the dynamic probabilistic frameworks; (3) the computational cost scales to the number of positive links only. In addition, we develop a new inference strategy, which first upward-and-backward propagates latent counts and then downward-and-forward samples variables, to enable efficient Gibbs sampling for the Recurrent-DBN. We apply the Recurrent-DBN to dynamic relational data problems. The extensive experiment results on real-world data validate the advantages of the Recurrent-DBN over the state-of-the-art models in interpretable latent structure discovery and improved link prediction performance.
Li, Y, Li, K, Jiang, S, Zhang, Z, Huang, C & Da Xu, RY 1970, 'Geometry-driven self-supervised method for 3D human pose estimation', AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, pp. 11442-11449.
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The neural network based approach for 3D human pose estimation from monocular images has attracted growing interest. However, annotating 3D poses is a labor-intensive and expensive process. In this paper, we propose a novel self-supervised approach to avoid the need of manual annotations. Different from existing weakly/self-supervised methods that require extra unpaired 3D ground-truth data to alleviate the depth ambiguity problem, our method trains the network only relying on geometric knowledge without any additional 3D pose annotations. The proposed method follows the two-stage pipeline: 2D pose estimation and 2D-to-3D pose lifting. We design the transform re-projection loss that is an effective way to explore multi-view consistency for training the 2D-to-3D lifting network. Besides, we adopt the confidences of 2D joints to integrate losses from different views to alleviate the influence of noises caused by the self-occlusion problem. Finally, we design a two-branch training architecture, which helps to preserve the scale information of re-projected 2D poses during training, resulting in accurate 3D pose predictions. We demonstrate the effectiveness of our method on two popular 3D human pose datasets, Human3.6M and MPI-INF-3DHP. The results show that our method significantly outperforms recent weakly/self-supervised approaches.
Li, Y, Li, Y, Liu, C, Zhu, L, Zhu, J & Lei, G 1970, 'Numerical Calculation for Power Transformer Vibration Based on Dynamic Magnetostriction Model', 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE, pp. 1-2.
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Li, Y, Shen, T, Long, G, Jiang, J, Zhou, T & Zhang, C 1970, 'Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention', Proceedings of the 28th International Conference on Computational Linguistics, Proceedings of the 28th International Conference on Computational Linguistics, International Committee on Computational Linguistics, pp. 1653-1664.
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Wrong labeling problem and long-tail relations are two main challenges caused by distant supervision in relation extraction. Recent works alleviate the wrong labeling by selective attention via multi-instance learning, but cannot well handle long-tail relations even if hierarchies of the relations are introduced to share knowledge. In this work, we propose a novel neural network, Collaborating Relation-augmented Attention (CoRA), to handle both the wrong labeling and long-tail relations. Particularly, we first propose relation-augmented attention network as base model. It operates on sentence bag with a sentence-to-relation attention to minimize the effect of wrong labeling. Then, facilitated by the proposed base model, we introduce collaborating relation features shared among relations in the hierarchies to promote the relation-augmenting process and balance the training data for long-tail relations. Besides the main training objective to predict the relation of a sentence bag, an auxiliary objective is utilized to guide the relation-augmenting process for a more accurate bag-level representation. In the experiments on the popular benchmark dataset NYT, the proposed CoRA improves the prior state-of-the-art performance by a large margin in terms of Precision@N, AUC and Hits@K. Further analyses verify its superior capability in handling long-tail relations in contrast to the competitors.
Li, Z, Chang, X, Yao, L, Pan, S, Zongyuan, G & Zhang, H 1970, 'Grounding Visual Concepts for Zero-Shot Event Detection and Event Captioning', Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 297-305.
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The flourishing of social media platforms requires techniques for understanding the content of media on a large scale. However, state-of-the art video event understanding approaches remain very limited in terms of their ability to deal with data sparsity, semantically unrepresentative event names, and lack of coherence between visual and textual concepts. Accordingly, in this paper, we propose a method of grounding visual concepts for large-scale Multimedia Event Detection (MED) and Multimedia Event Captioning (MEC) in zero-shot setting. More specifically, our framework composes the following: (1) deriving the novel semantic representations of events from their textual descriptions, rather than event names; (2) aggregating the ranks of grounded concepts for MED tasks. A statistical mean-shift outlier rejection model is proposed to remove the outlying concepts which are incorrectly grounded; and (3) defining MEC tasks and augmenting the MEC training set by the videos detected in MED in a zero-shot setting. To the best of our knowledge, this work is the first time to define and solve the MEC task, which is a further step towards understanding video events. We conduct extensive experiments and achieve state-of-the-art performance on the TRECVID MEDTest dataset, as well as our newly proposed TRECVID-MEC dataset.
Li, Z, Zhang, J, Gong, Y, Yao, Y & Wu, Q 1970, 'Field-wise learning for multi-field categorical data', Advances in Neural Information Processing Systems, Conference on Neural Information Processing Systems, On-line.
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We propose a new method for learning with multi-field categorical data. Multi-field categorical data are usually collected over many heterogeneous groups. These groups can reflect in the categories under a field. The existing methods try to learn a universal model that fits all data, which is challenging and inevitably results in learning a complex model. In contrast, we propose a field-wise learning method leveraging the natural structure of data to learn simple yet efficient one-to-one field-focused models with appropriate constraints. In doing this, the models can be fitted to each category and thus can better capture the underlying differences in data. We present a model that utilizes linear models with variance and low-rank constraints, to help it generalize better and reduce the number of parameters. The model is also interpretable in a field-wise manner. As the dimensionality of multi-field categorical data can be very high, the models applied to such data are mostly over-parameterized. Our theoretical analysis can potentially explain the effect of over-parametrization on the generalization of our model. It also supports the variance constraints in the learning objective. The experiment results on two large-scale datasets show the superior performance of our model, the trend of the generalization error bound, and the interpretability of learning outcomes. Our code is available at https://github.com/lzb5600/Field-wise-Learning.
Lian, J-W, Ban, Y-L, Zhu, H & Guo, YJ 1970, 'Uniplanar 2-D Butler Matrix for Multibeam Arrays', 2020 4th Australian Microwave Symposium (AMS), 2020 4th Australian Microwave Symposium (AMS), IEEE, Sydney, Australia, pp. 1-2.
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A 2-D Butler matrix (BM) in uniplanar configuration for designing multibeam array antenna (MAA) is proposed using substrate integrated waveguide (SIW) technology. Firstly, a novel topology for building uniplanar 2D BM is proposed, which successfully transforms the traditional 3-D topology to a 2-D (or uniplanar) one. To realize the planarization of basic components, a novel design of eight-port hybrid couplers, is developed to transform four spatially intersected couplers to a planar structure. To address the issue of excessive path intersections, a novel SIW eight-port crossover is proposed to reduce the number of path intersections from 16 to merely 4. Using this proposed 2-D BM, a 2-D MAA with 16 (4 × 4) beams can be realized.
Liang, B, Verma, S, Xu, J, Liang, S, Li, Z, Wang, Y & Chen, F 1970, 'A Data Driven Approach for Leak Detection with Smart Sensors', 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, Shenzhen, China, pp. 1311-1316.
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Preventing water pipe leaks and breaks has high priority for water utilities. It is a critical task for the utility to reduce water loss through leaks and breaks detection in water mains. The failure prediction and data analytics research have been conducted for an Australian water utility over the last few years to enhance the prediction of leaks and breaks detection in water mains. Intelligent sensing at sensitive locations with current research aids in prioritising investigation and prevention of potential breaks and leaks in water mains. The purpose of this work is to integrate the predictive analytics and intelligent sensing applications to identify high risk mains prior to failures. Predictive analytics and minimum night flow (MNF) analysis have been utilised to prioritise risky zones over the whole water network, and then risky pipes are identified to optimise sensors deployment. The sensing data is being collected for analysis and validation, and a machine learning model is being built based on the analysis results. This work is currently under progress and the planned outcomes will help the utility reduce water loss, improve leak detection, and enhance customer satisfaction by automating the process of leak detection using a data driven approach with smart sensors.
Liang, R, Zhang, Q, Lu, J, Zhang, G & Wang, J 1970, 'A cross-domain group recommender system with a generalized aggregation strategy', Developments of Artificial Intelligence Technologies in Computation and Robotics, 14th International FLINS Conference (FLINS 2020), WORLD SCIENTIFIC, Cologne, Germany, pp. 455-462.
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Developing group recommender systems has been a vital requirement due to the prevalence of group activities. However, existing group recommender systems still suffer from data sparsity problem because they rely on individual recommendation methods with a predefined aggregation strategy. To solve this problem, we propose a cross-domain group recommender system with a generalized aggregation strategy in this paper. A generalized aggregation strategy is developed to build group profile in the target domain with the help of individual preferences extracted from a source domain with sufficient data. By adding the constraints between the individual preference and the group profile, knowledge is transferred to assist in the group recommendation task in the target domain. Experiments on a real-world dataset justify the effectiveness and rationality of our proposed cross-domain recommender systems. The results show that we increase the accuracy of group recommendation on different sparse ratios with the help of individual data from the source domain.
Liao, W, Zhang, Q, Zhang, G & Lu, J 1970, 'Multi-source shared autoencoder for cross-domain recommendation', Developments of Artificial Intelligence Technologies in Computation and Robotics, 14th International FLINS Conference (FLINS 2020), WORLD SCIENTIFIC, Cologne, Germany, pp. 463-471.
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Cross-domain recommendation has been proved to be an effective solution to the data sparsity problem, which commonly exists in recommender systems. However, a challenging issue remains to be studied: how to transfer valuable knowledge from multiple source domains and balance the effect of them to the target domain under a sparse setting. To handle the issue, we develop a multi-source shared cross-domain recommender system, which aims to extract shared latent features from multiple domains to assist the recommendation task in a sparse target domain. It’s achieved through a multiple domain-shared autoencoder and an attentive module. Then we further propose an enhanced method by making it specific to each user so that it can provide personalized services. Experiments conducted on real world datasets show that the proposed methods perform well and improve the accuracy of recommendations in the target domain even though the datasets are quite sparse.
Lin, C-T, Huang, K-C, Pal, NR, Cao, Z, Liu, Y-T, Fang, C-N, Hsieh, T-Y, Lin, Y-Y & Wu, S-L 1970, 'Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface', 2020 International Conference on Fuzzy Theory and Its Applications (iFUZZY), 2020 International Conference on Fuzzy Theory and Its Applications (iFUZZY), IEEE, pp. 1-8.
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© 2020 IEEE. Kohonen's Adaptive Subspace Self-Organizing Map (ASSOM) learns several subspaces of the data where each subspace represents some invariant characteristics of the data. To deal with the imbalance classification problem, earlier we have proposed a method for oversampling the minority class using Kohonen's ASSOM. This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes an application to a Brain Computer Interface (BCI) problem. First we compare the performance of our method using some benchmark data sets with several state-of-The-Art methods. Finally, we apply the ASSOM-based technique to analyze a BCI based application using electroencephalogram (EEG) datasets. Our results demonstrate the effectiveness of the ASSOM-based method in dealing with imbalance classification problem.
Lin, J, Sun, G, Shen, J, Cui, T, Pritchard, D, Xu, D, Li, L, Wei, W, Beydoun, G & Chen, S 1970, 'Attention-Based High-Order Feature Interactions to Enhance the Recommender System for Web-Based Knowledge-Sharing Service.', WISE (1), Springer, pp. 461-473.
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© 2020, Springer Nature Switzerland AG. Providing personalized online learning services has become a hot research topic. Online knowledge-sharing services represents a popular approach to enable learners to use fragmented spare time. User asks and answers questions in the platform, and the platform also recommends relevant questions to users based on their learning interested and context. However, in the big data era, information overload is a challenge, as both online learners and learning resources are embedded in data rich environment. Offering such web services requires an intelligent recommender system to automatically filter out irrelevant information, mine underling user preference, and distil latent information. Such a recommender system needs to be able to mine complex latent information, distinguish differences between users efficiently. In this study, we refine a recommender system of a prior work for web-based knowledge sharing. The system utilizes attention-based mechanisms and involves high-order feature interactions. Our experimental results show that the system outperforms known benchmarks and has great potential to be used for the web-based learning service.
Lin, J, Sun, G, Shen, J, Pritchard, D, Cui, T, Xu, D, Li, L, Beydoun, G & Chen, S 1970, 'Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service.', AIED (2), International Conference on Artificial Intelligence in Education, Springer, Ifrane, Morocco,, pp. 168-173.
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Aims to provide flexible, effective and personalized online learning service, micro learning has gained wide attention in recent years as more people turn to use fragment time to grasp fragmented knowledge. Widely available online knowledge sharing is one of the most representative approaches to micro learning, and it is well accepted by online learners. However, information overload challenges such personalized online learning services. In this paper, we propose a deep cross attention recommendation model to provide online users with personalized resources based on users’ profile and historical online behaviours. This model benefits from the deep neural network, feature crossing, and attention mechanism mutually. The experiment result showed that the proposed model outperformed the state-of-the-art baselines.
Lin, J, Zhou, Z, Sun, G, Shen, J, Pritchard, D, Cui, T, Xu, D, Li, L & Beydoun, G 1970, 'Deep Sequence Labelling Model for Information Extraction in Micro Learning Service.', IJCNN, International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI), IEEE, ELECTR NETWORK, pp. 1-10.
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Micro learning aims to assist users in making good use of smaller chunks of spare time and provides an effective online learning service. However, to provide such personalized online services on the Web, a number of information overload challenges persist. Effectively and precisely mining and extracting valuable information from massive and redundant information is a significant pre-processing procedure for personalizing online services. In this study, we propose a deep sequence labelling model for locating, extracting, and classifying key information for micro learning services. The proposed model is general and combines the advantages of different types of classical neural network. Early evidence shows that it has satisfactory performance compared to conventional information extraction methods such as conditional random field and bi-directional recurrent neural network, for micro learning services.
Lin, S, Liao, S, Yang, Y, Xue, Q & Che, W 1970, 'High Gain Low-Profile Omnidirectional Yagi-Uda Array Antenna', 2020 IEEE MTT-S International Wireless Symposium (IWS), 2020 IEEE MTT-S International Wireless Symposium (IWS), IEEE, pp. 1-3.
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Lin, W, Ziolkowski, RW, Ahmad, WA, Yang, Y, Yuan, L, Ng, HJ, Wang, Y & Kissinger, D 1970, '320 GHz On-Chip Circularly-Polarized Antenna Array Realized with 0.13 μm BiCMOS Technology', 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, Montreal, QC, Canada, pp. 1467-1468.
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A 320 GHz on-chip circularly-polarized (CP) antenna array that has been facilitated with 0.13 μm SiGe BiCMOS technology is presented. It is the first designed and prototyped THz-band on-chip antenna array with circular polarization and high directivity. The antenna is realized by designing a 4 \times 4 microstrip-fed CP patch antenna array inside the silicon dioxide layer on top of a silicon base. A sequential phase rotation scheme is applied to the four 2 \times 2 subarrays to achieve wide axial ratio (AR) bandwidth (8.7 GHz). The antenna array was successfully prototyped in a 3.6\times 3.6 mm2 area on a silicon wafer. Consequently, it easily combined with other integrated circuit (IC) components. The developed THz on-chip CP antenna is highly desired for the emerging ultra-high speed wireless applications.
Lin, Z, Lv, T, Zhang, JA & Liu, RP 1970, 'Tensor-based High-Accuracy Position Estimation for 5G mmWave Massive MIMO Systems', ICC 2020 - 2020 IEEE International Conference on Communications (ICC), ICC 2020 - 2020 IEEE International Conference on Communications (ICC), IEEE, Dublin, Ireland, pp. 1-6.
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Highly accurate localization is important for wire-less communications. In this paper, we propose a new tensor-based positioning method for 5G wideband mmWave massive MIMO systems. We first develop an extended multidimensional interpolation (E-MI)-based method as the preprocessing step to suppress the frequency-dependence of the array steering vectors. By using this method, the data across the whole frequency band can be processed jointly, and the high temporal resolution offered by wideband mmWave signals can be exploited. Then, we propose a parameter decoupling (PD)-based tensor multiparameter estimation algorithm. This algorithm can suppress the noises in all of temporal, spatial and frequency domains, and thus all the parameters can be precisely estimated. A simplified perturbation term (S-PT)-based method is also presented to match the estimated parameters at low complexity. Based on the quasi-optical property of mmWave signals, we propose a novel method to compute the 3D coordinates of the target. Simulation results demonstrate the effectiveness of the proposed positioning method in the end.
Lindeck, J, Cheng, E, Machet, T, Boye, T, Daniel, S & Bhatia, T 1970, 'Using an online collaboration platform to facilitate group work', Proceedings of the AAEE2020 Conference Sydney, Australia, Annual Conference of the Australasian Association for Engineering Education, AAEE, Sydney.
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The onset of COVID-19 necessitated moving three large-enrolment introductory engineering and IT subjects online after just one week of face to face teaching. All three subjects focus on facilitating students' learning through group work to solve a self-identified problem. Considering a key Subject Learning Outcome is 'to collaborate effectively in team processes', group work is integral to the aims of these subjects. Studies for both online and face-to-face group work identify the influence educators play in achieving successful learning outcomes and group satisfaction; for example, the importance of group work management (Xu, Du & Fan, 2015). While many challenges faced in online group work in education are common to face-to-face teaching (Roberts & McInnerney, 2007), it has been shown that 'distance does matter' (Olson & Olson, 2000). The challenge was to facilitate the same level of cooperation between students and enable them to build teamwork skills without face-to-face interaction with teammates or educators. PURPOSE OR GOAL Moving three early-year subjects of approximately 600 students each onto an online collaboration platform over a short period provided new challenges. This paper will discuss the aspects of our transition to online group work that worked well, and those that did not, from the perspective of students and tutors. These insights into best-practice online learning will inform how teaching can shift into blended learning in 2021.METHODOLOGYFocus groups were conducted with students from one second-year and two first-year subjects. In these focus groups, students discussed their experiences of working in a group environment and how this experience can be improved. The comments from student feedback surveys and students’ comments from the SPARKplus peer assessments were also used. In addition, feedback on classes and materials were taken from tutors and compared to the students' view of the online classes. Transcripts and comments ...
Lister, R 1970, 'On the cognitive development of the novice programmer', Proceedings of the 9th Computer Science Education Research Conference, CSERC '20: the 9th Computer Science Education Research Conference, ACM, pp. 1-15.
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Litvinov, A, Gardner, A & Pradhan, S 1970, 'The presence of empathy in entrepreneurial subject outlines for IT and software engineering students', 31st Annual Conference of the Australasian Association for Engineering Education (AAEE 2020): Disrupting Business as Usual in Engineering Education, Australasian Association for Engineering Education Conference, Engineers Australia, Sydney, Australia, pp. 337-334.
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Technology startups are playing an increasingly important role in developed and developingeconomies. As well as technical expertise, the founders (and other employees) should possess social competencies such as communication, collaboration and empathy. A growing body of literature has identified empathetic behaviours as progressively important for the success of a technology startup. At universities, more entrepreneurial subjects are being introduced increasingly to IT and software engineering students. However, empathy as a phenomenon and its development may not be a common inclusion in engineering and information technology (IT) curricula
Liu, A, Zhang, G, Wang, K & Lu, J 1970, 'Fast Switch Naïve Bayes to Avoid Redundant Update for Concept Drift Learning', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK, pp. 1-7.
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In data stream mining, concept drift may cause the predictions given by machine learning models become less accurate as time passes. Existing concept drift detection and adaptation methods are built based on a framework that is buffering new samples if a drift-warming level is triggered and retraining a new model if a drift-alarm level is triggered. However, these methods neglected the problem that the performance of a learning model could be more sensitive to the amount of training data rather than the concept drift. In other words, a retrained model built on very few data instances could be even worse than the old model trained before the drift. To elaborate and address this problem, we propose a fast switch Naïve Bayes model (fsNB) for concept drift detection and adaptation. The intuition is to apply the idea of following the leader in online learning. We manipulate a sliding and an incremental Naïve Bayes classifier, if the sliding one overwhelms the incremental one, the model reports a drift. The experimental evaluation shows the advantages of fsNB and demonstrates that retraining may not be the best options for a marginal drift.
Liu, B, Shan, X, Zhu, J, Chen, C, Liu, Y, Wang, F & McGloin, D 1970, 'Self-optimizing ghost imaging with a genetic algorithm', 14th Pacific Rim Conference on Lasers and Electro-Optics (CLEO PR 2020), Conference on Lasers and Electro-Optics/Pacific Rim, Optica Publishing Group, ELECTR NETWORK, pp. C1G_3-C1G_3.
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To simplify the reconstruction algorithms in ghost imaging, we present a feedback-based approach to reduce reconstruction times. We introduce a genetic algorithm to optimize the illumination patterns in real-time to match with the object’s shape.
Liu, C, Chang, X & Shen, Y-D 1970, 'Unity Style Transfer for Person Re-Identification', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, ELECTR NETWORK, pp. 6886-6895.
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Style variation has been a major challenge for person re-identification, which aims to match the same pedestrians across different cameras. Existing works attempted to address this problem with camera-invariant descriptor subspace learning. However, there will be more image artifacts when the difference between the images taken by different cameras is larger. To solve this problem, we propose a UnityStyle adaption method, which can smooth the style disparities within the same camera and across different cameras. Specifically, we firstly create UnityGAN to learn the style changes between cameras, producing shape-stable style-unity images for each camera, which is called UnityStyle images. Meanwhile, we use UnityStyle images to eliminate style differences between different images, which makes a better match between query and gallery. Then, we apply the proposed method to Re-ID models, expecting to obtain more style-robust depth features for querying. We conduct extensive experiments on widely used benchmark datasets to evaluate the performance of the proposed framework, the results of which confirm the superiority of the proposed model.
Liu, C, Zowghi, D & Talaei-Khoei, A 1970, 'Empirical Evaluation of the Influence of EMR Alignment to Care Processes on Data Completeness', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences.
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Liu, F, Xu, W, Lu, J, Zhang, G, Gretton, A & Sutherland, DJ 1970, 'Learning Deep Kernels for Non-Parametric Two Sample Tests', Proceedings of the 37 th International Conference on Machine Learning, International Conference on Machine Learning, MLR Press, Online, pp. 1-11.
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We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution. Our tests are constructed from kernels parameterized by deep neural nets, trained to maximize test power. These tests adapt to variations in distribution smoothness and shape over space, and are especially suited to high dimensions and complex data. By contrast, the simpler kernels used in prior kernel testing work are spatially homogeneous, and adaptive only in lengthscale. We explain how this scheme includes popular classifier-based two-sample tests as a special case, but improves on them in general. We provide the first proof of consistency for the proposed adaptation method, which applies both to kernels on deep features and to simpler radial basis kernels or multiple kernel learning.In experiments, we establish the superior performance of our deep kernels in hypothesis testing on benchmark and real-world data. The code of our deep-kernel-based two sample tests is available at github.com/fengliu90/DK-for-TST.
Liu, F, Xu, W, Lu, J, Zhang, G, Gretton, A & Sutherland, DJ 1970, 'Learning deep kernels for non-parametric two-sample tests', 37th International Conference on Machine Learning, ICML 2020, International Conference on Machine Learning, MLR, Virtual, pp. 6272-6282.
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We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution. Our tests are constructed from kernels parameterized by deep neural nets, trained to maximize test power. These tests adapt to variations in distribution smoothness and shape over space, and are especially suited to high dimensions and complex data. By contrast, the simpler kernels used in prior kernel testing work are spatially homogeneous, and adaptive only in lengthscale. We explain how this scheme includes popular classifier-based two-sample tests as a special case, but improves on them in general. We provide the first proof of consistency for the proposed adaptation method, which applies both to kernels on deep features and to simpler radial basis kernels or multiple kernel learning. In experiments, we establish the superior performance of our deep kernels in hypothesis testing on benchmark and real-world data.
Liu, F, Zhang, G & Lu, J 1970, 'A Novel Non-parametric Two-Sample Test on Imprecise Observations', 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Glasgow, United Kingdom, pp. 1-6.
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In kernel non-parametric two-sample test, we aim to determine whether two sets of precise observations (i.e., samples) are from the same distribution based on a selected kernel. However, in real world, precise observations may be unavailable. For example, readings on an analogue measurement equipment are not precise numbers but intervals since there is only a finite number of decimals available. Hence, we consider a new and more realistic problem setting-two-sample test on imprecise observations. We show that the test power of existing kernel two- sample tests will drop significantly if they do not take care of the vagueness of the imprecise observations, and to this end, we propose a fuzzy-based maximum mean discrepancy (F-MMD), a powerful two-sample test on imprecise observations. F-MMD is based on a novel fuzzy-based kernel function that can measure the discrepancy between two imprecise observations. This novel kernel function takes care of the vagueness of the imprecise observations and its parameters are optimized to maximize the approximate test power of F-MMD. Experiments demonstrate that F-MMD significantly outperforms competitive two-sample test methods when facing imprecise observations.
Liu, J, Teng, J, Zhang, S & Sheng, D 1970, 'A frost heave model of unsaturated coarse-grained soil considering vapour transfer', E3S Web of Conferences, EDP Sciences, pp. 02017-02017.
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Substantial frost heave has been observed in coarse fills in high-speed railway embankments. These coarse fills have low fine contents and very low water content. The groundwater table is located below the coarse fills. The coarse fills were considered not susceptible to frost heave. Recent experimental results in the literature showed that vapour transfer has a considerable influence on the frost heaving of unsaturated coarse-grained soil. But vapour transfer has been rarely considered in the modelling of frost heave. This study presents a new frost heave model with considering vapour transfer and its contribution to ice formation. The rigid ice theory is applied to initiate an ice lens formation in the frozen fringe. An updated computer programme PCHeave is developed by considering the vapour transfer. The results of the proposed model are compared with laboratory test results, which show reasonable agreement. The prediction of the model agrees well with the measured frost heave and frost depth, which indicates that the proposed model can reasonably reflects the process of frost heave in unsaturated coarse soil.
Liu, J, Zhang, JA, Xu, R, Pearce, A, Ni, W & Hedley, M 1970, 'Gaussian Mixture Model based Convolutional Sparse Coding for Radar Heartbeat Detection', 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, Adelaide, Australia, pp. 1-6.
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Accurate detection of heartbeat through radar has many potential applications in, e.g., security and health. However, it is generally challenging to obtain clear heart-beat signature, due to its weak signal and relatively large interference caused by, e.g., body and respiration movement. In this paper, we propose an advanced algorithm based on convolutional sparse coding (CSC) and Gaussian mixture model (GMM) for suppressing the interference and extracting clear heartbeat signals. In this study, heartbeat signals are modelled by CSC and recovered by exploiting the sparsity of the signal. GMM is introduced to model the unknown noise, which could be a mixture from multiple noise/interference sources. The parameters of GMM, dictionary and codes are computed via the expectation maximization (EM) algorithm. To achieve faster processing, convolution computing is proposed to be processed in the frequency domain. The proposed method is tested and validated by simulation and experiments. The results show that our proposed algorithm can accurately extract the heartbeat components.
Liu, J, Zou, Z, Ye, X, Tan, X, Ding, E, Xu, F & Yu, X 1970, 'Leaping from 2D Detection to Efficient 6DoF Object Pose Estimation', Springer International Publishing, pp. 707-714.
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Liu, L, Guo, Y, Lei, G, Zhu, J & Jin, J 1970, 'Power Loss Analysis of High Speed Permanent Magnet Machine', 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE, China, pp. 1-2.
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Concerns about advantages such as high efficiency, high power density, small size, light weight, and fast dynamic response have contributed to the increasing industrial application of high speed permanent magnet machines (HSPMMs). Aiming at the complex power loss situations due to high operating speed and frequency, in this paper, the calculation models of HSPMM in terms of iron loss and copper loss are reviewed based on the thoroughly overview of previous research outcomes. Moreover, the research status and future directions about HSPMM power loss analysis are illustrated, which may provide a strong reference for the design and optimization of electrical drive systems with HSPMMs.
Liu, L, Zhang, T, Liu, Y, Leighton, B, Zhao, L, Huang, S & Dissanayake, G 1970, 'Parallax Bundle Adjustment on Manifold with Improved Global Initialization', Springer Proceedings in Advanced Robotics (SPAR), International Workshop on the Algorithmic Foundations of Robotics, Springer International Publishing, Mérida, México, pp. 621-638.
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In this paper we present a novel extension to the parallax feature based bundle adjustment (BA). We take parallax BA into a manifold form (PMBA) along with an observation-ray based objective function. This formulation faithfully mimics the projective nature in a camera’s image formation, resulting in a stable optimization configuration robust to low-parallax features. Hence it allows use of fast Dogleg optimization algorithm, instead of the usual Levenberg Marquardt. This is particularly useful in urban SLAM in which diverse outdoor environments and collinear motion modes are prevalent. Capitalizing on these properties, we propose a global initialization scheme in which PMBA is simplified into a pose-graph problem. We show that near-optimal solution can be achieved under low-noise conditions. With simulation and a series of challenging publicly available real datasets, we demonstrate PMBA’s superior convergence performance in comparison to other BA methods. We also demonstrate, with the “Bundle Adjustment in the Large” datasets, that our global initialization process successfully bootstrap the full BA in mapping many sequential or out-of-order urban scenes.
Liu, L, Zhou, T, Long, G, Jiang, J & Zhang, C 1970, 'Attribute Propagation Network for Graph Zero-Shot Learning', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), New York, pp. 4868-4875.
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The goal of zero-shot learning (ZSL) is to train a model to classify samples of classes that were not seen during training. To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes. In this paper, we aim to optimize the attribute space for ZSL by training a propagation mechanism to refine the semantic attributes of each class based on its neighbors and related classes on a graph of classes. We show that the propagated attributes can produce classifiers for zero-shot classes with significantly improved performance in different ZSL settings. The graph of classes is usually free or very cheap to acquire such as WordNet or ImageNet classes. When the graph is not provided, given pre-defined semantic embeddings of the classes, we can learn a mechanism to generate the graph in an end-to-end manner along with the propagation mechanism. However, this graph-aided technique has not been well-explored in the literature. In this paper, we introduce the “attribute propagation network (APNet)”, which is composed of 1) a graph propagation model generating attribute vector for each class and 2) a parameterized nearest neighbor (NN) classifier categorizing an image to the class with the nearest attribute vector to the image's embedding. For better generalization over unseen classes, different from previous methods, we adopt a meta-learning strategy to train the propagation mechanism and the similarity metric for the NN classifier on multiple sub-graphs, each associated with a classification task over a subset of training classes. In experiments with two zero-shot learning settings and five benchmark datasets, APNet achieves either compelling performance or new state-of-the-art results.
Liu, RP 1970, 'IoT and Blockchain: Technologies, Challenges, and Applications', APCCAS 2020: PROCEEDINGS OF THE 2020 IEEE ASIA PACIFIC CONFERENCE ON CIRCUITS AND SYSTEMS (APCCAS 2020), 16th IEEE Asia Pacific Conference on Circuits and Systems (IEEE APCCAS) / IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (PrimeAsia), IEEE, VIETNAM, Halong, pp. 2-2.
Liu, S, Pang, S, Zhu, L & Zhao, J 1970, 'A Feature Extraction Method Based on Few-shot Learning', 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE), 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE), IEEE, pp. 528-532.
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Liu, W, Kang, G, Huang, P-Y, Chang, X, Yu, L, Qian, Y, Liang, J, Gui, L, Wen, J, Chen, P & Hauptmann, AG 1970, 'Argus: Efficient Activity Detection System for Extended Video Analysis', 2020 IEEE Winter Applications of Computer Vision Workshops (WACVW), 2020 IEEE Winter Applications of Computer Vision Workshops (WACVW), IEEE, Snowmass, CO, pp. 126-133.
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We propose an Efficient Activity Detection System, Argus, for Extended Video Analysis in the surveillance scenario. For the spatial-temporal event detection in the surveillance video, we first generate video proposals by applying object detection and tracking algorithm which shared the detection features. After that, we extract several different features and apply sequential activity classification with them. Finally, we eliminate inaccurate events and fuse all the predictions from different features. The proposed system wins Trecvid Activities in Extended Video (ActEV1) challenge 2019. It achieves the first place with 60.5 mean weighted Pmiss, outperforming the second place system by 14.5 and the baseline R-C3D by 29.0. In TRECVID 2019 Challenge2, the proposed system wins the first place with pAUDC@0.2tfa 0.48407.
Liu, W, Wang, H, Zhang, Y, Qin, L & Zhang, W 1970, 'I/O Efficient Algorithm for c-Approximate Furthest Neighbor Search in High-Dimensional Space.', DASFAA (3), Springer, pp. 221-236.
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Furthest Neighbor search in high-dimensional space has been widely used in many applications such as recommendation systems. Because of the “curse of dimensionality” problem, c-approximate furthest neighbor (C-AFN) is a substitute as a trade-off between result accuracy and efficiency. However, most of the current techniques for external memory are only suitable for low-dimensional space. In this paper, we propose a novel algorithm called reverse incremental LSH based on Indyk’s LSH scheme to solve the problem with theoretical guarantee. Unlike the previous methods using hashing scheme, reverse incremental LSH (RI-LSH) is designed for external memory and can achieve a good performance on I/O cost. We provide rigorous theoretical analysis to prove that RI-LSH can return a-AFN result with a constant possibility. Our comprehensive experiment results show that, compared with other-AFN methods with theoretical guarantee, our algorithm can achieve better I/O efficiency.
Liu, Y, Yang, S, Han, C, Ni, W & Zhu, Y 1970, 'Variability in Regional Ecological Vulnerability: A Case Study of Sichuan Province, China', International Journal of Disaster Risk Science, Springer Science and Business Media LLC, pp. 696-708.
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AbstractRapid urbanization and natural hazards are posing threats to local ecological processes and ecosystem services worldwide. Using land use, socioeconomic, and natural hazards data, we conducted an assessment of the ecological vulnerability of prefectures in Sichuan Province for the years 2005, 2010, and 2015 to capture variations in its capacity to modulate in response to disturbances and to explore potential factors driving these variations. We selected five landscape metrics and two topological indicators for the proposed ecological vulnerability index (EVI), and constructed the EVI using a principal component analysis-based entropy method. A series of correlation analyses were subsequently performed to identify the factors driving variations in ecological vulnerability. The results show that: (1) for each of the study years, prefectures with high ecological vulnerability were located mainly in southern and eastern Sichuan, whereas prefectures in central and western Sichuan were of relatively low ecological vulnerability; (2) Sichuan’s ecological vulnerability increased significantly (p = 0.011) during 2005–2010; (3) anthropogenic activities were the main factors driving variations in ecological vulnerability. These findings provide a scientific basis for implementing ecological protection and restoration in Sichuan as well as guidelines for achieving integrated disaster risk reduction.
Liu, Y, Zhang, S, Zhang, C & Yu, JJQ 1970, 'FedGRU: Privacy-preserving Traffic Flow Prediction via Federated Learning', 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE, ELECTR NETWORK, pp. 1-6.
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Liu, Y, Zhu, L, Yamada, M & Yang, Y 1970, 'Semantic Correspondence as an Optimal Transport Problem', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Seattle, WA, USA, pp. 4462-4471.
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Establishing dense correspondences across semantically similar images is a challenging task. Due to the large intra-class variation and background clutter, two common issues occur in current approaches. First, many pixels in a source image are assigned to one target pixel, i.e., many to one matching. Second, some object pixels are assigned to the background pixels, i.e., background matching. We solve the first issue by global feature matching, which maximizes the total matching correlations between images to obtain a global optimal matching matrix. The row sum and column sum constraints are enforced on the matching matrix to induce a balanced solution, thus suppressing the many to one matching. We solve the second issue by applying a staircase function on the class activation maps to re-weight the importance of pixels into four levels from foreground to background. The whole procedure is combined into a unified optimal transport algorithm by converting the maximization problem to the optimal transport formulation and incorporating the staircase weights into optimal transport algorithm to act as empirical distributions. The proposed algorithm achieves state-of-the-art performance on four benchmark datasets. Notably, a 26\% relative improvement is achieved on the large-scale SPair-71k dataset.
Liu, Z, Wei, P, Jiang, J, Cao, W, Bian, J & Chang, Y 1970, 'MESA: Boost ensemble imbalanced learning with MEta-SAmpler', Advances in Neural Information Processing Systems.
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Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed based on some heuristic assumptions. They often suffer from unstable performance, poor applicability, and high computational cost in complex tasks where their assumptions do not hold. In this paper, we introduce a novel ensemble IL framework named MESA. It adaptively resamples the training set in iterations to get multiple classifiers and forms a cascade ensemble model. MESA directly learns the sampling strategy from data to optimize the final metric beyond following random heuristics. Moreover, unlike prevailing meta-learning-based IL solutions, we decouple the model-training and meta-training in MESA by independently train the meta-sampler over task-agnostic meta-data. This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks. Extensive experiments on both synthetic and real-world tasks demonstrate the effectiveness, robustness, and transferability of MESA. Our code is available at https://github.com/ZhiningLiu1998/mesa.
Liu, Z, Yao, L, Bai, L, Wang, X & Wang, C 1970, 'Spectrum-Guided Adversarial Disparity Learning', Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, San Diego, CA, United States, pp. 114-124.
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Liu, Z, Yao, L, Wang, X, Bai, L & An, J 1970, 'Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK, pp. 1-8.
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Loglio, A, Vigano, M, Borghi, M, Gentile, C, Facchetti, F, Perbellini, R, Soffredini, R, Lunghi, G, Rumi, M & Lampertico, P 1970, 'EARLY CHANGES OF PROXIMAL TUBULAR MARKERS IN CHB PATIENTS SWITCHED FROM TENOFOVIR DISOPROXIL FUMARATE TO TENOFOVIR ALAFENAMIDE ACCORDING TO EASL 2017 CRITERIA: A REAL-LIFE STUDY', HEPATOLOGY, Liver Meeting of the American-Association-for-the-Study-of-Liver-Diseases (AASLD), WILEY, ELECTR NETWORK, pp. 481A-482A.
Lyu, B, Hoang, DT, Gong, S & Yang, Z 1970, 'Intelligent Reflecting Surface Assisted Wireless Powered Communication Networks', 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), IEEE, Seoul, Korea (South), pp. 1-6.
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In this paper, we propose to use an intelligent reflecting surface (IRS) in a wireless powered communication network to boost both downlink energy transfer (ET) and uplink information transmission (IT) efficiency, where the IRS consisting of a large number of low-cost passive reflecting elements is deployed between a hybrid access point (HAP) and multiple wireless-powered users. In particular, all passive reflecting elements collaboratively adjust their phase shifts to first construct beamforming for ET from the HAP to all users and then provide additional transmission links for IT from the users to the HAP. Then, we formulate a sum-rate maximization problem by jointly optimizing the time scheduling for network, the phase shift matrix for ET, and the phase shift matrices for all users' IT. Since the formulated problem is non-convex, we first design the phase shift matrices for IT independently by exploiting the characteristics of IT and obtain an approximate solution by using the semidefinite relaxation technique and the Gaussian randomization method. After that, we propose a block coordinated decent based algorithm to solve the simplified problem by iteratively optimizing the time scheduling and the ET's phase shift matrix, the convergence of which is further analyzed. Simulation results confirm that the proposed scheme can achieve up to 350% sum- rate gain compared to the benchmarks.
Lyu, Y, Yuan, Y & Tsang, IW 1970, 'Subgroup-based rank-1 lattice Quasi-Monte Carlo', Advances in Neural Information Processing Systems.
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Quasi-Monte Carlo (QMC) is an essential tool for integral approximation, Bayesian inference, and sampling for simulation in science, etc. In the QMC area, the rank-1 lattice is important due to its simple operation, and nice properties for point set construction. However, the construction of the generating vector of the rank-1 lattice is usually time-consuming because of an exhaustive computer search. To address this issue, we propose a simple closed-form rank-1 lattice construction method based on group theory. Our method reduces the number of distinct pairwise distance values to generate a more regular lattice. We theoretically prove a lower and an upper bound of the minimum pairwise distance of any non-degenerate rank-1 lattice. Empirically, our methods can generate a near-optimal rank-1 lattice compared with the Korobov exhaustive search regarding the l1-norm and l2-norm minimum distance. Moreover, experimental results show that our method achieves superior approximation performance on benchmark integration test problems and kernel approximation problems.
Ma, F, Li, P, Zhu, L, Dong, X, Liu, Y & Yang, Y 1970, 'Activities in extended video', 2018 TREC Video Retrieval Evaluation, TRECVID 2018.
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In this paper, we present a system based on detection, tracking and 3D convolution neural network dealing with Activities in Extended Video (ActEV) task in TRECVID 2018. In the proposed system, videos are first unfolded into frames for training detection network, then we use it to generate bounding box for tracking areas where target activities could be happen. The tracking clips are then classified using a 3D convulution network.
Ma, F, Zhu, L, Yang, Y, Zha, S, Kundu, G, Feiszli, M & Shou, Z 1970, 'SF-Net: Single-Frame Supervision for Temporal Action Localization', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ECCV: European Conference on Computer Vision, Springer International Publishing, Glasgow, UK, pp. 420-437.
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In this paper, we study an intermediate form of supervision, i.e.,single-frame supervision, for temporal action localization (TAL). To obtain thesingle-frame supervision, the annotators are asked to identify only a singleframe within the temporal window of an action. This can significantly reducethe labor cost of obtaining full supervision which requires annotating theaction boundary. Compared to the weak supervision that only annotates thevideo-level label, the single-frame supervision introduces extra temporalaction signals while maintaining low annotation overhead. To make full use ofsuch single-frame supervision, we propose a unified system called SF-Net.First, we propose to predict an actionness score for each video frame. Alongwith a typical category score, the actionness score can provide comprehensiveinformation about the occurrence of a potential action and aid the temporalboundary refinement during inference. Second, we mine pseudo action andbackground frames based on the single-frame annotations. We identify pseudoaction frames by adaptively expanding each annotated single frame to itsnearby, contextual frames and we mine pseudo background frames from all theunannotated frames across multiple videos. Together with the ground-truthlabeled frames, these pseudo-labeled frames are further used for training theclassifier. In extensive experiments on THUMOS14, GTEA, and BEOID, SF-Netsignificantly improves upon state-of-the-art weakly-supervised methods in termsof both segment localization and single-frame localization. Notably, SF-Netachieves comparable results to its fully-supervised counterpart which requiresmuch more resource intensive annotations. The code is available athttps://github.com/Flowerfan/SF-Net.
Ma, Z, Xuan, J, Wang, YG, Li, M & Liò, P 1970, 'Path integral based convolution and pooling for graph neural networks', Advances in Neural Information Processing Systems.
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Graph neural networks (GNNs) extends the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and pooling is key to success. Borrowing ideas from physics, we propose a path integral based graph neural networks (PAN) for classification and regression tasks on graphs. Specifically, we consider a convolution operation that involves every path linking the message sender and receiver with learnable weights depending on the path length, which corresponds to the maximal entropy random walk. It generalizes the graph Laplacian to a new transition matrix we call maximal entropy transition (MET) matrix derived from a path integral formalism. Importantly, the diagonal entries of the MET matrix are directly related to the subgraph centrality, thus lead to a natural and adaptive pooling mechanism. PAN provides a versatile framework that can be tailored for different graph data with varying sizes and structures. We can view most existing GNN architectures as special cases of PAN. Experimental results show that PAN achieves state-of-the-art performance on various graph classification/regression tasks, including a new benchmark dataset from statistical mechanics we propose to boost applications of GNN in physical sciences.
Machet, T, Lindeck, J, Daniel, S, Boye, T, Cheng, E & Bhatia, T 1970, 'Relational Agency in First and Further Year Group Work', Proceedings of the AAEE2020 Conference Sydney, Australia, Annual Conference of the Australasian Association for Engineering Education, AAEE, Sydney.
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At the University of Technology Sydney (UTS), first and second-year students in engineering and IT develop professional practice skills through project-based work with significant group work components in large, 500+ student cohort subjects. Some students find group work challenging and do not appreciate its importance to their professional practice. In looking to improve the transition of our students into and through university, and then into professional practice, we are revising our subject activities. This paper looks at teamwork through the lens of building students’ capacity for relational agency.PURPOSE OR GOALRelational agency is a valuable capacity for professional practitioners working in complex, inter-professional environments (e.g., Edwards, 2010). This paper makes a case for the development of this capacity in engineering and IT students. As a first step to reviewing our group work activities, we report on a pilot study investigating the current capacity for relational agency in our students, and more broadly evaluating the relational agency framework as a tool to help us understand teamwork. The findings will inform further study into relational agency in students and tutors, and will form the basis for redesigning group work and tutor training.APPROACH OR METHODOLOGY/METHODSFocus groups on group work experience were held with students from one second-year and two first-year subjects. Inductive qualitative content analysis used data from the focus groups to look for evidence of relational agency and identify emerging themes. The results were triangulated using self and peer review data from the students and their teammates.ACTUAL OR ANTICIPATED OUTCOMESThe data indicates that the capacity for relational agency develops with time at university. This provides support for the proposed structure used to identify relational agency as progressing from 'novice' to 'professional'. Absent aspects of relational agency were identified, such a...
Madhuri, M, Gill, AQ & Khan, HU 1970, 'IoT-Enabled Smart Child Safety Digital System Architecture.', ICSC, 2020 IEEE 14th International Conference on Semantic Computing, IEEE, San Diego, CA, USA, pp. 166-169.
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Safety of a child in a large public event is a major concern for event organizers and parents. This paper addresses this important concern and proposes an architecture model of the IoT-enable smart child safety tracking digital system. This IoT-enabled digital system architecture integrates the Cloud, Mobile and GPS technology to precisely locate the geographical location of a child on an event map. The proposed architecture model describes the people, information, process, and technology architecture elements, and their relationships for the complex IoT-enable smart child safety tracking digital system. The proposed architecture model can be used as a reference or guide to assist in the safe architecture driven development of the various child tracking digital systems for different public events.
Makhdoom, I, Tofigh, F, Zhou, I, Abolhasan, M & Lipman, J 1970, 'PLEDGE: A Proof-of-Honesty based Consensus Protocol for Blockchain-based IoT Systems', 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), IEEE, Toronto, ON, Canada, pp. 1-3.
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Exhibition of malicious behavior during blockchain consensus, threats against reputation systems, and high TX latency are significant issues for blockchain-based IoT systems. Hence, to mitigate such challenges we propose 'Pledge', a unique Proof-of-Honesty based consensus protocol. Initial experimentation shows that Pledge is economical with low computations and communications complexity and low latency in transaction confirmation.
Makhdoom, I, Tofigh, F, Zhou, I, Abolhasan, M & Lipman, J 1970, 'PLEDGE: An IoT-oriented Proof-of-Honesty based Blockchain Consensus Protocol', 2020 IEEE 45th Conference on Local Computer Networks (LCN), 2020 IEEE 45th Conference on Local Computer Networks (LCN), IEEE, Australia, pp. 54-64.
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The existing lottery-based consensus algorithms, such as Proof-of-Work, and Proof-of-Stake, are mostly used for blockchain-based financial technology applications. Similarly, the Byzantine Fault Tolerance algorithms do provide consensus finality, yet they are either communications intensive, vulnerable to Denial-of-Service attacks, poorly scalable, or have a low faulty node tolerance level. Moreover, these algorithms are not designed for the Internet of Things systems that require near-real-time transaction confirmation, maximum fault tolerance, and appropriate transaction validation rules. Hence, we propose 'Pledge, 'a unique Proof-of-Honesty based consensus protocol to reduce the possibility of malicious behavior during blockchain consensus. Pledge also introduces the Internet of Things centric transaction validation rules. Initial experimentation shows that Pledge is economical and secure with low communications complexity and low latency in transaction confirmation.
Maleki, B, Alempijevic, A & Vidal-Calleja, T 1970, 'Continuous Optimization Framework for Depth Sensor Viewpoint Selection', Workshop on the Algorithmic Foundations of Robotics, Workshop on the Algorithmic Foundations of Robotics, Springer International Publishing, Merida, Mexico, pp. 357-372.
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Distinguishing differences between areas represented with point cloud data is generally approached by choosing a optimal viewpoint. The most informative view of a scene ultimately enables to have the optimal coverage over distinct points both locally and globally while accounting for the distance to the foci of attention. Measures of surface saliency, related to curvature inconsistency, extenuate differences in shape and are coupled with viewpoint selection approaches. As there is no analytical solution for optimal viewpoint selection, candidate viewpoints are generally discretely sampled and evaluated for information and require (near) exhaustive combinatorial searches. We present a consolidated optimization framework for optimal viewpoint selection with a continuous cost function and analytically derived Jacobian that incorporates view angle, vertex normals and measures of task related surface information relative to viewpoint. We provide a mechanism in the cost function to incorporate sensor attributes such as operating range, field of view and angular resolution. The framework is evaluated as competing favorably with the state-of-the-art approaches to viewpoint selection while significantly reducing the number of viewpoints to be evaluated in the process.
Marjanovic, O, Ariyachandra, T & Dinter, B 1970, 'Introduction to the Minitrack on Business Intelligence, Business Analytics and Big Data: Innovation, Deployment, and Management', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, p. 5348.
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Marsh, L, Cochrane, M, Lodge, R, Sims, B, Traish, J & Xu, R 1970, 'Autonomous Target Allocation Recommendations', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, Australia, pp. 1403-1410.
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We consider the problem of land vehicles under attack from a number of unmanned aerial systems. As the number of unmanned aerial systems increase, it may become difficult for human operators to coordinate actions across vehicles in a timely manner. In this paper, we study a number of algorithms designed to recommend actions to operators that will maximise the survivability of the vehicle fleet. We present a comparison of several assignment approaches including evolutionary strategies, genetic algorithms, multi-armed bandits, probability trees and basic heuristics. The performance of these algorithms is analysed across six different simulated scenarios. Our findings indicate that while there was no single best approach, Evolution Strategies, Ensemble and Genetic Algorithms were the strongest performers. It was also seen that a number of heuristic algorithms and the multi-armed bandits approach offered reliable performance in a number of scenarios without the need for any training.
Marynowsky, W, Ferguson, S, Fraietta, A & Bown, O 1970, ''The Ghosts of Roller Disco', a Choreographed, Interactive Performance for Robotic Roller Skates', Proceedings of the Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction, TEI '20: Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction, ACM, pp. 631-637.
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The project investigates how interactions with complex (biologically inspired swarming) behaviors of multiple robots are understood by human participants within a performative and dramaturgical system. Nonanthropomorphic robots in the form of roller skates are used in innovative ways by creating social formations from their movements, for example a leader and followers in a conga line. Synchronized audio signals and speech-like sonic structures are used in innovative ways by influencing and engaging the participant's interactions with the robots. Localization data of the robots in space is mapped to control the surround sound and lighting within the space. This is used to enhance audience immersion and engagement within the interactive performance work.
Mathieson, L & Moscato, P 1970, 'The Unexpected Virtue of Problem Reductions or How to Solve Problems Being Lazy but Wise', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 2381-2390.
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McEwan, M, Blackler, A, Wyeth, P & Johnson, D 1970, 'Intuitive Interaction with Motion Controls in a Tennis Video Game', Proceedings of the Annual Symposium on Computer-Human Interaction in Play, CHI PLAY '20: The Annual Symposium on Computer-Human Interaction in Play, ACM, pp. 321-333.
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McEwan, M, Phillips, C, Wyeth, P & Johnson, D 1970, 'Puppy island', Proceedings of the Interaction Design and Children Conference, IDC '20: Interaction Design and Children, ACM, pp. 532-540.
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McGregor, C, Inibhunu, C, Glass, J, Doyle, I, Gates, A, Madill, J & Pugh, JE 1970, 'Health Analytics as a Service with Artemis Cloud: Service Availability', 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, IEEE, United States, pp. 5644-5648.
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Critical care units internationally contain medical devices that generate Big Data in the form of high speed physiological data streams. Great opportunities exist for systemic and reliable approaches for the analysis of high speed physiological data for clinical decision support. This paper presents the instantiation of a Big Data analytics based Health Analytics as-a-Service model. The availability results of the deployment of two instances of Artemis Cloud to support two neonatal ICUs (NICUs) in Ontario Canada are presented.
Mehami, J, Vidal-Calleja, T & Alempijevic, A 1970, 'Observability driven Multi-modal Line-scan Camera Calibration', 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), IEEE, Karlsruhe, Germany, pp. 285-290.
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© 2020 IEEE. Multi-modal sensors such as hyperspectral line-scan and frame cameras can be incorporated into a single camera system, enabling individual sensor limitations to be compensated. Calibration of such systems is crucial to ensure data from one modality can be related to the other. The best known approach is to capture multiple measurements of a known planar pattern, which are then used to optimize calibration parameters through non-linear least squares. The confidence in the optimized parameters is dependent on the measurements, which are contaminated by noise due to sensor hardware. Understanding how this noise transfers through the calibration is essential, especially when dealing with line-scan cameras that rely on measurements to extract feature points. This paper adopts a maximum likelihood estimation method for propagating measurement noise through the calibration, such that the optimized parameters are associated with an estimate of uncertainty. The uncertainty enables development of an active calibration algorithm, which uses observability to selectively choose images that improve parameter estimation. The algorithm is tested in both simulation and hardware, then compared to a naive approach that uses all images to calibrate. The simulation results for the algorithm show a drop of 26.4% in the total normalized error and 46.8% in the covariance trace. Results from the hardware experiments also show a decrease in the covariance trace, demonstrating the importance of selecting good measurements for parameter estimation.
Mihaita, A-S, Papachatgis, Z & Rizoiu, M-A 1970, 'Graph modelling approaches for motorway traffic flow prediction', 2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), IEEE, ELECTR NETWORK.
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Mihaita, A-S, Papachatgis, Z & Rizoiu, M-A 1970, 'Graph modelling approaches for motorway traffic flow prediction', In 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC'20) (pp. 1--8). Rhodes, Greece (2020), IEEE International Conference on Intelligent Transportation Systems, IEEE, Virtual conference.
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Traffic flow prediction, particularly in areas that experience highly dynamicflows such as motorways, is a major issue faced in traffic management. Due toincreasingly large volumes of data sets being generated every minute, deeplearning methods have been used extensively in the latest years for both shortand long term prediction. However, such models, despite their efficiency, needlarge amounts of historical information to be provided, and they take aconsiderable amount of time and computing resources to train, validate andtest. This paper presents two new spatial-temporal approaches for buildingaccurate short-term prediction along a popular motorway in Sydney, by makinguse of the graph structure of the motorway network (including exits andentries). The methods are built on proximity-based approaches, denotedbacktracking and interpolation, which uses the most recent and closest trafficflow information for each of the target counting stations along the motorway.The results indicate that for short-term predictions (less than 10 minutes intothe future), the proposed graph-based approaches outperform state-of-the-artdeep learning models, such as long-term short memory, convolutional neuronalnetworks or hybrid models.
Miro, JV, Munoz, F & Miguel, FI 1970, 'An Arc-Shaped Rotating Magnet Solution for 3D Localisation of a Drug Delivery Capsule Robot', 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, USA, pp. 520-527.
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A method to estimate the three-dimensional (3D) position of a capsule robot used to deliver drugs in the gastrointestinal tract is proposed in this paper. By exploiting the unique characteristics of the rotating magnetic field created by an array of tangentially magnetised arc-shaped permanent magnets (ASMs), and its analytical formulation, a capsule robot equipped with on-board Hall-effect sensors can measure the rotating magnetic fields created to infer its pose. Extensive validation results provided from a small rotating ASM experimental rig built to test the concept, and a complementary robotic setup for large scale testing are supplied. Given the proven homothetic transformations of magnetic fields, this work demonstrates with validated practical experimentation in a scaled-down rig (1/10), that a full rotation of the ASMs about one axis is sufficient to obtain a mean pose error <10 mm in a magnetic system operating in scaled workspaces up to 250 mm, relevant for clinical use of capsule robots inside human bodies.
Mishra, DK, Panigrahi, TK, Mohanty, A & Ray, PK 1970, 'Effect of Superconducting Magnetic Energy Storage on Two Agent Deregulated Power System Under Open Market', Materials Today: Proceedings, Elsevier BV, pp. 1919-1929.
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Mittal, A, Shivakumara, P, Pal, U, Lu, T, Blumenstein, M & Lopresti, D 1970, 'A New Context-Based Method for Restoring Occluded Text in Natural Scene Images', Document Analysis Systems, International Workshop on Document Analysis Systems, Springer International Publishing, Wuhan, China, pp. 466-480.
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Text recognition from natural scene images is an active research area because of its important real world applications, including multimedia search and retrieval, and scene understanding through computer vision. It is often the case that portions of text in images are missed due to occlusion with objects in the background. Therefore, this paper presents a method for restoring occluded text to improve text recognition performance. The proposed method uses the GOOGLE Vision API for obtaining labels for input images. We propose to use PixelLink-E2E methods for detecting text and obtaining recognition results. Using these results, the proposed method generates candidate words based on distance measures employing lexicons created through natural scene text recognition. We extract the semantic similarity between labels and recognition results, which results in a Global Context Score (GCS). Next, we use the Natural Language Processing (NLP) system known as BERT for extracting semantics between candidate words, which results in a Local Context Score (LCS). Global and local context scores are then fused for estimating the ranking for each candidate word. The word that gets the highest ranking is taken as the correction for text which is occluded in the image. Experimental results on a dataset assembled from standard natural scene datasets and our resources show that our approach helps to improve the text recognition performance significantly.
Mittal, DA, Liu, S & Xu, G 1970, 'Electricity Price Forecasting using Convolution and LSTM Models', 2020 7th International Conference on Behavioural and Social Computing (BESC), 2020 7th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-4.
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Electricity Market uses Demand and Supply chain strategy. Also, it is prone to random fluctuations that directly impact profit. Therefore forecasting demand becomes very important to mitigate the consequences of price dynamics. This paper proposes a Deep Learning model using Long Short Term Memory (LSTM) and Convolution Neural Network to forecast future electricity prices on the Australian electricity market and compares them with other state of the art models. We have selected evaluation metrics to prove that our model outperforms the other existing models for electricity price prediction.
Mohamadzade, B, Simorangkir, RBVB, Hashmi, RM & Esselle, KP 1970, 'A Low Profile, UWB Circular Patch Antenna with Monopole-Like Radiation Characteristics', 2020 International Workshop on Antenna Technology (iWAT), 2020 International Workshop on Antenna Technology (iWAT), IEEE, Bucharest, Romania, pp. 1-3.
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a simple ultra-wideband antenna with monopolelike radiation pattern is presented in this paper. The structure of this low profile antenna with height of the 7 mm (0.65 ;min) is based on annular-ring circular patch. To improve the antenna's bandwidth, the main annular-ring circular patch is loaded with two concentric rings and two rectangular slots. The result shows the antenna achieves a 10 dB return loss bandwidth from 2.85 GHz to 8.6 GHz. The monopole-like radiation pattern is maintained throughout the frequency bands by combining four propagation modes of TM01, TM02, and TM03.
Mohammed, A, Hawryszkiewycz, I & Kozanoglu, DC 1970, 'Enabling Strategic Agility through Dynamic Cloud Capability', ACIS 2020 Proceedings - 31st Australasian Conference on Information Systems, Australasian Conference on Information System, Wellington, NZ, pp. 1-7.
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Organisations and its leadership team are confronted with the challenge of emerging digital economy, fast-changing innovations, globalisation and pandemics such as Covid-19. Organisations need to alter its business models to counter these effects, hence being strategically agile. Among the most prominent solutions proposed by various authors are a set of capabilities, such as strategic sensitivity, resource fluidity and leadership unity in organisational settings. In this research, we are proposing the Dynamic Cloud Capability (DCC) Framework which aims to help organisations realize an IT/IS strategy enabling them to improve Strategic Agility. DCC builds upon Dynamic IT Capability theory. We will be using a quantitative survey-based approach that involves IT SMEs in Australia, to investigate the effect of DCC on Resource Fluidity and Strategic Agility. This is a research in progress article, which intends to outline the literature review, theoretical underpinning, research methodology and expected results.
Mohammed, A, Hawryszkiewycz, IT & Kozanoglu, DC 1970, 'Enabling Strategic Agility through Dynamic Cloud Capability.', ACIS, pp. 78-78.
Mols, I, van den Hoven, E & Eggen, B 1970, 'Everyday Life Reflection', Proceedings of the Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction, TEI '20: Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction, ACM, Sydney, AUSTRALIA, pp. 67-79.
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Monjurul Hasan, ASM & Trianni, A 1970, 'Energy Management: Sustainable Approach Towards Industry 4.0', 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE, Singapore, Singapore, pp. 537-541.
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Industry 4.0 concept is captivating the attention globally in a substantial rate that encompasses industrial digitalization with the means of advanced technical features. Maintaining the highest standard of industrial processes, Industry 4.0 demands to ensure energy efficiency also. Industries must ensure energy efficiency during the process flows, keeping in mind about the energy cost, carbon emission, and resource efficiency. Unfortunately, despite a significant potential for energy efficiency exists, that can be addressed by industries, implementing energy management practices. However, industries are still disinclined to take advantages of such opportunities. Research in this domain has little explored the potential relationships between energy management and Industry 4.0. In the paper, we aim at offering an overview of industrial energy management and related tools as well as Industry 4.0, preliminary discussing potential opportunities and synergies.
More, FJ, Chaczko, Z & Kulbacka, J 1970, 'Early Detection of Coronary Artery Diseases Using Endocrine Markers', Intelligent Information and Database Systems, Asian Conference on Intelligent Information and Database Systems, Springer International Publishing, Phuket, Thailand, pp. 593-601.
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© 2020, Springer Nature Switzerland AG. Cardiovascular diseases including coronary artery disease is the leading cause of death in the well developed and developing countries of the 21st century and has a higher rate of mortality and morbidity. Dysfunction of the pituitary, thyroid, and parathyroid glands caused cardio/cardiovascular diseases including changes in blood pressure, contractility of myocardium - systolic and diastolic myocardial functions, endothelial and dyslipidemia. Dysfunction of thyroid, parathyroid and adrenocorticotropic hormones caused imbalance of endocrine system such as hyper and hypo function, effects on pathophysiology of the cardiovascular system.
Moreira, C, Hammes, M, Kurdoglu, RS & Bruza, P 1970, 'QuLBIT: Quantum-Like Bayesian Inference Technologies for Cognition and Decision', Proceedings for the 42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020, pp. 2520-2526.
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This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory. The main advantage of this framework is that it can cater for paradoxical and irrational human decision making. Although quantum approaches for cognition have demonstrated advantages over classical probabilistic approaches and bounded rationality models, they still lack explanatory power. To address this, we introduce a novel explanatory analysis of the decision-maker's belief space. This is achieved by exploiting quantum interference effects as a way of both quantifying and explaining the decision-maker's uncertainty. We detail the main modules of the unified framework, the explanatory analysis method, and illustrate their application in situations violating the Sure Thing Principle.
Muchtar, K, Munadi, K, Maulina, N, Pradhan, B, Arnia, F & Yanti, B 1970, 'Performance Evaluation of Binary Classification of Tuberculosis through Unsharp Masking and Deep Learning Technique', 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings, pp. 924-928.
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The latest World Health Organization (WHO) study in 2018 shows that about 1.5 million people died and around 10 million people are infected with tuberculosis (TBC) each year. Moreover, more than 4,000 people die every day from TBC. Important work can be found in automating the diagnosis by applying techniques of deep learning (DL) to the medical image. DL requires a large number of high-quality training samples to reach better performance. Due to the low contrast of TBC x-ray images, the image obtained is poor in quality. Our work assesses the effect of image enhancement on the performance of the DL technique based on this problem. An image enhancement algorithm will highlight the overall or local characteristics of the images, and highlight some interesting features. Specifically, an image enhancement algorithm called Unsharp Masking (UM), is evaluated. The enhanced image samples are then fed to the pre-trained ResNet model for transfer learning. In a TB image dataset, we achieve 88.69% and 96.15% of classification accuracy and AUC scores, respectively. All the results are obtained using the Shenzhen dataset which is available in the public domain.
Mughal, F, Raffe, W & Garcia, J 1970, 'Emotion Recognition Techniques for Geriatric Users: A Snapshot', 2020 IEEE 8th International Conference on Serious Games and Applications for Health (SeGAH), 2020 IEEE 8th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-8.
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Several elderly people prefer their independence, however due to cognitive impairment or other age-related ailments they cannot necessarily be left on their own. In order to aid the elderly in living independently, we consider the use of emotion recognition as a relatively autonomous monitoring approach for geriatric people. An analysis and comparison among various emotion recognition studies has shown that close to none of these studies have taken age related cognitive decline into account, which comes with various issues. The aim of this paper is to provide an overview of current emotion recognition techniques and why they may not necessarily be suitable or feasible for geriatric people. This analysis serves as a foundation for a proposed conceptual framework toward an autonomous monitoring system for geriatric people which could minimize the need for explicit user input or interaction while still monitoring the geriatric person(s) well-being.
Munasinghe, N & Paul, G 1970, 'Integrated 3-D printable temperature sensor for advanced manufacturing', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Queensland, Australia.
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As technology continues to develop at a rapid pace, the world progresses towards the fourth industrial revolution, Industry 4.0, with advancements in automation and machine intelligence, as well as manufacturing breakthroughs leading to more efficient and advanced methods. Additive manufacturing (AM), also known as 3D printing, is a type of manufacturing method that has experienced great development and has revolutionised end-product manufacturing. The authors are involved in a project to develop a large-scale industrial 3D printer to print equipment called a Gravity Separation Spiral (GSS), and in an effort to make the equipment “smart”, sensors need to be embedded inside to monitor the operating conditions remotely. This paper presents a temperature sensor able to be printed by a multi-material 3D printer, into 3D printed equipment. In this method, a conductive carbon-based filament has been used to print temperature-sensitive traces inside a Polylactic Acid (PLA) base. The printed sensor was temperature tested in a controlled environment using a programmable heat pad, and the change in resistance has been measured as a voltage change using a data acquisition device. Tests were conducted within in the expected operating range, between 25 ℃ and 36 ℃, and the absolute temperature error was found to be less than ±2 ℃.
Munasinghe, N & Paul, G 1970, 'Path Planning for Robot Based Radial Advanced Manufacturing Using Print Space Sampling', 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, Shenzhen, China, pp. 854-859.
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The world is embracing the fourth industrial revolution, Industry 4.0, which is enabling businesses to improve efficiency and optimise operations. The authors are part of a team that is researching and developing a large-scale industrial 3D printer to print smart, bespoke equipment called Gravity Separation Spirals (GSS). GSS are used in mining to separate minerals from the slurry. The printer under development employs two industrial robot arms mounted on vertical rails and the print direction is around a vertical rotating column in a radial direction. This paper presents a cost-based path planning method using print-space sampling to optimise distance error and manipulability during a printhead’s radial path as it travels outwards from the central column. Manipulability, distance error and rotation error have been calculated for each sampled point and a weighted cost function has been used to determine the optimal path. Simulated results show that this method reduces the instances of print failure and improves the overall manipulability of the robot during printing.
Nadeem, A, Abedin, B & Marjanovic, O 1970, 'Gender Bias in AI: A Review of Contributing Factors and Mitigating Strategies', ACIS 2020 Proceedings - 31st Australasian Conference on Information Systems, Australasian Conference on Information Systems, Association for Information Systems, Wellington.
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The impact of artificial intelligence (AI) is significant in almost every industry. As many important decisions are now being automated by various AI applications, fairness is fast becoming a vital concern in AI. Moreover, the related literature and industry press suggest that AI systems are often biased towards gender. Thus, there is a need to better understand the contributing factors behind gender bias in AI, along with the current approaches taken to address it. Therefore in this paper, we aim to contribute to the emerging IS literature on AI by presenting a consolidated picture of the most often discussed contributing factors and approaches taken in relation to gender bias in AI in the multidisciplinary literature. Our findings indicates that the more frequently discussed contributing factors include lack of diversity in both data and developers, programmer bias, and the existing gender bias in society, now amplified through AI. Additionally, our findings indicate the most discussed approaches for addressing gender bias in AI include the implementation of diversity in society and data and fairness in AI development, as well as reducing bias in algorithms. Based on our findings, we indicate some future IS research for the better development of AI systems.
Naderpour, M, Rizeei, HM & Ramezani, F 1970, 'Wildfire Prediction: Handling Uncertainties Using Integrated Bayesian Networks and Fuzzy Logic', 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Glasgow, United Kingdom, pp. 1-7.
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Wildfire is one of the most frequent natural hazards across the globe, one which has cast a malevolent shroud over many communities in recent years, causing significant risk to human lives, infrastructure, and property. Wildfires are hydrogeological events which are bound to escalate, especially due to climate change. They are different from other natural hazards as they are mainly triggered by human interventions rather than natural triggers. The wildfire risk management is a complex process with many uncertainties in the assessment, fire behavior and spread modelling, and decision making. To predict wildfires, sophisticated temporal geospatial methods are required. This paper develops a wildfire probability prediction method considering the capabilities of Bayesian networks and fuzzy logic that can handle uncertainties and update probabilities in response to the availability of new data. The model takes into account the data from a geographic information system (GIS) for a specific area at micro level to estimate the wildfire probability and is able to update the probability due to any planned or unplanned changes in the area. Therefore, the proposed method can feed to future macro and micro risk-based decision-making situations in wildfire prone areas. The method is evaluated through a sensitivity analysis and its performance is investigated through a case study in New South Wales (NSW), Australia.
Nagasubramanian, G, Sakthivel, RK, Patan, R, Ehtemami, A, Meyer-Baese, A, Tahmassebi, A & Gandomi, AH 1970, 'Detection and isolation of black hole attack in mobile ad hoc networks - a review', Disruptive Technologies in Information Sciences IV, Disruptive Technologies in Information Sciences IV, SPIE, ELECTR NETWORK, pp. 27-27.
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© 2020 SPIE. Mobile Ad hoc Network or MANET is a wireless network that allows communication between the nodes that are in range of each other and are self-configuring. The distributed administration and dynamic nature of MANET makes it vulnerable to many kind of security attacks. One such attack is Black hole attack which is a well known security threat. A node drops all packets which it should forward, by claiming that it has the shortest path to the destination. Intrusion Detection system identifies the unauthorized users in the system. An IDS collects and analyses audit data to detect unauthorized users of computer systems. This paper aims in identifying Black-Hole attack against AODV with Intrusion Detection System, to analyze the attack and find its countermeasure.
Nahar, K & Gill, AQ 1970, 'A Review Towards the Development of Ontology Based Identity and Access Management Metamodel.', AINA Workshops, International Conference on Advanced Information Networking and Applications, Springer, Caserta, Italy, pp. 223-232.
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Building an identity and access management (IAM) system that satisfies business needs and can evolve over time with the ever-changing business environment, is always a challenging endeavour. This calls for the need of an ontology based adaptive IAM metamodel, which can adapt to and instantiated for different situations. To achieve this objective, the first step is to identify the relevant elements and their relationships for developing a detailed IAM ontology. Thus, this paper mainly focuses on the review of the available key models as a starting point for the identification of relevant elements and relationships for the development of the adaptable and yet generic metamodel for our industry research partner. This paper uses the graph modelling approach to present the identified elements and their relationship as an ontology, which can be used for developing the metamodel.
Naji, M, Braytee, A, Anaissi, A, Sianaki, OA & Al-Ani, A 1970, 'Optimizing the Waiting Time for Airport Security Screening Using Multiple Queues and Servers', Complex, Intelligent, and Software Intensive Systems Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019), International Conference on Complex, Intelligent and Software Intensive Systems, Springer International Publishing, Australia, pp. 496-507.
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Airport security screening processes are essential to ensure the safety of passengers and the aviation industry. Security at airports has improved noticeably in recent years through the utilisation of state-of-the-art technologies and highly trained security officers. However, maintaining a high level of security can be costly to operate and implement and can cause delays for passengers and airlines. In optimising a security process it is essential to strike a balance between time delays, security and reduced operation cost. This paper uses queueing theory as a method to study the impact of queue formation and the size of the security area on the average waiting time for the case of multi-lane parallel servers. An experiment is conducted to validate the proposed approach.
Nalamati, M, Sharma, N, Saqib, M & Blumenstein, M 1970, 'Automated Monitoring in Maritime Video Surveillance System', 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ), 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ), IEEE, New Zealand, pp. 1-6.
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Maritime surveillance for intruders/illegal activities requires monitoring of a large area of the coastline. This task being manually exhaustive, would benefit immensely by application of object detection techniques to surveillance videos. However, object detection models trained on general objects datasets cannot be expected to give best performance for this scenario as marine vessels are only a small subset of these huge datasets and also do not classify the specific type of sea vehicle. Hence, their benchmarks are not appropriate for maritime surveillance. Some studies have been done with applications of Convolutional Neural Networks (CNN) for ship/boat detection on private and publicly available sea vessels datasets. This paper presents a summary of the benchmarks so far and presents our experiments of the latest object detection techniques for combined marine vessels dataset. A survey of the currently available datasets is also given. Results of our experiments in terms of mean Average Precision (mAP) and Frames Per Second (FPS) are presented.
Namisango, F, Kang, K & Rehman, J 1970, 'Organizational Generativity, Social Media and the Co-creation of Nonprofit Services: A Sociomateriality Perspective', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences.
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Nanda, P, Arain, A & Nagar, U 1970, 'Network Packet Breach Detection Using Cognitive Techniques', Smart Systems and IoT: Innovations in Computing, International Conference on Smart IoT Systems - Innovations in Computing, Springer Singapore, Jaipur, India, pp. 555-565.
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Machine learning approach is being extensively used in the area of cybersecurity in recent years developing solutions to protect Internet users. The use of state-based cognitive data and the increased prevalence of data mining has allowed for the amalgamation of statistical concepts with machine learning providing real-time network packet analysis with an aim to detect when an entity has intruded the network. In this paper, the use of mean squares error for packet payload aggregation, coupled with prediction techniques using Bayes and ensemble learning outputs to data clusters provide useful and important insight to generate hybrid solutions to existing data breach problems. The use of dynamic tolerance levels and countering this against the potential for false positives is central to the design of our proposed scheme. We believe that correlations between expected information against the aggregated payloads could provide sufficient level of accuracy, which is sufficient to flag certain packets for further human assessment.
Nandanwar, L, Shivakumara, P, Manna, S, Pal, U, Lu, T & Blumenstein, M 1970, 'A New DCT-FFT Fusion Based Method for Caption and Scene Text Classification in Action Video Images', Pattern Recognition and Artificial Intelligence, International Conference on Pattern Recognition and Artificial Intelligence, Springer International Publishing, Zhongshan, China, pp. 80-92.
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Achieving better recognition rate for text in video action images is challenging due to multi-type texts with unpredictable backgrounds. We propose a new method for the classification of captions (which is edited text) and scene texts (which is part of an image in video images of Yoga, Concert, Teleshopping, Craft, and Recipe classes). The proposed method introduces a new fusion criterion-based on DCT and Fourier coefficients to extract features that represent good clarity and visibility of captions to separate them from scene texts. The variances for coefficients of corresponding pixels of DCT and Fourier images are computed to derive the respective weights. The weights and coefficients are further used to generate a fused image. Furthermore, the proposed method estimates sparsity in Canny edge image of each fused image to derive rules for classifying caption and scene texts. Lastly, the proposed method is evaluated on images of five above-mentioned action image classes to validate the derived rules. Comparative studies with the state-of-the-art methods on the standard databases show that the proposed method outperforms the existing methods in terms of classification. The recognition experiments before and after classification show that the recognition performance rate improves significantly after classification.
Naseem, U & Musial, K 2019, 'DICE: Deep intelligent contextual embedding for twitter sentiment analysis', Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, International Conference on Document Analysis and Recognition, IEEE, Sydney, Australia, pp. 953-958.
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© 2019 IEEE. The sentiment analysis of the social media-based short text (e.g., Twitter messages) is very valuable for many good reasons, explored increasingly in different communities such as text analysis, social media analysis, and recommendation. However, it is challenging as tweet-like social media text is often short, informal and noisy, and involves language ambiguity such as polysemy. The existing sentiment analysis approaches are mainly for document and clean textual data. Accordingly, we propose a Deep Intelligent Contextual Embedding (DICE), which enhances the tweet quality by handling noises within contexts, and then integrates four embeddings to involve polysemy in context, semantics, syntax, and sentiment knowledge of words in a tweet. DICE is then fed to a Bi-directional Long Short Term Memory (BiLSTM) network with attention to determine the sentiment of a tweet. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of airline-related tweets.
Naseem, U, Khushi, M, Khan, SK, Waheed, N, Mir, A, Qazi, A, Alshammari, B & Poon, SK 1970, 'Diabetic Retinopathy Detection Using Multi-layer Neural Networks and Split Attention with Focal Loss', Springer International Publishing, pp. 26-37.
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Naseem, U, Musial, K, Eklund, P & Prasad, M 1970, 'Biomedical Named-Entity Recognition by Hierarchically Fusing BioBERT Representations and Deep Contextual-Level Word-Embedding', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK, pp. 1-8.
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© 2020 IEEE. Text mining in the biomedical domain is increasingly important as the volume of biomedical documents increases. Thanks to advances in natural language processing (NLP), extracting valuable information from the biomedical literature is gaining popularity among researchers, and deep learning has enabled the development of effective biomedical text mining models. However, directly applying advancements in NLP to biomedical sources often yields unsatisfactory results, due to a word distribution drift from the general language domain corpora to specific biomedical corpora, and this drift introduces linguistic ambiguities. To overcome these challenges, this paper presents a novel method for biomedical named entity-recognition (BioNER) through hierarchically fusing representations from BioBERT, which is trained on biomedical corpora and Deep contextual-level word embeddings to handle the linguistic challenges within biomedical literature. Proposed text representation is then fed to attention-based Bi-directional Long Short Term Memory (BiLSTM) with Conditional random field (CRF) for the BioNER task. The experimental analysis shows that our proposed end-to-end methodology outperforms existing state-of-the-art methods for the BioNER task.
Naseem, U, Razzak, I, Eklund, P & Musial, K 1970, 'Towards Improved Deep Contextual Embedding for the identification of Irony and Sarcasm', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK, pp. 1-7.
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Humans use tonal stress and gestural cues to reveal negative feelings that are expressed ironically using positive or intensified positive words when communicating vocally. However, in textual data, like posts on social media, cues on sentiment valence are absent, thus making it challenging to identify the true meaning of utterances, even for the human reader. For a given post, an intelligent natural language processing system should be able to identify whether a post is ironic/sarcastic or not. Recent work confirms the difficulty of detecting sarcastic/ironic posts. To overcome challenges involved in the identification of sentiment valence, this paper presents the identification of irony and sarcasm in social media posts through transformer-based deep, intelligent contextual embedding - T-DICE - which improves noise within contexts. It solves the language ambiguities such as polysemy, semantics, syntax, and words sentiments by integrating embeddings. T-DICE is then forwarded to attention-based Bidirectional Long Short Term Memory (BiLSTM) to find out the sentiment of a post. We report the classification performance of the proposed network on benchmark datasets for #irony and #sarcasm. Results demonstrate that our approach outperforms existing state-of-the-art methods.
Navaratnarajah, SK & Indraratna, B 1970, 'Application of Under Sleeper Pads to Enhance the Sleeper-Ballast Interface Behaviors', Springer Singapore, pp. 619-636.
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Nawazish Ali, SM, Hossain, MJ, Sharma, V & Kashif, M 1970, 'Tri-Objective LPV Controller Design for the Thermal Management of Motor Drive Parameters in an Electric Vehicle', 2020 IEEE Green Technologies Conference(GreenTech), 2020 IEEE Green Technologies Conference(GreenTech), IEEE, Oklahoma City, OK, USA, pp. 86-91.
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The increase in consumption of fossil fuels in transportation sector causes global warming and greenhouse gas emissions. Electric vehicles (EVs) provide an alternate solution to this combustion of fossil fuels. They use traction motor drives (mostly induction type) instead of combustion engines, but these drives suffer from parameter (stator, rotor resistance and mutual inductance) variations that result in the deterioration of drive as well as vehicular performance. The increase in the ambient and operating temperatures experienced by EV in its desired driving cycle is the major cause of such parameter variations. This paper proposes a tri-objective linear parameter varying (LPV) controller and observer design that allows these variations without affecting the performance of motor drive and vehicle. The controller design incorporates the linear matrix inequalities (LMIs) to guarantee the inherent system stability and L2 gain bound. The performance of LPV controller is compared with that of proportional-integral-derivative (PID) controller in case of motor drive and vehicular dynamics. The MATLABbased nonlinear simulations are carried out and the results are presented in terms of driveś terminal characteristics and highway fuel economy test (HWFET) drive cycle. These results ensure the excellent robust performance of the proposed control technique.
Negri, M, Cagno, E, Salemme, C & Trianni, A 1970, 'Industrial Wastewater Treatment Configuration: Insights from a Northern Italy Textile Manufacturing District', 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE, Singapore, Singapore, pp. 146-150.
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Industrial wastewater treatment is getting increased attention from academics, practitioners and regulators, due to the environmental hazard of discharging poorly treated wastewater into the environment. This paper analyzes the case of Como's textile district in Italy, to explore what factors are considered by firms in selecting the most appropriate wastewater treatment system configuration. The case studies highlighted that Como's wastewater consortium benefits the firms in the district, and it is a better solution compared to the presence of suboptimal private treatment plants. The firms mentioned internal stakeholders, factors related to the wastewater and technology, and economics as the most relevant.
Neira, S, Poblete, P, Cuzmar, R, Pereda, J & Aguilera, RP 1970, 'Sequential Phase-Shifted Model Predictive Control for a Multilevel Converter with Integrated Battery Energy Storage', 2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), IEEE, Dubrovnik, Croatia, pp. 29-34.
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Cascaded converters have risen as a suitable solution for the connection of Utility-scale Battery Energy Storage Systems (BESS) to the grid. These converters allow to split the battery array into the power modules, reducing the total series-connected battery cells and improving the reliability of the system. Different types of modules have been proposed to integrate the batteries in the converter. The three-port full-bridge module connects the batteries through a second deport decoupled from the harmful low-frequency oscillations and current peaks. However, the multi-variable controller required to manage the power interaction between the battery and the grid presents a challenge in terms of computational burden and scalability. This work proposes the use of the Sequential Phase-Shifted Model Predictive Control (PS-MPC) in a multilevel BESS implementation using three-port full-bridge modules. The proposed controller outperforms a standard FCS-MPC, as it obtains the optimal duty cycles for the operation of the converter with the same fast dynamic response, but also with the fixed spectrum of the PS-PWM and low computational burden, which facilitates its scalability to multilevel BESS with a large number of cells. Simulation results show the ability of the system to exchange different amounts of power with the grid, ensuring the best battery operational conditions.
Neira, S, Poblete, P, Pereda, J & Nunez, F 1970, 'Consensus-Based Distributed Control of a Multilevel Battery Energy Storage System', 2020 IEEE 21st Workshop on Control and Modeling for Power Electronics (COMPEL), 2020 IEEE 21st Workshop on Control and Modeling for Power Electronics (COMPEL), IEEE, pp. 1-7.
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Nerse, C & Wang, S 1970, 'Vibroacoustic characteristics of a damped box-type structure', Proceedings of 2020 International Congress on Noise Control Engineering, INTER-NOISE 2020.
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In industrial applications, rigid-walled cavities that are enclosed by flexible panels can be commonly encountered. Owing to the coupling of the velocity of the panel with the air pressure in the enclosure, noise and vibration in- and out of- the system is amplified. Such problems are frequently alleviated by passive vibration control, where damping treatments are effective in mid and high frequencies. It has been shown that when such treatments are applied nonproportionally, not only the vibration of the panel, but also the radiated sound pressure from the panel can be reduced, while limiting the mass increase. In this study, the governing relation for this phenomenon is expressed by using the uncoupled modal parameters of the panel and cavity. Complex modes that arise from nonproportionally damped systems are shown to be closely linked to optimal damping characteristics. We further show that the coupling strength between the cavity modes and panel modes are dependent on the spatial distribution of the damping. A damping layer topology optimization problem is formulated to demonstrate the interconnectedness of the modal parameters with optimal damping layer layout.
Neshat, M, Alexander, B, Sergiienko, NY & Wagner, M 1970, 'Optimisation of large wave farms using a multi-strategy evolutionary framework', Proceedings of the 2020 Genetic and Evolutionary Computation Conference, GECCO '20: Genetic and Evolutionary Computation Conference, ACM, pp. 1150-1158.
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Neuberger, B, Patten, T, Park, K & Vincze, M 1970, 'Self-initialized Visual Servoing for Accurate End-effector Positioning', 2020 6th International Conference on Control, Automation and Robotics (ICCAR), 2020 6th International Conference on Control, Automation and Robotics (ICCAR), IEEE, pp. 676-682.
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Ngo, CQ, Chai, R, Jones, TW & Nguyen, HT 1970, 'Electroencephalogram Reactivity to Hyperglycemia in Patients with Type 1 Diabetes', 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, IEEE, Canada, pp. 5224-5227.
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This paper is concerned with a study of hyperglycemia on four patients with type 1 diabetes at night time. We investigated the association between hyperglycemic episodes and electroencephalogram (EEG) signals using data from the central and occipital areas. The power spectral density of the brain waves was estimated to compare the difference between hyperglycemia and euglycemia using the hyperglycemic threshold of 8.3 mmol/L. The statistical results showed that alpha and beta bands were more sensitive to hyperglycemic episodes than delta and theta bands. During hyperglycemia, whereas the alpha power increased significantly in the occipital lobe (P<0.005), the power of the beta band increased significantly in all observed channels (P<0.01). Using the Pearson correlation, we assessed the relationship between EEG signals and glycemic episodes. The estimated EEG power levels of the alpha band and the beta band produced a significant correlation against blood glucose levels (P<0.005). These preliminary results show the potential of using EEG signals as a biomarker to detect hyperglycemia.
Ngo, HH, Guo, W, Ng, HY, Mannina, G & Pandey, A 1970, 'Preface', Conferences in Research and Practice in Information Technology Series, Elsevier, pp. xxi-xxii.
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Ngo, T & Indraratna, B 1970, 'Numerical Modelling of Track Behavior Capturing Particle Breakage under Dynamic Loading', Geo-Congress 2020, Geo-Congress 2020, American Society of Civil Engineers, Minneapolis, MN, pp. 374-382.
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© 2020 American Society of Civil Engineers. This paper presents a study on ballasted track behavior, capturing particle breakage under dynamic loading using large-scale laboratory testing, supplemented with computational modeling approaches. Four large-scale triaxial tests are conducted to investigate the ballast breakage responses subjected to cyclic loading subjected to varying frequencies, f=10-40Hz. Measured laboratory observations show that an increase in loading frequency and magnitude results in significantly increased degradation (breakage) and deformation of ballast. Computational modeling using a coupled discrete-continuum approach (coupled DEM-FEM) is introduced to provide insightful understanding of the deformation and breaking of ballast under cyclic loading. Discrete ballast grains are simulated by bonding of many cylinders together at appropriate sizes and locations. Selected elements located at corners, surfaces, and sharp edges of the simulated particles are connected by parallel bonds; and when those bonds are broken, they are considered to represent ballast breakage. The predicted axial strain ϵa, volumetric strain ϵv obtained from the coupled DEM-FEM model agree reasonably well with those observed experimentally. The coupled model is then used to investigate micromechanical aspects of ballast aggregates including the evolution of particle breakage, contact force distributions, and orientation of contacts during cyclic loading.
Nguyen, C & Hoang, D 1970, 'Software-Defined Virtual Sensors for Provisioning IoT Services on Demand', 2020 5th International Conference on Computer and Communication Systems (ICCCS), 2020 5th International Conference on Computer and Communication Systems (ICCCS), IEEE, Shanghai, China, pp. 796-802.
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Internet of Things (IoT) has been increasingly developed to provide essential IoT services ranging from personal health, smart homes to smart cities, and critical infrastructures. Sensor/IoT devices are indispensable elements in these systems/services. However, they are too rigid to permit reconfiguration for changes after their implementation. This makes it difficult to provision IoT services on demand and causes inefficient utilization of resources. Software-defined networking (SDN) and Network function virtualization (NFV) are emerging solutions to the programmability of network functions. Provisioning IoT services on demand is a natural utilization of programmability. Inspired by the benefits of SDN-NFV programmability, this paper proposes a softwaredefined virtual sensor (SDVS) that enables the programmability of IoT devices in accordance with IoT applications on demand. The paper presents the design and implementation of the proposed SDVS and demonstrates its use in an on-demand IoT services scenario.
Nguyen, CT, Nguyen, DN, Hoang, DT, Pham, H-A, Tuong, NH & Dutkiewicz, E 1970, 'Blockchain and Stackelberg Game Model for Roaming Fraud Prevention and Profit Maximization', 2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Seoul, pp. 1-6.
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Nguyen, D & Meixner, G 1970, 'A survey of gamified Augmented Reality systems for procedural tasks in industrial settings', IFAC-PapersOnLine, Elsevier BV, pp. 10096-10100.
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Nguyen, D-A, Tran, X-T, Dang, KN & Iacopi, F 1970, 'A lightweight Max-Pooling method and architecture for Deep Spiking Convolutional Neural Networks', 2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), 2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), IEEE, pp. 209-212.
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Nguyen, DDK, Lai, Y, Sutjipto, S & Paul, G 1970, 'Hybrid Multi-Robot System for Drilling and Blasting Automation', 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, Shenzhen, China, pp. 79-84.
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Multi-robot systems possess the potential of becoming the next generation of robots in the mining industry due to their robustness and scalability. However, they present challenges for the system to efficiently allocate tasks to each robot and allow them to navigate toward their targets safely. This paper introduces a hybrid approach method for a multi-robot system, alongside with a case study in drilling and blasting automation. A Centralized Control Unit delegates tasks and information among the robots in the system, each equipped with a decentralized motion planner that supports cooperative inter- robot collision avoidance. The proposed system inherits the advantage of a centralized multi-robot system in providing a time-wise optimal solution; while also possessing the computational benefit and scalability of a decentralized system. Simulations were conducted to validate the proposed method and discuss insights into the efficacy and performance of the proposed method.
Nguyen, H, Luo, S & Ramos, F 1970, 'Semi-supervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Training', Springer International Publishing, pp. 409-421.
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Nguyen, HAD, Nguyen, LV & Ha, QP 1970, 'IoT-enabled Dependable Co-located Low-cost Sensing for Construction Site Monitoring', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 37th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), Kitakyushu, Japan, pp. 616-624.
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This paper proposes an IoT-enabled network of low-cost sensors that are co-located for construction site monitoring. The network performance enhancement is achieved via its system dependability in terms of improved availability, integrity, reliability, maintainability, security and safety in real-time monitoring of environment parameters. The sensor motes of various sensing modules form a reliable wireless in-situ cluster for gathering on-site information of air temperature, soil moisture, air pressure, humidity, particulate matters (PM), emissions and weather variables. They are useful for the site management, improving safety and effective operation of construction equipment. The components for the development include inexpensive microcontrollers ESP32 embedded with wireless gateway function and energy-efficient motes featuring cost-effective sensors. Here, the adoption of the dependability concept for collocated sensor motes aims to introduce a level of redundancy to allow for improving fault-tolerance and reliability. Extensive field tests have been conducted in different environments. Experimental results as well as statistical analysis are provided to verify the merits of the proposed approach.
Nguyen, N-T, Nguyen, DN, Hoang, DT, Van Huynh, N, Nguyen, H-N, Nguyen, QT & Dutkiewicz, E 1970, 'Energy Trading and Time Scheduling for Energy-Efficient Heterogeneous Low-Power IoT Networks', GLOBECOM 2020 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - 2020 IEEE Global Communications Conference, IEEE, Taipei, Taiwan, pp. 1-6.
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In this paper, an economic model is proposed to jointly optimize profits for participants in a heterogeneous IoT wireless-powered backscatter communication network. In the network under considerations, a power beacon and IoT devices (with various communication types and energy constraints) are assumed to belong to different service providers, i.e., energy service provider (ESP) and IoT service provider (ISP), respectively. To jointly maximize the utility for both service providers in terms of energy efficiency and network throughput, a Stackelberg game model is proposed to study the strategic interaction between the ISP and ESP. In particular, the ISP first evaluates its benefits from providing IoT services to its customers and then sends its requested price together with the service time to the ESP. Based on the request from the ISP, the ESP offers an optimized transmission power that maximizes its utility while meeting energy demands of the ISP. To study the Stackelberg equilibrium, we first obtain a closed-form solution for the ESP and propose a low-complexity iterative method based on block coordinate descent (BCD) to address the non-convex optimization problem for the ISP. Through simulation results, we show that our approach can significantly improve the profits for both providers compared with those of conventional transmission methods, e.g., bistatic backscatter and harvest-then-transmit communication methods.
Nguyen, T-D, Maszczyk, T, Musial, K, Zöller, M-A & Gabrys, B 1970, 'AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), IDA 2020: Advances in Intelligent Data Analysis XVIII, Springer International Publishing, Konstanz, Germany, pp. 352-365.
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© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution.
Nguyen, TG, Phan, TV, Hoang, DT, Nguyen, TN & So-In, C 1970, 'Efficient SDN-Based Traffic Monitoring in IoT Networks with Double Deep Q-Network', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Computational Data and Social Networks, Springer International Publishing, Dallas, TX, USA, pp. 26-38.
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In an Internet of Things (IoT) environment, network traffic monitoring tasks are intractable to achieve due to various IoT traffic types. Recently, the development of Software-Defined Networking (SDN) enables outstanding flexibility and scalability abilities in network control and management, thereby providing a potential approach to mitigate challenges in monitoring the IoT traffic. In this paper, we propose an IoT traffic monitoring approach that implements deep reinforcement learning technique to maximize the fine-grained monitoring capability, i.e., level of traffic statistics details, for several IoT traffic groups. Specifically, we first study a flow-rule matching control system constrained by different expected levels of statistics details and by the flow-table limit of the SDN-based gateway device. We then formulate our control optimization problem by employing the Markov decision process (MDP). Afterwards, we develop Double Deep Q-Network (DDQN) algorithm to quickly obtain the optimal flow-rule matching control policy. Through the extensive experiments, the obtained results verify that the proposed approach yields outstanding improvements in terms of the ability to simultaneously provide different required degrees of statistics details while protecting the gateway devices from being overflowed in comparisons with those of the conventional Q-learning method and the typical SDN flow rule setting.
Nguyen, TK, Nguyen, HH & Tuan, HD 1970, 'Adaptive Successive Interference Cancellation in Cell-free Massive MIMO-NOMA', 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), IEEE, pp. 1-5.
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Nguyen, TN, Erkmen, E, Sanchez, LFM & Li, J 1970, 'A Probabilistic Homogenization Approach for the Computation of Stiffness Degradation in ASR-affected Concrete', The 16th International Conference on Alkali Aggregate Reaction in Concrete.
Ni, W & Cassidy, M 1970, 'City-wide traffic control: Modeling impacts of cordon queues', Transportation Research Part C: Emerging Technologies, Elsevier BV, pp. 164-175.
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Ni, Z, Zhang, JA, Huang, X, Yang, K & Gao, F 1970, 'Parameter Estimation and Signal Optimization for Joint Communication and Radar Sensing', 2020 IEEE International Conference on Communications Workshops (ICC Workshops), 2020 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, Dublin, Ireland, pp. 1-6.
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© 2020 IEEE. Joint communication and radar sensing (JCAS) integrates communication and radar sensing into one system, sharing one transmitted signal. In this paper, we study a JCAS system that uses a dedicated low-cost single-antenna receiver for sensing. We provide sensing parameter estimation algorithms for the JCAS system, and investigate the optimization of the precoding matrix to balance communication and sensing performance. A MUSIC-based estimation approach is proposed to obtain time delays and angle-of-arrivals of targets. A weighted signal optimization to balance between communication and sensing is then proposed. Numerical results are provided and verify the effectiveness of the proposed scheme.
O'Byrne, KJ, Kapeleris, J, Kulasinghe, A, Warkiani, ME, O'Leary, CG, Ladwa, R, Vela, I, Leo, P, Sternes, P & Punyadeera, C 1970, 'Culture of circulating tumour cells derived from non-small cell lung cancer.', JOURNAL OF CLINICAL ONCOLOGY, Annual Meeting of the American-Society-of-Clinical-Oncology (ASCO), LIPPINCOTT WILLIAMS & WILKINS, ELECTR NETWORK.
Olszak, C, Zurada, J & Kozanoglu, D 1970, 'Introduction to the Minitrack on Business Intelligence and Big Data for Innovative and Sustainable Development of Organizations', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, pp. 216-217.
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Oppermann, I, Nabaglo, J & Henecka, W 1970, 'A Measure of Personal Information in Mobile Data', 2020 2nd 6G Wireless Summit (6G SUMMIT), 2020 2nd 6G Wireless Summit (6G SUMMIT), IEEE, Levi, Finland, pp. 1-6.
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© 2020 IEEE. This paper describes fundamental aspects of a framework for privacy-preserving data sharing in a mobile context. The principal technical challenge is measuring the level of personal information (PI) in datasets that are shared for the delivery or enhancement of mobile enabled services. Another challenge is determining the threshold delineating a 'reasonable likelihood' of an individual being identifiable from the data. The risk of reidentification defines personally identifiable information (PII). The measure of PI must go beyond simply analysing personal attributes captured in data and consider preference revealed through use of services, temporal and spatial aspects of data, as well as context for use of services. Keywords-data sharing, privacy, mobile services.
Orth, D, Thurgood, C & van den Hoven, E 1970, 'Embodying Meaningful Digital Media', Proceedings of the Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction, TEI '20: Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction, ACM, Sydney NSW Australia, pp. 81-94.
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© 2020 Association for Computing Machinery. Technological products have become central to the ways in which many people communicate with others, conduct business and spend their leisure time. Despite their prevalence and significance in people's lives, these devices are often perceived to be highly replaceable. From a sustainability perspective, there is value in creating technological products with meaning directly associated with their materiality to reduce the rate of product consumption. We set out to explore the potential for design to promote the formation of product attachment by creating technological devices with meaningful materiality, closely integrating the physical form with the significance of its digital contents. We used the life stories and ongoing input of our intended user as inspiration for the creation of Melo, a bespoke music player. The evaluation and critical reflection of our design process and resulting artefact are used to propose a design strategy for promoting product attachment within the growing sector of technological devices.
Otter, L, Eder, K, Gim, J, Hovden, R, Kilburn, M, Yang, L, Cairney, JM & Jacob, DE 1970, 'New Perspectives on The Nacre-Organic Interface In Bivalve Shells', Goldschmidt Abstracts, Goldschmidt2020, Geochemical Society, pp. 2004-2004.
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Ou, L, Zeng, G, Chang, Y-C & Lin, C-T 1970, 'Multi-Objective Vibration-Based Particle-Swarm-Optimized Fuzzy Controller With Application to Boundary-Following of Mobile-Robot Simulation Environment', 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Toronto, ON, Canada, pp. 1893-1898.
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This paper presents a multi-objective vibration-based particle-swarm-optimization (MO-VBPSO) algorithm with enhanced exploration ability and convergence performance, for training fuzzy-controller (FC) to achieve robot control. The MO-VBPSO applies a reference point-based leader selection schema that assigns leaders for MO-PSOs' searching optimal parameters of the FC. Besides, the MO-VBPSO framework is integrated with a vibration factor to strengthen the exploration ability for resolving the local minima issue, which is inspired by the amplitude of the Firework Algorithm (FWA). The evaluation of MO-VBPSO focuses on the effect of the vibration factor by applying it to training a mobile robot in a simulation environment. The evaluation results are discussed concerning exploration ability, convergence performance, and performance stability. Experimental results reveal that the proposed MO- VBPSO lifts the performance of robot training significantly
Ou, Y, Mihaita, A-S & Chen, F 1970, 'Dynamic Train Demand Estimation and Passenger Assignment', 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE, Rhodes, Greece, pp. 1-6.
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Understanding real-time train occupancy is a critical problem for public transport management, especially in the service disruption scenarios. To address this problem, this paper proposes a public transport passenger assignment method for estimating the time-dependent train occupancy comprising of a three-step modelling approach. Firstly, we make use of train station tap-on and tap-off information collected by Automated Fare Collection systems to estimate the initial time-dependent Origin-Destination matrix (OD) of the train network. Secondly, we take advantage of real-time train scheduling data to calibrate the initial OD matrix according to travel time, transfer time and waiting times across train lines. Thirdly, the calibrated OD matrix together with train scheduling data are used to generate entire passenger travel trajectories from origins to destinations including all path segments, by following a probabilistic hybrid Markov-driven approach. Lastly, after knowing all passenger trajectories, we further estimate the passenger occupancy for every train in the entire network in a given short time window. The results are applied over the real Sydney train network in Australia, and showcase that the proposed method can accurately quantify time-dependent passenger flows at a station platform level of granularity.
Ouyang, D, Wen, D, Qin, L, Chang, L, Zhang, Y & Lin, X 1970, 'Progressive Top-K Nearest Neighbors Search in Large Road Networks.', SIGMOD Conference, SIGMOD/PODS '20: International Conference on Management of Data, ACM, ELECTR NETWORK, pp. 1781-1795.
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© 2020 Association for Computing Machinery. Computing top-k nearest neighbors (kNN) is a fundamental problem in road networks. Existing solutions either need a complicated parameter configuration in index construction or incur high costs when scanning an unbounded number of vertices in query processing. In this paper, we propose a novel parameter-free index-based solution for the kNN query based on the concept of tree decomposition in large road networks. Based on our index structure, we propose an efficient and progressive algorithm that returns each result in a bounded delay. We also optimize the index structure, which improves the efficiency of both index construction and index maintenance in large road networks. We conduct extensive experiments to show the efficiency of our proposed algorithms and the effectiveness of our optimization techniques in real-world road networks from ten regions.
Ouyang, L, Ploderer, B, Wyeth, P, Wang, MX & Andrew Brown, R 1970, 'Designing Tangible Interactions with Children for Pre-school Music Education', 32nd Australian Conference on Human-Computer Interaction, OzCHI '20: 32nd Australian Conference on Human-Computer-Interaction, ACM, pp. 687-691.
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Panta, A, Khushi, M, Naseem, U, Kennedy, P & Catchpoole, D 1970, 'Classification of Neuroblastoma Histopathological Images Using Machine Learning', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 3-14.
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Neuroblastoma is the most common cancer in young children accounting for over 15% of deaths in children due to cancer. Identification of the class of neuroblastoma is dependent on histopathological classification performed by pathologists which are considered the gold standard. However, due to the heterogeneous nature of neuroblast tumours, the human eye can miss critical visual features in histopathology. Hence, the use of computer-based models can assist pathologists in classification through mathematical analysis. There is no publicly available dataset containing neuroblastoma histopathological images. So, this study uses dataset gathered from The Tumour Bank at Kids Research at The Children’s Hospital at Westmead, which has been used in previous research. Previous work on this dataset has shown maximum accuracy of 84%. One main issue that previous research fails to address is the class imbalance problem that exists in the dataset as one class represents over 50% of the samples. This study explores a range of feature extraction and data undersampling and over-sampling techniques to improve classification accuracy. Using these methods, this study was able to achieve accuracy of over 90% in the dataset. Moreover, significant improvements observed in this study were in the minority classes where previous work failed to achieve high level of classification accuracy. In doing so, this study shows importance of effective management of available data for any application of machine learning.
Paranawithana, DLS, Gide, E, Wu, R & Chaudhry, G 1970, 'A comprehensive review on the influence of social media marketing in harnessing international students to Australia', 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), IEEE, pp. 1-7.
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Park, K, Patten, T & Vincze, M 1970, 'Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images'.
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Recent methods for 6D pose estimation of objects assume either textured 3Dmodels or real images that cover the entire range of target poses. However, itis difficult to obtain textured 3D models and annotate the poses of objects inreal scenarios. This paper proposes a method, Neural Object Learning (NOL),that creates synthetic images of objects in arbitrary poses by combining only afew observations from cluttered images. A novel refinement step is proposed toalign inaccurate poses of objects in source images, which results in betterquality images. Evaluations performed on two public datasets show that therendered images created by NOL lead to state-of-the-art performance incomparison to methods that use 13 times the number of real images. Evaluationson our new dataset show multiple objects can be trained and recognizedsimultaneously using a sequence of a fixed scene.
Parnell, J & Peng, J 1970, 'The relevance of the 2018 WHO Noise guidelines to Australasian road traffic noise objectives', Acoustics 2019, Sound Decisions: Moving Forward with Acoustics - Proceedings of the Annual Conference of the Australian Acoustical Society.
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Road traffic noise criteria are generally established by regulators with reference to exposure-response relationships. The release of the World Health Organisation (WHO) Noise Guidelines for the European Region in 2018 therefore had global relevance as it purported to present the most contemporary guidance on road traffic noise impacts. Consistent with European Union reporting requirements, the day-evening-night composite noise metric was referenced. In order to understand the implications of this WHO document on policies across Australasia it is necessary to undertake comparisons using a common noise descriptor. There are a range of noise metrics in use across the jurisdictions, however currently there is no robust process of converting the local noise metrics to the day-evening-night composite noise metric This paper uses a large data set of New South Wales (NSW) road traffic noise measurements collected from medium and highly trafficked routes as the basis for the development of such a process. This in turn allows comparison not only to the WHO studies, but also to ISO 1996-2:2017 and to the exposure-response studies that have underpinned the setting of noise objectives in NSW since 1999. In this respect, the conversion protocol has also provided for older studies to be reconstructed and compared to more contemporary studies.
Patibanda, R, Semertzidis, NA, Vranic-Peters, M, La Delfa, JN, Andres, J, Baytaş, MA, Martin-Niedecken, AL, Strohmeier, P, Fruchard, B, Leigh, S-W, Mekler, ED, Nanayakkara, S, Wiemeyer, J, Berthouze, N, Kunze, K, Rikakis, T, Kelliher, A, Warwick, K, van den Hoven, E, Mueller, FF & Mann, S 1970, 'Motor Memory in HCI', Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20: CHI Conference on Human Factors in Computing Systems, ACM, Honolulu, HI, pp. 1-8.
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There is mounting evidence acknowledging that embodiment is foundational to cognition. In HCI, this understanding has been incorporated in concepts like embodied interaction, bodily play, and natural user-interfaces. However, while embodied cognition suggests a strong connection between motor activity and memory, we find the design of technological systems that target this connection to be largely overlooked. Considering this, we are provided with an opportunity to extend human capabilities through augmenting motor memory. Augmentation of motor memory is now possible with the advent of new and emerging technologies including neuromodulation, electric stimulation, brain-computer interfaces, and adaptive intelligent systems. This workshop aims to explore the possibility of augmenting motor memory using these and other technologies. In doing so, we stand to benefit not only from new technologies and interactions, but also a means to further study cognition.
Pelchen, T, Mathieson, L & Lister, R 1970, 'On the Evidence for a Learning Hierarchy in Data Structures Exams', Proceedings of the Twenty-Second Australasian Computing Education Conference, ACE'20: Twenty-Second Australasian Computing Education Conference, ACM, pp. 122-131.
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© 2020 Association for Computing Machinery. ACM ISBN 978-1-4503-7686-0/20/02...$15.00 Several previous research studies have found a relationship between the ability of novices to trace and explain code, and the ability to write code. Harrington and Cheng refer to that relationship as the Learning Hierarchy. However, almost all of those studies examined students at the end of their first semester of learning to program (i.e. CS1). This paper is only the third paper to describe a study of explain in plain English questions on students at the end of an introductory data structures course. The preceding two papers reached contradictory conclusions. Corney et al. presented results consistent with the Learning Hierarchy identified in the CS1 studies. However, Harrington and Cheng presented results for data structures students suggesting that the hierarchy reversed by the time students had progressed to the level of learning about data structures; that is, tracing and explaining were skills that followed writing. In our study of data structures students, we present results that are consistent with the Learning Hierarchy derived from the CS1 students. We believe that the reversal identified by Harrington and Cheng can occur, but only as a consequence of a mismatch in the relative difficulty of tracing, explaining and writing questions.
Peng, J, Parnell, J & Kessissoglou, N 1970, 'Comparison of equivalent continuous noise levels and day-evening-night composite noise indicators for assessment of road traffic noise', Acoustics 2019, Sound Decisions: Moving Forward with Acoustics - Proceedings of the Annual Conference of the Australian Acoustical Society.
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Environmental road traffic noise exposure indicators adopted by Australasian road authorities, corresponding to equivalent continuous sound levels specified over different assessment time periods within a 24-hour period, are compared with 24-hour composite indicators comprising day-evening-night and day-night assessment periods that place higher importance on night-time noise impact. The aforementioned equivalent continuous sound levels specified over different assessment time periods and composite noise indicators are calculated using measured hourly road traffic noise levels at representative locations in urban and rural areas in New South Wales. Further, the corresponding road traffic data (full classification vehicle counts and vehicle speeds) are used as inputs to the well-established CNOSSOS-EU, CoRTN and FHWA-TNM road traffic noise prediction models, from which the equivalent continuous sound levels and composite noise indicators are then predicted. Using the noise indicators, measured noise levels and predicted noise levels from the three road traffic models at roadside locations along an urban arterial road and an interstate freight route are compared.
Peng, X, Long, G, Shen, T, Wang, S & Jiang, J 1970, 'Self-Attention Enhanced Patient Journey Understanding in Healthcare System', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Springer International Publishing, Ghent Belgium, pp. 719-735.
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Understanding patients' journeys in healthcare system is a fundamentalprepositive task for a broad range of AI-based healthcare applications. Thistask aims to learn an informative representation that can comprehensivelyencode hidden dependencies among medical events and its inner entities, andthen the use of encoding outputs can greatly benefit the downstreamapplication-driven tasks. A patient journey is a sequence of electronic healthrecords (EHRs) over time that is organized at multiple levels: patient, visitsand medical codes. The key challenge of patient journey understanding is todesign an effective encoding mechanism which can properly tackle theaforementioned multi-level structured patient journey data with temporalsequential visits and a set of medical codes. This paper proposes a novelself-attention mechanism that can simultaneously capture the contextual andtemporal relationships hidden in patient journeys. A multi-level self-attentionnetwork (MusaNet) is specifically designed to learn the representations ofpatient journeys that is used to be a long sequence of activities. The MusaNetis trained in end-to-end manner using the training data derived from EHRs. Weevaluated the efficacy of our method on two medical application tasks withreal-world benchmark datasets. The results have demonstrated the proposedMusaNet produces higher-quality representations than state-of-the-art baselinemethods. The source code is available in https://github.com/xueping/MusaNet.
Peng, X, Long, G, Shen, T, Wang, S, Jiang, J & Zhang, C 1970, 'BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes', 20th IEEE International Conference on Data Mining (ICDM), IEEE International Conference on Data Mining, Sorrento, Italy.
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Electronic health records (EHRs) are longitudinal records of a patient'sinteractions with healthcare systems. A patient's EHR data is organized as athree-level hierarchy from top to bottom: patient journey - all the experiencesof diagnoses and treatments over a period of time; individual visit - a set ofmedical codes in a particular visit; and medical code - a specific record inthe form of medical codes. As EHRs begin to amass in millions, the potentialbenefits, which these data might hold for medical research and medical outcomeprediction, are staggering - including, for example, predicting futureadmissions to hospitals, diagnosing illnesses or determining the efficacy ofmedical treatments. Each of these analytics tasks requires a domain knowledgeextraction method to transform the hierarchical patient journey into a vectorrepresentation for further prediction procedure. The representations shouldembed a sequence of visits and a set of medical codes with a specifictimestamp, which are crucial to any downstream prediction tasks. Hence,expressively powerful representations are appealing to boost learningperformance. To this end, we propose a novel self-attention mechanism thatcaptures the contextual dependency and temporal relationships within apatient's healthcare journey. An end-to-end bidirectional temporal encodernetwork (BiteNet) then learns representations of the patient's journeys, basedsolely on the proposed attention mechanism. We have evaluated the effectivenessof our methods on two supervised prediction and two unsupervised clusteringtasks with a real-world EHR dataset. The empirical results demonstrate theproposed BiteNet model produces higher-quality representations thanstate-of-the-art baseline methods.
Perdomo, W, Prior, J & Leaney, J 1970, 'How do Colombian software companies evaluate software product quality?', CEUR Workshop Proceedings.
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Software developers confuse product quality with process quality, leading them to think they are measuring product quality when they are not. This is the main finding of our study of software developers in young companies in Colombia. Software product quality (SPQ) reflects two perspectives: conformance to specifications, and satisfying expectations when it is used under specified conditions. Measuring product quality still remains a problem for software development companies in relation to factors such as cost, effort, time, and competitiveness. There are few studies that show the current state of SPQ in companies, how companies evaluate product quality, and which measures they use to develop and launch products that meet high-quality criteria. This paper presents a study of SPQ in seven young software development companies in a developing country. We used a qualitative research approach to understand, through their experiences and knowledge, how 20 employees—developers, testers, and project managers—and their companies evaluate SPQ, and which measures they apply in their companies. Our results demonstrate that software process quality is better understood, and applied, by these software companies than SPQ. A greater difficulty is that most study participants ‘overlaid’ the idea of product quality with process quality, i.e. they talked about product quality as if it were process quality. These findings have implications for companies that wish to increase competitiveness and productivity, as they must develop a working knowledge of SPQ that is not confused with software process quality. It also has implications for educators, to ensure that the distinction between process and product quality is explicitly taught.
Perez-Romero, ME, Alfaro-Garcia, VG, Merigo, JM & Flores-Romero, MB 1970, 'Covariance in Ordered Weighted Logarithm Aggregation Operators', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 36-41.
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Perry, S, Cong, HP, da Silva Cruz, LA, Prazeres, J, Pereira, M, Pinheiro, A, Dumic, E, Alexiou, E & Ebrahimi, T 1970, 'Quality Evaluation Of Static Point Clouds Encoded Using MPEG Codecs', 2020 IEEE International Conference on Image Processing (ICIP), 2020 IEEE International Conference on Image Processing (ICIP), IEEE, Abu Dhabi, United Arab Emirates, pp. 3428-3432.
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This paper presents a quality evaluation study of point cloud codecs that have been recently standardised by the MPEG committee. In particular, a subjective experiment to assess their performance in terms of bitrate against visual quality is designed and realized in four independent laboratories. The experimental setup of each laboratory varies; yet, the obtained subjective scores exhibit high inter laboratory correlation, confirming that the adopted assessment protocol is robust to equipment selection and viewing conditions, ensuring reliability and facilitating repeatability. Our study confirms the superior compression performance of the MPEG V-PCC, when compared to MPEG G-PCC, in the case of static contents. Finally, results from a benchmark of the most popular objective quality metrics using the obtained subjective scores as ground truth, reveal that the point2plane with mean square error is the most accurate quality predictor, closely followed by the point2point also using mean square error as distance measure.
Pham N., H, Mannen, T & Wada, K 1970, 'A Three-Phase Isolated Rectifier using Current Unfolding and Active Damping Methods', 2020 IEEE Energy Conversion Congress and Exposition (ECCE), 2020 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, Detroit, MI, USA, pp. 4587-4593.
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This paper discusses a three-phase isolated rectifier with integrated buck functionality, derived from an unfolding three-phase inverter and a 7-level forward converter. Its operating principle is based on current shaping and unfolding methods. The circuit consists of 10 diodes, 3 low-frequency bidirectional switches and 8 high-frequency switches. It operates as a current source converter and requires only 3 filter capacitors at the ac input and a dc filter inductor at the dc output. Parasitic inductances from the ac grid enable direct connection to the utility. This paper discusses the basic operating principle and control method of the proposed inverter. It proposes a new unsymmetrical modulation method that can reduce switching frequency while maintaining the accuracy of current sampling. Also, an active damping method is used to overcome intrinsic oscillation due to unfolding operation. Finally, experimental verification is carried out to confirm stable operation of the proposed three-phase isolated rectifier.
Pileggi, SF, Crain, H & Yahia, SB 1970, 'An Ontological Approach to Knowledge Building by Data Integration', International Conference on Computational Science, International Conference on Computational Science, Springer International Publishing, Amsterdam, pp. 479-493.
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This paper discusses the uncertainty in the automation of knowledge building from heterogeneous raw datasets. Ontologies play a critical role in such a process by providing a well consolidated support to link and semantically integrate datasets via interoperability, as well as semantic enrichment and annotations. By adopting Semantic Web technology, the resulting ecosystem is fully machine consumable. However, while the manual alignment of concepts from different vocabularies is reasonable at a small scale, fully automatic mechanisms are required once the target system scales up, leading to a significant uncertainty
Pillai, AG, Ahmadpour, N, Yoo, S, Kocaballi, AB, Pedell, S, Sermuga Pandian, VP & Suleri, S 1970, 'Communicate, Critique and Co-create (CCC) Future Technologies through Design Fictions in VR Environment', Companion Publication of the 2020 ACM Designing Interactive Systems Conference, DIS '20: Designing Interactive Systems Conference 2020, ACM, pp. 413-416.
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Design fiction enables HCI and design researchers to co-create, explore and speculate the future. It is growing in popularity given the growing complexities of emerging HCI systems and innovations. Diegetic props (like sound, videos, images) are sometimes used in design fiction to blur the lines between imagination and reality. These props enable the designers to be empathetic, feel present in the fiction as they investigate the complexity of technologies explored within the fiction, critique these technologies and think about their consequences. With a higher level of immersion and sense of embodiment, Virtual Reality (VR) can be a powerful tool for mediating and creating design fiction. However, there are few examples of VR as platform for design fiction. This workshop aims to investigate new opportunities for communicating, critiquing and co-creating design fiction narratives in immersive VR environments.
Popovic, M, Vidal-Calleja, T, Chung, JJ, Nieto, J & Siegwart, R 1970, 'Informative Path Planning for Active Field Mapping under Localization Uncertainty', 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Paris, France (Virtual), pp. 10751-10757.
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Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust, high-quality maps. To address this issue, we introduce an informative planning framework for active mapping that explicitly accounts for the pose uncertainty in both the mapping and planning tasks. Our strategy exploits a Gaussian Process (GP) model to capture a target environmental field given the uncertainty on its inputs. For planning, we formulate a new utility function that couples the localization and field mapping objectives in GP-based mapping scenarios in a principled way, without relying on manually-tuned parameters. Extensive simulations show that our approach outperforms existing strategies, reducing mean pose uncertainty and map error. We present a proof of concept in an indoor temperature mapping scenario.
Potena, C, Carpio, RF, Pietroni, N, Maiolini, J, Ulivi, G, Garone, E & Gasparri, A 1970, 'Suckers Emission Detection and Volume Estimation for the Precision Farming of Hazelnut Orchards.', CCTA, 2020 IEEE Conference on Control Technology and Applications (CCTA), IEEE, Montreal, QC, Canada, pp. 285-290.
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© 2020 IEEE. In this work, inspired by the needs of the H2020 European Project PANTHEON11http://www.project-pantheon.eu, we address the hazelnut sucker detection and canopy volume estimation problem on a per-plant basis. Sucker control is an essential but challenging practice in agriculture, given the fact that suckers, i.e., shoots that grow from the tree roots, compete with the tree itself for water and nutrients. This research is motivated by the observation that in current best-practice, sucker control is carried out by applying a non-calibrated amount of chemical inputs to each tree. Indeed, a proper sucker detection and estimation algorithm would represent the enabling technology for an environmentally friendly sucker control approach where the amount of herbicide could be properly calibrated according to the needs of each individual plant. In this work, we propose an end-to-end algorithm for detecting the presence of suckers and for estimating their canopy. First a sparse point cloud-based representation of the suckers is detected, then an approximated canopy estimation is achieved by means of a tailored meshing strategy that performs a leaf-based clustering and an iterative clusters connection. The volume is then estimated by the resulting mesh. Preliminary real-world experiments are provided to corroborate the effectiveness of the proposed canopy estimation strategy.
Pour, AB, Park, Y, Hong, JK, Muslim, AM & Pradhan, B 1970, 'Geological mapping in Morozumi range and Helliwell hills areas, northern Victoria Land (NVL), Antarctica using remote sensing imagery', 40th Asian Conference on Remote Sensing, ACRS 2019: &amp;amp;amp;amp;amp;quot;Progress of Remote Sensing Technology for Smart Future&amp;amp;amp;amp;amp;quot;.
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Many regions remain poorly studied in terms of geological mapping in inaccessible regions especially in the Arctic and Antarctica due to harsh conditions and logistical difficulties. Application of specialized image processing techniques is capable of revealing the hidden linearly mixed spectral sources in multispectral and hyperspectral satellite images. In this study, the application of Independent component analysis (ICA) and Constrained Energy Minimization (CEM) algorithms was evaluated for Landsat-8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data for geological mapping in Morozumi Range and Helliwell Hills areas, Northern Victoria Land (NVL), Antarctica. ICA algorithm was able to detect hidden linearly mixed spectral sources and low probability target materials in Landsat-8 and ASTER datasets. Fraction images of endmember target minerals such as hematite, goethite, jarosite, alunite, kaolinite, muscovite, epidote, chlorite, calcite, quartz, opal and chalcedony were produced using CEM algorithm for two spatial subsets of ASTER scene covering the Morozumi Range and Helliwell Hills areas. CEM classification image maps indicated that chlorite/hematite, goethite/jarosite/calcite and kaolinite/muscovite are governed in the Morozumi Range and goethite, chlorite, hematite and epidote are most dominated mineral assemblages in the Helliwell Hills area. GPS survey and XRD analysis verified the alteration mineral assemblages detected by ICA and CEM image processing algorithms. The results of this investigation demonstrate the capability of the two algorithms in distinguishing pixel and subpixel targets in the multispectral satellite data. The application of the methods for identifying poorly exposed geologic materials and subpixel exposures of alteration minerals has invaluable implications for geological mapping and mineral exploration in inaccessible regions.
Pour, AB, Park, Y, Hong, JK, Muslim, AM & Pradhan, B 1970, 'Geological mapping in Morozumi range and Helliwell hills areas, northern Victoria Land (NVL), Antarctica using remote sensing imagery', 40th Asian Conference on Remote Sensing, ACRS 2019: &amp;amp;amp;amp;amp;quot;Progress of Remote Sensing Technology for Smart Future&amp;amp;amp;amp;amp;quot;.
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© 2020 40th Asian Conference on Remote Sensing, ACRS 2019: 'Progress of Remote Sensing Technology for Smart Future'. All rights reserved. Many regions remain poorly studied in terms of geological mapping in inaccessible regions especially in the Arctic and Antarctica due to harsh conditions and logistical difficulties. Application of specialized image processing techniques is capable of revealing the hidden linearly mixed spectral sources in multispectral and hyperspectral satellite images. In this study, the application of Independent component analysis (ICA) and Constrained Energy Minimization (CEM) algorithms was evaluated for Landsat-8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data for geological mapping in Morozumi Range and Helliwell Hills areas, Northern Victoria Land (NVL), Antarctica. ICA algorithm was able to detect hidden linearly mixed spectral sources and low probability target materials in Landsat-8 and ASTER datasets. Fraction images of endmember target minerals such as hematite, goethite, jarosite, alunite, kaolinite, muscovite, epidote, chlorite, calcite, quartz, opal and chalcedony were produced using CEM algorithm for two spatial subsets of ASTER scene covering the Morozumi Range and Helliwell Hills areas. CEM classification image maps indicated that chlorite/hematite, goethite/jarosite/calcite and kaolinite/muscovite are governed in the Morozumi Range and goethite, chlorite, hematite and epidote are most dominated mineral assemblages in the Helliwell Hills area. GPS survey and XRD analysis verified the alteration mineral assemblages detected by ICA and CEM image processing algorithms. The results of this investigation demonstrate the capability of the two algorithms in distinguishing pixel and subpixel targets in the multispectral satellite data. The application of the methods for identifying poorly exposed geologic materials and subpixel exposures of alteration minerals has invaluable ...
Pour, AB, Park, Y, Hong, JK, Muslim, AM & Pradhan, B 1970, 'Geological mapping in Morozumi range and Helliwell hills areas, northern Victoria Land (NVL), Antarctica using remote sensing imagery', 40th Asian Conference on Remote Sensing, ACRS 2019: Progress of Remote Sensing Technology for Smart Future.
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Many regions remain poorly studied in terms of geological mapping in inaccessible regions especially in the Arctic and Antarctica due to harsh conditions and logistical difficulties. Application of specialized image processing techniques is capable of revealing the hidden linearly mixed spectral sources in multispectral and hyperspectral satellite images. In this study, the application of Independent component analysis (ICA) and Constrained Energy Minimization (CEM) algorithms was evaluated for Landsat-8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data for geological mapping in Morozumi Range and Helliwell Hills areas, Northern Victoria Land (NVL), Antarctica. ICA algorithm was able to detect hidden linearly mixed spectral sources and low probability target materials in Landsat-8 and ASTER datasets. Fraction images of endmember target minerals such as hematite, goethite, jarosite, alunite, kaolinite, muscovite, epidote, chlorite, calcite, quartz, opal and chalcedony were produced using CEM algorithm for two spatial subsets of ASTER scene covering the Morozumi Range and Helliwell Hills areas. CEM classification image maps indicated that chlorite/hematite, goethite/jarosite/calcite and kaolinite/muscovite are governed in the Morozumi Range and goethite, chlorite, hematite and epidote are most dominated mineral assemblages in the Helliwell Hills area. GPS survey and XRD analysis verified the alteration mineral assemblages detected by ICA and CEM image processing algorithms. The results of this investigation demonstrate the capability of the two algorithms in distinguishing pixel and subpixel targets in the multispectral satellite data. The application of the methods for identifying poorly exposed geologic materials and subpixel exposures of alteration minerals has invaluable implications for geological mapping and mineral exploration in inaccessible regions.
Pour, AB, Park, Y, Hong, JK, Muslim, AM & Pradhan, B 1970, 'Listvenite occurrences in the fault zones of northern Victoria Land, Antarctica: Aster-based mapping approach', 40th Asian Conference on Remote Sensing, ACRS 2019: Progress of Remote Sensing Technology for Smart Future.
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Listvenite, a Mg-carbonate-quartz-fuchsite±Cr-chlorite±pyrite±chromite rock, forms by hydrothermal/metasomatic alteration of mafic and ultramafic rocks and represents a key indicator for hydrothermal mineral deposits in orogenic belts. Hydrothermal/metasomatic alteration zones in the damage zones of accretionary plate boundaries are efficiently detected using multispectral satellite remote sensing data. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing data are used to identify listvenite occurrences and alteration mineral zones in the poorly exposed damage zones of the boundaries between the Wilson, Bowers and Robertson Bay terranes in Northern Victoria Land (NVL) of Antarctica. Spectral information for detecting alteration mineral assemblages and listvenite zones were extracted at pixel and sub-pixel levels using the Principal Component Analysis (PCA)/Independent Component Analysis (ICA) fusion technique, Linear Spectral Unmixing (LSU) and Constrained Energy Minimization (CEM) algorithms. Mineral phases containing Fe2+, Fe3+, Fe-OH, Al-OH, Mg-OH and CO3 spectral absorption features were are distinguished in the damage zones through PCA/ICA fusion of ASTER visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Silicate rocks are discriminated from the PCA/ICA fusion of the ASTER thermal infrared (TIR) bands. The extracted mineral images of goethite, hematite, jarosite, biotite, kaolinite, muscovite, antigorite, serpentine, talc, actinolite, chlorite, epidote, calcite, dolomite and siderite and listvenite occurrences were produced using the LSU and CEM algorithms. Listvenite occurrences are confined to mafic metavolcanic rocks (Glasgow Volcanics) in the Bowers Mountains. New field investigations verified the presence of listvenite in the mapped zones and further constrain on the efficiency of the integrative methodology used in this study.
Pour, AB, Park, Y, Hong, JK, Muslim, AM & Pradhan, B 1970, 'Listvenite occurrences in the fault zones of northern Victoria Land, Antarctica: Aster-based mapping approach', 40th Asian Conference on Remote Sensing, ACRS 2019: &amp;amp;amp;amp;amp;quot;Progress of Remote Sensing Technology for Smart Future&amp;amp;amp;amp;amp;quot;.
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Listvenite, a Mg-carbonate-quartz-fuchsite±Cr-chlorite±pyrite±chromite rock, forms by hydrothermal/metasomatic alteration of mafic and ultramafic rocks and represents a key indicator for hydrothermal mineral deposits in orogenic belts. Hydrothermal/metasomatic alteration zones in the damage zones of accretionary plate boundaries are efficiently detected using multispectral satellite remote sensing data. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing data are used to identify listvenite occurrences and alteration mineral zones in the poorly exposed damage zones of the boundaries between the Wilson, Bowers and Robertson Bay terranes in Northern Victoria Land (NVL) of Antarctica. Spectral information for detecting alteration mineral assemblages and listvenite zones were extracted at pixel and sub-pixel levels using the Principal Component Analysis (PCA)/Independent Component Analysis (ICA) fusion technique, Linear Spectral Unmixing (LSU) and Constrained Energy Minimization (CEM) algorithms. Mineral phases containing Fe2+, Fe3+, Fe-OH, Al-OH, Mg-OH and CO3 spectral absorption features were are distinguished in the damage zones through PCA/ICA fusion of ASTER visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Silicate rocks are discriminated from the PCA/ICA fusion of the ASTER thermal infrared (TIR) bands. The extracted mineral images of goethite, hematite, jarosite, biotite, kaolinite, muscovite, antigorite, serpentine, talc, actinolite, chlorite, epidote, calcite, dolomite and siderite and listvenite occurrences were produced using the LSU and CEM algorithms. Listvenite occurrences are confined to mafic metavolcanic rocks (Glasgow Volcanics) in the Bowers Mountains. New field investigations verified the presence of listvenite in the mapped zones and further constrain on the efficiency of the integrative methodology used in this study.
Pour, AB, Park, Y, Hong, JK, Muslim, AM & Pradhan, B 1970, 'Listvenite occurrences in the fault zones of northern Victoria Land, Antarctica: Aster-based mapping approach', 40th Asian Conference on Remote Sensing, ACRS 2019: &amp;amp;amp;amp;amp;quot;Progress of Remote Sensing Technology for Smart Future&amp;amp;amp;amp;amp;quot;.
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© 2020 40th Asian Conference on Remote Sensing, ACRS 2019: 'Progress of Remote Sensing Technology for Smart Future'. All rights reserved. Listvenite, a Mg-carbonate-quartz-fuchsite±Cr-chlorite±pyrite±chromite rock, forms by hydrothermal/metasomatic alteration of mafic and ultramafic rocks and represents a key indicator for hydrothermal mineral deposits in orogenic belts. Hydrothermal/metasomatic alteration zones in the damage zones of accretionary plate boundaries are efficiently detected using multispectral satellite remote sensing data. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing data are used to identify listvenite occurrences and alteration mineral zones in the poorly exposed damage zones of the boundaries between the Wilson, Bowers and Robertson Bay terranes in Northern Victoria Land (NVL) of Antarctica. Spectral information for detecting alteration mineral assemblages and listvenite zones were extracted at pixel and sub-pixel levels using the Principal Component Analysis (PCA)/Independent Component Analysis (ICA) fusion technique, Linear Spectral Unmixing (LSU) and Constrained Energy Minimization (CEM) algorithms. Mineral phases containing Fe2+, Fe3+, Fe-OH, Al-OH, Mg-OH and CO3 spectral absorption features were are distinguished in the damage zones through PCA/ICA fusion of ASTER visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Silicate rocks are discriminated from the PCA/ICA fusion of the ASTER thermal infrared (TIR) bands. The extracted mineral images of goethite, hematite, jarosite, biotite, kaolinite, muscovite, antigorite, serpentine, talc, actinolite, chlorite, epidote, calcite, dolomite and siderite and listvenite occurrences were produced using the LSU and CEM algorithms. Listvenite occurrences are confined to mafic metavolcanic rocks (Glasgow Volcanics) in the Bowers Mountains. New field investigations verified the presence of listven...
Prabowo, YA, Ranasinghe, R, Dissanayake, G, Riyanto, B & Yuliarto, B 1970, 'A Bayesian approach for gas source localization in large indoor environments', 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 4432-4437.
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Pradhan, S & Kreglicki, M 1970, 'Mentoring as a Tool: To Bridge the gap between industry and academia for undergraduate students', https://aaee.net.au/conferences, Australasian Association for Engineering Education, Sydney, Australia.
Prestigiacomo, R, Hadgraft, R, Hunter, J, Locker, L, Knight, S, van den Hoven, E & Martinez-Maldonado, R 1970, 'Learning-centred translucence', Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, LAK '20: 10th International Conference on Learning Analytics and Knowledge, ACM, Frankfurt, Germany, pp. 100-105.
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© 2020 Association for Computing Machinery. Teachers are increasingly being encouraged to embrace evidencebased practices. Learning analytics (LA) offer great promise in supporting these by providing evidence for teachers and learners to make informed decisions and transform the educational experience. However, LA limitations and their uptake by educators are coming under critical scrutiny. This is in part due to the lack of involvement of teachers and learners in the design of LA tools. In this paper, we propose a human-centred approach to generate understanding of teachers' data needs through the lens of three key principles of translucence: visibility, awareness and accountability. We illustrate our approach through a participatory design sprint to identify how teachers talk about classroom data. We describe teachers' perspectives on the evidence they need for making better-informed decisions and discuss the implications of our approach for the design of human-centred LA in the next years.
Prior, J & Leaney, J 1970, 'Software Quality and its Entanglements in Practice', Ethnographic Praxis in Industry Conference, Wiley Blackwell, Melbourne, Australia, pp. 163-176.
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Effective software quality assurance in large-scale, complex software systems is one of the most vexed issues in software engineering, and, it is becoming ever more challenging. Software quality and its assurance is part of software development practice, a messy, complicated and constantly shifting human endeavor. What emerged from our immersive study in a large Australian software development company is that software quality in practice is inextricably entangled with the phenomena of productivity, time, infrastructure and human practice. This ethnographic insight --- made visible to the organization and its developers via the rich picture and the concept of entanglements--- built their trust in our work and expertise. This led to us being invited to work with the software development teams on areas for change and improvement and moving to a participatory and leading role in organizational change.
Prysyazhnyuk, A & McGregor, C 1970, 'A wholistic approach to assessement of adaptation and resilience during spaceflight', Proceedings of the International Astronautical Congress, IAC.
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Human performance within the context of extreme environments both terrestrially and in outer space continues to lead the frontier of new physiological discoveries, further enhancing the knowledge on limitations of human mind and body systems, the role and activity of adaptation mechanisms, as well as assessment and development of resilience strategies. The acquired knowledge informs the development of innovative prognostic, diagnostic and therapeutic medical tools and resources aboard the spacecraft and in terrestrial medical centres. Despite decades of research and space exploration, the prognostic and diagnostic capacity aboard the spacecraft remains limited and fragmented, while health assessments constitute of questionnaires and collection of nominal physiological parameters, both of which are analyzed retrospectively, upon return to Earth, unless there is an apparent onset of medical contingency which necessitates immediate therapeutic intervention. Even then, the use of the acquired physiological data is limited, as it is being down-sampled to manageable data tuples for clinical evaluation and interpretation. In prior research we proposed the use of a big-data analytics platform, Artemis, for real-time assessment of adaptation during spaceflight. The capability of Artemis to support acquisition, storage and analysis of large volumes of physiological, environmental and activity data presents a great prospect for enhanced medical capacity during long duration spaceflights and deep space exploration. As such, we would like to propose a framework of an extension of Artemis to further incorporate activity data and mental health evaluations, so as to develop a more wholistic approach to assessment of crew's well-being during spaceflight. The proposed extension would also enable investigation of the team dynamics and how interpersonal relationships influence individual's performance and well-being. From a biomedical monitoring perspective, utilization of...
Qashlan, A, Nanda, P & He, X 1970, 'Automated Ethereum Smart Contract for Block Chain Based Smart Home Security', Automated Ethereum Smart Contract for Block Chain Based Smart Home Security, International Conference on Artificial Intelligence and Security, Springer Singapore, Jaipur, India, pp. 313-326.
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Presence of Internet of Things (IoT) based applications has been increasing in various domains including transportation, logistics, health care, and smart homes. Such applications involve deploying an enormous number of IoT devices, which generally lacks from security and often associates several vulnerabilities. These IoT devices need to communicate and synchronize with each other, which also increase the security and privacy challenges. Traditional security models are based on centralized and often include complicated approaches which, tend to be inapplicable and have some limitations. Therefore, one proposed solution is to use blockchain technology which could provide decentralize, secure, and peer-to-peer networks. In this paper, private blockchain implementation using Ethereum smart contract is developed for the smart home to ensure only the home owner can access and monitor home appliances. Simple smart contracts are designed to allow devices to communicate without the need for trusted third party. Our prototype demonstrates three key elements of blockchain-based smart security solution for smart home applications such as smart contract, blockchain-based access control and performance evaluation of the proposed scheme.
Qashlan, A, Nanda, P & He, X 1970, 'Security and Privacy Implementation in Smart Home: Attributes Based Access Control and Smart Contracts', 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), IEEE, pp. 951-958.
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Qi, Y, Indraratna, B & Tawk, M 1970, 'Use of Recycled Rubber Elements in Track Stabilisation', Geo-Congress 2020, Geo-Congress 2020, American Society of Civil Engineers, USA, pp. 49-59.
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This paper introduces two novel methods of using waste materials i.e., steel furnace slag (SFS), coal wash (CW), and rubber crumbs (RC) in rail tracks. One method is to optimize the mixtures of SFS, CW, and RC (SFS+CW+RC matrix) compacted with the standard compaction energy to serve as a subballast material. The other one is to examine the potential usage of CW and RC mixtures (CW+RC matrix) which are compacted under adjusted compaction effort. To investigate the geotechnical properties of these waste mixtures, comprehensive laboratory tests have been conducted. Based on the test results, the stress-strain relationship is studied with special focus on the effect of rubber content on the ductility and energy-absorbing potential of the proposed mixtures. In addition, for the CW+RC matrix, the role of rubber content and compaction effort on the compaction and degradation characteristics of the material is examined.
Qian, J, Begum, H & Lee, JE-Y 1970, 'Centrifugation of Microparticles Inside a Sessile Droplet on a Micromachined Silicon Chip Using Acoustic Tweezers', 2020 IEEE 33rd International Conference on Micro Electro Mechanical Systems (MEMS), 2020 IEEE 33rd International Conference on Micro Electro Mechanical Systems (MEMS), IEEE, pp. 1130-1133.
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Qian, J, Ren, J, Liu, Y, Lam, RHW & Lee, JE-Y 1970, 'Reusable acoustic tweezers enable 2D patterning of microparticles in microchamber on a disposable silicon chip superstrate', 2020 IEEE SENSORS, 2020 IEEE SENSORS, IEEE, pp. 1-4.
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Qian, Z, Xiao-jun, L & Lei, H 1970, 'Video Image Fire Recognition Based on Color Space and Moving Object Detection', 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE), 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE), IEEE, PEOPLES R CHINA, Beijing, pp. 367-371.
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Qiao, M, Yu, J, Liu, T, Wang, X & Tao, D 1970, 'Diversified Bayesian nonnegative matrix factorization', AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, pp. 5420-5427.
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Nonnegative matrix factorization (NMF) has been widely employed in a variety of scenarios due to its capability of inducing semantic part-based representation. However, because of the non-convexity of its objective, the factorization is generally not unique and may inaccurately discover intrinsic “parts” from the data. In this paper, we approach this issue using a Bayesian framework. We propose to assign a diversity prior to the parts of the factorization to induce correctness based on the assumption that useful parts should be distinct and thus well-spread. A Bayesian framework including this diversity prior is then established. This framework aims at inducing factorizations embracing both good data fitness from maximizing likelihood and large separability from the diversity prior. Specifically, the diversity prior is formulated with determinantal point processes (DPP) and is seamlessly embedded into a Bayesian NMF framework. To carry out the inference, a Monte Carlo Markov Chain (MCMC) based procedure is derived. Experiments conducted on a synthetic dataset and a real-world MULAN dataset for multi-label learning (MLL) task demonstrate the superiority of the proposed method.
Radhakrishnan, M, Misra, A & Balan, RK 1970, 'W8-Scope: Fine-Grained, Practical Monitoring of Weight Stack-based Exercises', 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom), IEEE, pp. 1-10.
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Radhakrishnan, M, Misra, A, Balan, RK & Lee, Y 1970, 'Gym Usage Behavior & Desired Digital Interventions', Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth '20: 14th EAI International Conference on Pervasive Computing Technologies for Healthcare, ACM, pp. 97-107.
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Radhakrishnan, M, Rathnayake, D, Han, OK, Hwang, I & Misra, A 1970, 'ERICA', Proceedings of the 18th Conference on Embedded Networked Sensor Systems, SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems, ACM, pp. 558-571.
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Rahman, M, Ahmed, F & Rahman, AMA 1970, 'A Low-Cost Dual Band Notched Planar Patch Antenna for Ultra-Wideband Applications', 2020 11th International Conference on Electrical and Computer Engineering (ICECE), 2020 11th International Conference on Electrical and Computer Engineering (ICECE), IEEE, pp. 109-112.
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Rahman, MM, Zhao, M, Islam, MS, Dong, K & Saha, SC 1970, 'Airflow dynamic and particle deposition in age-specific human lungs', Australasian Fluid Mechanics Conference (AFMC), 22nd Australasian Fluid Mechanics Conference AFMC2020, The University of Queensland.
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Ram, R, Kong, Q & Rizoiu, M-A 1970, 'Birdspotter: A Tool for Analyzing and Labeling Twitter Users', In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM'21), pp. 918-921. New York, NY, USA: ACM. 2021, WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining, ACM, online.
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The impact of online social media on societal events and institutions isprofound; and with the rapid increases in user uptake, we are just starting tounderstand its ramifications. Social scientists and practitioners who modelonline discourse as a proxy for real-world behavior, often curate large socialmedia datasets. A lack of available tooling aimed at non-data science expertsfrequently leaves this data (and the insights it holds) underutilized. Here, wepropose birdspotter -- a tool to analyze and label Twitter users --, andbirdspotter.ml -- an exploratory visualizer for the computed metrics.birdspotter provides an end-to-end analysis pipeline, from the processing ofpre-collected Twitter data, to general-purpose labeling of users, andestimating their social influence, within a few lines of code. The packagefeatures tutorials and detailed documentation. We also illustrate how to trainbirdspotter into a fully-fledged bot detector that achieves better thanstate-of-the-art performances without making any Twitter API online calls, andwe showcase its usage in an exploratory analysis of a topical COVID-19 dataset.
Ramakrishnan, RK, Ravichandran, AB, Talabattula, S, Vijayan, MK, Lund, AP & Rohde, PP 1970, 'Photonic Quantum Error Correction of Qudits Using W-state Encoding', 14th Pacific Rim Conference on Lasers and Electro-Optics (CLEO PR 2020), Conference on Lasers and Electro-Optics/Pacific Rim, Optica Publishing Group, pp. P5_23-P5_23.
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In this paper, we present a passive linear optics error correction scheme for qudits using W-state encoding, based on post selection, which reduces the effects of dephasing noise in photonic quantum communication.
Ravindranath, V, Ramasamy, S, Somula, R, Sahoo, KS & Gandomi, AH 1970, 'Swarm Intelligence Based Feature Selection for Intrusion and Detection System in Cloud Infrastructure', 2020 IEEE Congress on Evolutionary Computation (CEC), 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE, Glasgow, UK, pp. 1-6.
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© 2020 IEEE. Network intrusion and cyber attacks are the most severe concern for Cloud computing service providers. The vulnerability of attacks is on a hike that manual or simple rule-based detection of cyber-attacks is not robust. In order to tackle cyber attacks in a reliable manner, an automated Intrusion Detection system equipped with a swarm intelligence (SI) based machine learning model (ML) is essential to deploy at entry points of the network. Nowadays, the application of SI with ML is used in various research areas. For an efficient IDS, choosing relevant features from the noisy data is an open question. In this regard, this paper proposes a method that utilizes the Whale Pearson hybrid feature selection wrapper for reducing the irrelevancy in the IDS model. Whale Pearson hybrid wrapper is an improved version of the binary Whale optimization Algorithm (WOA). The WOA is a type of SI algorithm which is inspired by the behavior of humpback whales. The proposed method has chosen 8 out of 42 features from the Hackereath Network attack prediction data-set, which are sufficient for building an efficient Intrusion detection model. The model trained with the eight features produces an accuracy of 80%, which is 8% greater than the accuracy produced by the original data-set with the KNN algorithm on ten-fold cross-validation.
Raza, MA, Abolhasan, M, Lipman, J, Shariati, N & Ni, W 1970, 'Statistical Learning-Based Dynamic Retransmission Mechanism for Mission Critical Communication: An Edge-Computing Approach', 2020 IEEE 45th Conference on Local Computer Networks (LCN), 2020 IEEE 45th Conference on Local Computer Networks (LCN), IEEE, Australia, pp. 393-396.
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Mission-critical machine type communication (MC-MTC) systems in which machines communicate to perform various tasks such as coordination, sensing, and actuation, require stringent requirements of ultra-reliable and low latency communications (URLLC). Edge computing being an integral part of future wireless networks, provides services that support URLLC applications. In this paper, we use the edge computing approach and present a statistical learning-based dynamic retransmission mechanism. The proposed approach meets the desired latency-reliability criterion in MC-MTC networks employing framed ALOHA. The maximum number of retransmissions Nr under a given latency-reliability constraint is learned statistically by the devices from the history of their previous transmissions and shared with the base station. Simulations are performed in MATLAB to evaluate a framed-ALOHA system's performance in which an active device can have only one successful transmission in one round composed of (Nr + 1) frames, and the performance is compared with the diversity transmission-based framed-ALOHA.
Reddy, TK, Arora, V, Behera, L, Wang, Y-K & Lin, C-T 1970, 'Fuzzy Divergence Based Analysis for Eeg Drowsiness Detection Brain Computer Interfaces', 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Glasgow (UK), pp. 1-7.
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© 2020 IEEE. EEG signals can be processed and classified into commands for brain-computer interface (BCI). Stable deciphering of EEG is one of the leading challenges in BCI design owing to low signal to noise ratio and non-stationarities. Presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. Stationary Subspace methods discover subspaces in which data distribution remains steady over time. In this paper, we develop novel spatial filtering based feature extraction methods for dealing with nonstationarity in EEG signals from a drowsiness detection problem (a machine learning regression problem). The proposed method: DivOVR-FuzzyCSP-WS based features clearly outperformed fuzzy CSP based baseline features in terms of both RMSE and CC performance metrics. It is hoped that the proposed feature extraction method based on DivOVR-FuzzyCSP-WS will bring in a lot of interest in researchers working in developing algorithms for signal processing, in general, for BCI regression problems.
Rehman, J, Hawryszkiewycz, I, Sohaib, O & Namisango, F 1970, 'Building a knowledge-based competitive advantage in service firms: Role of high-performance work systems', Proceedings of the European Conference on Knowledge Management, ECKM, European Conference on Knowledge Management, Coventry, UK, pp. 658-667.
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The contemporary Professional Service Firms (PSFs) have enormously contributed to the advancement of the global services sector in general and knowledge-based economies in particular. Being knowledge-intensive firms, the PSFs are usually faced with the challenge of continually enhancing the knowledge competencies of their staff that form the basis of organizational Intellectual Capital (IC) and derives competitive advantage for them. This makes the role of High Performance Work Systems (HPWS) indispensible for managing IC resources in these firms. Therefore, by presenting a qualitatively-validated conceptual framework, this research offers a linking mechanism on how strategic HRM systems i.e. HPWS guide IC development in service firms. By empirically testing these in (Ability, Motivation & Opportunity)-enhancing bundles, the results demonstrate that HPWS play strategically significant role in building knowledge capital in the service firms.
Rehman, J, Hawryszkiewycz, I, Sohaib, O & Namisango, F 1970, 'Intellectual Capital Creates Value for the Organization: What About Other Stakeholders?', International Conference on Knowledge Management, Durham, NC, USA.
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The ever-increasing market turbulence has turned today‟s corporate landscape more competitive and complex. Particularly during the last two decades, the increased utilization of ICT systems and technologies globally transformed the services sector in terms of ease of business processes and improved client service delivery. However, in the current knowledge-based era, ICT-enabled systems and tools would only be meaningful if these are appropriately utilized by the knowledgeable and skilled workforce. However, leveraging these necessitates a knowledge-enabled work culture and recognizing that people are crucial to building a robust Intellectual Capital (IC) that is central to achieving long-term market competitiveness. IC comprising of intangible assets and knowledge resources is central to value creation for the firm as evident from the growth of the knowledge-based industries. Nevertheless, the true potential of IC for deriving value advantage for diverse organizational stakeholders has not been fully utilized. Hence, by conducting 12 face2face interviews with the senior executives within Australian Professional Service Firms (PSFs), this study offers renewedapproach to IC valuation by introducing „Triple Value Bottom-line‟ perspective in PSFs. The results highlight that the IC offers enormous potential towards deriving broader value outcomes for multiple organizational stakeholders.
Rehman, J, Hawryszkiewycz, I, Sohaib, O & Soomro, A 1970, 'Developing Intellectual Capital in Professional Service Firms Using High Performance Work Practices as Toolkit', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii, pp. 4983-4992.
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The role of Professional Service Firms (PSFs) has always been crucial in the development of knowledge economies. The effectiveness of these firms is highly attributed to the knowledge capabilities and skills embedded in its human resources and how effectively these resources are utilized in the optimal benefit of the firm. Owing to the ever-increasing growth of the services sector globally, it’s critical for the PSFs to gain in-depth awareness on the application of High Performance-Work-Practices (HPWPs) so as to continually maintain quality of their services to the clients. However, the mechanism for systematically designing and implementing these practices in intellectual capital context is still not fully developed.
This research, therefore, theoretically investigates and suggests a linkage mechanism on how Strategic HRM Practices (HPWPs) via (Ability, Motivation and Opportunity)-enhancing bundles stimulate intellectual capital development in professional service firms. By presenting a conceptual framework, this study offers practically meaningful insights to the managers in the service firms on how to implement these practices for effectively meeting client needs and sustaining a competitive advantage
Reid, W, Fitch, R, Göktoǧgan, AH & Sukkarieh, S 1970, 'Motion Planning for Reconfigurable Mobile Robots Using Hierarchical Fast Marching Trees', Algorithmic Foundations of Robotics XII Proceedings of the Twelfth Workshop on the Algorithmic Foundations of Robotics, Springer International Publishing, WAFR, pp. 656-671.
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Reconfigurable mobile robots are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. However, planning paths for these robots is challenging due to their many degrees of freedom. We propose a novel hierarchical variant of the Fast Marching Tree (FMT*) algorithm. Our algorithm assumes a decomposition of the full state space into multiple sub-spaces, and begins by rapidly finding a set of paths through one such sub-space. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full state space. This technique provides a novel way to incorporate prior knowledge of sub-spaces to efficiently bias search within the existing FMT* framework. Importantly, probabilistic completeness and asymptotic optimality are preserved. Experimental results are provided for a reconfigurable wheel-on-leg platform that benchmark the algorithm against state-of-the-art samplingbased planners. In minimizing an energy objective that combines the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion. These results illustrate the generality of the planner in exploiting the platform’s mechanical ability to fluidly transition between various physical geometric configurations, and wheeled/legged locomotion modes.
Reyes-Cubas, A & Abdo, P 1970, 'Simulation of Ventilation Flow at Different Conditions Through a Two-Dimensional Room Incorporated With Phase Change Materials', Volume 8: Energy, ASME 2020 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers.
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Abstract Climate change and global warming have raised many concerns, highlighting the necessity to reduce energy consumption associated with the building sector. HVAC systems account to almost 40% of the building’s energy consumption. Natural ventilation is the process of supplying and removing air through an indoor space by natural means. Windcatchers have been used over centuries for providing natural ventilation using wind power. Moreover, it is an effective passive method to provide healthy and comfortable indoor environment by decreasing moisture content in the air and reducing pollutants concentration significantly. Materials that change phase at certain temperature are frequently referred to as Phase Change Materials (PCMs). Phase Change Materials, also known as Thermal Energy Storage (TES), are substances with high latent heat storage capacity which absorb or release the heat from or to the surrounding environment. PCMs could be used in passive cooling systems and they are directly related to building energy efficiency. This study investigates air flow through a windcatcher into a two-dimensional room incorporated with phase change materials (PCMs). The temperature change in the room implementing PCM is analyzed to monitor the PCMs’ performance. To achieve this, Computational Fluid Dynamics (CFD) tool is used to simulate the air flow through a two-dimensional standard room (3 m × 5 m) fitted with a windcatcher at its roof. Ansys Fluent is utilized to simulate and display the contours of temperature, liquid fraction, and velocity of both PCM and air. The energy model as well as the solidification and melting model are employed, and the K-Epsilon turbulence model is implemented. PCM is placed at the right and left walls of the room, as well as at its bottom. The inlet velocity ranges between 1 m/s and 7 m/s, simulating the average wind ...
Roser, C, Langer, B & Deuse, J 1970, 'The Power of Six: Relation Between Time and Money in Manufacturing for Segments of the Value Stream', Lecture Notes in Networks and Systems, Springer International Publishing, pp. 21-28.
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© 2020, Springer Nature Switzerland AG. A major influence on the cost of a product is the time it takes to make this product. Traditional cost accounting can grasp part of this relation, but misses many critical aspects of having a faster time to the customer. Rajan Suri analyzed this relation empirically. Based on a data set with industrial data he determined an empirical mathematical relation between the turnaround time to the customer (or replenishment time) and the product cost for the entire value stream. This paper modifies the approach by Suri to be applied also to segments of the value stream, creating a relation between the cost within of a segment of a value stream and the time it takes for a part to pass through this segment of a value stream. This allows the estimation of the improvement in cost and the reduction in turnaround time also for sub-segments of the value stream, helping decision makers to better understand the impact of their decisions.
Roy, S, Shivakumara, P, Pal, U, Lu, T & Blumenstein, M 1970, 'New Moments Based Fuzzy Similarity Measure for Text Detection in Distorted Social Media Images', Pattern Recognition, Asian Conference on Pattern Recognition, Springer International Publishing, New Zealand, pp. 720-734.
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A trend towards capturing or filming images using cellphone and sharing images on social media is a part and parcel of day to day activities of humans. When an image is forwarded several times in social media it may be distorted a lot due to several different devices. This work deals with text detection from such distorted images. In this work, we consider images pass through three mobile devices on WhatsApp social media, which results in four images (including the original image) Unlike the existing methods that aim at developing new ways, we utilize the results detected by the existing ones to improve performances. The proposed method extracts Hu moments and fuzzy logic from detected texts of images. The similarity between text detection results given by three existing text detection methods is studied for determining the best pair of texts. The same similarity estimation is then used in a novel way to remove extra background or non-texts and restoring missing text information. Experimental results on own dataset and benchmark datasets of natural scene images, namely, MSRA-TD500, ICDAR2017-MLT, Total-Text, CTW1500 dataset and COCO datasets, show that the proposed method outperforms the existing methods.
Rufangura, P, Agrawal, A, Bosi, M, Folland, TG, Caldwell, JD & Iacopi, F 1970, 'Enhanced Mid -Infrared Reflectance with Graphene Coated Silicon Carbide Nanowires', 14th Pacific Rim Conference on Lasers and Electro-Optics (CLEO PR 2020), Conference on Lasers and Electro-Optics/Pacific Rim, Optica Publishing Group, pp. C11E_2-C11E_2.
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The mid-infrared optical spectrum hosts a variety of promising photonic applications. Herein we simulate and experimentally demonstrate reflectance enhancement of MIR light using graphene-coated silicon carbide nanowires on silicon, showing promise for on-chip MIR nanophotonics.
Saberi, M, Saberi, Z, Aasadabadi, MR, Hussain, OK & Chang, E 1970, 'A Customer-Oriented Assortment Selection in the Big Data Environment', Advances in E-Business Engineering for Ubiquitous Computing, International Conference on e-Business Engineering, Springer International Publishing, Shanghai, China, pp. 161-172.
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© 2020, Springer Nature Switzerland AG. Customers prefer the availability of a range of products when they shop online. This enables them to identify their needs and select products that best match their desires. This is addressed through assortment planning. Some customers have strong awareness of what they want to purchase and from which provider. When considering customer taste as an abstract concept, such customers’ decisions may be influenced by the existence of the variety of products and the current variant market may affect their initial desire. Previous studies dealing with assortment planning have commonly addressed it from the retailer’s point of view. This paper will provide customers with a ranking method to find what they want. We propose that this provision benefits both the retailer and the customer. This study provides a customer-oriented assortment ranking approach. The ranking model facilitates browsing and exploring the current big market in order to help customers find their desired item considering their own taste. In this study, a scalable and customised multi-criteria decision making (MCDM) method is structured and utilised to help customers in the process of finding their most suitable assortment while shopping online. The proposed MCDM method is tailored to fit the big data environment.
Sadeghi, F, Zhu, X & Li, J 1970, 'DAMAGE ANALYSIS OF STEEL-CONCRETE COMPOSITE BEAMS UNDER STATIC LOADS', Proceedings of the XI International Conference on Structural Dynamics, XI International Conference on Structural Dynamics, EASD, Athens, Greece, pp. 1053-1062.
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© 2020 European Association for Structural Dynamics. All rights reserved. This paper presents a study of the static behavior of steel-concrete composite beams with different types of damage. Since the behavior of a composite beam under load is governed by the shear connection, it is important to investigate the overall structural response due to different levels of damage in the interface and composite layers. A finite element (FE) model of a steel-concrete composite beam is developed based on two Euler-Bernoulli beams as the composite layers coupled with a deformable shear connection. Three different damage indices are defined for the concrete slab, the steel girder, and the distributed shear connection and then embedded into the stiffness matrix of the composite beam. This model is validated by comparing its load-displacement behavior with an equivalent FE model developed using the commercial FE software ABAQUS. The impact that the loading location has on the results is then investigated. A convergence study is also carried out in terms of the displacements and strains to determine the number of composite beam FEs. The maximum displacements and strains of composite beams with different types and levels of damage are then investigated. The numerical analysis showed that after an initial reduction when the number of FEs increase, the changes in displacement and strain at each location are very small. Moreover, the bonding slip has almost no effect on the measurements, and the changes in maximum displacement and strain from undamaged to maximum damage are almost the same.
Salgotra, R, Singh, U, Saha, S & Gandomi, AH 1970, 'Improving Cuckoo Search: Incorporating Changes for CEC 2017 and CEC 2020 Benchmark Problems', 2020 IEEE Congress on Evolutionary Computation (CEC), 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp. 1-7.
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© 2020 IEEE. Cuckoo search (CS) is a highly competitive single objective optimization technique. The algorithm has been widely applied in various diverse application domains and has been found to be efficient in solving various real-life problems. In the present work, we have proposed a new enhanced version of CS algorithm and tested its performance on recently proposed CEC 2017 and CEC 2020 benchmark test problems. The proposed algorithm has been named as CSsin and it employs four major modifications, i) new techniques for global and local search are devised, ii) dual search strategy is followed to enhance exploration and exploitation properties of CS algorithm, iii) a linearly decreasing switch probability has been used to add a balance between local and global search, and iv) linearly decreasing population size is used to reduce the computational burden. Apart from these modifications, the division of iterations has been employed as a further modification. The CSsin algorithm has been tested on IEEE CEC 2017 and CEC 2020 benchmark test problems having various dimension sizes and a comparative study has been performed with respect to stateof-the-art optimization algorithms for single objective bound constraint optimization problems. The results of statistical significance test affirm the competitiveness of the proposed algorithm with respect to state-of-the-art techniques.
Samanta, S, Singhar, SS, Gandomi, AH, Ramasubbareddy, S & Sankar, S 1970, 'A WiVi Based IoT Framework for Detection of Human Trafficking Victims Kept in Hideouts', Internet of Things - ICIOT 2020, International Conference on Internet of Things, Springer International Publishing, Honolulu, HI, USA, pp. 96-107.
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© 2020, Springer Nature Switzerland AG. Human trafficking is the trade of humans for the purpose of forced labor, sexual slavery, or commercial sexual exploitation for the trafficker or others. The traffickers often trick, defraud, or physically force victims into selling sex and forced labor. In others, victims are lied to, assaulted, threatened, or manipulated into working under inhumane, illegal, or otherwise unacceptable conditions. According to the estimation of the International Labor Organization, there are more than 40.3 million victims of human trafficking globally. It is a threat to the Nation as well as to humanity. There have been many efforts by government agencies & NGOs to stop human trafficking and rescuing victims, but the traffickers are getting smarter day by day. From multiple sources, it is observed that the traffickers generally hide humans in hidden rooms, sealed containers, and boxes disguised as goods. This congestion results in Critical mental and physical damages in some cases. It is practically impossible to physically go and check each box, containers or rooms. So in this paper, we propose an Wireless Vision based IoT framework, which uses the reflection of WiFi radio waves generated by WiFi to detect the presence of humans inside a cement or metal enclosure from outside.
Sangiovanni-Vincentelli, A, Sztipanovits, J, Zhu, Q & Yu, S 1970, 'Message from Organizers', 2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION), 2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION), IEEE, Sydney, NSW, Australia, pp. i-i.
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The second DESTION workshop focuses on co-design and co-simulation tools for CPS development, formal methods that address the needs and challenges of incorporating LEC in CPS and IoT, and the application of these approaches in transportation and energy domains. The program of the workshop includes a keynote, presentations of contributed and invited papers, and demonstrations. The COVID-19 pandemic forced us to make the workshop virtual. Consequently, we will miss the stimulating in-person discussions and interactions among researchers. Even with this unique challenge, we hope to continue our path towards becoming a premier forum for researchers and engineers from academia, industry, and government to present and discuss pressing technical challenges, promising solutions, and emerging applications in design automation for CPS and IoT.
Sankar, S, Ramasubbareddy, S, Chen, F & Gandomi, AH 1970, 'Energy-Efficient Cluster-based Routing Protocol in Internet of Things Using Swarm Intelligence', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, Canberra, ACT, Australia, pp. 219-224.
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© 2020 IEEE. Energy conservation is a difficult challenge, because the Internet of Things (IoT) connects limited resource devices. Clustering is an efficient method for energy saving in network nodes. The existing clustering algorithms have problems with the short lifespan of a network, an unbalanced load among the network nodes and increased end-to-end delays. This paper proposes a new Cluster Head (CH) selection and cluster formation algorithm to overcome these issues. The process has two phases. First, the CH is selected using a Swarm Intelligence Algorithm called Sailfish optimization Algorithm (SOA). Second, the cluster is formed by the Euclidean distance. The simulation is conducted using the NS2 simulator. The efficacy of the SOA is compared to Improved Ant Bee Colony optimization-based Clustering (IABCOCT), Enhanced Particle Swarm optimization Technique (EPSOCT) and Hierarchical Clustering-based CH Election (HCCHE). The final results of the simulation show that the proposed SOA improves network life and decreases node-to-sink delays.
Saputra, YM, Nguyen, DN, Hoangl, DT, Dutkiewicz, E & Mueck, MD 1970, 'Common Agency-Based Economic Model for Energy Contract in Electric Vehicle Networks', GLOBECOM 2020 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - 2020 IEEE Global Communications Conference, IEEE, Taipei, Taiwan, pp. 1-6.
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The rapid adoption of electric or hybrid vehicles (EVs) has called for wide deployment of charging stations. These stations can be launched/owned by different owners, referred to as charging station providers (CSPs), which make energy contracts with a smart grid provider (SGP). However, there exists a shortage of mutual economic strategy between the SGP and CSPs in an energy request/transfer competition due to the selfish nature among them. In this paper, we propose an economic model leveraging a multi-principal single-agent (referred to as common agency) contract policy, aiming at maximizing the utilities of multiple CSPs while optimizing the utility of the SGP in an EV network. In particular, we first develop the common agency-based contract problem as a non-cooperative energy contract optimization problem, in which each CSP can maximize its utility given the common constraints from the SGP and the contracts of other CSPs. To deal with this problem, we develop an iterative energy contract algorithm to find an equilibrium contract solution where the contracts from the CSPs can produce maximum utilities of the CSPs and satisfy the constraints of the SGP. Through numerical results, we show that our proposed model can improve the social welfare of the EV network up to 54% and the utilities of CSPs up to 60% compared with the baseline method in which each CSP obtains the amount of energy that is proportional to its energy request.
Sarathy, N, Alsawwaf, M & Chaczko, Z 1970, 'Investigation of an Innovative Approach for Identifying Human Face-Profile Using Explainable Artificial Intelligence', 2020 IEEE 18th International Symposium on Intelligent Systems and Informatics (SISY), 2020 IEEE 18th International Symposium on Intelligent Systems and Informatics (SISY), IEEE, Serbia, pp. 155-160.
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Human identification is a well-researched topic that keeps evolving. Advancement in technology has made it easy to train models or use ones that have been already created to detect several features of the human face. When it comes to identifying a human face from the side, there are many opportunities to advance the biometric identification research further. This paper investigates the human face identification based on their side profile by extracting the facial features and diagnosing the feature sets with geometric ratio expressions. These geometric ratio expressions are computed into feature vectors. The last stage involves the use of weighted means to measure similarity. This research addresses the problem of using an eXplainable Artificial Intelligence (XAI) approach. Findings from this research, based on a small data-set, conclude that the used approach offers encouraging results. Further investigation could have a significant impact on how face profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89.
Saroya, M, Best, G & Hollinger, GA 1970, 'Online Exploration of Tunnel Networks Leveraging Topological CNN-based World Predictions', 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 6038-6045.
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Sayem, ASM, Esselle, KP & Hashmi, RM 1970, 'Robustness Analysis of the Polymer-Conductive-Mesh Composite for the Realization of Transparent and Flexible Wearable Antennas', 2020 14th European Conference on Antennas and Propagation (EuCAP), 2020 14th European Conference on Antennas and Propagation (EuCAP), IEEE, Copenhagen, Denmark, pp. 1-4.
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In this paper the morphology of the polydimethylsiloxane (PDMS)-flexible-conductive-mesh composite has been studied to evaluate its suitability in the realization of robust, flexible, transparent, wearable antennas that can withstand multiple bending operations. We have utilized conductive mesh made out of VeilShield from Less EMF which has about 70% light transmittance and is highly flexible. On the other hand, PDMS is a highly flexible and optically transparent polymer. Uncured PDMS is in liquid form and upon curing it transforms to a robust flexible substrate and forms a strong bonding with the conductive mesh, VeilShield. We have examined the composite through Scanning Electron Microscope (SEM) images during and after multiple bending operations. Later, we have designed a simple patch antenna operating at 2.45 GHz band using our selected materials. For performance evaluation the antenna is tested in both free space and under bent conditions and the results are presented in this paper.
Scheide, E, Best, G & Hollinger, GA 1970, 'Learning Behavior Trees for Robotic Task Planning by Monte Carlo Search over a Formal Grammar', RSS Workshop on Learning (in) Task and Motion Planning.
Sedehi, O, Papadimitriou, C & Katafygiotis, L 1970, 'HIERARCHICAL BAYESIAN UNCERTAINTY QUANTIFICATION OF DYNAMICAL MODELS UTILIZING MODAL STATISTICAL INFORMATION', Proceedings of the XI International Conference on Structural Dynamics, XI International Conference on Structural Dynamics, EASD, pp. 3599-3606.
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Updating dynamical models based on experimental modal information has become an important topic in structural health monitoring. This paper revisits this significant problem and develops a new two-stage hierarchical Bayesian framework, aiming to improve the quantification of uncertainty. This framework employs the Bayesian FFT approach to identify the modal parameters along with their identification uncertainty, and then, it utilizes this modal information to update the stiffness matrix. It can quantify the variability of both modal and structural parameters over multiple data sets while characterizing their identification uncertainty as well. This framework also proposes a new basis to assign optimal and appropriate weights for modal features. It weights different modal parameters based on the sum of identification uncertainty and the ensemble variability such that more uncertain parameters will be assigned smaller weights. As a result, a coherent quantification of uncertainty is attained by following Bayesian probability logic. Ultimately, experimental data from a shear building structure is used for demonstrating the proposed framework.
Sergiienko, NY, Neshat, M, Da Silva, LSP, Alexander, B & Wagner, M 1970, 'Design optimisation of a multi-mode wave energy converter', Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE.
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A wave energy converter (WEC) similar to the CETO system developed by Carnegie Clean Energy is considered for design optimisation. This WEC is able to absorb power from heave, surge and pitch motion modes, making the optimisation problem nontrivial. The WEC dynamics is simulated using the spectraldomain model taking into account hydrodynamic forces, viscous drag, and power take-off forces. The design parameters for optimisation include the buoy radius, buoy height, tether inclination angles, and control variables (damping and stiffness). The WEC design is optimised for the wave climate at Albany test site in Western Australia considering unidirectional irregular waves. Two objective functions are considered: (i) maximisation of the annual average power output, and (ii) minimisation of the levelised cost of energy (LCoE) for a given sea site. The LCoE calculation is approximated as a ratio of the produced energy to the significant mass of the system that includes the mass of the buoy and anchor system. Six different heuristic optimisation methods are applied in order to evaluate and compare the performance of the best known evolutionary algorithms, a swarm intelligence technique and a numerical optimisation approach. The results demonstrate that if we are interested in maximising energy production without taking into account the cost of manufacturing such a system, the buoy should be built as large as possible (20 m radius and 30 m height). However, if we want the system that produces cheap energy, then the radius of the buoy should be approximately 11-14 m while the height should be as low as possible. These results coincide with the overall design that Carnegie Clean Energy has selected for its CETO 6 multimoored unit. However, it should be noted that this study is not informed by them, so this can be seen as an independent validation of the design choices.
Shafiei, S, Mihaita, A-S, Nguyen, H, Bentley, C & Cai, C 1970, 'Short-Term Traffic Prediction Under Non-Recurrent Incident Conditions Integrating Data-Driven Models and Traffic Simulation', Transportation Research Board 99th Annual Meeting, Transportation Research Board 99th Annual Meeting, Washington D.C..
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Predicting the traffic condition in urban networks is a priority for all traffic management centers around the world. This becomes very challenging especially when the network is affected by traffic incidents which vary in both time and space. Although data-driven machine learning (ML) modeling can be considered as an ideal tool for short-term traffic predictions, its performance is severely degraded when little historical traffic information is available under non-recurrent incident conditions. This paper addresses this challenge by integrating both data-driven and traffic simulation modeling. Instead of directly predicting the traffic states using limited historical data, we apply data-driven models to reinforce the traffic microsimulation. More explicitly, we employ ML models to predict origin-destination (OD) demand flows based on historical day-to-day demand flows. The traffic simulation uses the freshly reported incident information and the predicted OD demand flows obtained from ML models to forecast the future traffic states under non-recurrent incident conditions. Since accurate OD flows cannot directly measured in large-scale areas, we propose an OD demand rolling-horizon estimation problem to estimate demand flows based on the most recent measured link volumes. Results show that Decision Tree method outperforms other ML models in OD demand flow prediction. Finally, we showcase the capability of the proposed data-driven enforced traffic simulation platform for incident impact analysis in a real –life subnetwork from Sydney, Australia.
Shahariar, GMH, Bodisco, TA, Chu Van, T, Surawski, N, Sajjad, M, A., K, Ristovski, Z & Brown, RJ 1970, 'Optimisation of driving-parameters and emissions of a diesel-vehicle using principal component analysis (PCA)', Australasian Fluid Mechanics Conference (AFMC), 22nd Australasian Fluid Mechanics Conference AFMC2020, The University of Queensland.
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Shahid, I, Thalakotuna, D, Karmokar, DK & Heimlich, M 1970, 'Reactively Loaded Microstrip Line Based 1-D Periodic Structure with All-Pass, Low Pass and Stopband Filter Characteristics', 2020 4th Australian Microwave Symposium (AMS), 2020 4th Australian Microwave Symposium (AMS), IEEE, Sydney, Australia, pp. 1-2.
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A 1-D periodic structure having two shorted patches bearing dissimilar dimensions, in a unit cell, have been investigated for its application as a reconfigurable filter. To demonstrate the working mechanism, reactive loading to microstrip line is changed by periodically altering the patch connection to ground through presence/absence of shorting vias. Six filter structures with same unit cell dimensions and different switching patterns have been fabricated that represent different configurations of the filter. The proposed structure behaves as an all-pass filter in one state, and as a low pass filter and bandstop filter in other states with tunable cutoff frequencies.
Shang, D, Zhang, G & Lu, J 1970, 'Fast concept drift detection using unlabeled data', Developments of Artificial Intelligence Technologies in Computation and Robotics, 14th International FLINS Conference (FLINS 2020), WORLD SCIENTIFIC.
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Shannon, A, Atanassov, K, Sotirova, E & Vasilev, V 1970, 'Generalized Net Model for Creating and Evaluating of Educational Content', 2020 IEEE 10th International Conference on Intelligent Systems (IS), 2020 IEEE 10th International Conference on Intelligent Systems (IS), IEEE, pp. 517-520.
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© 2020 IEEE. The research expounded in this paper is a continuation of previous investigations into the modelling of information flow with a typical university. A generalized net model which describes the process of creating and evaluation of educational content using a set of criteria is constructed. The example for education of medical students in diabetes mellitus and possibilities for venous thrombotic disease is given.
Sharafkhani, N, Qiu, X & Wei, D 1970, 'Acoustic impedance of a folded rectangular cross shape cavity', Proceedings of 2020 International Congress on Noise Control Engineering, INTER-NOISE 2020.
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Accurate estimation of the acoustics impedance of a folded cavity is important for predicting its sound absorption performance. In this paper, the effect caused by a right angle sharp bend on the acoustic impedance of a folded cavity with a rectangular cross section is investigated by comparing the sound absorption coefficients for the folded cavities to the straight ducts with the same length and cross section area. Based on the simulation results, a corrected parameter is proposed to calculate the equivalent geometries for the bend by dividing the cavity into folded and straight sections. The experiment results confirm the feasibility of the proposed formula.
SHARARI, N, FATAHI, B & HOKMABADI, AS 1970, 'IMPACT OF WALL SUPPORT CONDITIONS ON SEISMIC RESPONSE OF GROUND-SUPPORTED REINFORCED CONCRETE CONTAINMENT TANKS', WIT Transactions on The Built Environment, SUSI 2020, WIT Press, pp. 139-151.
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Concrete liquid storage tanks are commonly used in regions that may be highly seismic, for the storage of water, petroleum products and other chemicals. In some cases, such as for liquefied natural gas (LNG) tanks, a secondary concrete containment is designed for external protection, ignoring any direct contact or interaction with the inner storage liquid by creating a gap, as another inner tank is used to hold the liquid. Typical secondary containment tanks for LNG are circular, upright concrete tanks, with fixed roofs, while the support wall conditions at its base can be hinged or fixed. In this study, the nonlinear behavior of ground supported circular reinforced concrete containment tank under the effect of the seismic loads is investigated for both hinged and fixed wall support conditions. A three-dimensional finite element model considering material nonlinearities was included. In particular, the Concrete Damage Plasticity (CDP) model, capturing the possible tensile cracking and compressive crushing of the concrete containment systems under seismic loads was adopted. By adopting time history analyses, deformation and stresses developed in the tank were assessed when subjected to large earthquakes, namely the 1994 Northridge and 1995 Kobe earthquakes, while frequency domain analyses were also conducted, to obtain the natural period and mode shapes for different wall support conditions. The results showed that in the hinged tank, the walls experience higher structural responses (in terms of shear force and bending moment); compared with the fixed tank, particularly around the mid-height zone of the tank wall. Conversely, at the base of the fixed tank, shear forces and bending moments were higher, compared with the hinged tank’s base. Under the effects of large earthquakes, both tanks experienced damage, yet larger seismic forces upon a hinged tank could potentially create more damage.
Sharma, S, Singh, M, Jayaram-Babu, N, Rao, KV & Singh, J 1970, 'Investigation of Properties of Mg and Al Based Nano Hybrid-Metallic Composites Processed Through Liquid Processing Technique', ADVANCES IN DESIGN, SIMULATION AND MANUFACTURING II, International Conference on Design, Simulation, Manufacturing, Springer International Publishing, Lutsk, Ukraine, pp. 466-476.
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This research paper provides comprehensive and extraordinary efforts to carry out novel mechanical behaviour, corrosion testing and metallurgical characterization of aluminium alloy (Al-6061) and Magnesium alloy (Mg-AZ91D) reinforced nano-hybrid metallic composites processed using the liquid processing stir casting technique. These stir casted hybrid nano-metallic composites were manufactured using nano size reinforcements that are SiC, Graphite and Alumina of a size of ~100 nm. The finding of comparative results of casted composites was done by using potentio-dynamic polarization tests in the form of capacitance performance of dielectric properties. The results were reported out and the best results were achieved for aluminium reinforced graphite based composites with better corrosion behaviour performances and high hardness and tensile value of the fabricated composites. The experimental data for Mg-Graphite/SiC/Al2O3 alloy show good arc-like performance over the frequency range with less impedance. The results also illustrated the good arc-like/weber behaviour over the frequency range examined, and indicate decent corrosion behaviour.
Sharma, V, Hossain, MJ, Ali, SMN, Kashif, M & Fernandez, E 1970, 'Design and Implementation of Trans-Z-Source Inverter-Fed Induction Motor Drive with Fault-Tolerant Capability', 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE, pp. 690-695.
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© 2020 IEEE. The traditional Z-source inverter suffers from large voltage stress across the switches, and discontinuous source current, which is not appropriate for the electric motor drives applications. This paper presents a design and thorough analysis of a trans-Z-source (transformer-based Z-source) with higher boost capability and negligible leakage inductance which overcomes the drawbacks of traditional Z-source inverters (ZSI). Additionally, the fault-tolerant capability of the proposed trans-ZSI is investigated for open-circuit and short-circuit faults occurring in the power semiconductor switches of the inverter module. It proposes a highly efficient faulty leg identification method which is independent of the temperature rise occurring due to high current in the faulty mode. The proposed fault-tolerant scheme is characterized by low cost, fast fault diagnosis irrespective of load, and maintaining post-fault speed characteristics of motor identical to pre-fault characteristics. The experimental results are presented to validate the effectiveness of the proposed method for induction motor drives. Also, a comparative study with similar fault diagnosis strategies is tabulated to validate the potential of the proposed fault-tolerant strategy.
Sharma, V, Mukhopadhyay, S, Hossain, MJ, Nawazish Ali, SM & Kashif, M 1970, 'A-source Inverter-fed PMSM drive with fault-tolerant capability for Electric Vehicles', 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), IEEE, Delft, Netherlands, pp. 241-246.
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© 2020 IEEE. This paper demonstrates the fault tolerance performance of the A-source inverter fed PMSM drive system for electric vehicles. The proposed topology of the A-source impedance network is implemented to obtain high gain dc output for the inverter module. This high magnitude dc output is fed to the PMSM drive system for electric vehicle applications. In addition to the design strategies of the proposed system, this paper presents a fast fault identification and diagnosis strategy under switch faults occurring in the inverter module. The utilized method is robust to common converter issues such as load variations and input power fluctuations. The simulation results of the proposed fault-tolerant operation have been presented in this paper. This efficient fault-tolerant operation substantially improved the reliability of the overall system. The achieved results demonstrate the effectiveness of the proposed system with fault-tolerant capability for electric vehicle applications.
Shen, S, Zhu, T, Ye, D, Yang, M, Liao, T & Zhou, W 1970, 'Simultaneously Advising via Differential Privacy in Cloud Servers Environment', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ICA3PP 2019: Algorithms and Architectures for Parallel Processing, Springer International Publishing, Melbourne, VIC, Australia, pp. 550-563.
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Due to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers’ capabilities and make optimal decisions on selecting the best available servers for users’ tasks. We consider the process that users learn servers’ capabilities as a multi-agent Reinforcement learning process. The learning speed and efficiency in Reinforcement learning can be improved by transferring the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by a requirement during experience transfer, which all learning agents in a Reinforcement learning environment must have the completely same available choices, also called actions. To address the above limit, this paper proposes a novel differential privacy agent advising approach in Reinforcement learning. Our proposed approach can significantly improve the conventional advising frameworks’ application when agents’ choices are not the completely same. The approach can also speed up the Reinforcement learning by the increase of possibility of experience transfer among agents with different available choices.
Shen, T, Geng, X, Long, G, Jiang, J, Zhang, C & Jiang, D 1970, 'Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering', PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 29th International Joint Conference on Artificial Intelligence, IJCAI-INT JOINT CONF ARTIF INTELL, ELECTR NETWORK, pp. 2227-2233.
Shen, T, Mao, Y, He, P, Long, G, Trischler, A & Chen, W 1970, 'Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning', Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, pp. 8980-8994.
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In this work, we aim at equipping pre-trained language models with structured knowledge. We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme that exploits relational knowledge underlying the text. This is fulfilled by using a linked knowledge graph to select informative entities and then masking their mentions. In addition, we use knowledge graphs to obtain distractors for the masked entities, and propose a novel distractor-suppressed ranking objective that is optimized jointly with masked language model. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training, to inject language models with structured knowledge via learning from raw text. It is more efficient than retrieval-based methods that perform entity linking and integration during finetuning and inference, and generalizes more effectively than the methods that directly learn from concatenated graph triples. Experiments show that our proposed model achieves improved performance on five benchmarks, including question answering and knowledge base completion.
Sheng, D & Sloan, SW 1970, 'Load stepping schemes and unbalanced force norms in geotechnical analysis', NUMERICAL MODELS IN GEOMECHANICS - NUMOG VII, 7th International Symposium on Numerical Models in Geomechanics (NUMOG), CRC Press, GRAZ, AUSTRIA, pp. 201-208.
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Sheng, Z, Tuan, HD, Nasir, AA & Poor, HV 1970, 'PLS for Wireless Interference Networks in the Short Blocklength Regime with Strong Wiretap Channels', GLOBECOM 2020 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - 2020 IEEE Global Communications Conference, IEEE, Taipei, Taiwan, pp. 1-6.
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This paper considers a wireless interference network in which the communication between multiple transmitter-user pairs is overheard by multiple eavesdroppers (EVs). Based on knowledge of the channel distribution, the goal is to maximize the worst users' secrecy rate under both long (infinite) blocklength and short (finite) blocklength transmissions. Under long blocklength transmission, the performance of the existing algorithms is unsatisfactory when the wiretapped channels are sufficiently strong. To address this drawback, we adopt a time-fraction based information and artificial noise (AN) transmission, under which first the information is transmitted within the initial fraction of the time slot and then AN is transmitted within the remaining fraction. Accordingly, the problem of join optimization of the time fractions, transmit power, and AN power to maximize the minimum secrecy rate is proposed and computed by a path-following algorithm, which iterates feasible points and converges at least to a locally optimal solution. A similar problem under short blocklength transmission is also proposed and computed. The provided simulations results clearly show the merits of the proposed approach.
Shi, L, Li, S, Zheng, Q, Cao, L, Yang, L & Pan, G 1970, 'Maximum Entropy Reinforcement Learning with Evolution Strategies', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Shi, Q, Wu, N, Wang, H, Nguyen, DN & Huang, X 1970, 'Joint Phase Noise Estimation and Decoding in OFDM-IM', GLOBECOM 2020 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - 2020 IEEE Global Communications Conference, IEEE, Taipei, Taiwan, pp. 1-6.
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This paper proposes a low-complexity joint phase noise (PHN) estimation and decoding algorithm for orthogonal frequency division multiplexing relying on index modulation (OFDM-IM) systems. A factor graph (FG) is constructed based on the truncated discrete cosine transform (DCT) expansion model for the variation of PHN. In order to explicitly take into account the structured and sparse a priori information of the frequency-domain symbols provided by the soft-in soft-out (SISO) decoder, the generalized approximate message passing (GAMP) algorithm is employed. Furthermore, to solve the unknown and nonlinear transform matrix problem introduced by the PHN, the mean-field (MF) method is invoked at the observation nodes on the FG. Monte Carlo simulations show the superiority of the proposed algorithm over the existing variational inference (VI) and extended Kalman filter (EKF) methods in terms of their bit error rate (BER) performance and complexity. In addition, we demonstrate that the OFDM-IM scheme outperforms its conventional OFDM counterpart in the presence of PHN.
Shi, T, Zhang, Q, Wang, X, Li, X, Xue, Z & Hao, J 1970, 'Description and Findings of OPPO’s Machine Translation Systems for CCMT 2020', MACHINE TRANSLATION, CCMT 2020, 16th China Conference on Machine Translation (CCMT), Springer Singapore, ELECTR NETWORK, pp. 83-97.
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Shi, Y, Yu, X, Campbell, D & Li, H 1970, 'Where Am I Looking At? Joint Location and Orientation Estimation by Cross-View Matching', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Seattle, WA, USA, pp. 4063-4071.
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Cross-view geo-localization is the problem of estimating the position and orientation (latitude, longitude and azimuth angle) of a camera at ground level given a large-scale database of geo-tagged aerial (e.g., satellite) images. Existing approaches treat the task as a pure location estimation problem by learning discriminative feature descriptors, but neglect orientation alignment. It is well-recognized that knowing the orientation between ground and aerial images can significantly reduce matching ambiguity between these two views, especially when the ground-level images have a limited Field of View (FoV) instead of a full field-of-view panorama. Therefore, we design a Dynamic Similarity Matching network to estimate cross-view orientation alignment during localization. In particular, we address the cross-view domain gap by applying a polar transform to the aerial images to approximately align the images up to an unknown azimuth angle. Then, a two-stream convolutional network is used to learn deep features from the ground and polar-transformed aerial images. Finally, we obtain the orientation by computing the correlation between cross-view features, which also provides a more accurate measure of feature similarity, improving location recall. Experiments on standard datasets demonstrate that our method significantly improves state-of-the-art performance. Remarkably, we improve the top-1 location recall rate on the CVUSA dataset by a factor of 1.5× for panoramas with known orientation, by a factor of 3.3× for panoramas with unknown orientation, and by a factor of 6× for 180 -FoV images with unknown orientation. ◦
Shi, Y, Yu, X, Liu, L, Zhang, T & Li, H 1970, 'Optimal feature transport for cross-view image geo-localization', AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, pp. 11990-11997.
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This paper addresses the problem of cross-view image geolocalization, where the geographic location of a ground-level street-view query image is estimated by matching it against a large scale aerial map (e.g., a high-resolution satellite image). State-of-the-art deep-learning based methods tackle this problem as deep metric learning which aims to learn global feature representations of the scene seen by the two different views. Despite promising results are obtained by such deep metric learning methods, they, however, fail to exploit a crucial cue relevant for localization, namely, the spatial layout of local features. Moreover, little attention is paid to the obvious domain gap (between aerial view and ground view) in the context of cross-view localization. This paper proposes a novel Cross-View Feature Transport (CVFT) technique to explicitly establish cross-view domain transfer that facilitates feature alignment between ground and aerial images. Specifically, we implement the CVFT as network layers, which transports features from one domain to the other, leading to more meaningful feature similarity comparison. Our model is differentiable and can be learned end-to-end. Experiments on large-scale datasets have demonstrated that our method has remarkably boosted the state-of-the-art cross-view localization performance, e.g., on the CVUSA dataset, with significant improvements for top-1 recall from 40.79% to 61.43%, and for top-10 from 76.36% to 90.49%.We expect the key insight of the paper (i.e., explicitly handling domain difference via domain transport) will prove to be useful for other similar problems in computer vision as well.
Shi, Z, Wu, D, Huang, J, Wang, Y-K & Lin, C-T 1970, 'Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow (UK), pp. 1-8.
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© 2020 IEEE. Dimensionality reduction is an important operation in information visualization, feature extraction, clustering, regression, and classification, especially for processing noisy high dimensional data. However, most existing approaches preserve either the global or the local structure of the data, but not both. Approaches that preserve only the global data structure, such as principal component analysis (PCA), are usually sensitive to outliers. Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph. We propose a novel linear dimensionality reduction approach, supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN), to integrate neighborhood-free supervised discriminative sparse PCA and projected clustering with adaptive neighbors. As a result, both global and local data structures, as well as the label information, are used for better dimensionality reduction. Classification experiments on nine high-dimensional datasets validated the effectiveness and robustness of our proposed SDSPCAAN.
Shi, Z, Zhang, JA, Xu, R, Cheng, Q & Pearce, A 1970, 'Towards Environment-Independent Human Activity Recognition using Deep Learning and Enhanced CSI', GLOBECOM 2020 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - 2020 IEEE Global Communications Conference, IEEE, Taipei, Taiwan, pp. 1-6.
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© 2020 IEEE. Deep learning has shown a strong potential in device-free human activity recognition (HAR). However, a fundamental challenge is ensuring accuracy, without re-training, when exposing a previously trained architecture to a new or unseen environment. To overcome the aforementioned challenge, this paper proposes an environment-robust channel state information (CSI) based HAR by leveraging the properties of a matching network (MatNet) and enhanced features (HAR-MN-EF). To improve the CSI quality, we propose a CSI cleaning and enhancement method (CSI-CE) that includes two key stages: activity-related information extraction (ARIE) and correlation feature extraction based on principal component analysis (CFE-PCA). The ARIE stage is able to effectively enhance the activity-dependent features whilst mitigating behavior-unrelated information. The CFE-PCA stage further improves the extracted features by filtering out the residual activity-unrelated data and the residual noise contained in signals from the former stage. The extracted features are then sequenced into the MatNet to create an environment-robust HAR. Experimental results confirm that an architecture trained by the proposed HAR-MN-EF can be directly adapted to a new environment, achieving reliable sensing accuracies without requiring additional effort.
Shi, Z, Zhang, JA, Xu, RY & Cheng, Q 1970, 'WiFi-Based Activity Recognition using Activity Filter and Enhanced Correlation with Deep Learning', 2020 IEEE International Conference on Communications Workshops (ICC Workshops), 2020 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, Dublin, Ireland, pp. 1-6.
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Device-free WiFi sensing utilizing channel state information (CSI) is attractive for human activity recognition (HAR). However, several challenging problems are yet to be resolved, e.g., difficulty in extracting proper features from input signals, susceptibility to the phase shift of CSI and difficulty in identifying similar behaviors (e.g., lying and standing). In this paper, we aim to tackle these problems by proposing a novel scheme for CSI-based HAR that uses activity filter-based deep learning network (HAR-AF-DLN) with enhanced correlation features. We first develop a novel CSI compensation and enhancement (CCE) method to compensate for the timing offset between the WiFi transmitter and receiver, enhance activity-related signals and reduce the dimension of inputs to DLN. Then, we design a novel activity filter (AF) to differentiate similar activities (e.g., standing and lying) based on the enhanced CSI correlation features obtained from CCE. Extensive simulation results demonstrate that our proposed HAR-AF-DLN scheme outperforms state-of-the-art methods with significantly improved recognition accuracy (especially for similar activities) and notably reduced training time.
Shivangi, TP, Rahimi, M, Gargiulo, G, Kailath, BJ & Hamilton, TJ 1970, 'A Silicon Neuron-based Bio-Front-End for Ultra Low Power Bio-Monitoring at the Edge', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 3043-3048.
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Shu, Y, Sui, Y, Zhang, H & Xu, G 1970, 'Perf-AL', Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), ESEM '20: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement, ACM, pp. 1-11.
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© 2020 IEEE Computer Society. All rights reserved. Context: Many software systems are highly configurable. Different configuration options could lead to varying performances of the system. It is difficult to measure system performance in the presence of an exponential number of possible combinations of these options. Goal: Predicting software performance by using a small configuration sample. Method: This paper proposes PERF-AL to address this problem via adversarial learning. Specifically, we use a generative network combined with several different regularization techniques (L1 regularization, L2 regularization and a dropout technique) to output predicted values as close to the ground truth labels as possible. With the use of adversarial learning, our network identifies and distinguishes the predicted values of the generator network from the ground truth value distribution. The generator and the discriminator compete with each other by refining the prediction model iteratively until its predicted values converge towards the ground truth distribution. Results:We argue that (i) the proposed method can achieve the same level of prediction accuracy, but with a smaller number of training samples. (ii) Our proposed model using seven real-world datasets show that our approach outperforms the state-of-the-art methods. This help to further promote software configurable performance. Conclusion: Experimental results on seven public real-world datasets demonstrate that PERF-AL outperforms state-of-the-art software performance prediction methods.
Singh, AK & Tao, X 1970, 'BCINet: An Optimized Convolutional Neural Network for EEG-Based Brain-Computer Interface Applications', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 582-587.
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Singh, AK, Aldini, S, Leong, D, Wang, Y-K, Carmichael, MG, Liu, D & Lin, C-T 1970, 'Prediction Error Negativity in Physical Human-Robot Collaboration', 2020 8TH INTERNATIONAL WINTER CONFERENCE ON BRAIN-COMPUTER INTERFACE (BCI), 8th International Winter Conference on Brain-Computer Interface (BCI), IEEE, SOUTH KOREA, Tech Univ Berlin, Korea Univ Machine Learning Grp, BK21 Plus Global Leader, Gangwon, pp. 58-63.
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Singh, AK, Aldini, S, Leong, D, Wang, Y-K, Carmichael, MG, Liu, D & Lin, C-T 1970, 'Prediction Error Negativity in Physical Human-Robot Collaboration', 2020 8th International Winter Conference on Brain-Computer Interface (BCI), 2020 8th International Winter Conference on Brain-Computer Interface (BCI), IEEE, Gangwon, Korea (South), pp. 1-6.
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Cognitive conflict is a fundamental phenomenon of human cognition, particularly during interaction with the real world. Understanding and detecting cognitive conflict can help to improve interactions in a variety of applications, such as in human-robot collaboration (HRC), which involves continuously guiding the semi-autonomous robot to perform a task in given settings. There have been several works to detect cognitive conflict in HRC but without physical control settings. In this work, we have conducted the first study to explore cognitive conflict using prediction error negativity (PEN) in physical human-robot collaboration (pHRC). Our results show that there was a statistically significant (p =. 047) higher PEN for conflict condition compared to normal conditions, as well as a statistically significant difference between different levels of PEN (p =. 020). These results indicate that cognitive conflict can be detected in pHRC settings and, consequently, provide a window of opportunities to improve the interaction in pHRC.
Singh, M & Maharana, S 1970, 'Investigating the EDM parameter effects on aluminium based metal matrix composite for high MRR', Materials Today: Proceedings, Elsevier BV, pp. 3858-3863.
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Song, B, Jing, Z, Jay Guo, Y, Liu, RP & Zhou, Q 1970, 'A Novel Measure to Quantify the Robustness of Social Network Under the Virus Attacks', Springer Singapore, pp. 189-200.
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Song, J, Bai, F, Zhao, L, Huang, S & Xiong, R 1970, 'Efficient two step optimization for large embedded deformation graph based SLAM', 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 9419-9425.
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© 2020 IEEE. Embedded deformation graph is a widely used technique in deformable geometry and graphical problems. Although the technique has been transmitted to stereo (or RGB-D) camera based SLAM applications, it remains challenging to compromise the computational cost as the model grows. In practice, the processing time grows rapidly in accordance with the expansion of maps. In this paper, we propose an approach to decouple the nodes of deformation graph in large scale dense deformable SLAM and keep the estimation time to be constant. We observe that only partial deformable nodes in the graph are connected to visible points. Based on this fact, the sparsity of the original Hessian matrix is utilized to split the parameter estimation into two independent steps. With this new technique, we achieve faster parameter estimation with amortized computation complexity reduced from O(n2) to almost O(1). As a result, the computational cost barely increases as the map keeps growing. Based on our strategy, the computational bottleneck in large scale embedded deformation graph based applications will be greatly mitigated. The effectiveness is validated by experiments, featuring large scale deformation scenarios.
Song, Y, Zhang, G, Lu, H & Lu, J 1970, 'A Fuzzy Drift Correlation Matrix for Multiple Data Stream Regression', 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Glasgow, United Kingdom, pp. 1-6.
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How to handle concept drift problem is a big challenge for algorithms designed for the data streams. Currently, techniques related to the concept drift problem focus on single data stream. However, it normally needs to handle multiple relevant data streams in the real-world application. Current concept drift methods can not be directly used in the multistream setting. They can only be limitedly applied on each stream separately, which omits the drift correlation between streams. In the multi-stream scenario, when drift occurs in a stream, other streams may face or have faced a similar drift problem as well. This pattern of simultaneous or delayed occurrence of drift is critical to analyze and predict multiple streams as a whole dynamic system. To fill the gap in the multi-stream scenario, this paper proposes a fuzzy drift variance (FDV) to measure the correlated drift patterns among streams. FDA is able to present how the pattern of drift occurrence for any two streams correlates and how delayed this correlation is. Seven synthetic streams are designed to validate FDA. The experimental results show a good presentation ability of FDA for drift-correlated multiple streams.
Soomro, WA, Guo, Y, Lu, HY & Jin, JX 1970, 'Advancements and Impediments in Applications of High-Temperature Superconducting Material', 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE, pp. 1-4.
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Soro, A, Brown, R, Wyeth, P & Turkay, S 1970, 'Towards a Smart and Socialised Augmented Reality', Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-8.
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Stratton, P, Wabnitz, A & Hamilton, TJ 1970, 'A Spiking Neural Network Based Auto-encoder for Anomaly Detection in Streaming Data', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, Australia, pp. 1981-1988.
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Anomaly Detection (AD) is useful for a range of applications including cyber security, health analytics, robotics, defense and big data. Automating the detection of anomalies is necessary to deal with large volumes of data and to satisfy real time processing constraints. Current Machine Learning (ML) methods have had some success in the automated detection of anomalies, but no ideal ML solutions have been found for any domain. Spiking Neural Networks (SNNs), an emerging ML technique, have the potential to do AD well, especially for Edge applications where it needs to be low power, readily adaptable, autonomous and reliable. Here we investigate SNNs doing anomaly detection on streams of text. We show that SNNs are well suited for detecting anomalous character sequences, that they can learn rapidly, and that there are many optimizations to the SNN architecture and training that can improve AD performance.
Su, Z, Lin, T, Xu, Q, Chen, N, Yu, S & Guo, S 1970, 'An Online Pricing Strategy of EV Charging and Data Caching in Highway Service Stations', 2020 16th International Conference on Mobility, Sensing and Networking (MSN), 2020 16th International Conference on Mobility, Sensing and Networking (MSN), IEEE, pp. 81-85.
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Sun, C, Ni, W & Wang, X 1970, 'Computation Offloading and Trajectory Design for UAV-assisted Mobile Computing Systems', 2020 International Conference on Wireless Communications and Signal Processing (WCSP), 2020 International Conference on Wireless Communications and Signal Processing (WCSP), IEEE, pp. 528-533.
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Sun, H-H, Ding, C, Zhu, H & Guo, YJ 1970, 'A Method for Bandwidth Enhancement of Cross-Dipole Antennas with Compact Configurations', 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, Montreal, QC, Canada, pp. 571-572.
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In this paper, a new method to broaden the bandwidth of dual-polarized cross-dipole antennas is presented. By connecting a thin loop to a traditional cross-dipole, additional resonant points are introduced and the bandwidth is broadened. This method does not increase the physical dimension of the antenna and has little influence on radiation performances. A loop-connected cross-dipole antenna is presented to verify the method. The bandwidth it achieves is 66.7% from 1.65 GHz to 3.30 GHz with a very compact radiator size. The antenna also has a high port isolation level and stable radiation performances, making it highly suitable for the base station application.
Sun, R, Zhu, Q, Chen, C, Wang, X, Zhang, Y & Wang, X 1970, 'Discovering Cliques in Signed Networks Based on Balance Theory', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 666-674.
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Enumerating cohesive subgraphs is a fundamental problem for signed network analysis. In this paper, we propose a novel model, called maximal signed k-clique, which aims to find cohesive subgraphs in signed networks based on clique property and balance theory. Given a signed graph G, a set of nodes C is a maximal signed k-clique if (1) $$|C|\ge k$$ and C is a clique without any unbalanced triangle; and (2) C is maximal. We show the problem of enumerating all maximal signed k-cliques is NP-hard. Novel pruning techniques are proposed to significantly filter the searching space. An efficient algorithm, SKC, is developed to handle large networks. Comprehensive experiments on four real-world datasets are conducted to demonstrate the efficiency and effectiveness of the proposed algorithms.
Sun, X, Jiang, Y & Li, W 1970, 'Residual Attention Based Network for Automatic Classification of Phonation Modes', 2020 IEEE International Conference on Multimedia and Expo (ICME), 2020 IEEE International Conference on Multimedia and Expo (ICME), IEEE, pp. 1-6.
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Sun, Z, Hua, X-S, Yao, Y, Wei, X-S, Hu, G & Zhang, J 1970, 'CRSSC: Salvage Reusable Samples from Noisy Data for Robust Learning', Proceedings of the 28th ACM International Conference on Multimedia, MM '20: The 28th ACM International Conference on Multimedia, ACM, pp. 92-101.
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Surawski, N, Tularam, A & Braddock, R 1970, 'Sustainability of groundwater extraction for the Pimpama Coastal-plain, Queensland, Australia', MODSIM 2005 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings, MODSIM 2005 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings, Melbourne, VIC, pp. 2366-2372.
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This paper presents results for field scale seawater intrusion simulations along the Pimpama coastal-plain using SALTFLOW. The simulations incorporated groundwater extraction and sea-level-rise, along with a sensitivity analysis for three physical forcings. The three physical forcings studied in the sensitivity analysis included variations in; freshwater inflow, sea-level-rise and groundwater extraction. The field site selected to perform the field scale seawater intrusion simulations was the Pimpama coastal-plain, which is located between Brisbane and the Gold Coast in South-East Queensland (see Figure 1). The region is undergoing significant population growth and is an area of hydrological interest, since groundwater extraction is relied upon to provide water for domestic and agricultural supply. In addition to land use change, further stresses will be placed upon the Pimpama aquifer due to a predicted sea-level rise, whilst population growth should lead to a concomitant rise in groundwater extraction. Whether the extraction of groundwater from the Pimpama aquifer is sustainable for meeting future water use needs is a significant resource supply problem and as yet no attempt has been made to assess the sustainability of groundwater extraction in this area. Thus, the simulations performed in this study represent the first attempt to assess the sustainability of groundwater extraction for the Pimpama coastal-plain, in light of the stresses of sea-level-rise, and a potential seawater intrusion. The SALTFLOW package was selected for the simulation of seawater intrusion for the Pimpama coastal-plain. Preliminary work tested the computational, physical and other modeling attributes for a suite of packages, as well as the performance of each package against benchmark problems from the literature. Ultimately, the SALTFLOW package was selected over PDE2D and SUTRA due to the ease with which time varying boundary conditions for groundwater flow could be incorpo...
Sutjipto, S, Lai, Y, Carmichael, M & Paul, G 2020, 'Fitts’ law in the presence of interface inertia', 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Annual International Conference of the IEEE Engineering in Medicine & Biology Society, IEEE, Montreal, QC, Canada, Canada, pp. 4749-4752.
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Collaborative robots are advancing the healthcare frontier, in applications such as rehabilitation and physical therapy. Effective physical collaboration in human-robot systems require an understanding of partner intent and capability. Various modalities exist to convey such information between human agents, however, natural interactions between humans and robots are difficult to characterise and achieve. To enhance inter-agent communication, predictive models for human movement have been devised. One such model is Fitts' law. Many works using Fitts' law rely on massless interfaces. However, this coupling between human and robot, and the inertial effects experienced, may affect the predictive ability of Fitts' law. Experiments were conducted on human-robot dyads during a target-directed force exertion task. From the interactions, the results indicate that there is no observable effect regarding Fitts' law's predictive ability.
Sutjipto, S, Lai, Y, Carmichael, MG & Paul, G 1970, 'Fitts’ law in the presence of interface inertia', 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, IEEE, Montreal, QC, Canada, Canada, pp. 4749-4752.
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Collaborative robots are advancing the healthcare frontier, in applications such as rehabilitation and physical therapy. Effective physical collaboration in human-robot systems require an understanding of partner intent and capability. Various modalities exist to convey such information between human agents, however, natural interactions between humans and robots are difficult to characterise and achieve. To enhance inter-agent communication, predictive models for human movement have been devised. One such model is Fitts' law. Many works using Fitts' law rely on massless interfaces. However, this coupling between human and robot, and the inertial effects experienced, may affect the predictive ability of Fitts' law. Experiments were conducted on human-robot dyads during a target-directed force exertion task. From the interactions, the results indicate that there is no observable effect regarding Fitts' law's predictive ability.
Syed, MS, Rafeie, M, Vandamme, D, Henderson, R & Warkiani, ME 1970, 'Selective separation of microalgae cells using inertial microfluidics', 21st International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2017, pp. 1375-1376.
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Microalgae are considered to be the most promising new source of biomass to meet world's growing demands. However, microalgal cultures are often contaminated, by bacteria or invading diatoms. The invasion of diatoms in a cell culture results in an overall decrease of biomass productivity and quality. This work demonstrates the application of inertial microfluidics for the selective separation and sorting of green microalgae cells from diatoms to obtain monocultures, at optimum conditions.
Tahmassebi, A, Martin, J, Meyer-Baese, A & Gandomi, AH 1970, 'An Interpretable Deep Learning Framework for Health Monitoring Systems: A Case Study of Eye State Detection using EEG Signals', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, Vanberra, Australia, pp. 211-218.
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Effective monitoring and early detection of deterioration in patients play an essential role in healthcare. This includes minimizing the number of emergency encounters, reducing the length of hospitalization stay, re-admission rates of the patients, and etc. Cutting-edge methods in artificial intelligence (AI) have the ability to significantly improve outcomes. However, the struggle to interpret these black box models presents a serious problem to the healthcare industry. When selecting a model, the decision to sacrifice accuracy for interpretability must be made. In this paper, we propose an interpretable framework with the ability of real-time prediction. To demonstrate the predictive power of the framework, a case study on eye state detection using electroencephalogram (EEG) signals was employed to investigate how a deep neural network (DNN) model makes a prediction, and how that prediction can be interpreted. The promising results can be used to employ more advanced models in healthcare solutions without any concern of sacrificing the interpretation.
Tancred, N, Turkay, S, Vickery, N, Wyeth, P & McCoombe, A 1970, 'Understanding Women Modders using the Serious Leisure Perspective', Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-13.
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Tang, W, Liu, L & Long, G 1970, 'Interpretable Time-series Classification on Few-shot Samples', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK, pp. 1-8.
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Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples. However, conventional time-series classification algorithms fail to tackle the few-shot scenario. Existing few-shot learning methods are proposed to tackle image or text data, and most of them are neural-based models that lack interpretability. This paper proposes an interpretable neural-based framework, namely Dual Prototypical Shapelet Networks (DPSN) for few-shot time-series classification, which not only trains a neural network-based model but also interprets the model from dual granularity: 1) global overview using representative time series samples, and 2) local highlights using discriminative shapelets. In particular, the generated dual prototypical shapelets consist of representative samples that can mostly demonstrate the overall shapes of all samples in the class and discriminative partial-length shapelets that can be used to distinguish different classes. We have derived 18 few-shot TSC datasets from public benchmark datasets and evaluated the proposed method by comparing with baselines. The DPSN framework outperforms state-of-the-art time-series classification methods, especially when training with limited amounts of data. Several case studies have been given to demonstrate the interpret ability of our model.
Tang, Y, Sui, Y, Wang, H, Luo, X, Zhou, H & Xu, Z 1970, 'All your app links are belong to us: understanding the threats of instant apps based attacks', Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ACM, pp. 914-926.
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Android deep link is a URL that takes users to a specific page of a mobile app, enabling seamless user experience from a webpage to an app. Android app link, a new type of deep link introduced in Android 6.0, is claimed to offer more benefits, such as supporting instant apps and providing more secure verification to protect against hijacking attacks that previous deep links can not. However, we find that the app link is not as secure as claimed, because the verification process can be bypassed by exploiting instant apps. In this paper, we explore the weakness of the existing app link mechanism and propose three feasible hijacking attacks. Our findings show that even popular apps are subject to these attacks, such as Twitter, Whatsapp, Facebook Message. Our observation is confirmed by Google. To measure the severity of these vulnerabilities, we develop an automatic tool to detect vulnerable apps, and perform a large-scale empirical study on 400,000 Android apps. Experiment results suggest that app link hijacking vulnerabilities are prevalent in the ecosystem. Specifically, 27.1% apps are vulnerable to link hijacking with smart text selection (STS); 30.0% apps are vulnerable to link hijacking without STS, and all instant apps are vulnerable to instant app attack. We provide an in-depth understanding of the mechanisms behind these types of attacks. Furthermore, we propose the corresponding detection and defense methods that can successfully prevent the proposed hijackings for all the evaluated apps, thus raising the bar against the attacks on Android app links. Our insights and findings demonstrate the urgency to identify and prevent app link hijacking attacks.
Tao, X, Zhang, D, Singh, AK, Prasad, M, Lin, C-T & Xu, D 1970, 'Weak Scratch Detection of Optical Components Using Attention Fusion Network', 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), IEEE, ELECTR NETWORK, pp. 855-862.
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© 2020 IEEE. Scratches on the optical surface can directly affect the reliability of the optical system. Machine vision-based methods have been widely applied in various industrial surface defect inspection scenarios. Since weak scratches imaging in the dark field has an ambiguous edge and low contrast, which brings difficulty in automatic defect detection. Recently, many existing visual inspection methods based on deep learning cannot effectively inspect weak scratches due to the lack of attention-aware features. To address the problems arising from industry-specific characteristics, this paper proposes 'Attention Fusion Network;', a convolutional neural network using attention mechanism built by hard and soft attention modules to generate attention-aware features. The hard attention module is implemented by integrating the brightness adjustment operation in the network, and the soft attention module is composed of scale attention and channel attention. The proposed model is trained on a real-world industrial scratch dataset and compared with other defect inspection methods. The proposed method can achieve the best performance to detect the weak scratch inspection of optical components compared to the traditional scratch detection methods and other deep learning-based methods.
Teymouri, D, Sedehi, O, Katafygiotis, LS & Papadimitriou, C 1970, 'REAL-TIME BAYESIAN PARAMETER, STATE AND INPUT ESTIMATION USING OUTPUT-ONLY VIBRATION MEASUREMENTS', Proceedings of the XI International Conference on Structural Dynamics, XI International Conference on Structural Dynamics, EASD, pp. 3590-3598.
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This paper presents a new sequential Bayesian method for the real-time estimation of state, input, parameters, and noise characteristics in dynamical systems using output-only measurements. It is an extension of the method developed by the authors for the joint input-state estimation in linear time-invariant systems [1], [2]. This method is built upon the Taylor series expansion of the state-space model and conjugate prior distributions, where the noise characteristics are described using Gaussian distributions and their covariance matrices are assumed to follow inverse Wishart distributions. When the Bayes rule is applied, explicit formulations for the posterior distributions are obtained, which allows efficient and real-time computations. The application of this method to a simple numerical example is demonstrated, which confirms its efficacy in handling this coupled estimation problem. It is observed that this method delivers accurate estimations for the state, input, model parameters, and noise covariance matrices when the results are compared with the actual values. Moreover, the proposed method has the potential to mitigate the low-frequency errors commonly produced in estimations of input forces and displacement responses when only acceleration responses are measured.
Thanh, TK, Chen, F & Gabrys, B 1970, 'An Improved Online Learning Algorithm for General Fuzzy Min-Max Neural Network', 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI), IEEE, ELECTR NETWORK.
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Thapa, S, Adhikari, S, Naseem, U, Singh, P, Bharathy, G & Prasad, M 1970, 'Detecting Alzheimer's Disease by Exploiting Linguistic Information from Nepali Transcript.', ICONIP (4), Springer, pp. 176-184.
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© 2020, Springer Nature Switzerland AG. Alzheimer’s disease (AD) is the most common form of neurodegenerating disorder accounting for 60–80% of all dementia cases. The lack of effective clinical treatment options to completely cure or even slow the progression of disease makes it even more serious. Treatment options are available to treat the milder stage of the disease to provide symptomatic short-term relief and improve quality of life. Early diagnosis is key in the treatment and management of AD as advanced stages of disease cause severe cognitive decline and permanent brain damage. This has prompted researchers to explore innovative ways to detect AD early on. Changes in speech are one of the main signs of AD patients. As the brain deteriorates the language processing ability of the patients deteriorates too. Previous research has been done in the English language using Natural Language Processing (NLP) techniques for early detection of AD. However, research using local languages and low resourced language like Nepali still lag behind. NLP is an important tool in Artificial Intelligence to decipher the human language and perform various tasks. In this paper, various classifiers have been discussed for the early detection of Alzheimer’s in the Nepali language. The proposed study makes a convincing conclusion that the difficulty in processing information in AD patients reflects in their speech while describing a picture. The study incorporates the speech decline of AD patients to classify them as control subjects or AD patients using various classifiers and NLP techniques. Furthermore, in this experiment a new dataset consisting of transcripts of AD patients and Control normal (CN) subjects in the Nepali language. In addition, this paper sets a baseline for the early detection of AD using NLP in the Nepali language.
Thapa, S, Singh, P, Jain, DK, Bharill, N, Gupta, A & Prasad, M 1970, 'Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer’s Disease', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK, pp. 1-8.
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© 2020 IEEE. Alzheimer's disease (AD) is a neurodegenerative disorder resulting in memory loss and cognitive decline caused due to the death of brain cells. It is the most common form of dementia and accounts for 60-80% of all dementia cases. There is no single test for diagnosis of AD, the doctors rely on medical history, neuropsychological assessments, computed tomography (CT) or magnetic resonance imaging (MRI) scan of the brain, etc. to confirm a diagnosis. In terms of the treatment, currently, there is neither a cure nor any way to slow the progression of AD. However, for people with mild or moderate stages of this disease, there are some medications available to temporarily reduce symptoms and help to improve quality of life. Hence, early diagnosis of AD is extremely crucial for overall better management of the disease. The researches have shown some relation between neuropsychological scores and atrophies of the brain. This can be leveraged for the early diagnosis of AD. This paper makes use of feature selection techniques to extract the most important features in the diagnosis of AD. This paper demonstrates the need to combine neuropsychological scores like mini-mental state examination (MMSE) with MRI features to provide better decisional space for early diagnosis of AD. Through the experiments, including MMSE along with other features are found to improve the classification of AD, significantly.
Thiyagarajan, K, Acharya, P, Piyathilaka, L & Kodagoda, S 1970, 'Numerical Modeling of the Effects of Electrode Spacing and Multilayered Concrete Resistivity on the Apparent Resistivity Measured Using Wenner Method', 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, Kristiansand, Norway, pp. 200-206.
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Thiyagarajan, K, Kodagoda, S & Ulapane, N 1970, 'Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature', 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, Shenzhen, China, pp. 1194-1199.
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Thiyagarajan, K, Kodagoda, S, Ulapane, N & Prasad, M 1970, 'A Temporal Forecasting Driven Approach Using Facebook’s Prophet Method for Anomaly Detection in Sewer Air Temperature Sensor System', 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, Kristiansand, Norway, pp. 25-30.
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To, KYC, Lee, JJH, Yoo, C, Anstee, S & Fitch, R 1970, 'Streamline-Based Control of Underwater Gliders in 3D Environments', 2019 IEEE 58th Conference on Decision and Control (CDC), 2019 IEEE 58th Conference on Decision and Control (CDC), IEEE, Nice, France, pp. 8303-8310.
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Autonomous underwater gliders use buoyancy control to achieve forwardpropulsion via a sawtooth-like, rise-and-fall trajectory. Because gliders areslow-moving relative to ocean currents, glider control must consider the effectof oceanic flows. In previous work, we proposed a method to control underwatervehicles in the (horizontal) plane by describing such oceanic flows in terms ofstreamlines, which are the level sets of stream functions. However, the generalanalytical form of streamlines in 3D is unknown. In this paper, we show howstreamline control can be used in 3D environments by assuming a 2.5D model ofocean currents. We provide an efficient algorithm that acts as a steeringfunction for a single rise or dive component of the glider's sawtoothtrajectory, integrate this algorithm within a sampling-based motion planningframework to support long-distance path planning, and provide several examplesin simulation in comparison with a baseline method. The key to our method'scomputational efficiency is an elegant dimensionality reduction to a 1D controlregion. Streamline-based control can be integrated within varioussampling-based frameworks and allows for online planning for gliders incomplicated oceanic flows.
To, KYC, Yoo, C, Anstee, S & Fitch, R 1970, 'Distance and Steering Heuristics for Streamline-Based Flow Field Planning', Proceedings - IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, IEEE, Paris, France, pp. 1867-1873.
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Motion planning for vehicles under the influence of flow fields can benefitfrom the idea of streamline-based planning, which exploits ideas from fluiddynamics to achieve computational efficiency. Important to such planners is anefficient means of computing the travel distance and direction between twopoints in free space, but this is difficult to achieve in strong incompressibleflows such as ocean currents. We propose two useful distance functions inanalytical form that combine Euclidean distance with values of the streamfunction associated with a flow field, and with an estimation of the strengthof the opposing flow between two points. Further, we propose steeringheuristics that are useful for steering towards a sampled point. We evaluatethese ideas by integrating them with RRT* and comparing the algorithm'sperformance with state-of-the-art methods in an artificial flow field and inactual ocean prediction data in the region of the dominant East AustralianCurrent between Sydney and Brisbane. Results demonstrate the method'scomputational efficiency and ability to find high-quality paths outperformingstate-of-the-art methods, and show promise for practical use with autonomousmarine robots.
Tofigh, F & Ziolkowski, RW 1970, 'A Stable Floating Non-Foster Circuit', 2020 4th Australian Microwave Symposium (AMS), 2020 4th Australian Microwave Symposium (AMS), IEEE, Sydney, Australia, pp. 1-2.
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© 2020 IEEE. Non-Foster elements have attracted considerable interest because of their role in enabling wide-band electrically small antennas and metamaterials. However, the potential instability of these elements continues to be an going challenge to their realization and, hence, practical acceptance. In this paper, we present a stable floating negative impedance converter (NIC) element using a RLC (resistor-inductor-capacitor) based subcircuit as the load.
Tohidi, F, Paul, M, Hooshmandasl, MR, Chakraborty, S & Pradhan, B 1970, 'Block-Wise Authentication and Recovery Scheme for Medical Images Focusing on Content Complexity', Image and Video Technology, Pacific-Rim Symposium on Image and Video Technology, Springer International Publishing, Sydney, NSW, Australia, pp. 86-99.
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© Springer Nature Switzerland AG 2020. Digital images are used to transfer most critical data in areas like medical, research, business, military, etc. The images transfer takes place over an unsecured Internet network. Therefore, there is a need for reliable security and protection for these sensitive images. Medical images play an important role in the field of Telemedicine and Tele surgery. Thus, before making any diagnostic decisions and treatments, the authenticity and the integrity of the received medical images need to be verified to avoid misdiagnosis. This paper proposes a block-wise and blind fragile watermarking mechanism for medical image authentication and recovery. By eliminating embedded insignificant data and considering different content complexity for each block during feature extraction and recovery, the capacity of data embedding without loss of quality is increased. This new embedding watermark method can embed a copy of the compressed image inside itself as a watermark to increase the recovered image quality. In our proposed hybrid scheme, the block features are utilized to improve the efficiency of data concealing for authentication and reduce tampering. Therefore, the scheme can achieve better results in terms of the recovered image quality and greater tampering protection, compared with the current schemes.
Tran, HT & Feuerlicht, G 1970, 'Implementation of a Cloud Services Management Framework', Advances in Service-Oriented and Cloud Computing Workshops of ESOCC 2018, Como, Italy, September 12–14, 2018, Revised Selected Papers, European Conference on Service-Oriented and Cloud Computing, Springer International Publishing, Como, Italy, pp. 67-78.
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© 2020, Springer Nature Switzerland AG. Rapid growth of various types of cloud services is creating new opportunities for innovative enterprise applications. As a result, enterprise applications are increasingly reliant on externally provided cloud services. It can be argued that traditional systems development methods and tools are not adequate in the context of cloud services and that new methods and frameworks that support these methods are needed for management of lifecycle of cloud services. In this paper, we describe the implementation of a Service Consumer Framework (SCF) – a framework for the management of design-time and runtime activities throughout the lifecycle of enterprise applications that use externally provided cloud services. The SCF framework has been evaluated during the implementation of a large-scale project and is being continuously improved to incorporate additional types of cloud services.
Tran, XT, Dinh, TH, Le, HV, Zhu, Q & Ha, Q 1970, 'Defect detection based on singular value decomposition and histogram thresholding', 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, Boston, MA, USA, pp. 1149-1154.
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This paper presents a novel method for defect detection based on singular value decomposition (SVD) and histogram thresholding. First, the input image is divided into blocks, where SVD is applied to determine if a region contains crack pixels. The detected crack blocks are then merged to construct a histogram to calculate the best binarization threshold by incoporating a recent technique for multiple peaks detection and Otsu algorithm. To validate the effectiveness and advantage of the proposed approach over related thresholding algorithms, experiments on images collected by an unmanned aerial vehicle have been conducted for surface crack detection. The obtained results have confirmed the merits of the proposed approach in terms of accuracy when using some well-known evaluation metrics.
Tyack, A, Wyeth, P & Johnson, D 1970, 'Restorative Play: Videogames Improve Player Wellbeing After a Need-Frustrating Event', Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-15.
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Ubaid, A, Dong, F & Hussain, FK 1970, 'Framework for Feature Selection in Health Assessment Systems', Advances in Intelligent Systems and Computing, International Conference on Advanced Information Networking and Applications, Springer International Publishing, Japan, pp. 313-324.
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Anomaly detection in health assessment systems has gained much attention in the recent past. Various feature selection techniques have been proposed for successful anomaly detection. However, these methods do not cater for the need to select features in health assessment systems. Most of the present techniques are data dependent and do not offer an option for incorporating domain information. This paper proposes a novel domain knowledge-driven feature selection framework named domain-driven selective wrapping (DSW) that can help in the selection of a correlated feature subset. The proposed framework uses an expert’s domain knowledge for the selection of subsets. The framework uses a custom-designed logic-driven anomaly detection block (LDAB) as a wrapper. The experiment results show that the proposed framework is able to select feature subsets more efficiently than traditional sequential selection methods and is very successful in detecting anomalies.
Ubaid, A, Hussain, FK & Charles, J 1970, 'Machine Learning-Based Regression Models for Price Prediction in the Australian Container Shipping Industry: Case Study of Asia-Oceania Trade Lane', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 52-59.
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© 2020, Springer Nature Switzerland AG. The objective of this paper is to train a data-driven price prediction model for container pricing based on demand and supply for the Australian container shipping industry. The sourcing of demand, supply and pricing data has been done from Australian ports, Sea-Intelligence maritime analysis and the Shanghai Freight Index (SCFI) respectively. Data-driven prediction have been realized by applying three different regression models that include support vector regression (SVR), random forest regression (RFR) and gradient booster regression (GBR) over the gathered datasets after initial feature engineering. A comparison of research outcomes shows that GBR outperforms all the other models by offering a test accuracy of 84%.
Uddin, MB, Chow, CM, Ling, SH & Su, SW 1970, 'A Robust Airflow Envelope Tracking and Digitization Approach for Automatic Detection of Apnea and Hypopnea Events', 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), IEEE, pp. 1-4.
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Sleep apnea hypopnea syndrome (SAHS) is a common sleep disorder that can significantly decrease the quality of life. Apnea hypopnea index, the number of apnea and hypopnea events per hour of sleep, is defined for the severity of SAHS. An automatic and accurate detection of apnea and hypopnea events can overcome the limitations of manual diagnosis of SAHS. This study explored the design of a novel automated algorithm to detect apnea and hypopnea events. From polysomnography records of the Sleep Heart Health Study, the airflow and pulse oximetry signals of 30 subjects were extracted. According to the updated American Academy of Sleep Medicine scoring manual, apnea and hypopnea events were scored by an experienced sleep physiologist. The peak signal excursion was precisely determined from the airflow envelope. An apnea event was detected by the precise determination of its pre-event baseline. A hypopnea event was detected when both the airflow reduction and oxygen desaturation were satisfied. Accordingly, the automated algorithm detected 5122 events (2215 apneas and 2907 hypopneas), against the manual scoring of 5021 events (2235 apneas and 2786 hypopneas). Strong correlations between scoring and detection of apnea, hypopnea, and combined events were achieved. The overall agreement between the scoring and detection of apnea, hypopnea, and combined events were respectively 99.1%, 95.7%, and 98.0%. This automatic algorithm is applicable to any portable sleep monitoring device for the accurate detection of apnea and hypopnea events.
Uddin, MB, Moi Chow, C, Ling, SH & Su, SW 1970, 'A Per-sample Digitized Algorithm for Automatically Detecting Apnea and Hypopnea Events from Airflow and Oximetry', 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, IEEE, Canada, pp. 5339-5342.
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Sleep apnea is a common sleep disorder that can significantly decrease the quality of life. An accurate and early diagnosis of sleep apnea is required before getting proper treatment. A reliable automated detection of sleep apnea can overcome the problems of manual diagnosis (scoring) due to variability in recording and scoring criteria (for example across Europe) and to inter-scorer variability. This study explored a novel automated algorithm to detect apnea and hypopnea events from airflow and pulse oximetry signals, extracted from 30 polysomnography records of the Sleep Heart Health Study. Apneas and hypopneas were manually scored by a trained sleep physiologist according to the updated 2017 American Academy of Sleep Medicine respiratory scoring rules. From pre-processed airflow, the peak signal excursion was precisely determined from the peak-to-trough amplitude using a sliding window, with a per-sample digitized algorithm for detecting apnea and hypopnea. For apnea, the peak signal excursion drop was operationalized at ≥85% and for hypopnea at ≥35% of its pre-event baseline. Using backward shifting of oximetry, hypopneas were filtered with ≥3% oxygen desaturation from its baseline. The performance of the automated algorithm was evaluated by comparing the detection with manual scoring (a standard practice). The sensitivity and positive predictive value of detecting apneas and hypopneas were respectively 98.1% and 95.3%. This automated algorithm is applicable to any portable sleep monitoring device for the accurate detection of sleep apnea.
Ulapane, N, Thiyagarajan, K & Kodagoda, S 1970, 'Binary Spectrum Feature for Improved Classifier Performance', 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, Shenzhen, China, pp. 1117-1122.
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Ulapane, N, Thiyagarajan, K & Kodagoda, S 1970, 'Hyper-Parameter Initialization for Squared Exponential Kernel-based Gaussian Process Regression', 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, Kristiansand, Norway, pp. 1154-1159.
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Ulapane, N, Thiyagarajan, K & Kodagoda, S 1970, 'System Identification of Static Nonlinear Elements: A Unified Approach of Active Learning, Over-fit Avoidance, and Model Structure Determination', 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, Kristiansand, Norway, pp. 1001-1006.
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Usayiwevu, M, Le Gentil, C, Mehami, J, Yoo, C, Fitch, R & Vidal-Calleja, T 1970, 'Information Driven Self-Calibration for Lidar-Inertial Systems', 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Las Vegas, NV, USA, pp. 9961-9967.
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Multi-modal estimation systems have the advantage of increased accuracy and robustness. To achieve accurate sensor fusion with these types of systems, a reliable extrinsic calibration between each sensor pair is critical. This paper presents a novel self-calibration framework for lidar-inertial systems. The key idea of this work is to use an informative path planner to find the admissible path that produces the most accurate calibration of such systems in an unknown environment within a given time budget. This is embedded into a simultaneous localization, mapping and calibration lidar-inertial system, which involves challenges in dealing with agile motions for excitation and large amount of data. Our approach has two stages: firstly, the environment is explored and mapped following a pre-defined path; secondly, the map is exploited to find a continuous and differentiable path that maximises the information gain within a sampling-based planner. We evaluate the proposed self-calibration method in a simulated environment and benchmark it with standard predefined paths to show its performance.
Van Den Hoven, E, Shaer, O, Loke, L, Van Dijk, J & Kun, A 1970, 'TEI 2020 Chairs? Welcome', TEI 2020 - Proceedings of the 14th International Conference on Tangible, Embedded, and Embodied Interaction, pp. III-IV.
Van Huynh, N, Nguyen, DN, Hoang, DT & Dutkiewicz, E 1970, 'Optimal Beam Association in mmWave Vehicular Networks with Parallel Reinforcement Learning', GLOBECOM 2020 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - 2020 IEEE Global Communications Conference, IEEE, Taipei, Taiwan, pp. 1-6.
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This paper develops a beam association framework for mm Wave vehicular networks to improve the system performance in terms of handover, disconnection time, and data rate under the high mobility of vehicles. In particular, we recruit the semi Markov decision process to capture the uncertainty and dynamic of the environment such as locations of beams, received signal strength indicator profiles, velocities, and blockages. Instead of adopting complex deep learning structures such as deep dueling and double deep Q-learning, we develop a lightweight yet very effective parallel Q-learning algorithm to quickly derive the optimal beam association policy by simultaneously learning from various vehicles on the road. Through extensive simulation results, we demonstrate that the proposed framework can reduce the average disconnection time by 33% and increase the data rate by 60% compared to other solutions. We also observed that the proposed parallel Q-learning algorithm converges much faster to the optimal solution than state-of-the-art deep-learning based algorithms.
Verma, S, Wang, J, Ge, Z, Shen, R, Jin, F, Wang, Y, Chen, F & Liu, W 1970, 'Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment Analysis', 2020 IEEE International Conference on Data Mining (ICDM), 2020 IEEE International Conference on Data Mining (ICDM), IEEE, Sorrento, Italy, pp. 561-570.
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Multimodal sentiment analysis utilizes multiple heterogeneous modalities for sentiment classification. The recent multimodal fusion schemes customize LSTMs to discover intra-modal dynamics and design sophisticated attention mechanisms to discover the inter-modal dynamics from multimodal sequences. Although powerful, these schemes completely rely on attention mechanisms which is problematic due to two major drawbacks 1) deceptive attention masks, and 2) training dynamics. Nevertheless, strenuous efforts are required to optimize hyperparameters of these consolidate architectures, in particular their custom-designed LSTMs constrained by attention schemes. In this research, we first propose a common network to discover both intra-modal and inter-modal dynamics by utilizing basic LSTMs and tensor based convolution networks. We then propose unique networks to encapsulate temporal-granularity among the modalities which is essential while extracting information within asynchronous sequences. We then integrate these two kinds of information via a fusion layer and call our novel multimodal fusion scheme as Deep-HOSeq (Deep network with higher order Common and Unique Sequence information). The proposed Deep-HOSeq efficiently discovers all-important information from multimodal sequences and the effectiveness of utilizing both types of information is empirically demonstrated on CMU-MOSEI and CMU-MOSI benchmark datasets. The source code of proposed Deep-HOSeq is available at https://github.com/sverma88/Deep-HOSeq-ICDM-2020.
Wahlroos, S, Wilkinson, A, Milioli, H, Portman, N, Gallego-Ortega, D & Lim, E 1970, 'Abstract P4-14-13: Concurrent exercise and chemotherapy in preclinical breast cancer models', Cancer Research, Abstracts: 2019 San Antonio Breast Cancer Symposium; December 10-14, 2019; San Antonio, Texas, American Association for Cancer Research (AACR), San Antonio, TX.
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Abstract The benefits of exercise following a cancer diagnosis is increasingly recognized. Increased physical activity is associated with reduced breast cancer recurrence and breast cancer specific mortality. However, the mechanisms underpinning this effect are still under investigation. The role of exercise as an adjunct to systemic therapy for breast cancer remains unclear. We hypothesize that exercise may exert an anti-tumor effect and a change in tumor immune cell infiltration. Methods We evaluated the effect of exercise alone and in combination with chemotherapy in preclinical patient-derived TNBC xenografts (PDX) established in Nude mice and PyMT mouse tumor models. Mice were individually housed in boxes equipped with running wheels and randomized to 1) clamped wheels (sedentary controls), 2) doxorubicin (Dox, 2mg/kg/week), 3) exercise (Ex) and 4) Ex + Dox. Daily distance run was measured. One week after randomization (acclimatization period), the intervention was commenced. Body composition was measured at randomization and at end point. Tumors were harvested after 5 weeks of intervention or at ethical endpoint. Tumor immune infiltrates were analyzed, and transcriptomic analysis performed. Results In the TNBC PDX model, there was no difference in tumor volume at randomization (p=0.96), or cumulative distance run after 1 week of acclimatization to the running wheel (p=0.47). At 5 weeks, Ex alone significantly reduced tumor growth rate compared with controls (relative reduction 10%, p=0.025). There was no difference between the other interventions. Mice randomized to Ex + Dox ran a shorter cumulative distance over 5 weeks compared with Ex alone (103.6 ± 16.2km vs 168.8 ±23km, p=0.028). There was no correlation between distance run and tumor volume in either of the treatment cohorts involving exercise (p=0.39). PyMT, transcriptomic and immune cell infiltration analysis will be reported. At baseline, th...
Wakakuwa, E, Nakata, Y & Hsieh, M-H 1970, 'One-Shot Trade-Off Bounds for State Redistribution of Classical-Quantum Sources', 2020 IEEE International Symposium on Information Theory (ISIT), 2020 IEEE International Symposium on Information Theory (ISIT), IEEE, pp. 1864-1869.
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Wang, C, Han, B, Pan, S, Jiang, J, Niu, G & Long, G 1970, 'Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure', 2020 IEEE International Conference on Data Mining (ICDM), 2020 IEEE International Conference on Data Mining (ICDM), IEEE, pp. 571-580.
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Wang, D, Arzhaeva, Y, Devnath, L, Qiao, M, Amirgholipour, S, Liao, Q, McBean, R, Hillhouse, J, Luo, S, Meredith, D, Newbigin, K & Yates, D 1970, 'Automated Pneumoconiosis Detection on Chest X-Rays Using Cascaded Learning with Real and Synthetic Radiographs', 2020 Digital Image Computing: Techniques and Applications (DICTA), 2020 Digital Image Computing: Techniques and Applications (DICTA), IEEE, pp. 1-6.
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Wang, H, Lian, D, Zhang, Y, Qin, L & Lin, X 1970, 'GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions.', CoRR, International Joint Conferences on Artificial Intelligence Organization, pp. 1317-1323.
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© 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved. Entity interaction prediction is essential in many important applications such as chemistry, biology, material science, and medical science. The problem becomes quite challenging when each entity is represented by a complex structure, namely structured entity, because two types of graphs are involved: local graphs for structured entities and a global graph to capture the interactions between structured entities. We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model. In this paper, we propose a Graph of Graphs Neural Network, namely GoGNN, which extracts the features in both structured entity graphs and the entity interaction graph in a hierarchical way. We also propose the dual-attention mechanism that enables the model to preserve the neighbor importance in both levels of graphs. Extensive experiments on real-world datasets show that GoGNN outperforms the state-of-the-art methods on two representative structured entity interaction prediction tasks: chemical-chemical interaction prediction and drug-drug interaction prediction. Our code is available at Github.
Wang, H, Xie, X, Li, Y, Wen, C, Li, Y, Liu, Y, Qin, S, Chen, H & Sui, Y 1970, 'Typestate-guided fuzzer for discovering use-after-free vulnerabilities', Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, ICSE '20: 42nd International Conference on Software Engineering, ACM, pp. 999-1010.
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Wang, J, Jin, J, Zhu, J, Guo, Y & Xing, Y 1970, 'Unified Control of APF and SMES Based on Fuzzy Logic Control', 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE, China, pp. 1-2.
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This paper proposes two single closed-loop control methods to achieve unified control of active power filter (APF) and superconducting magnetic energy storage (SMES). For the DC-DC chopper circuit, this paper proposes a fuzzy logic control (FLC) method to achieve the stabilization of the DC link voltage and the charge and discharge control of the SMES coil. For the back-stage DC-AC converter circuit, hysteresis current method realizes tracking control of reference current. The control method enables the SMES device to not only implement active filtering but also to suppress the active power oscillation of the system caused by sudden load changes. The simulation model of the whole system was built by Matlab/Simulink for verification analysis.
Wang, K, Lin, X, Qin, L, Zhang, W & Zhang, Y 1970, 'Efficient Bitruss Decomposition for Large-scale Bipartite Graphs.', CoRR, IEEE 36th International Conference on Data Engineering (ICDE), IEEE COMPUTER SOC, Dallas, TX, pp. 661-672.
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Cohesive subgraph mining in bipartite graphs becomes a popular research topic recently. An important structure k-bitruss is the maximal cohesive subgraph where each edge is contained in at least k butterflies (i.e., (2, 2)-bicliques). In this paper, we study the bitruss decomposition problem which aims to find all the k-bitrusses for k ≥ 0. The existing bottom-up techniques need to iteratively peel the edges with the lowest butterfly support. In this peeling process, these techniques are time-consuming to enumerate all the supporting butterflies for each edge. To relax this issue, we first propose a novel online index - the BE-Index which compresses butterflies into k-blooms (i.e., (2, k)-bicliques). Based on the BE-Index, the new bitruss decomposition algorithm BiT-BU is proposed, along with two batch-based optimizations, to accomplish the butterfly enumeration of the peeling process in an efficient way. Furthermore, the BiT-PC algorithm is devised which is more efficient against handling the edges with high butterfly supports. We theoretically show that our new algorithms significantly reduce the time complexities of the existing algorithms. Also, we conduct extensive experiments on real datasets and the results demonstrate that our new techniques can speed up the state-of-the-art techniques by up to two orders of magnitude.
Wang, K, Liu, A, Lu, J, Zhang, G & Xiong, L 1970, 'An Elastic Gradient Boosting Decision Tree for Concept Drift Learning', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 420-432.
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In a non-stationary data stream, concept drift occurs when different chunks of incoming data have different distributions. Hence, over time, the global optimization point of a learning model might permanently drift to the point where the model no longer adequately performs the task it was designed for. This phenomenon needs to be addressed to maintain the integrity and effectiveness of a model over the long term. In this paper, we propose a simple but effective drift learning algorithm called elastic Gradient Boosting Decision Tree (eGBDT). Since the prediction of a GBDT model is the sum output of a list of trees, we can easily append new trees to perform incremental learning or delete the last few trees to roll back to a previously known optimization point. The proposed eGBDT incrementally fits new data and detect drift by searching for the tree with the lowest residual. If the rollback deletions required would exceed the initial number of trees, a retraining process is triggered. Comparisons of eGBDT with five state-of-the-art methods on eight data sets show the efficacy of eGBDT.
Wang, K, Zhang, W, Lin, X, Zhang, Y, Qin, L & Zhang, Y 1970, 'Efficient and Effective Community Search on Large-scale Bipartite Graphs.', CoRR, IEEE COMPUTER SOC, ELECTR NETWORK, pp. 85-96.
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Bipartite graphs are widely used to model relation-ships between two types of entities. Community search retrieves densely connected subgraphs containing a query vertex, which has been extensively studied on unipartite graphs. However, com-munity search on bipartite graphs remains largely unexplored. Moreover, all existing cohesive subgraph models on bipartite graphs can only be applied to measure the structure cohesiveness between two sets of vertices while overlooking the edge weight in forming the community. In this paper, we study the significant (α, β)-community search problem on weighted bipartite graphs. Given a query vertex q, we aim to find the significant (α, β)-community ℛ of q which adopts (α, β)-core to characterize the engagement level of vertices, and maximizes the minimum edge weight (significance) within ℛ.To support fast retrieval of ℛ, we first retrieve the maximal connected subgraph of (α, β)-core containing the query vertex (the (α, β)-community), and the search space is limited to this subgraph with a much smaller size than the original graph. A novel index structure is presented which can be built in O(δ•m) time and takes O(δ•m) space where m is the number of edges in G, δ is bounded by \sqrt m and is much smaller in practice. Utilizing the index, the (α, β)-community can be retrieved in optimal time. To further obtain ℛ, we develop peeling and expansion algorithms to conduct searches by shrinking from the (α, β)-community and expanding from the query vertex, respectively. The experimental results on real graphs not only demonstrate the effectiveness of the significant (α, β)-community model but also validate the efficiency of our query processing and indexing techniques.
Wang, N, Wang, S, Wang, Y, Sheng, QZ & Orgun, M 1970, 'Modelling Local and Global Dependencies for Next-Item Recommendations', Web Information Systems Engineering – WISE 2020, International Conference on Web Information Systems Engineering, Springer International Publishing, The Netherlands, pp. 285-300.
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Session-based recommender systems (SBRSs) aim at predicting the next item by modelling the complex dependencies within and across sessions. Most of the existing SBRSs make recommendations only based on local dependencies (i.e., the dependencies between items within a session), while ignoring global dependencies (i.e., the dependencies across multiple sessions), leading to information loss and thus reducing the recommendation accuracy. Moreover, they are usually not able to recommend cold-start items effectively due to their limited session information. To alleviate these shortcomings of SBRSs, we propose a novel heterogeneous mixed graph learning (HMGL) framework to effectively learn both local and global dependencies for next-item recommendations. The HMGL framework mainly contains a heterogeneous mixed graph (HMG) construction module and an HMG learning module. The HMG construction module map both the session information and the item attribute information into a unified graph to connect items within and across sessions. The HMG learning module learns a unified representation for each item by simultaneously modelling the local and global dependencies over the HMG. The learned representation is then used for next-item recommendations. Results of extensive experiments on real-world datasets show the superiority of HMGL framework over the start-of-the-art methods in terms of recommendation accuracy.
Wang, S & Pradhan, S 1970, 'Exploring Factors Influencing Older Adults’ Willingness to Use Robo-Advisors', ACIS 2020 Proceedings - 31st Australasian Conference on Information Systems, Australasian Conference on Information Systems, Wellington. New Zealand.
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This exploratory study investigated factors that influence older adults’ (aged 50 or above) willingness to use robo-advisor, a type of financial technology (“FinTech”). In recent years, it has been receiving increasing attention from users because it is democratizing financial services. Many studies have been conducted on user adoption of robo-advisors. Only few empirical studies investigated how users’ literacy skills and behavioural traits affect their intentions to adopt robo-advisors, but none focusing on older population. In this study we collected survey data from 154 older adults living across United States. Our data analysis showed that trust and anxiety are significantly related to older adults’ willingness to use robo-advisors. Surprisingly, in contrast to many empirical findings, we found that e-literacy is negatively related to older adults’ willingness to use this technology. Based on these findings, we offered discussion regarding future direction for FinTech development and research to benefit the older demographics.
Wang, S, Hu, L, Wang, Y, Sheng, QZ, Orgun, M & Cao, L 1970, 'Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 6259-6266.
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Human behaviors are complex, which are often observed as a sequence of heterogeneous actions. In this paper, we take user choices for shopping baskets as a typical case to study the complexity of user behaviors. Most of existing approaches often model user behaviors in a mechanical way, namely treating a user action sequence as homogeneous sequential data, such as hourly temperatures, which fails to consider the complexity in user behaviors. In fact, users' choices are driven by certain underlying intentions (e.g., feeding the baby or relieving pain) according to Psychological theories. Moreover, the durations of intentions to drive user actions are quite different; some of them may be persistent while others may be transient. According to Psychological theories, we develop a hierarchical framework to describe the goal, intentions and action sequences, based on which, we design Intention Nets (IntNet). In IntNet, multiple Action Chain Nets are constructed to model the user actions driven by different intentions, and a specially designed Persistent-Transient Intention Unit models the different intention durations. We apply the IntNet to next-basket prediction, a recent challenging task in recommender systems. Extensive experiments on real-world datasets show the superiority of our Psychology-inspired model IntNet over the state-of-the-art approaches.
Wang, S, Hu, L, Wang, Y, Sheng, QZ, Orgun, M & Cao, L 1970, 'Intention2Basket: A Neural Intention-driven Approach for Dynamic Next-basket Planning', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization, pp. 2333-2339.
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User purchase behaviours are complex and dynamic, which are usually observed as multiple choice actions across a sequence of shopping baskets. Most of the existing next-basket prediction approaches model user actions as homogeneous sequence data without considering complex and heterogeneous user intentions, impeding deep under-standing of user behaviours from the perspective of human inside drivers and thus reducing the prediction performance. Psychological theories have indicated that user actions are essentially driven by certain underlying intentions (e.g., diet and entertainment). Moreover, different intentions may influence each other while different choices usually have different utilities to accomplish an intention. Inspired by such psychological insights, we formalize the next-basket prediction as an Intention Recognition, Modelling and Accomplishing problem and further design the Intention2Basket (Int2Ba in short) model. In Int2Ba, an Intention Recognizer, a Coupled Intention Chain Net, and a Dynamic Basket Planner are specifically designed to respectively recognize, model and accomplish the heterogeneous intentions behind a sequence of baskets to better plan the next-basket. Extensive experiments on real-world datasets show the superiority of Int2Ba over the state-of-the-art approaches.
Wang, T, Lu, W, Yan, Z & Liu, D 1970, 'DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances', 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Paris, France, pp. 1881-1887.
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This paper presents an observer-integrated Reinforcement Learning (RL) approach, called Disturbance OB-server Network (DOB-Net), for robots operating in environments where disturbances are unknown and time-varying, and may frequently exceed robot control capabilities. The DOB-Net integrates a disturbance dynamics observer network and a controller network. Originated from conventional DOB mechanisms, the observer is built and enhanced via Recurrent Neural Networks (RNNs), encoding estimation of past values and prediction of future values of unknown disturbances in RNN hidden state. Such encoding allows the controller generate optimal control signals to actively reject disturbances, under the constraints of robot control capabilities. The observer and the controller are jointly learned within policy optimization by advantage actor critic. Numerical simulations on position regulation tasks have demonstrated that the proposed DOB-Net significantly outperforms conventional feedback controllers and classical RL policy.
Wang, W, Liu, R, Wang, M, Wang, S, Chang, X & Chen, Y 1970, 'Memory-Based Network for Scene Graph with Unbalanced Relations', Proceedings of the 28th ACM International Conference on Multimedia, MM '20: The 28th ACM International Conference on Multimedia, ACM, ELECTR NETWORK, pp. 2400-2408.
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The scene graph which can be represented by a set of visual triples is composed of objects and the relations between object pairs. It is vital for image captioning, visual question answering, and many other applications. However, there is a long tail distribution on the scene graph dataset, and the tail relation cannot be accurately identified due to the lack of training samples. The problem of the nonstandard label and feature overlap on the scene graph affects the extraction of discriminative features and exacerbates the effect of data imbalance on the model. For these reasons, we propose a novel scene graph generation model that can effectively improve the detection of low-frequency relations. We use the method of memory features to realize the transfer of high-frequency relation features to low-frequency relation features. Extensive experiments on scene graph datasets show that our model significantly improved the performance of two evaluation metrics R@K and mR@K compared with state-of-the-art baselines.
Wang, W, Wang, M, Wang, S, Long, G, Yao, L, Qi, G & Chen, Y 1970, 'One-Shot Learning for Long-Tail Visual Relation Detection', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 12225-12232.
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The aim of visual relation detection is to provide a comprehensive understanding of an image by describing all the objects within the scene, and how they relate to each other, in < object-predicate-object > form; for example, < person-lean on-wall > . This ability is vital for image captioning, visual question answering, and many other applications. However, visual relationships have long-tailed distributions and, thus, the limited availability of training samples is hampering the practicability of conventional detection approaches. With this in mind, we designed a novel model for visual relation detection that works in one-shot settings. The embeddings of objects and predicates are extracted through a network that includes a feature-level attention mechanism. Attention alleviates some of the problems with feature sparsity, and the resulting representations capture more discriminative latent features. The core of our model is a dual graph neural network that passes and aggregates the context information of predicates and objects in an episodic training scheme to improve recognition of the one-shot predicates and then generate the triplets. To the best of our knowledge, we are the first to center on the viability of one-shot learning for visual relation detection. Extensive experiments on two newly-constructed datasets show that our model significantly improved the performance of two tasks PredCls and SGCls from 2.8% to 12.2% compared with state-of-the-art baselines.
Wang, X, Jin, D, Musial, K & Dang, J 1970, 'Topic Enhanced Sentiment Spreading Model in Social Networks Considering User Interest', Proceedings of the AAAI Conference on Artificial Intelligence, 34th AAAI Conference on Artificial Intelligence / 32nd Innovative Applications of Artificial Intelligence Conference / 10th AAAI Symposium on Educational Advances in Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), New York, NY, pp. 989-996.
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Emotion is a complex emotional state, which can affect our physiology and psychology and lead to behavior changes. The spreading process of emotions in the text-based social networks is referred to as sentiment spreading. In this paper, we study an interesting problem of sentiment spreading in social networks. In particular, by employing a text-based social network (Twitter) , we try to unveil the correlation between users' sentimental statuses and topic distributions embedded in the tweets, then to automatically learn the influence strength between linked users. Furthermore, we introduce user interest to refine the influence strength. We develop a unified probabilistic framework to formalize the problem into a topic-enhanced sentiment spreading model. The model can predict users' sentimental statuses based on their historical emotional status, topic distributions in tweets and social structures. Experiments on the Twitter dataset show that the proposed model significantly outperforms several alternative methods in predicting users' sentimental status. We also discover an intriguing phenomenon that positive and negative sentiment is more relevant to user interest than neutral ones. Our method offers a new opportunity to understand the underlying mechanism of sentimental spreading in online social networks.
Wang, X, Li, Q, Zhang, W, Xu, G, Liu, S & Zhu, W 1970, 'Joint Relational Dependency Learning for Sequential Recommendation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Singapore, pp. 168-180.
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© Springer Nature Switzerland AG 2020. Sequential recommendation leverages the temporal information of users’ transactions as transition dependencies for better inferring user preference, which has become increasingly popular in academic research and practical applications. Short-term transition dependencies contain the information of partial item orders, while long-term transition dependencies infer long-range user preference, the two dependencies are mutually restrictive and complementary. Although some work investigates unifying both long-term and short-term dependencies for better performance, they still neglect the fact that short-term interactions are multi-folds, which are either individual-level interactions or union-level interactions. Existing sequential recommendations mainly focus on user’s individual (i.e., individual-level) interactions but ignore the important collective influence at union-level. Since union-level interactions can reflect that human decisions are made based on multiple items he/she has already interacted, ignoring such interactions can result in the disability of capturing the collective influence between items. To alleviate this issue, we proposed a Joint Relational Dependency learning (JRD-L) for sequential recommendation that exploits both long-term and short-term preferences at individual-level and union-level. Specifically, JRD-L combines long-term user preferences with short-term interests by measuring short-term pair relations at individual-level and union-level. Moreover, JRD-L can alleviate the sparsity problem of union-level interactions by adding more descriptive details to each item, which is carried by individual-level relations. Extensive numerical experiments demonstrate JRD-L outperforms state-of-the-art baselines for the sequential recommendation.
Wang, X, Wu, Y, Zhu, L & Yang, Y 1970, 'Symbiotic Attention with Privileged Information for Egocentric Action Recognition', The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI, AAAI Conference on Artificial Intelligence, AAAI, New York Hilton Midtown, New York, pp. 12249-12256.
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Egocentric video recognition is a natural testbed for diverse interactionreasoning. Due to the large action vocabulary in egocentric video datasets,recent studies usually utilize a two-branch structure for action recognition,ie, one branch for verb classification and the other branch for nounclassification. However, correlation studies between the verb and the nounbranches have been largely ignored. Besides, the two branches fail to exploitlocal features due to the absence of a position-aware attention mechanism. Inthis paper, we propose a novel Symbiotic Attention framework leveragingPrivileged information (SAP) for egocentric video recognition. Finerposition-aware object detection features can facilitate the understanding ofactor's interaction with the object. We introduce these features in actionrecognition and regard them as privileged information. Our framework enablesmutual communication among the verb branch, the noun branch, and the privilegedinformation. This communication process not only injects local details intoglobal features but also exploits implicit guidance about the spatio-temporalposition of an on-going action. We introduce novel symbiotic attention (SA) toenable effective communication. It first normalizes the detection guidedfeatures on one branch to underline the action-relevant information from theother branch. SA adaptively enhances the interactions among the three sources.To further catalyze this communication, spatial relations are uncovered for theselection of most action-relevant information. It identifies the most valuableand discriminative feature for classification. We validate the effectiveness ofour SAP quantitatively and qualitatively. Notably, it achieves thestate-of-the-art on two large-scale egocentric video datasets.
Wang, X, Wu, Y, Zhu, L & Yang, Y 1970, 'Symbiotic attention with privileged information for egocentric action recognition', AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, pp. 12249-12256.
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Egocentric video recognition is a natural testbed for diverse interaction reasoning. Due to the large action vocabulary in egocentric video datasets, recent studies usually utilize a two-branch structure for action recognition, i.e., one branch for verb classification and the other branch for noun classification. However, correlation study between the verb and the noun branches have been largely ignored. Besides, the two branches fail to exploit local features due to the absence of position-aware attention mechanism. In this paper, we propose a novel Symbiotic Attention framework leveraging Privileged information (SAP) for egocentric video recognition. Finer position-aware object detection features can facilitate the understanding of actor’s interaction with the object. We introduce these features in action recognition and regard them as privileged information. Our framework enables mutual communication among the verb branch, the noun branch, and the privileged information. This communication process not only injects local details into global features, but also exploits implicit guidance about the spatio-temporal position of an on-going action. We introduce a novel symbiotic attention (SA) to enable effective communication. It first normalizes the detection guided features on one branch to underline the action-relevant information from the other branch. SA adaptively enhances the interactions among the three sources. To further catalyze this communication, spatial relations are uncovered for the selection of most action-relevant information. It identifies the most valuable and discriminative feature for classification. We validate the effectiveness of our SAP quantitatively and qualitatively. Notably, it achieves the state-of-the-art on two large-scale egocentric video datasets.
Wang, Y, Li, S, Ni, W, Abbott, D, Johnson, M, Pei, G & Hedley, M 1970, 'Automatic Device-Location Association based on Received Signal Strength Measurements', 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), IEEE, pp. 1-5.
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Wang, Y, Shi, K & Niu, Z 1970, 'A session-based job recommendation system combining area knowledge and interest graph neural networks', Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, pp. 489-492.
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Online job boards become one of the central components of the modern recruitment industry. Existing systems are mainly focused on content analysis of resumes and job descriptions, so they heavily rely on the accuracy of semantic analysis and the coverage of content modeling, in which case they usually suffer from rigidity and the lack of implicit semantic relations. In recent years, session recommendation has attracted the attention of many researchers, as it can judge the user's interest preferences and recommend items based on the user's historical clicks. Most existing session-based recommendation systems are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. We propose a novel method, Area Knowledge and Interest Graph Neural Networks(AIGNN). We add job area knowledge to job session recommendations, in which session sequences are modeled as graph-structured data, then GNN can capture complex transitions of items. Moreover, the attention mechanism is introduced to represent the user's interest. Experiments on real-world data set prove that the model we proposed better than other algorithms.
Wang, Z, Pei, Q, Liui, X, Ma, L, Li, H & Yu, S 1970, 'DAPS: A Decentralized Anonymous Payment Scheme with Supervision', Algorithms and Architectures for Parallel Processing, International Conference on Algorithms and Architectures for Parallel Processing, Springer International Publishing, Australia, pp. 537-550.
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With the emergence of blockchain-based multi-party trading scenarios, such as finance, government work, and supply chain management. Information on the blockchain poses a serious threat to users’ privacy, and anonymous transactions become the most urgent need. At present, solutions to the realization of anonymous transactions can only achieve a certain degree of trader identity privacy and transaction content privacy, so we introduce zero knowledge proof to achieve complete privacy. At the same time, unconditional privacy provides conditions for cybercrime. Due to the great application potential of the blockchain in many fields, supporting privacy protection and supervision simultaneously in the blockchain is a bottleneck, and existing works can not solve the problem of coexistence of privacy protection and supervision. This paper takes the lead in studying the privacy and supervision in multi-party anonymous transactions, and proposes a distributed anonymous payment scheme with supervision (DAPS) based on zk-SNARK, signature, commitment and elliptic curve cryptography, which enables users to be anonymous under supervision in transactions. The advantages of DAPS are twofold: enhanced privacy and additional supervision. We formally discussed the security of the whole system framework provided by the zero-knowledge proof, and verified its feasibility and practicability in the open source blockchain framework BCOS.
Wang, Z, Xu, M, Ye, N, Huang, H, Wang, R & Xiao, F 1970, 'RF-Mirror: Mitigating Mutual Coupling Interference in Two-Tag Array Labeled RFID Systems', 2020 17th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 2020 17th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), IEEE, pp. 1-9.
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© 2020 IEEE. Recent RFID systems start attaching a tag array consisting of two or more tags on an object to deal with polarization mismatch and RF phase periodicity for battery-free sensing and localization. The multi-tag solution can also provide target orientation estimation. However, when these tags are closely spaced apart, mutual coupling will be induced, producing the unexpected changes in reported RSSI and RF phase. In this paper, we present RF-Mirror that enables compensating the distortion in a two-tag array labeled RFID system. The system would output the accurate difference in tag-to-antenna distances between two tags, which is a fundamental parameter in previous works for use. Firstly, we model the backscatter signal of a responding tag in a two-tag scenario, and then formulate novel RSSI- and RF phase-distance models with coupling terms. Secondly, we design an algorithm to characterize the coupling effect on tag gain by fusing RSSI and RF phase. Thirdly, we design a decoupling algorithm based on an observation that tag mutual coupling is independent of the position of a tag array relative to a reader antenna. Our experiments show the effectiveness of our models and RF-Mirror achieves the decoupling error of 0.197 cm in calculating the tag-to-antenna distance difference.
Wen, D, Huang, Y, Zhang, Y, Qin, L, Zhang, W & Lin, X 1970, 'Efficiently Answering Span-Reachability Queries in Large Temporal Graphs.', ICDE, 2020 IEEE 36th International Conference on Data Engineering, IEEE, Dallas, TX, USA, pp. 1153-1164.
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Reachability is a fundamental problem in graph analysis. In applications such as social networks and collaboration networks, edges are always associated with timestamps. Most existing works on reachability queries in temporal graphs assume that two vertices are related if they are connected by a path with non-decreasing timestamps (time-respecting) of edges. This assumption fails to capture the relationship between entities involved in the same group or activity with no time-respecting path connecting them. In this paper, we define a new reachability model, called span-reachability, designed to relax the time order dependency and identify the relationship between entities in a given time period. We adopt the idea of two-hop cover and propose an index-based method to answer span-reachability queries. Several optimizations are also given to improve the efficiency of index construction and query processing. We conduct extensive experiments on 17 real-world datasets to show the efficiency of our proposed solution.
Wen, H, Wu, Y, Yang, C, Duan, H & Yu, S 1970, 'A Unified Federated Learning Framework for Wireless Communications: towards Privacy, Efficiency, and Security', IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, ELECTR NETWORK, pp. 653-658.
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Training high-quality machine learning models on distributed systems is a critical issue to achieve edge intelligence in wireless communications. Conventional data-driven machine learning approaches are infeasible due to non-IID data caused by privacy issues and the limited communication resources in wireless networks. Besides, considering the complex user identities, the training process also faces the challenges of Byzantine devices, which can inject poisoning information into models. In this paper, we propose a two-step federated learning framework, robust federated augmentation and distillation (RFA-RFD), to enable privacy-preserving, communication-efficient, and Byzantine-tolerant on-device machine learning in wireless communications. RFA is a method to tackle the problem of non-IID local data, which firstly trains local data generators on edge devices, then trains a global generator in the cloud server according to the IID dataset generated by the uploaded local generators, and finally, devices rectify non-IID dataset by downloading the global generator. After obtaining IID local data in edge devices, RFD is implemented to improve the performance of local models, in which devices only share the local information of models' outputs to reduce communication overhead. By employing a detection and discard mechanism in both RFA and RFD, our framework achieves robustness to the influence of Byzantine devices. Experiments show the effectiveness of RFA-RFD on preserving privacy, correcting non-IID data, reducing communication overhead, and resisting Byzantine devices, without much loss of accuracy compared with existing state-of-the-art methods.
Wen, Y, Liu, B, Xie, R, Zhu, Y, Cao, J & Song, L 1970, 'A Hybrid Model for Natural Face De-Identiation with Adjustable Privacy', 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), IEEE, pp. 269-272.
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Weng, J, Xiao, F & Cao, Z 1970, 'Uncertainty modelling in multi-agent information fusion systems', Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, pp. 1494-1502.
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In the field of informed decision-making, the usage of a single diagnostic expert system has limitations when dealing with complicated circumstances. The usage of a multi-agent information fusion (MAIF) system can mitigate this situation, as it allows multiple agents collaborating to solve the problems in a complex environment. However, the MAIF system needs to handle the uncertainty problem between different agents objectively at the same time. Target to this goal, this study reconstructs the generation of basic probability assignments (BPAs) based on the framework of evidence theory, and presents the uncertainty relationship between recognition sets, which are beneficial to the applications of the MAIF system. On the basis of evidence distance measurement, our method demonstrates the effectiveness and extendibility in numerical examples, and improves the accuracy and anti-interference ability during the identification process in the MAIF system.
Westling, FA, Abbas, R, Skinner, C, Hanus-Smith, M, Harris, A & Kirchner, N 1970, 'Applications of LiDAR for Productivity Improvement on Construction Projects: Case Studies from Active Sites', Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot, pp. 353-361.
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The McKinsey Global Institute's digitisation index ranks construction amongst the least digitised sectors globally. This translates to a relatively slow rate of labour-productivity growth which, according to McKinsey, costs the global economy US$1.6 trillion per year. One area ripe for improvement is validation of final components on construction sites. This is a critical step in the quality assurance process, but also one that consumes significant resources when performed manually. Digitisation, using reality capture technology, can enable rapid component analysis through automation. However, traditional survey tools, which focus on individual points or fixed locations, tend to provide limited coverage, are difficult to operate and hard to interpret. More recently, developments in Light Detection and Ranging (LiDAR) has enabled rapid digital representation of geometry. The resulting point clouds can then be processed using modern computing techniques, including the proprietary BuiltView platform used here, to perform automatic checks that are faster and more accurate than manual measurement and achieve greater coverage than traditional surveying technologies. As the technology develops to become cheaper and more readily available, potential on-site applications should be fully explored. To improve the understanding of options, applications and productivity benefits, we present case studies performed on active construction sites in which an aspect of the built environment was scanned with LiDAR and the data analysed to estimate value accretion for the builder. In floor flatness analysis and site visualisationwe demonstrate results that are prohibitively difficult to perform manually. In LiDAR-based precast scanning and formwork analysis we show promise for detecting defects before they cause delays and costs further down the value chain. We present the context and methodology for each case study, along with the benefits and difficulties encountered with...
White, SJU, Klauck, F, Tran, TT, Schmitt, N, Kianinia, M, Steinfurth, A, Heinrich, M, Toth, M, Szameit, A, Aharonovich, I & Solntsev, A 1970, 'Quantum Random Number Generation using a Solid-State Single-Photon Source', Proceedings of SPIE - The International Society for Optical Engineering, AOS Australian Conference on Optical Fibre Technology (ACOFT) / Australian Conference on Optics, Lasers, and Spectroscopy (ACOLS), SPIE-INT SOC OPTICAL ENGINEERING, Melbourne, AUSTRALIA.
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Quantum random number generation (QRNG) harnesses the intrinsic randomness ofquantum mechanical phenomena. Demonstrations of such processes have, however,been limited to probabilistic sources, for instance, spontaneous parametricdown-conversion or faint lasers, which cannot be triggered deterministically.Here, we demonstrate QRNG with a quantum emitter in hexagonal boron nitride; anemerging solid-state quantum source that can generate single photons on demandand operates at room temperature. We achieve true random number generationthrough the measurement of single photons exiting one of four integratedphotonic waveguides, and subsequently, verify the randomness of the sequencesin accordance with the National Institute of Standards and Technology benchmarksuite. Our results open a new avenue to the fabrication of on-chipdeterministic random number generators and other solid-state-basedquantum-optical devices.
White, SJU, Klauck, F, Tran, TT, Schmitt, N, Kianinia, M, Steinfurth, A, Heinrich, M, Toth, M, Szameit, A, Aharonovich, I & Solntsev, AS 1970, 'Quantum random number generation on a photonic chip using single photons from hexagonal boron nitride', 14th Pacific Rim Conference on Lasers and Electro-Optics (CLEO PR 2020), Conference on Lasers and Electro-Optics/Pacific Rim, Optica Publishing Group, pp. C8G_1-C8G_1.
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Quantum random number generation (QRNG) harnesses the intrinsic randomness of quantum mechanical phenomena. Here, we couple bright room-temperature single-photon emission from a hexagonal boron nitride atomic defect into a laser-written photonic chip and demonstrate QRNG. © 2020 The Author(s)
Wocker, M, Betz, NK, Feuersänger, C, Lindworsky, A & Deuse, J 1970, 'Unsupervised Learning for Opportunistic Maintenance Optimization in Flexible Manufacturing Systems', Procedia CIRP, Elsevier BV, pp. 1025-1030.
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© 2020 The Authors. Large scale manufacturing systems with a high degree in automation and the ability to produce several product variants in parallel meet current requirements of a highly flexible and at the same time productive manufacturing process. In practice, however, the non-transparency as well as the complexity of these systems overwhelm the maintenance department in the effective planning and implementation of maintenance tasks. As a result, major maintenance tasks are postponed to non-production times which causes increased maintenance cost as well as a decrease in system availability. This research explores a method that uses unsupervised learning algorithms to analyze type mixes and related process performances inside the system. The information is used to determine the optimal master production schedule prior to maintenance activities which leads to more frequent and extended time windows for maintenance activities during production time and thus to an increase in system availability.
Wostmann, R, Schlunder, P, Temme, F, Klinkenberg, R, Kimberger, J, Spichtinger, A, Goldhacker, M & Deuse, J 1970, 'Conception of a Reference Architecture for Machine Learning in the Process Industry', 2020 IEEE International Conference on Big Data (Big Data), 2020 IEEE International Conference on Big Data (Big Data), IEEE, pp. 1726-1735.
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Wu, K, Guo, YJ, Huang, X & Heath, RW 1970, 'Accurate Channel Estimation for Frequency-Hopping Dual-Function Radar Communications', 2020 IEEE International Conference on Communications Workshops (ICC Workshops), 2020 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, Dublin, Ireland, pp. 1-6.
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Dual-function radar communications (DFRC) is proposed recently to embed information into radar waveform, and hence performs data communications by sharing radar apertures and frequency resources. Exploiting a frequency-hopping (FH) MIMO radar, DFRC can achieve the symbol rate that is larger than the radar pulse frequency. However, this requires an accurate channel estimate, which is challenging to achieve due to the radar-prioritized transmission and the fast-changing FH waveform. In this paper, we propose an accurate channel estimation method for the DFRC based on FH-MIMO radars. We design a new FH-MIMO radar waveform which incurs no change to the ranging performance of the radar. The new waveform also enables a communication receiver to estimate the channel without knowing the pairing between hopping frequencies and antennas. We also develop a new angle estimation method at a single-antenna communication receiver using as few as one symbol, i.e., a single hop. Simulations are provided to validate the efficacy of the proposed channel estimation method. Specifically, the symbol error rate achieved based on the estimated channel approaches that based on the ideal channel.
Wu, M & Zhang, Y 1970, 'Intelligent bibliometrics for discovering the associations between genes and diseases: Methodology and case study', CEUR Workshop Proceedings, Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents, Sun SITE Central Europe, Virtual Event, China, pp. 8-15.
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Discovering disease-gene associations is an essential but challenging task in modern medicine. Within all the data-driven approaches targeting at this issue, literature-based knowledge discovery widely extends the discovering boundaries and uncovers implicit knowledge from unstructured textual data. However, most of the current literature-based methods require the involvement of specific expertise or prior knowledge. In this paper, we propose an adaptable and transferable methodology to 1) identify crucially genetic factors for a specific disease and 2) predict emerging genetic associations for the disease. Specifically, biomedical entities including diseases, chemicals, genes and genetic variations are extracted from literature data, then a heterogenous co-occurrence network is constructed and a semantic adjacency matrix is generated using the idea of Word2Vec. Following this, key genes and genetic variats are identified through centrality measurement on the network; emerging disease-gene associations are captured via a link prediction approach enhanced by the semantic matrix. We applied the proposed methodology to a literature dataset containing 54,219 scientific articles of atrial fibrillation (AF) to demonstrate its reliability. The results yielded a) crucial biomedical entities for AF highlighting five key gene groups and one potentially associated protein mutation; b) a list of emerging AF-genetic factors pairs that are worth in-depth exploration.
Wu, M, Pan, S, Zhou, C, Chang, X & Zhu, X 1970, 'Unsupervised Domain Adaptive Graph Convolutional Networks', Proceedings of The Web Conference 2020, WWW '20: The Web Conference 2020, ACM, Taipei, TAIWAN, pp. 1457-1467.
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Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only work in a single domain (graph) incapable of transferring knowledge from/to other domains (graphs), due to the challenges in both graph representation learning and domain adaptation over graph structures. In this paper, we present a novel approach, unsupervised domain adaptive graph convolutional networks (UDA-GCN), for domain adaptation learning for graphs. To enable effective graph representation learning, we first develop a dual graph convolutional network component, which jointly exploits local and global consistency for feature aggregation. An attention mechanism is further used to produce a unified representation for each node in different graphs. To facilitate knowledge transfer between graphs, we propose a domain adaptive learning module to optimize three different loss functions, namely source classifier loss, domain classifier loss, and target classifier loss as a whole, thus our model can differentiate class labels in the source domain, samples from different domains, the class labels from the target domain, respectively. Experimental results on real-world datasets in the node classification task validate the performance of our method, compared to state-of-the-art graph neural network algorithms.
Wu, M, Zhang, Y, Lu, J, Lin, H & Grosser, M 1970, 'Recommending scientific collaborators: Bibliometric networks for medical research entities', Developments of Artificial Intelligence Technologies in Computation and Robotics, 14th International FLINS Conference (FLINS 2020), WORLD SCIENTIFIC, Cologne, Germany, pp. 480-487.
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Aiming to recommend potential collaborators for academic entities such as researchers and institutions, this paper develops a social recommender system through bibliometric indicators and network analytics. Targeting to scholarly articles, the proposed recommender system exploits co-authorships as established social relations and proposes a link prediction model for discovering such potential relations in terms of a co-authorship network. A case study recommending scientific collaborators for research entities on generelated diseases demonstrates the reliability of this study.
Wu, S, Rizoiu, M-A & Xie, L 1970, 'Variation across Scales: Measurement Fidelity under Twitter Data Sampling', Proceedings of the Fourteenth International AAAI Conference on Web and Social Media, AAAI Conference on Web and Social Media, https://ojs.aaai.org//index.php/ICWSM/article/view/7337/7191, Atlanta, Georgia, pp. 715-725.
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A comprehensive understanding of data quality is the cornerstone ofmeasurement studies in social media research. This paper presents in-depthmeasurements on the effects of Twitter data sampling across differenttimescales and different subjects (entities, networks, and cascades). Byconstructing complete tweet streams, we show that Twitter rate limit message isan accurate indicator for the volume of missing tweets. Sampling also differssignificantly across timescales. While the hourly sampling rate is influencedby the diurnal rhythm in different time zones, the millisecond level samplingis heavily affected by the implementation choices. For Twitter entities such asusers, we find the Bernoulli process with a uniform rate approximates theempirical distributions well. It also allows us to estimate the true rankingwith the observed sample data. For networks on Twitter, their structures arealtered significantly and some components are more likely to be preserved. Forretweet cascades, we observe changes in distributions of tweet inter-arrivaltime and user influence, which will affect models that rely on these features.This work calls attention to noises and potential biases in social data, andprovides a few tools to measure Twitter sampling effects.
Wu, X, Ji, G, Dou, W, Yu, S & Qi, L 1970, 'Game Theory for Mobile Location Privacy', Proceedings of the 2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure, ASIA CCS '20: The 15th ACM Asia Conference on Computer and Communications Security, ACM, pp. 106-116.
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With the rapid growth of mobile network and infrastructures, location-related services accross multiple domains have received a great deal of attention. However, privacy is always an important problem that has a great influence on services. In order to research the attack and defence of privacy, we present the existing game-theoretic literatures for their interactions about mobile location privacy problems. We first demonstrate the necessity of game theory applied to location privacy. Then, we divide the literatures into four types according to different game players. Next, we describe the detailed content and analyse the equilibrium of privacy games. In addition, we also provide the works based on mechanism design to motive the defenders to increase the defence in various contexts. Finally, we also discuss the possible trends and challenges of the future research. Our survey provides a systematic and comprehensive understanding about location privacy preservation problems in mobile network.
Wu, Y, Cao, J & Xu, G 1970, 'FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraint', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 287-303.
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© Springer Nature Switzerland AG 2020. An excessive number of customers often leads to a degradation in service quality. However, the capacity constraints of services are ignored by recommender systems, which may lead to unsatisfactory recommendation. This problem can be solved by limiting the number of users who receive the recommendation for a service, but this may be viewed as unfair. In this paper, we propose a novel metric Top-N Fairness to measure the individual fairness of multi-round recommendations of services with capacity constraints. By considering the fact that users are often only affected by top-ranked items in a recommendation, Top-N Fairness only considers a sub-list consisting of top N services. Based on the metric, we design FAST, a Fairness Assured service recommendation STrategy. FAST adjusts the original recommendation list to provide users with recommendation results that guarantee the long-term fairness of multi-round recommendations. We prove the convergence property of the variance of Top-N Fairness of FAST theoretically. FAST is tested on the Yelp dataset and synthetic datasets. The experimental results show that FAST achieves better recommendation fairness while still maintaining high recommendation quality.
Wu, Z, Pan, S, Long, G, Jiang, J, Chang, X & Zhang, C 1970, 'Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks', Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 753-763.
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Modeling multivariate time series has long been a subject that has attractedresearchers from a diverse range of fields including economics, finance, andtraffic. A basic assumption behind multivariate time series forecasting is thatits variables depend on one another but, upon looking closely, it is fair tosay that existing methods fail to fully exploit latent spatial dependenciesbetween pairs of variables. In recent years, meanwhile, graph neural networks(GNNs) have shown high capability in handling relational dependencies. GNNsrequire well-defined graph structures for information propagation which meansthey cannot be applied directly for multivariate time series where thedependencies are not known in advance. In this paper, we propose a generalgraph neural network framework designed specifically for multivariate timeseries data. Our approach automatically extracts the uni-directed relationsamong variables through a graph learning module, into which external knowledgelike variable attributes can be easily integrated. A novel mix-hop propagationlayer and a dilated inception layer are further proposed to capture the spatialand temporal dependencies within the time series. The graph learning, graphconvolution, and temporal convolution modules are jointly learned in anend-to-end framework. Experimental results show that our proposed modeloutperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasetsand achieves on-par performance with other approaches on two traffic datasetswhich provide extra structural information.
Xiao, G, Du, X, Sui, Y & Yue, T 1970, 'HINDBR: Heterogeneous Information Network Based Duplicate Bug Report Prediction', 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), IEEE, Coimbra, Portugal, pp. 195-206.
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©2020 IEEE. Duplicate bug reports often exist in bug tracking systems (BTSs). Almost all the existing approaches for automatically detecting duplicate bug reports are based on text similarity. A recent study found that such approaches may become ineffective in detecting duplicates in bug reports submitted after the justin- time (JIT) retrieval, which is now a built-in feature of modern BTSs (e.g., Bugzilla). This is mainly because the embedded JIT feature suggests possible duplicates in a bug database when a bug reporter types in the new summary field, therefore minimizing the submission of textually similar reports. Although JIT filtering seems effective, a number of bug report duplicates remain undetected. Our hypothesis is that we can detect them using a semantic similarity-based approach. This paper presents HINDBR, a novel deep neural network (DNN) that accurately detects semantically similar duplicate bug reports using a heterogeneous information network (HIN). Instead of matching text similarity alone, HINDBR embeds semantic relations of bug reports into a low-dimensional embedding space where two duplicate bug reports represented by two vectors are close to each other in the latent space. Results show that HINDBR is effective.
Xie, H, Zheng, J, Wang, M & Chai, R 1970, 'Networked DC Motor Control with Time-Varying Delays and Application to a Mobile Robot', 2020 IEEE 16th International Conference on Control & Automation (ICCA), 2020 IEEE 16th International Conference on Control & Automation (ICCA), IEEE, pp. 171-176.
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Xie, H-B, Li, C, Mengersen, K, Wang, S & Xu, RYD 1970, 'Nonparametric Bayesian Nonnegative Matrix Factorization', Modeling Decisions for Artificial Intelligence, International Conference on Modeling Decisions for Artificial Intelligence, Springer International Publishing, Sant Cugat, Spain, pp. 132-141.
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© Springer Nature Switzerland AG 2020. Nonnegative Matrix Factorization (NMF) is an important tool in machine learning for blind source separation and latent factor extraction. Most of existing NMF algorithms assume a specific noise kernel, which is insufficient to deal with complex noise in real scenarios. In this study, we present a hierarchical nonparametric nonnegative matrix factorization (NPNMF) model in which the Gaussian mixture model is used to approximate the complex noise distribution. The model is cast in the nonparametric Bayesian framework by using Dirichlet process mixture to infer the necessary number of Gaussian components. We derive a mean-field variational inference algorithm for the proposed nonparametric Bayesian model. Experimental results on both synthetic data and electroencephalogram (EEG) demonstrate that NPNMF performs better in extracting the latent nonnegative factors in comparison with state-of-the-art methods.
Xing, Y, Guo, L, Xie, Z, Cui, L, Gao, L & Yu, S 1970, 'Non-Technical Losses Detection in Smart Grids: An Ensemble Data-Driven Approach', 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), IEEE, Hong Kong, pp. 563-568.
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Non technical losses (NTL) detection plays a crucial role in protecting the security of smart grids. Employing massive energy consumption data and advanced artificial intelligence (AI) techniques for NTL detection are helpful. However, there are concerns regarding the effectiveness of existing AI-based detectors against covert attack methods. In particular, the tampered metering data with normal consumption patterns may result in low detection rate. Motivated by this, we propose a hybrid data-driven detection framework. In particular, we introduce a wide deep convolutional neural networks (CNN) model to capture the global and periodic features of consumption data. We also leverage the maximal information coefficient algorithm to analysis and detect those covert abnormal measurements. Our extensive experiments under different attack scenarios demonstrate the effectiveness of the proposed method.
Xu, Y, Chen, L, Fang, M, Wang, Y & Zhang, C 1970, 'Deep Reinforcement Learning with Transformers for Text Adventure Games', 2020 IEEE Conference on Games (CoG), 2020 IEEE Conference on Games (CoG), IEEE, pp. 65-72.
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In this paper, we study transformers for text-based games. As a promising replacement of recurrent modules in Natural Language Processing (NLP) tasks, the transformer architecture could be treated as a powerful state representation generator for reinforcement learning. However, the vanilla transformer is neither effective nor efficient to learn with a huge amount of weight parameters. Unlike existing research that encodes states using LSTMs or GRUs, we develop a novel lightweight transformer-based representation generator featured with reordered layer normalization, weight sharing and block-wise aggregation. The experimental results show that our proposed model not only solves single games with much fewer interactions, but also achieves better generalization on a set of unseen games. Furthermore, our model outperforms state-of-the-art agents in a variety of man-made games.
Xu, Y, Fang, M, Chen, L, Du, Y, Zhou, JT & Zhang, C 1970, 'Deep reinforcement learning with stacked hierarchical attention for text-based games', Advances in Neural Information Processing Systems, Conference on Neural Information Processing Systems, NIPS, Virtual, pp. 1-13.
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We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to represent the environment information and language actions, existing RL agents are not empowered with any reasoning capabilities to deal with textual games. In this work, we aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure. We propose a stacked hierarchical attention mechanism to construct an explicit representation of the reasoning process by exploiting the structure of the knowledge graph. We extensively evaluate our method on a number of man-made benchmark games, and the experimental results demonstrate that our method performs better than existing text-based agents.
Xue, H, Liu, B, Din, M, Song, L & Zhu, T 1970, 'Hiding Private Information in Images From AI', ICC 2020 - 2020 IEEE International Conference on Communications (ICC), ICC 2020 - 2020 IEEE International Conference on Communications (ICC), IEEE, Dublin, Ireland, pp. 1-6.
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Privacy protection attracts increasing concerns these days. People tend to believe that large social platforms will comply with the agreement to protect their privacy. However, photos uploaded by people are usually not treated to achieve privacy protection. For example, Facebook, the world's largest social platform, was found leaking photos of millions of users to commercial organizations for big data analytics. A common analytical tool used by these commercial organizations is the Deep Neural Network (DNN). Today's DNN can accurately identify people's appearance, body shape, hobbies and even more sensitive personal information, such as addresses, phone numbers, emails, bank cards and so on. To enable people to enjoy sharing photos without worrying about their privacy, we propose an algorithm that allows users to selectively protect their privacy while preserving the contextual information contained in images. The results show that the proposed algorithm can select and perturb private objects to be protected among multiple optional objects so that the DNN can only identify non-private objects in images.
Xue, Y, Ma, M, Lin, Y, Sui, Y, Ye, J & Peng, T 1970, 'Cross-contract static analysis for detecting practical reentrancy vulnerabilities in smart contracts', Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, ASE '20: 35th IEEE/ACM International Conference on Automated Software Engineering, ACM, pp. 1029-1040.
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© 2020 ACM. Reentrancy bugs, one of the most severe vulnerabilities in smart contracts, have caused huge financial loss in recent years. Researchers have proposed many approaches to detecting them. However, empirical studies have shown that these approaches suffer from undesirable false positives and false negatives, when the code under detection involves the interaction between multiple smart contracts. In this paper, we propose an accurate and efficient cross-contract reentrancy detection approach in practice. Rather than design rule-of-thumb heuristics, we conduct a large empirical study of 11714 real-world contracts from Etherscan against three well-known general-purpose security tools for reentrancy detection. We manually summarized the reentrancy scenarios where the state-of-the-art approaches cannot address. Based on the empirical evidence, we present Clairvoyance, a cross-function and cross-contract static analysis to detect reentrancy vulnerabilities in real world with significantly higher accuracy. To reduce false negatives, we enable, for the first time, a cross-contract call chain analysis by tracking possibly tainted paths. To reduce false positives, we systematically summarized five major path protective techniques (PPTs) to support fast yet precise path feasibility checking. We implemented our approach and compared Clairvoyance with five state-of-the-art tools on 17770 real-worlds contracts. The results show that Clairvoyance yields the best detection accuracy among all the five tools and also finds 101 unknown reentrancy vulnerabilities.
Yan, Z, Traish, J, Li, R & Lu, J 1970, 'Dynamic scheduling of rail replacement bus timetables', Developments of Artificial Intelligence Technologies in Computation and Robotics, 14th International FLINS Conference (FLINS 2020), WORLD SCIENTIFIC, pp. 505-512.
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Yang, H, Chen, L, Lei, M, Niu, L, Zhou, C & Zhang, P 1970, 'Discrete Embedding for Latent Networks', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization, pp. 1223-1229.
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Discrete network embedding emerged recently as a new direction of network representation learning. Compared with traditional network embedding models, discrete network embedding aims to compress model size and accelerate model inference by learning a set of short binary codes for network vertices. However, existing discrete network embedding methods usually assume that the network structures (e.g., edge weights) are readily available. In real-world scenarios such as social networks, sometimes it is impossible to collect explicit network structure information and it usually needs to be inferred from implicit data such as information cascades in the networks. To address this issue, we present an end-to-end discrete network embedding model for latent networks DELN that can learn binary representations from underlying information cascades. The essential idea is to infer a latent Weisfeiler-Lehman proximity matrix that captures node dependence based on information cascades and then to factorize the latent Weisfiler-Lehman matrix under the binary node representation constraint. Since the learning problem is a mixed integer optimization problem, an efficient maximal likelihood estimation based cyclic coordinate descent (MLE-CCD) algorithm is used as the solution. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art network embedding methods.
Yang, T, Ding, C, Ziolkowski, RW & Guo, YJ 1970, 'Achieving a Terahertz Photonic Crystal Fiber with Enhanced Birefrigence', 2020 4th Australian Microwave Symposium (AMS), 2020 4th Australian Microwave Symposium (AMS), IEEE, Sydney, Australia, pp. 1-2.
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© 2020 IEEE. A method to achieve a photonic crystal fiber (PCF) with high birefringence for polarization maintenance in short range THz communication systems is introduced in this paper. Rectangular air slots are etched in the core region of the fiber; they make the X-polarized (XP) and Y-polarized (YP) propagation modes have different propagation constants, thus leading to the higher birefringence. In contrast to the widely-used fully-slotted (FS) configuration in which the fiber core is almost fully occupied by air slots, the proposed PCF has a partially-slotted (PS) core. The air slot in the core center is absent; only the dielectric background is present. Comparisons are made between the fully-slotted and partially-slotted PCFs to illustrate that the PS PCF overperforms the FS PCF. After optimization, the PS PCF attains a high birefringence value of 0.069 and a total loss of 0.071 cm-1 at 1.0 THz. Over a broad 0.4 THz working band, from 0.53 to 0.93 THz, the dispersion is within 0.06 ps/THz/cm.
Yang, T, Ding, C, Ziolkowski, RW & Jay Guo, Y 1970, 'An Ultra-Wideband Single-Polarization-Single-Mode Terahertz Photonic Crystal Fiber', 2020 IEEE International Conference on Computational Electromagnetics (ICCEM), 2020 IEEE International Conference on Computational Electromagnetics (ICCEM), IEEE, Singapore, SINGAPORE, pp. 21-22.
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© 2020 IEEE. A terahertz (THz) photonic crystal fiber (PCF) with an ultra-wide bandwidth and single-polarization-single-mode (SPSM) operation is designed and analyzed. Two air slots are introduced in the core region and epsilon-near-zero (ENZ) material is deposited in four specific air holes in the cladding of the PCF. The design achieves significantly different electric (E)-field distributions of the X-polarized (XP) and Y-polarized (YP) modes. The E-field components overlapping the ENZ material are attenuated because it is lossy. Gain material is then deposited in a rectangular slot in the core center to provide amplification of the E-field components overlapping this gain region. Changing the dimensions of the PCF modifies the amplification and attenuation rates to the wanted XP mode, the unwanted YP mode, and any unwanted higher order (HO) modes. The amplification of the wanted mode and the attenuation of the unwanted modes are maximized through optimization. The result is a PCF with an ultra-wide SPSM spectrum of 0.53 THz, from 1.00 to 1.53 THz. The minimum loss difference (MLD) across this bandwidth between the wanted mode and any unwanted modes is over 7.4 dB/cm. To the best of our knowledge, this is the widest SPSM bandwidth of a PCF fiber reported in THz regime.
Yang, T, Valls Miro, J, Wang, Y & Xiong, R 1970, 'Non-revisiting Coverage Task with Minimal Discontinuities for Non-redundant Manipulators', Robotics: Science and Systems XVI, Robotics: Science and Systems 2020, Robotics: Science and Systems Foundation, ELECTR NETWORK.
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Yang, X & Liu, W 1970, 'Population Location and Movement Estimation through Cross-domain Data Analysis', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization, Yokahama, Japan, pp. 5192-5193.
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Estimations on people movement behaviour within a country can provide valuable information to government strategic resource plannings. In this paper, we propose to utilize multi-domain statistical data to estimate people movements under the assumption that most population tend to move to areas with similar or better living conditions. We design a Multi-domain Matrix Factorization (MdMF) model to discover the underlying consistency patterns from these cross-domain data and estimate the movement trends using the proposed model. This research can provide important theoretical support to government and agencies in strategic resource planning and investments.
Yao, L, Zhang, Q, Chi, Q & Wang, L 1970, 'Teaching Reform of Java Web Programming Course Based on the Concept of OBE', 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), IEEE, PEOPLES R CHINA, Shenyang, pp. 456-459.
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Yao, Y, Hua, X, Gao, G, Sun, Z, Li, Z & Zhang, J 1970, 'Bridging the Web Data and Fine-Grained Visual Recognition via Alleviating Label Noise and Domain Mismatch', Proceedings of the 28th ACM International Conference on Multimedia, MM '20: The 28th ACM International Conference on Multimedia, ACM, Virtual, pp. 1735-1744.
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To distinguish the subtle differences among fine-grained categories, a large amount of well-labeled images are typically required. However, manual annotations for fine-grained categories is an extremely difficult task as it usually has a high demand for professional knowledge. To this end, we propose to directly leverage web images for fine-grained visual recognition. Our work mainly focuses on two critical issues including 'label noise' and 'domain mismatch' in the web images. Specifically, we propose an end-to-end deep denoising network (DDN) model to jointly solve these problems in the process of web images selection. To verify the effectiveness of our proposed approach, we first collect web images by using the labels in fine-grained datasets. Then we apply the proposed deep denoising network model for noise removal and domain mismatch alleviation. We leverage the selected web images as the training set for fine-grained categorization models learning. Extensive experiments and ablation studies demonstrate state-of-the-art performance gained by our proposed approach, which, at the same time, delivers a new pipeline for fine-grained visual categorization that is to be highly effective for real-world applications.
Yoo, C, Lensgraf, S, Fitch, R, Clemon, LM & Mettu, R 1970, 'Toward Optimal FDM Toolpath Planning with Monte Carlo Tree Search', Proceedings - IEEE International Conference on Robotics and Automation, pp. 4037-4043.
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The most widely used methods for toolpath planning in fused deposition 3Dprinting slice the input model into successive 2D layers in order to constructthe toolpath. Unfortunately slicing-based methods can incur a substantialamount of wasted motion (i.e., the extruder is moving while not printing),particularly when features of the model are spatially separated. In recentyears we have introduced a new paradigm that characterizes the space offeasible toolpaths using a dependency graph on the input model, along withseveral algorithms to search this space for toolpaths that optimize objectivefunctions such as wasted motion or print time. A natural question that arisesis, under what circumstances can we efficiently compute an optimal toolpath? Inthis paper, we give an algorithm for computing fused deposition modeling (FDM)toolpaths that utilizes Monte Carlo Tree Search (MCTS), a powerfulgeneral-purpose method for navigating large search spaces that is guaranteed toconverge to the optimal solution. Under reasonable assumptions on printergeometry that allow us to compress the dependency graph, our MCTS-basedalgorithm converges to find the optimal toolpath. We validate our algorithm ona dataset of 75 models and show it performs on par with our previous best localsearch-based algorithm in terms of toolpath quality. In prior work wespeculated that the performance of local search was near optimal, and weexamine in detail the properties of the models and MCTS executions that lead tobetter or worse results than local search.
You, A, Sukkar, F, Fitch, R, Karkee, M & Davidson, JR 1970, 'An Efficient Planning and Control Framework for Pruning Fruit Trees', 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Paris, Texas (Virtual), pp. 3930-3936.
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Dormant pruning is a major cost component of fresh market tree fruit production, nearly equal in scale to harvesting the fruit. However, relatively little focus has been given to the problem of pruning trees autonomously. In this paper, we introduce a robotic system consisting of an industrial manipulator, an eye-in-hand RGB-D camera configuration, and a custom pneumatic cutter. The system is capable of planning and executing a sequence of cuts while making minimal assumptions about the environment. We leverage a novel planning framework designed for high-throughput operation which builds upon previous work to reduce motion planning time and sequence cut points intelligently. In end-to-end experiments with a set of ten different branch configurations, the system achieved a high success rate in plan execution and a 1.5x speedup in throughput versus a baseline planner, representing a significant step towards the goal of practical implementation of robotic pruning.
Yu, E, Liu, W, Kang, G, Chang, X, Sun, J & Hauptmann, A 1970, 'Inf@TRECVID 2019: Instance search task', 2019 TREC Video Retrieval Evaluation, TRECVID 2019.
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We participated in one of the two types of Instance Search task in TRECVID 2019: Fully Automatic Search, without any human intervention. Firstly, the specific person and action are searched separately, and then we re-rank the two sorts of search results by ranking the one type scores according to the other type, as well as the score fusion. And thus, three kinds of final instance search results are submitted. Specifically, for the person search, our baseline consists of face detection, alignment and face feature selection. And for the action search, we integrate person detection, person tracking and feature selection into a framework to get the final 3D features for all tracklets in video shots. The official evaluations showed that our best search result gets the 4th place in the Automatic search.
Yu, H, Liu, A, Wang, B, Li, R, Zhang, G & Lu, J 1970, 'Real-Time Decision Making for Train Carriage Load Prediction via Multi-stream Learning', AI 2020: Advances in Artificial Intelligence, Australasian Joint Conference on Artificial Intelligence, Springer International Publishing, Canberra, ACT, Australia, pp. 29-41.
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© 2020, Springer Nature Switzerland AG. Real-time traffic planning and scheduling optimization are critical for developing low-cost, reliable, resilient, and efficient transport systems. In this paper, we present a real-time application that uses machine learning techniques to forecast the train carriage load when a train departure from a platform. With the predicted carriage load, crew can efficiently manage passenger flow, improving the efficiency of boarding and alighting, and thereby reducing the time trains spend at stations. We developed the application in collaboration with Sydney Trains. Most data are publicly available on Open Data Hub, which is supported by the Transport for NSW. We investigated the performance of different models, features, and measured their contributions to prediction accuracy. From this we propose a novel learning strategy, called Multi-Stream Learning, which merges streams having similar concept drift patterns to boost the training data size with the aim of achieving lower generalization errors. We have summarized our solutions and hope researchers and industrial users who might be facing similar problems will benefit from our findings.
Yu, M, Qin, L, Zhang, Y, Zhang, W & Lin, X 1970, 'AOT: Pushing the Efficiency Boundary of Main-memory Triangle Listing.', CoRR, International Conference on Database Systems for Advanced Applications, Springer International Publishing, Jeju, South Korea, pp. 516-533.
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© Springer Nature Switzerland AG 2020. Triangle listing is an important topic significant in many practical applications. Efficient algorithms exist for the task of triangle listing. Recent algorithms leverage an orientation framework, which can be thought of as mapping an undirected graph to a directed acylic graph, namely oriented graph, with respect to any global vertex order. In this paper, we propose an adaptive orientation technique that satisfies the orientation technique but refines it by traversing carefully based on the out-degree of the vertices in the oriented graph during the computation of triangles. Based on this adaptive orientation technique, we design a new algorithm, namely AOT, to enhance the edge-iterator listing paradigm. We also make improvements to the performance of AOT by exploiting the local order within the adjacent list of the vertices. We show that AOT is the first work which can achieve best performance in terms of both practical performance and theoretical time complexity. Our comprehensive experiments over 16 real-life large graphs show a superior performance of our AOT algorithm when compared against the state-of-the-art, especially for massive graphs with billions of edges. Theoretically, we show that our proposed algorithm has a time complexity of $$\varTheta (\sum _{ \langle u,v \rangle \in \mathbf {E} } \min \{ deg^{+}(u),deg^{+}(v)\}))$$, where $$\mathbf {E}$$ and $$deg^{+}(x)$$ denote the set of directed edges in an oriented graph and the out-degree of vertex x respectively. As to our best knowledge, this is the best time complexity among in-memory triangle listing algorithms.
Yu, SI, Jiang, L, Mao, Z, Chang, X, Du, X, Gan, C, Lan, Z, Xu, Z, Li, X, Cai, Y, Kumar, A, Miao, Y, Martin, L, Wolfe, N, Xu, S, Li, H, Lin, M, Ma, Z, Yang, Y, Meng, D, Shan, S, Sahin, PD, Burger, S, Metze, F, Singh, R, Raj, B, Mitamura, T, Stern, R & Hauptmann, A 1970, 'CMU informedia @ trecvid multimedia event detection', 2014 TREC Video Retrieval Evaluation, TRECVID 2014.
Yu, SI, Jiang, L, Xu, Z, Lan, Z, Xu, S, Chang, X, Li, X, Mao, Z, Gan, C, Miao, Y, Du, X, Cai, Y, Martin, L, Wolfe, N, Kumar, A, Li, H, Lin, M, Ma, Z, Yang, Y, Meng, D, Shan, S, Sahin, PD, Burger, S, Metze, F, Singh, R, Raj, B, Mitamura, T, Stern, R & Hauptmann, A 1970, 'CMU informedia@TrecVID 2015 MED', 2015 TREC Video Retrieval Evaluation, TRECVID 2015.
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We report on our system used in the TRECVID 2015 Multimedia Event Detection (MED) task. On the MED task, the CMU team submitted runs in the Semantic Query (SQ) and 10Ex settings. The proposed system is essentially the same as our MED 2014 system.
Yu, SI, Jiang, L, Xu, Z, Lan, Z, Xu, S, Chang, X, Li, X, Mao, Z, Gan, C, Miao, Y, Du, X, Cai, Y, Martin, L, Wolfe, N, Kumar, A, Li, H, Lin, M, Ma, Z, Yang, Y, Meng, D, Shan, S, Sahin, PD, Burger, S, Metze, F, Singh, R, Raj, B, Mitamura, T, Stern, R & Hauptmann, A 1970, 'Informedia@TrecVID 2014 Med and MER', 2014 TREC Video Retrieval Evaluation, TRECVID 2014.
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We report on our system used in the TRECVID 2014 Multimedia Event Detection (MED) and Multimedia Event Recounting (MER) tasks. On the MED task, the CMU team achieved leading performance in the Semantic Query (SQ), 000Ex, 010Ex and 100Ex settings. Furthermore, SQ and 000Ex runs are significantly better than the submissions from the other teams. We attribute the good performance to 4 main components: 1) large-scale semantic concept detectors trained on video shots for SQ/000Ex systems, 2) better features such as improved trajectories and deep learning features for 010Ex/100Ex systems, 3) a novel Multistage Hybrid Late Fusion method for 010Ex/100Ex systems and 4) improved reranking methods for Pseudo Relevance Feedback for 000Ex/010Ex systems. On the MER task, our system utilizes a subset of features and detection results from the MED system from which the recounting is then generated. Recounting evidence is presented by selecting the most likely concepts detected in the salient shots of a video. Salient shots are detected by searching for shots which have high response when predicted by the video level event detector.
Yu, X, Lyu, Y & Tsang, IW 1970, 'Intrinsic reward driven imitation learning via generative model', 37th International Conference on Machine Learning, ICML 2020, pp. 10856-10866.
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Imitation learning in a high-dimensional environment is challenging. Most inverse reinforcement learning (IRL) methods fail to outperform the demonstrator in such a high-dimensional environment, e.g., Atari domain. To address this challenge, we propose a novel reward learning module to generate intrinsic reward signals via a generative model. Our generative method can perform better forward state transition and backward action encoding, which improves the module’s dynamics modeling ability in the environment. Thus, our module provides the imitation agent both the intrinsic intention of the demonstrator and a better exploration ability, which is critical for the agent to outperform the demonstrator. Empirical results show that our method outperforms state-of-the-art IRL methods on multiple Atari games, even with one-life demonstration. Remarkably, our method achieves performance that is up to 5 times the performance of the demonstration.
Yu, X, Zhuang, Z, Koniusz, P & Li, H 1970, '6DoF Object Pose Estimation via Differentiable Proxy Voting Regularizer', 31st British Machine Vision Conference, BMVC 2020.
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Estimating a 6DOF object pose from a single image is very challenging due to occlusions or textureless appearances. Vector-field based keypoint voting has demonstrated its effectiveness and superiority on tackling those issues. However, direct regression of vector-fields neglects that the distances between pixels and keypoints also affect the deviations of hypotheses dramatically. In other words, small errors in direction vectors may generate severely deviated hypotheses when pixels are far away from a keypoint. In this paper, we aim to reduce such errors by incorporating the distances between pixels and keypoints into our objective. To this end, we develop a simple yet effective differentiable proxy voting regularizer (DPVR) which mimics the hypothesis selection in the voting procedure. By exploiting our voting regularizer, we are able to train our network in an end-to-end manner. Experiments on widely used datasets, i.e., LINEMOD and Occlusion LINEMOD, manifest that our DPVR improves pose estimation performance significantly and speeds up the training convergence.
Yuan, X, Feng, Z, Ni, W, Wei, Z & Liu, RP 1970, 'Waveform Optimization for MIMO Joint Communication and Radio Sensing Systems with Imperfect Channel Feedbacks', 2020 IEEE International Conference on Communications Workshops (ICC Workshops), 2020 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, Dublin, Ireland, pp. 1-6.
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In this paper, we study optimal waveform design to maximize mutual information (MI) for a joint communication and (radio) sensing (JCAS, a.k.a., radar-communication) multi-input multi-output (MIMO) downlink system. We consider a typical packet-based signal structure which includes training and data symbols. We first derive the conditional MI for both sensing and communication under correlated channels by considering the training overhead and channel estimation error (CEE). Based on the derived MI, we provide optimal waveform design methods for three scenarios, including maximizing MI for communication only and for sensing only, and maximizing a weighted sum MI for both communication and sensing. We also present extensive simulation results that provide insights on waveform design and validate the effectiveness of the proposed designs.
Zafra, E, Vazquez, S, Geyer, T, Aguilera, RP, Franquelo, LG & Leon, JI 1970, 'FCS-MPC and Observer Design in the dq Synchronous Frame: An Experimental Validation', 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), IEEE, Setubal, Portugal, pp. 445-450.
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The addition of an output LC filter to a voltage source inverter (VSI) allows the generation of high-quality output voltages with reduced harmonic distortion content. For this reason, this topology is widely used for uninterruptible power supply (UPS) applications. To control this system, finite control set model predictive control (FCS-MPC) has been assessed in previous research with promising results. In this paper, the dq-synchronous reference frame (dq-SRF) formulation of FCS-MPC and a state observer for an UPS system are presented and experimentally validated. The advantage of this proposal lies in the simplification of the observer design by considering a deadbeat observer along with the addition of a low-pass filter. In this way, the observer bandwidth is simply determined by the cutoff frequency of the low-pass filter. Simulation and experimental results are compared to assess the performance of the proposed solution.
Zhan, X, Chen, M, Yu, S & Zhang, Y 1970, 'Adaptive Detection Method for Packet-In Message Injection Attack in SDN', Algorithms and Architectures for Parallel Processing, International Conference on Algorithms and Architectures for Parallel Processing, Springer International Publishing, Australia, pp. 482-495.
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Packet-In message injection attack is severe in Software Defined Network (SDN), which will cause a single point of failure of the centralized controller and the crash of the entire network. Nowadays, there are many detection methods for it, including entropy detection and so on. We propose an adaptive detection method to proactively defend against this attack. We establish a Poisson probability distribution detection model to find the attack and use the flow table filter to mitigate it. We also use the EWMA method to update the expectation value of the model to adapt the actual network conditions. Our method has no need to send additional packets to request the switch information. The experiment results show that there is 92% true positive rate in case of attack with random destination IP packets injected, and true positive rate is 98.2% under the attack with random source IP packets injected.
Zhang, C, Mayr, P, Lu, W & Zhang, Y 1970, 'Extraction and Evaluation of Knowledge Entities from Scientific Documents', Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, JCDL '20: The ACM/IEEE Joint Conference on Digital Libraries in 2020, ACM, pp. 573-574.
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The goal of this workshop is to engage the related communities in open problems in the extraction and evaluation of knowledge entities from scientific documents. This workshop entitles this cuttingedge and cross-disciplinary direction Extraction and Evaluation of Knowledge Entity (EEKE), highlighting the development of intelligent methods for identifying knowledge claims in scientific documents, and promoting the application of knowledge entities. The website of this workshop is at: https://eeke2020.github.io/.
Zhang, C, Mayr, P, Lu, W & Zhang, Y 1970, 'Preface to the 1st workshop on extraction and evaluation of knowledge entities from scientific documents at JCDL 2020', CEUR Workshop Proceedings, pp. 1-5.
Zhang, C, Yao, Y, Zhang, J, Chen, J, Huang, P, Zhang, J & Tang, Z 1970, 'Web-Supervised Network for Fine-Grained Visual Classification', 2020 IEEE International Conference on Multimedia and Expo (ICME), 2020 IEEE International Conference on Multimedia and Expo (ICME), IEEE, London, United Kingdom, pp. 1-6.
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© 2020 IEEE. Fine-grained visual classification (FGVC) is a tough task due to its high annotation cost of the fine-grained subcategories. To build a large-scale dataset at low manual cost, straightforwardly learning from web images for FGVC has attracted broad attention. However, there exist two characteristics in the need of concerning for the web dataset: 1) Noisy images; 2) A large proportion of hard examples. In this paper, we propose a simple yet effective approach to deal with noisy images and hard examples during training. Our method is a pure web-supervised method for FGVC. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is much superior to the state-of-the-art web-supervised methods. The data and source code of this work have been posted available at: https://github.com/NUST-Machine-Intelligence-Laboratory/WSNFG.
Zhang, C, Zhang, F, Zhang, W, Liu, B, Zhang, Y, Qin, L & Lin, X 1970, 'Exploring Finer Granularity within the Cores: Efficient (k, p)-Core Computation.', ICDE, 2020 IEEE 36th International Conference on Data Engineering (ICDE), IEEE, Dallas, TX, USA, pp. 181-192.
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© 2020 IEEE. In this paper, we propose and study a novel cohesive subgraph model, named (k, p)-core, which is a maximal subgraph where each vertex has at least k neighbours and at least p fraction of its neighbours in the subgraph. The model is motivated by the finding that each user in a community should have at least a certain fraction p of neighbors inside the community to ensure user engagement, especially for users with large degrees. Meanwhile, the uniform degree constraint k, as applied in the k-core model, guarantees a minimum level of user engagement in a community, and is especially effective for users with small degrees. We propose an O(m) algorithm to compute a (k, p)-core with given k and p, and an O(dm) algorithm to decompose a graph by (k, p)-core, where m is the number of edges in the graph G and d is the degeneracy of G. A space efficient index is designed for time-optimal (k, p)-core query processing. Novel techniques are proposed for the maintenance of (k, p)-core index against graph dynamic. Extensive experiments on 8 reallife datasets demonstrate that our (k, p)-core model is effective and the algorithms are efficient.
Zhang, C, Zhang, S, Yu, JJQ & Yu, S 1970, 'An Enhanced Motif Graph Clustering-Based Deep Learning Approach for Traffic Forecasting', GLOBECOM 2020 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - 2020 IEEE Global Communications Conference, IEEE, Taipei, Taiwan, pp. 1-6.
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Traffic speed prediction is among the key problems in intelligent transportation system (ITS). Traffic patterns with complex spatial dependency make accurate prediction on traffic networks a challenging task. Recently, a deep learning approach named Spatio-Temporal Graph Convolutional Networks (STGCN) has achieved state-of-the-art results in traffic speed prediction by jointly exploiting the spatial and temporal features of traffic data. Nonetheless, applying STGCN to large-scale urban traffic network may develop degenerated results, which is due to redundant spatial information engaging in graph convolution. In this work, we propose a motif-based graph-clustering approach to apply STGCN to large-scale traffic networks. By using graphclustering, we partition a large urban traffic network into smaller clusters to prompt the learning effect of graph convolution. The proposed approach is evaluated on two real-world datasets and is compared with its variants and baseline methods. The results show that graph-clustering approaches generally outperform the other methods, and the proposed approach obtains the best performance.
Zhang, D, Zhang, Q, Zhang, G & Lu, J 1970, 'Recommender systems with heterogeneous information network for cold start items', Developments of Artificial Intelligence Technologies in Computation and Robotics, 14th International FLINS Conference (FLINS 2020), WORLD SCIENTIFIC, Cologne, Germany, pp. 496-504.
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Recommender System has been widely adopted in real-world applications. Collaborative Filtering (CF) and matrix based approach has been the forefront for the past decade in both implicit and explicit recommendation tasks. One prominent challenge that most recommendation approach facing is dealing with different data quality conditions. I.e. cold start and data sparsity. Some model based CF use condensed latent space to overcome sparsity problem. However, when dealing with constant cold start problem, CF based approach can be ineffective and costly. In this paper, we propose MERec, a novel approach that adopts graph meta-path embedding to learn item/user features independently besides learning from user-item interactions. It allows unseen data to be incorporated as part of user/item learning process. Our experiments demonstrated a effective impact reduction in cold start scenario for both new and sparse dataset.
Zhang, H, Huang, X, Zhang, JA & Guo, YJ 1970, 'Adaptive Transmission Based on MMSE Equalization over Fast Fading Channels', 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), IEEE, Victoria, BC, Canada, pp. 1-5.
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The sixth generation (6G) mobile systems will enable high mobility applications in both space and ground based networks. In this paper, we investigate low-complexity equalization and adaptive transmission schemes to combat fast fading channels due to high mobility. We first derive signal and channel models in fast fading channels, which allow low complexity minimum mean square error (MMSE) equalization. We then analyze the output signal-to-noise ratio (SNR) using eigenvalue decomposition for a generalized modulation representation. Assuming the channel state information (CSI) is known at the transmitter, we propose an adaptive transmission technique which utilizes the CSI to precode data symbols in order to improve the output SNR at the receiver. Simulation results show that the adaptive transmission scheme effectively improves the MMSE equalization performance in non-line-of-sight channels especially when the transmission signal frame is short.
Zhang, H, Zhu, L, Zhu, Y & Yang, Y 1970, 'Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior', Computer Vision – ECCV 2020 16th European Conference, European Conference on Computer Vision, Springer, Glasgow, UK, pp. 240-256.
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Deep neural networks are known to be susceptible to adversarial noise, whichare tiny and imperceptible perturbations. Most of previous work on adversarialattack mainly focus on image models, while the vulnerability of video models isless explored. In this paper, we aim to attack video models by utilizingintrinsic movement pattern and regional relative motion among video frames. Wepropose an effective motion-excited sampler to obtain motion-aware noise prior,which we term as sparked prior. Our sparked prior underlines frame correlationsand utilizes video dynamics via relative motion. By using the sparked prior ingradient estimation, we can successfully attack a variety of videoclassification models with fewer number of queries. Extensive experimentalresults on four benchmark datasets validate the efficacy of our proposedmethod.
Zhang, J, Wang, M, Li, Q, Wang, S, Chang, X & Wang, B 1970, 'Quadratic Sparse Gaussian Graphical Model Estimation Method for Massive Variables', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization, ELECTR NETWORK, pp. 2964-2972.
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We consider the problem of estimating a sparse Gaussian Graphical Model with a special graph topological structure and more than a million variables. Most previous scalable estimators still contain expensive calculation steps (e.g., matrix inversion or Hessian matrix calculation) and become infeasible in high-dimensional scenarios, where p (number of variables) is larger than n (number of samples). To overcome this challenge, we propose a novel method, called Fast and Scalable Inverse Covariance Estimator by Thresholding (FST). FST first obtains a graph structure by applying a generalized threshold to the sample covariance matrix. Then, it solves multiple block-wise subproblems via element-wise thresholding. By using matrix thresholding instead of matrix inversion as the computational bottleneck, FST reduces its computational complexity to a much lower order of magnitude (O(p2)). We show that FST obtains the same sharp convergence rate O(√(log max{p, n}/n) as other state-of-the-art methods. We validate the method empirically, on multiple simulated datasets and one real-world dataset, and show that FST is two times faster than the four baselines while achieving a lower error rate under both Frobenius-norm and max-norm.
Zhang, J, Yu, X, Li, A, Song, P, Liu, B & Dai, Y 1970, 'Weakly-Supervised Salient Object Detection via Scribble Annotations', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Seattle, WA, USA, pp. 12543-12552.
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Compared with laborious pixel-wise dense labeling, it is much easier to label data by scribbles, which only costs 1~2 seconds to label one image. However, using scribble labels to learn salient object detection has not been explored. In this paper, we propose a weakly-supervised salient object detection model to learn saliency from such annotations. In doing so, we first relabel an existing large-scale salient object detection dataset with scribbles, namely S-DUTS dataset. Since object structure and detail information is not identified by scribbles, directly training with scribble labels will lead to saliency maps of poor boundary localization. To mitigate this problem, we propose an auxiliary edge detection task to localize object edges explicitly, and a gated structure-Aware loss to place constraints on the scope of structure to be recovered. Moreover, we design a scribble boosting scheme to iteratively consolidate our scribble annotations, which are then employed as supervision to learn high-quality saliency maps. As existing saliency evaluation metrics neglect to measure structure alignment of the predictions, the saliency map ranking may not comply with human perception. We present a new metric, termed saliency structure measure, as a complementary metric to evaluate sharpness of the prediction. Extensive experiments on six benchmark datasets demonstrate that our method not only outperforms existing weakly-supervised/unsupervised methods, but also is on par with several fully-supervised state-of-The-Art models (Our code and data is publicly available at: https://github.com/JingZhang617/Scribble Saliency).
Zhang, J, Zhang, J, Chen, J & Yu, S 1970, 'GAN Enhanced Membership Inference: A Passive Local Attack in Federated Learning', ICC 2020 - 2020 IEEE International Conference on Communications (ICC), ICC 2020 - 2020 IEEE International Conference on Communications (ICC), IEEE, Dublin, Ireland, pp. 1-6.
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Federated learning has lately received great attention for its privacy protection feature. However, recent researches found that federated learning models are susceptible to various inference attacks. In this paper, we point out a membership inference attack method that can cause a serious privacy leakage in federated learning. An adversary who is a participant in federated learning can train a classification attack model to launch the membership inference attack, which determines if a data record is in the model's training dataset. The existing membership inference method is dissatisfied due to a lack of attack data since the training data of each participant are independent. To overcome the lack of attack data, an adversary can enrich attack data using the generative adversarial network (GAN), which is a practical method to increase data diversity. We substantiate that this GAN enhanced membership inference attack method has a 98 attack accuracy. We perform experiments to show that data diversity and the overfitting make federated learning models susceptible.
Zhang, JA, Hoang, L, Nguyen, D, Huang, X, Kekirigoda, A & Hui, K-P 1970, 'Multi-user MIMO Communications with Interference Mitigation in Time-varying Channels', 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, Adelaide, Australia, pp. 1-7.
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© 2020 IEEE. In this paper, we present a technique for realizing reliable multi-user MIMO communications in the presence of interference in time-varying channels. The null space of interfering channels is estimated and exploited for interference mitigation. We first introduce an improved superframe structure to enable frequent tracking of user channels and the null space of interfering channels. The different natures of the received user signals and interference require different processing methods. We improve and compare several adaptive equalizers to deal with time-varying user channels, and propose to use a subspace-based tracking algorithm to handle time-varying interfering channels. We simulate the proposed tracking algorithms in various settings, including when the interference signals are correlated. Simulation results are provided and validate the effectiveness of the proposed technique.
Zhang, L, Chang, X, Liu, J, Luo, M, Wang, S, Ge, Z & Hauptmann, A 1970, 'ZSTAD: Zero-Shot Temporal Activity Detection', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, ELECTR NETWORK, pp. 876-885.
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An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-To-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS'14 and the Charades datasets show promising performance in terms of detecting unseen activities.
Zhang, L, Wang, X, Yao, L & Zheng, F 1970, 'Zero-Shot Object Detection with Textual Descriptions Using Convolutional Neural Networks', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK, pp. 1-6.
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Zhang, L, Wang, X, Yao, L, Wu, L & Zheng, F 1970, 'Zero-Shot Object Detection via Learning an Embedding from Semantic Space to Visual Space', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization, Yokahama, Japan, pp. 906-912.
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Zero-shot object detection (ZSD) has received considerable attention from the community of computer vision in recent years. It aims to simultaneously locate and categorize previously unseen objects during inference. One crucial problem of ZSD is how to accurately predict the label of each object proposal, i.e. categorizing object proposals, when conducting ZSD for unseen categories.Previous ZSD models generally relied on learning an embedding from visual space to semantic space or learning a joint embedding between semantic description and visual representation. As the features in the learned semantic space or the joint projected space tend to suffer from the hubness problem, namely the feature vectors are likely embedded to an area of incorrect labels, and thus it will lead to lower detection precision. In this paper, instead, we propose to learn a deep embedding from the semantic space to the visual space, which enables to well alleviate the hubness problem, because, compared with semantic space or joint embedding space, the distribution in visual space has smaller variance. After learning a deep embedding model, we perform $k$ nearest neighbor search in the visual space of unseen categories to determine the category of each semantic description. Extensive experiments on two public datasets show that our approach significantly outperforms the existing methods.
Zhang, L, Zhang, J, Li, Z & Xu, J 1970, 'Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks For Multimodal Marketing Intention Detection', 2020 IEEE International Conference on Multimedia and Expo (ICME), 2020 IEEE International Conference on Multimedia and Expo (ICME), IEEE, London, United Kingdom, pp. 1-6.
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© 2020 IEEE. Inspired by the fact that spreading and collecting information through the Internet becomes the norm, more and more people choose to post for-profit contents (images and texts) in social networks. Due to the difficulty of network censors, malicious marketing may be capable of harming the society. Therefore, it is meaningful to detect marketing intentions online automatically. However, gaps between multimodal data make it difficult to fuse images and texts for content marketing detection. To this end, this paper proposes Two-Branch Collaborative Graph Neural Networks to collaboratively represent multimodal data by Graph Convolution Networks (GCNs) in an end-to-end fashion. We first separately embed groups of images and texts by GCNs layers from two views and further adopt the proposed multimodal fusion strategy to learn the graph representation collaboratively. Experimental results demonstrate that our proposed method achieves superior graph classification performance for marketing intention detection.
Zhang, M, Li, H, Pan, S, Chang, X & Su, S 1970, 'Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity Maximization', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 7806-7815.
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One-Shot Neural Architecture Search (NAS) significantly improves the computational efficiency through weight sharing. However, this approach also introduces multi-model forgetting during the supernet training (architecture search phase), where the performance of previous architectures degrades when sequentially training new architectures with partially-shared weights. To overcome such catastrophic forgetting, the state-of-the-art method assumes that the shared weights are optimal when jointly optimizing a posterior probability. However, this strict assumption is not necessarily held for One-Shot NAS in practice. In this paper, we formulate the supernet training in the One-Shot NAS as a constrained optimization problem of continual learning that the learning of current architecture should not degrade the performance of previous architectures. We propose a Novelty Search based Architecture Selection (NSAS) loss function and demonstrate that the posterior probability could be calculated without the strict assumption when maximizing the diversity of the selected constraints. A greedy novelty search method is devised to find the most representative subset to regularize the supernet training. We apply our proposed approach to two One-Shot NAS baselines, random sampling NAS (RandomNAS) and gradient-based sampling NAS (GDAS). Extensive experiments demonstrate that our method enhances the predictive ability of the supernet in One-Shot NAS and achieves remarkable performance on CIFAR-10, CIFAR-100, and PTB with efficiency.
Zhang, M, Li, H, Pan, S, Chang, X, Ge, Z & Su, S 1970, 'Differentiable neural architecture search in equivalent space with exploration enhancement', Advances in Neural Information Processing Systems.
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Recent works on One-Shot Neural Architecture Search (NAS) mostly adopt a bilevel optimization scheme to alternatively optimize the supernet weights and architecture parameters after relaxing the discrete search space into a differentiable space. However, the non-negligible incongruence in their relaxation methods is hard to guarantee the differentiable optimization in the continuous space is equivalent to the optimization in the discrete space. Differently, this paper utilizes a variational graph autoencoder to injectively transform the discrete architecture space into an equivalently continuous latent space, to resolve the incongruence. A probabilistic exploration enhancement method is accordingly devised to encourage intelligent exploration during the architecture search in the latent space, to avoid local optimal in architecture search. As the catastrophic forgetting in differentiable One-Shot NAS deteriorates supernet predictive ability and makes the bilevel optimization inefficient, this paper further proposes an architecture complementation method to relieve this deficiency. We analyze the proposed method’s effectiveness, and a series of experiments have been conducted to compare the proposed method with state-of-the-art One-Shot NAS methods.
Zhang, M, Li, H, Pan, S, Liu, T & Su, S 1970, 'One-Shot Neural Architecture Search via Novelty Driven Sampling', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization, Yokahama, Japan, pp. 3188-3194.
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One-Shot Neural architecture search (NAS) has received wide attentions due to its computational efficiency. Most state-of-the-art One-Shot NAS methods use the validation accuracy based on inheriting weights from the supernet as the stepping stone to search for the best performing architecture, adopting a bilevel optimization pattern with assuming this validation accuracy approximates to the test accuracy after re-training. However, recent works have found that there is no positive correlation between the above validation accuracy and test accuracy for these One-Shot NAS methods, and this reward based sampling for supernet training also entails the rich-get-richer problem. To handle this deceptive problem, this paper presents a new approach, Efficient Novelty-driven Neural Architecture Search, to sample the most abnormal architecture to train the supernet. Specifically, a single-path supernet is adopted, and only the weights of a single architecture sampled by our novelty search are optimized in each step to reduce the memory demand greatly. Experiments demonstrate the effectiveness and efficiency of our novelty search based architecture sampling method.
Zhang, M, Zhou, J, Zhang, G, Huang, L, Wang, T & Yu, S 1970, 'Scalable and Updatable Attribute-based Privacy Protection Scheme for Big Data Publishing', GLOBECOM 2020 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - 2020 IEEE Global Communications Conference, IEEE, Taipei, Taiwan, pp. 1-6.
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To ensure data security and privacy during big data publishing, it is challenging to design a security and privacy protection scheme for the big data environment with a large scale of users. At the same time, due to the users' dynamically joining and exiting, it is also very important to design a user's dynamic update mechanism. To address such challenges, we design a novel scalable and updatable attribute-based privacy protection scheme (SUAPP) for big data publishing. The proposed scheme can realize users' hierarchical management, which can reduce the overhead on key generation and management caused by the large scale of data users in the big data center (BDC). We set a user group for each attribute, then adapt the Chinese remaining theorem to dynamically assist the big data center to generate and update group keys for the attribute users group. Analyses and experiments show that while ensuring the privacy protection of big data publishing, our scheme also has low communication and computation overhead and higher efficiency compared with two state peer schemes.
Zhang, Q, Cao, Y, Chen, H, Li, F, Yang, S, Wang, Y, Yang, Z & Liu, Y 1970, 'airFinger: Micro Finger Gesture Recognition via NIR Light Sensing for Smart Devices', 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), IEEE, ELECTR NETWORK, pp. 552-562.
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Zhang, Q, Li, Y, Zhang, G & Lu, J 1970, 'A recurrent neural network-based recommender system framework and prototype for sequential E-learning', Developments of Artificial Intelligence Technologies in Computation and Robotics, 14th International FLINS Conference (FLINS 2020), WORLD SCIENTIFIC, Cologne, Germany, pp. 488-495.
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In the fast pace of life, E-learning has become a new way for self-improvement and competitiveness. The recommendation is needed in an E-learning system to filter suitable courses for users when they are facing a massive amount of information in course enrolment. However, due to the complexity of each learning course and the change of user interest, it is challenging to provide accurate recommendations. This paper proposes an E-learning recommender system that combines the recurrent neural network (RNN) and content-based technique to support users in course selection. The content-based techniques are to mine the relationships between courses, and the recurrent neural network is to extract user interests with a series of his/her enrolled courses. The proposed E-learning recommender system framework takes sequential connections into consideration. It intends to provide students with more precise course recommendations. The system is implemented with the Django framework and ElephantSQL cloud database and deployed on the Amazon Elastic Compute Cloud.
Zhang, Q, Lu, J & Zhang, G 1970, 'Cross-Domain Recommendation with Multiple Sources', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK, pp. 1-7.
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Data sparsity remains a challenging and common problem in real-world recommender systems, which impairs the accuracy of recommendation thus damages user experience. Cross-domain recommender systems are developed to deal with data sparsity problem through transferring knowledge from a source domain with relatively abundant data to the target domain with insufficient data. However, two challenging issues exist in cross-domain recommender systems: 1) domain shift which makes the knowledge from source domain inconsistent with that in the target domain; 2) knowledge extracted from only one source domain is insufficient, while knowledge is potentially available in many other source domains. To handle the above issues, we develop a cross-domain recommendation method in this paper to extract group-level knowledge from multiple source domains to improve recommendation in a sparse target domain. Domain adaptation techniques are applied to eliminate the domain shift and align user and item groups to maintain knowledge consistency during the transfer learning process. Knowledge is extracted not from one but multiple source domains through an intermediate subspace and adapted through flexible constraints of matrix factorization in the target domain. Experiments conducted on five datasets in three categories show that the proposed method outperforms six benchmarks and increases the accuracy of recommendations in the target domain.
Zhang, Q, Zhang, G, Lu, J & Lin, H 1970, 'A framework of clinical recommender system with genomic information', Developments of Artificial Intelligence Technologies in Computation and Robotics, 14th International FLINS Conference (FLINS 2020), WORLD SCIENTIFIC, Cologne, Germany, pp. 522-529.
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Clinicians make decisions that affect life and death, quality of life, every single day. It is important to support clinicians by discovering medical knowledge from the accumulated electronic health records (EHRs). The integration of genomic information and EHRs are long recognized by the medical community as the inherent feature of the disease. The demand for developing a clinical recommender system that is able to deal with both genomic and phenotypic data is urgent. This paper proposes a framework of clinical recommender system with genomic information, which is used in the clinical process and connects the four types of users: clinicians, patients, clinical labs, researchers. With models and methods in artificial intelligence (AI), five functions are designed in this framework: diagnosis prediction, disease risk prediction, test prediction, and event prediction. The proposed framework will help clinicians to make decisions on the next step in clinical care action for patients.
Zhang, R, Jin, J, Lin, Z & Guo, Y 1970, 'Study on the Effect of ReBCO Tape Arrangements on the Electromagnetic Field Distribution', 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE, Tianjin, China, pp. 1-2.
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ReBCO tapes are usually arranged horizontally or vertically to obtain a larger magnetic field or transmit more current. In this paper, the H-formulation is used to numerically analyze the electromagnetic field distribution combining with analytical method. The influence of the number of tapes and the arrangement interval on the electromagnetic characteristics is analyzed which is beneficial for superconducting device design and optimization.
Zhang, R, Jin, J, Zhu, J, Guo, Y & Abu-Siada, A 1970, 'Numerical Study on Electromagnetic Field and AC loss of HTS Air-core Transformer', 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE, China, pp. 1-2.
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Air-core transformer has advantages of no iron loss, no magnetic saturation, and compact in size, which make them attractive to be studied for practical applications. In this paper, the H-formulation is used to numerically analyze the electromagnetic field and AC loss of the HTS air-core transformer and optimize the air gap.
Zhang, R, Walder, CJ, Bonilla, EV, Rizoiu, M-A & Xie, L 1970, 'Quantile Propagation for Wasserstein-Approximate Gaussian Processes', ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 34th Conference on Neural Information Processing Systems (NeurIPS), NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), ELECTR NETWORK.
Zhang, R, Xu, M, Shi, Y, Fan, J, Mu, C & Xu, L 1970, 'Infrared Target Detection Using Intensity Saliency And Self-Attention', 2020 IEEE International Conference on Image Processing (ICIP), 2020 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 1991-1995.
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Zhang, S, Luo, L, Li, Z, Wang, Y, Chen, F & Xu, R 1970, 'Simultaneous Customer Segmentation and Behavior Discovery', Neural Information Processing, International Conference on Neural Information Processing, Springer International Publishing, Bangkok, Thailand, pp. 122-130.
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© 2020, Springer Nature Switzerland AG. Customer purchase behavior segmentation plays an important role in the modern economy. We proposed a Bayesian non-parametric (BNP)-based framework, named Simultaneous Customer Segmentation and Utility Discovery (UtSeg), to discover customer segmentation without knowing specific forms of utility functions and parameters. For the segmentation based on BNP models, the unknown type of functions is usually modeled as a non-homogeneous point process (NHPP) for each mixture component. However, the inference of these models is complex and time-consuming. To reduce such complexity, traditionally, economists will use one specific utility function in a heuristic way to simplify the inference. We proposed to automatically select among multiple utility functions instead of searching in a continuous space. We further unified the parameters for different types of utility functions with the same prior distribution to improve efficiency. We tested our model with synthetic data and applied the framework to real-supermarket data with different products, and showed that our results can be interpreted with common knowledge.
Zhang, X, Lu, W, Zhang, G, Li, F & Wang, S 1970, 'Chinese Sentence Semantic Matching Based on Multi-Granularity Fusion Model', Advances in Knowledge Discovery and Data Mining, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Singapore, pp. 246-257.
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Sentence semantic matching is the cornerstone of many natural language processing tasks, including Chinese language processing. It is well known that Chinese sentences with different polysemous words or word order may have totally different semantic meanings. Thus, to represent and match the sentence semantic meaning accurately, one challenge that must be solved is how to capture the semantic features from the multi-granularity perspective, e.g., characters and words. To address the above challenge, we propose a novel sentence semantic matching model which is based on the fusion of semantic features from character-granularity and word-granularity, respectively. Particularly, the multi-granularity fusion intends to extract more semantic features to better optimize the downstream sentence semantic matching. In addition, we propose the equilibrium cross-entropy, a novel loss function, by setting mean square error (MSE) as an equilibrium factor of cross-entropy. The experimental results conducted on Chinese open data set demonstrate that our proposed model combined with binary equilibrium cross-entropy loss function is superior to the existing state-of-the-art sentence semantic matching models.
Zhang, Y, Bai, G, Li, X, Curtis, C, Chen, C & Ko, RKL 1970, 'PrivColl: Practical Privacy-Preserving Collaborative Machine Learning'.
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Collaborative learning enables two or more participants, each with their owntraining dataset, to collaboratively learn a joint model. It is desirable thatthe collaboration should not cause the disclosure of either the raw datasets ofeach individual owner or the local model parameters trained on them. Thisprivacy-preservation requirement has been approached through differentialprivacy mechanisms, homomorphic encryption (HE) and secure multipartycomputation (MPC), but existing attempts may either introduce the loss of modelaccuracy or imply significant computational and/or communicational overhead. Inthis work, we address this problem with the lightweight additive secret sharingtechnique. We propose PrivColl, a framework for protecting local data and localmodels while ensuring the correctness of training processes. PrivColl employssecret sharing technique for securely evaluating addition operations in amultiparty computation environment, and achieves practicability by employingonly the homomorphic addition operations. We formally prove that it guaranteesprivacy preservation even though the majority (n-2 out of n) of participantsare corrupted. With experiments on real-world datasets, we further demonstratethat PrivColl retains high efficiency. It achieves a speedup of more than 45Xover the state-of-the-art MPC/HE based schemes for training linear/logisticregression, and 216X faster for training neural network.
Zhang, Y, Bai, G, Li, X, Curtis, C, Chen, C & Ko, RKL 1970, 'PrivColl: Practical Privacy-Preserving Collaborative Machine Learning', Springer International Publishing, pp. 399-418.
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Zhang, Y, Falque, R, Zhao, L, Huang, S & Hu, B 1970, 'Deep Learning Assisted Automatic Intra-operative 3D Aortic Deformation Reconstruction', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 660-669.
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Endovascular interventions rely on 2D X-ray fluoroscopy for 3D catheter manipulation. The dynamic nature of aorta prevents the pre-operative CT/MRI data to be used directly as the live 3D guidance since the vessel deforms during the surgery. This paper provides a framework that reconstructs the live 3D aortic shape by fusing a 3D static pre-operative model and the 2D intra-operative fluoroscopic images. The proposed framework recovers aortic 3D shape automatically and computationally efficient. A deep learning approach is adopted as the front-end for extracting features from fluoroscopic images. A signed distance field based correspondence method is employed for avoiding the repeated feature-vertex matching while maintaining the correspondence accuracy. The warp field of 3D deformation is estimated by solving a non-linear least squares problem based on the embedded deformation graph. Detailed phantom experiments are conducted, and the results demonstrate the accuracy of the proposed framework as well as the potential clinical value of the technique.
Zhang, Y, Liu, F, Fang, Z, Yuan, B, Zhang, G & Lu, J 1970, 'Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization, pp. 2526-2532.
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In unsupervised domain adaptation (UDA), classifiers for the target domain are trained with massive true-label data from the source domain and unlabeled data from the target domain. However, it may be difficult to collect fully-true-label data in a source domain given limited budget. To mitigate this problem, we consider a novel problem setting where the classifier for the target domain has to be trained with complementary-label data from the source domain and unlabeled data from the target domain named budget-friendly UDA (BFUDA). The key benefit is that it is much less costly to collect complementary-label source data (required by BFUDA) than collecting the true-label source data (required by ordinary UDA). To this end, complementary label adversarial network (CLARINET) is proposed to solve the BFUDA problem. CLARINET maintains two deep networks simultaneously, where one focuses on classifying complementary-label source data and the other takes care of the source-to-target distributional adaptation. Experiments show that CLARINET significantly outperforms a series of competent baselines.
Zhang, Y, Luo, L, Wang, Y & Wang, Z 1970, 'FCP Filter: A Dynamic Clustering-Prediction Framework for Customer Behavior', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Singapore, Singapore, pp. 580-591.
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Customer purchase behavior prediction plays an important role in modern retailing, but the performance of this task is often limited by the randomness of individual historic transaction data. In the meanwhile, Fragmentation and Coagulation Process (FCP), a stochastic partition model, has recently been proposed for identifying dynamic customer groups and modeling their purchase behavior. However, FCP is not able to forecast the purchase behavior because such a data-driven method requires transaction observations to conduct clustering. To tackle this challenge, we propose FCP filter, a clustering-prediction framework based on FCP, which can forecast purchase behavior and filter random noise of individual transaction data. In our model, FCP clusters customers into groups by their temporal interests to filter random noise of individual transaction data. Then a predictor is built on grouped data. The predicted results are also fed to FCP to adjust the parameter for prior knowledge at the next time step. Our model is superior in capturing temporal dynamics and having flexible number of groups. We conduct experiments on both synthetic and real-world datasets, demonstrating that our model is able to discover the latent group of individual customers and provides accurate predictions for dynamic purchase behavior.
Zhang, Y, Tsang, IW, Luo, Y, Hu, C-H, Lu, X & Yu, X 1970, 'Copy and Paste GAN: Face Hallucination From Shaded Thumbnails', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Seattle, WA, USA, pp. 7353-7362.
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Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in low or non-uniform illumination conditions. This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and non-uniform illumination. To this end, we develop two key components in our CPGAN: internal and external Copy and Paste nets (CPnets). Specifically, our internal CPnet exploits facial information residing in the input image to enhance facial details; while our external CPnet leverages an external HR face for illumination compensation. A new illumination compensation loss is thus developed to capture illumination from the external guided face image effectively. Furthermore, our method offsets illumination and upsamples facial details alternatively in a coarse-to-fine fashion, thus alleviating the correspondence ambiguity between LR inputs and external HR inputs. Extensive experiments demonstrate that our method manifests authentic HR face images in a uniform illumination condition and outperforms state-of-the-art methods qualitatively and quantitatively.
Zhang, Y, Wang, M, Saberi, M & Chang, E 1970, 'Towards Expert Preference on Academic Article Recommendation Using Bibliometric Networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 11-19.
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Expert knowledge can be valuable for academic article recommendation, however, hiring domain experts for this purpose is rather expensive as it is extremely demanding for human to deal with a large volume of academic publications. Therefore, developing an article ranking method which can automatically provide recommendations that are close to expert decisions is needed. Many algorithms have been proposed to rank articles but pursuing quality article recommendations that approximate to expert decisions has hardly been considered. In this study, domain expert decisions on recommending quality articles are investigated. Specifically, we hire domain experts to mark articles and a comprehensive correlation analysis is then performed between the ranking results generated by the experts and state-of-the-art automatic ranking algorithms. In addition, we propose a computational model using heterogeneous bibliometric networks to approximate human expert decisions. The model takes into account paper citations, semantic and network-level similarities amongst papers, authorship, venues, publishing time, and the relationships amongst them to approximate human decision-making factors. Results demonstrate that the proposed model is able to effectively achieve human expert-alike decisions on recommending quality articles.
Zhang, Y, Wang, Z, Xu, J, Liu, Y, Zhou, B, Zhang, N, He, M, Fan, J, Liu, X, Zhao, J, Yang, Q, Zhang, L, Cao, Y & Su, S 1970, 'Association Between Consecutive Ambient Air Pollution and Chronic Obstructive Pulmonary Disease Hospitalization: Time Series Study During 2015-2017 in Chengdu China', 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, IEEE, United States, pp. 5378-5381.
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This paper investigates the association between consecutive ambient air pollution and Chronic Obstructive Pulmonary Disease (COPD) hospitalization in Chengdu China. The three-year (2015-2017) time series data for both ambient air pollutant concentrations and COPD hospitalizations in Chengdu are approved for the study. The big data statistic analysis shows that Air Quality Index (AQI) exceeded the lighted air polluted level in Chengdu region are mainly attributed to particulate matters (i.e., PM2.5 and PM10). The time series study for consecutive ambient air pollutant concentrations reveal that AQI, PM2.5, and PM10 are significantly positive correlated, especially when the number of consecutive polluted days is greater than nine days. The daily COPD hospitalizations for every 10 μg/m3 increase in PM2.5 and PM10 indicate that consecutive ambient air pollution can lead to an appearance of an elevation of COPD admissions, and also present that dynamic responses before and after the peak admission are different. Support Vector Regression (SVR) is then used to describe the dynamics of COPD hospitalizations to consecutive ambient air pollution. These findings will be further developed for region specific, hospital early notifications of COPD in responses to consecutive ambient air pollution.
Zhang, Y, Xiao, G, Zheng, Z, Zhu, T, Tsang, IW & Sui, Y 1970, 'An Empirical Study of Code Deobfuscations on Detecting Obfuscated Android Piggybacked Apps', 2020 27th Asia-Pacific Software Engineering Conference (APSEC), 2020 27th Asia-Pacific Software Engineering Conference (APSEC), IEEE, Singapore, Singapore, pp. 41-50.
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Android piggybacked malware (i.e., apps that piggyback malicious code) are becoming ubiquitous in app stores. Malware writers often use obfuscation techniques to obfuscate piggybacked apps to evade detection by Android malware detectors. Previous studies in this field have focused on the impact of code obfuscations on the detection of piggybacked malware, but the impact of code deobfuscation on detecting obfuscated piggybacked apps has rarely been studied. Knowing about the impact of code deobfuscation can provide useful insights into obfuscated piggybacked apps and therefore the design of resilient Android malware detectors. In this paper we conduct an empirical study of code deobfuscations on detecting obfuscated Android piggybacked apps, focusing on three types of malware detectors: commercial anti-malware products, machine learning-based detectors, and similarity-based detectors. We observe that code deobfuscations can impact differently depending on the malware detectors. For example, some deobfuscation strategies can improve the precision of detecting obfuscated piggybacked apps. Also we observe that the examined deobfuscation tools (Simplify and Deguard) have a different impact on obfuscated piggybacked apps after deobfuscations.
Zhang, Y, Zhao, L & Huang, S 1970, 'Aortic 3D Deformation Reconstruction using 2D X-ray Fluoroscopy and 3D Pre-operative Data for Endovascular Interventions', 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Paris, France (Virtual), pp. 2393-2399.
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Current clinical endovascular interventions rely on 2D guidance for catheter manipulation. Although an aortic 3D surface is available from the pre-operative CT/MRI imaging, it cannot be used directly as a 3D intra-operative guidance since the vessel will deform during the procedure. This paper aims to reconstruct the live 3D aortic deformation by fusing the static 3D model from the pre-operative data and the 2D live imaging from fluoroscopy. In contrast to some existing deformation reconstruction frameworks which require 3D observations such as RGB-D or stereo images, fluoroscopy only presents 2D information. In the proposed framework, a 2D-3D registration is performed and the reconstruction process is formulated as a non-linear optimization problem based on the deformation graph approach. Detailed simulations and phantom experiments are conducted and the result demonstrates the reconstruction accuracy and robustness, as well as the potential clinical value of this framework.
Zhang, Z, Da Xu, RY, Jiang, S, Li, Y, Huang, C & Deng, C 1970, 'Illumination Adaptive Person Reid Based on Teacher-Student Model and Adversarial Training', 2020 IEEE International Conference on Image Processing (ICIP), 2020 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 2321-2325.
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Zhang, Z, Liu, H, Tang, Y, Xiang, Y & Gao, W 1970, 'Design of a comprehensive experiment of the synthesis of biodiesel catalyzed by CaO', IOP Conference Series: Earth and Environmental Science, IOP Publishing, pp. 012055-012055.
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Abstract An applied chemistry comprehensive experiment has been designed using CaO for synthesis of biodiesel. Effects of calcination temperature, methanol concentration, reaction temperature, reaction time and amount of catalyst on transesterification reaction are investigated. Based on experiment, the relationship between the catalyst structure and its properties was analyzed. Furthermore, this experiment can cultivate student's abilities of analyzing and solving problem. And it can build up the innovation consciousness, competition and team spirit of students. At the same time, the biodiesel and its preparation are line with the development requirements of green chemistry, so that students can establish environmental protection concept.
Zhang, Z, Yu, L, Zhang, J & Wu, Q 1970, 'A Vision Based Fish Processing System', 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), IEEE, Macau, China, pp. 260-260.
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The digital fish provenance and quality tracking system is essential for the seafood supply chain. As a part of this system, we develop a vision-based fish processing system to automatically perform fish freshness estimation, size measurement and species classification. Under the constrained illumination environment, our system is able to auto-process the fish selection, thus greatly reduce the human labour and bring trust and efficiency to the seafood supply chain from catch to market.
Zhang, Z, Yu, L, Zhang, J & Wu, Q 1970, 'A Vision Based Fish Processing System', 2020 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP), IEEE International Conference on Visual Communications and Image Processing (VCIP), IEEE, ELECTR NETWORK, pp. 260-260.
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Zhao, H, Lin, Y, Gao, S & Yu, S 1970, 'Evaluating and Improving Adversarial Attacks on DNN-Based Modulation Recognition', GLOBECOM 2020 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - 2020 IEEE Global Communications Conference, IEEE, pp. 1-5.
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Zhao, Y, Chen, J, Zhang, J, Wu, D, Teng, J & Yu, S 1970, 'PDGAN: A Novel Poisoning Defense Method in Federated Learning Using Generative Adversarial Network', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 595-609.
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© 2020, Springer Nature Switzerland AG. Federated learning can complete an enormous training task efficiently by inviting participants to train a deep learning model collaboratively, and the user privacy will be well preserved for the users only upload model parameters to the centralized server. However, the attackers can initiate poisoning attacks by uploading malicious updates in federated learning. Therefore, the accuracy of the global model will be impacted significantly after the attack. To address this vulnerability, we propose a novel poisoning defense generative adversarial network (PDGAN) to defend the poising attack. The PDGAN can reconstruct training data from model updates and audit the accuracy for each participant model by using the generated data. Precisely, the participant whose accuracy is lower than a predefined threshold will be identified as an attacker and model parameters of the attacker will be removed from the training procedure in this iteration. Experiments conducted on MNIST and Fashion-MNIST datasets demonstrate that our approach can indeed defend the poisoning attacks in federated learning.
Zhao, Y, Wang, S, Wang, Y, Liu, H & Zhang, W 1970, 'Double-Wing Mixture of Experts for Streaming Recommendations', Web Information Systems Engineering – WISE 2020, International Conference on Web Information Systems Engineering, Springer International Publishing, The Netherlands, pp. 269-284.
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Streaming Recommender Systems (SRSs) commonly train recommendation models on newly received data only to address user preference drift, i.e., the changing user preferences towards items. However, this practice overlooks the long-term user preferences embedded in historical data. More importantly, the common heterogeneity in data stream greatly reduces the accuracy of streaming recommendations. The reason is that different preferences (or characteristics) of different types of users (or items) cannot be well learned by a unified model. To address these two issues, we propose a Variational and Reservoir-enhanced Sampling based Double-Wing Mixture of Experts framework, called VRS-DWMoE, to improve the accuracy of streaming recommendations. In VRS-DWMoE, we first devise variational and reservoir-enhanced sampling to wisely complement new data with historical data, and thus address the user preference drift issue while capturing long-term user preferences. After that, we propose a Double-Wing Mixture of Experts (DWMoE) model to first effectively learn heterogeneous user preferences and item characteristics, and then make recommendations based on them. Specifically, DWMoE contains two Mixture of Experts (MoE, an effective ensemble learning model) to learn user preferences and item characteristics, respectively. Moreover, the multiple experts in each MoE learn the preferences (or characteristics) of different types of users (or items) where each expert specializes in one underlying type. Extensive experiments demonstrate that VRS-DWMoE consistently outperforms the state-of-the-art SRSs.
Zheng, H, Jiang, J, Wei, P, Long, G & Zhang, C 1970, 'Competitive and cooperative heterogeneous deep reinforcement learning', Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, pp. 1656-1664.
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Numerous deep reinforcement learning methods have been proposed, including deterministic, stochastic, and evolutionary-based hybrid methods. However, among these various methodologies, there is no clear winner that consistently outperforms the others in every task in terms of effective exploration, sample efficiency, and stability. In this work, we present a competitive and cooperative heterogeneous deep reinforcement learning framework called C2HRL. C2HRL aims to learn a superior agent that exceeds the capabilities of the individual agent in an agent pool through two agent management mechanisms: one competitive, the other cooperative. The competitive mechanism forces agents to compete for computing resources and to explore and exploit diverse regions of the solution space. To support this strategy, resources are distributed to the most suitable agent for that specific task and random seed setting, which results in better sample efficiency and stability. The other mechanic, cooperation, asks heterogeneous agents to share their exploration experiences so that all agents can learn from a diverse set of policies. The experiences are stored in a two-level replay buffer and the result is an overall more effective exploration strategy. We evaluated C2HRL on a range of continuous control tasks from the benchmark Mujoco. The experimental results demonstrate that C2HRL has better sample efficiency and greater stability than three state-of-the-art DRL baselines.
Zheng, H, Wei, P, Jiang, J, Long, G, Lu, Q & Zhang, C 1970, 'Cooperative heterogeneous deep reinforcement learning', Advances in Neural Information Processing Systems.
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Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws. In this work, we present a Cooperative Heterogeneous Deep Reinforcement Learning (CHDRL) framework that can learn a policy by integrating the advantages of heterogeneous agents. Specifically, we propose a cooperative learning framework that classifies heterogeneous agents into two classes: global agents and local agents. Global agents are off-policy agents that can utilize experiences from the other agents. Local agents are either on-policy agents or population-based evolutionary algorithms (EAs) agents that can explore the local area effectively. We employ global agents, which are sample-efficient, to guide the learning of local agents so that local agents can benefit from sample-efficient agents and simultaneously maintain their advantages, e.g., stability. Global agents also benefit from effective local searches. Experimental studies on a range of continuous control tasks from the Mujoco benchmark show that CHDRL achieves better performance compared with state-of-the-art baselines.
Zheng, J, Luo, Z & Jiang, C 1970, 'Design of multiscale composites using robust topology optimization of level sets', Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019, pp. 2060-2063.
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Most topology optimization methods are focused on structures with solid materials. Inspired by natural porous materials, topology optimization can be explored to concurrently optimize micro material distributions and the macro composite structure to achieve the desired performance. Under random and interval hybrid uncertainties, a robust topology optimization method based on level sets is then developed for the concurrent design of multiscale cellular composites. Both the topology of the representative microstructure and the topology of the macro composite structure are optimized. The robust objective function is defined as a weighted sum of the mean and standard variance of the objective function, e.g. structural compliance, under the worst case. The hybrid univariate dimension reduction method is used to estimate the interval mean and standard variance. The sensitivities of the robust objective function can be obtained after the hybrid uncertainty analysis. A benchmark numerical example is used to showcase the effectiveness of this method.
Zheng, X, Cao, Z & Bai, Q 1970, 'An Evoked Potential-Guided Deep Learning Brain Representation for Visual Classification', Neural Information Processing, International Conference on Neural Information Processing, Springer International Publishing, Thailand, pp. 54-61.
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The new perspective in visual classification aims to decode the feature representation of visual objects from human brain activities. Recording electroencephalogram (EEG) from the brain cortex has been seen as a prevalent approach to understand the cognition process of an image classification task. In this study, we proposed a deep learning framework guided by the visual evoked potentials, called the Event-Related Potential (ERP)-Long short-term memory (LSTM) framework, extracted by EEG signals for visual classification. In specific, we first extracted the ERP sequences from multiple EEG channels to response image stimuli-related information. Then, we trained an LSTM network to learn the feature representation space of visual objects for classification. In the experiment, 10 subjects were recorded by over 50,000 EEG trials from an image dataset with 6 categories, including a total of 72 exemplars. Our results showed that our proposed ERP-LSTM framework could achieve classification accuracies of cross-subject of 66.81% and 27.08% for categories (6 classes) and exemplars (72 classes), respectively. Our results outperformed that of using the existing visual classification frameworks, by improving classification accuracies in the range of 12.62%–53.99%. Our findings suggested that decoding visual evoked potentials from EEG signals is an effective strategy to learn discriminative brain representations for visual classification.
Zheng, Y, Yu, X, Liu, M & Zhang, S 1970, 'Residual multiscale based single image deraining', 30th British Machine Vision Conference 2019, BMVC 2019.
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Rain streaks deteriorate the performance of many computer vision algorithms. Previous methods represent rain streaks by different rain layers and then separate those layers from the background image. However, it is rather difficult to decouple a rain image into rain and background layers due to the complexity of real-world rain, such as various shapes, directions, and densities of rain streaks. In this paper, we propose a residual multiscale pyramid based single image deraining method to alleviate the difficulty of rain image decomposition. In particular, we remove rain streaks in a coarse-to-fine manner. In this fashion, the heavy rain can be significantly removed in the coarse-resolution level of the pyramid first, and the light rain will then be further removed in the high-resolution level. This allows us to avoid distinguishing the densities of rain streaks explicitly since the inaccurate classification of rain densities may lead to over- or insufficient-removal of rain. Furthermore, the residual between a recovered image and its corresponding rain image can provide vital clues of rain streaks. We therefore exploit such residual as an attention map for deraining in its consecutive finer-level. Benefiting from the residual attention maps, rain layers can be better extracted from a higher-resolution input image. Extensive experimental results on synthetic and real datasets demonstrate that our method outperforms the state of the art significantly.
Zheng, Z, Jiang, M, Wang, Z, Wang, J, Bai, Z, Zhang, X, Yu, X, Tan, X, Yang, Y, Wen, S & Ding, E 1970, 'Going Beyond Real Data: A Robust Visual Representation for Vehicle Re-identification', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, Seattle, WA, USA, pp. 2550-2558.
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In this report, we present the Baidu-UTS submission to the AICity Challenge in CVPR 2020. This is the winning solution to the vehicle re-identification (re-id) track. We focus on developing a robust vehicle re-id system for real-world scenarios. In particular, we aim to fully leverage the merits of the synthetic data while arming with real images to learn a robust representation for vehicles in different views and illumination conditions. By comprehensively investigating and evaluating various data augmentation approaches and popular strong baselines, we analyze the bottleneck restricting the vehicle re-id performance. Based on our analysis, we therefore design a vehicle re-id method with better data augmentation, training and post-processing strategies. Our proposed method has achieved the 1st place out of 41 teams, yielding 84.13% mAP on the private test set. We hope that our practice could shed light on using synthetic and real data effectively in training deep re-id networks and pave the way for real-world vehicle re-id systems.
Zhong, J, Xiao, T, Halkon, B, Kirby, R & Qiu, X 1970, 'An experimental study on the active noise control using a parametric array loudspeaker', Proceedings of 2020 International Congress on Noise Control Engineering, INTER-NOISE 2020, Seoul, Korea.
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An active noise control (ANC) system using a parametric array loudspeaker (PAL) was designed to cancel broadband noise at a person's ear, where a custom-made low-mass membrane pick-up from a retroreflective film and a laser Doppler vibrometer was used to form a remote sensing apparatus to determine the acoustic information with minimum obstructions to the person. The experiment results show that such an ANC system can achieve similar overall noise reductions from 1 kHz to 6 kHz at the ear as a similar one albeit using a traditional omnidirectional loudspeaker. The noise reductions at nine points around the person were used to evaluate the effects of the ANC system in the other areas, and the results show the side effect of the ANC system with the PAL is much smaller than that with the traditional loudspeaker due to the sharp radiation directivity of the PAL. It is also shown that when the PAL was placed away from the person, the ANC performance and the side effect to the other areas remained similar due to its low geometrical spreading attenuation, but the side effect caused by a traditional loudspeaker to the other areas increased with its distance to the person.
Zhong, Y, Huang, Y & Jiang, T 1970, 'Device-Free Sensing for Gesture Recognition by Wi-Fi Communication Signal Based on Auto-encoder/decoder Neural Network', Lecture Notes in Electrical Engineering, Springer Singapore, pp. 887-894.
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Gesture recognition has been found to be a vital mission for a variety of applications, such as smart surveillance, elder care, virtual reality, advanced user interface, etc. Recently, an emerging sensing technology, namely device-free sensing (DFS), has been introduced to the domain of gesture recognition which only uses radio-frequency (RF) signals without the need to equip any devices or extra hardware support; thus, it would be a natural choice to fully leverage ubiquitous Wi-Fi signals in almost every modern building. Although the feasibility of using this technology for gesture recognition has been explored to some extent, we observe that it still cannot perform promisingly for some gestures which maybe look nearly identical in a certain instant. Therefore, in this paper, we conduct experiments with several typical hand gestures in the opposite direction based on a proposed Auto-Encoder/Decoder (Auto-ED) deep neural network to address gesture recognition in our case. Compared with several traditional learning methods, experimental results demonstrate that our proposed approach can best tackle the challenge of gesture recognition for identical motions, which indicates its potential application values in the near future.
Zhong, Y, Huang, Y, Dutkiewicz, E, Wu, Q & Jiang, T 1970, 'Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing', Lecture Notes in Electrical Engineering, 7th International Conference on Communications, Signal Processing, and Systems (CSPS), Springer Singapore, China, pp. 553-560.
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Target classification in foliage environment is a challenging task in realistic due to the high-clutter background and unsettled weather. To detect a particular target, e.g., human, under such an environment, is an indispensable technique with significant application value. Traditional method such as computer vision techniques is hardly leveraged since the working condition is limited. Therefore, in this paper, we attempt to tackle human detection by using the radio frequency (RF) signal with a device-free sensing. To this end, we propose a differential evolution flower pollination algorithm support vector machine (DEFPA-SVM) approach to detect human among other targets, e.g., iron cupboard and wooden board. This task can be formally described as a target classification problem. In our experiment, the proposed DEFPA-SVM can effectively attain the best performance compared to other classical multi-target classification models and achieve a faster convergent speed than the traditional FPA-SVM.
Zhou, J, Chen, F, Berry, A, Reed, M, Zhang, S & Savage, S 1970, 'A Survey on Ethical Principles of AI and Implementations', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, Canberra, ACT, Australia, pp. 3010-3017.
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© 2020 IEEE. AI has powerful capabilities in prediction, automation, planning, targeting, and personalisation. Generally, it is assumed that AI can enable machines to exhibit human-like intelligence, and is claimed to benefit to different areas of our lives. Since AI is fueled by data and is a distinct form of autonomous and self-learning agency, we are seeing increasing ethical concerns related to AI uses. In order to mitigate various ethical concerns, national and international organisations including governmental organisations, private sectors as well as research institutes have made extensive efforts by drafting ethical principles of AI, and having active discussions on ethics of AI within and beyond the AI community. This paper investigates these efforts with a focus on the identification of fundamental ethical principles of AI and their implementations. The review found that there is a convergence around limited principles and the most prevalent principles are transparency, justice and fairness, responsibility, non-maleficence, and privacy. The investigation suggests that ethical principles need to be combined with every stages of the AI lifecycle in the implementation to ensure that the AI system is designed, implemented and deployed in an ethical manner. Similar to ethical framework used in biomedical and clinical research, this paper suggests checklist-style questionnaires as benchmarks for the implementation of ethical principles of AI.
Zhou, J, Huang, W & Chen, F 1970, 'A Radial Visualisation for Model Comparison and Feature Identification', 2020 IEEE Pacific Visualization Symposium (PacificVis), 2020 IEEE Pacific Visualization Symposium (PacificVis), IEEE, Tianjing, China, pp. 226-230.
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Zhou, W, Cheng, Z & Guo, YJ 1970, 'A Dual-Polarized Patch Antenna With Electric and Magnetic Coupling Feed for 5G Base Stations', 2020 IEEE 5th International Conference on Integrated Circuits and Microsystems (ICICM), 2020 IEEE 5th International Conference on Integrated Circuits and Microsystems (ICICM), IEEE, pp. 27-30.
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Zhou, X, Li, Y, Gururajan, R, Bargshady, G, Tao, X, Venkataraman, R, Barua, PD & Kondalsamy-Chennakesavan, S 1970, 'A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection', 2020 7th International Conference on Behavioural and Social Computing (BESC), 2020 7th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-4.
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Zhu, C, Zhang, Q, Cao, L & Abrahamyan, A 1970, 'Mix2Vec: Unsupervised Mixed Data Representation', 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp. 118-127.
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Zhu, F, Wang, X, Zhu, L, Dong, X & Yang, Y 1970, 'UTS CAI submission at TRECVID 2018 ad-hoc video search task', 2018 TREC Video Retrieval Evaluation, TRECVID 2018.
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This work describes our approach used for the fully automatic Ad-hoc Video Search (AVS) task [7] for TRECVID[1] 2018. Our model is divided into two parts, visual model and language model. Our motivation is mapping video embedding and language embedding into same semantic space. We observe that by constructing triplets in the feature space we can take better advantage of large batches and hard examples. Our models are trained on MSR-VTT [12] and TGIF [5] dataset with different visual and language architectures. The final ensemble model achieves 6.7% mAP.
Zhu, F, Zhu, Y, Chang, X & Liang, X 1970, 'Vision-Language Navigation With Self-Supervised Auxiliary Reasoning Tasks', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 10009-10019.
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Vision-Language Navigation (VLN) is a task where an agent learns to navigate following a natural language instruction. The key to this task is to perceive both the visual scene and natural language sequentially. Conventional approaches fully exploit vision and language features in cross-modal grounding. However, the VLN task remains challenging, since previous works have implicitly neglected the rich semantic information contained in environments (such as navigation graphs or sub-trajectory semantics). In this paper, we introduce Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised auxiliary reasoning tasks to exploit the additional training signals derived from these semantic information. The auxiliary tasks have four reasoning objectives: explaining the previous actions, evaluating the trajectory consistency, estimating the progress and predict the next direction. As a result, these additional training signals help the agent to acquire knowledge of semantic representations in order to reason about its activities and build a thorough perception of environments. Our experiments demonstrate that auxiliary reasoning tasks improve both the performance of the main task and the model generalizability by a large margin. We further demonstrate empirically that an agent trained with self-supervised auxiliary reasoning tasks substantially outperforms the previous state-of-the-art method, being the best existing approach on the standard benchmark.
Zhu, H & Guo, YJ 1970, 'Circularly-Polarized Differential Antenna Array Fed by Single-Ended-to-Balanced Power Dividers with High Common-Mode Rejection', 2020 4th Australian Microwave Symposium (AMS), 2020 4th Australian Microwave Symposium (AMS), IEEE, Sydney, Australia, pp. 1-2.
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This paper presents a differential feeding network comprising of a new type of single-ended-to-balanced power dividers with high level of common-mode rejection. The single-ended-to-balanced power dividers are built based on slotline-to-microstrip transitions, which are able to provide high common-mode suppression and low differential-mode-to-common-mode conversion levels. A wideband differential circularly-polarized (CP) antenna array is designed fabricated and tested using the differential feeding network. The experimental results verify that the presented differential feeding network can be used in feeding differential CP arrays to achieve high gain, symmetrical patterns and a wide axial ratio bandwidth.
Zhu, J & Marjanovic, O 1970, 'How do platform cooperatives contribute to sustainable development goals?', 26th Americas Conference on Information Systems, AMCIS 2020, Americas Conference on Information Systems, AISEL, Virtual.
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Sustainable Development Goals (SDGs), set by United Nations General Assembly in 2015 and to be achieved by 2030, consist of 17 inter-dependent global goals that call for global partnership for a more sustainable planet. Achieving SDGs successfully and on time requires efforts from governments, civil societies, private sectors, individuals, and we posit, new types of digital organizations such as platform cooperatives. Enabled by digital platform technology, they are rapidly emerging and often established to tackle certain societal problems. Platform cooperatives are co-owned and democratically governed by their members. Besides creating economic value, they also focus on environmental and social value. Therefore, platform cooperatives appear to be a natural match for SDGs. This research aims to investigate how platform cooperatives contribute to SDGs. This aim is achieved by analysis and mapping of over 100 platform coops to SDGs, focusing on their products and services as well as different value creation mechanisms.
Zhu, J, McGloin, D, Yang, Y & Liu, B 1970, '0.32 THz dual circularly polarized reflectarray', 14th Pacific Rim Conference on Lasers and Electro-Optics (CLEO PR 2020), Conference on Lasers and Electro-Optics/Pacific Rim, Optica Publishing Group, ELECTR NETWORK, pp. C11B_3-C11B_3.
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A terahertz (THz) reflect-array is proposed. Dual circularly polarized (left- and right-hand-circular-polarizations) collimated beams are independently manipulated. In our model, the left-hand-circularly-polarized and right-hand-circularly-polarized beams reflect at 23-degrees along the y-direction and x-direction respectively.
Zhu, L & Yang, Y 1970, 'ActBERT: Learning Global-Local Video-Text Representations', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Seattle, WA, USA, pp. 8743-8752.
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© 2020 IEEE. In this paper, we introduce ActBERT for self-supervised learning of joint video-text representations from unlabeled data. First, we leverage global action information to catalyze the mutual interactions between linguistic texts and local regional objects. It uncovers global and local visual clues from paired video sequences and text descriptions for detailed visual and text relation modeling. Second, we introduce an ENtangled Transformer block (ENT) to encode three sources of information, i.e., global actions, local regional objects, and linguistic descriptions. Global-local correspondences are discovered via judicious clues extraction from contextual information. It enforces the joint videotext representation to be aware of fine-grained objects as well as global human intention. We validate the generalization capability of ActBERT on downstream video-and language tasks, i.e., text-video clip retrieval, video captioning, video question answering, action segmentation, and action step localization. ActBERT significantly outperform the state-of-the-arts, demonstrating its superiority in video-text representation learning.
Zhu, L & Yang, Y 1970, 'Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Seattle, WA, USA, pp. 4343-4352.
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There have been increasing interests in modeling long-tailed data. Unlike artificially collected datasets, long-tailed data are naturally existed in the real-world and thus more realistic. To deal with the class imbalance problem, we introduce an Inflated Episodic Memory (IEM) for long-tailed visual recognition. First, our IEM augments the convolutional neural networks with categorical representative features for rapid learning on tail classes. In traditional few-shot learning, a single prototype is usually leveraged to represent a category. However, long-tailed data has higher intra-class variances. It could be challenging to learn a single prototype for one category. Thus, we introduce IEM to store the most discriminative feature for each category individually. Besides, the memory banks are updated independently, which further decreases the chance of learning skewed classifiers. Second, we introduce a novel region self-attention mechanism for multi-scale spatial feature map encoding. It is beneficial to incorporate more discriminative features to improve generalization on tail classes. We propose to encode local feature maps at multiple scales, and the spatial contextual information should be aggregated at the same time. Equipped with IEM and region self-attention, we achieve state-of-the-art performance on four standard long-tailed image recognition benchmarks. Besides, we validate the effectiveness of IEM on a long-tailed video recognition benchmark, i.e., YouTube-8M.
Zhu, Q, Qiu, X & Burnett, I 1970, 'An Acoustic Modelling Based Remote Error Sensing Approach for Quiet Zone Generation in a Noisy Environment', ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Barcelona, Spain, pp. 8424-8428.
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Remote error sensing is required in active noise control systems when they are used to create a quiet zone in a noisy environment with the constraint that the error microphones cannot be inside the zone. The challenge in remote error sensing is to estimate the sound pressure in the target zone with a small number of physical microphones outside it. The spatial harmonic decomposition method uses wave domain sound field parameterisation to reduce the required number of the error microphones but can only provide accurate estimation below a certain frequency. This paper presents an improved approach to increase the effective frequency range based on the acoustic modelling. The simulation results demonstrate the proposed method can provide more than 20 dB noise reduction up to 1650 Hz for a quiet zone with a radius of 0.1 m by using only three microphones under the studied situations.
Zhu, Y, He, J, Ye, J, Qin, L, Huang, X & Yu, JX 1970, 'When Structure Meets Keywords: Cohesive Attributed Community Search.', CIKM, ACM, pp. 1913-1922.
Zhu, Y, Zhu, F, Zhan, Z, Lin, B, Jiao, J, Chang, X & Liang, X 1970, 'Vision-Dialog Navigation by Exploring Cross-Modal Memory', 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 10727-10736.
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Vision-dialog navigation posed as a new holy-grail task in vision-language disciplinary targets at learning an agent endowed with the capability of constant conversation for help with natural language and navigating according to human responses. Besides the common challenges faced in visual language navigation, vision-dialog navigation also requires to handle well with the language intentions of a series of questions about the temporal context from dialogue history and co-reasoning both dialogs and visual scenes. In this paper, we propose the Cross-modal Memory Network (CMN) for remembering and understanding the rich information relevant to historical navigation actions. Our CMN consists of two memory modules, the language memory module (L-mem) and the visual memory module (V-mem). Specifically, L-mem learns latent relationships between the current language interaction and a dialog history by employing a multi-head attention mechanism. V-mem learns to associate the current visual views and the cross-modal memory about the previous navigation actions. The cross-modal memory is generated via a vision-to-language attention and a language-to-vision attention. Benefiting from the collaborative learning of the L-mem and the V-mem, our CMN is able to explore the memory about the decision making of historical navigation actions which is for the current step. Experiments on the CVDN dataset show that our CMN outperforms the previous state-of-the-art model by a significant margin on both seen and unseen environments. 1
Zhuang, Z, Yu, X & Mahony, R 1970, 'LyRN (Lyapunov Reaching Network): A Real-Time Closed Loop approach from Monocular Vision', 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 8331-8337.
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Zou Zhikang, Liu Yifan, Xu Shuangjie, Wei Wei, Wen Shiping & Zhou Pan 1970, 'Crowd Counting via Hierarchical Scale Recalibration Network', Frontiers in Artificial Intelligence and Applications, IOS Press, pp. 2864-2871.
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The task of crowd counting is extremely challenging due to complicated difficulties, especially the huge variation in vision scale. Previous works tend to adopt a naive concatenation of multiscale information to tackle it, while the scale shifts between the feature maps are ignored. In this paper, we propose a novel Hierarchical Scale Recalibration Network (HSRNet), which addresses the above issues by modeling rich contextual dependencies and recalibrating multiple scale-associated information. Specifically, a Scale Focus Module (SFM) first integrates global context into local features by modeling the semantic inter-dependencies along channel and spatial dimensions sequentially. In order to reallocate channel-wise feature responses, a Scale Recalibration Module (SRM) adopts a step-by-step fusion to generate final density maps. Furthermore, we propose a novel Scale Consistency loss to constrain that the scale-associated outputs are coherent with groundtruth of different scales. With the proposed modules, our approach can ignore various noises selectively and focus on appropriate crowd scales automatically. Extensive experiments on crowd counting datasets (ShanghaiTech, MALL, WorldEXPO'10, and UCSD) show that our HSRNet can deliver superior results over all state-of-the-art approaches. More remarkably, we extend experiments on an extra vehicle dataset, whose results indicate that the proposed model is generalized to other applications.
Zou, Y, Gong, S, Xu, J, Cheng, W, Hoang, DT & Niyato, D 1970, 'Joint Energy Beamforming and Optimization for Intelligent Reflecting Surface Enhanced Communications', 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), IEEE, Seoul, Korea (South), pp. 1-6.
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To face the scarcity of wireless spectrum resources and explosive energy consumption due to rapid growth of mobile devices and Internet of Things terminals, intelligent reflecting surface (IRS) has recently gained a lot of attention and become as one of the promising solutions. In this paper, we consider an IRS-enhanced multiple-input single- output (MISO) system, in which the IRS is wireless powered by the access point (AP) in power splitting scheme. We aim to maximize the signal-to-noise ratio (SNR) of the end user by jointly optimizing the AP's beamforming as well as the phase-shift and the power splitting ratio of the IRS elements. To tackle the non-convexity of the formulated problem due to the coupling of optimization variables, we devise a two-stage approximation algorithm by analyzing and then decomposing the structure of the problem. Specifically, the algorithm first tunes the phase-shift of IRS elements to align the equivalent channel of IRS reflected path to that of the direct link. After that, we adopt a successive convex approximation based method to achieve a near optimal solution for the reformulated problem iteratively. The simulation results show that our proposed two-stage approximation algorithm can solve the jointly SNR maximization problem efficiently.
Zou, Y, Xie, Y, Zhang, C, Gong, S, Hoang, DT & Niyato, D 1970, 'Optimization-driven Hierarchical Deep Reinforcement Learning for Hybrid Relaying Communications', 2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Seoul, Korea (South), pp. 1-6.
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In this paper, we employ multiple wireless-powered user devices as wireless relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. To improve energy efficiency, we design a hybrid relaying communication strategy in which wireless relays are allowed to operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall SNR by jointly optimizing the access point's beamforming strategy as well as individual relays' radio modes and operating parameters. Due to the non-convex and combinatorial structure of the SNR maximization problem, we develop a deep reinforcement learning approach that adapts the beamforming and relaying strategies dynamically. In particular, we propose a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach that integrates the model-based optimization into the framework of conventional DDPG approach. It decomposes the discrete relay mode selection into the outer-loop by using deep Q-network (DQN) algorithm and then optimizes the continuous beamforming and relays' operating parameters by using the inner-loop DDPG algorithm. Simulation results reveal that the H-DDPG is robust to the hyper parameters and can speed up the learning process compared to the conventional DDPG approach.
Zuo, H, Lu, J & Zhang, G 1970, 'Multiple-source Domain Adaptation in Rule-based Neural Network', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-6.
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© 2020 IEEE. Domain adaptation uses the previously acquired knowledge (source domain) to support predicted tasks in the current domain without sufficient labeled data (target domain). Although many methods have been developed in domain adaptation, one issue hasn't been solved: how to implement knowledge transfer when more than one source domain is available. In this paper we present a neural network-based method which extracts domain knowledge in the form of rules to facilitate knowledge transfer, merge rules from all source domains and further select related rules for target domain and clip redundant rules. The method presented is validated on datasets that simulate the multi-source scenario and the experimental results verify the superiority of our method in handling multi-source domain adaptation problems.
Zwinkau, R, Frentrup, S, Möhle, R & Deuse, J 1970, 'Automatic Particle Classification Through Deep Learning Approaches for Increasing Productivity in the Technical Cleanliness Laboratory', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 34-44.
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© Springer Nature Switzerland AG 2020. Understanding the properties of particles plays a vital role in assessing the component cleanliness and its origin in the manufacturing process. We propose a classification method using deep convolutional neural networks. Using a dataset of 70,000 annotated images, we achieve a accuracy of 97.7% for a binary classification in metal and non-metal particles comparable to state-of-the-art polarized light microscopy according to VDA 19-1 and ISO 16232. Manual follow-up checks in a cleanliness laboratory are not required due to the robustness of the classification system.
