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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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.
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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, Gite, S & Alamri, A 2020, 'Building Footprint Extraction from High Resolution Aerial Images Using Generative Adversarial Network (GAN) Architecture', IEEE Access, vol. 8, pp. 209517-209527.
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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.
Abdollahi, M, Ghobadian, B, Najafi, G, Hoseini, SS, Mofijur, M & Mazlan, M 2020, 'Impact of water – biodiesel – diesel nano-emulsion fuel on performance parameters and diesel engine emission', Fuel, vol. 280, pp. 118576-118576.
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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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© 2020 The Authors 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.
Abeywickrama, HV, He, Y, Dutkiewicz, E, Jayawickrama, BA & Mueck, M 2020, 'A Reinforcement Learning Approach for Fair User Coverage Using UAV Mounted Base Stations Under Energy Constraints', IEEE Open Journal of Vehicular Technology, vol. 1, pp. 67-81.
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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 landslide e...
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 seasons (2017–...
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 extreme rainfall...
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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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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Abuhilaleh, M, Li, L & Hossain, MJ 2020, 'Power management and control coordination strategy for autonomous hybrid microgrids', IET Generation, Transmission & Distribution, vol. 14, no. 1, pp. 119-130.
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© The Institution of Engineering and Technology 2019. This study presents an improved power management and control coordination strategy for autonomous hybrid microgrids (MGs). The new strategy aims to reduce the continuous operation of interlinking converters (ILCs) under all load conditions, and thereby avoids the significant power loss on the ILCs that adversely affects the operational feasibility of the hybrid MGs. The hybrid MG considered in this study consists of multiple alternating current and direct current sub-microgrids (SMGs) with different voltage levels. The hierarchal coordination of power management and control strategy for autonomous hybrid MG is introduced and analysed. The designed system includes both the primary and secondary control levels to ensure a seamless and accurate transfer of power among the SMGs. A new technique to ensure the continuous power flow among the SMGs while minimising the continuous operation of the ILCs in the islanded mode is presented with a focus on the secondary control level. Five scenarios of transferring power among SMGs are analysed using MATLAB/Simulink. The results indicate that the system's high level of flexibility in managing the power flow at different control levels can be achieved by the proposed approach.
Adegbosin, AE, Stantic, B & Sun, J 2020, 'Efficacy of deep learning methods for predicting under-five mortality in 34 low-income and middle-income countries', BMJ Open, vol. 10, no. 8, pp. e034524-e034524.
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ObjectivesTo explore the efficacy of machine learning (ML) techniques in predicting under-five mortality (U5M) in low-income and middle-income countries (LMICs) and to identify significant predictors of U5M.DesignThis is a cross-sectional, proof-of-concept study.Settings and participantsWe analysed data from the Demographic and Health Survey. The data were drawn from 34 LMICs, comprising a total of n=1 520 018 children drawn from 956 995 unique households.Primary and secondary outcome measuresThe primary outcome measure was U5M; secondary outcome was comparing the efficacy of deep learning algorithms: deep neural network (DNN); convolution neural network (CNN); hybrid CNN-DNN with logistic regression (LR) for the prediction of child’s survival.ResultsWe found that duration of breast feeding, number of antenatal visits, household wealth index, postnatal care and the level of maternal education are some of the most important predictors of U5M. We found that deep learning techniques are superior to LR for the classification of child survival: LR sensitivity=0.47, specificity=0.53; DNN sensitivity=0.69, specificity=0.83; CNN sensitivity=0.68, specificity=0.83; CNN-DNN sensitivity=0.71, specificity=0.83.ConclusionOur findings provide an understanding of determinants of U5M in LMICs. It also demonstrates that deep learning models are more efficacious than traditional analytical approach.
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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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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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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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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© 2020 Institution of Engineering and Technology. All rights reserved. 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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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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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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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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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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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 associated wi...
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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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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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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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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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. 247, 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.
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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Al-Karagoly, MKU, Ayani, M-B, Mamourian, M & Razavi Bazaz, S 2020, 'Experimental parametric study of a deep groove within a pin fin arrays regarding fin thermal resistance', International Communications in Heat and Mass Transfer, vol. 115, pp. 104615-104615.
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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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© 2020 Wiley-VCH GmbH Photo-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, vol. 132, no. 48, pp. 21576-21580.
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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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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 & 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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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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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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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.
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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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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© 2020 Institution of Engineering and Technology. All rights reserved. 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 postdisturbance 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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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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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
array structu...
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, vol. 38, no. 6, pp. 062202-062202.
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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. 569-569.
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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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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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Anshar, M, Sadjad, RS, Dewiani, Hanan, M, Prayudha, R & Abry, M 2020, 'Design and Implementation Monitoring and Booking Systems for Smart Parking at Engineering Faculty Campus', IOP Conference Series: Materials Science and Engineering, vol. 875, no. 1, pp. 012036-012036.
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Abstract
Parking our vehicles sometimes become troublesome and situation gets worse when the user is required to park their cars at the unknown parking availability. Not to mention, this situation also occurs within the new Campus of Engineering Faculty of Universitas Hasanuddin. This campus has a motto to be the centre of technology, so it is something unfortunate that the parking management does not incorporate technology to support the parking system. This paper emphases on designing smart parking model which is suitable to be adopted within this new campus. Technology based Internet of Things is used to modernised the parking system with advanced features of monitoring and booking mechanisms. These features are accessible through smart phones which increases the usability of the proposed smart parking system. The experiments show that the parking management system can function accurate and effectively in enhancing the quality of parking system, with the two-mentioned features of the proposed smart parking system, which are monitoring and booking capabilities.
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 future researc...
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 is GLM, Ma...
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 yields of ...
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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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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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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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.
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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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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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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Atov, I, Chen, K-C, Kamal, A & Yu, S 2020, 'Data Science and Artificial Intelligence for Communications', IEEE Communications Magazine, vol. 58, no. 1, pp. 10-11.
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AUNG, MH, TSUTSUI, H & MIYANAGA, Y 2020, 'WiFi Fingerprint Based Indoor Positioning Systems Using Estimated Reference Locations', IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E103.A, no. 12, pp. 1483-1493.
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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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Aykin, I & Krunz, M 2020, 'Efficient Beam Sweeping Algorithms and Initial Access Protocols for Millimeter-Wave Networks', IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2504-2514.
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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', vol. 99, pp. 193-235.
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© 2020 Elsevier Inc. 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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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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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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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.
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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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Bag, S, Yadav, G, Wood, LC, Dhamija, P & Joshi, S 2020, 'Industry 4.0 and the circular economy: Resource melioration in logistics', Resources Policy, vol. 68, pp. 101776-101776.
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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. The result...
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, 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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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.
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.
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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 RF outper...
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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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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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.
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.
Beehan-Quirk, C, Jarman, L, Maharaj, S, Simpson, A, Nassif, N & Lal, S 2020, 'Investigating the effects of fatigue on blood glucose levels – Implications for diabetes', Translational Metabolic Syndrome Research, vol. 3, pp. 17-20.
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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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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.
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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
.
Ben, X, Gong, C, Zhang, P, Yan, R, Wu, Q & Meng, W 2020, 'Coupled Bilinear Discriminant Projection for Cross-View Gait Recognition', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 3, pp. 734-747.
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© 1991-2012 IEEE. A problem that hinders good performance of general gait recognition systems is that the appearance features of gaits are more affected-prone by views than identities, especially when the walking direction of the probe gait is different from the register gait. This problem cannot be solved by traditional projection learning methods because these methods can learn only one projection matrix, and thus for the same subject, it cannot transfer cross-view gait features into similar ones. This paper presents an innovative method to overcome this problem by aligning gait energy images (GEIs) across views with the coupled bilinear discriminant projection (CBDP). Specifically, the CBDP generates the aligned gait matrix features for two views with two sets of bilinear transformation matrices, so that the original GEIs' spatial structure information can be preserved. By iteratively maximizing the ratio of inter-class distance metric to intra-class distance metric, the CBDP can learn the optimal matrix subspace where the GEIs across views are aligned in both horizontal and vertical coordinates. Therefore, the CBDP is also able to avoid the under-sample problem. We also theoretically prove that the upper and lower bounds of the objective function sequence of the CBDP are both monotonically increasing, so the convergence of the CBDP is demonstrated. In the terms of accuracy, the comparative experiments on the CASIA (B) and OU-ISIR gait databases show that our method is superior to the state-of-the-art cross-view gait recognition methods. More impressively, encouraging performance is obtained by our method even in matching a lateral-view gait with a frontal-view gait.
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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Bhuiya, MMK, Rasul, M, Khan, M, Ashwath, N & Mofijur, M 2020, 'Comparison of oil extraction between screw press and solvent (n-hexane) extraction technique from beauty leaf (Calophyllum inophyllum L.) feedstock', Industrial Crops and Products, vol. 144, pp. 112024-112024.
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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.
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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Biswas, PC, Rani, S, Hossain, MA, Islam, MR & Canning, J 2020, 'Multichannel Smartphone Spectrometer Using Combined Diffraction Orders', IEEE Sensors Letters, vol. 4, no. 9, pp. 1-4.
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© 2017 IEEE. A robust multichannel smartphone spectrometer exploiting multiorder diffraction imaging on a smartphone camera is reported. The instrument utilizes a thin film diffractive element generating multiple orders of diffracted light from a broadband visible source (white LED, λ = 400-700 nm). The smartphone's CMOS camera captures all the diffraction orders simultaneously, producing a 2-D image. Each of these diffraction orders can be utilized as a single optical channel for dedicated samples enabling simultaneous multiple sample analysis. The wavelength distribution along the diffraction direction produces a tunable spectral resolution of δλ ∼1.6 to 3.0 nm/pixel over the bandwidth of Δλ = 300 nm. A customized app processes each diffraction image into spectra. 3-D printing is used to create the entire instrument prototype. As an initial demonstration, simultaneous absorption measurements of reference and sample cells using the first diffraction orders (m = +1 and -1) is shown. Absorption spectra for a laser dye (Rhodamine B) and a pH-responsive buffer (bromothymol blue) are measured. This offers a low cost (<30) portable instrument layout comparable to conventional double beam benchtop instruments in performance but more rugged for field use.
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 flat ‘refl...
Blanco-Mesa, F & Merigó, JM 2020, 'Bonferroni Distances and Their Application in Group Decision Making', Cybernetics and Systems, vol. 51, no. 1, pp. 27-58.
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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, 123 pati...
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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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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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.
Bradbury, P, Wu, H, Choi, JU, Rowan, AE, Zhang, H, Poole, K, Lauko, J & Chou, J 2020, 'Modeling the Impact of Microgravity at the Cellular Level: Implications for Human Disease', Frontiers in Cell and Developmental Biology, vol. 8.
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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.
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, Y, Tang, W, Li, L, Zhang, B, Zhang, L & Wang, Y 2020, 'Multi‐mode adaptive local reactive power control method based on PV inverters in low voltage distribution networks', IET Generation, Transmission & Distribution, vol. 14, no. 4, pp. 542-551.
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© The Institution of Engineering and Technology 2019. Low voltage distribution networks with the high penetration of photovoltaic (PV) units are facing four types of challenges, including over-voltage issues, under-voltage issues, voltage fluctuation issues and high power losses. In order to mitigate the above issues, this study proposes a multi-mode adaptive local reactive power control method based on Q(P) characteristics. A new concept, node virtual injection active power (NVIP), is developed to take the PV active power, load active power and load reactive power as a whole and as the basis to regulate the PV reactive power. Then, an NVIP-based multi-mode local Q(P) framework is proposed to consider the four types of challenges based on four operation modes, respectively, that can be adaptively switched according to the NVIP value and the NVIP variation. In addition, a systematic parameter design for the Q(P) framework is proposed based on an optimisation model to further enhance the effectiveness of the proposed multi-mode control. The simulation results demonstrate the effectiveness of the proposed method in mitigating voltage violations and voltage fluctuations, and improving the power losses and power factor.
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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© 2020 Wiley Periodicals, Inc. In 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. Lastly, those...
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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Canning, J & Ziyani, A 2020, 'Chirping fiber Bragg gratings within additively manufactured polymer packages', Optics Letters, vol. 45, no. 8, pp. 2235-2235.
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Fiber Bragg gratings are embedded within 3D printed polymer packages.
Information about both induced and applied stresses, and operator
error, can be determined from the observed spectral shifts and
chirping. A novel way to produce packaged broadband
gratings, with
Δ
λ
B
W
>
7
n
m
/
c
m
, is proposed and demonstrated.
Canning, J, Wang, Y, Lancry, M, Luo, Y & Peng, G-D 2020, 'Helical distributed feedback fiber Bragg gratings and rocking filters in a 3D printed preform-drawn fiber', Optics Letters, vol. 45, no. 19, pp. 5444-5444.
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Using induced UV attenuation across a twisted fiber asymmetric core drawn from a 3D printed preform, linear fiber Bragg gratings (FBGs) are produced on one side of the core. By removing the twist, a helical grating with a period matching the twist rate is produced. Balancing the rate with the polarization beat length in a form birefringent fiber allows the production of a combined rocking filter and FBG device with tunable properties. Direct observation of the fiber grating dispersion within the rocking filter rejection band is possible.
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 of
coupling relationships that reflect interactions between factors related to
technical, business (domain-specific) and environmental (including
socio-cultural and economic) aspects. There are diverse forms of couplings
embedded in poor-structured and ill-structured data. Such couplings are
ubiquitous, implicit and/or explicit, objective and/or subjective,
heterogeneous and/or homogeneous, presenting complexities to existing learning
systems in statistics, mathematics and computer sciences, such as typical
dependency, association and correlation relationships. Modeling and learning
such couplings thus is fundamental but challenging. This paper discusses the
concept of coupling learning, focusing on the involvement of coupling
relationships in learning systems. Coupling learning has great potential for
building a deep understanding of the essence of business problems and handling
challenges that have not been addressed well by existing learning theories and
tools. This argument is verified by several case studies on coupling learning,
including handling coupling in recommender systems, incorporating couplings
into coupled clustering, coupling document clustering, coupled recommender
algorithms 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 appropriate
understanding of data DNA and its organisms relies on the new field of data
science and its keystone, analytics. Although it is widely debated whether big
data 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 by
the research, innovation, business, profession, and education of data science.
This paper provides a comprehensive survey and tutorial of the fundamental
aspects of data science: the evolution from data analysis to data science, the
data science concepts, a big picture of the era of data science, the major
challenges and directions in data innovation, the nature of data analytics, new
industrialization and service opportunities in the data economy, the profession
and competency of data education, and the future of data science. This article
is the first in the field to draw a comprehensive big picture, in addition to
offering rich observations, lessons and thinking about data science and
analytics.
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 science
and what makes it as a science. In reviewing hundreds of pieces of literature
which include data science in their titles, we find that the majority of the
discussions essentially concern statistics, data mining, machine learning, big
data, or broadly data analytics, and only a limited number of new data-driven
challenges and directions have been explored. In this paper, we explore the
intrinsic challenges and directions inspired by comprehensively exploring the
complexities and intelligence embedded in data science problems. We focus on
the research and innovation challenges inspired by the nature of data science
problems 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 significant
controversy. A critical matter for the healthy development of data science in
its early stages is to deeply understand the nature of data and data science,
and to discuss the various pitfalls. These important issues motivate the
discussions 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 a
crucial means for disclosing interior driving forces, causes and impact on
businesses in handling many challenging issues. The modeling and analysis of
behaviors in virtual organizations is an open area. Traditional behavior
modeling mainly relies on qualitative methods from behavioral science and
social science perspectives. The so-called behavior analysis is actually based
on human demographic and business usage data, where behavior-oriented elements
are hidden in routinely collected transactional data. As a result, it is
ineffective or even impossible to deeply scrutinize native behavior intention,
lifecycle and impact on complex problems and business issues. We propose the
approach of Behavior Informatics (BI), in order to support explicit and
quantitative behavior involvement through a conversion from source data to
behavioral data, and further conduct genuine analysis of behavior patterns and
impacts. BI consists of key components including behavior representation,
behavioral data construction, behavior impact analysis, behavior pattern
analysis, behavior simulation, and behavior presentation and behavior use. We
discuss the concepts of behavior and an abstract behavioral model, as well as
the research tasks, process and theoretical underpinnings of BI. Substantial
experiments have shown that BI has the potential to greatly complement the
existing empirical and specific means by finding deeper and more informative
patterns leading to greater in-depth behavior understanding. BI creates new
directions and means to enhance the quantitative, formal and systematic
modeling 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 for
irrelevant, duplicate, or uninteresting products and services. A critical
reason for such bad recommendations lies in the intrinsic assumption that
recommended users and items are independent and identically distributed (IID)
in existing theories and systems. Another phenomenon is that, while tremendous
efforts have been made to model specific aspects of users or items, the overall
user and item characteristics and their non-IIDness have been overlooked. In
this paper, the non-IID nature and characteristics of recommendation are
discussed, followed by the non-IID theoretical framework in order to build a
deep and comprehensive understanding of the intrinsic nature of recommendation
problems, from the perspective of both couplings and heterogeneity. This
non-IID recommendation research triggers the paradigm shift from IID to non-IID
recommendation research and can hopefully deliver informed, relevant,
personalized, and actionable recommendations. It creates exciting new
directions and fundamental solutions to address various complexities including
cold-start, sparse data-based, cross-domain, group-based, and shilling
attack-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 role
in driving modern economies, society, technology, and many other areas. Smart
FinTech is the new-generation FinTech, largely inspired and empowered by data
science and new-generation AI and (DSAI) techniques. Smart FinTech synthesizes
broad DSAI and transforms finance and economies to drive intelligent,
automated, whole-of-business and personalized economic and financial
businesses, services and systems. The research on data science and AI in
FinTech involves many latest progress made in smart FinTech for BankingTech,
TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech,
cryptocurrencies, and blockchain, and the DSAI techniques including complex
system methods, quantitative methods, intelligent interactions, recognition and
responses, data analytics, deep learning, federated learning,
privacy-preserving processing, augmentation, optimization, and system
intelligence enhancement. Here, we present a highly dense research overview of
smart financial businesses and their challenges, the smart FinTech ecosystem,
the DSAI techniques to enable smart FinTech, and some research directions of
smart 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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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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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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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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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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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, 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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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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New knowledge presents opportunities for commercial value and can hence be a critical asset for entrepreneurial ecosystems. In particular, general purpose technologies are major drivers of entrepreneurship, thus, a nuanced understanding on technological knowledge and its spillovers among actors within an entrepreneurial ecosystem (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 paper 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 paper 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 paper 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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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, McLachlan, CS, Gustin, SM & 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.
Chamoli, U, Umali, J, Kleuskens, MWA, Chepurin, D & Diwan, AD 2020, 'Morphological characteristics of the kangaroo lumbar intervertebral discs and comparison with other animal models used in spine research', European Spine Journal, vol. 29, no. 4, pp. 652-662.
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PURPOSE:Animal models are frequently used to elucidate pathomechanism and pathophysiology of various disorders of the human intervertebral disc (IVD) and also to develop therapeutic approaches. Here we report morphological characteristics of the kangaroo lumbar IVDs and compare them with other animal models used in spine research. METHODS:Twenty-five fresh-frozen cadaveric lumbar spines (T12-S1) derived from kangaroo carcases (Macropus giganteus) of undetermined age were first scanned in a C-Arm X-ray machine. A photograph of the axial section of the disc including a calibrated metric scale was also acquired. The digital radiographs and photographs were processed in ImageJ to determine the axial and sagittal plane dimensions for the whole disc (WD) and the nucleus pulposus (NP) and the mid-sagittal disc height for all the lumbar levels. RESULTS:Our results suggest that the L6-S1 IVD in kangaroos is distinctly large compared with the upper lumbar IVDs. Based on previously published data, human lumbar IVDs are the largest of all the animal IVDs used in spine research, with camelid cervical IVDs being the closest relative in absolute dimensions (llamas: 78% in disc height, 40% in WD volume, and 38% in NP volume). Kangaroo L6-S1 IVD was approximately 51% in height, 20% in WD volume, and 20% in NP volume of the human lumbar IVD. CONCLUSIONS:We conclude that morphological similarities exist between a kangaroo and human lumbar IVD, especially with the lima bean shape in the axial plane, wedge shape in the sagittal plane, convexity at the cephalad endplates, and percentage volume occupied by the NP in the IVD. These slides can be retrieved under Electronic Supplementary Material.
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.
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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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.
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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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, F, Xiao, Z, Cui, L, Lin, Q, Li, J & Yu, S 2020, 'Blockchain for Internet of things applications: A review and open issues', Journal of Network and Computer Applications, vol. 172, pp. 102839-102839.
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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, 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, Xu, W, Ding, Y, Xue, P, Sheng, P, Qiao, H, Wang, S & Yu, Y 2020, 'Mechanical and Thermal Properties of All-Wood Biocomposites through Controllable Dissolution of Cellulose with Ionic Liquid', Polymers, vol. 12, no. 2, pp. 361-361.
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All-wood biocomposites were prepared with an efficient method. The ionic liquid of 1-butyl-3-methylimidazolium chloride (BMIMCl) was used to impregnate manchurian ash (MA) before hot-pressing, and the all-wood biocomposites were prepared by controllable dissolving and regenerating the cellulose in MA. The Fourier transform infrared analysis suggested that all the components of MA remained unchanged during the preparation. X-ray diffraction, thermogravimetric and scanning electron microscope analysis were carried out to study the process parameters of hot-pressing pressure and time on the crystallinity, thermal properties and microstructure of the all-wood biocomposites. The tensile strength of the prepared all-wood biocomposites reached its highest at 212.6 MPa and was increased by 239% compared with that of the original MA sample. The thermogravimetric analysis indicated that as the thermo-stability of the all-wood biocomposites increased, the mass of the residual carbon increased from 19.7% to 22.7% under a hot-pressing pressure of 10 MPa. This work provides a simple and promising pathway for the industrial application of high-performance and environmentally friendly all-wood biocomposites.
Chen, K, Xue, J, Lu, J & Qiu, X 2020, 'Improving active noise control without secondary path modeling using subband phase estimation', The Journal of the Acoustical Society of America, vol. 147, no. 2, pp. 1275-1283.
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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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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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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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Chen, S, Fu, A, Shen, J, Yu, S, Wang, H & Sun, H 2020, 'RNN-DP: A new differential privacy scheme base on Recurrent Neural Network for Dynamic trajectory privacy protection', Journal of Network and Computer Applications, vol. 168, pp. 102736-102736.
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© 2020 Elsevier Ltd Mobile devices furnish users with various services while on the move, but also raise public concerns about trajectory privacy. Unfortunately, traditional privacy protection methods, such as anonymity and generalization, are not secure because they cannot resist attackers with background knowledge. The emergence of differential privacy provides an effective solution to this problem. Still, the existing schemes are almost designed based on the collected aggregate historical data (so-called static trajectory privacy protection), which are not suitable for real-time dynamic trajectory privacy protection of mobile users. Furthermore, due to the complexity and redundancy features of the full trajectory data, the efficiency and accuracy of the privacy protection model are significantly limited by the existing schemes. In this paper, we propose a new differential privacy scheme base on the Recurrent Neural Network for Dynamic trajectory privacy Protection (RNN-DP). We firstly introduce a recurrent neural network model to handle the real-time data effectively instead of the full data. Secondly, we novelty leverage the dynamic velocity attribute to form a quaternion to indicate the status of the users. Moreover, we design a prejudgment mechanism to increase the availability of differential privacy technology. Compared with the current state-of-the-art mechanisms, the experimental results demonstrate that RNN-DP displays excellent performance in privacy protection and data availability for dynamic trajectory data.
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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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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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, Li, Y, Xue, W, Shahabi, H, Li, S, Hong, H, Wang, X, Bian, H, Zhang, S, Pradhan, B & Ahmad, BB 2020, 'Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods', Science of The Total Environment, vol. 701, pp. 134979-134979.
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Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets.
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, 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, 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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Chen, X, Tang, M-C, Yi, D & Ziolkowski, RW 2020, 'Wideband, Electrically Small, Near-Field Resonant Parasitic Dipole Antenna With Stable Radiation Performance', IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 5, pp. 826-830.
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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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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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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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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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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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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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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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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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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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Chen, Z, Zhang, Q, Li, C, Fu, S, Qiu, X, Wang, X & Wu, H 2020, 'Geometric Nonlinear Model for Prediction of Frequency–Temperature Behavior of SAW Devices for Nanosensor Applications', Sensors, vol. 20, no. 15, pp. 4237-4237.
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Surface acoustic wave (SAW)-based sensors have become highly valued for their use as nanosensors in industrial applications. Accurate prediction of the thermal stability is a key problem for sensor design. In this work, a numerical tool based on the finite element method combined with piezoelectric Lagrangian equations has been developed to accurately predict the thermal sensitivity characteristics of surface acoustic wave devices. Theoretical analysis for the geometric nonlinearity contributing to the frequency–temperature characteristic and material constants’ dependency on temperature were taken into consideration. The thermomechanical equilibrium equation built on the three-dimensional finite element method (3D-FEM) mesh node took mesh movement into account because thermal expansion was employed. The frequency–temperature characteristics of different SAW modes, including Rayleigh waves and leaky waves excited on a piezoelectric substrate of quartz or lithium tantalate, respectively, were calculated. The theoretical accuracy of the proposed numerical tool was verified by experiments.
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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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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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, G, Zhang, M, Han, C, Liang, Y & Zhao, K 2020, 'Achieving solar-to-hydrogen evolution promotion using TiO2 nanoparticles and an unanchored Cu co-catalyst', Materials Research Bulletin, vol. 129, pp. 110891-110891.
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Spherical Cu particles were successfully synthesized by a facile strategy at room temperature. The crystal phase and structure were characterized by XRD, SEM, and TEM measurements. The physical mixing of the as-synthesized copper spheres with TiO2 nanoparticles could initial and promote solar-to-hydrogen evolution in methanol aqueous solution. TEM result showed that the partial of Cu co-catalyst attached on the surface of the host TiO2 nanoparticles, although the interface of TiO2-Cu non-existed in the synthetic process. On the basis of photo/electro-chemical measurements, it was proposed that charge transfer was accomplished via collisions between the TiO2 and Cu nanoparticles, which promoted charge separation and subsequently photocatalytic hydrogen evolution in the suspension.
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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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.
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Chi, C, Li, C, Wu, D, Buys, N, Wang, W, Fan, H & Sun, J 2020, 'Effects of Probiotics on Patients with Hypertension: a Systematic Review and Meta-Analysis.', Curr Hypertens Rep, vol. 22, no. 5, p. 34.
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PURPOSE OF REVIEW: This meta-analysis and systematic review was conducted to evaluate the effect of probiotics on blood pressure, body mass index (BMI), and blood glucose changes in patients with hypertension. RECENT FINDINGS: We searched the PubMed, Cochrane, Embase, and ProQuest databases using a combination of MeSH and free text, from the inception of these databases to 20 January 2020, with no language restrictions. The quantitative PEDro scale method was used to assess the quality of the included studies. We used the random effects models to estimate the outcomes, with heterogeneity among the studies assessed using Cochran's Q statistic. Fourteen included studies published between 2002 and 2019 were included in the meta-analysis, reporting results of 846 hypertension participants. A significant reduction in SBP by - 2.05 mmHg (95% CI - 3.87, -0.24, P = 0.03), DBP by - 1.26 mmHg (95% CI - 2.51, - 0.004, P = 0.047), BMI by - 1.03 (95% CI - 1.28, - 0.97, P < 0.01), and blood glucose by - 0.18 mmol/L (95% CI - 0.30 - 0.05, P = 0.007) was observed following probiotics intervention. Our meta-analysis showed a modest but a significant reduction in SBP and DBP in patients with hypertension, particularly in those with diabetes mellitus, following probiotic supplementation. This effect was associated with treatment duration, dosage, and the age of subject but was not associated with single or multiple strains usage. Additionally, probiotic supplement had a beneficial effect in reducing BMI and blood glucose.
Chi, C, Xue, Y, Liu, R, Wang, Y, Lv, N, Zeng, H, Buys, N, Zhu, B, Sun, J & Yin, C 2020, 'Effects of a formula with a probiotic Bifidobacterium lactis Supplement on the gut microbiota of low birth weight infants', European Journal of Nutrition, vol. 59, no. 4, pp. 1493-1503.
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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. 1-8.
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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. Numerical
approaches 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.
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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© 2019 Elsevier Ltd 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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Choudhury, TT, Paul, SK, Rahman, HF, Jia, Z & Shukla, N 2020, 'A systematic literature review on the service supply chain: research agenda and future research directions', Production Planning & Control, vol. 31, no. 16, pp. 1363-1384.
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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, 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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Chu, H, Yi, B, Zhang, G & Zhang, W 2020, 'Performance Improvement of Consensus Tracking for Linear Multiagent Systems With Input Saturation: A Gain Scheduled Approach', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 3, pp. 734-746.
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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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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.
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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Engineered 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%.
Costa, E, Climent, E, Ast, S, Weller, MG, Canning, J & Rurack, K 2020, 'Development of a lateral flow test for rapid pyrethroid detection using antibody-gated indicator-releasing hybrid materials', The Analyst, vol. 145, no. 10, pp. 3490-3494.
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The employment of type-I pyrethroids for airplane disinfection in recent years underlines the necessity to develop sensing schemes for the rapid detection of these pesticides directly at the point-of-use.
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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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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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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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.
Dandachi, G, De Domenico, A, Hoang, DT & Niyato, D 2020, 'An Artificial Intelligence Framework for Slice Deployment and Orchestration in 5G Networks', IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 2, pp. 858-871.
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Network slicing is a key enabler to successfully support 5G services with specific requirements and priorities. Due to the diversity of these services, slice deployment and orchestration are essential to guarantee service performance in a cost-effective way. Here, we propose an Artificial Intelligence framework for cross-slice admission and congestion control that simultaneously considers communication, computing, and storage resources to maximize resources utilization and operator revenue. First, we propose a smart feature extraction solution to analyze the characteristics of incoming requests together with the already deployed slices, and then automatically evaluates the request requirements to make appropriate decisions. Second, we design an online algorithm that controls the slice admission based on their priorities, the arrival and departure characteristics, and the available resources. To mitigate system overloading, our framework dynamically adjusts resources allocated to low priority slices, thereby reducing the dropping probability of new slice requests. The proposed algorithm offers outstanding advantages over traditional static approaches by automatically adapting the controller decisions to the system changes. Simulation results show that our framework significantly improves the resource utilization and reduces the slice request dropping probabilities up to 44% as compared to the baseline schemes.
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.
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.
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In Australia and beyond, journalism is reportedly an industry in crisis, a
crisis exacerbated by COVID-19. However, the evidence revealing the crisis is
often anecdotal or limited in scope. In this unprecedented longitudinal
research, we draw on data from the Australian journalism jobs market from
January 2012 until March 2020. Using Data Science and Machine Learning
techniques, we analyse two distinct data sets: job advertisements (ads) data
comprising 3,698 journalist job ads from a corpus of over 8 million Australian
job ads; and official employment data from the Australian Bureau of Statistics.
Having matched and analysed both sources, we address both the demand for and
supply of journalists in Australia over this critical period. The data show
that the crisis is real, but there are also surprises. Counter-intuitively, the
number of journalism job ads in Australia rose from 2012 until 2016, before
falling into decline. Less surprisingly, for the entire period studied the
figures reveal extreme volatility, characterised by large and erratic
fluctuations. The data also clearly show that COVID-19 has significantly
worsened the crisis. We then tease out more granular findings, including: that
there are now more women than men journalists in Australia, but that gender
inequity is worsening, with women journalists getting younger and worse-paid
just as men journalists are, on average, getting older and better-paid; that,
despite the crisis besetting the industry, the demand for journalism skills has
increased; and that, perhaps concerningly, the skills sought by journalism job
ads 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 force
workers to transition to new jobs. This may be because new technologies emerge
or production is moved abroad. Perhaps it is a global crisis, such as COVID-19,
which shutters industries and displaces labor en masse. Regardless of the
impetus, people are faced with the challenge of moving between jobs to find new
work. Successful transitions typically occur when workers leverage their
existing skills in the new occupation. Here, we propose a novel method to
measure the similarity between occupations using their underlying skills. We
then build a recommender system for identifying optimal transition pathways
between occupations using job advertisements (ads) data and a longitudinal
household survey. Our results show that not only can we accurately predict
occupational transitions (Accuracy = 76%), but we account for the asymmetric
difficulties of moving between jobs (it is easier to move in one direction than
the other). We also build an early warning indicator for new technology
adoption (showcasing Artificial Intelligence), a major driver of rising job
transitions. By using real-time data, our systems can respond to labor demand
shifts as they occur (such as those caused by COVID-19). They can be leveraged
by policy-makers, educators, and job seekers who are forced to confront the
often distressing challenges of finding new jobs.
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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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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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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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.
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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Oral 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 Monitoring in situ Mitochondrial Thermal Dynamics by Upconversion Nanoparticles'.
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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 allows in situ thermal 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−1 in 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 study in situ vital 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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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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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 variation betwe...
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 variables...
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 investigated area. Th...
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 urgently neede...
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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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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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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Ding, R, Zhou, X, Zhang, R & Lu, W 2020, 'Retrieval, reporting and methodological characteristics forsystematic reviews/meta-analyses of animal models: a metaepidemiologicalstudy', Energy Engineering, vol. 117, no. 1, pp. 1-17.
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© 2020, Tech Science Press. All rights reserved. Free convection inside an attic enclosure in which sinusoidal heat flux applied on the inclined walls and a constant temperature applied on the base wall has been investigated numerically to demonstrate the primary flow characteristics and heat transfer within the attic enclosure over daily routine cycles. To solve the governing equations, the finite volume technique has been utilized. After performing the grid independency and time step size tests, the roles of Rayleigh number (Ra) and the attic aspect ratio (AR) on the unsteady flow structure and heat transfer phenomenon are explained for a constant Prandtl number (0.72) for the air. Results are illustrated as a form of stream function and isotherms. Moreover, heat transfer is calculated in terms of Nusselt number. The numerical simulations reveal stratified flow within the enclosure during the daytime nonlinear heating stage. However, during night-time, nonlinear cooling stage the flow turns into unstable as the forms of rising and sinking plumes for sufficiently higher Rayleigh number.
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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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.
Ding, Y, Fan, H, Xu, M & Yang, Y 2020, 'Adaptive Exploration for Unsupervised Person Re-identification', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 1, pp. 1-19.
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Due to domain bias, directly deploying a deep person re-identification (re-ID) model trained on one dataset often achieves considerably poor accuracy on another dataset. In this article, we propose an Adaptive Exploration (AE) method to address the domain-shift problem for re-ID in an unsupervised manner. Specifically, in the target domain, the re-ID model is inducted to (1) maximize distances between all person images and (2) minimize distances between similar person images. In the first case, by treating each person image as an individual class, a non-parametric classifier with a feature memory is exploited to encourage person images to move far away from each other. In the second case, according to a similarity threshold, our method adaptively selects neighborhoods for each person image in the feature space. By treating these similar person images as the same class, the non-parametric classifier forces them to stay closer. However, a problem of the adaptive selection is that, when an image has too many neighborhoods, it is more likely to attract other images as its neighborhoods. As a result, a minority of images may select a large number of neighborhoods while a majority of images has only a few neighborhoods. To address this issue, we additionally integrate a balance strategy into the adaptive selection. We evaluate our methods with two protocols. The first one is called “target-only re-ID”, in which only the unlabeled target data is used for training. The second one is called “domain adaptive re-ID”, in which both the source data and the target data are used during training. Experimental results on large-scale re-ID datasets demonstrate the effectiveness of our method. Our code has been released at https://github.com/dyh127/Adaptive-Exploration-for-Unsupervised-Person-Re-Identification.
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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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.
Dixon, A, Robertson, K, Yung, A, Que, M, Randall, H, Wellalagodage, D, Cox, T, Robertson, D, Chi, C & Sun, J 2020, 'Efficacy of Probiotics in Patients of Cardiovascular Disease Risk: a Systematic Review and Meta-analysis', Current Hypertension Reports, vol. 22, no. 9.
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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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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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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, pp. 21-29.
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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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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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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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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'.
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Neural architecture search (NAS) has attracted a lot of attention and has
been illustrated to bring tangible benefits in a large number of applications
in the past few years. Architecture topology and architecture size have been
regarded as two of the most important aspects for the performance of deep
learning models and the community has spawned lots of searching algorithms for
both aspects of the neural architectures. However, the performance gain from
these searching algorithms is achieved under different search spaces and
training setups. This makes the overall performance of the algorithms to some
extent incomparable and the improvement from a sub-module of the searching
model unclear. In this paper, we propose NATS-Bench, a unified benchmark on
searching for both topology and size, for (almost) any up-to-date NAS
algorithm. NATS-Bench includes the search space of 15,625 neural cell
candidates for architecture topology and 32,768 for architecture size on three
datasets. We analyze the validity of our benchmark in terms of various criteria
and performance comparison of all candidates in the search space. We also show
the versatility of NATS-Bench by benchmarking 13 recent state-of-the-art NAS
algorithms on it. All logs and diagnostic information trained using the same
setup for each candidate are provided. This facilitates a much larger community
of researchers to focus on developing better NAS algorithms in a more
comparable and computationally cost friendly environment. All codes are
publicly 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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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 engineers wi...
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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Dou, Y, Li, Y, Zhang, C, Yue, S & Zhu, J 2020, 'Effects of Uniaxial Stress Along Different Directions on Alternating Magnetic Properties of Silicon Steel Sheets', IEEE Transactions on Magnetics, vol. 56, no. 3, pp. 1-4.
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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 pathology i...
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, G, Xu, W, Zhu, J & Huang, N 2020, 'Effects of Design Parameters on the Multiphysics Performance of High-Speed Permanent Magnet Machines', IEEE Transactions on Industrial Electronics, vol. 67, no. 5, pp. 3472-3483.
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Du, G, Xu, W, Zhu, J & Huang, N 2020, 'Power Loss and Thermal Analysis for High-Power High-Speed Permanent Magnet Machines', IEEE Transactions on Industrial Electronics, vol. 67, no. 4, pp. 2722-2733.
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© 1982-2012 IEEE. For high-speed permanent magnet machines (HSPMMs), the permanent magnet (PM) is more likely to suffer irreversible demagnetization because the heat dissipation is serious in the HSPMMs, especially for the high-power machines. This paper focuses on the comprehensive research results on the power loss and thermal characteristic for a high-power HSPMM. First, the power loss at the rated load is investigated by finite-element analysis. Then, the temperature distribution of four cooling schemes is compared by the electromagnetic-thermal iteration calculation. The effect of different parameters on thermal behavior is obtained to reduce rotor temperature, which includes an examination of the axial flow duct, cooling medium, sleeve thickness, and sleeve thermal conductivity. Finally, an improved loss separation method is employed to obtain the loss distribution from the measured total loss, and the comprehensive experiments are implemented based on one HSPMM prototype (800 kW, 15 000 rpm) to verify the related theoretical analysis.
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, Lu, Z & Wu, D 2020, 'An intelligent recognition model for dynamic air traffic decision-making', Knowledge-Based Systems, vol. 199, pp. 105274-105274.
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© 2019 Air traffic flow management system (ATFMS) is becoming increasingly important due to the rapid growth of air traffic and serious flight delay nowadays. To aware the air traffic flow density and identify the heat airspace in terminal areas of large hub airports is essential for an ATFMS. Due to numerous parameters in air traffic flow, traditional methods based on one single parameter fail to reflect the true complexity relationship between these parameters. This study aims to develop an intelligent air traffic flow heat airspace recognition model using advanced data science technique for establishing a real-time cloud map in the terminal airspace of airports, which attempts to use machine learning models to represent the complex relationship among these parameters. In the proposed intelligent recognition model, high dimensions of parameters (basic parameters, additional parameters and time parameters) are processed to achieve a comprehensive and accurate situation awareness for support dynamic air traffic decision-making. An aircraft trajectories points clustering method is developed to generate a 4D heat airspace map. The basic parameters and time parameters are used to identify the heat airspaces; the changes of additional parameters which influence the heat airspaces are identified and analyzed by use of grid graphs of flight trajectories; probability fitting graphs are used to verify accuracy of 4D results in order to support air traffic decision-making. A case study on Beijing International Airport (PEK) is conducted to test our model and has obtained two main research findings: there are two areas of PEK that have the high density and there are hot peaks at two different heights; flight trajectories and speed of trajectories also effect on the heat airspace. The study realizes that the proposed 4D heat airspace model is better for detailed and accurate information construction, expression of spatial changes, and visualization of multiple parameters of...
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, 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 the
variational hybrid quantum-classical scheme, which remains largely unknown due
to the non-convex optimization landscape, the measurement error, and the
unavoidable gate errors introduced by noisy intermediate-scale quantum (NISQ)
machines. Our contributions in this paper are multi-fold. First, we derive the
utility bounds of QNN towards empirical risk minimization, and show that large
gate noise, few quantum measurements, and deep circuit depth will lead to the
poor utility bounds. This result also applies to the variational quantum
circuits with gradient-based classical optimization, and can be of independent
interest. 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 learned
by 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 on
noiseless or noisy quantum machines. We last exhibit that the quantum
statistical query (QSQ) model can be effectively simulated by noisy QNN. Since
the QSQ model can tackle certain tasks with runtime speedup, our result
suggests that the modified QNN implemented on NISQ devices will retain the
quantum 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 if
they can not guarantee privacy. To fill this knowledge gap, here we devise an
efficient quantum differentially private (QDP) Lasso estimator to solve sparse
regression tasks. Concretely, given $N$ $d$-dimensional data points with $N\ll
d$, we first prove that the optimal classical and quantum non-private Lasso
requires $\Omega(N+d)$ and $\Omega(\sqrt{N}+\sqrt{d})$ runtime, respectively.
We next prove that the runtime cost of QDP Lasso is \textit{dimension
independent}, i.e., $O(N^{5/2})$, which implies that the QDP Lasso can be
faster than both the optimal classical and quantum non-private Lasso. Last, we
exhibit that the QDP Lasso attains a near-optimal utility bound
$\tilde{O}(N^{-2/3})$ with privacy guarantees and discuss the chance to realize
it on near-term quantum chips with advantages.
Du, Y, Huang, T, You, S, Hsieh, M-H & Tao, D 2020, 'Quantum circuit architecture search for variational quantum algorithms', npj Quantum Information, vol. 8, p. 62.
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Variational quantum algorithms (VQAs) are expected to be a path to quantum
advantages on noisy intermediate-scale quantum devices. However, both empirical
and theoretical results exhibit that the deployed ansatz heavily affects the
performance of VQAs such that an ansatz with a larger number of quantum gates
enables a stronger expressivity, while the accumulated noise may render a poor
trainability. To maximally improve the robustness and trainability of VQAs,
here we devise a resource and runtime efficient scheme termed quantum
architecture search (QAS). In particular, given a learning task, QAS
automatically seeks a near-optimal ansatz (i.e., circuit architecture) to
balance benefits and side-effects brought by adding more noisy quantum gates to
achieve a good performance. We implement QAS on both the numerical simulator
and real quantum hardware, via the IBM cloud, to accomplish data classification
and quantum chemistry tasks. In the problems studied, numerical and
experimental results show that QAS can not only alleviate the influence of
quantum noise and barren plateaus, but also outperforms VQAs with pre-selected
ansatze.
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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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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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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Dunbar, M, Belieres, S, Shukla, N, Amirghasemi, M, Perez, P & Mishra, N 2020, 'A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network', Annals of Operations Research, vol. 290, no. 1-2, pp. 923-941.
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Modern-day logistics companies require increasingly shorter lead-times in order to cater for the increasing popularity of on-demand services. There is consequently an urgent need for fast scheduling algorithms to provide high quality, real-time implementable solutions. In this work we model a spare part delivery problem for an on-demand logistics company, as a variant of vehicle routing problem. For large delivery networks, the optimisation solution technique of column generation has been employed successfully in a variety of vehicle routing settings and is often used in combination with exact methods for solving problems with a large number of variables. Challenges may arise when the pricing subproblem is difficult to solve in a realistic period due to complex constraints or a large number of variables. The problem may become intractable when the network structure varies daily or is known with less certainty over longer period. In such instances, a high quality heuristic solution may be more preferable than an exact solution with excessive run time. We propose an improved version of column generation approach integrating an efficient genetic algorithm to obtain fast and high-quality solutions for a sustainable spare parts delivery problem. More specifically, we propose to retain the traditional column generation iterative framework, with master problem solved exactly, but with pricing subproblem solved using a metaheuristic. Computational results on a real dataset indicate that this approach yields improved solutions compared to the current best-case business-as-usual costs. It also substantially decreases the computational time; allowing for high-quality, tractable solutions to be obtained in few minutes. We propose to strike a balance between the practical and efficient solution aspects of metaheuristic algorithms, and the exact decomposition and iterative aspect of the column generation solution technique.
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 important aspects ...
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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El Sayed, H, Zeadally, S & Puthal, D 2020, 'Design and evaluation of a novel hierarchical trust assessment approach for vehicular networks', Vehicular Communications, vol. 24, pp. 100227-100227.
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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.
Eskandari, M, Li, L, Moradi, MH, Siano, P & Blaabjerg, F 2020, 'Optimal Voltage Regulator for Inverter Interfaced Distributed Generation Units Part І: Control System', IEEE Transactions on Sustainable Energy, vol. 11, no. 4, pp. 2813-2824.
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The stable operation of conventional power systems greatly depends on coherent impedances of the bulk power networks' elements. However, penetration of inverter interfaced distributed generation (IIDG) units put the stability of modern power systems into a risk due the vague and arbitrary output impedance of IIDG units. Besides, the impedance specification of IIDGs can only be established by means of a virtual impedance loop, which needs extra control efforts also imposes voltage drops. Especially, the virtual impedance depends on the output current and cannot be thus freely adjusted. To this end, an optimal voltage regulator (OVR) is proposed for controlling IIDG units to achieve a free/wide range of impedance shaping. The OVR facilitates the optimal impedance shaping based on the control requirement and grid's impedance characteristics, which makes the IIDG units consistent with the power network thus contributing to stabilizing modern power systems. The OVR's control system is based on the state feedback control and the impedance shaping is achieved through an appropriate feedback gain adjustment process. Simulation results prove the effectiveness of the method to achieve the desired impedance shaping.
Eskandari, M, Li, L, Moradi, MH, Siano, P & Blaabjerg, F 2020, 'Simultaneous reactive power sharing and voltage regulation in an autonomous networked microgrid', IET Generation, Transmission & Distribution, vol. 14, no. 7, pp. 1366-1377.
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Eskandari, M, Li, L, Moradi, MH, Wang, F & Blaabjerg, F 2020, 'A Control System for Stable Operation of Autonomous Networked Microgrids', IEEE Transactions on Power Delivery, vol. 35, no. 4, pp. 1633-1647.
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The interaction of droop controllers through power network is high in networked microgrids (NMGs) due to the low X/R ratio of the power lines impedance and lack of inertia in converter-based NMGs, which has raised stability concerns. On the other hand, inaccurate reactive power sharing and poor power quality due to the voltage and frequency deviations still remain as noticeable issues in NMGs. In this paper, a novel fuzzy consensus protocol is proposed to improve the droop controller performance in power sharing by incorporating the X/R ratio of the power lines impedance into droop loops. Power quality is also enhanced by restoring the average voltage profile based on a new consensus protocol, which is designed to be in coordination with reactive power sharing. In order to guarantee stability of the closed-loop system, linear matrix inequality method is adopted to determine the consensus signal coefficients as structured static output feedback gains. To this end, a novel small-signal model is proposed for NMGs to be adopted in the design process, by which the cross-coupling as well as interaction of droop controllers through the power network is properly realized. The numerical results in MATLAB/SIMULINK prove the effectiveness and accuracy of the proposed method.
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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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.
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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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.
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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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, Yin, C, Zhu, J, Ge, C, Tanveer, M, Jolfaei, A & Cao, Z 2020, 'Privacy Protection for Medical Data Sharing in Smart Healthcare', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 3s, pp. 1-18.
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In virtue of advances in smart networks and the cloud computing paradigm, smart healthcare is transforming. However, there are still challenges, such as storing sensitive data in untrusted and controlled infrastructure and ensuring the secure transmission of medical data, among others. The rapid development of watermarking provides opportunities for smart healthcare. In this article, we propose a new data-sharing framework and a data access control mechanism. The applications are submitted by the doctors, and the data is processed in the medical data center of the hospital, stored in semi-trusted servers to support the selective sharing of electronic medical records from different medical institutions between different doctors. Our approach ensures that privacy concerns are taken into account when processing requests for access to patients’ medical information. For accountability, after data is modified or leaked, both patients and doctors must add digital watermarks associated with their identification when uploading data. Extensive analytical and experimental results are presented that show the security and efficiency of our proposed scheme.
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, Y, Huang, X, Qin, L, Zhang, Y, Zhang, W, Cheng, R & Lin, X 2020, 'A survey of community search over big graphs.', VLDB J., vol. 29, pp. 353-392.
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Fang, Y, Huang, X, Qin, L, Zhang, Y, Zhang, W, Cheng, R & Lin, X 2020, 'Correction: A survey of community search over big graphs', The VLDB Journal, vol. 29, no. 5, pp. 1219-1219.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. In the original article, the Table 1 was published with incorrect figures. The correct Table 1 is given below:(Table presented).
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 were derived....
Far, H 2020, 'Flexural Behavior of Cold-Formed Steel-Timber Composite Flooring Systems', Journal of Structural Engineering, vol. 146, no. 5, pp. 06020003-06020003.
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Composite flooring systems comprising cold-formed steel beams and timber floorboards have been proposed recently for building flooring as efficient, economical, durable, and sustainable solutions. In this study, a parameterised three-dimensional (3D) finite-element model for analyzing the performance of such flooring systems has been developed and validated against experimental data reported for a specific steel/particle board (PB) flooring system in the literature. Once validated, the model was used to investigate the flexural behavior of various composite floors by replacing the PB with a range of sustainable engineered products with known mechanical properties. The results of this study provide insight into the use of various sustainable engineered board products in novel composite flooring systems and the importance of their elastic constants. It has also become apparent that the flexural behavior of the studied composite flooring systems is governed mainly by the connections between the cold-formed steel beams and the floorboards.
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.
Farokhipour, E, Mehrabi, M, Komjani, N & Ding, C 2020, 'A Spoof Surface Plasmon Polaritons (SSPPs) Based Dual-Band-Rejection Filter with Wide Rejection Bandwidth', Sensors, vol. 20, no. 24, pp. 7311-7311.
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This paper presents a novel single-layer dual band-rejection-filter based on Spoof Surface Plasmon Polaritons (SSPPs). The filter consists of an SSPP-based transmission line, as well as six coupled circular ring resonators (CCRRs) etched among ground planes of the center corrugated strip. These resonators are excited by electric-field of the SSPP structure. The added ground on both sides of the strip yields tighter electromagnetic fields and improves the filter performance at lower frequencies. By removing flaring ground in comparison to prevalent SSPP-based constructions, the total size of the filter is significantly decreased, and mode conversion efficiency at the transition from co-planar waveguide (CPW) to the SSPP line is increased. The proposed filter possesses tunable rejection bandwidth, wide stop bands, and a variety of different parameters to adjust the forbidden bands and the filter’s cut-off frequency. To demonstrate the filter tunability, the effect of different elements like number (n), width (WR), radius (RR) of CCRRs, and their distance to the SSPP line (yR) are surveyed. Two forbidden bands, located in the X and K bands, are 8.6–11.2 GHz and 20–21.8 GHz. As the proof-of-concept, the proposed filter was fabricated, and a good agreement between the simulation and experiment results was achieved.
Farrok, O, Islam, MR, Muttaqi, KM, Sutanto, D & Zhu, J 2020, 'Design and Optimization of a Novel Dual-Port Linear Generator for Oceanic Wave Energy Conversion', IEEE Transactions on Industrial Electronics, vol. 67, no. 5, pp. 3409-3418.
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Farrokhi, F, Buchlak, QD, Sikora, M, Esmaili, N, Marsans, M, McLeod, P, Mark, J, Cox, E, Bennett, C & Carlson, J 2020, 'Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms', World Neurosurgery, vol. 134, pp. e325-e338.
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BACKGROUND:Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to 1) investigate preoperative clinical risk factors and 2) build machine learning models to predict adverse outcomes. METHODS:This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n = 501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data. Performance was evaluated using area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and accuracy. RESULTS:Logistic regression showed that the risk of complication was related to the operating institution in which the surgery was performed (odds ratio [OR] = 0.44, confidence interval [CI] = 0.25-0.78), body mass index (OR = 0.94, CI = 0.89-0.99), and diabetes (OR = 2.33, CI = 1.18-4.60). Patients with diabetes were almost 3× more likely to return to the operating room (OR = 2.78, CI = 1.31-5.88). Patients with a history of smoking were 4× more likely to experience postoperative infection (OR = 4.20, CI = 1.21-14.61). Supervised learning algorithms demonstrated high discrimination performance when predicting any complication (AUC = 0.86), a complication within 12 months (AUC = 0.91), return to the operating room (AUC = 0.88), and infection (AUC = 0.97). Age, body mass index, procedure side, gender, and a diagnosis of Parkinson disease were influential features. CONCLUSIONS:Multiple significant complication risk factors were identified, and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery.
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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Feng, Q, Wu, Y, Fan, H, Yan, C, Xu, M & Yang, Y 2020, 'Cascaded Revision Network for Novel Object Captioning', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 10, pp. 3413-3421.
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Feng, Y & Ying, M 2020, 'Quantum Hoare logic with classical variables.', CoRR, vol. abs/2008.06812.
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.
Feng, Y, Wu, D, Liu, L, Gao, W & Tin-Loi, F 2020, 'Safety assessment for functionally graded structures with material nonlinearity', Structural Safety, vol. 86, pp. 101974-101974.
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© 2020 Elsevier Ltd A machine learning aided reliability assessment framework is presented for functionally graded material (FGM) structures under plane strain/stress conditions with the consideration of elastoplasticity. The material nonlinearity of the FGM is modelled through the implementation of the Tamura-Tomota-Ozawa (TTO) model. For safety evaluation of FGM structures, the volume fraction of FGM has been modelled through spatially dependent uncertainty as random field for the concerned composite. In order to solve the complex stochastic elastoplastic problem, a further developed machine learning aided technique called the extended support vector regression (X-SVR) with a generalized Dirichlet feature mapping function has been introduced and then, the corresponding probabilistic features, including the statistical moments, probability density functions (PDFs), and cumulative distribution functions (CDFs), of the concerned structural responses can be effectively established for assessing the reliability of FGM structures. Moreover, the proposed approach is competent to deliver critical information regarding the uncertain system inputs which can be beneficial for subsequent safety assessment and structural designs for the FGM. Two test functions and two numerical examples have been adopted to visualise the accuracy, stability and capability of the proposed safety assessment framework for FGM structures.
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.
Ferreira, B, Maharaj, S, Simpson, A, Nassif, N & Lal, S 2020, 'The metabolic role of depression and burnout in nurses', Translational Metabolic Syndrome Research, vol. 3, pp. 9-11.
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Ferro, V, Chuai, M, McGloin, D & Weijer, C 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, pp. 1-1-.
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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 are fitted best with a simple visco-elastic 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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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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Frederiks, ER, Romanach, LM, Berry, A & Toscas, P 2020, 'Making energy surveys more impactful: Testing material and non-monetary response strategies', Energy Research & Social Science, vol. 63, pp. 101409-101409.
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© 2019 Elsevier Ltd Recent years have seen a growth in energy research that integrates social and behavioural sciences. A core component of this work involves collecting human data, commonly via surveys and field experiments. But there are often barriers to recruiting large and representative samples of participants, with sampling bias and non-response error posing threats to validity. Identifying cost-effective ways to increase participation in energy research is therefore important for strengthening the rigor, utility and generalisability of studies in this area. To this end, the current study harnesses an experimental design to test pathways for making energy surveys more impactful – specifically by improving response rates and times, lowering sampling bias, and enhancing overall cost-effectiveness. As part of a postal survey on household energy use in Australia, a set of randomised controlled trials were conducted to test the impact of four strategies: incentives, an envelope message, a handwritten sticky note, and a reminder postcard. A 3 x 2 x 2 x 2 factorial design was applied to assess both individual and interactive effects. While material incentives in the form of an upfront token gift and prize draw were ineffective in improving response relative to the control survey, results revealed that a handwritten sticky note expressing upfront thanks for participating – designed to serve as an intrinsically motivating attentional cue – improved both the rate and timeliness of response. Three combinations of strategies yielded significantly higher response rates than the control, but they were more expensive on a ‘dollar cost per response’ basis. Implications for research and practice are discussed.
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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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.
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Gai, K, Guo, J, Zhu, L & Yu, S 2020, 'Blockchain Meets Cloud Computing: A Survey', IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 2009-2030.
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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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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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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, 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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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, 'Correction: Video-rate upconversion display from optimized lanthanide ion doped upconversion nanoparticles', Nanoscale, vol. 12, no. 36, pp. 18987-18987.
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Correction for ‘Video-rate upconversion display from optimized lanthanide ion doped upconversion nanoparticles’ by Laixu Gao et al., Nanoscale, 2020, DOI: 10.1039/d0nr03076g.
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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Gao, S, Lu, R, Wang, X, Chou, J, Wang, N, Huai, X, Wang, C, Zhao, Y & Chen, S 2020, 'Immune response of macrophages on super-hydrophilic TiO2 nanotube arrays', Journal of Biomaterials Applications, vol. 34, no. 9, pp. 1239-1253.
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Macrophages are attracting increasing attention in promoting implant-mediated osteogenesis by modulating the microenvironment of the implant site. Biomaterial surface properties including topography and wettability regulate macrophage responses to influence tissue repair. The objective of our present study was to investigate the effects of hydrogenated titanium dioxide nanotube surfaces on the immune response of macrophages in vitro. Hydrogenated titanium dioxide nanotubes (TNTs) were synthesized on Ti surfaces by anodic oxidation and hydrogenation to form super-hydrophilic nanotubular surfaces. Macrophages were seeded directly onto three substrates (hydrogenated TNTs (H2-TNTs), air-annealed TNTs, and commercially pure Ti substrates) and grown under standard or lipopolysaccharide-stimulated culture conditions. Cell proliferation and viability were evaluated by Cell Counting Kit-8 and live/dead staining at 24 and 48 h. Secretion and expression of pro- and anti-inflammatory cytokines were evaluated at 24 and 48 h to determine whether the surfaces elicited differential macrophage behaviors. Gene expression of the M1/M2 surface markers of macrophages was analyzed to assess the effect of the H2-TNT surface on macrophage polarization. The results showed that hydrogenation of the TNT surface resulted in a super-hydrophilic substrate, which exhibited markedly improved wettability attributable to the formation of oxygen vacancies in the nanotubes. The H2-TNT group induced a significantly lower macrophage proliferation rate and up-regulated anti-inflammatory cytokines (interleukin-10, bone morphogenetic protein-2, and transforming growth factor-β1) irrespective of lipopolysaccharide stimulation, while alleviating the secretion of pro-inflammatory cytokines (tumor necrosis factor-α, interleukin-6, nitric oxide, and macrophage chemotactic protein-1) induced by lipopolysaccharide. Moreover, the H2-TNT surface elicited up-regulated gene expression of M2 surface markers and dow...
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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García-Rincón, J, Gatsios, E, Rayner, JL, McLaughlan, RG & Davis, GB 2020, 'Laser-induced fluorescence logging as a high-resolution characterisation tool to assess LNAPL mobility', Science of The Total Environment, vol. 725, pp. 138480-138480.
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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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© 2020 Institution of Engineering and Technology. All rights reserved. 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.
Gautam, S, Lu, Y, Hassan, W, Xiao, W & Lu, DD-C 2020, 'Single phase NTD PLL for fast dynamic response and operational robustness under abnormal grid condition', Electric Power Systems Research, vol. 180, pp. 106156-106156.
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© 2019 Elsevier B.V. Non-frequency-dependent transfer delay (NTD) approach is one of the simplest and the fastest solution for single-phase AC systems to achieve phase-locked loops (PLLs). It also exhibits immunity to error in estimated quantities in grid frequency variations. However it is susceptible to grid voltage disturbances, such as DC offset and distortions. This paper proposed an improved NTD scheme to achieve both fast dynamic response and operational robustness against abnormal grid conditions. The solution is based on a digital phase lead compensator (PLC), which is cascaded with the MAF inside the PLL control loop to enhance dynamic response and mitigate disturbance, DC offset, and harmonics. The filtering structure is also designed to be adaptive to grid frequency variations. The effectiveness of the proposed solution is verified via simulation and experimental results.
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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Geekiyanage, N, Sauret, E, Saha, S, Flower, R & Gu, Y 2020, 'Modelling of Red Blood Cell Morphological and Deformability Changes during In-Vitro Storage', Applied Sciences, vol. 10, no. 9, pp. 3209-3209.
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Storage lesion is a critical issue facing transfusion treatments, and it adversely affects the quality and viability of stored red blood cells (RBCs). RBC deformability is a key indicator of cell health. Deformability measurements of each RBC unit are a key challenge in transfusion medicine research and clinical haematology. In this paper, a numerical study, inspired from the previous research for RBC deformability and morphology predictions, is conducted for the first time, to investigate the deformability and morphology characteristics of RBCs undergoing storage lesion. This study investigates the evolution of the cell shape factor, elongation index and membrane spicule details, where applicable, of discocyte, echinocyte I, echinocyte II, echinocyte III and sphero-echinocyte morphologies during 42 days of in-vitro storage at 4 °C in saline-adenine-glucose-mannitol (SAGM). Computer simulations were performed to investigate the influence of storage lesion-induced membrane structural defects on cell deformability and its recoverability during optical tweezers stretching deformations. The predicted morphology and deformability indicate decreasing quality and viability of stored RBCs undergoing storage lesion. The loss of membrane structural integrity due to the storage lesion further degrades the cell deformability and recoverability during mechanical deformations. This numerical approach provides a potential framework to study the RBC deformation characteristics under varying pathophysiological conditions for better diagnostics and treatments.
Gentile, C, Kesteven, S, Wu, J, Davies, MJ, Bursill, C, Feneley, M & Figtree, G 2020, 'Endothelial nitric oxide synthase plays a protective role in endothelial cells and cardiomyocytes against myocardial infarction', Journal of Molecular and Cellular Cardiology, vol. 140, pp. 31-31.
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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, SS, 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.
Giubilato, R, Vayugundla, M, Schuster, MJ, Sturzl, W, Wedler, A, Triebel, R & Debei, S 2020, 'Relocalization With Submaps: Multi-Session Mapping for Planetary Rovers Equipped With Stereo Cameras', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 580-587.
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© 2016 IEEE. To enable long term exploration of extreme environments such as planetary surfaces, heterogeneous robotic teams need the ability to localize themselves on previously built maps. While the Localization and Mapping problem for single sessions can be efficiently solved with many state of the art solutions, place recognition in natural environments still poses great challenges for the perception system of a robotic agent. In this paper we propose a relocalization pipeline which exploits both 3D and visual information from stereo cameras to detect matches across local point clouds of multiple SLAM sessions. Our solution is based on a Bag of Binary Words scheme where binarized SHOT descriptors are enriched with visual cues to recall in a fast and efficient way previously visited places. The proposed relocalization scheme is validated on challenging datasets captured using a planetary rover prototype on Mount Etna, designated as a Moon analogue environment.
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, 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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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 that
relies only on minimal information-theoretic axioms, namely monotonicity under
mixing and data-processing as well as additivity for product distributions. We
find that these axioms induce sufficient structure to establish continuity in
the interior of the probability simplex and meaningful upper and lower bounds,
e.g., we find that every relative entropy must lie between the R\'enyi
divergences of order $0$ and $\infty$. We further show simple conditions for
positive definiteness of such relative entropies and a characterisation in term
of a variant of relative trumping. Our main result is a one-to-one
correspondence 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 a
larger one. We find that all extensions of resource measures are bounded
between two quantities that we call the minimal and maximal extensions. We
discuss various applications of our framework. We show that any relative
entropy (i.e. an additive function on pairs of quantum states that satisfies
the data processing inequality) must be bounded by the min and max relative
entropies. We prove that the generalized trace distance, the generalized
fidelity, and the purified distance are optimal extensions. And in entanglement
theory we introduce a new technique to extend pure state entanglement measures
to mixed bipartite states.
Govindarajan, P, Soundarapandian, RK, Gandomi, AH, Patan, R, Jayaraman, P & Manikandan, R 2020, '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.
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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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.
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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, Q, Huang, Y, Zhong, Z, Zheng, Z, Zheng, L & Yang, Y 2020, 'Thorax disease classification with attention guided convolutional neural network', Pattern Recognition Letters, vol. 131, pp. 38-45.
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© 2019 Elsevier B.V. This paper considers the task of thorax disease diagnosis on chest X-ray (CXR) images. Most existing methods generally learn a network with global images as input. However, thorax diseases usually happen in (small) localized areas which are disease specific. Thus training CNNs using global images may be affected by the (excessive) irrelevant noisy areas. Besides, due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. For addressing the above problems, we propose to integrate the global and local cues into a three-branch attention guided convolution neural network (AG-CNN) to identify thorax diseases. An attention guided mask inference based cropping strategy is proposed to avoid noise and improve alignment in the global branch. AG-CNN also integrates the global cues to compensate the lost discriminative cues by the local branch. Specifically, we first learn a global CNN branch using global images. Then, guided by the attention heatmap generated from the global branch, we infer a mask to crop a discriminative region from the global image. The local region is used for training a local CNN branch. Lastly, we concatenate the last pooling layers of both the global and local branches for fine-tuning the fusion branch. Experiments on the ChestX-ray14 dataset demonstrate that after integrating the local cues with the global information, the average AUC scores are improved by AG-CNN.
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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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.
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.
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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 effects of...
Guo, J & Ying, M 2020, 'Software Pipelining for Quantum Loop Programs.', CoRR, vol. abs/2012.12700.
Guo, K & Guo, Y 2020, '3D Nonlinear Equivalent Magnetic Circuit Model Analysis of a Flux Reversal Linear Rotary Permanent Magnet Machine', Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, vol. 35, no. 20, pp. 4278-4286.
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An improved 3D nonlinear equivalent magnetic circuit model (EMCM) of a flux reversal linear rotary permanent magnet machine (FR-LRPMM) was established by adding a variable reluctance unit to 3D magnetic circuit structure. The magnetic reluctance expressions of the stator and mover sections were derived by magnetic nodal method. The reluctance of the variable-reluctance unit was derived by 2D finite element method (FEM), which changed from 0 to infinity periodically with different locations of the mover when it was in linear or rotary motion. The 3D air-gap permeance expression was derived by segmentation method. The electromagnetic characteristics, such as no-load air gap flux density, were calculated and analyzed by an iterative method. Compared with the 3D FEM, the calculation time of this proposed model is reduced greatly. The proposed model could take in account the saturation of the stator core material, PM local flux leakage and pole-pole flux leakage. The analyzed results of the electromagnetic characteristics, including the back EMF, cogging torque and detent force, are consistent with those of 3D FEM and test measurement, which verifies the improved EMCM.
Guo, K, Guo, Y & Li, J 2020, 'Decoupling Control Analysis of a Flux Reversal Linear Rotary Permanent Magnet Actuator', Journal of Electrical Engineering & Technology, vol. 15, no. 4, pp. 1693-1704.
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Guo, W, Nguyen, PD, You, S-J & Lin, C 2020, 'Editorial - Special issue on green technologies for waste treatment', Bioresource Technology Reports, vol. 11, pp. 100495-100495.
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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, 18.7–23.6 °C...
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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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.
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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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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Drug 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 sensor
head vibrations as if they are vibrations of the target surface itself. This
paper presents practical correction schemes to solve this important problem.
The study begins with a theoretical analysis, for arbitrary vibration and any
scanning configuration, which shows that the only measurement required is of
the vibration velocity at the incident point on the final steering mirror in
the direction of the outgoing laser beam and this underpins the two correction
options investigated. Correction sensor location is critical; the first scheme
uses an accelerometer pair located on the SLDV front panel, either side of the
emitted laser beam, while the second uses a single accelerometer located along
the optical axis behind the final steering mirror. Initial experiments with a
vibrating sensor head and stationary target confirmed the sensitivity to sensor
head vibration together with the effectiveness of the correction schemes which
reduced overall error by 17 dB (accelerometer pair) and 27 dB (single
accelerometer). In extensive further tests with both sensor head and target
vibration, conducted across a range of scan angles, the correction schemes
reduced error by typically 14 dB (accelerometer pair) and 20 dB (single
accelerometer). RMS phase error was also up to 30% lower for the single
accelerometer option, confirming it as the preferred option. The theory
suggests a geometrical weighting of the correction measurements and this
provides a small additional improvement. Since the direction of the outgoing
laser beam and its incident point on the final steering mirror both change as
the mirrors scan the laser beam, the use of fixed axis correction transducers
mounted in fixed locations makes the correction imperfect. The associated
errors are estimated and expected to be generally small, and the theoretical
basis...
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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Han, C, Li, W, Liu, HK, Dou, S & Wang, J 2020, 'Principals and strategies for constructing a highly reversible zinc metal anode in aqueous batteries', Nano Energy, vol. 74, pp. 104880-104880.
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© 2020 Elsevier Ltd Among all the electrochemical energy storage systems, zinc-based batteries, such as zinc-air, zinc-metal, zinc-ion batteries, etc., have been recognized as an important group of candidates that could be potential alternatives to the currently dominant lead-acid and lithium-ion battery systems, because they have many unbeatable merits, including direct use of zinc metal as electrode; compatible with low cost, non-flammable, and environement-friendly aqueous electrolyte; assembly in ambient conditions; environmental benignity; and high safety. Currently, however, the capacitance, cycle life, and safety of zinc-based batteries were significantly degraded by zinc-water interaction problems that took place on the zinc metal electrode, including corrosion, passivation, shape change, and dendrite formation. This review gives a specific, comprehensive and in-depth summary of the mechanisms behind these problems; as well as state-of-the-art progress in the protection of the zinc electrode via intrinsic zinc alloy, zinc surface coating and electrolyte engineering in full pH range aqueous electrolyte. Future development trends, perspective and outlooks on the further blossom of these strategies are also presented.
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, 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.
Hanawal, MK, Nguyen, DN & Krunz, M 2020, 'Cognitive Networks With In-Band Full-Duplex Radios: Jamming Attacks and Countermeasures', IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 296-309.
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Although in-band full-duplex (IBFD) radios promise to double the throughput of a wireless link, they are more vulnerable to jamming attacks than their out-of-band full-duplex (OBFD) counterparts. For two communicating OBFD nodes, a jammer needs to attack both the uplink and the downlink channels to completely break the communication link. In contrast, only one common channel needs to be jammed in the case of two IBFD nodes. Even worse, a jammer with self-interference suppression (SIS) capabilities (the underlying technique of IBFD radios) can learn the transmitters’ activity while injecting interference, allowing it to react instantly to the transmitter’s strategies. In this work, we consider a power-constrained IBFD “reactive-sweep” jammer that sweeps through the set of channels by jamming a subset of them simultaneously. We model the interactions between the IBFD radios and the jammer as a stochastic constrained zero-sum Markov game in which nodes adopt the frequency hopping (FH) technique as their strategies to counter jamming attacks. Beside the IBFD transmission-reception (TR) mode, we introduce an additional operation mode, called transmission-detection (TD), in which an IBFD radio transmits and leverages its SIS capability to detect jammers. The aim of the TD mode is to make IBFD radios more cognitive to jamming. The nodes’ optimal defense strategy that guides them when to hop and which operational mode (TD or TR) to use is then established from the equilibrium of the stochastic Markov game. We prove that this optimal policy has a threshold structure, in which IBFD nodes stay on the same channel up to a certain number of time slots before hopping. Simulation results show that our policy significantly improves the throughput of IBFD nodes under jamming attacks.
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.
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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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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.
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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.
Hao, Z, Cook, K, Canning, J, Chen, H-T & Martelli, C 2020, '3-D Printed Smart Orthotic Insoles: Monitoring a Person's Gait Step by Step', IEEE Sensors Letters, vol. 4, no. 1, pp. 1-4.
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This article reports a 3-D printing intelligent insole gait monitoring system based on an embedded fiber Bragg grating (FBG). The smart insole combines 3-D printing technology and FBG sensors providing high sensitivity and end-point low cost. Results using pressure points measured by four FBGs are sufficient to differentiate foot loads and gait types.
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.
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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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, 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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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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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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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.
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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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.
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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, L, Lu, Z, Geng, L, Zhang, J, Li, X & Guo, X 2020, 'Environmental economic dispatch of integrated regional energy system considering integrated demand response', International Journal of Electrical Power & Energy Systems, vol. 116, pp. 105525-105525.
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He, S, Huang, D, Feng, X, Deng, J, Li, J & Zhu, J 2020, 'Transient potential distribution on transformer winding considering the effect of core lamination stack', AIP Advances, vol. 10, no. 1, pp. 015024-015024.
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© 2020 Author(s). The potential distribution of windings under impulse voltage is very important for the design of transformer inter-turn insulation especially for large capacity transformers such as ultra-high voltage direct current (UHVDC) converter transformer. Quite a lot of equivalent circuit models for transformer winding have been proposed for the potential distribution calculation assuming that the influence of magnetic core is negligible at frequencies higher than 10 kHz. However, lightning impulse or VFTO waveforms usually contain abundant frequency components higher than 10 kHz. At above situations the magnetic core plays an important role during the transient procedure. To obtain a more comprehensive model and also to provide a more accurate potential distribution of transformer winding, in this paper, a wide frequency magnetic properties of silicon steel sheet were measured and the relationship between relative permeability of lamination stack and frequency is studied and implemented in the calculation of frequency-dependent parameters such as resistance, self- and mutual-inductances. Then the equivalent circuit model of UHVDC converter transformer is established considering the properties of core lamination stack. Coding the program in MATLAB to solve the matrix equation and the potential distribution properties are extracted from the calculation results under lightning situation. The inter-turn potential distribution is also analyzed and the results may provide more accurate information for transformer inter-turn insulation design.
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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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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He, Y, Dong, X, Kang, G, Fu, Y, Yan, C & Yang, Y 2020, 'Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks.', IEEE Transactions on Cybernetics.
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Deeper and wider convolutional neural networks (CNNs) achieve superior performance but bring expensive computation cost. Accelerating such overparameterized neural network has received increased attention. A typical pruning algorithm is a three-stage pipeline, i.e., training, pruning, and retraining. Prevailing approaches fix the pruned filters to zero during retraining and, thus, significantly reduce the optimization space. Besides, they directly prune a large number of filters at first, which would cause unrecoverable information loss. To solve these problems, we propose an asymptotic soft filter pruning (ASFP) method to accelerate the inference procedure of the deep neural networks. First, we update the pruned filters during the retraining stage. As a result, the optimization space of the pruned model would not be reduced but be the same as that of the original model. In this way, the model has enough capacity to learn from the training data. Second, we prune the network asymptotically. We prune few filters at first and asymptotically prune more filters during the training procedure. With asymptotic pruning, the information of the training set would be gradually concentrated in the remaining filters, so the subsequent training and pruning process would be stable. The experiments show the effectiveness of our ASFP on image classification benchmarks. Notably, on ILSVRC-2012, our ASFP reduces more than 40% FLOPs on ResNet-50 with only 0.14% top-5 accuracy degradation, which is higher than the soft filter pruning by 8%.
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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Heathcote, K, Wullschleger, M, Gardiner, B, Morgan, G, Barbagello, H & Sun, J 2020, 'The Importance of Place of Residence on Hospitalized Outcomes for Severely Injured Trauma Patients: A Trauma Registry Analysis', The Journal of Rural Health, vol. 36, no. 3, pp. 381-393.
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Heffernan, AJ, Sime, FB, Sun, J, Lipman, J, Kumar, A, Andrews, K, Ellwood, D, Grimwood, K & Roberts, J 2020, 'β-lactam antibiotic versus combined β-lactam antibiotics and single daily dosing regimens of aminoglycosides for treating serious infections: A meta-analysis', International Journal of Antimicrobial Agents, vol. 55, no. 3, pp. 105839-105839.
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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.
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 particles from...
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 tumor 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 characterization of CTC clusters is becoming important as heterotypic clusters can provide a mechanism for immune evasion. This review summarizes the latest advances in CTC cluster-mediated metastasis and clinical significance. Expert opinion: Comprehensive characterization 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 symptoms, a...
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 (Preprint)'.
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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 mea...
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 intervention over a ...
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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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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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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Hlalele, TG, Naidoo, RM, Bansal, RC & Zhang, J 2020, 'Multi-objective stochastic economic dispatch with maximal renewable penetration under renewable obligation', Applied Energy, vol. 270, pp. 115120-115120.
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Hlalele, TG, Naidoo, RM, Zhang, J & Bansal, RC 2020, 'Dynamic Economic Dispatch With Maximal Renewable Penetration Under Renewable Obligation', IEEE Access, vol. 8, pp. 38794-38808.
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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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Hong, H, Li, X, Pan, Y & Tsang, I 2020, 'Domain-adversarial Network Alignment', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Network alignment is a critical task to a wide variety of fields. Many existing works leverage on representation learning to accomplish this task without eliminating domain representation bias induced by domain-dependent features, which yield inferior alignment performance. This paper proposes a unified deep architecture (DANA) to obtain a domain-invariant representation for network alignment via an adversarial domain classifier. Specifically, we employ the graph convolutional networks to perform network embedding under the domain adversarial principle, given a small set of observed anchors. Then, the semi-supervised learning framework is optimized by maximizing a posterior probability distribution of observed anchors and the loss of a domain classifier simultaneously. We also develop a few variants of our model, such as, direction-aware network alignment, weight-sharing for directed networks and simplification of parameter space. Experiments on three real-world social network datasets demonstrate that our proposed approaches achieve state-of-the-art alignment results.
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.
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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Hosen, ASMS, Singh, S, Sharma, PK, Rahman, MS, Ra, I-H, Cho, GH & Puthal, D 2020, 'A QoS-Aware Data Collection Protocol for LLNs in Fog-Enabled Internet of Things', IEEE Transactions on Network and Service Management, vol. 17, no. 1, pp. 430-444.
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Hoshyar, AN, Samali, B, Liyanapathirana, R, Houshyar, AN & Yu, Y 2020, 'Structural damage detection and localization using a hybrid method and artificial intelligence techniques', Structural Health Monitoring, vol. 19, no. 5, pp. 1507-1523.
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In this article, an intelligent scheme for structural damage detection and localization is introduced by implementing a hybrid method using the Hilbert–Huang transform and the wavelet transform. First, the second derivatives of the Discrete Laplacian are computed on Hilbert spectrum parameters at each frequency coordinate, and then, in order to highlight the influence of damage on signals, the data are rescaled and weighted with respect to the variance to adjust the differences in amplitude at different scales. Afterwards, the anti-symmetric extension is applied to deal with the boundary distortion phenomenon. A two-dimensional map is created using the multi two-dimensional discrete wavelet transform. This generates the coefficient matrices of level 2 approximation and horizontal, vertical and diagonal details. Horizontal detail coefficients are used to localize damages due to its sensitiveness to any perturbation. Finally, the validity of the algorithm corresponding to various damage states, the single state damage and multiple state damage, is examined through experimental analysis. The results indicate that the proposed framework can effectively localize cracks on concrete and reinforced concrete beams and can provide reliable crack localization in the presence of noise up to 5% more than the expected noise. In addition, the detection problem is mapped to machine learning tasks (support vector machine, k-nearest neighbours and ensemble methods) to automate the damage detection process. The quality of the models is evaluated and validated using the features extracted from the horizontal detail coefficients. The numerical results show that the ensemble models outperform the other models with respect to accuracy, prediction speed and training time.
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, MA, Canning, J & Yu, Z 2020, 'Fluorescence-Based Determination of Olive Oil Quality Using an Endoscopic Smart Mobile Spectrofluorimeter', IEEE Sensors Journal, vol. 20, no. 8, pp. 4156-4163.
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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.
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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.
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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.
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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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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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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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.
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 investiga...
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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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, 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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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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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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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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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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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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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, W, Hua, W, Chen, F & Zhu, J 2020, 'Enhanced Model Predictive Torque Control of Fault-Tolerant Five-Phase Permanent Magnet Synchronous Motor With Harmonic Restraint and Voltage Preselection', IEEE Transactions on Industrial Electronics, vol. 67, no. 8, pp. 6259-6269.
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Huang, W, Hua, W, Chen, F, Hu, M & Zhu, J 2020, 'Model Predictive Torque Control With SVM for Five-Phase PMSM Under Open-Circuit Fault Condition', IEEE Transactions on Power Electronics, vol. 35, no. 5, pp. 5531-5540.
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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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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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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, 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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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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Boron's uniqueposition in the periodic table, i.e. 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 and energy-efficient products has seen boron playing key roles in energy-related research, from activating and synthesizing energy-rich small molecules, to storing chemical and electrical energy, to converting electrical energy to 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 great tunability in 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 the useful family of weakly coordinating anions. This review summarizes recent advances in the study of boron compounds for energy-related research and applications, driven by modern demands and enabled by breakthroughs in the synthesis and understanding of boron chemistry.
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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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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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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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 reinforcement
learning-based deception strategy to deal with reactive jamming attacks. In
particular, for a smart and reactive jamming attack, the jammer is able to
sense the channel and attack the channel if it detects communications from the
legitimate transmitter. To deal with such attacks, we propose an intelligent
deception strategy which allows the legitimate transmitter to transmit 'fake'
signals to attract the jammer. Then, if the jammer attacks the channel, the
transmitter can leverage the strong jamming signals to transmit data by using
ambient backscatter communication technology or harvest energy from the strong
jamming signals for future use. By doing so, we can not only undermine the
attack ability of the jammer, but also utilize jamming signals to improve the
system performance. To effectively learn from and adapt to the dynamic and
uncertainty of jamming attacks, we develop a novel deep reinforcement learning
algorithm using the deep dueling neural network architecture to obtain the
optimal policy with thousand times faster than those of the conventional
reinforcement algorithms. Extensive simulation results reveal that our proposed
DeepFake framework is superior to other anti-jamming strategies in terms of
throughput, packet loss, and learning rate.
Huynh, NV, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2020, 'Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach'.
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In intelligent transportation systems (ITS), vehicles are expected to feature
with advanced applications and services which demand ultra-high data rates and
low-latency communications. For that, the millimeter wave (mmWave)
communication has been emerging as a very promising solution. However,
incorporating the mmWave into ITS is particularly challenging due to the high
mobility of vehicles and the inherent sensitivity of mmWave beams to dynamic
blockages. This article addresses these problems by developing an optimal beam
association framework for mmWave vehicular networks under high mobility.
Specifically, we use the semi-Markov decision process to capture the dynamics
and uncertainty of the environment. The Q-learning algorithm is then often used
to find the optimal policy. However, Q-learning is notorious for its
slow-convergence. Instead of adopting deep reinforcement learning structures
(like most works in the literature), we leverage the fact that there are
usually multiple vehicles on the road to speed up the learning process. To that
end, we develop a lightweight yet very effective parallel Q-learning algorithm
to quickly obtain the optimal policy by simultaneously learning from various
vehicles. Extensive simulations demonstrate that our proposed solution can
increase the data rate by 47% and reduce the disconnection probability by 29%
compared to other solutions.
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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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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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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Ibrar, I, Altaee, A, Zhou, JL, Naji, O & Khanafer, D 2020, 'Challenges and potentials of forward osmosis process in the treatment of wastewater', Critical Reviews in Environmental Science and Technology, vol. 50, no. 13, pp. 1339-1383.
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An emerging osmotically driven membrane process, forward osmosis has attracted growing attention in the field of desalination and wastewater treatment. The present study provides a critical review of the forward osmosis process for wastewater treatment focusing on most recent studies. Forward osmosis is one of the technologies that has been widely studied for the treatment of a wide range of wastewater because of its low fouling and energy consumption compared to conventional techniques for wastewater treatment. To date, forward osmosis has limited applications in the field of wastewater treatment due to several technical and economic concerns. Although membrane cost is one of the critical issues that limit the commercial application of forward osmosis, there are other obstacles such as membrane fouling, finding an ideal draw solution that can easily be recycled, concentration polarization and reverse salt diffusion. Innovative technologies for in-situ real-time fouling monitoring can give us new insights into fouling mechanisms and fouling control strategies in forward osmosis. This study evaluated recent advancements in forward osmosis technology for wastewater treatment and the main challenges that need to be addressed in future research work.
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.
Imran, K, Ullah, K, Khattak, A, Zhang, J, Pal, A, Rafique, MN & Baig, SM 2020, 'Matchmaking model for bilateral trading decisions of load serving entity', Electric Power Systems Research, vol. 183, pp. 106281-106281.
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Imran, K, Zhang, J, Pal, A, Khattak, A, Ullah, K & Baig, SM 2020, 'Bilateral negotiations for electricity market by adaptive agent-tracking strategy', Electric Power Systems Research, vol. 186, pp. 106390-106390.
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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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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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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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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.
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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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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Jafari, M, Malekjamshidi, Z, Zhu, J & Khooban, M-H 2020, 'A Novel Predictive Fuzzy Logic-Based Energy Management System for Grid-Connected and Off-Grid Operation of Residential Smart Microgrids', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 8, no. 2, pp. 1391-1404.
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IEEE In this paper, a novel energy management system with two operating horizons is proposed for a residential micro-grid application. The micro-grid utilises the energies of a photovoltaic (PV), a fuel cell and a battery bank to supply the local loads through a combination of electric and magnetic buses. The proposed micro-grid operates in a large number of grid-connected and off-grid operation modes. The energy management system includes a long-term data prediction unit based on a 2D dynamic programming and a short-term fuzzy controller. The long-term prediction unit is designed to determine the appropriate variation range of the battery state of charge and fuel cell state of hydrogen. The efficiency performance of the micro-grid components, predicted energy generation and demand, energy cost and the system constraints are taken into account. The resultant data then is sent to the short-term fuzzy controller which determines the operation mode of the micro-grid based on the real-time condition of the micro-grid elements. A prototype of the proposed micro-grid including the energy management system is developed, and experimental tests are conducted for three different energy management scenarios. The proposed management technique is validated through energy distribution and cost analysis.
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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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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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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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.
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, DP & Qiu, X 2020, 'Sound transmission loss of porous materials in ducts with embedded periodic scatterers', The Journal of the Acoustical Society of America, vol. 147, no. 2, pp. 978-983.
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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 accuracy of 0.94...
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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Jena, R, Pradhan, B, Al-Amri, A, Lee, CW & Park, H-J 2020, 'Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning', Sensors, vol. 20, no. 16, pp. 4369-4369.
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Earthquake prediction is a popular topic among earth scientists; however, this task is challenging and exhibits uncertainty therefore, probability assessment is indispensable in the current period. During the last decades, the volume of seismic data has increased exponentially, adding scalability issues to probability assessment models. Several machine learning methods, such as deep learning, have been applied to large-scale images, video, and text processing; however, they have been rarely utilized in earthquake probability assessment. Therefore, the present research leveraged advances in deep learning techniques to generate scalable earthquake probability mapping. To achieve this objective, this research used a convolutional neural network (CNN). Nine indicators, namely, proximity to faults, fault density, lithology with an amplification factor value, slope angle, elevation, magnitude density, epicenter density, distance from the epicenter, and peak ground acceleration (PGA) density, served as inputs. Meanwhile, 0 and 1 were used as outputs corresponding to non-earthquake and earthquake parameters, respectively. The proposed classification model was tested at the country level on datasets gathered to update the probability map for the Indian subcontinent using statistical measures, such as overall accuracy (OA), F1 score, recall, and precision. The OA values of the model based on the training and testing datasets were 96% and 92%, respectively. The proposed model also achieved precision, recall, and F1 score values of 0.88, 0.99, and 0.93, respectively, for the positive (earthquake) class based on the testing dataset. The model predicted two classes and observed very-high (712,375 km2) and high probability (591,240.5 km2) areas consisting of 19.8% and 16.43% of the abovementioned zones, respectively. Results indicated that the proposed model is superior to the traditional methods for earthquake probability assessment in terms of accuracy. Aside from fac...
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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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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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, P, Li, K, Chen, X, Dan, R & Yu, Y 2020, 'Magnetic and Hydrophobic Composite Polyurethane Sponge for Oil–Water Separation', Applied Sciences, vol. 10, no. 4, pp. 1453-1453.
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Crude oil spills from offshore oil fields will cause serious pollution to the marine ecological environment. Many 3D porous materials have been used for oil–water separation, but they cannot be widely used due to complex preparation processes and expensive preparation costs. Here, a facile and cheap approach to disperse expanded graphite (EG), stearic acid, and Fe3O4 magnetic nanoparticles on the skeleton surface of polyurethane (PU) sponge to prepare the magnetic and hydrophobic composite polyurethane sponge for oil–water separation. The results show that the composite PU sponge had a strong oil absorption capacity for various oils, the oil adsorption capacities has reached 32–40 g/g, and it has become more hydrophobic. The addition of Fe3O4 magnetic nanoparticles endowed the sponge with magnetic responsivity, and the composite PU sponge still had a strong oil adsorption capacity after several adsorbing-squeezing cycles. The magnetic and hydrophobic composite polyurethane sponge is a very promising material for practical oil adsorption and oil–water separation.
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, Gu, X, Wu, D, Hang, W, Xue, J, Qiu, S & Chin-Teng, L 2020, 'A Novel Negative-Transfer-Resistant Fuzzy Clustering Model with a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation', IEEE/ACM Transactions on Computational Biology and Bioinformatics, pp. 1-1.
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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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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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Joshi, S, Sharma, M & Singh, RK 2020, 'Performance Evaluation of Agro-tourism Clusters using AHP–TOPSIS', Journal of Operations and Strategic Planning, vol. 3, no. 1, pp. 7-30.
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The aim of this article is to recognize the key critical success factors (CSFs) that determine the performance of agritourism clusters using combined analytical hierarchy process (AHP)-the technique for order of preference by similarity to ideal solution (TOPSIS) method. The operational performance of agritourism clusters was determined using integrated approach, and 11 CSFs were initially identified through systematic literature review. Furthermore, experts validated and finalized nine CSFs that can determine the sustainable performance of agritourism network. Totally 15 experts (including practitioners and agritourism supply chain partners), were engaged in data collection process. The AHP application exhibits that destination attractiveness (D5) has the highest priority (0.156) among all the dimensions followed by local community contribution and sustainable livelihoods (D9). Furthermore, TOPSIS demonstrates that Pithoragarh (C7) has the ideal best value (0.7705) indicating that this cluster is performing the best among all the clusters followed by Almora (0.7209). The identification of CSFs to evaluate the performance of clusters in Uttarakhand, India can serve as guiding tool for the agritourism practitioners. The examination of pertinent issues with the focus on major support required, bottlenecks in adoption, key benefits of sustainable agritourism activities and most common sustainable practices are expected to provide new understanding about the sustainable agritourism business-based issues in India. The only limitation of this study is the geographic coverage and limited demography of the respondents. This study is one of the first efforts on assessing agritourism supply chain practices in India, with the special reference to Uttarakhand state.
Joshi, S, Singh, RK & Sharma, M 2020, 'Sustainable Agri-food Supply Chain Practices: Few Empirical Evidences from a Developing Economy', Global Business Review, pp. 097215092090701-097215092090701.
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This study aims to identify the factors influencing the adoption of sustainable agribusiness practices in India and examines the most pertinent issues related to sustainable agri-supply chains and their management. The constructs of the study were identified through two sequential phases involving a review of literature and semi-structured interviews with supply chain enablers and intermediary (traders, government officials, local mandis or local markets) operating in Uttarakhand region, India. Data were collected from 1100 supply chain entities (including agri-farmers, partners and enablers across the supply chain) using semi-structured interviews. Twenty five items emerged as significantly influencing the adoption of sustainable agribusiness practices among farmers which have branched out of five factors. Further, it was found that low adoption capabilities and lack of uniform sustainable agribusiness policy were the major issues reported by the respondents. Identification of factors influencing adoption of sustainable agribusiness in Uttarakhand, India, can serve as a guiding tool for agribusiness regulators (government departments including Department of Rural Development), intermediaries (traders, NGOs, etc.). The examination of pertinent issues with the focus on major support required, bottlenecks in adoption, key benefits of sustainable agribusiness activities and most common sustainable practices is expected to provide a new understanding about the sustainable agribusiness-based issues in India.
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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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.
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 transformation i...
Kantemur, A, Tak, J, Siyari, P, Abdelrahman, AH, Krunz, M & Xin, H 2020, 'A Novel Compact Reconfigurable Broadband Antenna for Cognitive Radio Applications', IEEE Transactions on Antennas and Propagation, vol. 68, no. 9, pp. 6538-6547.
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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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Tumour tissue-based information is li