Abbasipour, M, Milimonfared, J, Yazdi, SSH & Rouzbehi, K 2020, 'Power injection model of IDC-PFC for NR-based and technical constrained MT-HVDC grids power flow studies', ELECTRIC POWER SYSTEMS RESEARCH, vol. 182.
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Abdin, Z, Zafaranloo, A, Rafiee, A, Merida, W, Lipinski, W & Khalilpour, KR 2020, 'Hydrogen as an energy vector', RENEWABLE & SUSTAINABLE ENERGY REVIEWS, vol. 120.
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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.
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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, 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.
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© 2020 by the authors. One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.
Abdulkareem, SA, Augustijn, E-W, Filatova, T, Musial, K & Mustafa, YT 2020, 'Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning.', PLoS One, vol. 15, no. 1, pp. e0226483-e0226483.
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Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agent-based modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socio-environmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.
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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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 (Switzerland), vol. 12, no. 3, pp. 1-19.
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© 2020 by the authors. 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 Systeme Hydrologique Europeen 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 (Basel, Switzerland), vol. 20, no. 9.
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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 (2017-2019), a...
Abuhilaleh, M, Li, L & Hossain, MJ 2020, 'Power management and control coordination strategy for autonomous hybrid microgrids', IET Generation, Transmission and 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.
Abuzied, SM & Pradhan, B 2020, 'Hydro-geomorphic assessment of erosion intensity and sediment yield initiated debris-flow hazards at Wadi Dahab Watershed, Egypt', Georisk.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. This study attempts to assess slope and channel erosion for modelling their implications on debris-flow occurrences in Wadi Dahab Watershed (WDW). Remote sensing and Geographic Information System (GIS) were integrated to appraise erosion rates from a hillslope and channel storage throughout WDW. A mass-wasting database was built initially for modelling hazard zones and validating the final map using a bivariate statistical analysis. An Erosion Hazard Model (EHM) was developed to evaluate the erosion intensity and sediment yield throughout WDW and to prognosticate the hazard zones due to debris-flows. The EHM was developed based on hydrological and geomorphic controls which are responsible for disintegrating bedrocks, delivering detritus downslopes, and accelerating debris through channels. Multi-source datasets, including topographic and geologic maps, climatic, satellite images, aerial photographs, and field-based datasets, were used to derive factors associated with the hydro-geomorphic processes. A spatial prediction of erosion intensity was obtained by the integration of both static and dynamic factors generated hazards in GIS platform. The erosion intensity map classifies WDW relatively to five intensity zones in which the most hazardous zones are distributed in steep sloping terrains and structurally controlled channels covered by metamorphic and clastic rocks. The erosion intensity map was correlated and tested against the debris-flows dataset which was not used during the spatial modelling process. The statistical correlation analysis has confirmed that the debris-flow locations increase exponentially in the high erosion intensity zones. The holistic integration approach provides the promising model for forecasting critical zones prone to erosion intensity and their associated hazards in WDW.
Adak, C, Chaudhuri, BB, Lin, CT & Blumenstein, M 2020, 'Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis', IEEE Transactions on Information Forensics and Security.
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IEEE In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly. Such within-writer variation throws a challenge for automatic writer inspection, where the state-of-the-art methods do not perform well. To deal with intra-variability, we analyze the idiosyncrasy in individual handwriting. We identify/verify the writer from highly idiosyncratic text-patches. Such patches are detected using a deep recurrent reinforcement learning-based architecture. An idiosyncratic score is assigned to every patch, which is predicted by employing deep regression analysis. For writer identification, we propose a deep neural architecture, which makes the final decision by the idiosyncratic score-induced weighted average of patch-based decisions. For writer verification, we propose two algorithms for patch-fed deep feature aggregation, which assist in authentication using a triplet network. The experiments were performed on two databases, where we obtained encouraging results.
Afroz, F & Braun, R 2020, 'Energy-efficient MAC protocols for wireless sensor networks: a survey', International Journal of Sensor Networks, vol. 33, no. 3, pp. 150-173.
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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.
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Ahmad, HA, Ni, SQ, 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.
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© 2020 Elsevier Ltd Anaerobic ammonium oxidation (anammox) process appears a suitable substitute to nitrification-denitrification at a lower C/N ratios. Anammox is a chemolithoautotrophic process, belong to phylum Planctomycetes, and they are slow growing bacteria. Different strategies, e.g., biofilm formation, granulation and gel immobilization, have been applied to maintain a critical mass of bacterial cells in the system by avoiding washout from the bioreactor. Gel immobilization of anammox appears the best alternative to the natural process of biofilm formation and granulation. Polyvinyl alcohol-sodium alginate, polyethylene glycol, and waterborne polyurethane are the most reported materials used for the entrapment of anammox bacteria. However, dissolution of the gel beads refrains its application for long term bioprocess. Magnetic powder could coat on the surface of the beads which may increase the mechanical strength and durability of pellets. Application and problem of immobilization technology for the commercialization of this technology also addressed.
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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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.
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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.
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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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.
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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.
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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.', J Clin Endocrinol Metab.
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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+ from the prospective Dubbo Osteoporosis Epidemiology Study followed from 2000 to 2018 for incident fracture. Clinical data, appendicular lean mass/ht2, BMD, quadriceps strength/ht (QS), timed get-up-go (TGUG), sit-to-stand (5xSTS), and gait speed (GS) measured biennially. Rates of decline in muscle parameters were calculated using ordinal least square regression and fracture risk 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 (HRs ranging from 2.1 (95%CI:1.5-2.9) for GS to 2.7 (1.9-3.6) for 5xSTS) while in men, only the decline in GS was associated with fracture risk (HR: 3.4 (1.8-6.3)). Baseline performance and strength were also associated with increased fracture risk in men (HRs ranging from 1.8 (1.1-3.0) for QS to 2.5 (1.5-4.1) for TGUG) but not in women. CONCLUSION: Rate of decline in physical performance in both genders, and baseline strength and performance in men contributed independently to fracture risk. 5xSTS and GS were the tests most consistently associated with fractures. Further studies are required to determine whether muscle strength/performance improve the predictive accuracy of fracture prediction models.
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.
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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.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. This paper examines human auditory interaction with an architectural design hypothesized to decrease users’ vocal effort and thus enhance their speech privacy. This detailed design increased sound scattering in semi-enclosed meeting rooms within open plan offices. To achieve desirable speech intelligibility, a live sound environment is strongly recommended for meeting rooms. The research explores the hypothesis that by adding early reflections to the direct sound energy with an integrated design, the speaker as a self-listener might benefit from perceiving their own voice with more clarity. This can cause adaptive changes to subconscious vocal effort and increase the corresponding speech privacy of the space. An architecture-driven talker-quality experiment in a natural situation has been conducted in two rounds and in two different acoustic environments with 20 participants. The results implied the importance of human visual and spatial perception of privacy over auditory interaction with the environment on decreasing vocal effort. Such factors could thus be considered within the architectural design process.
Alfouneh, M, Ji, J & Luo, Q 2020, 'Damping design of harmonically excited flexible structures with graded materials to minimize sound pressure and radiation', Engineering Optimization.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. Topology optimization is an effective method in the design of acoustic media. This article presents optimization for graded damping materials to minimize sound pressure at a reference point or sound power radiation under harmonic excitation. The Helmholtz integral equation is used to calculate an acoustic field to satisfy the Sommerfeld conditions. The equation of motion is solved using a unit dynamic load method. Formulations for the sound pressure or sound power radiation in an integral form are derived in terms of mutual kinetic and strain energy densities. These integrals lead to novel physical response functions for solving the proposed optimization problem to design graded damping materials. The response function derived for individual frequency is utilized to solve the multi-objective optimization problem of minimizing sound pressure at the reference point for excitations with a range of frequencies. Numerical examples are presented to verify the efficiency of the present formulations.
Alfouneh, M, Ji, J & Luo, Q 2020, 'Optimal design of multi-cellular cores for sandwich panels under harmonic excitation', Composite Structures, vol. 248.
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© 2020 Elsevier Ltd Sandwich panels with cellular cores are increasingly used in engineering due to their superb dynamic performance. In this type of structure, core design significantly affects its mechanical property. This article is to study optimal design of a multi-cellular core to minimize dynamic response of the sandwich panel under harmonic excitation by use of topology optimization. In this study, structural dynamic responses to harmonic excitation are discussed and formulations of the dynamic response in terms of strain and kinetic energy densities are derived. Topology optimization with multi-fractional volume constraint is conducted for multi-cellular core design to minimize the dynamic response of the sandwich panel under harmonic excitation. The optimization to minimize or maximize the dynamic responses are discussed in optimal core designs of sandwich panels. A multi-objective optimisation problem is also considered to optimally suppress harmonic vibrations with a range of several frequencies. Numerical examples are presented to validate the derived formulations and to optimally design multi-cellular cores for sandwich panels to achieve better dynamic performance.
Alghamdi, K & Braun, R 2020, 'Software Defined Network (SDN) and OpenFlow Protocol in 5G Network', Communications and Network, vol. 12, no. 01, pp. 28-40.
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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.
Alismaili, SZ, Li, M, Shen, J, Huang, P, He, Q & Zhan, W 2020, 'Organisational-Level Assessment of Cloud Computing Adoption: Evidence from the Australian SMEs', JOURNAL OF GLOBAL INFORMATION MANAGEMENT, vol. 28, no. 2, pp. 73-89.
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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.
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Almotairy, SM, Boostani, AF, Hassani, M, Wei, D & Jiang, ZY 2020, 'Mechanical Properties of Aluminium Metal Matrix Nanocomposites Manufactured by Assisted-Flake Powder Thixoforming Process', Metals and Materials International.
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© 2019, The Korean Institute of Metals and Materials. Abstract: This study discusses the superior effect of thixoforming process on enhancing the tensile properties of aluminium matrix composite produced using flake metallurgy route. The flake metallurgy process was utilised to manufacture aluminium matrix composites followed by thixoforming process. Microstructural investigations carried out using transmission electron microscope have shown the synergic effect of thixoforming process on rendering uniform distribution of SiC nanoparticles associated with lower porosity content. X-ray diffraction characterisations have revealed the promising effect of uniform dispersion of SiC nanoparticles on restricting the grain growth of aluminium matrix within nanoscale regime (90 nm) even at high semi-solid thixoforming temperatures (575 °C). The achieved results of tensile tests have shown a profound effect of flake metallurgy of aluminium powder through dual speed ball milling. These results are higher than those achieved by low speed and high speed even with higher SiC content. This was attributed to the uniform confinement of SiC nanoparticles within the samples produced using flake-assisted forming process compared to the ones manufactured using ball milling-assisted processes. Graphic abstract: [Figure not available: see fulltext.].
Almotairy, SM, Fadavi Boostani, A, 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.
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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.
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Alshahrani, AA, Algamdi, MS, Alsohaimi, IH, Nghiem, LD, Tu, KL, Al-Illwajfeh, AE & Panhuis, MIH 2020, 'The rejection of mono- and di-valent ions from aquatic environmental by MWNT/chitosan buckypaper composite membranes: Influences of chitosan concentrations', SEPARATION AND PURIFICATION TECHNOLOGY, vol. 234.
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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, TM 2020, 'Grid-connected renewable energy sources: Review of the recent integration requirements and control methods', JOURNAL OF CLEANER PRODUCTION, vol. 253.
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Altaee, A 2020, 'Organic Fouling in Forward Osmosis: A Comprehensive Review', Water, vol. 12, no. 5.
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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.
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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, Khalaifat, N & Zhou, J 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.
Altaee, A, Khlaifat, N, Zhou, J & Huang, Y 2020, 'A review of the key sensitive parameters on the aerodynamic performance of a horizontal wind turbine using Computational Fluid Dynamics modelling', AIMS Energy, vol. 8, no. 3, pp. 493-524.
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Renewable energy technologies are receiving much attention to replacing power plants operated by fossil and nuclear fuels. Of all the renewable technologies, wind power has been successfully implemented in several countries. There are several parameters in the aerodynamic characteristics and design of the horizontal wind turbine. This paper highlights the key sensitive parameters that affect the aerodynamic performance of the horizontal wind turbine, such as environmental conditions, blade shape, airfoil configuration and tip speed ratio. Different turbulence models applied to predict the flow around the horizontal wind turbine using Computational Fluid Dynamics modeling are reviewed. Finally, the challenges and concluding remarks for future research directions in wind turbine design are discussed.
Altaee, A, Khlaifat, N, Zhou, J, Huang, Y & Braytee, A 2020, 'Optimization of a Small Wind Turbine for a Rural Area: A Case Study of Deniliquin, New South Wales, Australia', Energies, vol. 13.
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The performance of a wind turbine is profoundly affected by wind conditions. Small wind turbines usually achieve the demand for electricity in rural areas. The shape of the blade greatly influences the performance of the wind turbine. The present study aims to optimize the performance of a 20 kW horizontal-axis wind turbine (HAWT) under local wind conditions at Deniliquin, New South Wales, Australia. ANSYS Fluent was used to investigate the aerodynamic performance of the 20 KW HAWT. The effects of four Reynolds Averaged Navier Stokes (RANS) turbulence models on predicting the flow over the wind turbine under separation condition were examined. Transition SST model had the best agreement with NREL CER data, which was used to investigate the mechanical output at different rotational speeds and variable pitch angles. Then the aerodynamic shape of the rotor of the wind turbine was optimized to maximize the annual energy production (AEP) in the Deniliquin region. Statistical wind analysis was applied to define the Weibull function and scale parameters which were 2.096 and 5.042 m/s, respectively. HARP_Opt was enhanced with design variables concerning the shape of the blade, rated rotational speed, and pitch angle. Pitch angle remained at 0ᵒ while the rising wind speed improved rotor speed to 148.4482 rpm at rated speed. This optimization improved the AEP rate by 9.068% when compared to the original NREL design.
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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© 2013 IEEE. Agile software development performance depends on active communication. Active communication is an arduous task when agile teams are geographically distributed. Agile enterprise architecture was reported to enhance such active communication and performance. There is little empirical evidence on how agile enterprise architecture can enhance communication and performance of geographically distributed software development. This paper contributes to this research gap by empirically examining the relationships between agile enterprise architecture, active communication, and performance (on-time completion, on-budget completion, software functionality, and software quality). Using a quantitative data analysis approach, the PLS results of survey responses of 160 research participants suggest that agile enterprise architecture has positive effects on active communication, on-budget completion, functionality, and quality. The results also indicate that communication efficiency has positive effects on on-time and on-budget completion; while communication effectiveness has positive effects on functionality, quality, and on-budget completion.
Al-Zubi, M & Sanagavarapu, A 2020, 'Implantable Biosensor Interface Platform for Monitoring of Atherosclerosis', IEEE Sensors Letters.
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The drug-eluting stents (DESs) have been considered as an effective technique to reduce the severity of atherosclerotic stenosis. Recent technological advances have opened the door for developing new and innovative smart cardiovascular stents by integrating various electronics components and sensors to improve health monitoring and diagnosis. Rupture of atherosclerotic plaque represents the major cause of cardiovascular disease (CVD) such as heart attacks due to blockages of the arterial lumen. Pentraxin-3 (PTX3) is a novel localized inflammatory biomarker associated with the development of atherosclerosis and plaque vulnerability. In this paper, we propose a biosensor interface platform for monitoring the plaque vulnerability via detection and prediction of the inflammatory biomarker (PTX3). We propose mathematical and stochastic models using molecular communication paradigm for detection of Pentraxin-3 (PTX3) molecules in the atherosclerotic arterial wall using biosensor attached to a vascular stent inside the artery. The proposed platform and models can help in the localized sensing of the atherosclerotic biomarkers for monitoring of the plaque progression and for early warning of certain disorders such as heart attack.
Amin, DB, Tavakoli, J, Freeman, BJC & Costi, JJ 2020, 'Mechanisms of Failure Following Simulated Repetitive Lifting: A Clinically Relevant Biomechanical Cadaveric Study', Spine, vol. 45, no. 6, pp. 357-367.
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© 2018 Wolters Kluwer Health, Inc. All rights reserved. Study Design.A biomechanical analysis correlating internal disc strains and tissue damage during simulated repetitive lifting.Objective.To understand the failure modes during simulated safe and unsafe repetitive lifting.Summary of Background Data.Repetitive lifting has been shown to lead to lumbar disc herniation (LDH). In vitro studies have developed a qualitative understanding of the effect of repetitive loading on LDH. However, no studies have measured internal disc strains and subsequently correlated these with disc damage.Methods.Thirty human cadaver lumbar functional spinal units were subjected to an equivalent of 1 year of simulated repetitive lifting under safe and unsafe levels of compression, in combination with flexion (13-15°), and right axial rotation (2°) for 20,000 cycles or until failure. Safe or unsafe lifting were applied as a compressive load to mimic holding a 20kg weight either close to, or at arm's length, from the body, respectively. Maximum shear strains (MSS) were measured, and disc damage scores were determined in nine regions from axial post-Test magnetic resonance imaging (MRI) and macroscopic images.Results.Twenty percent of specimens in the safe lifting group failed before 20,000 cycles due to endplate failure, compared with 67% in the unsafe group. Over half of the specimens in the safe lifting group failed via either disc protrusion or LDH, compared with only 20% via protrusion in the unsafe group. Significant positive correlations were found between MRI and macroscopic damage scores in all regions (rs>0.385, P<0.049). A significant positive correlation was observed in the left lateral region for MSS versus macroscopic damage score (rs=0.486, P<0.037) and MSS versus failure mode (rs=0.724, P=0.018, only specimens with disc failure). Pfirrmann Grade 3 discs were strongly associated with subsequent LDH (P=0.003).Conclusion.Increased shear strains were observed in the contralater...
Amjadipour, M, Su, D & Iacopi, F 2020, 'Graphitic-Based Solid-State Supercapacitors: Enabling Redox Reaction by In Situ Electrochemical Treatment', BATTERIES & SUPERCAPS.
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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.
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Anshar, M & Williams, M-A 2020, 'Simplified Pain Matrix Method for Artificial Pain Activation Embedded into Robot Framework', INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS.
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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.
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© 2020 Elsevier B.V. A GIS-based hybrid approach for gully erosion susceptibility mapping (GESM) in the Biarjamand watershed in Iran is presented. A database comprised of 15 geo-environmental factors (GEFs) was compiled and used to predict the spatial distribution of 358 gully locations; 70% (251) of which were extracted for training and 30% (107) for validation. A Dempster-Shafer (DS) statistical model was employed to map susceptibility. Next, the results of four kernels (binary logistic, reg logistic, binary logitraw, and reg linear) of a boosted regression tree (BRT) model were combined to increase the efficiency and accuracy of the mapping. Area under receiver operating characteristics (AUROC), true skill statistic (TSS) and efficiency (E) metrics were used to rank the five validated models. The results show that elevation and distance to road play crucial roles in gullying. Integrating BRT and DS enhanced prediction accuracy. Among the four BRT kernels, binary logistic performed best (AUROC of 0.886, TSS of 0.854 and E equal to 0.880). The worst results were produced by the individual DS model (AUROC = 0.849, TSS = 0.774 and E = 0.834). The hybrid binary logistic-BRT and DS map categorized 14.50% of the study area as having very-low susceptibility, 16.99% low susceptibility, 22.77% moderate susceptibility, 24.12% high susceptibility, and 21.59% very-high susceptibility.
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.
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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.
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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.
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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.
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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.
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© 2020 Elsevier B.V. Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total of 96 locations of LS and 12 LS conditioning factors (LSCFs) were collected. Each feature in the LS inventory map (LSIM) was randomly assigned to one of four groups or folds, each comprising 25% of cases. The novel ensemble model was trained using 75% (3 folds) and validated with the remaining 25% (1 fold) in a four-fold cross-validation (CV) system, which is used to control for the effects of the random selection of the training and validation datasets. LSCFs for LS prediction were selected using the information-gain ratio and multi-collinearity test methods. Factor significance was evaluated using a random forest (RF) model. Groundwater drawdown, land use and land cover, elevation, and lithology were the most important LSCFs. Using the k-fold CV approaches, twelve LS susceptibility maps (LSSMs) were prepared as each fold employed all three models (ANN-bagging, ANN, and bagging). The LS susceptibility mapping showed that between 5.7% and 12.6% of the plain had very high LS susceptibility. All three models produced LS susceptibility maps with acceptable prediction accuracies and goodness-of-fits, but the best maps were produced by the ANN-bagging ensemble method. Overall, LS risk was highest in agricultural areas with high groundwater drawdown in the flat l...
Arabameri, A, Tiefenbacher, JP, Blaschke, T, Pradhan, B & Bui, DT 2020, 'Morphometric analysis for soil erosion susceptibility mapping using novel gis-based ensemble model', Remote Sensing, vol. 12, no. 5.
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© 2020 by the author. Licensee MDPI, Basel, Switzerland. The morphometric characteristics of the Kalvari 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 Kalvari 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 ...
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.
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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.
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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.
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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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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.
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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© The Royal Society of Chemistry 2020. Metal-organic frameworks (MOFs) have high porosity, large surface area, and tunable functionality and have been widely used for drug loading. The aim of this study was to exploit unique features of zeolitic imidazolate framework-8 (ZIF8) to develop an innovative composite microcarrier (MC) for human mesenchymal stem cells (hMSCs) adhesion and proliferation. ZIF8 MCs were prepared by immobilization of polydopamine/polyethyleneimine (PDA/PEI) and ZIF8 on the surface of polystyrene beads. The chemical properties of MCs such as coating stability and homogeneity were characterized by different techniques such as ATR-FTIR, XRD, EDX, SEM, and contact angle. The prepared MCs were tested using human adipose-derived mesenchymal stem cells (hADSCs) in both static and dynamic conditions, and results were compared to a commercially available MC (Star-Plus), polydopamine coated MCs and amine-functionalized MCs as a control. Results show that PDA/PEI/ZIF8 coated MCs (in short: ZIF8 MCs) provides an excellent biocompatible environment for hADSCs adhesion and growth. In conclusion, ZIF8 MCs represent suitable and low-cost support for hADSCs culture and expansion, which can maximize cell yield and viability while preserving hADSCs multipotency. The present findings have revealed this strategy has the potential for chemical and topographical modification of MCs in tissue engineering applications.
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.
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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, KC, 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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Awan, I, Younas, M & Hussain, F 2020, 'Emerging challenges and frontiers in cloud computing', Concurrency Computation, vol. 32, no. 1.
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Ayanian, N, Robuffo Giordano, P, Fitch, R, Franchi, A & Sabattini, L 2020, 'Guest editorial: special issue on multi-robot and multi-agent systems', Autonomous Robots, vol. 44, no. 3-4, pp. 297-298.
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Azadi, M, Emrouznejad, A, Ramezani, F & Hussain, FK 2020, 'Efficiency measurement of cloud service providers using network data envelopment analysis', IEEE Transactions on Cloud Computing.
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IEEE An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements (Duan, 2017). To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, variable returns to scale (VRS), the non-oriented network slacks-based measure (SBM) model and input-oriented and output-oriented SBM models are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models.
Azeez, OS, Pradhan, B & Jena, R 2020, 'Urban tree classification using discrete-return LiDAR and an object-level local binary pattern algorithm', GEOCARTO INTERNATIONAL.
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Urban trees have the potential to mitigate some of the harm brought about by rapid urbanization and population growth, as well as serious environmental degradation (e.g. soil erosion, carbon pollution and species extirpation), in cities. This paper presents a novel urban tree extraction modelling approach that uses discrete laser scanning point clouds and object-based textural analysis to (1) develop a model characterised by four sub-models, including (a) height-based split segmentation, (b) feature extraction, (c) texture analysis and (d) classification, and (2) apply this model to classify urban trees. The canopy height model is integrated with the object-level local binary pattern algorithm (LBP) to achieve high classification accuracy. The results of each sub-model reveal that the classification of urban trees based on the height at 47.14 (high) and 2.12 m (low), respectively, while based on crown widths were highest and lowest at 22.5 and 2.55 m, respectively. Results also indicate that the proposed algorithm of urban tree modelling is effective for practical use.
Aziminezhad, M, Mardi, S, Hajikarimi, P, Nejad, FM & 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.
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Azizi, N, CheshmehBeigi, HM & Rouzbehi, K 2020, 'Optimal placement of direct current power system stabiliser (DC-PSS) in multi-terminal HVDC grids', IET Generation, Transmission and Distribution, vol. 14, no. 12, pp. 2315-2322.
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© The Institution of Engineering and Technology 2020. This study focuses on the selection of the most effective location of direct current power system stabiliser (DC-PSS) in multi-terminal high-voltage direct current (MT-HVDC) grids that is applied to voltage source converter (VSC) to voltage stability enhancement and oscillation damping. Due to the adverse interaction between having multiple number of DC-PSS, it is necessary to select the best location for installing DC-PSS in MT-HVDC grid. In this study, the participation factor (PF) method is used. This method assists to find which power converters are more sensitive and have a major impact on the instability of the entire system. In this approach, the whole of the system is modelled as a multi-input multi-output (MIMO) dynamic system. PFs related to each power converter are intended by the oscillatory mode sensitivity analysis against the components of the MIMO matrix of the system. The PF analysis determines which VSC station is more participating in the small-signal instability of the system. In order to endorse the efficiency of the proposed method, time-domain MATLAB simulations are conducted in Cigré MT-HVDC grid benchmark.
Azizivahed, A, Arefi, A, Ghavidel Jirsaraie, 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, pp. 1-1.
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Azizivahed, A, Razavi, SE, Arefi, A, Jabbari Ghadi, M, 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, pp. 1-1.
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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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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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Bajan, S & Hutvagner, G 2020, 'RNA-Based Therapeutics: From Antisense Oligonucleotides to miRNAs', CELLS, vol. 9, no. 1.
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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.
Balogun, AL, 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.
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© 2020, by the authors. 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. Howeve...
Bao, T, Damtie, MM, Hosseinzadeh, A, Wei, W, Jin, J, Hoang, NPV, 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.
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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, pp. 21:1-21:1.
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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Barzegarkhoo, R, Siwakoti, YP, Vosoughi, N & Blaabjerg, F 2020, 'Six-Switch Step-up Common-Grounded Five-Level Inverter with Switched-Capacitor Cell for Transformerless Grid-Tied PV Applications', IEEE Transactions on Industrial Electronics, pp. 1-1.
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Bazaz, SR, Mashhadian, A, Ehsani, A, Saha, SC, Kruger, T & Warkiani, ME 2020, 'Computational inertial microfluidics: a review', LAB ON A CHIP, vol. 20, no. 6, pp. 1023-1048.
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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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Behmanesh, R, Rahimi, I & Gandomi, AH 2020, 'Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study', Archives of Computational Methods in Engineering.
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© 2020, CIMNE, Barcelona, Spain. Many optimization problems encountered in the real-world have more than two objectives. To address such optimization problems, a number of evolutionary many-objective optimization algorithms were developed recently. In this paper, we tested 18 evolutionary many-objective algorithms against well-known combinatorial optimization problems, including knapsack problem (MOKP), traveling salesman problem (MOTSP), and quadratic assignment problem (mQAP), all up to 10 objectives. Results show that some of the dominance and reference-based algorithms such as non-dominated sort genetic algorithm (NSGA-III), strength Pareto-based evolutionary algorithm based on reference direction (SPEA/R), and Grid-based evolutionary algorithm (GrEA) are promising algorithms to tackle MOKP and MOTSP with 5 and 10 while increasing the number of objectives. Also, the dominance-based algorithms such as MaOEA-DDFC as well as the indicator-based algorithms such as HypE are promising to solve mQAP with 5 and 10 objectives. In contrast, decomposition based algorithms present the best on almost problems at saving time. For example, t-DEA displayed superior performance on MOTSP for up to 10 objectives.
Belotti, Y, McGloin, D & Weijer, C 2020, 'Chemotaxis overrides Barotaxis during Directional Decision-Making in Dictyostelium discoideum'.
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Abstract Neutrophils and dendritic cells have, besides their well characterised chemotactic movement responses, been shown to be able to detect and respond to local differences in hydraulic resistance ( barotaxis ). Furthermore, for neutrophils, it has been suggested that barotaxis overrides chemotaxis. Here, we investigate whether Dictyostelium cells also respond to hydraulic resistance or primarily to chemical gradients using an asymmetric bifurcating micro-channel. This channel design allows us to decouple hydraulic and chemical stimuli, by providing a choice between moving up a chemical gradient or down a chemical gradient into a channel with 100 times lower hydraulic resistance. Under these conditions chemotaxis always overrides barotaxis. Cells confronted by a microchannel bifurcation are observed to often partially split their leading edge and to start moving into both channels. Cells in steeper cAMP gradients, that move faster, split more readily. The decision to retract the pseudopod moving away from the cAMP source is made when the average velocity of the pseudopod moving up the cAMP gradient is 20% higher than the average velocity of the pseudopod moving down the gradient. Surprisingly, this decision threshold is independent of the steepness of the cAMP gradient and speed of movement. It indicates that a critical force imbalance threshold underlies the repolarisation decision. Significance Statement We investigate the directional ‘decision-making’ of Dictyostelium discoideum cells migrating within engineered micro-channels harbouring asymmetric bifurcations. Unlike neutrophils and immature dendritic cells, Dictyostelium cells strongly prioritise chemical over barotactic guidance cues. Cells in steeper cAMP gradients migrate at higher speeds, split their leading edges more readily when confronted with a bifurcation in the channel. The decision to retract a pseudopod pointing in an unfavourable direction occurs when a critical tension gradient between tw...
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.
Beydoun, G, Hoffmann, A, Valencia-García, R, Shen, J & Gill, A 2020, 'Towards an assessment framework of reuse: a knowledge level analysis approach', Complex and Intelligent Systems.
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.
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Bian, Y, Wang, D, Liu, X, Yang, Q, Liu, Y, Wang, Q, Ni, BJ, Li, H & Zhang, Y 2020, 'The fate and impact of TCC in nitrifying cultures', Water Research, vol. 178.
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© 2020 Elsevier Ltd 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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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. One of the well-known methods for evaluating Heterogeneous wireless multimedia sensor networks (HWMSNs) in Internet of Things have drawn attention of the research community because this type of networks possesses great advantages of both coverage and performance. One of the most fundamental issues in HWMSNs is the barrier coverage problem which evaluates the surveillance capability of the network systems, especially those designed for security purposes. Among multiple approaches to solve this issue, finding the minimal exposure path (MEP), which corresponds to the worst-case coverage of the network is the most popular and efficient way. However, the MEP problem in HWMSNs (hereinafter heterogeneous multimedia MEP or HM-MEP) is specifically complex and challenging with the unique features of the HWMSNs. Thus, the problem is then converted into numerical functional extreme with high dimension, non-differential and non-linearity. Adapting to these features, two efficient meta-heuristic algorithms, Hybrid Evolutionary Algorithm (HEA) and Gravitation Particle Swarm Optimization (GPSO) are proposed for solving the problem. The HEA is a hybrid evolutionary algorithm in combination with local search while the GPSO is a novel particle swarm optimization based on the gravity force theory. Experimental results on extensive instances indicate that the proposed algorithms are suitable for the HM-MEP problem and perform well in term of both solution accuracy and computation time compared to existing approaches.
Bishop, AN & Moral, PD 2020, 'An Explicit Floquet-Type Representation of Riccati Aperiodic Exponential Semigroups', International Journal of Control.
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The article presents a rather surprising Floquet-type representation of
time-varying transition matrices associated with a class of nonlinear matrix
differential Riccati equations. The main difference with conventional Floquet
theory comes from the fact that the underlying flow of the solution matrix is
aperiodic. The monodromy matrix associated with this Floquet representation
coincides with the exponential (fundamental) matrix associated with the
stabilizing fixed point of the Riccati equation. The second part of this
article is dedicated to the application of this representation to the stability
of matrix differential Riccati equations. We provide refined global and local
contraction inequalities for the Riccati exponential semigroup that depend
linearly on the spectral norm of the initial condition. These refinements
improve upon existing results and are a direct consequence of the Floquet-type
representation, yielding what seems to be the first results of this type for
this class of models.
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.
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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...
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.
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© 2020 Creating a reliable groundwater vulnerability map is a key solution for protecting the groundwater resources and further planning in coastal aquifers. The GALDIT framework, which is an abbreviation for six parameters of Groundwater occurrence, Aquifer hydraulic conductivity, Level of groundwater above sea level, Distance from the shoreline, Impact of the existing status of seawater intrusion, and Thickness of the aquifer, is a well-known framework for evaluating the groundwater vulnerability in coastal zones. In this study, two meta-heuristic algorithms of Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA) were proposed to optimize the weights of GALDIT framework. This framework was tested in the Gharesoo-Gorgan Rood coastal aquifer located in the north of Iran. The GALDIT index illustrated poor evaluation of vulnerability to seawater intrusion. Contrariwise, GALDIT-GWO and GALDIT-GA frameworks provided results that are more reasonable. Both vulnerability maps showed a close similarity in terms of vulnerability to seawater intrusion. The vulnerability maps demonstrated that the west and northwest parts of the study area suffer from seawater intrusion. Additionally, the values of Spearman's rank correlation coefficient between the indices of GALDIT, GALDIT-GWO and GALDIT-GA and the parameter of Cl/HCO3 were obtained as 0.31, 0.53, and 0.49 and the corresponding values for the parameter of TDS were obtained 0.45, 0.64 and 0.60, respectively. Therefore, it can be concluded that the proposed optimization models are able to provide accurate results. Furthermore, these models reduce the subjectivity and increase the capability of the GALDIT index.
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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© Copyright © 2020 Bradbury, Wu, Choi, Rowan, Zhang, Poole, Lauko and Chou. A lack of gravity experienced during space flight has been shown to have profound effects on human physiology including muscle atrophy, reductions in bone density and immune function, and endocrine disorders. At present, these physiological changes present major obstacles to long-term space missions. What is not clear is which pathophysiological disruptions reflect changes at the cellular level versus changes that occur due to the impact of weightlessness on the entire body. This review focuses on current research investigating the impact of microgravity at the cellular level including cellular morphology, proliferation, and adhesion. As direct research in space is currently cost prohibitive, we describe here the use of microgravity simulators for studies at the cellular level. Such instruments provide valuable tools for cost-effective research to better discern the impact of weightlessness on cellular function. Despite recent advances in understanding the relationship between extracellular forces and cell behavior, very little is understood about cellular biology and mechanotransduction under microgravity conditions. This review will examine recent insights into the impact of simulated microgravity on cell biology and how this technology may provide new insight into advancing our understanding of mechanically driven biology and disease.
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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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, JC, 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.
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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', Geotechnique, vol. 70, no. 2, pp. 161-172.
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© 2020 ICE Publishing. All rights reserved. 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.
C. A. Tirado Cortes, H. Chen, D. Sturnieks, J. Garcia Marin, S. Lord & C. Lin 2020, 'Evaluating Balance Recovery Techniques for Users Wearing Head-Mounted Display in VR', IEEE Transactions on Visualization and Computer Graphics, pp. 1-1.
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Room-scale 3D position tracking allows users to explore the virtual environment by physically walking. However, since the eyesight of the user is blocked by a Head-Mounted Display (HMD), the user might lose her balance because of bumping into real-world obstacles or shifting the body weight onto virtual objects that are inexistent in the real-world. This paper investigates assistive fall recovery methods under the assumption that the onset of the fall is given. Our experiment simulated the forward loss-of-balance with a tether-release protocol. A magnetic lock attached to a counter-weight was released while the subject was in a static leaning posture and engaged in a secondary 3D object selection task. The experiment uses a two by two design that examines two assistive techniques, i.e. video-see-through and auditory warning, at two different timing, i.e. fall onset and 500ms prior to onset. The data from 17 subjects show that the video-see-through triggered 500ms before the onset of fall can effectively help users recover from falls. Surprisingly, the video-see-through at the fall onset has a significant negative impact on the fall recovery providing similar results to the baseline condition (no intervention).
Ca, Y, Tang, W, Li, L, Zhang, B, Zhan, L & Wang, Y 2020, 'Multi-mode adaptive local reactive power control method based on pv inverters in low voltage distribution networks', IET Generation, Transmission and 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, 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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Cai, Y, Pan, S, Wang, X, Chen, H, Cai, X & Zuo, M 2020, 'Measuring distance-based semantic similarity using meronymy and hyponymy relations', Neural Computing and Applications.
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© 2018, The Natural Computing Applications Forum. The assessment of semantic similarity between lexical terms plays a critical part in semantic-oriented applications for natural language processing and cognitive science. The optimization of calculation models is still a challenging issue for improving the performance of similarity measurement. In this paper, we investigate WordNet-based measures including distance-based, information-based, feature-based and hybrid. Among them, the distance-based measures are considered to have the lowest computational complexity due to simple distance calculation. However, most of existing works ignore the meronymy relation between concepts and the non-uniformity of path distances caused by various semantic relations, in which path distances are simply determined by conceptual hyponymy relation. To solve this problem, we propose a novel model to calculate the path distance between concepts, and also propose a similarity measure which nonlinearly transforms the distance to semantic similarity. In the proposed model, we assign different weights in accordance with various relations to edges that link different concepts. On basis of the distance model, we use five structure properties of WordNet for similarity measurement, which consist of multiple meanings, multiple inheritance, link type, depth and local density. Our similarity measure is compared against state-of-the-art WordNet-based measures on M&C dataset, R&G dataset and WS-353 dataset. According to experiment results, the proposed measure in this work outperforms others in terms of both Pearson and Spearman correlation coefficients, which indicates the effectiveness of our distance model. Besides, we construct six additional benchmarks to prove that the proposed measure maintains stable performance.
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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Canning, J & Ziyani, A 2020, 'Chirping fiber Bragg gratings within additively manufactured polymer packages', OPTICS LETTERS, vol. 45, no. 8, pp. 2235-2238.
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Cao, DL, Hong, G & Le, AT 2020, 'Applying chemical heat storage to saving exhaust gas energy in diesel engines: Principle, design and experiment', Journal of Energy Storage, vol. 28.
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© 2020 Elsevier Ltd Thermal energy storage has become more and more important to improving the overall efficiency of energy systems by utilising the wasted energy. This study was aimed to develop a chemical heat storage (CHS) system using magnesium hydroxide (Mg(OH)2) and its dehydration and hydration reactions to recover the thermal energy wasted by the exhaust gases in internal combustion (IC) engines. Experiments were conducted on a Diesel engine (D1146TI) to estimate the percentage of exhaust gas energy stored by CHS technology in the heat storage process. In this process, at 80% engine load, 61.4% of chemical material reacted and 5.05% heat energy of exhaust gas was stored in an hour. The percentage of the stored exhaust gas energy decreased with the engine loads. In the heat output process, in one of the proposed applications, the engine intake air was heated with the stored energy by hydrating MgO when the temperature of the inner wall of the reactor (T6) increased from 45 °C to 86.4 °C. The experimental results show that at the ambient temperature 23 °C, the intake air can be heated to the temperature 5.7 °C-17.3 °C higher than the ambient temperature. The applications of CHS to heating the engine, lubricant or batteries in hybrid vehicles are proposed.
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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© 2019 Elsevier Ltd This paper investigates the global exponential synchronization problem of delayed memristive neural networks (MNNs) with reaction–diffusion terms. First, by utilizing the pinning control technique, two novel kinds of control methods are introduced to achieve synchronization of delayed MNNs with reaction–diffusion terms. Then, with the help of inequality techniques, pinning control technique, the drive–response concept and Lyapunov functional method, two sufficient conditions are obtained in the form of algebraic inequalities, which can be used for ensuring the exponential synchronization of the proposed delayed MNNs with reaction–diffusion terms. Moreover, the obtained results based on algebraic inequality complement and improve the previously known results. Finally, two illustrative examples are given to support the effectiveness and validity of the obtained theoretical results.
Cao, Z, Ding, W, Wang, YK, Hussain, FK, Al-Jumaily, A & Lin, CT 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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Carmichael, CL, Wang, J, Nguyen, T, Kolawole, O, Benyoucef, A, De Mazière, C, Milne, A, Samuel, S, Gillinder, KR, 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, N, Haigh, K, Thoms, JAI, Toulmin, E, Curtis, DJ, Oxley, EP, Dickins, RA, Beck, D, Perkins, AC, 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.
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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.
Cetindamar Kozanoglu, 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, pp. 1-38.
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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.
Cetindamar Kozanoglu, D, Lammers, T, Kocaoglu, D & Zhang, Y 2020, 'The Anniversary Tribute of PICMET: 1989-2018', IEEE Transactions on Engineering Management, pp. 1-15.
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The Portland International Conference for Management
of Engineering and Technology (PICMET) has become a
world-leading organization in the field of management of engineering
and technology management (MET) since its inception in
1989. PICMET provides a strong platform for academics, industry
professionals, and government representatives to exchange new
knowledge in the field. To celebrate its 30-year journey, this article
examines 20 conferences organized by PICMET covering 6601
accepted papers in order to show the trends in MET research and
implementation through topics, authors, journals, and countries. In
addition to the analysis of the PICMET data, the article delves into
the past ten years (2009–2018) to carry out an in-depth bibliometric
analysis of the citations of more than 3000 PICMET papers available
at Scopus. The detailed analysis sheds light on how PICMET
has developed a rich network of researchers and practitioners
through its conferences over time. PICMET contributes to the
interdisciplinary nature of the MET field and is also affected by
the changes of the field. The article ends with key observations and
a few suggestions for further studies.
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.
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PURPOSE:This work has two related objectives. The first is to estimate the relative biological effectiveness of two radioactive heavy ion beams based on experimental measurements, and compare these to the relative biological effectiveness of corresponding stable isotopes to determine whether they are therapeutically equivalent. The second aim is to quantitatively compare the quality of images acquired postirradiation using an in-beam whole-body positron emission tomography scanner for range verification quality assurance. METHODS:The energy deposited by monoenergetic beams of 11 C at 350 MeV/u, 15 O at 250 MeV/u, 12 C at 350 MeV/u, and 16 O at 430 MeV/u was measured using a cruciform transmission ionization chamber in a water phantom at the Heavy Ion Medical Accelerator in Chiba (HIMAC), Japan. Dose-mean lineal energy was measured at various depths along the path of each beam in a water phantom using a silicon-on-insulator mushroom microdosimeter. Using the modified microdosimetric kinetic model, the relative biological effectiveness at 10% survival fraction of the radioactive ion beams was evaluated and compared to that of the corresponding stable ions along the path of the beam. Finally, the postirradiation distributions of positron annihilations resulting from the decay of positron-emitting nuclei were measured for each beam in a gelatin phantom using the in-beam whole-body positron emission tomography scanner at HIMAC. The depth of maximum positron-annihilation density was compared with the depth of maximum dose deposition and the signal-to-background ratios were calculated and compared for images acquired over 5 and 20 min postirradiation of the phantom. RESULTS:In the entrance region, the hboxRBE10 was 1.2 ± 0.1 for both 11 C and 12 C beams, while for 15 O and 16 O it was 1.4 ± 0.1 and 1.3 ± 0.1, respectively. At the Bragg peak, the RBE10 was 2.7 ± 0.4 for 11 C and 2.9 ± 0.4 for 12 C, while for 15 O and 16 O it was 2.7 ± 0.4 and 2.8 ± 0.4, respectively. In ...
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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Chaczko, Z, Kulbacki, M, Gudzbeler, G, Alsawwaf, M, Thai-Chyzhykau, I & Wajs-Chaczko, P 2020, 'Exploration of Explainable AI in Context of Human-Machine Interface for the Assistive Driving System', Lecture Notes in Artificial Intelligence, vol. Part II, no. 10752.
Chai, J, Tsang, IW & Chen, W 2020, 'Large Margin Partial Label Machine.', IEEE transactions on neural networks and learning systems.
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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.
Challa, S, Das, AK, Gope, P, Kumar, N, Wu, F & Vasilakos, AV 2020, 'Design and analysis of authenticated key agreement scheme in cloud-assisted cyber–physical systems', Future Generation Computer Systems, vol. 108, pp. 1267-1286.
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© 2018 Elsevier B.V. With advancements in engineering and science, the application dimensions of Cyber–Physical System (CPS) are increasing due to their improving efficiency, safety, reliability, usability and autonomy. By providing on-demand access to shared processing resources, cloud computing reduces infrastructure costs. Ensuring quality of service and information privacy and security is important in such environments. In this paper, we design a new authentication scheme related to the cloud-assisted CPS in two directions: (1) authentication between a user and a cloud server, and (2) authentication between a smart meter and a cloud server. In the former situation, any external party (user) can access the information stored in a cloud server provided that the user is legal and has the right to access information. In the later situation, a smart meter and a cloud server authentication is needed for secure communication of data stored in the cloud server. In both cases, both entities first mutually authenticate each other and only after successful authentication with the help of a trusted authority, establish a session key for their future secure communication. The proposed scheme deals with both the cases and provides high security as compared to other related works, which is shown through formal and informal security analysis. In addition, the mutual authentication using the widely-accepted Burrows–Abadi–Needham logic (BAN logic) and also formal security verification using the broadly-used Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool demonstrate further that the scheme is strong in security. Finally, the proposed scheme is shown to be efficient in terms of communication and computation costs as compared to those for other related existing schemes.
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 : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 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.
Chandran, D & Alammari, A 2020, 'Influence of Power Distance on Knowledge Sharing Adoption among Academic Staff in eLearning Virtual Communities in Saudi Arabia', Information Systems Frontiers.
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Knowledge sharing is a significant component of success in knowledge management. In Saudi Arabia, knowledge management is often lacking when it comes to knowledge sharing adoption, especially between academic staff. This research aims to investigate various factors of knowledge sharing adoption for eLearning communities in Saudi Arabia and to examine the effect of culture as a moderating role on the relationships between these factors and academics’ attitude. Therefore, a framework is aimed at sharing knowledge within the eLearning communities is developed. Data has been collected from public universities in Saudi Arabia. Partial Least Square approach has been applied to analyse the data. The results show individual factors (such as openness in communication, interpersonal trust) and technology acceptance factors (perceived usefulness and perceived ease of use) significantly influence knowledge sharing attitude, while the relationship between people self-motivation and knowledge sharing attitude is insignificant. Subjective norm and attitude significantly impact behavioral intention toward knowledge sharing adoption in Saudi universities’ eLearning communities.
Chandran, M, Bhadada, SK, Ebeling, PR, Gilchrist, NL, Khan, AH, Halbout, P, Lekamwasam, S, Lyubomirsky, G, Mitchell, PJ, Nguyen, TV & Tiu, KL 2020, 'IQ driving QI: the Asia Pacific Consortium on Osteoporosis (APCO): an innovative and collaborative initiative to improve osteoporosis care in the Asia Pacific', OSTEOPOROSIS INTERNATIONAL.
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Chang, Z, Long, G, Zhou, JL & Ma, C 2020, 'Valorization of sewage sludge in the fabrication of construction and building materials: A review', Resources, Conservation and Recycling, vol. 154.
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© 2019 Elsevier B.V. With increasing amount of sewage sludge becoming an urgent and inevitable issue for every country, its applications in the production of construction and building materials provide an alternative solution for sludge disposal and resource recovery. Similar to clay and Portland cement, the main oxides in sewage sludge are SiO2 (10–25 %), Al2O3 (5–10 %) and CaO (10–30 %) which are increased in sludge ash after incineration to 25–50 %, 10–20 % and 15–30 %. Therefore, this solid waste can be utilized not only as raw material for the production of eco-cement, bricks, ceramic materials and lightweight aggregates through sintering process, but also as supplementary admixtures in cementitious materials such as pozzolanic component, fine aggregate or filling material. By critically reviewing current utilizations of sewage sludge, it is feasible to replace up to 15 % natural raw materials with sewage sludge in cement production and the manufactured eco-cement clinkers show comparable performance to traditional Portland cement. Whilst as raw feed in the fabrication of bricks, ceramic materials and lightweight aggregates, 20 % of sewage sludge substitution is acceptable to produce good quality products (within 8 % firing shrinkage and 15 % water absorption). Though high content of organic matter in raw sludge causes a decrease in mechanical strength and delay in hydration process, controlled low-strength materials offer an innovative reuse with large amount of sludge. The immobilization of heavy metals in products prevents sewage sludge causing secondary environmental pollution. Furthermore, suggestions for future research are proposed in order to strengthen the high value-added applications of sewage sludge.
Changani, Z, Razmjou, A, Taheri-Kafrani, A, Warkiani, ME & Asadnia, M 2020, 'Surface modification of polypropylene membrane for the removal of iodine using polydopamine chemistry', CHEMOSPHERE, vol. 249.
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Chen, B, Yu, S, Yu, Y & Zhou, Y 2020, 'Acoustical damage detection of wind turbine blade using the improved incremental support vector data description', Renewable Energy, vol. 156, pp. 548-557.
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© 2020 Elsevier Ltd The blade is a crucial part of wind turbine for generating electricity and prone to damage due to harsh external environment. Accurate damage detection of wind turbine blade (WTB) is still a prominent challenge. This paper presents an acoustical detection method for damage identification of the WTB based on pattern recognition. In the proposed method, sound pulse extraction of the WTB is first investigated through physical method in combination with the filter and sliding window. Subsequently, the wavelet packet energy ratios of acoustic signal are introduced to characterize the discrepancy between intact and cracked sound pulses, and the support vector data description (SVDD) model is built for WTB damage detection. Besides, an improved incremental learning method is presented and employed to adaptively update the SVDD model, which aims at simplifying calculation procedure. Finally, the performance of proposed method is evaluated using experimental data collected from the WTBs with both intact and damaged conditions in commercial wind farms. It is demonstrated that proposed method has improvement in prediction accuracy compared to previous incremental SVDD models and performs the best on training time.
Chen, H, Fang, Y, Zhang, Y, Zhang, W & Wang, L 2020, 'ESPM: Efficient Spatial Pattern Matching', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 6, pp. 1227-1233.
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Chen, H, Heidari, AA, Chen, H, Wang, M, Pan, Z & Gandomi, AH 2020, 'Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies', Future Generation Computer Systems, vol. 111, pp. 175-198.
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© 2020 Elsevier B.V. The first powerful variant of the Harris hawks optimization (HHO) is proposed in this work. HHO is a recently developed swarm-based stochastic algorithm that has previously shown excellent performance. In fact, the original HHO has features that can still be improved as it may experience convergence problems or may easily become trapped in local optima. To overcome these shortcomings of the original HHO, the first powerful variant of HHO integrates chaos strategy, topological multi-population strategy, and differential evolution (DE) strategy. For this, chaos mechanism is first introduced into the original algorithm to improve the exploitation propensities of HHO. The multi-population strategy with three mechanisms is embedded to augment the global search ability of the method. Finally, the DE mechanism is introduced into the HHO to enhance the quality of the solutions. Based on these well-regarded evolutionary mechanisms, we propose an enhanced DE-driven multi-population HHO (CMDHHO) algorithm. In this work, the proposed CMDHHO is compared with a range of other methods, including four original meta-heuristic algorithms, conventional HHO, twelve advanced algorithms based on IEEE CEC2017 benchmark functions, and IEEE CEC2011 real-world problems. Furthermore, the Friedman test and the non-parametric statistical Wilcoxon sign rank test are used to verify the significance of the results. The results of the experiments show that the three embedded mechanisms can effectively enhance the exploratory and exploitative traits of HHO. The time required for HHO to converge was substantially shortened. We suggest using the proposed CMDHHO as an effective tool to solve complex optimization problems.
Chen, H, Li, F, Wang, L, Jin, Y, Chi, C-H, Kurgan, L, Song, J & Shen, J 2020, 'Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions.', Briefings in bioinformatics.
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In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.
Chen, H, Shen, J, Wang, L & Song, J 2020, 'A framework towards data analytics on host–pathogen protein–protein interactions', Journal of Ambient Intelligence and Humanized Computing.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. With the rapid development of high-throughput technologies, systems biology is now embracing a great opportunity made possible by the increased accumulation of data available online. Biological data analytics is considered as a critical means to contribute to a better understanding on such data through extraction of the latent features, relationships and the associated mechanisms. Therefore, it is important to evaluate how to involve data analytics from both computational and biological perspectives in practice. This paper has investigated interaction relationships in the proteomics area, which provide insights of the critical molecular processes within infection mechanisms. Specifically, we focused on host–pathogen protein–protein interactions, which represented the primary challenges associated with infectious diseases and drug design. Accordingly, a novel framework based on data analytics and machine learning techniques is detailed for analyzing these areas and we will describe the analytical results from host–pathogen protein–protein interactions (HP-PPI). Based on this framework, which serves as a pipeline solution for extracting and learning from the raw proteomics data, we have firstly evaluated several models from literature using different analytic technologies and performance measurements. An unsupervised deep learning model based on stacked denoising autoencoders, is subsequently proposed to capture higher level feature regarding the sequence information in the framework. The achieved performance indicates a superior capability of the unsupervised deep learning model in dealing with the host–pathogen protein interactions scenario among all of these models. The results will further help to enrich a theoretical and technical foundation for analyzing HP-PPI networks.
Chen, H, Xu, J-X, Kong, Z-H, Chen, W-H & Zhang, XY 2020, 'Broadband High-Efficiency Power Amplifier With Quasi-Elliptic Low-Pass Response', IEEE ACCESS, vol. 8, pp. 52566-52574.
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Chen, J, Zhou, J, Cao, Z, Vasilakos, AV, Dong, X & Choo, KKR 2020, 'Lightweight Privacy-Preserving Training and Evaluation for Discretized Neural Networks', IEEE Internet of Things Journal, vol. 7, no. 4, pp. 2663-2678.
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© 2014 IEEE. Machine learning, particularly the neural network (NN), is extensively exploited in dizzying applications. In order to reduce the burden of computing for resource-constrained clients, a large number of historical private datasets are required to be outsourced to the semi-trusted or malicious cloud for model training and evaluation. To achieve privacy preservation, most of the existing work either exploited the technique of public key fully homomorphic encryption (FHE) resulting in considerable computational cost and ciphertext expansion, or secure multiparty computation (SMC) requiring multiple rounds of interactions between user and cloud. To address these issues, in this article, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized NNs (DiNNs) is proposed. First, we put forward an efficient single key fully homomorphic data encapsulation mechanism (SFH-DEM) without exploiting public key FHE. Based on SFH-DEM, a series of atomic calculations over the encrypted domain, including multivariate polynomial, nonlinear activation function, gradient function, and maximum operations are devised as building blocks. Furthermore, a lightweight privacy-preserving model training and evaluation scheme LPTE for DiNNs is proposed, which can also be extended to convolutional NN. Finally, we give the formal security proofs for dataset privacy, model training privacy, and model evaluation privacy under the semi-honest environment and implement the experiment on real dataset MNIST for recognizing handwritten numbers in DiNN to demonstrate the high efficiency and accuracy of our proposed LPTE.
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.
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© 2020 by the authors. 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', 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.
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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, Zhang, N, Sun, HM, Chang, CC, Yu, S & Choo, KKR 2020, 'Secure search for encrypted personal health records from big data NoSQL databases in cloud', Computing.
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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, RS, Zhu, L, Lin, JY, Wong, SW, Li, Y, Yang, Y & He, Y 2020, 'Miniaturized Full-Metal Dual-Band Filter Using Dual-Mode Circular Spiral Resonators', IEEE Microwave and Wireless Components Letters, vol. 30, no. 6, pp. 573-576.
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© 2001-2012 IEEE. A miniaturized full-metal dual-band bandpass filter using a dual-mode circular spiral resonator (CSR) in a single metal cavity is proposed. Two spirals are combined together to form a dual-mode resonator, and the transmission zero (TZ) produced by the source-load coupling can separate the two modes and achieve the desired dual-band property. Then, a second-order dual-band filter is designed based on the dual-mode resonator, and each band can be individually synthesized, designed, and tuned to achieve the desired filtering performances. In addition, each band has TZs on both sides of the passband to achieve high selectivity. The size of the second-order dual-band filter is only 0.073\lambda _{0} \times 0.055\lambda _{0} \times 0.015\lambda _{0} calculated at the center frequency of the lower band. Finally, the filter is fabricated and measured, and a good agreement is achieved between the measurement and simulation.
Chen, 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, 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.', The 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.
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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, Castillo, JV, 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.
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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.
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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, 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, Huang, S & Fitch, R 2020, 'Active SLAM for Mobile Robots with Area Coverage and Obstacle Avoidance', IEEE/ASME Transactions on Mechatronics, vol. 25, no. 3, pp. 1182-1192.
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© 1996-2012 IEEE. In this article, we present an active simultaneous localization and mapping (SLAM) framework for a mobile robot to obtain a collision-free trajectory with good performance in SLAM uncertainty reduction and in an area coverage task. Based on a model predictive control framework, these two tasks are solved by the introduction of a control switching mechanism. For SLAM uncertainty reduction, graph topology is used to approximate the original problem as a constrained nonlinear least squares problem. A convex half-space representation is applied to relax nonconvex spatial constraints that represent obstacle avoidance. Using convex relaxation, the problem is solved by a convex optimization method and a rounding procedure based on singular value decomposition. The area coverage task is addressed with a sequential quadratic programming method. A submap joining approach, called linear SLAM, is used to address the associated challenges of avoiding local minima, minimizing control switching, and potentially high computational cost. Finally, various simulations and experiments using an aerial robot are presented that verify the effectiveness of the proposed method, showing that our method produces a more accurate SLAM result and is more computationally efficient compared with multiple existing methods.
Chen, Y, Leighton, B, Zhu, H, Ke, X, Liu, S & Zhao, L 2020, 'Submap-based indoor navigation system for the fetch robot', IEEE Access, vol. 8, pp. 81479-81491.
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© 2013 IEEE. In this paper, we present a novel navigation framework for the Fetch robot in a large-scale environment based on submapping techniques. This indoor navigation system is divided into a submap mapping part and an on-line localization part. For the mapping part, in order to deal with large environments or multi-story buildings, a submap mapping framework fusing two-dimensional (2D) laser scan and 3D point cloud from RGBD sensor is proposed using Google Cartographer. Meanwhile, several image datasets with corresponding poses are created from the RGBD sensor. Thanks to the submap framework, the error is limited corresponding to the size of the map, thus localization accuracy will be improved. For the on-line localization, so as to switch the submaps, the on-line images from the RGBD sensor are used to match the database images using DeepLCD, a deep learning based library for loop closure. Based on the information from DeepLCD and odometry, adaptive Monte Carlo localization (AMCL) is reinitialized to finish the localization task. In order to validate the result accuracy, reflectors and a motion capture system are used to compute the absolute trajectory error (ATE) and the relative pose error (RPE) based on the Gaussian-Newton (GN) algorithm. Finally, the proposed framework is tested on the Fetch simulator and the real Fetch robot, including both submap mapping and on-line localization.
Chen, Y, Mao, Y, Liang, H, Yu, S, Wei, Y & Leng, S 2020, 'Data poison detection schemes for distributed machine learning', IEEE Access, vol. 8, pp. 7442-7454.
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© 2013 IEEE. Distributed machine learning (DML) can realize massive dataset training when no single node can work out the accurate results within an acceptable time. However, this will inevitably expose more potential targets to attackers compared with the non-distributed environment. In this paper, we classify DML into basic-DML and semi-DML. In basic-DML, the center server dispatches learning tasks to distributed machines and aggregates their learning results. While in semi-DML, the center server further devotes resources into dataset learning in addition to its duty in basic-DML. We firstly put forward a novel data poison detection scheme for basic-DML, which utilizes a cross-learning mechanism to find out the poisoned data. We prove that the proposed cross-learning mechanism would generate training loops, based on which a mathematical model is established to find the optimal number of training loops. Then, for semi-DML, we present an improved data poison detection scheme to provide better learning protection with the aid of the central resource. To efficiently utilize the system resources, an optimal resource allocation approach is developed. Simulation results show that the proposed scheme can significantly improve the accuracy of the final model by up to 20% for support vector machine and 60% for logistic regression in the basic-DML scenario. Moreover, in the semi-DML scenario, the improved data poison detection scheme with optimal resource allocation can decrease the wasted resources for 20-100%.
Chen, Y, 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, 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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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.
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© 2019 Elsevier Ltd 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, Huu, HN, Guo, W, Chang, SW, Dinh, DN, 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.
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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.
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© 2020 Enzymatic processing has been considered an interesting technology as enzymes play important roles in the process of waste bioconversion, whilst heling to develop valuable products from animal wastes. In this paper, the application of enzymes in animal waste management were critically reviewed in short with respect to utilization in: (i) animal wastewater treatment and (ii) animal manure management. The results indicate that the application of enzymes could increase both chemical oxygen demand (COD) removal efficiency and production of biogas. The enzymatic bioprocesses were found to be affected by the type, source and dosage of enzymes and the operating conditions. Further studies on optimizing the operating conditions and developing cost-effective enzymes for the future large-scale application are therefore necessary.
Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, 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.
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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, 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.
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© 2020 The proper treatment of swine wastewater with relatively high concentrations of antibiotics is very important to protect environmental safety and human health. Microbial fuel cell (MFC) technology shows much promise for removing pollutants and producing electricity simultaneously. A double-chamber MFC was investigated in this study. Synthetic swine wastewater with the addition of sulfonamides was used as the fuels in the anode chamber. Results indicated that COD could be effectively removed (>95%) and virtually not affect by the presence of sulfonamides in the MFC. A stable voltage output was also observed. The removal efficiencies of sulfamethoxazole (SMX), sulfadiazine (SDZ), and sulfamethazine (SMZ) in the MFC were in the 99.46–99.53%, 13.39–66.91% and 32.84–67.21% ranges, respectively. These totals were higher than those reported for a traditional anaerobic reactor. Hence, MFC revealed strong resistance to antibiotic toxicity and high potential to treat swine wastewater with antibiotics.
Cheng, EJ, Prasad, M, Yang, J, Khanna, P, Chen, BH, Tao, X, Young, KY & Lin, CT 2020, 'A fast fused part-based model with new deep feature for pedestrian detection and security monitoring', Measurement: Journal of the International Measurement Confederation, vol. 151.
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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, Q, Nguyen, D, Dutkiewicz, E & Mueck, M 2020, 'Preserving Honest/Dishonest Users' Operational Privacy with Blind Interference Calculation in Spectrum Sharing System', IEEE Transactions on Mobile Computing.
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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
Chiang, YK, Oberst, S, Melnikov, A, Quan, L, Marburg, S, Alù, A & Powell, D 2020, 'Reconfigurable acoustic metagrating for high efficiency anomalous reflections', Physcal Review Applied, vol. 13, pp. 064067-064067.
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Recent study revealed that the scattering behaviors of bianisotropic scatterers can be controlled by an additional degree of freedom, represented as Willis coupling, which can be endowed with asymmetric wave scattering to form an acoustic metagrating for wavefront manipulation. Here, we introduce a flexible acoustic metagrating, formed by periodic arrays of properly design Willis scatterers, for anomalous reflection with nearly unitary efficiency and significantly less necessity of fine discretization. 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.
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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, XS, Nanda, P & Tan, Z 2020, 'Hybrid Tree-rule Firewall for High Speed Data Transmission', IEEE Transactions on Cloud Computing.
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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.
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, pp. 1-22.
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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, pp. 1-1.
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Chu, P, Zhang, JA, Wang, X, Fei, Z, Fang, G & Wang, D 2020, 'Interference Characterization and Power Optimization for Automotive Radar with Directional Antenna', IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 3703-3716.
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© 1967-2012 IEEE. Wide deployment of radar sensors on automotive vehicles can potentially lead to a severe interference problem. Such interference has been characterized without considering directional antenna patterns, which could lead to results significantly larger than the actual ones. In this paper, we study the mean power of effective echo signals and interference, by considering both front- and side- mounted radars equipped with directional antennas. We employ the stochastic geometry method to characterize the randomness of vehicles and hence radars in both two-lane and multi-lane scenarios, and derive closed-form expressions for the mean interference by approximating the radiation pattern by Gaussian waveforms. Simulation results are shown to match the analytical results very well, and insights are obtained for the impact of radar parameters on interference. Based on the interference analysis, we aim to minimize the total transmission power of each vehicle with constraints on the required signal to interference and noise ratio. An optimal solution is obtained based on linear programming techniques and corroborated by simulation results.
Chude-Okonkwo, UAK, Maharaj, BT, Vasilakos, AV & Malekian, R 2020, 'Information-Theoretic Model and Analysis of Molecular Signaling in Targeted Drug Delivery', IEEE Transactions on Nanobioscience, vol. 19, no. 2, pp. 270-284.
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© 2002-2011 IEEE. Targeted drug delivery (TDD) modality promises a smart localization of appropriate dose of therapeutic drugs to the targeted part of the body at reduced system toxicity. To achieve the desired goals of TDD, accurate analysis of the system is important. Recent advances in molecular communication (MC) present prospects to analyzing the TDD process using engineering concepts and tools. Specifically, the MC platform supports the abstraction of TDD process as a communication engineering problem in which the injection and transportation of drug particles in the human body and the delivery to a specific tissue or organ can be analyzed using communication engineering tools. In this paper we stand on the MC platform to present the information-theoretic model and analysis of the TDD systems. We present a modular structure of the TDD system and the probabilistic models of the MC-abstracted modules in an intuitive manner. Simulated results of information-theoretic measures such as the mutual information are employed to analyze the performance of the TDD system. Results indicate that uncertainties in drug injection/release systems, nanoparticles propagation channel and nanoreceiver systems influence the mutual information of the system, which is relative to the system's bioequivalence measure.
Clegg, SR & Burdon, S 2020, 'Exploring creativity and innovation in broadcasting', Human Relations, pp. 001872671988800-001872671988800.
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We consider the emergence of design innovations in process, emerging around the form of polyarchy. This is done by using a case study of innovation conducted by a production organization’s project that was embedded in and hosted by a bureaucratic public institution, the Australian Broadcasting Corporation (ABC). The research reported here was part of a larger project comparing the BBC and ABC’s use of different modes of organization. It focused mainly on the organization designed to deliver a six-part television series, The Code. The innovative process of Scribe, the organization in question, in producing the story is a good example of idea work being instituted in a polyarchic design process. Scribe represents a new organizational design characterized by a polyarchic structure, which is soft and decentralized, with strict and relatively insuperable social and symbolic boundaries. This results in a project-based organization to coordinate collective innovation that is curated by making the writer also the creative director or showrunner. The research contributes further to exploring organizational idea work, through prioritizing creativity and innovation by an explicit positioning of a product and collaborative generative idea work.
Cong, TN, Walker, PD, Zhang, N & Ruan, J 2020, 'Efficiency improvement of a novel dual motor powertrain for plug-in hybrid electric buses', PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, vol. 234, no. 7, pp. 1869-1882.
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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', ANALYST, vol. 145, no. 10, pp. 3490-3494.
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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.
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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, 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 and Management, vol. 57, no. 2.
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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.
Dai, M, Xiao, G, Cheng, S, Wang, D & He, X 2020, 'Structural correlation filters combined with a Gaussian particle filter for hierarchical visual tracking', Neurocomputing.
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© 2020 Visual tracking is a key problem for many computer vision applications such as human-computer interaction, intelligent medical diagnosis, navigation and traffic control management. Most of the existing tracking methods are mainly based on correlation filters. However, boundary effect, scale estimation and template updating have not been fully resolved. Herein, this paper presents a new hierarchical tracking method combining structural correlation filters with a Gaussian Particle Filter (GPF), named KCF-GPF. Weak KCF classifiers are constructed via a Lukas-Kanade (LK) method and the preliminary target location is presented as a weighted sum of these classifiers. Specially, a facile weight strategy is implemented to estimate the reliability of each weak classifier. On the basis of the preliminary target location, the GPF using features from a Convolutional Neural Network (CNN) is employed to predict the location and scale of a target. Extensive experiments with the OTB-2013 and the OTB-2015 databases demonstrate that the proposed algorithm performs favourably against state-of-the-art trackers.
Dai, P, Lu, W, Le, K & Liu, D 2020, 'Sliding Mode Impedance Control for contact intervention of an I-AUV: Simulation and experimental validation'.
Dai, P, Lu, W, Le, K & Liu, D 2020, 'Sliding Mode Impedance Control for contact intervention of an I-AUV: Simulation and experimental validation', Ocean Engineering, vol. 196.
Dang, D, Hoang, D & Nguyen, D 2020, 'Trust-based Scheduling Framework for Big Data Processing with MapReduce', IEEE Transactions on Services Computing (TSC).
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Security and privacy have become a great concern in cloud computing platforms in which users risk the leakage of their private data. The leakage can happen while the data is at rest (in storage), in processing, or on moving within a cloud or between different cloud infrastructures, e.g., from private to public clouds. This paper focuses on protecting data "in processing". For big data applications, the MapReduce framework has been proven as an efficient solution and has been widely deployed, e.g., in healthcare and business data analysis. In this article, we propose a trust-based framework for MapReduce in big data processing tasks. Specifically, we first quantify and propose to assign the sensitive values for data and trust values for map and reduce slots. We then compute the trust value of each resource employed in the big data processing tasks. Depending on the data's sensitivity level of a task, the task requires a given level of trust (i.e., higher sensitive data requires servers/slots with higher trust level). The MapReduce scheduling problem is then formulated as the maximum weighted matching problem of a bipartite graph that aims to maximize the total trust value over all possible assignments subject to various trust requirement of different tasks. The problem is known to be NP-hard. To tackle it, we observe that within a computing node (VM), slots share the same trust value granted from the secured transformation phase. This helps reduce the number of slot nodes of a weight bipartite graph. Leveraging this fact, we propose an efficient heuristic algorithm that achieves 94.7% of the optimal solution obtained via exhaustive search. Extensive simulations show that the trust-based scheduling scheme provides much higher protection for data sensitivity while ensuring good performance for big data applications.
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.
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This study proposes a novel ground modification technique utilising fibre reinforced load transfer platform (FRLTP) and deep cement mixing (DCM) columns 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 firstly 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, 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 show that the embankment with FRLTP can effectively diminish the total settlement and the lateral deformation of the embankment, meanwhile improve the stress concentration ratio and the embankment stability to a great extent. The findings of the extensive parametric study indicate that the FRLTP shear strength properties appear to be the most influence factors to be considered in the design procedure of a target CS-FRLTP-embankment system.
Dao, N-N, Na, W, Tran, A-T, Nguyen, DN & Cho, S 2020, 'Energy-Efficient Spectrum Sensing for IoT Devices', IEEE Systems Journal, pp. 1-9.
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de Koning, K & Filatova, T 2020, 'Repetitive floods intensify outmigration and climate gentrification in coastal cities', Environmental Research Letters, vol. 15, no. 3, pp. 034008-034008.
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Deepanraj, B, Senthilkumar, N, Ranjitha, J, Jayaraj, S & Ong, HC 2020, 'Biogas from food waste through anaerobic digestion: optimization with response surface methodology', Biomass Conversion and Biorefinery.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. In the current study, anaerobic digestion method efficiency on biogas production and chemical oxygen demand (COD) degradation was assessed through a sequence of laboratory-scale batch experimentations to compute the role of chosen process parameters, viz., solid concentration (5–15%), pH (5–9), temperature (30–60 °C), and co-digestion (0–40% of poultry manure). Biogas production and COD degradation were significantly dependent on the selected process parameters with independent conditions to accomplish active performance of the process. Central composite design (CCD)-based response surface methodology (RSM) was adopted for evaluation and optimizing of the combined performance of system considering two responses. Among various combinations, it was observed that solid concentration of 7.38%, pH value as 7, temperature at 48.43 °C, and co-digestion as 29% produce biogas of 6344 ml and COD degradation as 38%. Confirmation experiment performed shows a deviation of 4.93% maximum between the predicted and experimental results.
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.
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Dehkordi, SA, Farajzadeh, K, Rezazadeh, J, Farahbakhsh, R, Sandrasegaran, K & Dehkordi, MA 2020, 'A survey on data aggregation techniques in IoT sensor networks', WIRELESS NETWORKS, vol. 26, no. 2, pp. 1243-1263.
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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.
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© 2020 Elsevier Ltd The purpose of this study is to determine the key properties of Australian hemp particles which are used for manufacturing hempcrete. Hemp characteristics have a wide variability due to the influence of the environment conditions in various farmed areas. This study focuses on the measurements of the mechanical, thermal and acoustic performances of three Australian hemp: unretted hemp hurd, retted hemp hurd, and hemp fines. Hemp hurd is usually used in non-load bearing building walls, and hemp fine, which is the by-product of hemp manufacturing industry, is usually incorporated into a render. The experimental results show that the main impact of the retting process is a decrease in bulk density and leading to an improvement in thermal and acoustic properties. Without compaction, the bulk density is ranged from 97 and 118.8 kg.m−3, the max sound absorption coefficient from 0.88 and 0.99, and the thermal conductivity from 64 to 97 mW.m-1. K-1. Hemp fines have excellent thermal and acoustic properties and appear to be an efficient aggregate to produce an insulating render. Australian hemps investigated in this study have shown very similar characteristics to European hemps.
Deng, 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.
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Dharmapriya, S, Kiridena, S & Shukla, N 2020, 'Multiagent Optimization Approach to Supply Network Configuration Problems With Varied Product-Market Profiles', IEEE Transactions on Engineering Management.
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IEEE This article demonstrates the application of a novel multiagent modeling approach to support supply network configuration (SNC) decisions toward addressing several challenges reported in the literature. These challenges include: enhancing supply network (SN)-level performance in alignment with the goals of individual SN entities; addressing the issue of limited information sharing between SN entities; and sustaining competitiveness of SNs in dynamic business environments. To this end, a multistage, multiechelon SN consisting of geographically dispersed SN entities catering to distinct product-market profiles was modeled. In modeling the SNC decision problem, two types of agents, each having distinct attributes and functions, were used. The modeling approach incorporated a reverse-auctioning process to simulate the behavior of SN entities with differing individual goals collectively contributing to enhance SN-level performance, by means of setting reserve values generated through the application of a genetic algorithm. A set of Pareto-optimal SNCs catering to distinct product-market profiles was generated using Nondominated Sorting Genetic Algorithm II. Further evaluation of these SNCs against additional criteria, using a rule-based approach, allowed the selection of the most appropriate SNC to meet a broader set of conditions. The model was tested using a refrigerator SN case study drawn from the literature. The results reveal that a number of SNC decisions can be supported by the proposed model, in particular, identifying and evaluating robust SNs to suit varied product-market profiles, enhancing SC capabilities to withstand disruptions and developing contingencies to recover from disruptions.
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, pp. 1-1.
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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.
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© 2020 Chongqing University of Posts and Telecommunications Intelligent vehicles are advancing at a fast speed with the improvement of automation and connectivity, which opens up new possibilities for different cyber-attacks, including in-vehicle attacks (e.g., hijacking attacks) and vehicle-to-everything communicationattacks (e.g., data theft). These problems are becoming increasingly serious with the development of 4G LTE and 5G communication technologies. Although many efforts are made to improve the resilience to cyber attacks, there are still many unsolved challenges. This paper first identifies some major security attacks on intelligent connected vehicles. Then, we investigate and summarize the available defences against these attacks and classify them into four categories: cryptography, network security, software vulnerability detection, and malware detection. Remaining challenges and future directions for preventing attacks on intelligent vehicle systems have been discussed as well.
Dikshit, A, Sarkar, R, Pradhan, B, Acharya, S & Alamri, AM 2020, 'Spatial landslide risk assessment at Phuentsholing, Bhutan', Geosciences (Switzerland), vol. 10, no. 4.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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 extend...
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-BASEL, vol. 10, no. 7.
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Dikshit, A, Satyam, N, Pradhan, B & Kushal, S 2020, 'Estimating rainfall threshold and temporal probability for landslide occurrences in Darjeeling Himalayas', Geosciences Journal, vol. 24, no. 2, pp. 225-233.
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© 2020, The Association of Korean Geoscience Societies and Springer. The Indian Himalayan region has been severely affected by landslides causing an immense loss in terms of human lives and economic loss. The landslides are usually induced by rainfall which can be slow and continuous or heavy downpour. The incidences of landslide events in Indian Himalayas have been further aggravated due to the rapid increase in urbanization and thus its increasing impact on socio-economic aspects. There is a dire need for understanding landslide phenomena, estimating its occurrence potential and formulating strategies to minimize the damage caused by them. One of the most affected area is Kalimpong of Darjeeling Himalayas where significant studies have been conducted on zonation, threshold estimation and other related aspects. However, a comprehensive study in terms of temporal prediction for this region remains unattended. The paper deals with assessing landslide hazard using a rainfall threshold model involving daily and cumulative antecedent rainfall values for landslide events. The threshold values were determined using daily rainfall and antecedent rainfall using precipitation and landslide records for 2010–2016. The results show that 20-day antecedent rainfall provides the best fit for landslide occurrences in the region. The rainfall thresholds were further validated using rainfall and landslide data of 2017, which was not considered for threshold estimation. Finally, the results were used to determine the temporal probability for landslide incidence using a Poisson probability model. The validated results suggest that the model has the potential to be used as a preliminary early warning system.
Dimuro, GP, Lucca, G, Bedregal, B, Mesiar, R, Sanz, JA, Lin, CT & 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, C, Sun, H-H, Zhu, H & Guo, YJ 2020, 'Achieving Wider Bandwidth with Full-Wavelength Dipoles (FWDs) for 5G Base Stations', IEEE Transactions on Antennas and Propagation, vol. 68, no. 2, pp. 1119-1127.
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Ding, W, Lin, C-T & Pedrycz, W 2020, 'Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce.', IEEE Transactions on Cybernetics.
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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, CT, Liew, AWC, 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, Triguero, I, Cao, Z & Lin, CT 2020, 'Multigranulation Super-Trust Model for Attribute Reduction', IEEE Transactions on Fuzzy Systems.
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IEEE As big data often contains a significant amount of uncertain, unstructured and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this paper, we present a novel multigranulation super-trust fuzzy-rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation super-trust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme is adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular-coevolution is employed to ensure a wide range of balancing of exploration and exploitation and can classify super elitists’ preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables.
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, pp. 1-13.
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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.
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© 2020 ACM. 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 : a publication of the IEEE Signal Processing Society.
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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.
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.', The 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.
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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, 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.
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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.
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© 2020 Elsevier Ltd The cyclic compression and four series of fixed magnitude impact loads with an increment of 50 times were conducted alternatively on the smart carbon nanotubes (CNTs) reinforced cementitious composites, to evaluate the piezoresistive sensitivity and repeatability of composites after exposed to different drop impact energies. The results show that the impacts procedure suddenly increased in electrical resistivity due to the emerged micro-cracks and pores, and higher impact energy led to faster resistivity increase. On the other hand, when the impact is repeatedly applied, a high impact resistance of the cementitious composites could be observed, which was attributed to the dense microstructures. Moreover, instead of instable and uneven output of electrical resistivity during cyclical compression, more stable and uniform fractional changes of resistivity were achieved after exposed to impact load. However, severe nonlinearity with swift resistivity reduction of cementitious composites under low loads was observed at the beginning and the end of cyclic compression after subjected to many impacts with impact energy of 18.72 × 10−4 J/cm3. The related outcomes of smart conductive cementitious composites subjected to cyclic compression and impact will provide an insight into the stable electrical signal output and promote the applications of cement-based sensors for structural health monitoring under various loading conditions.
Dong, W, Li, W, 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.
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© 2020 Elsevier Ltd Piezoresistivity of smart carbon nanotube/cementitious composite has been experimentally investigated, but the piezoresistive performance had been rarely studied when exposed to elevated temperatures. In this study, the physicochemical and mechanical properties, and piezoresistive behaviours of multi-walled carbon nanotube (MWCNT) reinforced smart cementitious composite were investigated under heat treatments of elevated temperatures of 300 °C and 600 °C. The microstructures, crystal deterioration and thermal gravity relationships were characterized by scanning electron microscope (SEM), X-ray diffraction (XRD) and thermos-gravimetric (TG) analysis. The results show that the compressive strength and elastic modulus of MWCNT/cementitious composite after heat treatments gradually decreased, especially under the high temperature of 600 °C. There was a sudden growth of fractional changes of resistivity (FCR) after heat treatment. The higher temperature treatments led to more extensive sudden increase in the piezoresistivity. In the linear part of the relationship curves of FCR to the strain, the gauge factor even increased at the temperature of 300 °C. Moreover, the mechanism for the altered piezoresistivity was fundamentally explained and discussed by the MWCNT purification and destructions of MWCNT, cement matrix and agglomerations after heat treatments. Therefore, the related outcomes will promote the understanding and application of smart MWCNT/cementitious composite for structural health monitoring (SHM) under extreme environments.
Dong, W, Li, W, Wang, K, Luo, Z & Sheng, D 2020, 'Self-sensing capabilities of cement-based sensor with layer-distributed conductive rubber fibres', SENSORS AND ACTUATORS A-PHYSICAL, vol. 301.
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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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Dong, X, Gong, Y & Cao, L 2020, 'e-RNSP: An Efficient Method for Mining Repetition Negative Sequential Patterns.', IEEE Transactions on Cybernetics.
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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, Y & Fatahi, B 2020, 'Discrete element simulation of cavity expansion in lightly cemented sands considering cementation degradation', COMPUTERS AND GEOTECHNICS, vol. 124.
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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.
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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.
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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.
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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, p. 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 in 40% (10/25) of cases. Pharmacists requested more direction from the CDSS in relation to automated prompts for user input and page navigation. Overall system usability was rated as excellent (mean score 92/100, SD 6.5; 90th percentile compared with similar systems), with acceptance rated as good to excellent. CONCLUSIONS:A novel CDSS (high-fidelity prototype) to enhance pharmacist care of LB...
Drumond, PDP, Ball, JE, Moura, P & Lara Pinto Coelho, MM 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.
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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, pp. 1-1.
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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, X, Lu, Z & Wu, D 2020, 'An intelligent recognition model for dynamic air traffic decision-making', Knowledge-Based Systems, vol. 199.
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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.
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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.
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.
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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.
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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 in proce...
Ekanayake, UGM, Seo, DH, Faershteyn, K, O'Mullane, AP, Shon, H, MacLeod, J, Golberg, D & Ostrikov, K 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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Elgharabawy, A, Mukesh, P & CT, L 2020, 'Preference Neural Network', Transaction of Neural network and learning system.
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This paper proposes a preference neural network (PNN) to address the problem of indifference preferences orders with new activation function. PNN also solves the Multi-label ranking problem, where labels may have indifference preference orders or subgroups are equally ranked. PNN follows a multi-layer feedforward architecture with fully connected neurons. Each neuron contains a novel smooth stairstep activation function based on the number of preference orders. PNN inputs represent data features and output neurons represent label indexes. The proposed PNN is evaluated using new preference mining dataset that contains repeated label values which have not experimented before. PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.
Elsawah, S, Filatova, T, Jakeman, AJ, Kettner, AJ, Zellner, ML, Athanasiadis, IN, Hamilton, SH, Axtell, RL, Brown, DG, Gilligan, JM, Janssen, MA, Robinson, DT, Rozenberg, J, Ullah, IIT & Lade, SJ 2020, 'Eight grand challenges in socio-environmental systems modeling', Socio-Environmental Systems Modelling, vol. 2, pp. 16226-16226.
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Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.
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.
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. 133-159.
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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.
Fahmideh, M & Beydoun, G 2020, 'Big data analytics architecture design.', CoRR, vol. abs/2004.08021.
Fahmideh, M & Beydoun, G 2020, 'Reusing empirical knowledge during cloud computing adoption.', CoRR, vol. abs/2004.11268.
Fahmideh, M, Beydoun, G & Low, G 2020, 'Experiential probabilistic assessment of cloud services.', CoRR, vol. abs/2004.10858.
Fahmideh, M, Daneshgar, F, Beydoun, G & Rabhi, FA 2020, 'Challenges in migrating legacy software systems to the cloud an empirical study.', CoRR, vol. abs/2004.10724.
Fahmideh, M, Low, G, Beydoun, G & Daneshgar, F 2020, 'Cloud Migration Process A Survey Evaluation Framework and Open Challenges.', CoRR, vol. abs/2004.10725.
Fan, K, Bao, Z, Liu, M, Vasilakos, AV & Shi, W 2020, 'Dredas: Decentralized, reliable and efficient remote outsourced data auditing scheme with blockchain smart contract for industrial IoT', Future Generation Computer Systems.
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© 2019 Elsevier B.V. The development of cloud computing and the Internet of things (IOT) attracts more and more enterprises to outsource the data from their Industrial Internet of things (IIOT) to cloud servers in order to save operating costs and improve efficiency. However, in this environment, protecting the security and privacy of data storage is an important challenge for IIOT and cloud server provider (CSP). Data auditing could allow data owner discover malicious behaviors of CSP which destroy their outsourced data. The public auditing authorizes the trusted third part auditor (TPA) to audit the owner's outsourced data and frees owner from regular tasks. However, the public auditing using TPA is considered a centralized auditing, and the TPA is assumed totally honest, but it is difficult to find a reliable auditing organization. In this paper, a novel decentralized auditing smart contract in Ethereum is proposed. By replacing the TPA with a designed smart contract, a decentralized auditing scheme (Dredas) is proposed, where anyone can obtain the auditing result from Ethereum without worrying about semi-honest TPA. Compared with traditional auditing, apart from being able to perform traditional auditing functions, Dredas has three important benefits over previous work. First, the random values of challenge are more secure. Dredas chooses the current blockchain nonce as a random seed to prevent any party forging random values. Secondly, in order to achieve a safe, regular, proactive auditing, the protocol writes the auditing rules into the blockchain, and uses the number blocks on the Ethereum as the security timestamp. Finally, data owner, user and CSP must pay some ether for smart contract as deposit. This way not only inhibits the malicious behavior of these three parties, but also makes it more reasonable in real life. We implement Dredas to show that the computation costs are reasonable and efficient.
Fan, X, Xiang, C, Chen, X, Song, X, Yang, P, Gong, L, Nanda, P & He, X 2020, 'BuildSenSys: Reusing Building Sensing Data for Traffic Prediction with Cross-domain Learning', IEEE Transactions on Mobile Computing.
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With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by
the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and
cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to
predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of
cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two
major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal
complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more
daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and
time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic
volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain
Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building
sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction
based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the
building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal
attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive
experimental studies demonstrate that BuildSenSys outp...
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.
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© 2020 Elsevier B.V. In this study, the growth of a spherulitic structure at the surface and sub-layer of the polyvinylidene fluoride (PVDF) hollow fiber membrane was controlled by co-extruding nucleation agent (NA) solutions as the outermost layer of a triple-orifice spinneret in the thermally induced phase separation (TIPS) process. Using this method, the spherulites were tailored to be a variety of morphologies having different combinations with the bi-continuous network at the membrane surface and sub-layer, while the bulk membrane structure remained almost unchanged. These typically tailored spherulitic structures dramatically enhanced the surface roughness, hydrophobicity, liquid entry pressure, and permeation stability of the TIPS-prepared PVDF hollow fiber membranes. The experimental results from the pressure-driven filtration and the direct contact membrane distillation demonstrated great potential of the tailored membranes to achieve water treatment and seawater desalination. The electrostatic interaction between NA and PVDF played a key role in tailoring the special membrane spherulite structures, providing new possibilities for engineering TIPS-prepared hollow fiber membranes with desirable structures and performances to address current and future water shortages.
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, 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.
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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, Y, Huang, X, Qin, L, Zhang, Y, Zhang, W, Cheng, R & Lin, X 2020, 'A survey of community search over big graphs', The VLDB Journal, vol. 29, no. 1, pp. 353-392.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently, a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus, it is desirable to have a comprehensive review of these works. In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze and compare the quality of communities under their models, and the performance of different solutions. Furthermore, we point out new research directions. This survey does not only help researchers to have better understanding of existing community search solutions, but also provides practitioners a better judgment on choosing the proper solutions.
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.
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, (2019), 10.1007/s00778-019-00556-x)', VLDB Journal.
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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.
Fani, A, Golroo, A, Ali Mirhassani, S & Gandomi, AH 2020, 'Pavement maintenance and rehabilitation planning optimisation under budget and pavement deterioration uncertainty', International Journal of Pavement Engineering.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. One of the key parts of a pavement management system is the maintenance and rehabilitation planning. The planning is usually developed under the assumption that all parameters are known with certainty. In practice, there are various parameters afflicted with large uncertainty. Ignoring the uncertainty may lead to a suboptimal plan adversely affecting the network conditions. The objective of this study is to develop an optimisation framework for network-level pavement maintenance and rehabilitation planning considering the uncertain nature of pavement deterioration and the budget with an applicable approach. A multistage stochastic mixed-integer programming model is proposed to find the optimal plan that is feasible for all possible scenarios of uncertainty and optimise the expectation of objective function. Two case studies of 4 and 21 pavement sections are presented to show the applicability of the proposed method. The value of stochastic solution and the expected value of perfect information which are the indices for evaluating the benefits of using the stochastic model are, respectively, 30% and 85% of the objective function of here and now model for the first case study and 26% and 42% of it regarding the second one. The indices are high indicating the effectiveness of the stochastic solution.
Far, H 2020, 'Flexural Behavior of Cold-Formed Steel-Timber Composite Flooring Systems', Journal of Structural Engineering (United States), vol. 146, no. 5.
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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.
Faramarzi, A, Heidarinejad, M, Mirjalili, S & Gandomi, AH 2020, 'Marine Predators Algorithm: A nature-inspired metaheuristic', EXPERT SYSTEMS WITH APPLICATIONS, vol. 152.
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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. 1-1.
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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.
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.
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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.
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Feng, F, Li, KC, Shen, J, Zhou, Q & Yang, X 2020, 'Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification', IEEE Access, vol. 8, pp. 69979-69996.
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© 2013 IEEE. Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data. As already known, the General Vector Machine (GVM) algorithm has good generalization ability, though it does not work well for the imbalanced classification. Additionally, the state-of-the-art Binary Ant Lion Optimizer (BALO) algorithm has high exploitability and fast convergence rate. Based on these facts, we have proposed in this paper a Cost-sensitive Feature selection General Vector Machine (CFGVM) algorithm based on GVM and BALO algorithms to tackle the imbalanced classification problem, delivering different cost weights to different classes of samples. In our method, the BALO algorithm determines the cost weights and extract more significant features to improve the classification performance. Experiments conducted on eleven imbalanced data sets have shown that the CFGVM algorithm significantly improves the classification performance of minority class samples. By comparing with similar algorithms and state-of-the-art algorithms, the proposed algorithm significantly outperforms in performance and produces better classification results.
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, pp. 1-1.
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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: the journal of the Society for International Development.
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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.
Fis, MA & Cetindamar Kozanoglu, D 2020, 'Unlocking the Relationship between Corporate Entrepreneurship and Firm Performance', Entrepreneurship Research Journal, pp. 1-47.
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This paper explores the relationship between corporate entrepreneurship and performance by developing a comprehensive theoretical model based on Schumpeterian understanding of entrepreneurship supported with the Theory of Planned Behavior from social psychology. The model shows how organizational culture (value) triggers a chain effect through its influence on entrepreneurial orientation (attitude) and managerial support (intentions) that ultimately generate impact on corporate entrepreneurship (behavior). We test our model in an emerging economy context and present our results with implications to theory and practice.
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.
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© 2020 Elsevier Ltd Indoor air quality (IAQ) is of growing public health concern which has prompted the use of plants to phytoremediate air pollution in interior spaces. Active green walls are emerging as a means of reducing indoor contaminants and have demonstrated efficacy comparable to conventional air filtering technologies. However, the use of active airflow through organic substrates has the potential to emit bioaerosols into the surrounding environment, where the potential risk to human health is largely unknown. In this study, we demonstrate that two indoor green walls (with and without active airflow) contribute significantly to the ambient fungal load, however concentrations remained well below WHO safety guidelines. Bacterial dynamics within the rhizosphere/substrate of the operational botanical biofilters displayed variability across plant species. Phyla-wide distribution generally aligned with previous literature; however, differences from those previously reported were observed at the genus level, possibly due to geographic location, substrate composition, or plant species selection. Targeted assessment of Legionella aerosol contamination, an under-addressed potential pathogen for these active systems, yielded no positive identification during the sampling period. We conclude that active green walls host a unique bacterial profile and do not emit harmful levels of fungal propagules or pose significant risk of aerosolised Legionella species, provided systems are well monitored and maintained.
Frederiks, ER, Romanach, LM, Berry, A & Toscas, P 2020, 'Making energy surveys more impactful: Testing material and non-monetary response strategies', Energy Research and Social Science, vol. 63.
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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.
Frost, SA, Kelly, A, Gaudin, J, Mc Evoy, L, Wilson, C, Marov, L, El Haddad, C, Center, J, Eisman, JA, Nguyen, TV & Hassett, G 2020, 'Establishing baseline absolute risk of subsequent fracture among adults presenting to hospital with a minimal-trauma-fracture', BMC MUSCULOSKELETAL DISORDERS, vol. 21, no. 1.
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Fu, L, Lin, P, Vasilakos, AV & Wang, S 2020, 'An overview of recent multi-view clustering', Neurocomputing.
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© 2020 Elsevier B.V. With the widespread deployment of sensors and the Internet-of-Things, multi-view data has become more common and publicly available. Compared to traditional data that describes objects from single perspective, multi-view data is semantically richer, more useful, however more complex. Since traditional clustering algorithms cannot handle such data, multi-view clustering has become a research hotspot. In this paper, we review some of the latest multi-view clustering algorithms, which are reasonably divided into three categories. To evaluate their performance, we perform extensive experiments on seven real-world data sets. Three mainstream metrics are used, including clustering accuracy, normalized mutual information and purity. Based on the experimental results and a large number of literature reading, we also discuss existing problems in current multi-view clustering and point out possible research directions in the future. This research provides some insights for researchers in related fields and may further promote the development of multi-view clustering algorithms.
Gai, K, Guo, J, Zhu, L & Yu, S 2020, 'Blockchain Meets Cloud Computing: A Survey', IEEE Communications Surveys and Tutorials.
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IEEE Blockchain technology has been deemed to be an ideal choice for strengthening existing computing systems in varied manners. As one of the network-enabled technologies, cloud computing has been broadly adopted in the industry through numerous cloud service models. Fusing blockchain technology with existing cloud systems has a great potential in both functionality/ performance enhancement and security/ privacy improvement. The question remains on how blockchain technology inserts into current deployed cloud solutions and enables the reengineering of cloud datacenter. This survey addresses this issue and investigates recent efforts in the technical fusion of blockchain and clouds. Three technical dimensions roughly are covered in this work. First, we concern the service model and review an emerging cloud-relevant blockchain service model, Blockchain-as-a-Service (BaaS); second, security is considered a key technical dimension in this work and both access control and searchable encryption schemes are assessed; finally, we examine the performance of cloud datacenter with supports/ participance of blockchain from hardware and software perspectives. Main findings of this survey will be theoretical supports for future reference of blockchain-enabled reengineering of cloud datacenter.
Gan, YY, Ong, HC, Chen, WH, Sheen, HK, Chang, JS, 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.
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© 2020 Elsevier Ltd Wet torrefaction is a promising method to converts the high moisture microalgae to biofuel. In this study, the microalgae were torrefied in water or dilute acid solutions with the aid of microwaves irradiation. The reaction temperature and heating time were fixed at 160 °C and 10 min. The effects of sulfuric, phosphorus, and succinic acids on two different microalgae species (Chlorella vulgaris ESP-31 and FSP-E) were investigated. As a result, the disruption of the microalga FSP-E (high protein) was not notable in the acidic solution. The higher heating value of wet torrefied microalga ESP-31 (high carbohydrate) in succinic acid was enhanced up to 40% with at least 45% of energy yield. The use of 0.1 M of phosphorus acid in wet torrefaction produced the highest ash content of 1.61 and 11.60 wt% for microalgae ESP-31 and FSP-E, respectively. Thermogravimetric analysis revealed that the carbohydrate content of microalga ESP-31 has the highest degradation in sulfuric acid solution. In contrast, crystalline cellulose in the microalgae is hardly affected by the wet torrefaction process in low-temperature acidic solution. In terms of liquid product, a maximum glucose extraction of 35.39 g/L occurred with the use of 0.1 M of sulfuric acid. In conclusion, biochar produced using organic acid is desirable as solid fuel, whereas the use of sulfuric acid is more suitable to produce sugar for bioethanol production.
Gandomi, AH & Atefi, E 2020, 'Software review: the GPTIPS platform', Genetic Programming and Evolvable Machines.
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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.
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© 2020 Elsevier B.V. Real-world optimization problems usually involve constraints and sometimes even finding a single feasible solution is a challenging task. This study introduces a new approach for implicitly handling constraints. The proposed approach reduces the consideration of infeasible solutions by directly updating variable bounds with constraints, which is called the boundary update (BU) method. Two illustrative examples are used to explain the proposed approach, followed by applying it to mathematical and engineering constrained optimization problems. Finally, a surrogate-based problem and a large-dimensional and highly constrained problem are used to evaluate the BU method on these types of problems. The BU method is coupled with seven well-known evolutionary and mathematical optimization algorithms and the results show that the proposed BU method is a practical and effective approach and leads to better solutions with fewer function evaluations in nearly all cases, particularly for population-based optimization algorithms. This study should motivate optimization researchers and practitioners to pay more attention to the direct handling of constraints, rather than constraint handling by penalty or other fix-ups.
Gao, 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, K, Liu, Z, Jia, J, Liu, Z, Yisimayili, A, Qi, Z, Wu, Y & Li, S 2020, 'Study on Flame Spread Characteristics of Flame-Retardant Cables in Mine', ADVANCES IN POLYMER TECHNOLOGY, vol. 2020.
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Gao, K, Qi, Z, Jia, J, Li, S, Liu, Z & Liu, Z 2020, 'Investigation of coupled control of gas accumulation and spontaneous combustion in the goaf of coal mine', AIP ADVANCES, vol. 10, no. 4.
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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', 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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© The Author(s) 2020. 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 s...
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.
Garcia-Rincon, 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.
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Gautam, S, Lu, DDC, 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, DDC 2020, 'Single phase NTD PLL for fast dynamic response and operational robustness under abnormal grid condition', Electric Power Systems Research, vol. 180.
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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.
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-BASEL, vol. 10, no. 9.
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Geissinger, A, Laurell, C, Öberg, C, Sandström, C, Sick, N & Suseno, Y 2020, 'Social media analytics for knowledge acquisition of market and non-market perceptions in the sharing economy', Journal of Knowledge Management.
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© 2020, Emerald Publishing Limited. Purpose: Using the case of Foodora, this paper aims to assess the impact of technological innovation of an emerging actor in the sharing economy through stakeholders’ perceptions in the market and non-market domains. Design/methodology/approach: Using a methodological approach called social media analytics (SMA) to explore the case of Foodora, 3,250 user-generated contents in social media are systematically gathered, coded and analysed. Findings: The findings indicate that, while Foodora appears to be a viable provider in the marketplace, there is mounting public concern about the working conditions of its employees. In the market domain, Foodora manages its status as an online delivery platform and provider well, but at the same time, it struggles with its position in the non-market sphere, suggesting that the firm is vulnerable to regulatory change. These insights highlight the importance of simultaneously exploring and balancing market and non-market perceptions when assessing the impact of disruptive innovation. Originality/value: This study offers originality by providing an integrative approach to consider both the market and non-market domains. It is also novel in its use of SMA as a tool for knowledge acquisition and management to evaluate the impact of emerging technologies in the sharing economy.
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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Ghadi, MJ, 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 Compressed Air Energy Storage Systems', IEEE Transactions on Smart Grid, pp. 1-1.
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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.
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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.
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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.
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Ghorbani, F, Fathi, F, Aghebati-Maleki, L, Abolhasan, R, Rikhtegar, R, Dolatabadi, JEN, Babaloo, Z, Khalilzadeh, B, Warkiani, ME, 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.', Eur J Pharm Sci, 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.
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© 2020 by the authors. 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.
Giubilato, R, Vayugundla, M, Schuster, MJ, Stuerzl, 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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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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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.
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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.
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© 2020 American Society of Civil Engineers. Across the globe, rapid urbanization demands the construction of tunnels within the vicinity of high-rise buildings supported on piled raft foundations. As a consequence, ground movements caused by such tunneling works could interfere with the serviceability of the foundations. Hence, the prediction of tunnel-induced ground deformation is of utmost importance. In the current study, a three-dimensional numerical analysis was carried out to study the response of an existing piled raft foundation with a 2 × 2 pile arrangement subjected to ground movements induced by a 6 m-diameter tunnel. The results obtained from the numerical simulations were further compared with a field study. The field measured lateral pile deflections were found to be in fair agreement with those computed using the present method. The numerical analysis was then extended to understand the influence of various factors, such as tunnel diameter, tunnel longitudinal axis, pile diameter, pile position from tunnel, and soil stiffness on the response of pile raft in the vicinity of an adjacent tunnel. The results obtained from the parametric study was finally summarized in simple semi-empirical equations to estimate the maximum lateral deflection and bending moment for both short and long piles.
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.
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© 2020 Elsevier B.V. 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, Oberst, S & Wang, X 2020, 'An experimentally validated rubber shear spring model for vibrating flip-flow screens', Mechanical Systems and Signal Processing, vol. 139.
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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, Y, Zhang, L, Yu, K & Liu, R 2020, 'Exploring Uplink Achievable Rate for HPO MIMO Through Quasi-Monte Carlo and Variance Reduction Techniques', IEEE ACCESS, vol. 8, pp. 75874-75883.
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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, 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.
Gravina da Rocha, C, El Ghoz, HBC & Guadanhim, S 2020, 'A model for implementing product modularity in buildings design', Engineering, Construction and Architectural Management.
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© 2019, Emerald Publishing Limited. Purpose: The purpose of this paper is to examine the fundamental underpinnings of product modularity and how these can be adapted to construction and its specificities (e.g. one-off products delivered by temporary supply chains) to create a model to design modular buildings. Design/methodology/approach: This research adopts a design science research approach. Explanation I (substantive theory devising based on the analysis of an artefact ‒ a low-income housing project) is used, followed by Solution Incubation (a model to implement product modularity in buildings design). Findings: The model allows product modularity to be implemented at distinct levels (i.e. building, systems and components) at a single stage (building design), different from manufacturing where each level is considered at a distinct stage. This is in line with the project investigated: modularity was considered for house layouts, roof types and gable formats. Practical implications: The model provides a hands-on tool for practitioners to design modular buildings. The low-income project is also extensively detailed: three-dimensional models, floor plans and conceptual diagrams (outlining how fundamental underpinnings were applied at each level) are presented. There is a lack of comprehensive accounts such as the one presented here to demonstrate the application of product modularity in real-world projects. Originality/value: This paper identifies and adapts the fundamental underpinnings of product modularity to construction, and it details how these were applied to a low-income housing design at distinct levels. Based on these two endeavours, a model to implement product modularity in buildings design is proposed.
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, 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, 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.
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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Guo, K, Guo, Y & Li, J 2020, 'Decoupling Control Analysis of a Flux Reversal Linear Rotary Permanent Magnet Actuator', Journal of Electrical Engineering and Technology, vol. 15, no. 4, pp. 1693-1704.
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© 2020, The Korean Institute of Electrical Engineers. In order to meet the requirement of industrial production, a control system of a novel flux reversal linear rotary permanent magnet actuator (FR-LRPMA) is developed in this paper, and the electromagnetic and motion decoupled process is analyzed in detail. Firstly, the control equation of FR-LRPMA is derived in the nine phase stator coordinate system and dual dq-axis mover coordinate system. Then the control model of FR-LRPMA is built in MATLAB/Simulink using improved space vector pulse width modulation (SVPWM), and the linear, rotary and spiral motions of the proposed actuator are achieved. Finally, the prototype of the actuator is manufactured, and the experiment platform of FR-LRPMA is set up to measure the cogging torque and linear detent force, validating the simulation results. It is concluded that the control system of FR-LRPMA can be converted into two independent single freedom motors by dual dq transformation. The improved SVPWM is effective in the control system, and the method can be used to solve the electromagnetic and motion decoupling problems.
Guo, YC, Xiao, SH, Shi, SW, Zeng, JJ, Wang, WQ & Zhao, HC 2020, 'Axial compressive behavior of concrete-filled FRP-steel wire reinforced thermoplastics pipe hybrid columns', Composite Structures, vol. 244.
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© 2020 Elsevier Ltd This paper presents investigations on a new column form, namely, concrete-filled fiber-reinforced polymer-steel wire reinforced thermoplastics pipe (FRP-SRTP) columns (CFFSCs) which consist of a SRTP tube with an outer FRP confinement system and concrete filled in the core area. A series of axial compression tests were performed on CFFSCs, and parameters including the number of FRP layers, the FRP fiber type and the clear spacing of the FRP rings were investigated. The test results demonstrate that the concrete-filled SRTP tube columns have an excellent ductility and the outer FRP confinement system provides efficient confinement to CFFSCs. The dilation behavior of the concrete in CFFSCs with polyethylene terephthalate FRP is different to that of the concrete in CFFSCs with carbon FRP. Four existing design-oriented models are adopted to generate the ultimate axial stresses and their corresponding strains of concrete in CFFSCs. By comparing the test results and the theoretical predictions, it is found that the existing models provides inaccurate predictions for CFFSCs. The new column form is particularly suitable as a form of standing support for underground mines, where a novel supporting system requires both a large deformation capacity and a good structural integrity.
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.
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© 2020 Elsevier Ltd The limited adsorption capacity of the substrate and the concentration of dissolved oxygen in constructed wetlands (CWs) have inhibited their ability to efficiently remove polycyclic aromatic hydrocarbons (PAHs) from wastewater. Presently, biochar and activated carbon modified with Fe3+ and Mn4+ were used as effective sorbents in the removal of benzofluoranthene (BbFA), a typical PAH, in CW microcosms. The addition of metallic ions embedded carbon increased NO3-N accumulation by the reduction of Fe3+ and Mn4+, which led to improved BbFA degradation. Additionally, plant adsorption in root and stem sections were observed separately. The abundance of PAH-degrading microbes in the rhizosphere substrate was higher with the metallic ions embedded carbon than control group. The Fe3+, Mn4+ and NO3-N served as electron acceptors increased BbFA microbial degradation. The removal pathways of BbFA in the modified CWs were proposed which involved settlement in the substrate, plant absorption, and microbial degradation.
Gupta, A, Agrawal, RK, Kirar, JS, Andreu-Perez, J, Ding, W-P, Lin, C-T & Prasad, M 2020, 'On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-13.
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Gupta, A, Agrawal, RK, Kirar, JS, Kaur, B, Ding, W, Lin, CT, 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.
Hadgraft, RG & Kolmos, A 2020, 'Emerging learning environments in engineering education', Australasian Journal of Engineering Education.
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© 2020, © 2020 Engineers Australia. Three major challenges, sustainability, the fourth industrial revolution, and employability, will require new types of engineering programs, to help students develop skills in cross-disciplinarity, complexity, and contextual understanding. Future engineering students should be able to understand the needs for technological, sustainable solutions in context. The engineering graduates should be able to act in complex and chaotic situations. The question is how engineering institutions are responding now and how they should respond in the future. This article analyses the general responses from engineering education over the last 20 years. These responses are student-centred learning, integration of theory and practice, digital and online learning, and the definition of professional competencies. Examples are given of institutions that are already applying several of these components in the curriculum. On the long-term horizon, more personalised curriculum models are emerging based on students developing and documenting their own learning and career trajectories, as part of their lifelong learning strategy.
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, B & Rothberg, S 2020, 'Establishing correction solutions for Scanning Laser Doppler Vibrometer measurements affected by sensor head vibration'.
Hamidi, BA, Hosseini, SA, Hayati, H & Hassannejad, R 2020, 'Forced axial vibration of micro and nanobeam under axial harmonic moving and constant distributed forces via nonlocal strain gradient theory', MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES.
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Han, P, Kimura, F & Sandu, S 2020, 'Household-level analysis of the impacts of electricity consumption on welfare and the environment in Cambodia: Empirical evidence and policy implications', Economic Modelling.
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© 2019 Cambodia's biomass consumption is the most dominant energy source at residential sector, and its use is mainly for cooking and heating which could affect health due to indoor air pollution. The biomass is mainly sourced from wood cutting and forest-encroachment that could impact the environment due to reduction of forest at considerable scale. By using the data 2015 of Cambodia Socio-Economic Survey, the study investigates the impacts of electricity consumption on household welfare, such as earnings and the school performance of children in the households, and further to investigate its impacts on the environment. The study found that household's access to electricity with ability to spend on electricity consumption contributes to the positive household welfare effects and environment via a reduction of biomass consumption, and the more household spends on biomass, the more they are prone to sickness of lung problem. The study also confirmed the important role of human capital formation for the positive impact on the welfare and the environment. These findings lead to policy implications that would improve affordable access to electricity to ensure that all households can use electricity for their basic needs and productivity, and also to reduce the negative effects on environment.
Han, Y, Deng, Y, Cao, Z & Lin, CT 2020, 'An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making', Neural Computing and Applications.
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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, M, Nguyen, D & Krunz, M 2020, 'Cognitive Networks with In-band Full-duplex Radios: Jamming Attacks and Countermeasures', IEEE Transactions on Cognitive Communications and Networking.
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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.
Handayani, K, Filatova, T, Krozer, Y & Anugrah, P 2020, 'Seeking for a climate change mitigation and adaptation nexus: Analysis of a long-term power system expansion', APPLIED ENERGY, vol. 262.
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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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© 2020, The Author(s). State 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, 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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Hao, Q, Jia, G, Wei, W, Vinu, A, Wang, Y, Arandiyan, H & Ni, BJ 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.
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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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Hao, Z, Cook, K, Canning, J, Chen, HT & Martelli, C 2020, '3-D printed smart orthotic insoles: Monitoring a person's gait step by step', IEEE Sensors Letters, vol. 4, no. 1.
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© 2017 IEEE. 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.
Hasanpour, S, Siwakoti, Y, Mostaan, A & Blaabjerg, F 2020, 'New Semi-Quadratic High Step-Up DC/DC Converter for Renewable Energy Applications', IEEE Transactions on Power Electronics, pp. 1-1.
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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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Hassan, M & Liu, D 2020, 'PPCPP: A Predator–Prey-Based Approach to Adaptive Coverage Path Planning', IEEE Transactions on Robotics, pp. 1-18.
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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 & BIOELECTRONICS, vol. 155.
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Hayat, T, Afzal, MU, Ahmed, F, Zhang, S, Esselle, KP & Vardaxoglou, JYC 2020, 'Low-Cost Ultra-Wideband High-Gain Compact Resonant Cavity Antenna', IEEE Antennas and Wireless Propagation Letters, pp. 1-1.
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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 and Energy Systems, vol. 116.
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© 2019 Elsevier Ltd To reduce the emissions of greenhouse gases and air pollutants, various low-emission measures have been taken in the power system, which are gradually intensifying the interdependency among different energy systems. Considering the carbon trading scheme and different air pollutant control technologies, this paper proposes an environmental economic dispatch model for the coordinated operation of an integrated regional energy system, which consists of a regional electricity supply network and a natural gas network, along with district energy hubs. Each energy hub contains a combined heat and power unit, a CO2-capture-based power to gas facility, a heat pump, a gas furnace and different energy storage facilities. To achieve an optimized balance between operational cost and emissions during the environmental economic dispatch of this integrated regional energy system, a price-based integrated demand response program is introduced in the energy hub. Then the proposed model is converted into a mixed-integer linear programming problem to find solutions efficiently. Case studies are carried out to demonstrate the effectiveness of proposed environmental economic dispatch model of the integrated regional energy system.
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.
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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, 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%.
Heidary, A, Radmanesh, H, Bakhshi, A, Samandarpour, S, Rouzbehi, K & Shariati, N 2020, 'A Compound Ferroresonance Overvoltage and Fault Current Limiter for Power System Protection', IET Energy Systems Integration.
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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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Heidary, A, Radmanesh, H, Rouzbehi, K & CheshmehBeigi, HM 2020, 'A Multifunction High-Temperature Superconductive Power Flow Controller and Fault Current Limiter', IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, vol. 30, no. 5.
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Heidary, A, Radmanesh, H, Rouzbehi, K, Mehrizi-Sani, A & Gharehpetian, GB 2020, 'Inductive fault current limiters: A review', Electric Power Systems Research, vol. 187, pp. 106499-106499.
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Heizmann, H, Ahuja, S & Mastio, E 2020, 'Stuck in Defensive Professionalism: Undermining Organizational Change in an Intellectual Property Law Firm', Journal of Professions and Organization.
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.
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Hendryx, M, Islam, MS, Dong, GH & 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.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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.
Hien, NT, Lan, HN, Huu, TV, Thi, DN, Thi, HVN, Thi, HHC, Tien, VN, Van, TT, Xuan, HV & 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.
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Hill, M, Mason, D, Monteiro Marques, T, Gama Carvalho, M & Tran, N 2020, 'Developing a virus-microRNA interactome using cytoscape', MethodsX, vol. 7.
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© 2019 It is currently difficult to determine the effect of oncogenic viruses on the global function and regulation of pathways within mammalian cells. A thorough understanding of the molecular pathways and individual genes altered by oncogenic viruses is needed for the identification of targets that can be utilised for early diagnosis, prevention, and treatment methods. We detail a logical step-by-step guide to uncover viral-protein-miRNA interactions using publically available datasets and the network building program, Cytoscape. This method may be applied to identify specific pathways that are altered in viral infection, and contribute to the oncogenic transformation of cells. To demonstrate this, we constructed a gene regulatory interactome encompassing Human Papillomavirus Type 16 (HPV16) and its control of specific miRNAs. This approach can be broadly applied to understand and map the regulatory functions of other oncogenic viruses, and determine their role in altering the cellular environment in cancer. Availability and Implementation Cytoscape (Shannon et al. (2003), Smoot et al. (2010)) is freely available at https://cytoscape.org/. • This method allows for the analysis and visualization of large datasets to generate an interactome that integrates key players of molecular biology • This approach may be applied to any oncogenic virus to map its regulatory functions, and its secondary impact on gene regulation via microRNAs.
Hirsimaki, C, Outram, JG, Millar, GJ & Altaee, A 2020, 'Process simulation of High pH reverse osmosis systems to facilitate reuse of coal seam gas associated Water', Journal of Environmental Chemical Engineering, pp. 104122-104122.
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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, DT, Nguyen, DN, Alsheikh, MA, Gong, S, Dutkiewicz, E, Niyato, D & Han, Z 2020, '"Borrowing Arrows with Thatched Boats": The Art of Defeating Reactive Jammers in IoT Networks', IEEE Wireless Communications Magazine.
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 AND SOIL POLLUTION, vol. 231, no. 1.
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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.
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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, VT, Phung, MD, Dinh, TH & Ha, QP 2020, 'System Architecture for Real-Time Surface Inspection Using Multiple UAVs', IEEE Systems Journal, pp. 1-12.
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Hong, L, Ju, S, Yang, Y, Zheng, J, Xia, G, Huang, Z, Liu, X & Yu, X 2020, 'Hollow-shell structured porous CoSe2 microspheres encapsulated by MXene nanosheets for advanced lithium storage', SUSTAINABLE ENERGY & FUELS, vol. 4, no. 5, pp. 2352-2362.
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Hoque, MAA, Pradhan, B & Ahmed, N 2020, 'Assessing drought vulnerability using geospatial techniques in northwestern part of Bangladesh', Science of the Total Environment, vol. 705.
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© 2019 Elsevier B.V. Drought is a damaging and costly natural disaster that frequently affects many climatic regions in the world. A multi-criteria-based approach for integrated spatial drought vulnerability mapping that combines all drought categories is required to generate detailed vulnerability information for formulating drought mitigation strategies. This study presents a spatial multi-criteria integrated approach for mapping comprehensive drought vulnerability using geospatial techniques and an analytical hierarchy process (AHP). The developed approach was applied in the northwestern region of Bangladesh to justify its applicability. A total of 17 criteria under 4 drought categories, namely, meteorological, agricultural, hydrological and socio-economic, were selected. Moreover, spatial layers for each criterion were developed. AHP was used to calculate the weights for each criterion and drought types using pair-wise comparison matrices. Individual categories of drought and overall drought vulnerability maps were developed using the weighted overlay technique by integrating the corresponding criteria. The produced maps effectively defined the spatial extents and levels (e.g. normal, mild, moderate, severe and extreme) of drought vulnerability. Results demonstrated that approximately 77% of the total area of the north-western region of Bangladesh was moderately to extremely vulnerable to drought. The output of the developed approach was successfully validated using the receiver operating characteristics and area under the curve techniques. The findings suggest that the proposed approach is highly effective in mapping comprehensive drought vulnerability for formulating strong drought mitigation strategies.
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.
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© The Author(s) 2019. 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.
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, P 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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Hossain, SI, Gandhi, NS, Hughes, ZE & Saha, S 2020, 'The role of SP-B1-25 peptides in lung surfactant monolayers exposed to gold nanoparticles', Physical Chemistry Chemical Physics.
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Lung surfactant (LS) monolayers that continuously expand and compress during breathing cycles, act as the first line barrier for inhaled nanoparticles. It is known that nanoparticles which adsorb to the...
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.
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Hossain, SMG & McLaughlan, RG 2020, 'Non-equilibrium 2, 4-DCP uptake onto pine chips from aqueous solutions', ENVIRONMENTAL TECHNOLOGY.
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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.
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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.
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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, Zhao, S, Cheng, B, Chen, S & Chen, J 2020, 'Decentralized Real-Time Optimization of Voltage Reconfigurable Cloud Computing Data Center', IEEE Transactions on Green Communications and Networking, vol. 4, no. 2, pp. 577-592.
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© 2017 IEEE. Dynamic Voltage and Frequency Scaling, and Adaptive Body Biasing are increasingly adopted hardware techniques to improve energy efficiency of multi-core servers by adjusting reconfigurable supply and body bias voltages. Existing algorithms cannot fulfill the potential of the techniques because random variations of workload and background traffic can lead to coupling of voltage configurations over time and hinder effective real-time reconfigurations. This paper proposes a new approach which enables multi-core servers to optimize in real-time their configurations under random traffic variations. The approach asymptotically minimizes the time-averaged energy consumption of cloud computing while maintaining platform stability in a fully decentralized fashion. Lyapunov optimization is employed to decouple and separately optimize the voltage configuration, inter- and intra-server offloading schedules among servers and over time. The voltage configuration which is non-convex is proved to increasingly exhibit convexity with growing workloads. The optimality loss from the non-convexity asymptotically diminishes. Simulations show our approach dramatically reduces the power if the cloud is lightly loaded, or converts the power to processing capacity otherwise. Embraced by theoretical breakthroughs, the approach can potentially revolutionize cloud computing.
Hou, S, Ni, W, Zhao, S, Cheng, B, Chen, S & Chen, J 2020, 'Frequency-Reconfigurable Cloud Versus Fog Computing: An Energy-Efficiency Aspect', IEEE Transactions on Green Communications and Networking, vol. 4, no. 1, pp. 221-235.
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© 2017 IEEE. Cloud and fog computing are two emerging Internet-based collaborative technologies for big data analytics. An interesting question arising is whether the two technologies can resonate with significant gains of energy efficiency, especially in the case where advanced cloud platforms with Dynamic Voltage and Frequency Scaling (DVFS) are considered. This paper answers the question by formulating the optimization of a cloud with and without the assistance of fog, and deriving asymptotically optimal distributed solutions for the two cases. We also identify the critical condition under which fog computing helps the cloud to reduce the time-averaged queue lengths. The condition depends on the configurations of the fog, and the configurations of the connections between the fog and cloud. Extensive simulations exhibit good consistency with our analysis of the conditional benefits of fog computing. Evident from experimental datasets, the proposed fog-assisted cloud platform is able to increase the time-averaged energy efficiency by about 32.2%, and decrease the time-averaged queue length by around 37.0%, compared to a fog-coordinated counterpart where fog nodes only dispatch data and do not process the data.
Hou, Z, Tang, J-F, Ferrie, C, Xiang, G-Y, Li, C-F & Guo, G-C 2020, 'Experimental realization of self-guided quantum process tomography', PHYSICAL REVIEW A, vol. 101, no. 2.
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Hu, C, Liu, X, Lu, J & Wang, CH 2020, 'Operations scheduling of waste-to-energy plants under uncertainty', Journal of Cleaner Production, vol. 253.
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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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© 2017 IEEE. The advancements in smart power grid and the advocation of 'green communications' have inspired the wireless communication networks to harness energy from ambient environments and operate in an energy-efficient manner for economic and ecological benefits. This article presents a contemporary review of recent breakthroughs on the utilization, redistribution, trading and planning of energy harvested in future wireless networks interoperating with smart grids. This article starts with classical models of renewable energy harvesting technologies. We embark on constrained operation and optimization of different energy harvesting wireless systems, such as point-to-point, multipoint-to-point, multipoint-to-multipoint, multi-hop, and multi-cell systems. We also review wireless power and information transfer technologies which provide a special implementation of energy harvesting wireless communications. A significant part of the article is devoted to the redistribution of redundant (unused) energy harvested within cellular networks, the energy planning under dynamic pricing when smart grids are in place, and two-way energy trading between cellular networks and smart grids. Applications of different optimization tools, such as convex optimization, Lagrangian dual-based method, subgradient method, and Lyapunov-based online optimization, are compared. This article also collates the potential applications of energy harvesting techniques in emerging (or upcoming) 5G/B5G communication systems. It is revealed that an effective redistribution and two-way trading of energy can significantly reduce the electricity bills of wireless service providers and decrease the consumption of brown energy. A list of interesting research directions are provided, requiring further investigation.
Hu, 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.', The Science of the total environment, 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, 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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Hua, J, Wang, G, Huang, ML, Hua, S & Yang, S 2020, 'A Visual Approach for the SARS (Severe Acute Respiratory Syndrome) Outbreak Data Analysis', International Journal of Environmental Research and Public Health, vol. 17, no. 11, pp. 1-16.
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Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades in the world. SARS-CoV (Severe Acute Respiratory Syndrome
Associated Coronavirus) is a member of the coronavirus family, so its study is useful for relevant
virus data research. In this work, we conduct a proposed approach that is non-medical/clinical,
generate graphs from five features of the SARS outbreak data in five countries and regions, and
offer insights from a visual analysis perspective. The results show that prevention measures such as
quarantine are the most common control policies used, and areas with strict measures did have
fewer peak period days; for instance, Hong Kong handled the outbreak better than other areas. Data
conflict issues found with this approach are discussed as well. Visual analysis is also proved to be a
useful technique to present the SARS outbreak data at this stage; furthermore, we are proceeding to apply a similar methodology with more features to future COVID-19 research from a visual analysis
perfective.
Huang, C, Yao, L, Wang, X, Benatallah, B & Sheng, QZ 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, 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.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 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, Liu, F & Zhang, Y 2020, 'Overlapping Community Discovery for Identifying Key Research Themes', IEEE Transactions on Engineering Management, pp. 1-13.
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Huang, L, Liu, Z, Wu, C & Liang, J 2020, 'Interaction between a tunnel and alluvial valley under plane SV waves of earthquakes by IBIEM', European Journal of Environmental and Civil Engineering.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This paper investigates the dynamic interaction between a lined tunnel and an alluvial valley under plane SV waves with the indirect boundary integral equation method (IBIEM). The impact of different parameters on the displacement of sedimentary valley and the dynamic stress concentration factors (DSCF) of the lining inner and outer walls are studied. The dynamic response of a tunnel embedded in an alluvial valley is considerably different from that of a tunnel not embedded in an alluvial valley. Numerical results indicate that under the same frequency, different buried depths can change the distribution of displacement in the sedimentary area. The deeper the tunnel is buried, the more resonance points the response spectrum curve has. In general, the DSCF on the inner surfaces of the tunnels embedded in the sedimentary valley is obviously greater than that of the tunnels in the half space. However, the DSCF on the outer surfaces of the former is smaller than that of the latter. The results reported here will provide a quantitative basis to the security assessment and seismic design of lined tunnels.
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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© 2013 IEEE. Cyber-Physical-Social System (CPSS) provides users secure and high-quality mobile service applications to share and exchange data in the cyberspace and physical world. With the explosive growth of data, it is necessary to introduce cloud storage service, which allows devices frequently resort to the cloud for data storage and sharing, into CPSS. In this paper, we propose a data storage and sharing scheme for CPSS with the help of cloud storage service. Since data integrity assurance is an inevitable problem in cloud storage, we first design a secure and efficient data storage scheme based on the technology of public auditing and bilinear map, which also ensures the security of the verification. In order to meet the real-time and reliability requirements of the CPSS, the rewards of timeliness incentive and effectiveness incentive are considered in the scheme. Secondly, based on the proposed storage scheme and ElGamal encryption, we propose a lightweight access model for users to access the final data processed by cloud server. We formally prove the security of the proposed scheme, and conduct performance evaluation to validate its high efficiency. The experimental results show that the proposed scheme has lower overheads in communication and access as compared to the technique CDS.
Huang, M, Liu, A, Xiong, NN, Wang, T & Vasilakos, AV 2020, 'An effective service-oriented networking management architecture for 5G-enabled internet of things', Computer Networks, vol. 173.
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© 2020 The development of a 5G-enabled Internet of Things has led to a dramatic increase in network traffic load, which has presented tremendous challenges to network management. In this paper, a service-oriented network architecture is proposed to support the effective management of 5G-enabled IoT systems. This architecture effectively reduces the traffic load and simplifies network management by introducing a service aggregation and caching (SAaC) scheme. Specifically, SAaC first breaks through the data-centric network architecture by converting data into services. Then, SAaC significantly reduces traffic load and energy consumption by service aggregation. Finally, SAaC introduces service caching, and each content router caches new services locally after aggregating received services so that user requests are handled at the network layer. Experimental results demonstrate that compared with traditional solutions, the SAaC scheme improves the request response time by 20.52%–56.09%, reduces the traffic load by 10.85%–37.67%, and reduces energy consumption by more than 50%.
Huang, ML, Lu, L, Ren, X, Cui, C & Luo, Y 2020, 'Tensor mutual information and its application', Concurrency and Computation: Practice and Experience, vol. 2020, pp. 1-10.
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Correlation analysis has long been a question of great interest in measuring the relationship among different variables and has been applied in many fields, such as dimension reduction, classification, and so on. However, current methods of correlation analysis take into account the linear relationship between multiple variables and only few works on nonlinear interaction of two variables have been considered. In this article, we first present a nonlinear analysis method of multiple (two or more) variables based on mutual information for tensor analysis (MITA). In addition, we extend the mutual‐information matrix analysis directly to MITA and show the multivariable mutual information formula based on Venn diagram. Experiments on multiview dimension reduction, including attacking internet traffic prediction, advertisement classification, and biometric structure prediction illustrate the effectiveness of the proposed method, especially in the case of low‐dimensional subspace.
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, 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.
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Huang, S, Xu, Z, Tsang, IW & Kang, Z 2020, 'Auto-weighted multi-view co-clustering with bipartite graphs', Information Sciences, vol. 512, pp. 18-30.
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© 2019 Co-clustering aims to explore coherent patterns by simultaneously clustering samples and features of data. Several co-clustering methods have been proposed in the past decades. However, in real-world applications, datasets are often with multiple modalities or composed of multiple representations (i.e., views), which provide different yet complementary information. Hence, it is essential to develop multi-view co-clustering models to solve the multi-view application problems. In this paper, a novel multi-view co-clustering method based on bipartite graphs is proposed. To make use of the duality between samples and features of multi-view data, a bipartite graph for each view is constructed such that the co-occurring structure of data can be extracted. The key point of utilizing the bipartite graphs to deal with the multi-view co-clustering task is to reasonably integrate these bipartite graphs and obtain an optimal consensus one. As for this point, the proposed method can learn an optimal weight for each bipartite graph automatically without introducing an additive parameter as previous methods do. Furthermore, an efficient algorithm is proposed to optimize this model with theoretically guaranteed convergence. Extensive experimental results on both toy data and several benchmark datasets have demonstrated the effectiveness of the proposed model.
Huang, 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, WC, Yam, YS, 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.
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© 2020 Elsevier B.V. This study investigated real world in-use vehicle emissions using two regulatory techniques simultaneously, namely on-road remote sensing (RS) systems and air quality (AQ) monitoring stations, aiming to provide a full pollution profile from tailpipe to roadside and atmosphere. Two large AQ and RS datasets collected during 2012–2018 were analyzed. The effects of various emission control programmes on the trends of tailpipe emissions and air quality were evaluated. Correlations between tailpipe emissions and roadside and ambient air quality were also explored. The results showed a decreasing trend of NO2 at both roadside and ambient AQ stations from 2013 to 2016, which was attributed to the intensive implementation of a series of vehicle emissions control programmes. Although NO2 was decreasing, O3 was generally increasing for all AQ stations. AQ data showed that O3 had little correlation with either NO2 or NOx, but was mainly determined by NO2/NOx ratio. Roadside NO2/NOx ratio increased first and then decreased or stabilized after 2014, while ambient NO2/NOx ratio increased steadily. RS data showed that the overall NO decreased quickly during 2012–2015 and then decreased moderately after 2015. The decrease was mainly attributed to the effective NO reduction from LPG vehicles. However, diesel NO remained high and reduced relatively slowly during the study period. Gasoline vehicles were relatively clean compared with LPG and diesel vehicles. Finally, good correlations were demonstrated between NO measured by RS sites and NOx measured by roadside AQ stations, indicating that vehicle emissions were the major contributor to roadside NOx pollution. Ambient NOx emissions could be affected by various sources, leading to different correlation levels between RS and ambient AQ results.
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.
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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, 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, pp. 1-1.
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Huang, Y, Zhu, F, Porter, AL, Zhang, Y, Zhu, D & Guo, Y 2020, 'Exploring Technology Evolution Pathways to Facilitate Technology Management: From a Technology Life Cycle Perspective', IEEE Transactions on Engineering Management.
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IEEE Technological innovation is a dynamic process that spans the life cycle of an idea, from scientific research to production. Within this process, there are often a few key innovations that significantly impact a technology's development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In this article, we present a framework for identifying the technology's main evolutionary pathway. What is unique about this framework is that we introduce new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology's development. We also show how information about a family of patents can be used to build a comprehensive patent citation network. Finally, we apply integrated approaches of main path analysis (MPA)—namely global MPA and global key-route main analysis—for extracting technological trajectories at different technological stages. We illustrate this approach with dye-sensitized solar cells (DSSCs), a low-cost solar cell belonging to the group of thin-film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision makers manage the technological stages of their innovation processes more effectively.
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 research and applications.', Angewandte Chemie (International ed. in English).
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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.
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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Huynh, P, Phan, KT, Liu, B & Ross, R 2020, 'Throughput analysis of buffer-aided decode-and-forward wireless relaying with RF energy harvesting', Sensors (Switzerland), vol. 20, no. 4.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In this paper, we investigated a buffer-aided decode-and-forward (DF) wireless relaying system over fading channels, where the source and relay harvest radio-frequency (RF) energy from a power station for data transmissions. We derived exact expressions for end-to-end throughput considering half-duplex (HD) and full-duplex (FD) relaying schemes. The numerical results illustrate the throughput and energy efficiencies of the relaying schemes under different self-interference (SI) cancellation levels and relay deployment locations. It was demonstrated that throughput-optimal relaying is not necessarily energy efficiency-optimal. The results provide guidance on optimal relaying network deployment and operation under different performance criteria.
Huynh, TV, Wilkinson, RH, Lech, M & Cheng, E 2020, 'A Hybrid Neural Network for Graph-Based Human Pose Estimation From 2D Images', IEEE ACCESS, vol. 8, pp. 52830-52840.
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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.
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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, pp. 1-45.
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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.
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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.
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© 2020 Bilateral transactions hedge both sides against uncertain price and volume risks of day-ahead auction and make up major portions of trading in electricity markets. Peer-to-peer bilateral transactions avoid broker fees but involve challenges of balancing between cooperative and competitive strategies for multi-round negotiations. To solve these challenges, this paper develops novel utility-based and adaptive agent-tracking strategies for bilateral negotiations. Relying on bilateral transaction volume and utility curves determined over a price range during unilateral pre-negotiation, utility-based strategies are developed for generation company (GenCo) agent (load serving entity (LSE) agent) to offer (bid) volumes and prices during multi-round bilateral negotiations. GenCo agent is also equipped with a new adaptive agent-tracking strategy that estimates reservation price of each LSE agent by Bayesian learning and updates the estimates in each round. The adaptive agent-tracking strategy facilitates cooperative yet competitive responses. Integration of new bilateral negotiation strategies with existing day-ahead auction in a renowned agent-based platform also enables combined simulation of the two market types. The case study demonstrates that the adaptive agent-tracking strategy empowers GenCoagents to swing bilateral negotiation results in their favor and yield 7% more payoff than the utility-based strategy, while achieving 100% improvement in frequency of failure of negotiation.
Indraratn, B, Korkitsuntornsan, W & Thanh, TN 2020, 'Influence of Kaolin content on the cyclic loading response of railway subgrade', TRANSPORTATION GEOTECHNICS, vol. 22.
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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.
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© 2020 American Society of Civil Engineers. 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.
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© 2019 American Society of Civil Engineers. This paper presents a study on the deformation and degradation responses of railway ballast using large-scale laboratory testing and computational modeling approaches. A series of large-scale triaxial tests were carried out to investigate the ballast breakage responses under cyclic train loading subjected to varying frequencies, f=10-40 Hz. The role of recycled rubber energy-absorbing mats (REAMs) on reducing ballast breakage was also examined. Laboratory test results show that the ballast experiences significant degradation (breakage) and deformation, while the inclusion of REAMs can reduce the ballast breakage up to about 35%. Numerical modeling using the coupled discrete-continuum approach [coupled discrete-element method-finite-difference method (DEM-FDM)] is introduced to provide insightful understanding on the deformation and breaking of ballast under cyclic loading. Discrete ballast grains were simulated by bonding of many circular elements together at appropriate sizes and locations. Selected cylinders located at corners, surfaces, and sharp edges of the simulated particles were connected by parallel bonds; and when those bonds were broken, they were considered to represent ballast breakage. The subgrade and rubber mat were simulated as a continuum media using FDM. The predicted axial strain ϵa and volumetric strain ϵv obtained from the coupled DEM-FDM model are in good agreement with those measured in the laboratory. The model was then used to explore micromechanical aspects of ballast aggregates including the evolution of particle breakage, contact force distributions, and orientation of contacts during cyclic loading. These findings are imperative for a more insightful understanding of the breakage behavior of ballast from the perspective of microstructure characteristics of discrete particle assemblies.
Indraratna, B, 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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Iqbal, S, Parker, LM, Everest-Dass, AV, Moh, ESX, Sayyadi, N, Hutchinson, MR & Packer, NH 2020, 'Lipopolysaccharide and Morphine-3-Glucuronide-Induced Immune Signalling Increases the Expression of Polysialic Acid in PC12 Cells.', Molecular neurobiology, vol. 57, no. 2, pp. 964-975.
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Polysialic acid (polySia), a long homopolymer of 2,8-linked sialic acids, is abundant in the embryonic brain and is restricted largely in adult brain to regions that exhibit neurogenesis and structural plasticity. In the central nervous system (CNS), polySia is highly important for cell-cell interactions, differentiation, migration and cytokine responses, which are critical neuronal functions regulating intercellular interactions that underlie immune signalling in the CNS. In recent reports, a metabolite of morphine, morphine-3-glucuronide (M3G), has been shown to cause immune signalling in the CNS. In this study, we compared the effects of neurite growth factor (NGF), lipopolysaccharide (LPS) and M3G exposure on the expression of polySia in PC12 cells using immunocytochemistry and Western blot analysis. PolySia was also extracted from stimulated cell proteins by endo-neuraminidase digestion and quantitated using fluorescent labelling followed by HPLC analysis. PolySia expression was significantly increased following NGF, M3G or LPS stimulation when compared with unstimulated cells or cells exposed to the TLR4 antagonist LPS-RS. Additionally, we analyzed the effects of test agent exposure on cell migration and the oxidative stress response of these cells in the presence and absence of polySia expression on their cell surface. We observed an increase in oxidative stress in cells without polySia as well as following M3G or LPS stimulation. Our study provides evidence that polySia expression in neuronal-like PC12 cells is influenced by M3G and LPS exposure alike, suggestive of a role of TLR4 in triggering these events.
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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© 1969-2012 IEEE. Modern grid codes demand the integration of voltage support capability with photo-voltaic (PV) generators to ensure a secure operation of power systems. On the other hand, short-term voltage instability (STVI) of distribution networks (DNs) is one of the key issues to be addressed. It can occur due to high proportion of induction motor loads. However, the existing literature lacks a thorough analysis of short-term voltage stability (STVS) of DNs following an asymmetrical fault. Furthermore, an effective voltage-support strategy for PV units, which can improve the STVS while mitigating the excessive voltage swell, is essential for secure operation. Hence, firstly this paper thoroughly investigates the STVS of DNs subjected to asymmetrical faults. It is perceived that voltage support through conventional methods can increase the risk of STVI and excessive voltage swell. Secondly, a new voltage-support strategy is proposed based on the negative sequence voltage at point of common coupling (PCC) aimed at mitigating the STVI and regulating the phase voltages within the margins. The key features of the proposed strategy are, fast and accurate estimation of the network's impedance at PCC is not required and it can be easily re-designed considering the network's dynamic behaviors. The efficacy of the proposed method is validated on two IEEE benchmark test systems, and the results demonstrate the effectiveness in improving the STVS and alleviating over voltage issues.
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, 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.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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.
Jackson, T, Shen, J, Nikolic, S & Xia, G 2020, 'Managerial factors that influence the success of knowledge management systems: A systematic literature review', Knowledge and Process Management, vol. 27, no. 2, pp. 77-92.
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© 2020 John Wiley & Sons Ltd The purpose of this research is to remove the ambiguity that clouds the analysis of knowledge management systems (KMSs). This is because of an overall lack of consensus on how KMSs adapt to the new “knowledge explosion” embraced by the booming “Big Data” hype. In this paper, a refreshing synthesis of literature will uncover benefits and identify gaps in current knowledge. These findings will also be of benefit to researchers and industries as it allows for the holistic analysis of a KMS. This systematic literature review collected 54 papers for qualitative analysis. This analysis led to a synthesis of factors evident in the research and how they could be combined and collected as key categories. Once each factor was categorized, the future directions of research was analysed and documented. The primary factors discussed include (a) formal processes, (b) company culture, (c) top-down support, (d) motivation, (e) clear goals, and (f) quality of KMS. This research has created a baseline for the further evaluation of KMSs in the real world.
Jafari, M, Malekjamshidi, Z, Zhu, J & Khooban, MH 2020, 'Novel Predictive Fuzzy Logic-Based Energy Management System for Grid-connected and Off-grid Operation of Residential Smart Micro-grids', 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.
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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.
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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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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.
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.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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', 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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© 2019, King Abdulaziz University and Springer Nature Switzerland AG. 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 compreh...
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 & 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.
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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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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.
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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.
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.
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© 2019 Elsevier Ltd Constructed wetlands (CWs) have been widely utilized for various types of wastewater treatment due to their merits, including high cost-effectiveness and easy operation. However, a few intrinsic drawbacks have always restricted their application and long-term stability, especially their weak performance at temperatures under 10 °C (low temperatures) due to the deterioration of microbial assimilation and plant uptake processes. The existing modifications to improve CWs performance from the direct optimization of internal components to the indirect adjunction of external resources promoted the wastewater treatment efficiency to a certain degree, but the sustainability and sufficiency of pollutants removal remains a challenge. With the goal of optimizing CW components, the integrity of the CW ecosystem and the removal of emerging pollutants, future directions for research should include radiation plant breeding, improvements to CW ecosystems, and the combination or integration of certain treatment processes with CWs to enhance wastewater treatment effects at low temperatures.
Jiang, J, Ji, S & Long, G 2020, 'Decentralized Knowledge Acquisition for Mobile Internet Applications', World Wide Web.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Mobile internet applications on smart phones dominate large portions of daily life for many people. Conventional machine learning-based knowledge acquisition methods collect users’ data in a centralized server, then train an intelligent model, such as recommendation and prediction, using all the collected data. This knowledge acquisition method raises serious privacy concerns, and also violates the rules of the newly published General Data Protection Regulation. This paper proposes a new attention-augmented federated learning framework that can conduct decentralized knowledge acquisition for mobile Internet application scenarios, such as mobile keyboard suggestions. In particular, the attention mechanism aggregates the decentralized knowledge which has been acquired from each mobile using its own data locally. The centralized server aggregates knowledge without direct access to personal data. Experiments on three real-world datasets demonstrate that the proposed framework performs better than other baseline methods in terms of perplexity and communication cost.
Jiang, P, Li, K, Chen, X, Dan, R & Yu, Y 2020, 'Magnetic and Hydrophobic Composite Polyurethane Sponge for Oil-Water Separation', APPLIED SCIENCES-BASEL, vol. 10, no. 4.
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Jiang, P, Li, R, Lu, H & Zhang, X 2020, 'Modeling of electricity demand forecast for power system', Neural Computing and Applications.
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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.
Jin, D, Zhang, B, Song, Y, He, D, Feng, Z, Chen, S, Li, W & Musial, K 2020, 'ModMRF: A modularity-based Markov Random Field method for community detection', Neurocomputing, vol. 405, pp. 218-228.
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© 2020 Elsevier B.V. Complex networks are widely used in the research of social and biological fields. Analyzing real community structure in networks is the key to the study of complex networks. Modularity optimization is one of the most popular techniques in community detection. However, due to its greedy characteristic, it leads to a large number of incorrect partitions and more communities than in reality. Existing methods use the modularity as a Hamiltonian at the finite temperature to solve the above problem. Nevertheless, modularity is not formalized as a statistical model in the method, which makes many statistical inference methods limited and cannot be used. Moreover, the method uses the sum-product version of belief propagation (BP) and its performance is not as good as the max-sum version, since it calculates per-variable marginal probabilities rather than the joint probability. To address these issues, we propose a novel Markov Random Field (MRF) method by formalizing modularity as an energy function based on the rich structures of MRF to represent properties and constraints of this problem, and use the max-sum BP to infer model parameters. In order to analyze our method and compare it with existing methods, we conducted experiments on both real-world and synthetic networks with ground-truth of communities, showing that the new method outperforms the state-of-the-art methods.
Jin, 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.
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Jones, GT, Siwakoti, YP & Rogers, DJ 2020, 'Active Gate Drive to Increase the Power Capacity of Hard-Switched IGBTs', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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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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Ju, R, Zhou, P, Wen, S, Wei, W, Xue, Y, Huang, X & Yang, X 2020, '3D-CNN-SPP: A Patient Risk Prediction System From Electronic Health Records via 3D CNN and Spatial Pyramid Pooling', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-15.
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Jung, HS, Lee, S & Pradhan, B 2020, 'Sustainable applications of remote sensing and geospatial information systems to earth observations', Sustainability (Switzerland), vol. 12, no. 6.
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© 2020 by the authors. 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.
Jupp, JR & Parkes, M 2020, 'Integrated Construction Enterprise Systems: A Strategic Approach to Model-based Data and Process Management', Automation in Construction, vol. (to appear).
Karami, E, Khalilinia, A, Bali, A & Rouzbehi, K 2020, 'Monte-Carlo-based simulation and investigation of 230 kV transmission lines outage due to lightning', HIGH VOLTAGE, vol. 5, no. 1, pp. 83-91.
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Karimi, M, Croaker, P, Maxit, L, Robin, O, Skvortsov, A, Marburg, S & Kessissoglou, N 2020, 'A hybrid numerical approach to predict the vibrational responses of panels excited by a turbulent boundary layer', Journal of Fluids and Structures, vol. 92, pp. 102814-102814.
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Karimi, M, Maxit, L, Croaker, P, Robin, O, Skvortsov, A, Marburg, S, Noureddine, A & Kessissoglou, N 2020, 'Analytical and numerical prediction of acoustic radiation from a panel under turbulent boundary layer excitation', Journal of Sound and Vibration.
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The vibroacoustic response of a simply supported panel excited by turbulent flow is analytically and numerically investigated. In the analytical model, the radiated sound power is described in terms of the cross spectrum density of the wall pressure field and sensitivity functions for the acoustic pressure and fluid particle velocity. For the numerical model, a hybrid approach based on the finite element method is described in which the cross spectrum of the wall pressure field is represented by a set of uncorrelated wall plane waves. Realisations of the wall pressure field are used as deterministic input loads to the panel. The structural and acoustic responses of the panel subject to turbulent boundary layer excitation are then obtained from an ensemble average of the different realisations. Analytical and numerical results are compared with experimental data measured in an anechoic wind tunnel, showing good agreement. The effect of adding stiffeners on the vibroacoustic response of the panel is also examined using the proposed numerical approach.
Karimidastenaei, Z, Torabi Haghighi, A, Rahmati, O, Rasouli, K, Rozbeh, S, Pirnia, A, Pradhan, B & Kløve, B 2020, 'Fog-water harvesting Capability Index (FCI) mapping for a semi-humid catchment based on socio-environmental variables and using artificial intelligence algorithms.', The Science of the total environment, vol. 708, pp. 135115-135115.
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Fog is an important component of the water cycle in northern coastal regions of Iran. Having accurate tools for mapping the precise spatial distribution of fog is vital for water harvesting within integrated water resources management in this semi-humid region. In this study, environmental variables were considered in prediction mapping of areas with high concentrations of fog in the Vazroud watershed, Iran. Fog probability maps were derived from four artificial intelligence algorithms (Generalized Linear Model, Generalized Additive Model, Generalized Boosted Model, and Generalized Dissimilarity Model). Models accuracy were assessed using Receiver Operating characteristic Curve (ROC). Three social variables were also selected according to their relevance for fog suitability mapping. Finally, Fog-water harvesting Capability Index (FCI) maps were produced by multiplying fog probability by fog suitability maps. The results showed high accuracy in fog probability mapping for the study area, with all models proving capable of identifying areas with high fog concentrations in the south and southeast. For all models, the highest values of importance were obtained for sky view factor and the lowest for slope curvature. Analytic Hierarchy Process results showed the relative importance of social conditioning factors in fog suitability mapping, with the highest weight given to distance to residential area, followed by distance to livestock buildings and distance to road. Based on the fog suitability map, southeast and southern parts of the study area are most suitable for fog water harvesting. The fog spatial distribution maps obtained can increase fog water harvesting efficiency. They also indicate areas for future study with regions where fog is a critical component in the water cycle.
Karmokar, DK & Chen, S-L 2020, 'Slot antenna miniaturization by loading dumbbell-shaped slots and strips, square-shaped loops, and shorting vias', MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, vol. 62, no. 6, pp. 2307-2310.
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Karmokar, DK, Chen, S-L, Thalakotuna, D, Qin, P-Y, Bird, TS & Guo, YJ 2020, 'Continuous Backward-to-Forward Scanning 1-D Slot-Array Leaky-Wave Antenna With Improved Gain', IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 19, no. 1, pp. 89-93.
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Karmokar, DK, Guo, YJ, Chen, S-L & Bird, TS 2020, 'Composite Right/Left-Handed Leaky-Wave Antennas for Wide-Angle Beam Scanning With Flexibly Chosen Frequency Range', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 1, pp. 100-110.
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Kashani, AR, Gandomi, M, Camp, CV & Gandomi, AH 2020, 'Optimum design of shallow foundation using evolutionary algorithms', Soft Computing, vol. 24, no. 9, pp. 6809-6833.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. In the current study, the performance of three evolutionary algorithms, differential algorithm (DE), evolution strategy (ES), and biogeography-based optimization algorithm (BBO), is examined for foundation design optimization. Moreover, four recent variations of evolutionary-based algorithms [i.e., improved differential evolution algorithm based on an adaptive mutation scheme, weighted differential evolution algorithm (WDE), linear population size reduction success-history-based adaptive differential evolution algorithm, and biogeography-based optimization with covariance matrix-based migration] have been tackled for handling the current problem. The objective function is based on the cost of shallow foundation designs that satisfy ACI 318-05 requirements is formulated as the objective function. This study addresses shallow footing optimization with two attitudes, routine optimization, and sensitivity analysis. As a further study, the effect of the location of the column at the top of the foundation is examined by adding two additional design variables. Three numerical case studies are used for both routine and sensitivity analysis. Moreover, the most common evolutionary-based technique, genetic algorithm (GA), is considered as a benchmark to evaluate the proposed methods’ efficiency. Based on the results, there is no algorithm which works as the most efficient solver over all the cases; while, BBO and WDE showed an acceptable performance because of satisfying records in most cases. There were several cases in which GA, DE, and ES were incapable of finding a valid solution which meets all the constraints simultaneously.
Ke, G, Li, S-N, Han, R, Li, R-Z, Liu, Z-M, Qi, Z-P & Liu, Z-Y 2020, 'Study on the propagation law of gas explosion in the space based on the goaf characteristic of coal mine', SAFETY SCIENCE, vol. 127.
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Keall, P, Nguyen, DT, O'Brien, R, Hewson, E, Ball, H, Poulsen, P, Booth, J, Greer, P, Hunter, P, Wilton, L, Bromley, R, Kipritidis, J, Eade, T, Kneebone, A, Hruby, G, Moodie, T, Hayden, A, Turner, S, Arumugam, S, Sidhom, M, Hardcastle, N, Siva, S, Tai, KH, Gebski, V & Martin, J 2020, 'Real-Time Image Guided Ablative Prostate Cancer Radiation Therapy: Results From the TROG 15.01 SPARK Trial', International Journal of Radiation Oncology Biology Physics.
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© 2020 Elsevier Inc. Purpose: Kilovoltage intrafraction monitoring (KIM) is a novel software platform implemented on standard radiation therapy systems and enabling real-time image guided radiation therapy (IGRT). In a multi-institutional prospective trial, we investigated whether real-time IGRT improved the accuracy of the dose patients with prostate cancer received during radiation therapy. Methods and Materials: Forty-eight patients with prostate cancer were treated with KIM-guided SABR with 36.25 Gy in 5 fractions. During KIM-guided treatment, the prostate motion was corrected for by either beam gating with couch shifts or multileaf collimator tracking. A dose reconstruction method was used to evaluate the dose delivered to the target and organs at risk with and without real-time IGRT. Primary outcome was the effect of real-time IGRT on dose distributions. Secondary outcomes included patient-reported outcomes and toxicity. Results: Motion correction occurred in ≥1 treatment for 88% of patients (42 of 48) and 51% of treatments (121 of 235). With real-time IGRT, no treatments had prostate clinical target volume (CTV) D98% dose 5% less than planned. Without real-time IGRT, 13 treatments (5.5%) had prostate CTV D98% doses 5% less than planned. The prostate CTV D98% dose with real-time IGRT was closer to the plan by an average of 1.0% (range, –2.8% to 20.3%). Patient outcomes showed no change in the 12-month patient-reported outcomes compared with baseline and no grade ≥3 genitourinary or gastrointestinal toxicities. Conclusions: Real-time IGRT is clinically effective for prostate cancer SABR.
Kerferd, B, Eggler, D, Karimi, M & Kessissoglou, N 2020, 'Active acoustic cloaking of cylindrical shells in low Mach number flow', Journal of Sound and Vibration.
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The vibro-acoustic responses of a two-dimensional cylindrical shell in low Mach number flow are herein derived. The analytical model takes into account the structural elasticity and coupling of the shell vibration with its interior and exterior acoustic fields in the presence of a moving fluid. The cylindrical shell is modelled using Donnell-Mushtari theory. Taylor transformations are employed to transfer the convected wave equation into the ordinary wave equation which was then solved using scattering theory. Three excitation cases corresponding to a plane wave, an external monopole source and a radial point force applied directly to the shell are considered. Shell circumferential resonances and interior acoustic resonances are identified. Two active control strategies are then applied to acoustically cloak the cylindrical shell at its acoustic and structural resonances. The first control approach employs acoustic control sources in the exterior fluid domain. In the second approach, control forces are applied to directly excite the elastic shell, whereby the structural response is actively modified to manipulate the scattered and radiated acoustic fields arising from plane wave excitation of the shell. Results show that the second approach is superior in terms of both reduced control effort and cloaking of the global exterior domain. For both control approaches, the performance of the active cloak is shown to deteriorate if the convected flow field is not accounted for in the control process.
Khalilpour, KR & Lusis, P 2020, 'Network capacity charge for sustainability and energy equity: A model-based analysis', APPLIED ENERGY, vol. 266.
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Khalilpour, KR & Zafaranloo, A 2020, 'Generic techno-economic optimization methodology for concurrent design and operation of solvent-based PCC processes', International Journal of Greenhouse Gas Control, vol. 99, pp. 103079-103079.
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Khalilpour, KR, Pace, R & Karimi, F 2020, 'Retrospective and prospective of the hydrogen supply chain: A longitudinal techno-historical analysis', International Journal of Hydrogen Energy.
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© 2020 Hydrogen Energy Publications LLC The objective of this study was to investigate the evolution of hydrogen research and its international scientific collaboration network. From the Scopus database, 58,006 relevant articles, published from 1935 until mid-2018, were retrieved. To review this massive volume of publication records, we took a scientometric network analysis approach and investigated the social network of the publication contents based on keywords co-occurrence as well as international collaboration ties. An interesting observation is that despite publications on hydrogen occurring since 1935, the growth of this research field ignited with the Kyoto Protocol of 1997. The publication profile reveals that more than 93% of the existing records have been published over the last two decades. More recently, the accelerated growth of renewables has further motivated hydrogen research with almost 36,000 academic records having been indexed from 2010 till mid-2018. This accounts for ~62% of the total historical publications on hydrogen. The conventional hydrogen production pathway is fossil fuel-based, involving fossil fuel reforming for synthesis gas generation. The keyword analysis also shows a paradigm shift in hydrogen generation to renewables. While all components of hydrogen supply chain research are now growing, the topic areas of biohydrogen and photocatalysis seem to be growing the fastest. Analysis of international collaboration networks also reveals a strong correlation between the increase of collaboration ties on hydrogen research and the publications. Until the 1970s, only 25 countries had collaborated, while this has reached 108 countries as of 2018, with over 17,500 collaboration ties. The collaborations have also evolved into a substantially more integrated network, with a few strong clusters involving China, the United States, Germany, and Japan. The longitudinal network evolution maps also reveal a shift, over the last two decades, from ...
Khan, HA, Castel, A & Khan, MSH 2020, 'Corrosion investigation of fly ash based geopolymer mortar in natural sewer environment and sulphuric acid solution', Corrosion Science, vol. 168.
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© 2020 Elsevier Ltd The objective of this research is to estimate the durability of low-calcium fly ash based geopolymer mortar (FA-GPm) in comparison with sulphate resistant Portland cement mortars (SRPCm) exposed to natural sewer environment. Their performance is also investigated in the sulphuric acid (H2SO4) solution to highlight the difference in the corrosion mechanisms between these two exposure conditions. Mortar samples were removed from natural sewer and 1.5 % sulphuric acid solution after 12, 24 months and 6 months of exposure, respectively. Visual and physical analyses showed greater neutralization and loss in alkalinity in FA-GPm compared to SRPCm. However, mass loss and strength reduction observed for SRPCm was greater compared to FA-GPm. Microstructural analysis showed widespread gypsum crystallization within SRPCm matrix compared to FA-GPm, leading to more severe matrix deterioration. Differences in corrosion mechanism were identified between natural and sulphuric acid exposure conditions which led to the variation in estimated corrosion depth. Data collected from these microstructural and physical investigations were utilized to develop simplified linear models to express the depth of corrosion, surface pH, mass loss and neutralization depth of FA-GPm and SRPCm as a dependent of exposure time, temperature and H2S concentration in natural sewer environments.
Khan, HU, Aruya, JA & Gill, A 2020, 'Web 2.0 Technologies Adoption Barriers for External Contacts and Participation: A Case Study of Federal Establishment of Africa', International Journal of Business Information Systems.
Khan, I, Xu, T, Khan, MSH, Castel, A & Gilbert, RI 2020, 'Effect of Various Supplementary Cementitious Materials on Early-Age Concrete Cracking', JOURNAL OF MATERIALS IN CIVIL ENGINEERING, vol. 32, no. 4.
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Khan, MA, Zhu, Y, Yao, Y, Zhang, P, Agrawal, A & Reece, PJ 2020, 'Impact of metal crystallinity-related morphologies on the sensing performance of plasmonic nanohole arrays', Nanoscale, vol. 12, no. 14, pp. 7577-7585.
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© The Royal Society of Chemistry . Plasmonic nanohole arrays for biosensing applications have attracted tremendous attention because of their flexibility in optical signature design, high multiplexing capabilities, simple optical alignment setup, and high sensitivity. The quality of the metal film, including metal crystallinity and surface roughness, plays an important role in determining the sensing performance because the interaction between free electrons in the metal and incident light is strongly influenced by the metal surface morphology. We systematically investigated the influence of metal crystallinity-related morphologies on the sensing performance of plasmonic nanohole arrays after different metal deposition processes. We utilised several non-destructive nanoscale surface characterisation techniques to perform a quantitative and comparative analysis of the Au quality of the fabricated sensor. We found empirically how the surface roughness and grain sizes influence the permittivity of the Au film and thus the sensitivity of the fabricated sensor. Finally we confirmed that the deposition conditions that provide both low surface roughness and large metal grain sizes improve the sensitivity of the plasmonic sensor.
Khan, MNH, Forouzesh, M, Siwakoti, YP, Li, L & Blaabjerg, F 2020, 'Switched Capacitor Integrated (2n+1)-Level Step-Up Single-Phase Inverter', IEEE TRANSACTIONS ON POWER ELECTRONICS, vol. 35, no. 8, pp. 8248-8260.
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Khan, MNH, Forouzesh, M, Siwakoti, YP, Li, L, Kerekes, T & Blaabjerg, F 2020, 'Transformerless Inverter Topologies for Single-Phase Photovoltaic Systems: A Comparative Review', IEEE Journal of Emerging and Selected Topics in Power Electronics.
Khan, MNH, Siwakoti, YP, Li, L & Blaabjerg, F 2020, 'H-Bridge Zero-Voltage Switch Controlled Rectifier (HB-ZVSCR) Transformerless Mid-Point-Clamped Inverter for Photovoltaic Applications', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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A single-phase transformerless mid-point clamped H-bridge zero-voltage switch-controlled rectifier inverter topology is proposed in this paper for photovoltaic (PV) systems to address the issue of common mode (CM) voltage and leakage currents. Apart from the full H-bridge inverter, the proposed voltage clamping circuit consists of two switches and a full-bridge diode which clamps the AC terminal to the DC midpoint (consisting of two DC-link capacitors) during the freewheeling period. As a result, the common mode voltage is held constant which makes it suitable for the grid-connected PV system. The operating principle and CM effect of the proposed topology are analysed and compared with the conventional topologies. This is followed by the thermal analysis and loss calculation, which shows that the proposed circuit is more efficient over the conventional topologies. Validation is carried out using MATLAB-Simulink using the PLECS toolbox followed by a scale down prototype of 1.5 kW. It is shown that the proposed inverter has the 98±1% efficiency over a wide range of loads with a peak efficiency of 98.96%, and the total harmonic distortion of the output current relatively low (≤1.8 %). The leakage current (icm) is measured for different values of parasitic capacitance that reaches a maximum of 16.65 mA for 330 nF capacitor under consideration which is well below the limit set by different safety standards
Khan, NU, Wan, W & Yu, S 2020, 'Location-Based Social Network's Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China', ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, vol. 9, no. 2.
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Khan, NU, Wan, W & Yu, S 2020, 'Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network's Data from Weibo', ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, vol. 9, no. 2.
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Khan, SA, Guo, Y, Siwakoti, YP, Lu, DD-C & Zhu, J 2020, 'A Disturbance Rejection-Based Control Strategy for Five-Level T-Type Hybrid Power Converters With Ripple Voltage Estimation Capability', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 67, no. 9, pp. 7364-7374.
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Khan, SA, Guo, Y, Siwakoti, YP, Lu, DD-C & Zhu, J 2020, 'A Disturbance Rejection-Based Control Strategy for Five-Level T-Type Hybrid Power Converters With Ripple Voltage Estimation Capability.', IEEE Trans. Ind. Electron., vol. 67, pp. 7364-7374.
Khan, TA & Ling, SH 2020, 'An improved gravitational search algorithm for solving an electromagnetic design problem', JOURNAL OF COMPUTATIONAL ELECTRONICS, vol. 19, no. 2, pp. 773-779.
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Khanzada, NK, Farid, MU, Kharraz, JA, Choi, J, Tang, CY, Nghiem, LD, Jang, A & An, AK 2020, 'Removal of organic micropollutants using advanced membrane-based water and wastewater treatment: A review', Journal of Membrane Science, vol. 598.
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© 2019 Elsevier B.V. The rising consumption of pharmaceuticals, personal care products, and endocrine disruptive compounds for healthcare purposes and improving living standards has resulted in the widespread occurrence of organic micropollutants (MPs) in water and wastewater. Conventional water/wastewater treatment plants are faced with inherent limitations in tackling these compounds, leading to difficulties in the provision of secure and safe water supplies. In this context, membrane technology has been found to be a promising method for resolving this emerging concern. To ensure the suitability of membrane-based treatment processes in full-scale applications, we first need to develop a better understanding of the behavior of MPs and the mechanisms behind their removal using advanced membrane technologies. This review provides a thorough overview of the advanced membrane-based treatment methods available for the effective removal of MPs, including reverse osmosis, nanofiltration, ultrafiltration, forward osmosis, and membrane distillation.
Khari, M, Garg, AK, Gandomi, AH, Gupta, R, Patan, R & Balusamy, B 2020, 'Securing Data in Internet of Things (IoT) Using Cryptography and Steganography Techniques', IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, vol. 50, no. 1, pp. 73-80.
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Khorsand, M, Tavakoli, J, Guan, H & Tang, Y 2020, 'Artificial intelligence enhanced mathematical modeling on rotary triboelectric nanogenerators under various kinematic and geometric conditions', Nano Energy, vol. 75.
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© 2020 Elsevier Ltd The triboelectric nanogenerator (TENG) has been introduced as a revolutionary technology in the renewable electrical energy generation at micro/nanoscale. In the current study, experimental and theoretical models for augmented rotary TENGs are presented. The power generated by TENGs is found to be a function of the number of segments, rotational speed, and tribo-surface spacing. Mathematical modeling combined with artificial intelligence is applied to characterize the TENG output under various kinematics and geometric conditions. Sensitivity analysis reveals that the generated energy and the matched resistance depend highly on segmentation and angular velocity rate. It is shown that the optimized harvested energy reaches 0.369 mJ at each cycle. The TENG dynamic outputs for various structural parameters are found and described. This study enhances understanding of rotation-induced periodic TENGs and reveals optimized characteristics for disk-shaped TENG energy harvesters.
Khosravi, F, Hosseini, SA & Hayati, H 2020, 'Free and forced axial vibration of single walled carbon nanotube under linear and harmonic concentrated forces based on nonlocal theory', INTERNATIONAL JOURNAL OF MODERN PHYSICS B, vol. 34, no. 8.
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Khuat, TT & Le, MH 2020, 'Evaluation of Sampling-Based Ensembles of Classifiers on Imbalanced Data for Software Defect Prediction Problems', SN Computer Science, vol. 1, no. 2.
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Kieu, LM, Ou, Y, Truong, LT & Cai, C 2020, 'A class-specific soft voting framework for customer booking prediction in on-demand transport', Transportation Research Part C: Emerging Technologies, vol. 114, pp. 377-390.
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© 2020 Elsevier Ltd Customer booking prediction is essential for On-Demand Transport services, especially for those in rural and suburban areas where the demand is low, variable and often regarded as unpredictable. Existing literature tends to focus more on the prediction of demand for traffic, classical public transport, and urban On-Demand Transport service such as taxi, Uber or Lyft, in areas with higher and less variable demand, in which popular time-series prediction methods can be employed. This paper proposes an ensemble learning framework to predict the customer booking behaviour and demand using the observed data of a suburban On-Demand Transport service where data scarcity is a challenge. The proposed method, which is called as Class-specific Soft Voting, is found to be the most accurate prediction method when compared to popular supervised classification methods such as Logistic Regression, Random Forest, Support Vector Machine and other ensemble techniques.
Kim, D, Nguyen, LN & Oh, S 2020, 'Ecological impact of the antibiotic ciprofloxacin on microbial community of aerobic activated sludge.', Environmental geochemistry and health.
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This study investigated the effects and fate of the antibiotic ciprofloxacin (CIP) at environmentally relevant levels (50-500 µg/L) in activated sludge (AS) microbial communities under aerobic conditions. Exposure to 500 µg/L of CIP decreased species diversity by about 20% and significantly altered the phylogenetic structure of AS communities compared to those of control communities (no CIP exposure), while there were no significant changes upon exposure to 50 µg/L of CIP. Analysis of community composition revealed that exposure to 500 µg/L of CIP significantly reduced the relative abundance of Rhodobacteraceae and Nakamurellaceae by more than tenfold. These species frequently occur in AS communities across many full-scale wastewater treatment plants and are involved in key ecosystem functions (i.e., organic matter and nitrogen removal). Our analyses showed that 50-500 µg/L CIP was poorly removed in AS (about 20% removal), implying that the majority of CIP from AS processes may be released with either their effluents or waste sludge. We therefore strongly recommend further research on CIP residuals and/or post-treatment processes (e.g., anaerobic digestion) for waste streams that may cause ecological risks in receiving water bodies.
Kim, DI, Gonzales, RR, Dorji, P, Gwak, G, Phuntsho, S, Hong, S & Shon, H 2020, 'Efficient recovery of nitrate from municipal wastewater via MCDI using anion-exchange polymer coated electrode embedded with nitrate selective resin', DESALINATION, vol. 484.
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King, JT, Prasad, M, Tsai, T, Ming, YR & Lin, CT 2020, 'Influence of Time Pressure on Inhibitory Brain Control During Emergency Driving', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE It is believed that failures of people's reaction to emergencies occurred during driving are closely related to the inhibitory mechanism of brain's operations. To investigate the role of this function in emergency driving, two virtual realistic driving conditions based on stop signal task were designed and time limitation was manipulated to increase the stress in one condition. Sixteen subjects with behavioral encephalography recordings were collected and analyzed. By comparing successful and unsuccessful stop trials with event-related spectral perturbation analysis, δ and θ band power increases in frontal and central areas are correlated with driving inhibitory control of the brain. Moreover, β and ɣ band power in frontal and central areas showed more increases upon stress condition. Time pressure in driving could adjust the operation of brain's inhibition control, to benefit the people's reactive ability upon emergency.
Kishore Kumar, D, Kříž, J, Bennett, N, Chen, B, Upadhayaya, H, Reddy, KR & Sadhu, V 2020, 'Functionalized metal oxide nanoparticles for efficient dye-sensitized solar cells (DSSCs): A review', Materials Science for Energy Technologies, vol. 3, pp. 472-481.
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Kocaballi, AB, Quiroz, JC, Rezazadegan, D, Berkovsky, S, Magrabi, F, Coiera, E & Laranjo, L 2020, 'Responses of Conversational Agents to Health and Lifestyle Prompts: Investigation of Appropriateness and Presentation Structures', JOURNAL OF MEDICAL INTERNET RESEARCH, vol. 22, no. 2.
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Kong, FH & Manchester, IR 2020, 'Contraction analysis of nonlinear noncausal iterative learning control', Systems and Control Letters, vol. 136.
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© 2019 Elsevier B.V. Iterative learning control (ILC) is a method for learning input signals for repetitive control tasks. In this paper, we provide a new method based on convex optimization for certifying convergence and estimating convergence rate in ILC schemes involving a nonlinear plant and a noncausal update law, which are common in practice. Using sum-of-squares (SOS) optimization, we compute the convergence rate of an example nonlinear, noncausal ILC system and verify its accuracy in experiment.
Kong, FH, Zhao, J, Zhao, L & Huang, S 2020, 'Analysis of Minima for Geodesic and Chordal Cost for a Minimal 2-D Pose-Graph SLAM Problem', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 323-330.
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© 2016 IEEE. In this letter, we show that for a minimal 2D pose-graph SLAM problem, even in the ideal case of perfect measurements and spherical covariance, using geodesic distance (in 2D, the 'wrap function') to compare angles results in multiple suboptimal local minima. We numerically estimate regions of attraction to these local minima for some examples, give evidence to show that they are of nonzero measure, and that these regions grow in size as noise is added. In contrast, under the same assumptions, we show that the chordal distance representation of angle error has a unique minimum up to periodicity. For chordal cost, we find that initial conditions failing to converge to the global minimum are far fewer, fail because of numerical issues, and do not seem to grow with noise in our examples.
Kong, L, Qu, W, Yu, J, Zuo, H, Chen, G, Xiong, F, Pan, S, Lin, S & Qiu, M 2020, 'Distributed Feature Selection for Big Data Using Fuzzy Rough Sets', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 28, no. 5, pp. 846-857.
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Kovaleva, M, Bulger, D & Esselle, KP 2020, 'Comparative Study of Optimization Algorithms on the Design of Broadband Antennas', IEEE Journal on Multiscale and Multiphysics Computational Techniques, vol. 5, pp. 89-98.
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Kumar, DK, Loskot, J, Kriz, J, Bennett, N, Upadhyaya, HM, Sadhu, V, Reddy, CV & Reddy, KR 2020, 'Synthesis of SnSe quantum dots by successive ionic layer adsorption and reaction (SILAR) method for efficient solar cells applications', SOLAR ENERGY, vol. 199, pp. 570-574.
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Laccone, F, Malomo, L, Froli, M, Cignoni, P & Pietroni, N 2020, 'Automatic Design of Cable-Tensioned Glass Shells', Computer Graphics Forum.
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© 2019 The Authors Computer Graphics Forum © 2019 The Eurographics Association and John Wiley & Sons Ltd. We propose an optimization algorithm for the design of post-tensioned architectural shell structures, composed of triangular glass panels, in which glass has a load-bearing function. Due to its brittle nature, glass can fail when it is subject to tensile forces. Hence, we enrich the structure with a cable net, which is specifically designed to post-tension the shell, relieving the underlying glass structure from tension. We automatically derive an optimized cable layout, together with the appropriate pre-load of each cable. The method is driven by a physically based static analysis of the shell subject to its service load. We assess our approach by applying non-linear finite element analysis to several real-scale application scenarios. Such a method of cable tensioning produces glass shells that are optimized from the material usage viewpoint since they exploit the high compression strength of glass. As a result, they are lightweight and robust. Both aesthetic and static qualities are improved with respect to grid shell competitors.
Laengle, S, Lobos, V, Merigó, JM, Herrera-Viedma, E, Cobo, MJ & De Baets, B 2020, 'Forty years of Fuzzy Sets and Systems: A bibliometric analysis', Fuzzy Sets and Systems.
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© 2020 Elsevier B.V. Fuzzy Sets and Systems is a leading international journal in computer science and applied mathematics that was created in 1978. In 2018, the journal celebrated its 40th anniversary. The aim of this study is to present a bibliometric overview of the leading trends occurring in the journal between 1978 and 2016 by analysing the most productive and influential authors, institutions and countries as well as the publication and citation structure. Additionally, this work presents a graphical visualization of the bibliographic data by using the visualization of similarities (VOS) viewer and the science mapping analysis tool (SciMAT) software. The results show the strong growth of fuzzy set theory over time and a huge diversity of publications from all over the world, especially from Europe, North America and East Asia.
Laengle, S, Merigó, JM, Modak, NM & Yang, JB 2020, 'Bibliometrics in operations research and management science: a university analysis', Annals of Operations Research.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Many universities around the World have made important contributions in the field of operations research and management science. This article presents the most productive and influential universities between 1991 and 2015. For doing so, we use the Web of Science database in order to search for the information which is usually regarded as the most relevant for scientific research. The results show the country of origin of the leading universities being mainly from North America and Asia and especially from USA and China. The Centre National de la Recherche Scientifique (CNRS) of France is the most productive university while the Massachusetts Institute of Technology (MIT) of USA is the most influential one. The temporal evolution shows that USA is trailing its dominancy while China progressing quickly. The evaluation also reveals that Asian universities outperform North American universities during the last 5 years.
Lai, S, Fan, X, Ye, Q, Tan, Z, Zhang, Y, He, X & Nanda, P 2020, 'FairEdge: A Fairness-Oriented Task Offloading Scheme for Iot Applications in Mobile Cloudlet Networks', IEEE Access, vol. 8, pp. 13516-13526.
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Lalbakhsh, A, Afzal, MU, Esselle, KP & Smith, SL 2020, 'Low-Cost Nonuniform Metallic Lattice for Rectifying Aperture Near-Field of Electromagnetic Bandgap Resonator Antennas', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 5, pp. 3328-3335.
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Lam, SC & Sandrasegaran, K 2020, 'Fractional Frequency Reuse in Multi-tier Networks: Performance Analysis and Optimization', International Journal of Wireless Information Networks, vol. 27, no. 1, pp. 164-183.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Frequency Reuse (FR) is an efficient approach to improve the network performance in a multi-cell cellular network. In this paper, we investigate the performance of heterogeneous networks utilising two well-known frequency reuse algorithms, called Strict FR and Soft FR, with a reuse factor of Δ (Δ> 1). Based on a two-phase operation of the FR algorithm, we develop a new approach to analyse cellular networks based on the Poisson Point Process (PPP) model which successfully demonstrates the impact of the network parameters (such as the number of allocated Resource Blocks (RBs) and bias factor) and FR parameters (such as Base Station (BS) transmit power) on the performance of a user and overall network. Compared to related works, we propose the following novel approaches: (i) we investigate flexible FR networks in which users have connections with the BSs which deliver the highest performance; (ii) the BS observes SINR on the data channel to classify each user into either a Cell-Edge User (CEU) or Cell-Center User (CCU); (iii) the initial state of the network is considered to establish the initial network interference when new users arrive and request connections to the BSs. In the case of a single-tier network, it is proved that our analytical approach is more accurate than previous works. In the case of a two-tier network, the analytical results indicate that compared to the 3GPP model, our proposed model not only reduces up to 40.79% and 3.8% power consumption of a BS on the data channel but also achieves 16.08% and 18.63% higher data rates in the case of Strict FR and Soft FR respectively. Furthermore, the paper presents an approach to find an optimal value of SINR thresholds and bias factor to achieve the maximum network performance.
Lama, S, Pradhan, S & Shrestha, A 2020, 'Exploration and implication of factors affecting e-tourism adoption in developing countries: a case of Nepal', Information Technology and Tourism, vol. 22, no. 1, pp. 5-32.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. E-tourism plays a pivotal role in delivering services to tourists by enhancing travel experiences. While small tourism service providers in the developed economies can efficiently take advantage of e-tourism by partnering with global online platforms such as TripAdvisor or Booking.com, small tourism service providers in developing countries often struggle to reach global markets due to factors that are unique to the country they operate in. The aim of this study, therefore, is to identify the key barriers and motivators of e-tourism adoption for small and medium tourism enterprises (SMTEs) in developing countries. Nepal is selected as the case study due to its enormous tourism potential that is plagued by typical challenges. Based on the ‘Technology, Organization and Environment’ framework and ‘e-readiness’ model, this study identified ten key factors that affect e-tourism adoption by SMTEs in Nepal. A mixed-method approach, using interviews with seven key stakeholders and a survey with 198 SMTEs, were employed for data collection and validation of the proposed factors. Finally, an e-tourism adoption model highlighting the barriers and motivators for e-tourism by SMTEs is presented. This research found that e-tourism adoption by SMTEs in Nepal is affected by environmental factors related to national infrastructure, market size, country-specific contextual factors and organizational factors comprising e-tourism awareness, ICT resources, value proposition, and top management support. This study offers implications for policy and practice towards effective e-tourism adoption in other developing countries.
Lau, AKS, Bilad, MR, Nordin, NAHM, Faungnawakij, K, Narkkun, T, Wang, DK, Mahlia, TMI & Jaafar, J 2020, 'Effect of membrane properties on tilted panel performance of microalgae biomass filtration for biofuel feedstock', RENEWABLE & SUSTAINABLE ENERGY REVIEWS, vol. 120.
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Lazaar, A, Mouazen, AM, EL Hammouti, K, Fullen, M, Pradhan, B, Memon, MS, Andich, K & Monir, A 2020, 'The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco', International Soil and Water Conservation Research, vol. 8, no. 2, pp. 195-204.
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Le Gentil, C, Vidal-Calleja, T & Huang, S 2020, 'Gaussian Process Preintegration for Inertial-Aided State Estimation', IEEE ROBOTICS AND AUTOMATION LETTERS, vol. 5, no. 2, pp. 2108-2114.
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Le, AT, Tran, LC, Huang, X & Guo, YJ 2020, 'Analog Least Mean Square Loop for Self-Interference Cancellation: A Practical Perspective', SENSORS, vol. 20, no. 1.
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Le, AT, Tran, LC, Huang, X & Guo, YJ 2020, 'Beam-Based Analog Self-Interference Cancellation in Full-Duplex MIMO Systems', IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2460-2471.
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© 2002-2012 IEEE. Self-interference (SI) cancellation for full-duplex (FD) multiple input multiple output (MIMO) systems is challenging due to both hardware and signal processing complexity. In this paper, a beam-based adaptive filter structure with analog least mean square (ALMS) loops is proposed to significantly reduce the complexity of SI cancellation for FD MIMO systems. With this structure, the number of adaptive filters required for SI cancellation scales linearly with the number of transmit beams rather than quadratically with the number of antennas. Furthermore, to avoid additional transmit chains used to up-convert the beam signals to generate reference signals for the ALMS loops, a novel method is proposed to select the optimized reference signals from all transmitted signals. In addition, our stationary analysis shows that the proposed structure for FD MIMO systems outperforms the ALMS loop employed for an FD single input single output system. Simulations are conducted to confirm the theoretical analyses.
Lee, D, Woo, YC, Park, KH, Phuntsho, S, Tijing, LD, Yao, M, Shim, WG & Shon, HK 2020, 'Polyvinylidene fluoride phase design by two-dimensional boron nitride enables enhanced performance and stability for seawater desalination', Journal of Membrane Science, vol. 598.
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© 2019 The instability of polyvinylidene fluoride (PVDF) membranes in membrane distillation (MD) for seawater desalination is still a problem, despite the tremendous effort expended to resolve this issue. Here, a simple and feasible approach for improving desalination performance through the incorporation of two-dimensional boron nitride nanosheets (BNNSs) in polyvinylidene fluoride-co-hexafluoropropene (PVDF-co-HFP) electrospun nanofiber membrane (BNs-PH) is proposed as well as demonstrate its origin for fundamental understanding. The BNs-PH membrane exhibits a stable water vapor flux (18 LMH) and superior salt rejection (99.99%), even after operation for 280 h (commercial PVDF: steep decay within 28 h; neat PH: wetting within 4 h). From structural/chemical analyses, the BNNSs play a crucial role in forming favorable phases of the PH polymer crystal structure, inducing a superhydrophobic surface with greater nanoporosity and higher heterogeneity as well as enhanced mechanical properties (increase of UTS: 13.4%; modulus: 1.2%) for long-term operation. Theoretical modeling results of an air-gap MD system are consistent with our experimental results. The approach introduced in this study can be applied to other desalination systems to boost various water treatment applications.
Lee, SS, Lim, CS, Siwakoti, YP & Lee, KB 2020, 'Dual-T-Type Five-Level Cascaded Multilevel Inverter with Double Voltage Boosting Gain', IEEE Transactions on Power Electronics, vol. 35, no. 9, pp. 9524-9531.
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© 1986-2012 IEEE. The switched-capacitor-based cascaded multilevel inverters (CMI) have been emerging due to their voltage boosting capability. Unfortunately, they suffer from impulse charging current and nonuniform operation. This article presents a topology termed as dual-T-type five-level CMI to resolve these problems without compromising the desirable voltage boosting characteristic. The main idea is to integrate a half bridge and an inductor to soft charge a capacitor that is connected in series with the dc source. The capacitor enables the voltage gain boosted to two, while the control of eight power switches that constitutes a dual-T structure enables five voltage levels generation. In addition, uniform operation is achieved for cascaded extensions. The operating principle of the proposed topology is analyzed and elaborated. For validation, simulation, and experimental results of a prototype are presented.
Lee, SS, Lim, CS, Siwakoti, YP & Lee, K-B 2020, 'Hybrid 7-Level Boost Active-Neutral-Point-Clamped (H-7L-BANPC) Inverter', IEEE Transactions on Circuits and Systems II: Express Briefs, pp. 1-1.
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Lee, SS, Siwakoti, YP, Lim, CS & Lee, K-B 2020, 'An Improved PWM Technique to Achieve Continuous Input Current in Common-Ground Transformerless Boost Inverter', IEEE Transactions on Circuits and Systems II: Express Briefs, pp. 1-1.
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Lee, XJ, Ong, HC, Gan, YY, Chen, WH & Mahlia, TMI 2020, 'State of art review on conventional and advanced pyrolysis of macroalgae and microalgae for biochar, bio-oil and bio-syngas production', Energy Conversion and Management, vol. 210.
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© 2020 Elsevier Ltd Algal biomass including macroalgae and microalgae show great potential as pyrolysis feedstock in generating energy-dense and valuable pyrolytic products such as bio-oil, biochar and bio-syngas. The chemical constituents of macroalgae and microalgae show great variations, especially their lipid, carbohydrate and protein contents, which could affect the qualities of the pyrolytic products. From the established conventional pyrolysis, the products produced from both macroalgae and microalgae show moderate energy contents (<34 MJ/kg). The review identifies the issues associated with development of conventional pyrolysis such as flash and intermediate pyrolysis. To enhance the production of biofuels from algal biomass, advanced or non-conventional pyrolysis techniques have been employed. Catalytic pyrolysis on algal biomass could reduce the nitrogenates and oxygenates in the biofuels. On top of that, co-pyrolysis with suitable feedstock shows great enhancement on the bio-oil yield. As for hydropyrolysis of algal biomass, their generated biofuels can produce up to 48 MJ/kg with high yield of bio-oil up to 50 wt%, comparable to conventional fuels. Microwave-assisted pyrolysis of algal biomass greatly shortens the processing time through advanced heating; however, favours the formation of bio-syngas by improving the yield up to 84 wt% depending on the feedstock used. Therefore, formation of biofuel fraction suitable for energy generation highly depends on the selected pyrolysis technologies.
Lei, F, Lv, X, Fang, J, Sun, G & Li, Q 2020, 'Multiobjective discrete optimization using the TOPSIS and entropy method for protection of pedestrian lower extremity', Thin-Walled Structures, vol. 152.
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© 2019 Elsevier Ltd Design of vehicle frontal structures signifies an important topic of studies on protection of pedestrian lower extremity. Conventional optimization has been largely focused on continuous variable problems without involving complexity of human model. Nevertheless, design of frontal structures is commonly discrete from a manufacturing perspective and the responses of the lower extremity of pedestrian are highly nonlinear in nature. For this reason, this study aimed to develop a multiobjective discrete optimization (MODO) algorithm for design of frontal structures involving a pedestrian model. In the proposed MODO method, the order preference by similarity to ideal solution (TOPSIS) was coupled with the entropy algorithm to develop a multiple attribute decision making (MADM) model for converting multiple conflicting objectives into a unified single cost function. The presented optimization procedure is iterated using the successive orthogonal experiment to deal with a large number of design variables and design levels. In this study, the proposed method was first verified by two benchmark examples; and then was applied to a more sophisticated real-life design case of vehicle frontal structure to better protect pedestrian lower extremity from impacting injury. The finite element model was validated via experimental tests first and the optimized design was then prototyped for the further physical tests. The results showed that the algorithm is able to achieve an optimal design in a fairly efficient manner. The proposed algorithm exhibits considerable potential to solve other complex engineering design problems.
Lei, G, Bramerdorfer, G, Liu, C, Guo, Y & Zhu, J 2020, 'Robust Design Optimization of Electrical Machines: A Comparative Study and Space Reduction Strategy', IEEE Transactions on Energy Conversion, pp. 1-1.
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Lei, G, Bramerdorfer, G, MA, BO, Guo, Y & Zhu, J 2020, 'Robust Design Optimization of Electrical Machines: Multi-objective Approach', IEEE Transactions on Energy Conversion, pp. 1-1.
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Li, DL, Prasad, M, Liu, CL & Lin, CT 2020, 'Multi-View Vehicle Detection Based on Fusion Part Model With Active Learning', IEEE Transactions on Intelligent Transportation Systems.
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IEEE Computer vision-based vehicle detection techniques are widely used in real-world applications. However, most of these techniques aim to detect only single-view vehicles, and their performances are easily affected by partial occlusion. Therefore, this paper proposes a novel multi-view vehicle detection system that uses a part model to address the partial occlusion problem and the high variance between all types of vehicles. There are three features in this paper; firstly, different from Deformable Part Model, the construction of part models in this paper is visual and can be replaced at any time. Secondly, this paper proposes some new part models for detection of vehicles according to the appearance analysis of a large number of modern vehicles by the active learning algorithm. Finally, this paper proposes the method that contains color transformation along with the Bayesian rule to filter out the background to accelerate the detection time and increase accuracy. The proposed method outperforms other methods on given dataset.
Li, F, Cao, Z, Xu, P, Yi, C, Liao, Y, Jiang, Y, Si, Y, Song, L, Zhang, T, Yao, D & Zhang, Y 2020, 'Reconfiguration of Brain Network between Rest-ing-state and P300 task', IEEE Transactions on Cognitive and Developmental Systems.
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IEEE Previous studies explore the power spectra from the resting-state condition to the oddball task, but whether brain network existing significant difference is still unclear. Our study aims to address how the brain reconfigures its architecture from a resting-state condition (i.e., baseline) to the P300 task in the visual oddball task. In this study, electroencephalograms (EEGs) were collected from 24 subjects, who were required to only mentally count the number of target stimulus; afterwards, EEG networks constructed in different bands were compared between baseline and task to evaluate the reconfiguration of functional connectivity. Compared to the baseline, our results showed the significantly enhanced delta/theta functional connectivity and decreased alpha default mode network in the progress of brain reconfiguration to the task. Furthermore, the reconfigured coupling strengths were found to relate to P300 amplitudes, which were then regarded as features to train a classifier to differentiate the brain states and the high and low P300 groups with an accuracy of 100% and 77.78%, respectively. The findings of our study help us to under-stand the updates in functional connectivity from resting-state to the oddball task, and the reconfigured network structure has the potential for the selection of good subjects for P300-based brain-computer interface.
Li, G, Feng, B, Zhou, H, Zhang, Y, Sood, K & Yu, S 2020, 'Adaptive service function chaining mappings in 5G using deep Q-learning', Computer Communications, vol. 152, pp. 305-315.
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© 2020 Elsevier B.V. With introduction of Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies, mobile network operators are able to provide on-demand Service Function Chaining (SFC) to meet various needs from users. However, it is challenging to map multiple SFCs to substrate networks efficiently, particularly in a number of key scenarios of forthcoming 5G, where user requests have different priorities and various resource demands. To this end, we first formulate the mapping of multiple SFCs with priorities as a multi-step Linear Integer Programming (ILP) problem, of which the mapping strategy (i.e., the objective function) in each step is configurable to improve overall CPU and bandwidth resource utilization rates. Secondly, to solve the strategy selection problem in each step and alleviate the complexity of ILP, we propose an adaptive deep Q-learning based SFC mapping approach (ADAP), where an agent is learned to make decisions from two low-complexity heuristic SFC mapping algorithms. Finally, we conduct extensive simulations using multiple SFC requests with randomly generated CPU and bandwidth demands in a real-world substrate network topology. Related results demonstrate that compared with a single strategy or random selections of strategies under the ILP-based approach or the proposed heuristic algorithms, our ADAP approach can improve whole-system resource efficiency by scheduling this two simply designed heuristic algorithms properly after limited training episodes.
Li, G, Zhou, H, Feng, B, Zhang, Y & Yu, S 2020, 'Efficient Provision of Service Function Chains in Overlay Networks using Reinforcement Learning', IEEE Transactions on Cloud Computing.
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IEEE Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies facilitate deploying Service Function Chains (SFCs) at clouds in efficiency and flexibility. However, it is still challenging to efficiently chain Virtualized Network Functions (VNFs) in overlay networks without knowledge of underlying network configurations. Although there are many deterministic approaches for VNF placement and chaining, they have high complexity and depend on state information of substrate networks. Fortunately, Reinforcement Learning (RL) brings opportunities to alleviate this challenge as it can learn to make suitable decisions without prior knowledge. Therefore, in this paper, we propose an RL approach for efficient SFC provision in overlay networks, where the same VNFs provided by multiple vendors are with different performance. Specifically, we first formulate the problem into an Integer Linear Programming (ILP) model for benchmarking. Then, we present the online SFC path selection into a Markov Decision Process (MDP) and propose a corresponding policy-gradient-based solution. Finally, we evaluate our proposed approach with extensive simulations with randomly generated SFC requests and a real-world video streaming dataset, and implement an emulation system for feasibility verification. Related results demonstrate that performance of our approach is close to the ILP-based method and better than deep Q-learning, random, and load-least-greedy methods.
Li, H, Li, Y & Li, J 2020, 'Negative stiffness devices for vibration isolation applications: A review', Advances in Structural Engineering, vol. 23, no. 8, pp. 1739-1755.
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© The Author(s) 2020. In recent years, negative stiffness vibration isolation device with nonlinear characteristic has become an emerging research area and attracted a significant amount of attentions in the community due to the promising potentials it brought into the field. Its high-static-low-dynamic stiffness property endows the capacity to realize effective vibration isolation and in the meantime to maintain the system stability. This article presents a comprehensive review of the recent research and developments on negative stiffness vibration isolation device. It begins with an introduction on the concept of negative stiffness and then provides a summary and discussion regarding the realization and characteristics of negative stiffness vibration isolation device. The article places its special interest on the principles, structure design, and device characterisation of different types of negative stiffness vibration isolation devices, including spring type, pre-bucked beam type, magnetism type, geometrically nonlinear structural type, and composite structural type. Besides, the applications of negative stiffness vibration isolation device, as well as negative stiffness damper, are summarized and discussed based on the current state-of-the-art. Finally, the conclusions and further discussion provide highlights of the investigation.
Li, H, Yang, Y, Dai, Y, Yu, S & Xiang, Y 2020, 'Achieving Secure and Efficient Dynamic Searchable Symmetric Encryption over Medical Cloud Data', IEEE Transactions on Cloud Computing, vol. 8, no. 2, pp. 484-494.
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© 2013 IEEE. In medical cloud computing, a patient can remotely outsource her medical data to the cloud server. In this case, only authorized doctors are allowed to access the data since the medical data is highly sensitive. Before outsourcing, the data is commonly encrypted, where the corresponding secret key is sent to authorized doctors. However, performing searches on encrypted medical data is difficult without decryption. In this paper, we propose two Secure and Efficient Dynamic Searchable Symmetric Encryption (SEDSSE) schemes over medical cloud data. First, we utilize the secure k-Nearest Neighbor (kNN) and Attribute-Based Encryption (ABE) techniques to construct a dynamic searchable symmetric encryption scheme, which can achieve forward privacy and backward privacy simultaneously. These tow security properties are vital and very challenging in the area of dynamic searchable symmetric encryption. Then, we propose an enhanced scheme to solve the key sharing problem which widely exists in the kNN based searchable encryption scheme. Compared with existing proposals, our schemes are better in terms of storage, search and updating complexity. Extensive experiments demonstrate the efficiency of our schemes on storage overhead, index building, trapdoor generating and query.
Li, H-Y, Xu, J-X & Zhang, XY 2020, 'Single-to-Balanced and Balanced-to-Balanced Dual-channel Filters Using Multilayer Substrate Integrated Waveguide Cavities', IEEE Transactions on Industrial Electronics, pp. 1-1.
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Li, HY, Xu, JX, Yang, Y & Zhang, XY 2020, 'Novel Switchable Filtering Circuit With Function Reconfigurability Between SPQT Filtering Switch and Four-Way Filtering Power Divider', IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 3, pp. 867-876.
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© 2019 IEEE. In this article, we propose a circuit model for designing the switchable filtering circuit with reconfigurable functions between a single-pole quad-throw (SPQT) filtering switch (State 1) and a four-way filtering power divider (State 2) without additional switchable impedance matching network (IMN). The proposed circuit model can be utilized as the feeding network of a switchable directive and omni-directional antenna system to eliminate the omni-directional antenna in conventional solutions to reduce the size and cost. It consists of four filter channels with a common port. In State 1, it works as a SPQT filtering switch, where only one channel is turned on to realize good filtering responses and the impedances at the other three channels are adapted to infinity to realize high isolation. The four channels are turned on simultaneously in State 2 to work as a four-way filtering power divider, and the input impedance of the common port is the same as that in State 1. Thus, a switchable IMN is not required for port matching when switching between the two states. Transmission zeros are generated to enhance the skirt selectivity. For verification, the filtering circuit is constructed using high- Q coaxial resonators. Experimental results show excellent performance in both states acting as a SPQT filtering switch and a four-way filtering power divider.
Li, J & Little, C 2020, 'An In Vitro study to investigate the effects of stem cell therapy for treating Osteoarthritis', Osteoarthritis and Cartilage, vol. 28, pp. S503-S504.
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Li, J, Liu, M, Xue, Z, Fan, X & He, X 2020, 'RTVD: A Real-Time Volumetric Detection Scheme for DDoS in the Internet of Things', IEEE ACCESS, vol. 8, pp. 36191-36201.
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Li, J, Pan, Y, Sui, Y & Tsang, IW 2020, 'Secure Metric Learning via Differential Pairwise Privacy', IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3640-3652.
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Li, J, Zhu, X, Law, S-S & Samali, B 2020, 'Time-varying characteristics of bridges under the passage of vehicles using synchroextracting transform', MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 140.
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Li, K, Liu, AX & Yu, S 2020, 'Special issue on natural computation, fuzzy systems and knowledge discovery from the ICNC&FSKD 2017', Neurocomputing.
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Li, L, Ju, N, He, C, Li, C & Sheng, D 2020, 'A computationally efficient system for assessing near-real-time instability of regional unsaturated soil slopes under rainfall', Landslides.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. The objective of this paper is to obtain an applicable assessment method and a Web-GIS-based prediction system for regional landslides. The traditional Richards function is reconstructed using the soil-water characteristic curve (SWCC) and the coordinate transformation technique. The analytical pore pressure for a slope model is derived by solving the modified Richard equation via the Green function and Fourier transformation. The obtained transient pore pressure field is then incorporated with Brakensiek’s matric suction theory, to build a conceptual model for rainfall-induced shallow landslides. The safety factor is obtained by solving the limit equilibrium equation of the conceptual model. The method is then implemented in a Web-GIS system, considering influence of slope geometry features, geology parent material, and near-real-time rainfall intensity of the study area. It is verified that this method is computationally efficient and reliable for gentle slopes and short rainfall durations. Moreover, an extensive parameter study shows that the two commonly used coefficients in the intensity-duration equation are both correlated to rainfall inter-event time via exponential functions, and rainfall event time via power functions. The primary influential factor for regional landslides is the initial water content, followed by the rainfall duration and intensity, and least by soil thickness.
Li, L, Song, K, Yeerken, S, Geng, S, Liu, D, Dai, Z, Xie, F, Zhou, X & Wang, Q 2020, 'Effect evaluation of microplastics on activated sludge nitrification and denitrification', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 707.
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Li, P, Gao, X, Wang, K, Tam, VWY & Li, W 2020, 'Hydration mechanism and early frost resistance of calcium sulfoaluminate cement concrete', CONSTRUCTION AND BUILDING MATERIALS, vol. 239.
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Li, P, Li, W, Yu, T, Qu, F & Tam, VWY 2020, 'Investigation on early-age hydration, mechanical properties and microstructure of seawater sea sand cement mortar', Construction and Building Materials, vol. 249.
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© 2020 Elsevier Ltd Using seawater for concrete manufacturing promisingly provides significant economical and environmental benefits. In this study, ordinary Portland cement (OPC) hydration in distilled water and seawater and the corresponding evolution of solid phases was investigated by heat evolution, hydrated phase, hydration kinetics, and microstructure characterization. The results show that seawater can promote the early hydration of tricalcium silicate (C3S) during the hydration acceleration period. The hydrated phase assemblage was affected by the dissolved ions in seawater. Friedel's salt was detected as a specific hydration phase in seawater, which was formed by chemical combination between the aluminate ferrite monosulfate (AFm) phase and chloride ions. The monocarboaluminate can be converted into a stable phase as Friedel's salt in the seawater, due to the reaction with chloride ions. Furthermore, the ettringite becomes more stable when coexists with Friedel's salt than that with monocarboaluminate, and thus ettringite formed in seawater remains 67% higher than that formed in distilled water at the later curing age. Moreover, additional unhydrated cement and less amorphous calcium silicate hydrate (C-S-H) were formed in seawater, which might be responsible for the slightly lower compressive strength of cement mortar prepared by seawater and sea sand. A modeled evolution of the solid phase and pore solution have been established, which agrees well with the characteristics of the dissolution of mineral phase, precipitation of hydration products and changes of pore solution. The related results can provide an insight into the applications of seawater and sea sand concrete for marine infrastructures.
Li, Q, Cao, Z, Ding, W & Li, Q 2020, 'A multi-objective adaptive evolutionary algorithm to extract communities in networks', SWARM AND EVOLUTIONARY COMPUTATION, vol. 52.
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Li, Q, Cao, Z, Tanveer, M, Pandey, HM & Wang, C 2020, 'A Semantic Collaboration Method Based on Uniform Knowledge Graph', IEEE INTERNET OF THINGS JOURNAL, vol. 7, no. 5, pp. 4473-4484.
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Li, Q, Wu, D, Gao, W & Tin-Loi, F 2020, 'Size-dependent instability of organic solar cell resting on Winkler–Pasternak elastic foundation based on the modified strain gradient theory', International Journal of Mechanical Sciences, vol. 177.
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© 2019 The present study employs the modified strain gradient theory (MSGT) in conjunction with the refined shear deformation plate theory to explore the buckling behaviour of simply supported and clamped OSC. The Winkler-Pasternak elastic foundation is implemented to idealise the foundation. The size-dependent effect of the OSC is captured by the three length scale parameters within the MSGT. The Hamilton principle is used to derive the equations of motion and the boundary conditions, and the Galerkin procedure is subsequently implemented to obtain the critical buckling load. Subsequently, the framework is extended to the thermally induced buckling behaviour, and three types of temperature rise patterns, namely uniform, linear and nonlinear temperature variations, along the thickness of the OSC are considered. Several verification studies are conducted to illustrate the accuracy of the present method. Besides, size-dependent material properties are taken into consideration during the numerical experiments. Thorough studies are conducted to demonstrate the difference between critical buckling loads obtained from the MSGT, the modified couple stress theory (MCST), and the classical plate theory (CPT) models. Furthermore, the effects of length scale parameter (h/l), the aspect ratio (a/b), the length-to-thickness ratio (a/h) and the Winkler-Pasternak elastic foundation parameters on the buckling behaviour of the OSC are also revealed by the numerical results.
Li, Q, Zhong, J, Cao, Z & Li, X 2020, 'Optimizing streaming graph partitioning via a heuristic greedy method and caching strategy', Optimization Methods and Software.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Graph partitioning is an important method for accelerating large distributed graph computation. Streaming graph partitioning is more efficient than offline partitioning, and it has been developed continuously in the application of graph partitioning in recent years. In this work, we first introduce a heuristic greedy streaming partitioning method and show that it outperforms the state-of-the-art streaming partitioning methods, leading to exact balance and fewer cut edges. Second, we propose a cache structure for streaming partitioning, called an adjacent edge structure, which can improve the partition efficiency several times on a single commodity type computer without affecting the partition quality. Regardless as to whether the memory capacity is limited (local cache) or not (global cache), our strategy can also improve the partition quality by restreaming partitioning. Taking linear weight greedy streaming algorithm as an example, the experimental results on 19 real-world graphs show that the average partitioning time of the new method is 4.9 times faster than that of the original method, which proves the effectiveness and superiority of the cache structure mentioned in this paper.
Li, R, Wu, B, Ying, M, Sun, X & Yang, G 2020, 'Quantum Supremacy Circuit Simulation on Sunway TaihuLight', IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 4, pp. 805-816.
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© 1990-2012 IEEE. With the rapid progress made by industry and academia, quantum computers with dozens of qubits or even larger size are being realized. However, the fidelity of existing quantum computers often sharply decreases as the circuit depth increases. Thus, an ideal quantum circuit simulator on classical computers, especially on high-performance computers, is needed for benchmarking and validation. We design a large-scale simulator of universal random quantum circuits, often called 'quantum supremacy circuits', and implement it on Sunway TaihuLight. The simulator can be used to accomplish the following two tasks: 1) Computing a complete output state-vector; 2) Calculating one or a few amplitudes. We target the simulation of 49-qubit circuits. For task 1), we successfully simulate such a circuit of depth 39, and for task 2) we reach the 55-depth level. To the best of our knowledge, both of the simulation results reach the largest depth for 49-qubit quantum supremacy circuits.
Li, S, Liang, Y, Li, Y, Li, J & Zhou, Y 2020, 'Investigation of dynamic properties of isotropic and anisotropic magnetorheological elastomers with a hybrid magnet shear test rig', Smart Materials and Structures.
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Li, S, Tian, T, Wang, H, Li, Y, Li, J, Zhou, Y & Wu, J 2020, 'Development of a four-parameter phenomenological model for the nonlinear viscoelastic behaviour of magnetorheological gels', Materials & Design, pp. 108935-108935.
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Li, S, Watterson, P, Li, Y, Wen, Q & Li, J 2020, 'Improved magnetic circuit analysis of a laminated magnetorheological elastomer devices featuring both permanent magnets and electromagnets', Smart Materials and Structures.
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Li, T, Fong, S, Li, X, Lu, Z & Gandomi, AH 2020, 'Swarm Decision Table and Ensemble Search Methods in Fog Computing Environment: Case of Day-Ahead Prediction of Building Energy Demands Using IoT Sensors', IEEE INTERNET OF THINGS JOURNAL, vol. 7, no. 3, pp. 2321-2342.
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Li, W, Dong, W, Shen, L, Castel, A & Shah, SP 2020, 'Conductivity and piezoresistivity of nano-carbon black (NCB) enhanced functional cement-based sensors using polypropylene fibres', Materials Letters, vol. 270.
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© 2020 Elsevier B.V. The cement-based sensors have a great potential for structural health monitoring, especially functional sensors filled with nano-carbon black (NCB) particles. To improve the sensing efficiency of NCB filled cementitious composite, polypropylene (PP) fibres were premixed with NCB during the manufacturing of cement-based sensor. Although the compressive strength is slightly decreased, the electrical conductivity and piezoresistivity of the NCB filled cementitious composite are improved by PP fibres. Microstructural characterization indicated that NCB attached to the surface of PP fibres significantly promotes the generation of conductive paths and contact points in cement-based sensors. The results can provide a new insight into the application of nonconductive fibres to enhance the conductivity and piezoresistivity of spherical conductors filled cement-based sensor.
Li, W, Huang, L & Ji, J 2020, 'GLOBALLY EXPONENTIALLY STABLE PERIODIC SOLUTION IN A GENERAL DELAYED PREDATOR-PREY MODEL UNDER DISCONTINUOUS PREY CONTROL STRATEGY', DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B, vol. 25, no. 7, pp. 2639-2664.
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Li, W, Huang, L, Guo, Z & Ji, J 2020, 'Global dynamic behavior of a plant disease model with ratio dependent impulsive control strategy', MATHEMATICS AND COMPUTERS IN SIMULATION, vol. 177.
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Li, W, Ji, J & Huang, L 2020, 'Global dynamic behavior of a predator–prey model under ratio-dependent state impulsive control', Applied Mathematical Modelling, vol. 77, pp. 1842-1859.
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© 2019 This paper studies the global dynamic behavior of a prey–predator model with square root functional response under ratio-dependent state impulsive control strategy. It is shown that the boundary equilibrium point of the controlled system is globally asymptotically stable. An order-k periodic orbit is obtained by employing the Brouwer's fixed point theorem. Furthermore, the critical values are determined for the existence of orbitally asymptotically stable order-1 and order-2 periodic orbits in finite time. These critical values play an important role in determining different kinds of order-k periodic orbits and can also be used for designing the control parameters to obtain the desirable dynamic behavior of the controlled prey–predator system. Moreover, it is found that the local equilibrium point is also globally asymptotically stable under the control strategy. Numerical examples are provided to validate the effectiveness and feasibility of the theoretical results.
Li, W, Ji, J, Huang, L & Wang, J 2020, 'Bifurcations and dynamics of a plant disease system under non-smooth control strategy', NONLINEAR DYNAMICS, vol. 99, no. 4, pp. 3351-3371.
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Li, W, Tang, Z, Tam, VWY, Zhao, X & Wang, K 2020, 'A Review on Durability of Alkali-activated System from Sustainable Construction Materials to Infrastructures', ES Materials & Manufacturing.
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Li, X, Chen, L, Ji, Y, Li, M, Dong, B, Qian, G, Zhou, J & Dai, X 2020, 'Effects of chemical pretreatments on microplastic extraction in sewage sludge and their physicochemical characteristics.', Water research, vol. 171, pp. 115379-115379.
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Sewage sludge is a primary pathway for microplastics (MPs) entering into terrestrial ecosystems. However, a standardized method to analyze MP in sludge is lacking due to its high organic matter. This study investigated the extraction efficiency of six MPs in five solid matrices, i.e. sewage sludge, cattle manure, soil, sediment and silicon dioxide. Results show lower extraction efficiency of 87.2% for MPs in sludge compared with that in other matrices, especially polyethylene terephthalate (PET) (only 27.8%). The possible reason was that the presence of extracellular polymeric substances within the sludge hinders the MPs to float. Therefore, five protocols, i.e. hydrogen peroxide (H2O2), Fenton, nitric acid (HNO3), hydrochloric acid (HCl) and sodium hydroxide (NaOH) were used to pretreat the sludge and optimize the MP extraction. The sludge pretreated by H2O2, Fenton and 1 M of acids had higher MP extraction efficiency than the raw sludge due to higher extraction of the PET. The MP extraction efficiency in the sludge first increased, and subsequently decreased with the soluble chemical oxygen demand (SCOD) content, implying that moderate dissolution of sludge organic matter is beneficial to the MP extraction. Quantitative analysis of the changes in the MP physicochemical characteristics after the pretreatments indicated that polyamide (PA) and PET are not resistant to acid and alkali treatment, respectively. Principal component analysis shows that the effect of pretreatments on the MPs follows a decreasing sequence: alkali > high concentration of acids > low concentration of acids > H2O2 and Fenton. Additionally, the susceptibility of the MPs to the pretreatments follows a decreasing sequence: PET, PA and polymethyl methacrylate (PMMA) > polystyrene (PS) > polyethylene (PE) and polypropylene (PP). The findings supply novel insights into the effect of chemical pretreatments on MP extraction in sewage sludge.
Li, X, Ji, M, Nghiem, LD, Zhao, Y, Liu, D, Yang, Y, Wang, Q, Trinh, QT, Vo, D-VN, Van, QP & Ngoc, HT 2020, 'A novel red mud adsorbent for phosphorus and diclofenac removal from wastewater', JOURNAL OF MOLECULAR LIQUIDS, vol. 303.
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Li, X, Li, X, Kuang, Z, Kuang, Z, Zhang, J, Zhang, J, Liu, X, Liu, X, Hu, J, Hu, J, Xu, Q, Wang, D, Wang, D, Liu, Y, Wang, Q, Yang, Q, Yang, Q & Li, H 2020, 'Performance and Mechanism of Potassium Ferrate(VI) Enhancing Dark Fermentative Hydrogen Accumulation from Waste Activated Sludge', ACS Sustainable Chemistry and Engineering, vol. 8, no. 23, pp. 8681-8691.
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© 2020 American Chemical Society. Potassium ferrate (K2FeO4, PF), as a multifunctional green oxidant, has been used for oxidative degradation of pollutants and recovery of resources in sludge. However, its impact on the generation of hydrogen in anaerobic fermentation of waste activated sludge (WAS) is still unclear. The purpose of this work is to study the influence of PF on the dark fermentative hydrogen production. Experimental result suggested that as PF increased from 0 to 0.09 g/g of TSS (total suspended solids), the maximal hydrogen production increased from 1.47 to 8.35 mL/g VSS (volatile suspended solids). A further increase to 0.12 g/g of TSS resulted in a decrease in hydrogen yield. Mechanism studies revealed that that the addition of PF not only facilitated the disruption of sludge cell and extracellular polymeric substances (EPS) but also increased the proportion of biodegradable organics, providing more bioavailable organics for subsequent reactions involved in hydrogen accumulation. Although the activities of microorganisms relevant to dark fermentation were suppressed to a certain extent in the presence of PF, the induced suppression to hydrogen consumers was more severe. Microbial studies indicated that the relative abundances of hydrogen producers (such as Petrimonas and Proteiniborus) were augmented while hydrogen consumers (such as Methanosaeta and Methylocaldum) decreased in the presence of PF.
Li, Y, Wang, D, Yang, G, Yuan, X, Xu, Q, Yang, Q, Liu, Y, Wang, Q, Ni, B-J, Tang, W & Jiang, L 2020, 'Enhanced dewaterability of anaerobically digested sludge by in-situ free nitrous acid treatment', WATER RESEARCH, vol. 169.
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Li, Y, Zeng, X, Zhou, J, Liu, H, Gu, Y, Pan, Z, Zeng, Y & Zeng, Y 2020, 'Incorporation of disposed oil-contaminated soil in cement-based materials', Resources, Conservation and Recycling, vol. 160.
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© 2020 Elsevier B.V. To realize the win-win objective of environmental conservation and waste recycling, oil-contaminated soil was utilized as an additive in cement-based materials. The effect of diesel and engine oil and the corresponding oil-contaminated soil on cement-based materials were studied, including the heat release of cement hydration, rheological and flow properties, flexural and compressive strength, hydration products and oil leaching values. The results showed that oil-contaminated soil increased the heat release of hydration of unit mass cement and reduced rheological and flow properties of cement paste and mortars. However, when the dosage of oil-contaminated soil is about 4%, the optimum values of the flexural and compressive strength of mortar, in standard curing 7 and 28 days, were obtained. The leaching values of oil in the disposition satisfied the requirement of China standards. The results confirmed that utilizing an appropriate dosage of oil-contaminated soil in cement-based materials improved the flexural and compressive strength, which is stable to dispose of the waste. This shows that using disposed oil-contaminated soil in cement-based materials will serve as a cost-effective and environmental solution.
Li, Y, Zhu, J, Zhu, L, Li, Y & Lei, G 2020, 'A Dynamic Magnetostriction Model of Grain-Oriented Sheet Steels Based on Becker-Doring Crystal Magnetization Model and Jiles-Atherton Theory of Magnetic Hysteresis', IEEE TRANSACTIONS ON MAGNETICS, vol. 56, no. 3.
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Li, Y, Zhu, Y, Wang, D, Yang, G, Pan, L, Wang, Q, Ni, BJ, Li, H, Yuan, X, Jiang, L & Tang, W 2020, 'Fe(II) catalyzing sodium percarbonate facilitates the dewaterability of waste activated sludge: Performance, mechanism, and implication', Water Research, vol. 174.
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© 2020 Elsevier Ltd In this work, Fe(II) catalyzing sodium percarbonate (Fe(II)/SPC) was managed to facilitate waste activated sludge (WAS) dewatering for the first time. The results showed that after WAS was treated by 20 mg/g total suspended solids (TSS) Fe(II) and 50 mg/g TSS SPC, the water content of sludge cake (WCSC) by press filtration and capillary suction time (CST) dropped from 90.8% ± 1.6% and 96.1 ± 4.0 s (the control) to 55.6% ± 1.4% and 30.1 ± 2.5 s, respectively. The mechanism investigations indicated that four intermediates or products (i.e., •OH, H2O2, Fe(II), and Fe(III)) generated in the Fe(II)/SPC process were responsible for the improved WAS dewaterability, and •OH and Fe(III) were the two major contributors. It was found that •OH collapsed and fragmented extracellular polymeric substances, damaged cell wall and permeabilized cytoplasmic membrane, and transformed conformation of the extracellular proteins secondary structure via both affecting the hydrogen bond maintaining α-helix and cracking disulfide bond in cysteine residues while Fe(III), the oxidization product of Fe(II), decreased the surface electronegativity and water-affinity surface areas of WAS flocs. As a result, the bound water release, flocculability, surface hydrophobicity, drain capability, and flowability of WAS flocs were strengthened whereas the compact surface structure, colloidal forces, network strength, gel-like structure, and apparent viscosity of WAS flocs were weakened. In addition, Fe(II)/SPC process also reduced the recalcitrant organics and fecal coliforms in sludge, which facilitated land application of dewatered sludge. The findings acquired in this work not only deepens our understanding of Fe(II)/SPC-involved WAS treatment process but also may guide engineers to develop both effective and promising strategies to better condition WAS for dewatering in the future.
Li, Z, Tang, X, Yang, Y, Lu, D & Cai, Z 2020, 'Three-pole wide-tuning-range balanced frequency-agile bandpass filter with constant absolute bandwidth and 3 transmission zeros', Microwave and Optical Technology Letters.
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© 2020 Wiley Periodicals, Inc. This paper proposes a 3-pole wide-tuning-range, balanced frequency-agile bandpass filter (BAL-FA-BPF) with constant absolute bandwidth (CABW) and 3 transmission zeros (TZs). Under the differential-mode (DM) operation, the cascaded trisection coupling topology with source-load coupling is constructed. Thus, 3 adaptive TZs are generated for the DM responses, which improves the passband selectivity and stopband suppression. Under the common-mode (CM) operation, the short stubs and lumped elements are added at the symmetric plane which effectively improves the CM suppression. Using the advanced matrix synthesis, a demonstrative BAL-FA-BPF is designed with stable CABW and controlled by single-DC-bias. The measured frequency tuning range of the DM passband is 2.0 to 3.0 GHz (40%) and 3 dB-bandwidth is kept at 174 ± 3 MHz (±1.7%). The CM suppression is better than 25 dB.
Li, Z, Tao, M, Du, K, Cao, W & Wu, C 2020, 'Dynamic stress state around shallow-buried cavity under transient P wave loads in different conditions', Tunnelling and Underground Space Technology, vol. 97.
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© 2019 Elsevier Ltd The failure of underground structure is always caused by local stress redistribution resulted from transient impact loads. An analytical model is established and solved by using the complex variable function method to illustrate the dynamic stress concentration around a shallow-buried cavity under transient P wave loads. The transient wave response is attained as an integration of steady-state wave response in circular frequency domain. By adopting a Butterworth filter, the jump points in the dynamic stress concentration factor (DSCF) curve which is not in line with the overall trend is filtered out to obtain more reasonable results. The convergence and accuracy of the solutions are verified and discussed in detail. DSCF distributions of high wavenumber incidences differ from that of low wavenumber incidences significantly in amplitudes and distribution patterns. The effects of the cavity depth, incidence angle and position of wave peak on DSCF distributions are illustrated.
Lian, J-W, Ban, Y-L, Zhu, H & Guo, YJ 2020, 'Reduced-Sidelobe Multibeam Array Antenna Based on SIW Rotman Lens', IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 19, no. 1, pp. 188-192.
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Lian, J-W, Ban, Y-L, Zhu, H & Guo, YJ 2020, 'Uniplanar Beam-Forming Network Employing Eight-Port Hybrid Couplers and Crossovers for 2-D Multibeam Array Antennas', IEEE Transactions on Microwave Theory and Techniques, pp. 1-1.
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Liang, Q, Wu, W, Yang, Y, Zhang, R, Peng, Y & Xu, M 2020, 'Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm', APPLIED SCIENCES-BASEL, vol. 10, no. 3.
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Lim, S, Akther, N, Van, HT, Bae, T-H, Phuntsho, S, Merenda, A, Dumee, LF & Shon, HK 2020, 'Covalent organic framework incorporated outer-selective hollow fiber thin-film nanocomposite membranes for osmotically driven desalination', DESALINATION, vol. 485.
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Lim, S, Park, KH, Van, HT, Akther, N, Phuntsho, S, Choi, JY & Shon, HK 2020, 'Size -controlled graphene oxide for highly permeable and fouling -resistant outer -selective hollow fiber thin-film composite membranes for forward osmosis', JOURNAL OF MEMBRANE SCIENCE, vol. 609.
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Lim, WYB, Luong, NC, Hoang, DT, Jiao, Y, Liang, YC, Yang, Q, Niyato, D & Miao, C 2020, 'Federated Learning in Mobile Edge Networks: A Comprehensive Survey', IEEE Communications Surveys and Tutorials.
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IEEE In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.
Lin, A, Lu, J, Xuan, J, Zhu, F & Zhang, G 2020, 'A Causal Dirichlet Mixture Model for Causal Inference from Observational Data', ACM Transactions on Intelligent Systems and Technology, vol. 11, no. 3.
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© 2020 ACM. Estimating causal effects by making causal inferences from observational data is common practice in scientific studies, business decision-making, and daily life. In today's data-driven world, causal inference has become a key part of the evaluation process for many purposes, such as examining the effects of medicine or the impact of an economic policy on society. However, although the literature contains some excellent models, there is room to improve their representation power and their ability to capture complex relationships. For these reasons, we propose a novel prior called Causal DP and a model called CDP. The prior captures the complex relationships between covariates, treatments, and outcomes in observational data using a rational probabilistic dependency structure. The model is Bayesian, nonparametric, and generative and is not based on the assumption of any parametric distribution. CDP is designed to estimate various kinds of causal effects - average, conditional average, average treated, quantile, and so on. It performs well with missing covariates and does not suffer from overfitting. Comparative experiments on synthetic datasets against several state-of-the-art methods demonstrate that CDP has a superior ability to capture complex relationships. Further, a simple evaluation to infer the effect of a job training program on trainee earnings from real-world data shows that CDP is both effective and useful for causal inference.
Lin, C-T, King, J-T, Chuang, C-H, Ding, W, Chuang, W-Y, Liao, L-D & Wang, Y-K 2020, 'Exploring the Brain Responses to Driving Fatigue Through Simultaneous EEG and fNIRS Measurements.', International Journal of Neural Systems, vol. 30, no. 1.
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Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO2) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO2 decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO2 oscillations suggest that activations in HbO2 are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain's responses as evidence of state changes during fatigue driving.
Lin, C-T, Yu, Y-H, King, J-T, Liu, C-H & Liao, L-D 2020, 'Augmented Wire-Embedded Silicon-Based Dry-Contact Sensors for Electroencephalography Signal Measurements', IEEE SENSORS JOURNAL, vol. 20, no. 7, pp. 3831-3837.
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Lin, J, Sun, G, Cui, T, Shen, J, Xu, D, Beydoun, G, Yu, P, Pritchard, D, Li, L & Chen, S 2020, 'From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning', World Wide Web.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.
Lin, W & Ziolkowski, RW 2020, 'Electrically Small Huygens CP Rectenna With a Driven Loop Element Maximizes Its Wireless Power Transfer Efficiency', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 1, pp. 540-545.
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Lin, W, Yin, X, Wang, S & Khosravi, MR 2020, 'A Blockchain-enabled decentralized settlement model for IoT data exchange services', Wireless Networks.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. IoT data exchange services are emerging to connect various and distributed IoT data sources, which facilitate data owners exchange their IoT data flexibly. Traditional IoT data exchange services rely on a centralized third-party to negotiate the settlement of trades between data consumers and providers. Such settlement model suffers from issues including single point of failure, extra settlement fee, un-transparency of settlement details, to name a few. New settlement model is imperative to overcome such limitations. Blockchain is an innovative technology that is competent in governing the decentralized network, which poses great opportunity to implement fair settlement in decentralized manner. In this paper, we propose a Blockchain-enabled settlement model for decentralized IoT data exchange services. Firstly, Bitcoin-based time commitment scheme is adopted to build fair and autonomous settlement model. Furthermore, to enhance the accountability of all transactions, an optimized practical Byzantine fault tolerant consensus protocol named ReBFT, is proposed to enable all members involved in the IoT data exchange application achieve identical shared ledger recording all transactions. Finally, experiments are conducted to verify the feasibility of our proposal.
Lin, Y, Wu, Y, Yan, C, Xu, M & Yang, Y 2020, 'Unsupervised person re-identification via cross-camera similarity exploration', IEEE Transactions on Image Processing, vol. 29, pp. 5481-5490.
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© 1992-2012 IEEE. Most person re-identification (re-ID) approaches are based on supervised learning, which requires manually annotated data. However, it is not only resource-intensive to acquire identity annotation but also impractical for large-scale data. To relieve this problem, we propose a cross-camera unsupervised approach that makes use of unsupervised style-transferred images to jointly optimize a convolutional neural network (CNN) and the relationship among the individual samples for person re-ID. Our algorithm considers two fundamental facts in the re-ID task, i.e., variance across diverse cameras and similarity within the same identity. In this paper, we propose an iterative framework which overcomes the camera variance and achieves across-camera similarity exploration. Specifically, we apply an unsupervised style transfer model to generate style-transferred training images with different camera styles. Then we iteratively exploit the similarity within the same identity from both the original and the style-transferred data. We start with considering each training image as a different class to initialize the Convolutional Neural Network (CNN) model. Then we measure the similarity and gradually group similar samples into one class, which increases similarity within each identity. We also introduce a diversity regularization term in the clustering to balance the cluster distribution. The experimental results demonstrate that our algorithm is not only superior to state-of-the-art unsupervised re-ID approaches, but also performs favorably compared with other competing unsupervised domain adaptation methods (UDA) and semi-supervised learning methods.
Ling, Y, Wang, K, Wang, X & Li, W 2020, 'Prediction of engineering properties of fly ash-based geopolymer using artificial neural networks', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Fly ash-based geopolymer has been studied extensively in recent years due to its comparable properties to Portland cement and its environmental benefits. However, the uncertainty and complexity of design parameters, such as the SiO2/Na2O mole ratio in alkaline solution, the alkaline solution concentration in liquid phase, and the liquid-to-fly ash mass ratio (L/F), have made it very difficult to create a systematic approach for geopolymer mix design. These mix design parameters, along with fly ash properties and curing conditions (temperature and time), significantly influence key properties of the material, such as setting time and compressive strength. In this study, an artificial neural network (ANN) was used to develop models for predicting the key properties of high-calcium fly ash-based geopolymer according to its mix design parameters. The correlations between experimental measurements and ANN model predictions of setting time, compressive strength, and heat of geopolymerization were established based on the results of tests on 36, 273, and 72 geopolymer mixes, respectively. The results show that the correlations between the experimental measurements and ANN model predictions of the properties studied are all strong. ANN modeling was found to be a suitable computing method to analyze the effects of design parameters on geopolymer properties and showed that L/F exhibited the greatest effect on setting time, alkaline solution concentration had the greatest influence on compressive strength, and a mole ratio larger than 1.5 significantly impacted heat at the geopolymerization peak. The developed ANN models can be used as guidance for mix design of high-calcium fly ash geopolymer in engineering applications.
Liu, A, Lu, J & Zhang, G 2020, 'Concept Drift Detection via Equal Intensity k-Means Space Partitioning.', IEEE transactions on cybernetics.
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The data stream poses additional challenges to statistical classification tasks because distributions of the training and target samples may differ as time passes. Such a distribution change in streaming data is called concept drift. Numerous histogram-based distribution change detection methods have been proposed to detect drift. Most histograms are developed on the grid-based or tree-based space partitioning algorithms which makes the space partitions arbitrary, unexplainable, and may cause drift blind spots. There is a need to improve the drift detection accuracy for the histogram-based methods with the unsupervised setting. To address this problem, we propose a cluster-based histogram, called equal intensity k-means space partitioning (EI-kMeans). In addition, a heuristic method to improve the sensitivity of drift detection is introduced. The fundamental idea of improving the sensitivity is to minimize the risk of creating partitions in distribution offset regions. Pearson's chi-square test is used as the statistical hypothesis test so that the test statistics remain independent of the sample distribution. The number of bins and their shapes, which strongly influence the ability to detect drift, are determined dynamically from the sample based on an asymptotic constraint in the chi-square test. Accordingly, three algorithms are developed to implement concept drift detection, including a greedy centroids initialization algorithm, a cluster amplify-shrink algorithm, and a drift detection algorithm. For drift adaptation, we recommend retraining the learner if a drift is detected. The results of experiments on the synthetic and real-world datasets demonstrate the advantages of EI-kMeans and show its efficacy in detecting concept drift.
Liu, A, Lu, J & Zhang, G 2020, 'Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation.', IEEE transactions on neural networks and learning systems, vol. PP.
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Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved to be an efficient method of handling concept drift. However, the best way to create and maintain ensemble diversity with evolving streams is still a challenging problem. In contrast to estimating diversity via inputs, outputs, or classifier parameters, we propose a diversity measurement based on whether the ensemble members agree on the probability of a regional distribution change. In our method, estimations over regional distribution changes are used as instance weights. Constructing different region sets through different schemes will lead to different drift estimation results, thereby creating diversity. The classifiers that disagree the most are selected to maximize diversity. Accordingly, an instance-based ensemble learning algorithm, called the diverse instance-weighting ensemble (DiwE), is developed to address concept drift for data stream classification problems. Evaluations of various synthetic and real-world data stream benchmarks show the effectiveness and advantages of the proposed algorithm.
Liu, B, Chen, C, Di, X, Liao, J, Wen, S, Su, QP, Shan, X, Xu, Z-Q, Ju, LA, Mi, C, Wang, F & Jin, D 2020, 'Upconversion nonlinear structured illumination microscopy.', Nano letters.
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Video-rate super-resolution imaging through biological tissue can visualize and track biomolecule interplays and transportations inside cellular organisms. Structured illumination microscopy allows for wide-field super resolution observation of biological samples but is limited by the strong extinction of light by biological tissues, which restricts the imaging depth and degrades its imaging resolution. Here we report a photon upconversion scheme using lanthanide-doped nanoparticles for wide-field super-resolution imaging through the biological transparent window, featured by near-infrared and low-irradiance nonlinear structured illumination. We demonstrate that the 976 nm excitation and 800 nm up-converted emission can mitigate the aberration. We found that the nonlinear response of upconversion emissions from single nanoparticles can effectively generate the required high spatial frequency components in the Fourier domain. These strategies lead to a new modality in microscopy with a resolution below 131 nm, 1/7th of the excitation wavelength, and an imaging rate of 1 Hz.
Liu, C, Zhu, L, He, X & Chen, J 2020, 'Enabling Privacy-Preserving Shortest Distance Queries on Encrypted Graph Data', IEEE Transactions on Dependable and Secure Computing.
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IEEE When coming to perform shortest distance queries on encrypted graph data outsourced in external storage infrastructure such as cloud, a significant challenge is how to compute the shortest distance in an accurate, efficient and secure way. This issue is addressed by a recent work, which makes use of somewhat homomorphic encryption (SWHE) to encrypt distance values output by a 2-hop cover labeling (2HCL) scheme. However, it may import large errors and even yield negative results. Besides, SWHE would be too time-consuming for normal clients. In this paper, we propose GENOA, a novel Graph ENcryption scheme for shOrtest distAnce queries. GENOA employs only efficient symmetric-key primitives while significantly enhances the accuracy compared to the prior work. As a reasonable trade-off, it additionally reveals the order information among queried distance values in the 2HCL index. We theoretically prove the accuracy and security of GENOA under rigorous cryptographic model. Detailed experiments on eight real-world graphs demonstrate that GENOA is efficient and can produce almost exact results.
Liu, C, Zowghi, D & Talaei-Khoei, A 2020, 'An empirical study of the antecedents of data completeness in electronic medical records', International Journal of Information Management, vol. 50, pp. 155-170.
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© 2019 Elsevier Ltd There is a body of research that highlights the role of data management to improve the quality of data, which in return improves organizational performance. The literature in data management has indicated the five theoretical constructs used to understand the factors influencing data quality, including top management support, capability on the regulation and process management, business-IT alignment, staff participation, and integration of information systems. However, it is unclear how these theoretical constructs can be utilized to understand the antecedents of data completeness as a dimension of data quality. Following that stream of research, the current paper examines the factors influencing data completeness in electronic medical records (EMR). The scope of this study is by only surveying medical professionals at healthcare settings in northern Nevada. The empirical results reveal that resources should be added as one of the antecedents of data completeness in EMR.
Liu, F, Liu, Y, Han, F, Ban, Y-L & Guo, YJ 2020, 'Synthesis of Large Unequally Spaced Planar Arrays Utilizing Differential Evolution With New Encoding Mechanism and Cauchy Mutation', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 6, pp. 4406-4416.
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Liu, F, Zhang, G & Lu, J 2020, 'Heterogeneous Domain Adaptation: An Unsupervised Approach.', IEEE transactions on neural networks and learning systems.
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Domain adaptation leverages the knowledge in one domain--the source domain--to improve learning efficiency in another domain--the target domain. Existing heterogeneous domain adaptation research is relatively well-progressed but only in situations where the target domain contains at least a few labeled instances. In contrast, heterogeneous domain adaptation with an unlabeled target domain has not been well-studied. To contribute to the research in this emerging field, this article presents: 1) an unsupervised knowledge transfer theorem that guarantees the correctness of transferring knowledge and 2) a principal angle-based metric to measure the distance between two pairs of domains: one pair comprises the original source and target domains and the other pair comprises two homogeneous representations of two domains. The theorem and the metric have been implemented in an innovative transfer model, called a Grassmann-linear monotonic maps-geodesic flow kernel (GLG), which is specifically designed for heterogeneous unsupervised domain adaptation (HeUDA). The linear monotonic maps (LMMs) meet the conditions of the theorem and are used to construct homogeneous representations of the heterogeneous domains. The metric shows the extent to which the homogeneous representations have preserved the information in the original source and target domains. By minimizing the proposed metric, the GLG model learns the homogeneous representations of heterogeneous domains and transfers knowledge through these learned representations via a geodesic flow kernel (GFK). To evaluate the model, five public data sets were reorganized into ten HeUDA tasks across three applications: cancer detection, the credit assessment, and text classification. The experiments demonstrate that the proposed model delivers superior performance over the existing baselines.
Liu, J, Hou, J, Huang, X, Xiang, Y & Zhu, T 2020, 'Secure and efficient sharing of authenticated energy usage data with privacy preservation', Computers and Security, vol. 92.
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© 2020 Elsevier Ltd As a technological innovation, smart grid improves electricity services in terms of substantiality, economics, efficiency, and reliability. This owes to the bi-directional communication property which not only enables the fine-grained energy usage data to be available for different entities but also facilitates the automated grid management. However, sharing such energy usage data with other parties could potentially cause the leakage of customers’ sensitive information. In addition, the data are vulnerable to be tampered with by any internal or external attacker such that the value of data could be destroyed. Therefore, it is crucial to preserve customers’ privacy and provide authenticity and verifiability while sharing energy usage data with other parties. In this paper, we propose a bilinear-map accumulator based redactable signature scheme (RSS-BMA) which allows customers to safeguard their privacy while guaranteeing the verifiability of shared data. Furthermore, the batch-data-block verification property of our design enhances not only efficiency but also security by prohibiting additional redaction to a batch of data blocks. We analyze the efficiency and security of our proposed scheme extensively, and the results indicate our construction is more practical than others.
Liu, K, Li, Q, Wu, C, Li, X & Li, J 2020, 'Optimization of spherical cartridge blasting mode in one-step raise excavation using pre-split blasting', International Journal of Rock Mechanics and Mining Sciences, vol. 126.
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© 2019 Elsevier Ltd In one-step raise excavation, spherical cartridge blasting mode is easy to be implemented due to minimal requirement of the hole-deviation. Nevertheless, it may induce cumulative damage to the country rock. This study firstly uses a validated rock model (Johnson-Holmquist model) to simulate the damage evolution process of a raise by spherical cartridge blasting mode in LS-DYNA software. Then a field test is carried out to examine the numerical results of spherical cartridge blasting mode. Both the numerical and test results indicate that because of highly confined rock mass and restricted free face in deep raise, a large charge is required in spherical cartridge blasting mode which leads to extensive damage on the wall of the raise. In order to solve such a problem, the pre-split blasting technique is developed to optimize spherical cartridge blasting mode. According to the subsequent numerical results, the improved spherical cartridge blasting mode is successfully applied in another filling raise. This study provides an effective solution to the difficulties that are encountered in one-step raise excavation by spherical cartridge blasting mode.
Liu, K, Wu, C, Li, X, Li, Q, Fang, J & Liu, J 2020, 'A modified HJC model for improved dynamic response of brittle materials under blasting loads', Computers and Geotechnics, vol. 123.
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© 2020 Elsevier Ltd The original Holmquist-Johnson-Cook (HJC) model, which can only capture the compressive behaviour but not the tensile behaviour of brittle materials, is modified in this study. First, the Lode-angle function is introduced to the yield strength surface to consider the changes in the substantial shear strength between the tensile and compressive meridians. A hyperbolic function is used to describe the strain rate effect under dynamic tensile loading. An exponential model is introduced to express the tensile softening stage of brittle material and define the tensile damage variable. Then the modified HJC model is implemented in LS-DYNA via user subroutine UMAT. Further the modifications are validated by single element tests and rock mechanical laboratory experiments. The verification results show that the modified HJC model can effectively describe the tensile responses of brittle material under static and dynamic loading. Last, a comparison between a blasting-crater field test and the corresponding numerical prediction by the modified HJC model is carried out. The comparison results demonstrate that the shape and size of the crater predicted by the modified HJC model show a good agreement with the field test data. Therefore, the modified HJC model has the capacity to capture the tensile and compressive behaviours and damage evolution of brittle material.
Liu, L, Guo, R, Ji, J, Miao, Z & Zhou, J 2020, 'Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates', International Journal of Robust and Nonlinear Control, vol. 30, no. 10, pp. 3831-3847.
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© 2020 John Wiley & Sons, Ltd. This paper proposes a sliding-mode control (SMC) method to achieve practical cooperative consensus tracking for a network of multiple nonholonomic wheeled mobile robots (MNWMRs) with input disturbances. A novel SMC surface under the nonholonomic constraints is first formulated to characterize the network communication interactions among the networked robots under the framework of polar coordinates. A unified distributed consensus tracking strategy is then proposed by systematically combining a position controller and a direction controller. Furthermore, a simple yet general criterion is derived to achieve the desired practical consensus of trajectory tracking and posture stabilization for MNWMRs. In particular, for a specific common consensus trajectory, the complete asymptotic tracking in heading direction can be fully guaranteed when the perfect asymptotic position-tracking errors are realized. Accordingly, the developed consensus tracking strategy for MNWMRs demonstrates some advantages of control performance including stability, robustness, and effectiveness over the existing control method proposed for their single-robot counterparts. Some comparative simulation results are given to confirm the effectiveness of the proposed cooperative consensus control method.
Liu, M, Luo, Y, Yang, C, Pang, S, Puthal, D, Ren, K & Zhang, X 2020, 'Privacy-preserving matrix product based static mutual exclusive roles constraints violation detection in interoperable role-based access control', Future Generation Computer Systems.
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© 2018 Elsevier B.V. Secure interoperation is an important technology to protect shared data in multi-domain environments. IRBAC (Interoperable Role-based Access Control) 2000 model has been proposed to achieve security interoperation between two or more RBAC administrative domains. Static Separation of Duties (SSoD) is an important security policy in RBAC, but it has not been enforced in the IRBAC 2000 model. As a result, some previous works have studied the problem of SMER (Statically Mutually Exclusive Roles) constraints violation between two RBAC domains in the IRBAC 2000 model. However all of them do not enforce how to preserve privacy of RBAC policies, such as roles, roles hierarchies and user-role assignment while detecting SMER constraints violation, if the two interoperable domains do not want to disclose them each other and to others. In order to enforce privacy-preserving detection of SMER constraints violation, we first introduce a solution without privacy-preserving mechanism using matrix product. Then a privacy-preserving solution is proposed to securely detect SMER constraints violation without disclosing any RBAC policy based on a secure three-party protocol to matrix product computation. By efficiency analysis and experimental results comparison, the secure three-party computation protocol to matrix product based on the Paillier cryptosystem is more efficient and practical.
Liu, P, Tang, Q, Su, S, Hu, J & Yu, Y 2020, 'Modeling and Analysis of the Two-Dimensional Axisymmetric Acoustofluidic Fields in the Probe-Type and Substrate-Type Ultrasonic Micro/Nano Manipulation Systems.', Micromachines, vol. 11, no. 1.
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The probe-type and substrate-type ultrasonic micro/nano manipulation systems have proven to be two kinds of powerful tools for manipulating micro/nanoscale materials. Numerical simulations of acoustofluidic fields in these two kinds of systems can not only be used to explain and analyze the physical mechanisms of experimental phenomena, but also provide guidelines for optimization of device parameters and working conditions. However, in-depth quantitative study and analysis of acoustofluidic fields in the two ultrasonic micro/nano manipulation systems have scarcely been reported. In this paper, based on the finite element method (FEM), we numerically investigated the two-dimensional (2D) axisymmetric acoustofluidic fields in the probe-type and substrate-type ultrasonic micro/nano manipulation systems by the perturbation method (PM) and Reynolds stress method (RSM), respectively. Through comparing the simulation results computed by the two methods and the experimental verifications, the feasibility and reasonability of the two methods in simulating the acoustofluidic fields in these two ultrasonic micro/nano manipulation systems have been validated. Moreover, the effects of device parameters and working conditions on the acoustofluidic fields are clarified by the simulation results and qualitatively verified by the experiments.
Liu, Q, Huang, H, Xuan, J, Zhang, G, Gao, Y & Lu, J 2020, 'A Fuzzy Word Similarity Measure for Selecting Top-k Similar Words in Query Expansion', IEEE Transactions on Fuzzy Systems, pp. 1-1.
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Liu, Q, Kang, B, Yu, K, Qi, X, Li, J, Wang, S & Li, HA 2020, 'Contour-Maintaining-Based Image Adaption for an Efficient Ambulance Service in Intelligent Transportation Systems', IEEE Access, vol. 8, pp. 12644-12654.
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© 2013 IEEE. Ambulance services play a vital role in intelligent transportation systems (ITS). In an intelligent ambulance system, the medical images can help doctors quickly and accurately understand the patients' condition during first aid. On various display devices in different kinds of ambulances, content-aware image adaption can be used to better present the medical image among different display resolutions and aspect ratios. Most existing methods mainly focus on visual protection of salient areas, such as specific organ parts of the human body, with less attention paid to the visual effect of unimportant areas. However, the human visual system is more sensitive to the edge and contour of images, which are important for ambulance services. To improve the visual effect of adapted images, a contour-maintaining-based image adaption method for an efficient ambulance service in ITS is proposed here. Firstly, the proposed method innovatively combines the weighted gradient, saliency, and edge maps into an importance map. Secondly, energy is optimized for reducing contour distortion and interruption according to the visual slope and curvature of contours and edges in non-salient areas. Finally, applying the sub-procedure of a forward seam carving method, the optimal seams can more evenly pass through the contour areas. The experimental results demonstrate that the proposed method is more effective than other similar methods.
Liu, Q, Liufu, K, Cui, Z, Li, J, Fang, J & Li, Q 2020, 'Multiobjective optimization of perforated square CFRP tubes for crashworthiness', THIN-WALLED STRUCTURES, vol. 149.
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Liu, T, Zhang, W, Yuwono, M, Zhang, M, Ueland, M, Forbes, SL & Su, SW 2020, 'A data-driven meat freshness monitoring and evaluation method using rapid centroid estimation and hidden Markov models', SENSORS AND ACTUATORS B-CHEMICAL, vol. 311.
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Liu, W, Chang, X, Chen, L, Phung, D, Zhang, X, Yang, Y & Hauptmann, AG 2020, 'Pair-based uncertainty and diversity promoting early active learning for person re-identification', ACM Transactions on Intelligent Systems and Technology, vol. 11, no. 2.
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© 2020 Association for Computing Machinery. The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and practical problem called Early Active Learning, which is applied to the early stage of experiments when there is no pre-labeled sample available as references for human annotating. Previous early active learning methods suffer from two limitations for Re-ID. First, these instance-based algorithms select instances rather than pairs, which can result in missing optimal pairs for Re-ID. Second, most of these methods only consider the representativeness of instances, which can result in selecting less diverse and less informative pairs. To overcome these limitations, we propose a novel pair-based active learning for Re-ID. Our algorithm selects pairs instead of instances from the entire dataset for annotation. Besides representativeness, we further take into account the uncertainty and the diversity in terms of pairwise relations. Therefore, our algorithm can produce the most representative, informative, and diverse pairs for Re-ID data annotation. Extensive experimental results on five benchmark Re-ID datasets have demonstrated the superiority of the proposed pair-based early active learning algorithm.
Liu, W, Gong, D, Tan, M, Shi, JQ, Yang, Y & Hauptmann, AG 2020, 'Learning Distilled Graph for Large-Scale Social Network Data Clustering', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 7, pp. 1393-1404.
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© 1989-2012 IEEE. Spectral analysis is critical in social network analysis. As a vital step of the spectral analysis, the graph construction in many existing works utilizes content data only. Unfortunately, the content data often consists of noisy, sparse, and redundant features, which makes the resulting graph unstable and unreliable. In practice, besides the content data, social network data also contain link information, which provides additional information for graph construction. Some of previous works utilize the link data. However, the link data is often incomplete, which makes the resulting graph incomplete. To address these issues, we propose a novel Distilled Graph Clustering (DGC) method. It pursuits a distilled graph based on both the content data and the link data. The proposed algorithm alternates between two steps: in the feature selection step, it finds the most representative feature subset w.r.t. an intermediate graph initialized with link data; in graph distillation step, the proposed method updates and refines the graph based on only the selected features. The final resulting graph, which is referred to as the distilled graph, is then utilized for spectral clustering on the large-scale social network data. Extensive experiments demonstrate the superiority of the proposed method.
Liu, X, He, D, Wu, Y, Xu, Q, Wang, D, Yang, Q, Liu, Y, Ni, BJ, Wang, Q & Li, X 2020, 'Freezing in the presence of nitrite pretreatment enhances hydrogen production from dark fermentation of waste activated sludge', Journal of Cleaner Production, vol. 248.
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© 2019 Elsevier Ltd Due to the poor biodegradability of released organics and the rapid consumption of hydrogen, hydrogen production from the untreated waste activated sludge (WAS) and/or inocula is still limited. In this study, it was found that the dark fermentative hydrogen production was largely enhanced from WAS pretreated by freezing in the presence of nitrite. With an increase of nitrite addition from 100 to 400 mg NO2−-N/L during freezing pretreatment (−5 °C for 4 h), the maximal hydrogen yield increased from 7.96 to 19.40 mL/g VS (volatile solids), which was 5.5–13.4 times of that in the control (without freezing and nitrite addition). Mechanism explorations revealed that the proposed pretreatment not only accelerated the disintegration of sludge but also promoted the proportion of biodegradable organics released, thereby provided more bio-available substrates for subsequent hydrogen production. Proposed pretreatment severely suppressed the sludge microorganisms responding to homoacetogenesis (−32.1%), methanogenesis (−58.4%), and sulfate-reducing process (−51.5%), inhibited the consumption of hydrogen. Moreover, there was more acetic and butyric (76% versus 57.5%) but less propionic acid (22.6% versus 13.4%) in this pretreated fermenter, which was in correspondence with the theory of fermentation type affecting hydrogen production. Long-term fermentation experiments indicated that the proposed pretreatment boosted the [FeFe]-hydrogenase activities while suppressed the activities of carbon monoxide dehydrogenase, coenzyme F420, and adenylyl sulfate reductase.
Liu, X, Huang, X, Wu, Y, Xu, Q, Du, M, Wang, D, Yang, Q, Liu, Y, Ni, BJ, Yang, G, Yang, F & Wang, Q 2020, 'Activation of nitrite by freezing process for anaerobic digestion enhancement of waste activated sludge: Performance and mechanisms', Chemical Engineering Journal, vol. 387.
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© 2020 Elsevier B.V. Nitrite-based pretreatment was demonstrated to effectively improve anaerobic digestion of waste activated sludge. It was found in this work that the freezing activated nitrite pretreatment could further enhance the performances. With the increase of nitrite addition from 0 to 600 mg NO2−-N/L during freezing process, the biochemical methane potential of pretreated-sludge gradually increased from 191.3 ± 8.0 to 233.2 ± 10.6 mL per gram volatile solid (VS), while only 178.6 ± 7.3 mL/g VS was obtained in the raw sludge. Mechanism explorations revealed that the freezing activated nitrite pretreatment remarkably facilitated the disintegration of sludge. Excitation emission matrix and fluorescence regional integration analyses further revealed that nitrite addition during freezing process promoted the proportion of biodegradable organics released, thereby providing more bio-available substrates for subsequent anaerobic digestion. Freezing condition induced reactive derivatives from nitrite (e.g., free nitrite acid, NO2[rad], N2O3) were assumed to be the major contributors to the enhanced sludge disintegration and recalcitrant organics (e.g., humic acid-like substances) degradation. It was also found that 600 mg NO2−-N/L addition activated by freezing pretreatment produced an anaerobically digested sludge with an improved dewaterability, as indicated by the decrease of the specific resistance to filterability and moisture content of dewatered cake. Moreover, 600 mg NO2−-N/L addition activated by freezing pretreatment and subsequent anaerobic digestion largely inactivated the pathogens to the levels below Class A biosolids requirements. Considering that nitrite can be in-situ produced in wastewater treatment plants through nitritation of the digestion liquid, this nitrite-based freezing process for sludge pretreatment was environmental-friendly and economically attractive.
Liu, X, Song, W, Musial, K, Zhao, X, Zuo, W & Yang, B 2020, 'Semi-supervised stochastic blockmodel for structure analysis of signed networks', Knowledge-Based Systems, vol. 195.
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© 2020 Elsevier B.V. Finding hidden structural patterns is a critical problem for all types of networks, including signed networks. Among all of the methods for structural analysis of complex network, stochastic blockmodel (SBM) is an important research tool because it is flexible and can generate networks with many different types of structures. However, most existing SBM learning methods for signed networks are unsupervised, leading to poor performance in terms of finding hidden structural patterns, especially when handling noisy and sparse networks. Learning SBM in a semi-supervised way is a promising avenue for overcoming the above difficulty. In this type of model, a small number of labelled nodes and a large number of unlabelled nodes, coupled with their network structures, are simultaneously used to train SBM. We propose a novel semi-supervised signed stochastic blockmodel and its learning algorithm based on variational Bayesian inference, with the goal of discovering both assortative (the nodes connect more densely in same clusters than that in different clusters) and disassortative (the nodes link more sparsely in same clusters than that in different clusters) structures from signed networks. The proposed model is validated through a number of experiments wherein it compared with the state-of-the-art methods using both synthetic and real-world data. The carefully designed tests, allowing to account for different scenarios, show our method outperforms other approaches existing in this space. It is especially relevant in the case of noisy and sparse networks as they constitute the majority of the real-world networks.
Liu, X, Zhou, A, Shen, SL, Li, J & Sheng, D 2020, 'A micro-mechanical model for unsaturated soils based on DEM', Computer Methods in Applied Mechanics and Engineering, vol. 368.
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© 2020 Elsevier B.V. A micro-mechanical model to study the microscopic and macroscopic behavior of unsaturated soils under different suctions is proposed in this study. In the model, a novel pore-scale numerical method for simulating the liquid–solid interfaces is proposed first. A discretization of the particle surface using Fibonacci-Lattice is then introduced to calculate the capillary forces from the complex liquid–solid interfaces. The joint influence of capillary forces and the interparticle contact forces on the motion of the particles are handled by the discrete element method (DEM). The effective stress parameter estimated by the model is compared with the experimental results for unsaturated soils, which confirms the validity of the proposed micro-mechanical model. The microscopic responses (liquid–solid interfaces, capillary forces, contact forces and coordination numbers) and macroscopic responses (strength, stress–strain relationship and volume change) of unsaturated soils in desaturation tests and triaxial tests are studied by the proposed model.
Liu, Y, Lan, C, Blumenstein, M & Li, J 2020, 'Bi-level error correction for PacBio long reads', IEEE/ACM Transactions on Computational Biology and Bioinformatics.
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IEEE The latest sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines can generate long reads at the length of thousands of nucleic bases which is much longer than the reads at the length of hundreds generated by Illumina machines. However, these long reads are prone to much higher error rates, for example 15%, making downstream analysis and applications very difficult. Error correction is a process to improve the quality of sequencing data. Hybrid correction strategies have been recently proposed to combine Illumina reads of low error rates to fix sequencing errors in the noisy long reads with good performance. In this paper, we propose a new method named Bicolor, a bi-level framework of hybrid error correction for further improving the quality of PacBio long reads. At the first level, our method uses a de Bruijn graph-based error correction idea to search paths in pairs of solid < formula > < tex > $k$ < /tex > < /formula > -mers iteratively with an increasing length of < formula > < tex > $k$ < /tex > < /formula > -mer. At the second level, we combine the processed results under different parameters from the first level. In particular, a multiple sequence alignment algorithm is used to align those similar long reads, followed by a voting algorithm which determines the final base at each position of the reads. We compare the superior performance of Bicolor with three state-of-the-art methods on three real data sets. Results demonstrate that Bicolor always achieves the highest identity ratio. Bicolor also achieves a higher alignment ratio ( < formula > < tex > $ & #x003E; 1.3\%$ < /tex > < /formula > ) and a higher number of aligned reads than the current methods on two data sets. On the third data set, our method is closely competitive to the current methods in terms of number of aligned reads and genome coverage. The C++ source codes of our algorithm are freely available at https://github.com/yuansliu/Bicolor.
Liu, Y, Li, M, Haupt, RL & Guo, YJ 2020, 'Synthesizing Shaped Power Patterns for Linear and Planar Antenna Arrays Including Mutual Coupling by Refined Joint Rotation/Phase Optimization', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 6, pp. 4648-4657.
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Liu, Y, Luo, G, Ngo, HH, Guo, W & Zhang, S 2020, 'Advances in thermostable laccase and its current application in lignin-first biorefinery: A review.', Bioresource technology, pp. 122511-122511.
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As the most abundant aromatic polymers on the Earth, lignin has great potential to produce biofuels and aromatic chemicals due to their high carbon content and low oxygen content. Lignin-first biorefinery methods have attracted increasing attention recently for their high-value of aromatic chemicals, and high biofuels productivity from lignocellulosic wastes. Thermostable laccase has proven to be an excellent alternative catalyst in degrading lignin for its versatile catalytic abilities under industrial conditions and pollution-free by-products. Thermostable laccases can be found in native extreme environments or modified by biologically based technologies such as gene recombination expression and enzyme direct evolution. This review demonstrated thermostable laccases and their application in lignin degradation. Future research should focus more on the investigation of the reaction of thermostable laccases with lignin substrates.
Liu, Y, Ngo, HH, Guo, W, Wang, D, Peng, L, Wei, W & Ni, B-J 2020, 'Impact of coexistence of sludge flocs on nitrous oxide production in a granule-based nitrification system: A model-based evaluation', WATER RESEARCH, vol. 170.
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Liu, Y, Wang, F, Lu, H, Fang, G, Wen, S, Chen, C, Shan, X, Xu, X, Zhang, L, Stenzel, M & Jin, D 2020, 'Super-Resolution Mapping of Single Nanoparticles inside Tumor Spheroids', SMALL, vol. 16, no. 6.
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Liu, Y, Yang, Y, Han, F, Liu, QH & Guo, YJ 2020, 'Improved Beam-Scannable Ultra-Wideband Sparse Antenna Arrays by Iterative Convex Optimization Based on Raised Power Series Representation', IEEE Transactions on Antennas and Propagation.
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IEEE A novel method is presented to design beam-scannable ultra-wideband (UWB) sparse arrays. A concept of design frequency is introduced which transforms the beam-scannable UWB array design to the problem of synthesizing a broadside-beam array at single-frequency. The raised power series (RPS) representation with appropriate parameter selection is adopted to generate initial element positions, and then an iterative convex optimization is applied to successively optimize the element positions for further sidelobe level (SLL) reduction. Multiple constraints for controlling the first-order Taylor expansion accuracy, the minimum element spacing and the array aperture are incorporated in the iterative convex optimization to obtain stable and practical synthesis results. Several examples for synthesizing UWB arrays with different frequency bands, beam scanning ranges and element counts, are conducted to validate the effectiveness and advantages of the proposed method. It is shown that the proposed method achieves much lower SLLs than those by the original RPS method for all test cases, and it also significantly outperforms some conventional stochastic optimization methods for large UWB array cases.
Liu, Y, Yang, Y, Wu, P, Ma, X, Li, M, Xu, KD & Guo, YJ 2020, 'Synthesis of Multibeam Sparse Circular-Arc Antenna Arrays Employing Refined Extended Alternating Convex Optimization', IEEE Transactions on Antennas and Propagation.
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IEEE A refined extended alternating convex optimization (REACO) method is presented to synthesize multibeam sparse circular-arc antenna arrays with minimum element spacing control by considering real antenna array structure characteristics. This method consists of initial step and a few refining steps. At the initial step, an initial array with dense elements distributed on a circular-arc is considered, and its array manifold vector is described by rotating a simulated isolated element pattern (IEP) without considering element mutual coupling. The collective excitation coefficient vector (CECV) and its energy bound are introduced for each element, and consequently the common element positions for generating desired multibeam patterns can be found by minimizing the number of active CECVs under multiple constraints. This minimization problem is further formulated as performing a sequence of alternating convex optimization (ACO) in which the CECV and an auxiliary weighting vector are alternately chosen as the optimization variables, so that the mimimum element spacing constraint can be easily dealt with. Once the initial optimization step is finished, a few refining steps are performed in which the element positions and excitations are successively updated in each step by renewing the array manifold vector through rotating the simulated nearby active element patterns (AEPs) of the antenna array obtained at the previous step. In such a way, the mutual coupling can be incorporated into the multibeam sparse array synthesis. An example of synthesizing a sparse circular-arc conformal array with 23 beams covering the space from–63.25° to 63.25° is conducted to validate the effectiveness and advantage of the proposed method.
Liu, Y, Zhao, T, Su, Z, Zhu, T & Ni, B-J 2020, 'Evaluating the roles of coexistence of sludge flocs on nitrogen removal and nitrous oxide production in a granule-based autotrophic nitrogen removal system.', The Science of the total environment, vol. 730, p. 139018.
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Certain levels of sludge flocs would always coexist in granule-based reactors due to the biomass detachment from granules. Such inevitable coexistence could affect both total nitrogen (TN) removal and nitrous oxide (N2O) production in autotrophic nitrogen removal systems. This work utilized a mathematical approach to systematically study the influence of the coexisting sludge flocs on TN removal and N2O production in a granular nitritation-anaerobic ammonium oxidation (Anammox) process for the first time, based on a 2-pathway N2O production model concept. The modelling results reveal that the highest TN removal efficiency decreases from ca. 87-88% to ca. 41-49% as the fraction of sludge flocs in the system increases from 10% to 40%, while the N2O production rate gradually increases with such increase. Meanwhile, both bulk dissolved oxygen (DO, 0.05-0.3 mg/L) and the size of granule (200-400 μm) could also influence the TN removal efficiency and N2O production. As the fraction of sludge flocs increases from 10% to 40%, the contribution of granular biomass to total N2O production is reduced due to increase of N2O-producing ammonia-oxidizing bacteria (AOB) in the sludge flocs, and the increase of granule size could intensify such decrease. In addition, the hydroxylamine oxidation pathway dominates the nitrifier denitrification pathway in both granules and sludge flocs under various testing conditions, whereas the increasing contribution of the latter would occur at a certain DO range, higher fraction of sludge flocs and smaller granule size. These results disclose an important influence of the coexisting sludge flocs on the performance of granular nitritation-Anammox systems.
Liu, Z, Cao, J, Tan, Y, Xiao, Q & Prasad, M 2020, 'Planning Above the API Clouds Before Flying Above the Clouds: A Real-Time Personalized Air Travel Planning Approach', International Journal of Parallel Programming, vol. 48, no. 1, pp. 137-156.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. The rapid growth of the airline industry has resulted in the availability of a large number of flights, however this can also create a paralyzing problem. Flight information on all airlines across the world can be obtained via the Internet. Today, passengers trend to be interested in user personalized service. How to effectively find a passenger’s most preferred air travel plan, which might include multiple transfers from millions of possible choices with certain constraints, such as time and price, is a critical challenge. This paper presents an efficient air travel planning approach, which can find a number of air travel plans by invoking the APIs offered by airline companies. At the same time, these plans also best match the customer’s preference based on an analysis of historical orders. An algorithm to extract user preference features is introduced and heuristic rules to speed up the K path search process under constraints are presented. The experiment results show that the proposed model finds optimal air travel plans efficiently on a real-world dataset.
Liu, Z, Xiao, F, Lin, C-T, Kang, BH & Cao, Z 2020, 'A Generalized Golden Rule Representative Value for Multiple-Criteria Decision Analysis', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-12.
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Liu, Z, Zhang, L, Ni, W & Collings, IB 2020, 'Uncoordinated Pseudonym Changes for Privacy Preserving in Distributed Networks', IEEE Transactions on Mobile Computing, vol. 19, no. 6, pp. 1465-1477.
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© 2002-2012 IEEE. Pseudonyms have been adopted to preserve identity privacy of nodes in distributed networks. Frequent and unlinkable changes of pseudonyms need to be enabled by having at least $k$k nodes change together to confuse potential eavesdroppers. Existing approaches either depend on the coordination from central controllers, or involve interactive signaling between the nodes. This can potentially compromise privacy. This paper proposes a fully uncoordinated approach to change pseudonyms in distributed networks, where each node uses a pseudonym until its expiration and then changes after a random delay. We develop a new model to analyse the time-varying population of changing pseudonyms. Critical conditions are analytically established, under which individual nodes can independently change their pseudonyms while their identity privacy is preserved. The conditions are validated by illustrative examples. Corroborated by simulations, the accuracy of the analytical model improves, as the number of nodes increases. The analysis confirms that, the $k$k-anonymity can be achieved at a negligible throughput loss in the case of large networks.
Liyanaarachchi, S, Jegatheesan, V, Shu, L, Shon, HK, Muthukumaran, S & Li, CQ 2020, 'Evaluating the feasibility of forward osmosis in diluting ro concentrate using pretreatment backwash water', Membranes, vol. 10, no. 3.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Forward osmosis (FO) is an excellent membrane process to dilute seawater (SW) reverse osmosis (RO) concentrate for either to increase the water recovery or for safe disposal. However, the low fluxes through FO membranes as well the biofouling/scaling of FO membranes are bottlenecks of this process requiring larger membrane area and membranes with anti‐fouling properties. This study evaluates the performance of hollow fibre and flat sheet membranes with respect to flux and biofouling. Ferric hydroxide sludge was used as impaired water mimicking the backwash water of a filter that is generally employed as pretreatment in a SWRO plant and RO concentrate was used as draw solution for the studies. Synthetic salts are also used as draw solutions to compare the flux produced. The study found that cellulose triacetate (CTA) flat sheet FO membrane produced higher flux (3–6 L m−2 h−1) compared to that produced by polyamide (PA) hollow fibre FO membrane (less than 2.5 L m−2 h−1) under the same experimental conditions. Therefore, long‐term studies conducted on the flat sheet FO membranes showed that fouling due to ferric hydroxide sludge did not allow the water flux to increase more than 3.15 L m−2 h−1.
Loganathan, P, Gradzielski, M, Bustamante, H & Vigneswaran, S 2020, 'Progress, challenges, and opportunities in enhancing NOM flocculation using chemically modified chitosan: A review towards future development', Environmental Science: Water Research and Technology, vol. 6, no. 1, pp. 45-61.
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© 2019 The Royal Society of Chemistry. Natural organic matter (NOM) occurs ubiquitously in water bodies and this can greatly affect feed or raw water quality (taste, colour, odour, bacterial growth). Furthermore, it reduces the performance of the coagulation-based water treatment process. Because the NOM content and its chemical complexity are increasing throughout the world, the removal of NOM from water has become a major challenge in supplying the required amounts of good quality water. The coagulation-flocculation process is widely used for purifying urban water supplies. However, it is not always sufficiently successful in removing the augmented NOM in the feed water, mostly because the polyelectrolytes currently used as coagulants/flocculants cannot effectively interact with all the NOM components consisting of different functional groups, molecular weights, charges, and hydrophobicity. Within the class of polyelectrolytes, chitosan (Cs), which is produced by the deacetylation of abundantly available chitin, has been tested in removing NOM. The effectiveness of Cs can be further improved by chemically modifying the abundant free amino and hydroxyl groups along the Cs chain backbone. This will provide new functional groups that can increase the positive charges, molecular weight, and allow for solubility over a wider pH range, as well as introduce tailored groups which interact in an optimised way with NOM, thereby reducing the solubility of the formed complexes. This paper critically reviews the chemistry of the formed polyelectrolyte/NOM complexes and provides information on how this can be taken advantage of, to identify modified chemical structures of Cs to improve NOM removal in water treatment strategies.
Logeswaran, J, Shamsuddin, AH, Silitonga, AS & Mahlia, TMI 2020, 'Prospect of using rice straw for power generation: a review.', Environmental science and pollution research international, vol. 27, no. 21, pp. 25956-25969.
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With the ever-increasing energy demands, fossil fuels are gradually depleting and eventually, these nonrenewable sources of energy will be exhausted. Hence, there is an urgent need to formulate alternative fuels that are both renewable and sustainable. Biomass is one of the reliable sources of energy because it is replenishable. Rice is the staple food in many countries, particularly in Asia. The number of paddy fields has increased tremendously over the years and is expected to increase in the future in response to the growing world population. This will lead to significant amounts of agricultural wastes annually, particularly rice straw. In some countries, open burning and soil incorporation are used to manage agricultural wastes. Open burning is the preferred method because it is inexpensive. However, this method is highly undesirable because of its detrimental impact on the environment resulting from the release of carbon dioxide and methane gas. Hence, it is important to develop an energy-harvesting method from rice straw for power generation. More studies need to be carried out on the availability and characteristics of rice straw as well as logistic analysis to assess the potential of rice straw for power generation. This paper is focused on reviewing studies pertaining to the characteristics and potential of rice straw for power generation, current rice straw management practices, and logistic analysis in order to develop a suitable energy-harvesting method from rice straw in Malaysia.
Lomax, BA, Conti, M, Khan, N, Bennett, NS, Ganin, AY & Symes, MD 2020, 'Proving the viability of an electrochemical process for the simultaneous extraction of oxygen and production of metal alloys from lunar regolith', Planetary and Space Science, vol. 180.
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© 2019 Elsevier Ltd The development of an efficient process to simultaneously extract oxygen and metals from lunar regolith by way of in-situ resource utilisation (ISRU) has the potential to enable sustainable activities beyond Earth. The Metalysis-FFC (Fray, Farthing, Chen) process has recently been proven for the industrial-scale production of metals and alloys, leading to the present investigation into the potential application of this process to regolith-like materials. This paper provides a proof-of-concept for the electro-deoxidation of powdered solid-state lunar regolith simulant using an oxygen-evolving SnO2 anode, and constitutes the first in-depth study of regolith reduction by this process that fully characterises and quantifies both the anodic and cathodic products. Analysis of the resulting metallic powder shows that 96% of the total oxygen was successfully extracted to give a mixed metal alloy product. Approximately a third of the total oxygen in the sample was detected in the off-gas, with the remaining oxygen being lost to corrosion of the reactor vessel. We anticipate, with appropriate adjustments to the experimental set-up and operating parameters, to be able to isolate essentially all of the oxygen from lunar regolith simulants using this process, leading to the exciting possibility of concomitant oxygen generation and metal alloy production on the lunar surface.
Long, H, Liu, C, Shao, C, Cheng, Y, Tao, J, Qiu, X & Liu, X 2020, 'Tunable and broadband asymmetric sound absorptions with coupling of acoustic bright and dark modes', JOURNAL OF SOUND AND VIBRATION, vol. 479.
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Long, Z, Meng, H, Li, T & Li, S 2020, 'Compact geometric representation of qualitative directional knowledge', Knowledge-Based Systems, vol. 195.
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© 2020 Elsevier B.V. To effectively and efficiently deal with large-scale spatial data is critical for applications in the age of information technology. Compact representation of spatial knowledge is one of the emerging research techniques that contribute to this capability. In this article, we consider the problem of compactly representing qualitative directional relations between extended objects, modelled in the Cardinal Direction Calculus (CDC) of Goyal and Egenhofer. For a large dataset of regions, this approach first constructs a simplified geometry for each region, which preserves CDC relations between regions, and then represents each simplified geometry compactly, so that the storage size is small while retrieving CDC relations from the representation is still reasonably fast. More specifically, the method called necessary cut is used to construct simple geometries, and the two methods, viz. the polygon representation and the rectangle representation, are devised to compactly represent the constructed geometries in cubic time w.r.t. the size of the corresponding simple geometry. Theoretical analyses demonstrate that the two representations, especially the rectangle representation, are promising to have small storage size. Moreover, our empirical evaluations on real-world datasets show that, for each dataset the new approach can produce a rectangle representation that has dominant performance against the state of the art techniques in reducing the storage size of the relations, while the average efficiency of retrieving CDC relations based on the rectangle representation is about the same as the fastest method in the literature.
Lu, J, Liu, A, Song, Y & Zhang, G 2020, 'Data-driven decision support under concept drift in streamed big data', COMPLEX & INTELLIGENT SYSTEMS.
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Data-driven decision-making ( D3 M) is often confronted by the problem of uncertainty or unknown dynamics in streaming data. To provide real-time accurate decision solutions, the systems have to promptly address changes in data distribution in streaming data—a phenomenon known as concept drift. Past data patterns may not be relevant to new data when a data stream experiences significant drift, thus to continue using models based on past data will lead to poor prediction and poor decision outcomes. This position paper discusses the basic framework and prevailing techniques in streaming type big data and concept drift for D3 M. The study first establishes a technical framework for real-time D3 M under concept drift and details the characteristics of high-volume streaming data. The main methodologies and approaches for detecting concept drift and supporting D3 M are highlighted and presented. Lastly, further research directions, related methods and procedures for using streaming data to support decision-making in concept drift environments are identified. We hope the observations in this paper could support researchers and professionals to better understand the fundamentals and research directions of D3 M in streamed big data environments.
Lu, L, Ren, X, Cui, C, Luo, Y & Huang, M 2020, 'Tensor mutual information and its applications', Concurrency Computation.
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© 2020 John Wiley & Sons, Ltd. Correlation analysis has long been a question of great interest in measuring the relationship among different variables and has been applied in many fields, such as dimension reduction, classification, and so on. However, current methods of correlation analysis take into account the linear relationship between multiple variables and only few works on nonlinear interaction of two variables have been considered. In this article, we first present a nonlinear analysis method of multiple (two or more) variables based on mutual information for tensor analysis (MITA). In addition, we extend the mutual-information matrix analysis directly to MITA and show the multivariable mutual information formula based on Venn diagram. Experiments on multiview dimension reduction, including attacking internet traffic prediction, advertisement classification, and biometric structure prediction illustrate the effectiveness of the proposed method, especially in the case of low-dimensional subspace.
Lu, W & Liu, D 2020, 'A2: Extracting cyclic switchings from DOB-nets for rejecting excessive disturbances: A2: Extracting cyclic switchings from DOB-nets for rejecting excessive disturbances', Neurocomputing, vol. 400, pp. 161-172.
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© 2020 Reinforcement Learning (RL) is limited in practice by its poor explainability, which is responsible for insufficient trustiness from users, unsatisfied interpretation for human intervention, inadequate analysis for future improvement, etc. This paper seeks to partially characterize the interplay between dynamical environments and a previously-proposed Disturbance OBserver net (DOB-net). The DOB-net is trained via RL and offers optimal control for a set of Partially Observable Markovian Decision Processes (POMDPs). The transition function of each POMDP is largely determined by the environments (excessive external disturbances). This paper proposes an Attention-based Abstraction (A2) approach to extract a finite-state automaton, referred to as a Key Moore Machine Network (KMMN), to capture the switching mechanisms exhibited by the DOB-net in dealing with multiple such POMDPs. A2 first quantizes the controlled platform by learning continuous-discrete interfaces. Then it extracts the KMMN by finding the key hidden states and transitions that attract sufficient attention from the DOB-net. Within the resultant KMMN, three patterns of cyclic switchings (between key hidden states) are found, and saturated controls are shown synchronized with unknown disturbances. Interestingly, the found switchings have previously appeared in the control design for often-saturated systems. They are interpreted via an analogy to the discrete-event subsystem of hybrid control.
Lu, Z, Geng, L, Huo, G, Zhao, H, Yao, W, Li, G, Guo, X & Zhang, J 2020, 'A novel hybrid multi-objective bacterial colony chemotaxis algorithm', Soft Computing, vol. 24, pp. 2013-2032.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Abstract: In this article, a novel hybrid multi-objective bacterial colony chemotaxis (HMOBCC) algorithm is proposed to solve multi-objective optimization problems. A mechanism of particle swarm optimization is introduced to multi-objective bacterial colony chemotaxis (MOBCC) algorithm to improve the performance of MOBCC algorithm. Also, three other techniques, including dynamic reverse learning operator, external archive multiplying operator and adaptive diversity maintenance operator, are further applied to improve the diversity and convergence of the algorithm. The proposed algorithm is validated using 12 benchmark problems, and three performance measures are implemented for 5 benchmark problems to compare its performance with existing popular algorithms such as MOBCC, multi-objective bacterial colony chemotaxis based on grid algorithm, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective evolutionary algorithm based on decomposition. The results show that the proposed HMOBCC is very effective against existing algorithms. Graphical abstract: The graphical abstract of this study.[Figure not available: see fulltext.].
Luo, S, Chu, VW, Li, Z, Wang, Y, Zhou, J, Chen, F & Wong, RK 2020, 'Multi-task learning by hierarchical Dirichlet mixture model for sparse failure prediction', International Journal of Data Science and Analytics.
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Luo, Y, Canning, J, Zhang, J & Peng, GD 2020, 'Toward optical fibre fabrication using 3D printing technology', Optical Fiber Technology, vol. 58.
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© 2020 Elsevier Inc. 3D printing is a disruptive technology that could revolutionise the fabrication of optical fibre by offering greater flexibility and diversity in material and structure designs. This paper reviews the recent research and development of material and fabrication techniques toward 3D printing-based polymer optical fibre (POF) and silica optical fibre (SOF).
Luo, Y, Zhang, JA, Huang, X, Ni, W & Pan, J 2020, 'Multibeam Optimization for Joint Communication and Radio Sensing Using Analog Antenna Arrays', IEEE Transactions on Vehicular Technology, pp. 1-1.
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Luo, Z, Li, W, Gan, Y, Mendu, K & Shah, SP 2020, 'Applying grid nanoindentation and maximum likelihood estimation for N-A-S-H gel in geopolymer paste: Investigation and discussion', Cement and Concrete Research, vol. 135.
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© 2020 Elsevier Ltd Static nanoindentation and Maximum Likelihood Estimation (MLE) were applied for the nano/micromechanical properties investigation of alkali-activated fly ash (AAFA) in this study. Some critical issues of statistical nanoindentation were fully discussed, including properties of pure gel phase, influence of bin size when using least-square estimation (LSE), and suitable number of components for deconvolution. Results indicate that the model estimated by MLE method can effectively reflect the micromechanical distribution of AAFA. The number of components needed to separate sodium aluminosilicate hydrate (N-A-S-H) gels is sometimes more than the normally used 3 or 4, depending on the sample and testing factors. The gel phase does not always display as a prominent peak in the histogram and is easy to be mixed with other adjacent peaks even if the bin size is small, indicating the challenges of employing the LSE method to investigate the gel phase in highly heterogeneous materials, such as geopolymer.
Ly, QV, Matindi, C, Kuvarega, AT, Ngo, HH, Le, QV, Nam, VH & Li, J 2020, 'Exploring the novel PES/malachite mixed matrix membrane to remove organic matter for water purification', Chemical Engineering Research and Design, vol. 160, pp. 63-73.
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© 2020 Institution of Chemical Engineers This study presents a greener approach to fabricate mixed matrix membranes (MMMs) by introducing malachite nanoparticles (MLC NPs) synthesized from copper sulfate, a toxic waste from many industrial applications, into polyethersulfone (PES) membrane to remove organic matter for water reclamation. The PES/MLC MMMs were characterized by XRD, FTIR, SEM(EDX), Raman Spectroscopy, Photoluminescence Spectrometry, and Contact angle measurements. Results showed that the incorporation of MLC NPs into PES altered the membrane morphology, and made the membrane more hydrophilic. At the optimal amount of 0.1 wt.% MLC NPs, PES/MLC showed the best water flux of 931.1 L/(m2 h) (LMH) with the highest breaking stability of 5.06 ± 0.33 MPa. The membrane also exhibited enhanced adsorption of organic foulants with a corresponding flux recovery rate of 0.55 ± 0.03%. The inevitable agglomeration of MLC NPs witnessed at MLC NP dosage of 1 wt.% compromised either the throughput or the mechanical integrity. The elevated NP loading rate from 0 to 1 wt.% enhanced the removal rate of organic carbon (C) from 20.8 ± 1.7% to 26.3 ± 2.3%, respectively. Optical methods demonstrated the preferential rejection of aromatic constituents by the fabricated membranes suggests their potential applicability of optical methods, particularly the fluorescence spectrometry for better prediction of treated water quality by membrane filtration.
Lyu, B, Hoang, DT & Yang, Z 2020, 'Backscatter then Forward: A Relaying Scheme for Batteryless IoT Networks', IEEE Wireless Communications Letters.
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IEEE In this paper, we introduce a novel relaying scheme together with a joint energy beamforming (EB) and time allocation optimization to meet requirements about energy efficiency and hardware constraints of batteryless IoT networks. First, we propose an intelligent relaying scheme using RF-powered gateways as relay nodes to deliver information from batteryless IoT devices to a hybrid access point (HAP). The HAP can also transfer energy to the gateways and batteryless devices using EB techniques. The energy from HAP will be then used to supply power for gateways and as a communications means to transmit data for batteryless devices. We then formulate a sum-rate maximization problem by jointly optimizing the EB vectors, time scheduling, and power allocation. Since the optimization problem is non-convex, we exploit EB characteristics for data backscattering and employ variable substitutions and semidefinite relaxation techniques to transform it into a convex one. After that, a low-complexity method is proposed to obtain the optimal solution in a closed-form. Simulation results confirm that the proposed scheme can achieve significant sum-rate gain.
Lyu, J, Bi, X & Ling, SH 2020, 'Multi-Level Cross Residual Network for Lung Nodule Classification', SENSORS, vol. 20, no. 10.
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Lyu, Q, Lu, W, Wang, W & Chen, Y 2020, 'Mechanism and optimum design of shared tuned mass damper for twin-tower structures connected at the top by an isolated corridor', Structural Design of Tall and Special Buildings, vol. 29, no. 8.
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© 2020 John Wiley & Sons, Ltd. The concept of shared tuned mass damper (STMD) for twin towers linked by a sky corridor using flexible joints is proposed in this paper. The analytical expressions of the transform functions and random earthquake responses of the flexibly connected structures are derived using a three-degrees-of-freedom model system. The seismic reduction mechanism of the STMD is revealed using comparative analysis between the structures with STMD and those connected using a viscoelastic damper. The effect of the nondimensional parameters such as the frequency ratio of the two primary structures, mass ratio, tuning frequency ratio of the corridor, and damping ratio of the passive control devices on the structural seismic response is investigated. Optimum parametric analysis is performed using the principle of minimizing the displacements of both towers, and the optimal parameter formulas are established. Numerical analysis is conducted to verify the control effectiveness of the connected multi-degree-of-freedom system subjected to the El Centro earthquake ground motion. The results show that the earthquake responses of the towers can be effectively reduced if the parameters of the flexible connecting elements are appropriately selected for a certain corridor mass. Moreover, a remarkable seismic reduction effect can be achieved if the towers have similar dynamic properties.
M. Vanaki, S, Holmes, D, Saha, SC, Chen, J, Brown, RJ & Jayathilake, PG 2020, 'Muco-ciliary clearance: A review of modelling techniques', Journal of Biomechanics, vol. 99.
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© 2019 Elsevier Ltd The airways of the human respiratory system are covered by a protective layer, which is known as airway surface liquid (ASL). This layer consists of two relatively distinct sub-layers; a mucus layer (ML), and a periciliary liquid layer (PCL). In addition, the airways are lined with a dense mat of hair-like structures, called cilia, which beat back and forth in a co-ordinated manner and mainly propel the mucus layer. Such interaction between the cilia and mucus is called ‘muco-ciliary clearance’ (MCC) which is essential to clear the respiratory airways from the inhaled toxic particles deposited on the mucus. The complex nature of lung clearance mechanisms limit the ability to conduct experiments to investigate micro-scale physiological phenomena. As such, modelling techniques are commonly implemented to investigate the effects of biological parameters on the lung muco-ciliary clearance. In the present work, modelling techniques of cilia-ASL interactions – including continuum cilia modelling and discrete cilia modelling – are reviewed and the numerical procedures and level of complexity related to each technique are explained. This is followed by a detailed analysis of the airway surface liquid modelling approaches. In addition, findings of numerical investigations related to the effects of various parameters such as ciliary beat frequency (CBF), mucus rheology, metachronal waves of cilia, surface tension at the PCL-mucus interface, ciliary length, ciliary density, and airway surface liquid depth on the bronchial and tracheal ASL transport are reviewed. This review also explains how these biological parameters can alter the internal power required to perform ciliary beating. Lastly, the main limitations of current numerical works are discussed and significant research directions are brought forward that may be considered in future models to better understand this complex human biological system and its vital clearance mechanism.
Ma, B, Zheng, J, Lei, G, Zhu, J, Jin, P & Guo, Y 2020, 'Topology Optimization of Ferromagnetic Components in Electrical Machines', IEEE Transactions on Energy Conversion.
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IEEE This paper presents the topology optimization of ferromagnetic components in electrical machines by using the density method. Explicit expressions of machine performance based on the principle of electromechanical energy conversion are derived and incorporated in the finite element model. Because the gradient-based algorithm is employed for efficient optimization, the performance sensitivities with respect to the design variables are derived subsequently. An optimization framework is then proposed to optimize effectively the electrical machine performance, such as the flux linkage, back electromotive force, torque profile including torque density and torque ripples. Two design examples are reported to confirm the feasibility and effectiveness of the presented topology optimization approach. The accuracy of optimal solutions is verified by using the commercial electromagnetic analysis software ANSYS MAXWELL.
Ma, B, Zheng, J, Zhu, J, Wu, J, Lei, G & Guo, Y 2020, 'Robust Design Optimization of Electrical Machines Considering Hybrid Random and Interval Uncertainties', IEEE Transactions on Energy Conversion, pp. 1-1.
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Ma, H, Wang, Y, Xiong, R, Kodagoda, S & Tang, L 2020, 'DeepGoal: Learning to drive with driving intention from human control demonstration', Robotics and Autonomous Systems, vol. 127.
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© 2020 Elsevier B.V. Recent research on automotive driving has developed an efficient end-to-end learning mode that directly maps visual input to control commands. However, it models distinct driving variations in a single network, which increases learning complexity and is less adaptive for modular integration. In this paper, we re-investigate human's driving style and propose to learn an intermediate driving intention region to relax the difficulties in end-to-end approach. The intention region follows both road structure in image and direction towards goal in public route planner, which addresses visual variations only and figures out where to go without conventional precise localization. Then the learned visual intention is projected on vehicle local coordinate and fused with reliable obstacle perception to render a navigation score map that is widely used for motion planning. The core of the proposed system is a weakly-supervised cGAN-LSTM model trained to learn driving intention from human demonstration. The adversarial loss learns from limited demonstration data with one local planned route and enables reasoning of multi-modal behaviors with diverse routes while testing. Comprehensive experiments are conducted with real-world datasets. Results indicate the proposed paradigm can produce more consistent motion commands with human demonstration and shows better reliability and robustness to environment change. Our code is available at https://github.com/HuifangZJU/visual-navigation.
Ma, J, Liang, P, Yu, W, Chen, C, Guo, X, Wu, J & Jiang, J 2020, 'Infrared and visible image fusion via detail preserving adversarial learning', Information Fusion, vol. 54, pp. 85-98.
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© 2019 Elsevier B.V. TargefTablets can be detected easily from the background of infrared images due to their significantly discriminative thermal radiations, while visible images contain textural details with high spatial resolution which are beneficial to the enhancement of target recognition. Therefore, fused images with abundant detail information and effective target areas are desirable. In this paper, we propose an end-to-end model for infrared and visible image fusion based on detail preserving adversarial learning. It is able to overcome the limitations of the manual and complicated design of activity-level measurement and fusion rules in traditional fusion methods. Considering the specific information of infrared and visible images, we design two loss functions including the detail loss and target edge-enhancement loss to improve the quality of detail information and sharpen the edge of infrared targets under the framework of generative adversarial network. Our approach enables the fused image to simultaneously retain the thermal radiation with sharpening infrared target boundaries in the infrared image and the abundant textural details in the visible image. Experiments conducted on publicly available datasets demonstrate the superiority of our strategy over the state-of-the-art methods in both objective metrics and visual impressions. In particular, our results look like enhanced infrared images with clearly highlighted and edge-sharpened targets as well as abundant detail information.
Ma, XY, Dong, K, Tang, L, Wang, Y, Wang, XC, Ngo, HH, Chen, R & Wang, N 2020, 'Investigation and assessment of micropollutants and associated biological effects in wastewater treatment processes', Journal of Environmental Sciences (China), vol. 94, pp. 119-127.
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© 2020 Currently, the wastewater treatment plants (WWTPs) attempt to achieve the shifting from general pollution parameters control to reduction of organic micropollutants discharge. However, they have not been able to satisfy the increasing ecological safety needs. In this study, the removal of micropollutants was investigated, and the ecological safety was assessed for a local WWTP. Although the total concentration of 31 micropollutants detected was reduced by 83% using the traditional biological treatment processes, the results did not reflect chemicals that had poor removal efficiencies and low concentrations. Of the five categories of micropollutants, herbicides, insecticides, and bactericides were difficult to remove, pharmaceuticals and UV filters were effectively eliminated. The specific photosynthesis inhibition effect and non-specific bioluminescence inhibition effect from wastewater were detected and evaluated using hazardous concentration where 5% of aquatic organisms are affected. The photosynthesis inhibition effect from wastewater in the WWTP was negligible, even the untreated raw wastewater. However, the bioluminescence inhibition effect from wastewater which was defined as the priority biological effect, posed potential ecological risk. To decrease non-specific biological effects, especially of macromolecular dissolved organic matter, overall pollutant reduction strategy is necessary. Meanwhile, the ozonation process was used to further decrease the bioluminescence inhibition effects from the secondary effluent; ≥ 0.34 g O3/g DOC of ozone dose was recommended for micropollutants elimination control and ecological safety.
Mahamedi, B, Eskandari, M, Fletcher, JE & Zhu, J 2020, 'Sequence-Based Control Strategy with Current Limiting for the Fault Ride-Through of Inverter-Interfaced Distributed Generators', IEEE Transactions on Sustainable Energy.
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IEEE Three-phase three-leg inverters comprise the majority of inverters that are integrated into power grids. They can develop positive- and negative-sequence components under unbalanced conditions including asymmetrical faults, but not zero sequence. This indicates that a sequence-based control strategy is required for a comprehensive control of inverter currents and voltages, hence positive- and negative-sequence components must be controlled separately with their own dedicated controllers. Given this critical need, a sequence-based control technique for grid-forming and grid-feeding inverter-interfaced distributed generators (IIDG) is proposed. As a necessary part of the fault ride-through (FRT) capability of IIDGs, a current limiter is developed which can limit inverter currents at a pre-defined threshold for both balanced and unbalanced faults/voltage sags. Whilst the current regulators work in the synchronous reference frame with simple PI controllers, the limiter works in natural reference frame and thus treats each phase current separately, which results in current limiting at the threshold for all phases. The advantage of the proposed limiter is that it virtually acts instantaneously such that the inverter current can be limited from the start of the disturbance. The simulation results in MATLAB/SIMULINK reveal promising performance of the proposed control strategy with the proposed current limiting.
Mahlia, TMI, Syazmi, ZAHS, Mofijur, M, Abas, AEP, Bilad, MR, Ong, HC & Silitonga, AS 2020, 'Patent landscape review on biodiesel production: Technology updates', RENEWABLE & SUSTAINABLE ENERGY REVIEWS, vol. 118.
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Mahmood, AH, Foster, SJ & Castel, A 2020, 'Development of high-density geopolymer concrete with steel furnace slag aggregate for coastal protection structures', Construction and Building Materials, vol. 248.
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© 2020 Elsevier Ltd Anticipated changes in coastal wave conditions due to various climate change impact scenarios along coastlines may expose coastal protection structures to greater wave energies and higher damage rates than designed for, especially during episodic storm events. Some existing coastal breakwaters need upgrading to withstand the projected conditions. Breakwater armour unit design equations and physical model tests predict a large gain in stability with a modest increase in the armour material density and indicate reduced armour unit size requirements when utilising high-density concrete. In this study, a high-density geopolymer concrete mix with steel furnace slag (SFS) aggregate was developed based on several trials; the material properties were evaluated for on-site applications under ambient curing conditions. The use of SFS aggregate offers higher bulk density to concrete and mixes were proportioned to achieve good workability and setting time. Most importantly, the fly ash-blast furnace slag blended binder used in this study leads to adequate strength gain in ambient curing and allows the diffusion of the free lime associated with the SFS aggregate into the geopolymer matrix to eliminate the delayed hydration and expansion of the aggregate. This research provides a pathway to both upgradings of existing breakwaters and construction of new structures with a reduction to the carbon footprint in breakwater construction.
Mahmoudi, T, Tazehkand, AP, Pourhassan-Moghaddam, M, Alizadeh-Ghodsi, M, Ding, L, Baradaran, B, Bazaz, SR, Jin, D & Warkiani, ME 2020, 'PCR-free paper-based nanobiosensing platform for visual detection of telomerase activity via gold enhancement', MICROCHEMICAL JOURNAL, vol. 154.
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Mahmoudi, Z, Mohammadnejad, J, Razavi Bazaz, S, Abouei Mehrizi, A, Saidijam, M, Dinarvand, R, Ebrahimi Warkiani, M & Soleimani, M 2020, 'Promoted chondrogenesis of hMCSs with controlled release of TGF-β3 via microfluidics synthesized alginate nanogels', Carbohydrate Polymers, pp. 115551-115551.
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Mahmud, K, Rahman, MS, Ravishankar, J, Hossain, MJ & Guerrero, JM 2020, 'Real-Time Load and Ancillary Support for a Remote Island Power System Using Electric Boats', IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1516-1528.
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© 2005-2012 IEEE. Powering small islands with reliable, affordable, and green electricity is a big challenge due to their dispersed geographical location with limited number of consumers and the heavy dependence on fossil fuels. This paper aims to address this challenge of reducing dependency on fossil fuel generators by providing an easy and feasible solution using available and accessible energy resources. The proposed method utilizes the bidirectional energy transfer mechanism available in electric boats (EBs) to support the consumers' power demand. It proposes a new real-time load-support (RTLS) system with a coordinated control using EBs, community generators, and battery energy storage systems. It analyzes the management of the intermittent source-dependent small-scale grid in real time, under various weather, load, and battery state-of-charge conditions. The RTLS system coordinates the customers' load demand with the available EBs, photovoltaics, and battery storage to provide efficient load support and to regulate the bus voltage and frequency. The efficacy of the proposed system is validated both computationally in a real network and in a laboratory setup. It is found that this novel system can substantially reduce the grid load demand and maintain the power quality under various load/source uncertainties and fault conditions. The system robustness is also evaluated considering undesirable conditions, such as severe three-phase faults and sudden EB disconnections. The performance of the proposed method is compared with that of the day-ahead load management approach to validate its effectiveness under various scenarios.
Mahmud, K, Ravishankar, J, Hossain, MJ & Dong, ZY 2020, 'The Impact of Prediction Errors in the Domestic Peak Power Demand Management', IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 16, no. 7, pp. 4567-4579.
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Makhdoom, I, Zhou, I, Abolhasan, M, Lipman, J & Ni, W 2020, 'PrivySharing: A blockchain-based framework for privacy-preserving and secure data sharing in smart cities', COMPUTERS & SECURITY, vol. 88.
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Malik, N, Nanda, P, He, X & Liu, RP 2020, 'Vehicular networks with security and trust management solutions: proposed secured message exchange via blockchain technology', Wireless Networks, vol. 26, no. 6, pp. 4207-4226.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. In vehicular ad hoc networks (VANET), effective trust establishment with authentication is an important requirement. Trust management among communicating vehicles is significant for secure message transmission; however, very less contributions have been made towards evaluating the trustworthiness of the node. This research work intends to introduce a new trust management system in VANET with two major phases: secured message transmission and node trustability prediction. The security assured message passing is carried out by incorporating the privacy preservation model under the data sanitization process. The key used for the sanitization process is optimally tuned by a new hybrid algorithm termed Sea Lion Explored-Whale Optimization Algorithm, which is the combination of Whale Optimization Algorithm and Sea Lion Optimization Algorithm, respectively. The blockchain technology is assisted to handle the key generated by the nodes. Subsequently, the trustability of the node is evaluated under novel specifics “two-level evaluation process” with a rule-based and machine learning-based evaluation process. Finally, the performance of the proposed model is verified and proved over other conventional methods for certain measures.
Malisetty, RS, Indraratna, B & Vinod, JS 2020, 'Multilaminate mathematical framework for analyzing the deformation of coarse granular materials', International Journal of Geomechanics, vol. 20, no. 6.
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© 2020 American Society of Civil Engineers. Coarse granular materials such as railway ballast and rockfill are often subjected to three-dimensional (3D) stress conditions including the influence of intermediate principal stress. Modeling the deformation and breakage of these materials under the presence of intermediate principal stress is important for assessing their long-term performance. This paper presents a mathematical model to describe the mechanical behavior of granular materials incorporating the intermediate principal stress and capture particle breakage. The model formulation encompasses interparticle contact planes using a multilaminate mathematical framework based on generalized plasticity and associated critical state concepts. The model that has been calibrated based on recent experimental data on latite basalt, captures the stress-strain and volumetric strain behavior for a range of confining pressures under triaxial compression. This paper also describes the influence of intermediate principal stress on the strength and deformation response of selected granular materials following 3D stress paths. It is evident from the results that the current modeling technique successfully captured the effects of particle breakage, intermediate principal stress, and confining pressure on the shear behavior of various granular assemblies. The results also highlight the influence of intermediate principal stress in reducing the peak deviatoric strength of the material. The model predictions were validated using four independent sets of past experimental data on crushed basalt, limestone, sandstone, and granite aggregates.
Manikandan, R, Patan, R, Gandomi, AH, Sivanesan, P & Kalyanaraman, H 2020, 'Hash polynomial two factor decision tree using IoT for smart health care scheduling', EXPERT SYSTEMS WITH APPLICATIONS, vol. 141.
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Marin, JG, Baba, AA, Hesselbarth, J, Hashmi, RM & Esselle, KP 2020, 'Millimeter-Wave Low-Loss Multifeed Superstrate-Based Antenna', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 5, pp. 3387-3396.
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Martínez-López, FJ, Merigó, JM, Gázquez-Abad, JC & Ruiz-Real, JL 2020, 'Industrial marketing management: Bibliometric overview since its foundation', Industrial Marketing Management, vol. 84, pp. 19-38.
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© 2019 Elsevier Inc. Industrial Marketing Management (IMM) is an outstanding journal in the field of business-to-business marketing. This paper focuses on this journal, with an extensive bibliometric analysis of IMM from its foundation in 1971 to 2017, the last year analyzed in this study. It identifies, among others, the annual evolution of publications, the most influential countries, the most relevant authors, the most prominent institutions supporting research, as well as the citations of IMM papers in major marketing, but also other, business and management journals. To do so, this research uses the Web of Science Core Collection and Scopus databases, and analyzes a wide range of bibliometric indicators, including the total number of publications and citations, citations per paper, the h-index, m-value and citation thresholds, and also develops a graphical analysis of the bibliographical material using the visualization of similarities (VOS) viewer software. Finally, by applying a cluster analysis by fractional accounting, this research identifies trends and proposes future topics and research lines, such as: trust, innovation, performance, relationship marketing, the future role of new technologies in industrial marketing research, online marketing and corporate image.
Martinez-Maldonado, R, Schulte, J, Echeverria, V, Gopalan, Y & Shum, SB 2020, 'Where is the teacher? Digital analytics for classroom proxemics', JOURNAL OF COMPUTER ASSISTED LEARNING.
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Mastio, E, Chew, E & Dovey, KA 2020, 'The learning organization as a context for value co-creation', The Learning Organization, vol. ahead-of-print, no. ahead-of-print.
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Purpose
This paper aims to explore the relationship between the concept of the learning organization and that of the co-creation of value.
Design/methodology/approach
The paper is conceptual in nature and draws on data from a case study of a small highly innovative Australian company.
Findings
The authors show that, from a value co-creation perspective, the learning organization can be viewed as an open, collaborative, social/economic actor engaged in social/economic activities with other interdependent actors (organizations or stakeholders) in a network or ecosystem of actors to serve its mission/purpose and the well-being of the ecosystem.
Research limitations/implications
As a conceptual paper, the authors rely primarily on previous research as the basis for the argument. The implications of the findings are that, as value co-creation practices are founded upon the generation and leveraging of specific intangible capital resources, more research located in alternative research paradigms is required.
Practical implications
There are important implications for organizational leadership in that the practices that underpin value co-creation require the leadership to be able to work constructively with multiple forms of systemic and agentic power.
Social implications
In increasingly turbulent and hyper-competitive global operational contexts, sustainable value creation is becoming recognized as a ...
Maxit, L, Guasch, O, Meyer, V & Karimi, M 2020, 'Noise radiated from a periodically stiffened cylindrical shell excited by a turbulent boundary layer', Journal of Sound and Vibration, pp. 115016-115016.
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Maxit, L, Karimi, M, Meyer, V & Kessissoglou, N 2020, 'Vibroacoustic responses of a heavy fluid loaded cylindrical shell excited by a turbulent boundary layer', Journal of Fluids and Structures.
Mazaheri, H, Ong, HC, Masjuki, HH, Arslan, A, Chong, WT & Amini, Z 2020, 'Friction and wear characteristics of rice bran oil based biodiesel using calcium oxide catalyst derived from Chicoreus Brunneus shell', Energy Sources, Part A: Recovery, Utilization and Environmental Effects.
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© 2019, © 2019 Taylor & Francis Group, LLC. In this study, an investigation has been carried out to examine the tribological features of Rice bran oil (RBO) based biodiesel as an agent to improve lubricity. RBOB was successfully generated by the means of transesterification and the studied fuels were pure biodiesel (RBOB100), 10% (RBOB10), 30% (RBOB30), and 50% (RBOB50) of biodiesel combined with diesel and pure diesel (RBOB0). Examination in agreement with the ASTM D 4172 method was carried out under following conditions for all examined fuels: a constant load of 40 kg, a steady sliding speed of 1200 rpm, a constant temperature of 75°C and a reaction time of 3600 s. Frictional torque was recorded on line in the course of wear examination. Optical microscopy was implemented to investigate the wear scars of tested balls. Result showed that friction diminished with the decrease of biodiesel concentration. Moreover, formation of wear scars increased with increasing the biodiesel concentration. It could be concluded that lubricity decreases due to increasing the biodiesel concentration. Surface morphology analysis showed that pure biodiesel and diesel formed adhesive wears. However, all the wears formed by biodiesel-diesel blends were fallen into the abrasive wear group.
Medawela, S & Indraratna, B 2020, 'Computational modelling to predict the longevity of a permeable reactive barrier in an acidic floodplain', Computers and Geotechnics, vol. 124, pp. 103605-103605.
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Meena, NK, Nimbalkar, S, Fatahi, B & Yang, G 2020, 'Effects of soil arching on behavior of pile-supported railway embankment: 2D FEM approach', COMPUTERS AND GEOTECHNICS, vol. 123.
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Mehmood, A, Zameer, A, Chaudhary, NI, Ling, SH & Raja, MAZ 2020, 'Design of meta-heuristic computing paradigms for Hammerstein identification systems in electrically stimulated muscle models', NEURAL COMPUTING & APPLICATIONS.
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Mehrabi, M, Pradhan, B, Moayedi, H & Alamri, A 2020, 'Optimizing an adaptive neuro-fuzzy inference system for spatial prediction of landslide susceptibility using four state-of-the-art metaheuristic techniques', Sensors (Switzerland), vol. 20, no. 6.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area.
Melnikov, A, Maeder, M, Friedrich, N, Pozhanka, Y, Oberst, S, Wollmann, A, Scheffler, M, Powell, D & Marburg, S 2020, 'Acoustic metamaterial capsule for reduction of stage machinery noise', Journal of the Acoustical Society of America, vol. 147, pp. 1491-1491.
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Noise mitigation of stage machinery can be quite demanding and requires innovative solutions. In this work we propose an acoustic metamaterial capsule to reduce the noise emission of several stage machinery drive trains, while still allowing the ventilation required for cooling. The metamaterial capsule consists of c-shape meta atoms, which have a simple structure that facilitates manufacturing. We design, simulate, manufacture, and experimentally validate two different metamaterial capsules, which utilize an ultra-sparse and air-permeable reflective meta-grating. Both designs demonstrate transmission loss peaks that effectively suppress gear mesh noise or other narrow band noise sources. The ventilation by natural convection was numerically verified, and was shown to give adequate cooling, whereas a conventional sound capsule would lead to overheating. The noise spectra of three common stage machinery drive trains are numerically modelled, enabling us to design meta-gratings and determine their noise suppression performance. The results fulfill the stringent stage machinery noise limits, highlighting the benefit of using metamaterial capsule of simple c-shape structure.
Meng, Q, Wu, C, Hao, H, Li, J, Wu, P, Yang, Y & Wang, Z 2020, 'Steel fibre reinforced alkali-activated geopolymer concrete slabs subjected to natural gas explosion in buried utility tunnel', Construction and Building Materials, vol. 246.
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© 2020 Elsevier Ltd Accidental gas explosions in the buried utility tunnels around the world have caused massive losses in economy and human lives. The buried utility tunnel with adequate blast resistance capacity is therefore required to withstand the possible accidental gas explosions. In this study, a novel construction material, alkali-activated steel fibre reinforced geopolymer composite is introduced and the blast resistance capacity of slabs made of this material is studied in a full-scale buried utility tunnel. Fly ash and S95 grade ground granulated blast-furnace slag powder (GGBS) were used as the major binders in this geopolymer concrete. The plain geopolymer concrete had a compressive strength of 61 MPa and the steel fibre reinforced geopolymer concrete had a compressive strength of 74 MPa. The elastic modulus of the plain geopolymer concrete was found to be lower than the conventional C30 concrete. The methane gas explosion test was conducted in a full-scale (12 m × 1.8 m × 0.6 m) tunnel segment to investigate the structural performance of selected slab specimen (1.8 m × 0.4 m × 0.09 m). The test results and numerical simulations of structural responses subjected to methane gas explosion are presented. The results indicate the fibre reinforced geopolymer concrete slab has good capacity to resist methane gas explosion load.
Metia, S, Ha, QP, Duc, HN & Scorgie, Y 2020, 'Urban air pollution estimation using unscented Kalman filtered inverse modeling with scaled monitoring data', Sustainable Cities and Society, vol. 54.
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© 2019 The increasing rate of urbanization requires effective and reliable techniques for air quality monitoring and control. For this, the Air Pollution Model and Chemical Transport Model (TAPM-CTM) has been developed and used in Australia with emissions inventory data, synoptic data and terrain data used as its input parameters. Since large uncertainties exist in the emissions inventory (EI), further refinements and improvements are required for accurate air quality prediction. This study evaluates the performance of urban air quality forecasting, using TAPM-CTM, and improves accuracy of air pollution estimation by using a two-stage optimization technique to upgrade EI with validation from monitoring data. The first stage is based on statistical analysis for EI correction and the second stage is based on the unscented Kalman filter (UKF) to take into account the spatio-temporal distributions of air pollutant levels utilizing a Matérn covariance function. The predicted nitrogen monoxide (NO) and nitrogen dioxide (NO2) concentrations with a priori emissions are first compared with observations at monitoring stations in the New South Wales (NSW). Ozone (O3) is also considered since at the ground level it represents a major air pollutant affecting human health and the environment. In the second stage, with the improved EI, TAPM-CTM model errors are reduced further by using the UKF to calibrate EI. Results obtained show effectiveness of the proposed technique, which is promising for air quality inverse modeling, an important aspect of air pollution control in smart cities to achieve environmental sustainability.
Mhiesan, H, Wei, Y, Siwakoti, Y & Mantooth, A 2020, 'A Fault-Tolerant Hybrid Cascaded H-Bridge Multilevel Inverter', IEEE Transactions on Power Electronics, pp. 1-1.
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Mi, Y, Liu, Z, Wang, W, Yang, Y & Wu, C 2020, 'Experimental study on residual axial bearing capacity of UHPFRC-filled steel tubes after lateral impact loading', Structures, vol. 26, pp. 549-561.
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Miah, SJ, Miah, M & Shen, J 2020, 'Editorial note: Learning management systems and big data technologies for higher education', Education and Information Technologies, vol. 25, no. 2, pp. 725-730.
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Micheli, GJL, Cagno, E, Mustillo, G & Trianni, A 2020, 'Green supply chain management drivers, practices and performance: A comprehensive study on the moderators', Journal of Cleaner Production, vol. 259.
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© 2020 Elsevier Ltd The growing level of attention toward global warming, reduction of non-renewable resources and pollution calls manufacturing firms to implement sustainable, and specifically green initiatives into their supply chains (Green Supply Chain Management, GSCM). So far, too little studies have provided clear empirical evidence on the actual impact of these initiatives on firms’ performance, especially within the European manufacturing context, and on the actual impact of possible drivers on the implementation of the above-mentioned initiative. Thus, the aim of this study is to analyse possible moderation factors that affect the relationships between drivers-practices and practices-performance through a survey carried out in 169 Italian manufacturing firms belonging to a range of different sectors. The moderation analysis shows that some drivers strongly influence the relationships between drivers-practices and practices-performance, and a few contributions from the existing literature are challenged and discussed. Our findings may be particularly interesting for managers and supply chain specialists, as well as for policymakers, who could be inspired by the role of particular drivers on the implementation of GSCM practices, and by the level of performance achievable thanks to the adoption of a set of green practices. As for the academic impact, the issue has been tackled for the first time in an attempt of a comprehensive view, which paves the way to a number of research lines to further investigate both the confirmed and unconfirmed moderations, so as to understand the related rationales in the comprehensive view proposed by the authors.
Mihandoust, A, Bazaz, SR, Maleki-Jirsaraei, N, Alizadeh, M, Taylor, RA & Warkiani, ME 2020, 'High-throughput particle concentration using complex cross-section microchannels', Micromachines, vol. 11, no. 4.
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© 2020 by the authors. High throughput particle/cell concentration is crucial for a wide variety of biomedical, clinical, and environmental applications. In this work, we have proposed a passive spiral microfluidic concentrator with a complex cross-sectional shape, i.e., a combination of rectangle and trapezoid, for high separation efficiency and a confinement ratio less than 0.07. Particle focusing in our microfluidic system was observed in a single, tight focusing line, in which higher particle concentration is possible, as compared with simple rectangular or trapezoidal cross-sections with similar flow area. The sharper focusing stems from the confinement of Dean vortices in the trapezoidal region of the complex cross-section. To quantify this effect, we introduce a new parameter, complex focusing number or CFN, which is indicative of the enhancement of inertial focusing of particles in these channels. Three spiral microchannels with various widths of 400 μ m, 500 μ m, and 600 μ m, with the corresponding CFNs of 4.3, 4.5, and 6, respectively, were used. The device with the total width of 600 μ m was shown to have a separation efficiency of ~98%, and by recirculating, the output concentration of the sample was 500 times higher than the initial input. Finally, the investigation of results showed that the magnitude of CFN relies entirely on the microchannel geometry, and it is independent of the overall width of the channel cross-section. We envision that this concept of particle focusing through complex cross-sections will prove useful in paving the way towards more efficient inertial microfluidic devices.
Miner, AS, Laranjo, L & Kocaballi, AB 2020, 'Chatbots in the fight against the COVID-19 pandemic.', NPJ digital medicine, vol. 3, p. 65.
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We are all together in a fight against the COVID-19 pandemic. Chatbots, if effectively designed and deployed, could help us by sharing up-to-date information quickly, encouraging desired health impacting behaviors, and lessening the psychological damage caused by fear and isolation. Despite this potential, the risk of amplifying misinformation and the lack of prior effectiveness research is cause for concern. Immediate collaborations between healthcare workers, companies, academics and governments are merited and may aid future pandemic preparedness efforts.
Moayedi, H, Mehrabi, M, Bui, DT, Pradhan, B & Foong, LK 2020, 'Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility', Journal of Environmental Management, vol. 260.
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© 2019 Elsevier Ltd Forests are important dynamic systems which are widely affected by fire worldwide. Due to the complexity and non-linearity of the forest fire problem, employing hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threat. Three fuzzy-metaheuristic ensembles, based on adaptive neuro-fuzzy inference systems (ANFIS) incorporated with genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) evolutionary algorithms are used to produce the forest fire susceptibility map (FFSM) of a fire-prone region in Iran. A sensitivity analysis is also executed to evaluate the effectiveness of the proposed ensembles in terms of time and complexity. The results revealed that all models produce FFSMs with acceptable accuracy. However, the superiority of the GA-ANFIS was shown in both recognizing the pattern (AUROCtrain = 0.912 and Error = 0.1277) and predicting unseen fire events (AUROCtest = 0.850 and Error = 0.1638). The optimized structures of the proposed GA-ANFIS and PSO-ANFIS ensembles could be good alternatives to traditional forest fire predictive models, and their FFSMs can be promisingly used for future planning and decision making in the proposed area.
Modak, NM, Lobos, V, Merigó, JM, Gabrys, B & Lee, JH 2020, 'Forty years of computers & chemical engineering: A bibliometric analysis', Computers and Chemical Engineering, vol. 141.
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© 2020 Elsevier Ltd Computers & Chemical Engineering (CCE) is one of the premier international journals in the field of chemical engineering. CCE published its first issue in 1977 and completed forty years in 2016. More than four decades of continuous and successful journey influenced us to celebrate its contribution through a comprehensive bibliometric study. Using the Web of Science Core Collection database we depict trends of the journal in terms of papers, topics, authors, institutions, and countries. Networks visualization of co-citation of journals and authors, bibliographic coupling institutions and countries, and co-occurrence of author keywords are prepared using the visualization of similarities (VOS) viewer software. The present analysis explores publication and citation patterns of the journal. Professor Ignacio E. Grossmann, Carnegie Mellon University, and USA respectively appear as the most productive and influential author, institution, and country in CCE publications. Optimization based research topics received most attention in CCE publications.
Mofijur, M, Kusumo, F, Fattah, IMR, Mahmudul, HM, Rasul, MG, Shamsuddin, AH & Mahlia, TMI 2020, 'Resource Recovery from Waste Coffee Grounds Using Ultrasonic-Assisted Technology for Bioenergy Production', ENERGIES, vol. 13, no. 7.
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Moghaddam, HA, Sarmadian, A, Asnaashari, A, Joushani, HAN, Islam, MS, Saha, SC, Ghasemi, G & Shafaee, M 2020, 'Condensation heat transfer and pressure drop characteristics of Isobutane in horizontal channels with twisted tape inserts', International Journal of Refrigeration.
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Mojiri, A, Zhou, JL, Robinson, B, Ohashi, A, Ozaki, N, Kindaichi, T, Farraji, H & Vakili, M 2020, 'Pesticides in aquatic environments and their removal by adsorption methods', CHEMOSPHERE, vol. 253.
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Moon, DH, Chung, WJ, Chang, SW, Lee, SM, Kim, SS, Jeung, JH, Ro, YH, Ahn, JY, Guo, W, Ngo, HH & Dinh, DN 2020, 'Fabrication and characterization of Ni-Ce-Zr ternary disk-shaped catalyst and its application for low-temperature CO2 methanation', FUEL, vol. 260.
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Mortazavi, M, Sharafi, P, Kildashti, K & Samali, B 2020, 'Prefabricated hybrid steel wall panels for mid-rise construction in seismic regions', JOURNAL OF BUILDING ENGINEERING, vol. 27.
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Mudassar, M, Zhai, Y, Liao, L & Shen, J 2020, 'A Decentralized Latency-Aware Task Allocation and Group Formation Approach With Fault Tolerance for IoT Applications', IEEE ACCESS, vol. 8, pp. 49212-49223.
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Mueller, M, Zolfaghari, R, Briggs, A, Furtado, H, Booth, J, Keall, P, Nguyen, D, O'Brien, R & Shieh, C-C 2020, 'The first prospective implementation of markerless lung target tracking in an experimental quality assurance procedure on a standard linear accelerator.', Physics in medicine and biology, vol. 65, no. 2, pp. 025008-025008.
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The ability to track tumour motion without implanted markers on a standard linear accelerator (linac) could enable wide access to real-time adaptive radiotherapy for cancer patients. We previously have retrospectively validated a method for 3D markerless target tracking using intra-fractional kilovoltage (kV) projections acquired on a standard linac. This paper presents the first prospective implementation of markerless lung target tracking on a standard linac and its quality assurance (QA) procedure. The workflow and the algorithm developed to track the 3D target position during volumetric modulated arc therapy treatment delivery were optimised. The linac was operated in clinical QA mode, while kV projections were streamed to a dedicated computer using a frame-grabber software. The markerless target tracking accuracy and precision were measured in a lung phantom experiment under the following conditions: static localisation of seven distinct positions, dynamic localisation of five patient-measured motion traces, and dynamic localisation with treatment interruption. The QA guidelines were developed following the AAPM Task Group 147 report with the requirement that the tracking margin components, the margins required to account for tracking errors, did not exceed 5 mm in any direction. The mean tracking error ranged from 0.0 to 0.9 mm (left-right), -0.6 to -0.1 mm (superior-inferior) and -0.7 to 0.1 mm (anterior-posterior) over the three tests. Larger errors were found in cases with large left-right or anterior-posterior and small superior-inferior motion. The tracking margin components did not exceed 5 mm in any direction and ranged from 0.4 to 3.2 mm (left-right), 0.7 to 1.6 mm (superior-inferior) and 0.8 to 1.5 mm (anterior-posterior). This study presents the first prospective implementation of markerless lung target tracking on a standard linac and provides a QA procedure for its safe clinical implementation, potentially enabling real-time adaptive radiother...
Mujtaba, MA, Muk Cho, H, Masjuki, HH, Kalam, MA, Ong, HC, Gul, M, Harith, MH & Yusoff, MNAM 2020, 'Critical review on sesame seed oil and its methyl ester on cold flow and oxidation stability', Energy Reports, vol. 6, pp. 40-54.
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© 2019 The demand for renewable energy is steadily increasing due to rapid population growth and economic development worldwide. An additional reason is that fossil fuel reserves are limited, and this situation results in their non-uniform availability globally. Furthermore, the attitudes of the society, energy policies and technology choices are constantly changing. Thus, renewable energy resources are now considered good alternatives to fossil fuels. In the meantime, liquid energy, such as methyl ester from locally produced vegetable oils, is well accepted by many countries, even though it is currently being blended up to 20% with petroleum fuels. Recently, the industrialisation of biodiesel is a major problem because of its poor cold flow properties and oxidative stability. Vegetable oils are also being blended in an appropriate proportion before transesterification to obtain the desired properties in biodiesel. Similarly, poor cold flow properties and oxidative stability can be improved by choosing suitable vegetable oils for making blends. Amongst all available vegetable oils, sesame seed oil (SSO) has unique cold flow properties and oxidation stability, particularly because of naturally occurring antioxidants and preservatives, which enhance the stability of oil towards rancidity. Therefore, SSO can be used as a potential feedstock for blending with other vegetable oils to enhance the overall cold flow and oxidation stability properties. This overview summarises sesame cultivation, SSO production, the physicochemical properties of SSO and its potential as an alternative renewable fuel source. In this review, the physicochemical properties of sesame biodiesel are compared with those of biodiesel derived from other vegetable oils. Results show that blending SSO with palm oil before transesterification will successfully improve the cold flow properties and oxidation stability of palm methyl ester (biodiesel).
Mukai, H, Sakata, K, Devitt, SJ, Wang, R, Zhou, Y, Nakajima, Y & Tsai, JS 2020, 'Pseudo-2D superconducting quantum computing circuit for the surface code: Proposal and preliminary tests', New Journal of Physics, vol. 22, no. 4.
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© 2020 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft. Among the major hardware platforms for large-scale quantum computing, one of the leading candidates is superconducting quantum circuits. Current proposed architectures for quantum error-correction with the promising surface code require a two-dimensional layout of superconducting qubits with nearest-neighbor interactions. A major hurdle for the scalability in such an architecture using superconducting systems is the so-called wiring problem, where qubits internal to a chipset become difficult to access by the external control/readout lines. In contrast to the existing approaches which address the problem through intricate three-dimensional wiring and packaging technology, leading to a significant engineering challenge, here we address this problem by presenting a modified microarchitecture in which all the wiring can be realized through a newly introduced pseudo two-dimensional resonator network which provides the inter-qubit connections via airbridges. Our proposal is completely compatible with current standard planar circuit technology. We carried out experiments to examine the feasibility of the new airbridge component. The measured quality factor of the airbridged resonator is below the simulated surface-code threshold required for a coupling resonator, and it should not limit simulated gate fidelity. The measured crosstalk between crossed resonators is at most-49 dB in resonance. Further spatial and frequency separation between the resonators should result in relatively limited crosstalk between them, which would not increase as the size of the chipset increases. This architecture and the preliminary tests indicate the possibility that a large-scale, fully error-corrected quantum computer could be constructed by monolithic integration technologies without additional overhead or special packaging know-how.
Munasinghe, N & Paul, G 2020, 'Ultrasonic-based Sensor Fusion Approach to Measure Flow Rate in Partially Filled Pipes', IEEE Sensors Journal.
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Flow rate measurement in pipes is essential for many applications. Thus, there have been a variety of flow meters developed that incorporate different technologies. However, a typical limitation in flow meters is that the pipe must be full in order to get an accurate flow reading. In many cases, this is not possible for practical reasons. When the pipe is full, ultrasonic flow meters can calculate the flow rate using known properties of the pipe and fluid, namely the cross-section, propagation path and fluid sound velocity. However, when the pipe is only partially filled, the propagation path is unknown which leads to an inability to calculate the correct flow rate. This paper presents a cost-effective sensor fusion approach to extend the capabilities of transit time ultrasonic flow meters to handle such scenarios. The approach determines the propagation path using capacitance-based level sensing, combined with fluid velocities ascertained via an ultrasonic sensor, leading to a significantly more accurate estimation of flow rates. Experiments in low flow rate situations validated the efficacy of the proposed model, with a 92% reduction of mean error in the lowest water height when compared to a conventional ultrasonic flow meter.
Muniyasamy, A, Sivaporul, G, Gopinath, A, Lakshmanan, R, Altaee, A, Achary, A & Velayudhaperumal Chellam, P 2020, 'Process development for the degradation of textile azo dyes (mono-, di-, poly-) by advanced oxidation process - Ozonation: Experimental & partial derivative modelling approach', Journal of Environmental Management, vol. 265, pp. 110397-110397.
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Nagasubramanian, G, Sakthivel, RK, Patan, R, Gandomi, AH, Sankayya, M & Balusamy, B 2020, 'Securing e-health records using keyless signature infrastructure blockchain technology in the cloud', NEURAL COMPUTING & APPLICATIONS, vol. 32, no. 3, pp. 639-647.
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Nagele, C, Ilo-Okeke, EO, Rohde, PP, Dowling, JP & Byrnes, T 2020, 'Relativity of quantum states in entanglement swapping', PHYSICS LETTERS A, vol. 384, no. 15.
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Naghibi, SA, Vafakhah, M, Hashemi, H, Pradhan, B & Alavi, SJ 2020, 'Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms', Natural Resources Research, vol. 29, no. 3, pp. 1915-1933.
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© 2019, International Association for Mathematical Geosciences. Lack of water resources is a common issue in many countries, especially in the Middle East. Flood spreading project (FSP) is an artificial recharge technique, which is generally suggested for arid and semi-arid areas with two major aims including (1) flood mitigation and (2) artificial recharge of groundwater. This study implemented three state-of-the-art popular models including frequency ratio (FR), k-nearest neighbours (KNN), and random forest (RF) for determining the suitability of land for FSP. At the first step, suitable areas for FSP were identified according to the national guidelines and the literature. The identified areas were then verified by multiple field surveys. To produce FSP land suitability maps, several FSP conditioning factors such as topographical (i.e. slope, plan curvature, and profile curvature), hydrogeological (i.e. transmissivity, aquifer thickness, and electrical conductivity), hydrological (i.e. rainfall, distance from rivers, river density, and permeability), lithology, and land use were considered as input to the models. For the FR modelling, classified layers of the aforementioned variables were used, while their continuous layers were implemented in the KNN and RF algorithms. At the last step, receiver operating characteristic (ROC) curve was used to assess the ability and accuracy of the applied algorithms. Based on the findings, the area under the curve of ROC for the RF, KNN, and FR models was 97.1, 94.6, and 89.2%, respectively. Furthermore, transmissivity, slope, aquifer thickness, distance from rivers, rainfall, and electrical conductivity were recognized as the most influencing factors in the modelling procedure. The findings of this study indicated that the application of RF, KNN, and FR can be suggested for identification of suitable areas for FSP establishment in other regions.
Naidu, G, Tijing, L, Johir, MAH, Shon, H & Vigneswaran, S 2020, 'Hybrid membrane distillation: Resource, nutrient and energy recovery', JOURNAL OF MEMBRANE SCIENCE, vol. 599.
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Naji, O, Al-Juboori, RA, Bowtell, L, Alpatova, A & Ghaffour, N 2020, 'Direct contact ultrasound for fouling control and flux enhancement in air-gap membrane distillation.', Ultrasonics sonochemistry, vol. 61, pp. 104816-104816.
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Air Gap Membrane distillation (AGMD) is a thermally driven separation process capable of treating challenging water types, but its low productivity is a major drawback. Membrane fouling is a common problem in many membrane treatment systems, which exacerbates AGMD's low overall productivity. In this study, we investigated the direct application of low-power ultrasound (8-23 W), as an in-line cleaning and performance boosting technique for AGMD. Two different highly saline feedwaters, namely natural groundwater (3970 μS/cm) and RO reject stream water (12760 μS/cm) were treated using Polytetrafluoroethylene (PTFE) and polyvinylidene fluoride (PVDF) membranes. Theoretical calculations and experimental investigations are presented, showing that the applied ultrasonic power range only produced acoustic streaming effects that enhanced cleaning and mass transfer. Attenuated Total Reflection Fourier-Transform Infrared Spectroscopy (ATR FT-IR) analysis showed that ultrasound was capable of effectively removing silica and calcium scaling. Ultrasound application on a fouled membrane resulted in a 100% increase in the permeate flux. Cleaning effects accounted for around 30-50% of this increase and the remainder was attributed to mass transfer improvements. Contaminant rejection percentages were consistently high for all treatments (>99%), indicating that ultrasound did not deteriorate the membrane structure. Scanning Electron Microscopy (SEM) analysis of the membrane surface was used to confirm this observation. The images of the membrane surface demonstrated that ultrasound successfully cleaned the previously fouled membrane, with no signs of structural damage. The results of this study highlight the efficient and effective application of direct low power ultrasound for improving AGMD performance.
Namadmalan, A, Rouzbehi, K, Manuel Escano, J & Bordons, C 2020, 'Dual-Active Bridge Series Resonant Electric Vehicle Charger: A Self-Tuning Method', ELECTRONICS, vol. 9, no. 2.
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Nan, Y, Huang, X & Guo, YJ 2020, 'A Millimeter-Wave GCW-SAR Based on Deramp-on-Receive and Piecewise Constant Doppler Imaging', IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 1, pp. 680-690.
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© 2019 IEEE. A novel generalized continuous-wave synthetic aperture radar (GCW-SAR) based on deramp-on-receive operating in millimeter-wave frequency is proposed in this article. With deramp-on-receive, the receiver sampling rate is drastically reduced, and the downsampled 1-D raw data can be obtained from the received beat signal. Further adopting piecewise constant Doppler (PCD) imaging in the digital domain, a GCW-SAR image can be easily reconstructed by using the existing frequency-modulated continuous-wave (FMCW) radar system. The effects of deramp-on-receive in PCD imaging are analyzed accordingly. The short wavelength of the millimeter-wave carrier used in the proposed GCW-SAR enables high azimuth resolution as well as a short synthetic aperture, which, in turn, significantly reduces the imaging computational complexity. Simulation and experimental results confirm the advantages of the proposed GCW-SAR.
Nan, Y, Huang, X & Guo, YJ 2020, 'Piecewise Constant Doppler Algorithm: Performance Analysis, Further Simplification and Motion Compensation', IEEE Transactions on Aerospace and Electronic Systems.
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IEEE The piecewise constant Doppler (PCD) algorithm is a novel radar imaging process recently proposed for the generalized continuous wave synthetic aperture radar (GCW-SAR). This paper presents a detailed theoretical analysis on the PCD algorithm's performance and proposes a further complexityreduced PCD algorithm with motion compensation (MOCO) suitable for practical applications. Firstly, the difference between conventional SAR imaging and PCD imaging, i.e., the zeroth order versus the first order slant range approximation, is revealed. Exact ambiguity function expressions of the PCD imaging in range and azimuth directions respectively are then derived. An error function of the PCD imaging as compared with the ideal matched filtering method is further defined and shown to be a function of an image quality factor which can be used to quantify the PCD imaging performance. Finally, a faster and more flexible imaging process, called decimated PCD algorithm, is proposed, by which the image azimuth spacing can be easily extended and hence the computational complexity can be significantly reduced. The decimated PCD implementation incorporated with the MOCO is developed for practical GCWSAR applications and its imaging error lower-bounded by the PCD imaging error function is analyzed accordingly. Simulation and experimental results validate the theoretical analysis of the PCD imaging and show that the decimated PCD algorithm can achieve a high imaging quality at low cost.
Naseer, A, Rani, M, Naz, S, Razzak, MI, Imran, M & Xu, G 2020, 'Refining Parkinson’s neurological disorder identification through deep transfer learning', Neural Computing and Applications, vol. 32, no. 3, pp. 839-854.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work.
Nasir, AA, Tuan, HD, Duong, TQ & Hanzo, L 2020, 'Transmitter-Side Wireless Information- and Power-Transfer in Massive MIMO Systems', IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, vol. 69, no. 2, pp. 2322-2326.
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Nasir, AA, Tuan, HD, Duong, TQ & Poor, HV 2020, 'MIMO-OFDM-Based Wireless-Powered Relaying Communication with an Energy Recycling Interface', IEEE Transactions on Communications, vol. 68, no. 2, pp. 811-824.
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© 2019 IEEE. This paper considers wireless-powered relaying multiple-input-multiple-output (MIMO) communication, where all four nodes (information source, energy source, relay, and destination) are equipped with multiple antennas. Orthogonal frequency division multiplexing (OFDM) is applied for information processing to compensate the frequency selectivity of communication channels between the information source and the relay and between the relay and the destination as these nodes are assumed to be located far apart from each. The relay is equipped with a full-duplexing interface for harvesting energy not only from the wireless transmission of the dedicated energy source but also from its own transmission while relaying the source information to the destination. The problem of designing the optimal power allocation over OFDM subcarriers and transmit antennas to maximize the overall spectral efficiency is addressed. Due to a very large number of subcarriers, this design problem poses a large-scale nonconvex optimization problem involving a few thousand variables of power allocation, which is very computationally challenging. A novel path-following algorithm is proposed for computation. Based on the developed closed-form calculation of linear computational complexity at each iteration, the proposed algorithm rapidly converges to an optimal solution. Compared to the best existing solvers, the computational complexity of the proposed algorithm is reduced at least 105 times, making it very efficient and practical for online computation while existing solvers are ineffective. Numerical results for a practical simulation setting show promising results by achieving high spectral efficiency.
Nasir, AA, Tuan, HD, Duong, TQ, Vincent Poor, H & Hanzo, L 2020, 'Hybrid Beamforming for Multi-User Millimeter-Wave Networks', IEEE Transactions on Vehicular Technology, vol. 69, no. 3, pp. 2943-2956.
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© 1967-2012 IEEE. This paper considers hybrid beamforming by combining an analog beamformer with a new regularized zero forcing baseband one, for multi-user millimeter-wave networks under a limited number of radio frequency (RF) chains. Three popular scenarios are examined: i) the number of users is up to the number of RF chains in a single-cell network, ii) the number of users is up to twice the number of RF chains in a single-cell network, and iii) the number of users is up to twice the number of RF chains in each cell of a two-cell network. In the second and third scenarios, we group the users into two categories of cell-center users as well as cell-edge users and serve them in two different time fractions. In the third scenario, we propose to suppress the inter-cell interference by serving the cell-center and cell-edge users in alternate fractional-time slots. In all the three scenarios, we determine the optimal power allocation maximizing the users' minimum rate. Finally, low-complexity path-following algorithms having rapid convergence are developed for the computation of the optimal power. Our simulation results show that the proposed algorithms achieve a clear performance gain over the existing benchmarkers.
Natarajan, S, Vairavasundaram, S, Natarajan, S & Gandomi, AH 2020, 'Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data', EXPERT SYSTEMS WITH APPLICATIONS, vol. 149.
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Navaratnarajah, SK & Indraratna, B 2020, 'Stabilisation of Stiffer Rail Track Substructure Using Artificial Inclusion', Indian Geotechnical Journal, vol. 50, no. 2, pp. 196-203.
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© 2020, Indian Geotechnical Society. The railway transport system in many countries plays a significant role in the passage of bulk freight and passengers. However, increased train speeds and higher freight loads (large dynamic wheel loads) accelerate the deterioration of rail track substructure. This problem is more critical in isolated rail track locations where the track substructure is much stiffer than the regular surface track assembly such as track at the bridges and tunnels. Ballast is a key track foundation material placed underneath the sleepers which provides structural support against high cyclic and impact stresses caused by moving trains. Inclusion of rubber mats called under ballast mats (UBMs) placed between the ballast and stiffer base layer is one of the measures to minimise the ballast deterioration. In this study, cyclic loads representing fast and heavy haul trains were simulated on stiffer track foundation condition using a large-scale process simulation prismoidal triaxial apparatus to investigate the mitigation of strain, stress and degradation characteristics of ballast stabilised with UBM. These UBMs were locally manufactured from recycled tyre wastes. The results show that ballast on a stiff foundation substructure stabilised with UBM experienced significantly less vertical and lateral deformation, ballast interface and inter-particle stresses and degradation. This study also confirmed that the recycled tyre UBMs used in this study had adequate damping to absorb the energy transmitted to the moving train to the track, thus preventing excessive plastic deformation and degradation of the ballast layer.
Ngo, CQ, Chai, R, Nguyen, T, Jones, TW & Nguyen, HT 2020, 'Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia', IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, vol. 24, no. 5, pp. 1237-1245.
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Nguyen, AQ, Nguyen, LN, Johir, MAH, Ngo, HH, Chaves, AV & Nghiem, LD 2020, 'Derivation of volatile fatty acid from crop residues digestion using a rumen membrane bioreactor: A feasibility study', Bioresource Technology, vol. 312.
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© 2020 This study evaluates the feasibility of a novel rumen membrane bioreactor (rumen MBR) to produce volatile fatty acids (VFA) from crop residues (i.e. lignocellulosic biomass). Rumen MBR can provide a sustainable route for VFA production by mimicking the digestive system of ruminant animals. Rumen fluid was inoculated in a reactor coupled with ultrafiltration (UF) membrane and fed with maize silage and concentrate feed at 60:40% (w/w). Continuous VFA production was achieved at an average daily yield of 438 mg VFA/g substrate. The most abundant VFA were acetic (40–80%) and propionic (10–40%) acids. The majority (73 ± 15%) of produced VFA was transferred through the UF membrane. Shifts in dominant rumen microbes were observed upon the transition from in vivo to in vitro environment and during reactor operation, however, stable VFA yield was maintained for 35 days, providing the first proof-of-concept of a viable rumen MBR.
Nguyen, HC, Wang, FM, Dinh, KK, Pham, TT, Juan, HY, Nguyen, NP, Ong, HC & Su, CH 2020, 'Microwave-assisted noncatalytic esterification of fatty acid for biodiesel production: A kinetic study', Energies, vol. 13, no. 9.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This study developed a microwave-mediated noncatalytic esterification of oleic acid for producing ethyl biodiesel. The microwave irradiation process outperformed conventional heating methods for the reaction. A highest reaction conversion, 97.62%, was achieved by performing esterification with microwave irradiation at a microwave power of 150 W, 2:1 ethanol:oleic acid molar ratio, reaction time of 6 h, and temperature of 473 K. A second-order reaction model (R2 of up to 0.997) was established to describe esterification. The reaction rate constants were promoted with increasing microwave power and temperature. A strong linear relation of microwave power to pre-exponential factors was also established, and microwave power greatly influenced the reaction due to nonthermal effects. This study suggested that microwave-assisted noncatalytic esterification is an efficient approach for biodiesel synthesis.
Nguyen, HT, Tuan, HD, Duong, TQ, Poor, HV & Hwang, WJ 2020, 'Nonsmooth Optimization Algorithms for Multicast Beamforming in Content-Centric Fog Radio Access Networks', IEEE Transactions on Signal Processing, vol. 68, pp. 1455-1469.
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© 1991-2012 IEEE. This paper considers a content-centric fog radio access network (F-RAN). Its multi-antenna remote radio heads (RRHs) are capable of caching and executing signal processing for content delivery to its users. The fronthaul traffic is thus saved since its baseband processing unit (BBU) needs to transfer only the cache-missed content items to the RRHs via limited-capacity fronthaul links. The problem of beamforming design maximizing the energy efficiency in content delivery subject to the quality-of-content-service constraints in terms of content throughput and fronthaul limited-capacity is addressed. Unlike the user's throughput in user-centric networks, the content throughput in content-centric networks is no longer a differentiable function of the beamforming vectors. The problem is inherently high-dimensional due to the involvement of many beamforming vectors even in simple cases of three RRHs serving three users. Path-following algorithms, which invoke a simple convex quadratic optimization problem to generate a better feasible point, are proposed for computation of this nonsmooth and high-dimensional optimization problem. We also employ generalized zero-forcing beamforming, which forces the multi-content interference to zero or nearly to zero to reduce the problem dimensionality for computational efficiency. Numerical results are provided to demonstrate their computational effectiveness. They also reveal that when the fronthaul traffic becomes more flexible, hard-transfer fronthauling is more energy efficient than soft-transfer fronthauling.
Nguyen, HT, Yoon, Y, Ngo, HH & Jang, A 2020, 'The application of microalgae in removing organic micropollutants in wastewater', Critical Reviews in Environmental Science and Technology.
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© 2020, © 2020 Taylor & Francis Group, LLC. Micropollutants have become a serious environmental problem with several negative outcomes for human health and ecosystems. Many efforts have been made to remove micropollutants using a variety of physical, chemical and biological methods. By far, the most attention has been paid to microalgae-based technologies for wastewater treatment in order to obtain high-quality effluents, recover algal biomass for fertilizers, protein-rich feed, biofuel, and put them to other practical use. This paper reviews the potential of microalgae-based systems for the removal of organic micropollutants from open ponds to closed photobioreactors coupled by suspended microalgal cells, immobilized cells, or microalgae-microbial consortia. The inhibition of micropollutants on microalgae growth as well as micropollutant removal mechanisms performed by microalgae-based systems are also discussed. Other treatment methods for the removal of micropollutants are analyzed to show the advantages and limitations of microalgae-based treatment strategies, from which some possible combined systems can be suggested. Finally, some recommendations for future studies on this topic are proposed. (Figure presented.).
Nguyen, L & Nguyen, HT 2020, 'Mobility based network lifetime in wireless sensor networks: A review'.
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Increasingly emerging technologies in micro-electromechanical systems and
wireless communications allows a mobile wireless sensor networks (MWSN) to be a
more and more powerful mean in many applications such as habitat and
environmental monitoring, traffic observing, battlefield surveillance, smart
homes and smart cities. Nevertheless, due to sensor battery constraints,
energy-efficiently operating a MWSN is paramount importance in those
applications; and a plethora of approaches have been proposed to elongate the
network longevity at most possible. Therefore, this paper provides a
comprehensive review on the developed methods that exploit mobility of sensor
nodes and/or sink(s) to effectively maximize the lifetime of a MWSN. The survey
systematically classifies the algorithms into categories where the MWSN is
equipped with mobile sensor nodes, one mobile sink or multiple mobile sinks.
How to drive the mobile sink(s) for energy efficiency in the network is also
fully reviewed and reported.
Nguyen, LN, Commault, AS, Kahlke, T, Ralph, PJ, Semblante, GU, Johir, MAH & Nghiem, LD 2020, 'Genome sequencing as a new window into the microbial community of membrane bioreactors - A critical review.', Science of the Total Environment, vol. 704, pp. 135279-135279.
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Recent developed sequencing techniques have resulted in a new and unprecedented way to study biological wastewater treatment, in which most organisms are uncultivable. This review provides (i) an insight on state-of-the-art sequencing techniques and their limitations; (ii) a critical assessment of the microbial community in biological reactor and biofouling layer in a membrane bioreactor (MBR). The data from high-throughput sequencing has been used to infer microbial growth conditions and metabolisms of microorganisms present in MBRs at the time of sampling. These data shed new insight to two fundamental questions about a microbial community in the MBR process namely the microbial composition (who are they?) and the functions of each specific microbial assemblage (what are their function?). The results to date also highlight the complexity of the microbial community growing on MBRs. Environmental conditions are dynamic and diverse, and can influence the diversity and structural dynamics of any given microbial community for wastewater treatment. The benefits of understanding the structure of microbial communities on three major aspects of the MBR process (i.e. nutrient removal, biofouling control, and micropollutant removal) were symmetrically delineated. This review also indicates that the deployment of microbial community analysis for a practical engineering context, in terms of process design and system optimization, can be further realized.
Nguyen, LN, Truong, MV, Nguyen, AQ, Johir, MAH, Commault, AS, Ralph, PJ, Semblante, GU & Nghiem, LD 2020, 'A sequential membrane bioreactor followed by a membrane microalgal reactor for nutrient removal and algal biomass production', Environmental Science: Water Research and Technology, vol. 6, no. 1, pp. 189-196.
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© 2019 The Royal Society of Chemistry. A hybrid process combining a single compartment aerobic membrane bioreactor (MBR) and a membrane microalgal reactor (MMR) was evaluated for nutrient removal and microalgal biomass production. When operated without biomass extraction, the microalgal biomass in the MMR reached 920 mg L-1 on day 18 and then collapsed, rendering nutrient removal ineffective. Stable operation of the MMR was achieved by regular biomass extraction (i.e. 1/30 of the microalgal biomass in the reactor daily). The biomass production at steady state was approximately 26 g m-3 d-1. The NO3- and PO43- uptake values by microalgae were 4.0 ± 1.1 and 1.5 ± 0.9 g m-3 d-1, respectively. A facile flocculation and separation technique capable of recovering 98% microalgal biomass was demonstrated. Although the hybrid process can significantly enhance nutrient removal and biomass production, further research is needed to intensify the microalgal growth rate. At the current microalgal growth rate, a large MMR volume (37 times that of the MBR) is necessary for synchronous operation.
Nguyen, LV, Hu, G & Spanos, CJ 2020, 'Efficient Sensor Deployments for Spatio-Temporal Environmental Monitoring', IEEE Transactions on Systems Man and Cybernetics: Systems.
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This paper addresses the problem of efficiently deploying sensors in spatial environments, e.g., buildings, for the purposes of monitoring spatio-temporal environmental phenomena. By modeling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality-cost function of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is proven not to be affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment optimization problem is then solved by a practical and feasible polynomial algorithm, where its solutions are theoretically proven to be guaranteed. The proposed method is also theoretically and experimentally compared with the existing works. The effectiveness of the proposed algorithm is demonstrated by implementation in a real tested space in a university building, where the obtained results are highly promising.
Nguyen, NC, Duong, HC, Nguyen, HT, Chen, SS, Le, HQ, Ngo, HH, Guo, W, Duong, CC, Le, NC & Bui, XT 2020, 'Forward osmosis–membrane distillation hybrid system for desalination using mixed trivalent draw solution', Journal of Membrane Science, vol. 603.
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© 2020 Elsevier B.V. Finding suitable draw solutions is still a major problem when developing FO technologies. This study represents the first time a mixed trivalent draw solution containing of EDTA–2Na and Na3PO4 was systemically studied for FO performance. The objective here was to achieve simultaneously low reverse salt flux and high water flux. The FO results showed that the mixed trivalent draw solution-based 0.3 M EDTA–2Na and 0.55 M Na3PO4 underwent higher water flux (Jw = 9.17 L/m2⋅h) than that of pure 0.85 M EDTA-2Na (Jw = 7.02 L/m2⋅h) due to its lower viscosity. Additionally, the specific reverse salt flux caused by mixing 0.3 M EDTA–2Na with 0.55 M Na3PO4 draw solution was only 0.053 g/L using DI water as the feed solution. Donnan equilibrium force and formed complexation of [EDTANa]3-, [HPO4Na]- with the FO membrane are believed to constitute the main mechanism for minimizing salt leakage from the mixed draw solution. Moreover, the FO desalination process utilizing the mixed trivalent draw solution achieved water fluxes of 6.12 L/m2⋅h with brackish water (TDS = 5000 mg/L) and 3.10 L/m2⋅h with seawater (TDS = 35,000 mg/L) as the feed solution. Lastly, diluted mixed trivalent draw solution following the FO process was effectively separated using the MD process with salt rejection >99.99% at a mild feed temperature of 55 °C.
Nguyen, QD & Castel, A 2020, 'Reinforcement corrosion in limestone flash calcined clay cement-based concrete', Cement and Concrete Research, vol. 132.
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© 2020 Elsevier Ltd Limestone calcined clay cement (LC3) concrete has attracted world-wide attention as a newly promising low-carbon concrete. In this study, long-term reinforcement corrosion in LC3 concrete was investigated. Both chloride and carbonation-induced reinforcing bar corrosion were examined. Open circuit corrosion potential, polarization resistance, Tafel constants were monitored at regular intervals up to 500 days. Gravimetric mass loss was measured and compared to the loss of mass calculated using electrochemical methods. The performance of concrete with flash calcined clay and limestone was similar to that of traditional Portland cement concrete in long-term investigation. Traditional corrosion methods and classifications used widely to assess of steel in concrete can be applied to concrete containing LC3 providing a recalibration of polarization resistance range for passitivity condition.
Nguyen, QD, Afroz, S & Castel, A 2020, 'Influence of Calcined Clay Reactivity on the Mechanical Properties and Chloride Diffusion Resistance of Limestone Calcined Clay Cement (LC3) Concrete', JOURNAL OF MARINE SCIENCE AND ENGINEERING, vol. 8, no. 5.
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Nguyen, TAH, Ngo, HH, Guo, WS, Nguyen, THH, Soda, S, Vu, ND, Bui, TKA, Vo, TDH, Bui, XT, Nguyen, TT & Pham, TT 2020, 'White hard clam (Meretrix lyrata) shells media to improve phosphorus removal in lab-scale horizontal sub-surface flow constructed wetlands: Performance, removal pathways, and lifespan', Bioresource Technology, vol. 312.
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© 2020 Elsevier Ltd This work examined the phosphorus (P) removal from the synthetic pretreated swine wastewater using lab-scale horizontal sub-surface flow constructed wetlands (HSSF-CWs). White hard clam (Meretrix lyrata) shells (WHC) and Paspalum atratum were utilized as substrate and plant, respectively. The focus was placed on treatment performance, removal mechanisms and lifespan of the HSSF-CWs. Results indicated that WHC-based HSSF-CW with P. atratum exhibited a high P removal (89.9%). The mean P efluent concentration and P removal rate were 1.34 ± 0.95 mg/L and 0.32 ± 0.03 g/m2/d, respectively. The mass balance study showed that media sorption was the dominant P removal pathway (77.5%), followed by microbial assimilation (14.5%), plant uptake (5.4%), and other processes (2.6%). It was estimated the WHC-based bed could work effectively for approximately 2.84 years. This WHC-based HSSF-CWs technology will therefore pave the way for recycling Ca-rich waste materials as media in HSSF-CWs to enhance P-rich wastewater purification.
Nguyen, TH, Tran, HN, Vu, HA, Trinh, MV, Nguyen, TV, Loganathan, P, Vigneswaran, S, Nguyen, TM, Trinh, VT, Vu, DL & Nguyen, THH 2020, 'Laterite as a low-cost adsorbent in a sustainable decentralized filtration system to remove arsenic from groundwater in Vietnam.', The Science of the total environment, vol. 699, pp. 134267-134267.
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In the Red River Delta, Vietnam, arsenic (As) contamination of groundwater is a serious problem where more than seventeen million people are affected. Millions of people in this area are unable to access clean water from the existing centralized water treatment systems. They also cannot afford to buy expensive household water filters. Similar dangerous situations exist in many other countries and for this reason there is an urgent need to develop a cost-effective decentralized filtration system using new low-cost adsorbents for removing arsenic. In this study, seven locally available low-cost materials were tested for arsenic removal by conducting batch adsorption experiments. Of these materials, a natural laterite (48.7% Fe2O3 and 18.2% Al2O3) from Thach That (NLTT) was deemed the most suitable adsorbent based on arsenic removal performance, local availability, stability/low risk and cost (US$ 0.10/kg). Results demonstrated that the adsorption process was less dependent on the solution pH from 2.0 to 10. The coexisting anions competed with As(III) and As(V) in the order, phosphate > silicate > bicarbonate > sulphate > chloride. The adsorption process reached a fast equilibrium at approximately 120-360 min, depending on the initial arsenic concentrations. The Langmuir maximum adsorption capacities of NLTT at 30 °C were 512 μg/g for As(III) and 580 μg/g for As(V), respectively. Thermodynamic study conducted at 10 °C, 30 °C, and 50 °C suggested that the adsorption process of As(III) and As(V) was spontaneous and endothermic in nature. A water filtration system packed with NLTT was tested in a childcare centre in the most disadvantaged community in Ha Nam province, Vietnam, to determine arsenic removal performance in an operation lasting six months. Findings showed that the system reduced total arsenic concentration in groundwater from 122 to 237 μg/L to below the Vietnam drinking water standard of 10 μg/L.
Nguyen, TKL, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Nguyen, TV & Nghiem, DL 2020, 'Contribution of the construction phase to environmental impacts of the wastewater treatment plant', Science of The Total Environment, vol. 743, pp. 140658-140658.
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Nguyen, TKL, Ngo, HH, Guo, WS, Chang, SW, Nguyen, DD, Nghiem, LD & Nguyen, TV 2020, 'A critical review on life cycle assessment and plant-wide models towards emission control strategies for greenhouse gas from wastewater treatment plants', Journal of Environmental Management, vol. 264.
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© 2020 Elsevier Ltd For decades, there has been a strong interest in mitigating greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs). Numerous models were developed to measure the emissions and propose the quantification. Existing studies looked at the relationship between GHG emissions and operational cost (OCI), which is one of the most important indicators for decision-makers. Other parameters that can influence the control strategies include the effluent quality (EQI) and total environmental impacts. Plant-wide models are reliable methods to examine the OCI, EQI and GHG emissions while Life cycle assessment (LCA) works to assess the potential environmental impacts. A combined LCA and plant-wide model proved to be a valuable tool evaluating and comparing strategies for the best performance of WWTPs. For this study involving a WWTP, the benchmark model is used while LCA is the decision tool to find the most suitable treatment strategy. LCA adds extra criteria that complement the existing criteria provided by such models. Complementing the cost/performance criteria is proposed for plant-wide models, including environmental evaluation, based on LCA, which provides an overall better assessment of WWTPs. It can capture both the dynamic effects and potential environmental impacts. This study provides an overview of the integration between plant-wide models and LCA.
Nguyen, TT & Indraratna, B 2020, 'The energy transformation of internal erosion based on fluid-particle coupling', Computers and Geotechnics, vol. 121.
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© 2020 Elsevier Ltd The transformation of energy is an intrinsic process that is needed to trigger the internal erosion of soils subjected to a fluid flow, but how to capture this process is not understood very well. This is why this study aims to address these complex processes through a numerical fluid-particle coupling simulation. The computational fluid dynamics (CFD) is used to model fluid flows which is coupled with the discrete element method (DEM) employed to simulate soil particles. Detailed migration of particles and fluid variables are recorded to enable their kinetic energy to be computed. Successful experiments are used to demonstrate how the numerical method can be used to model the internal erosion associated with energy computation. This study shows a good agreement between the numerical and experimental results in terms of the hydraulic conductivity and erosion rate of soils subjected to upward flows. A significant loss in energy is also found as fluid flows through the soil whereas only a small amount of kinetic energy is needed to make particles migrate at a considerable degree. The influence that the porosity and uniformity of soils has on the transformation of energy is also discussed in the paper.
Nguyen, TT, Ngo, HH, Guo, W, Nguyen, HQ, Luu, C, Dang, KB, Liu, Y & Zhang, X 2020, 'New approach of water quantity vulnerability assessment using satellite images and GIS-based model: An application to a case study in Vietnam', Science of the Total Environment, vol. 737.
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© 2020 Water deficiency due to climate change and the world's population growth increases the demand for the water industry to carry out vulnerability assessments. Although many studies have been done on climate change vulnerability assessment, a specific framework with sufficient indicators for water vulnerability assessment is still lacking. This highlights the urgent need to devise an effective model framework in order to provide water managers and authorities with the level of water exposure, sensitivity, adaptive capacity and water vulnerability to formulate their responses in implementing water management strategies. The present study proposes a new approach for water quantity vulnerability assessment based on remote sensing satellite data and GIS ModelBuilder. The developed approach has three layers: (1) data acquisition mainly from remote sensing datasets and statistical sources; (2) calculation layer based on the integration of GIS-based model and the Intergovernmental Panel on Climate Change's vulnerability assessment framework; and (3) output layer including the indices of exposure, sensitivity, adaptive capacity and water vulnerability and spatial distribution of remote sensing indicators and these indices in provincial and regional scale. In total 27 indicators were incorporated for the case study in Vietnam based on their availability and reliability. Results show that the most water vulnerable is the South Central Coast of the country, followed by the Northwest area. The novel approach is based on reliable and updated spatial-temporal datasets (soil water stress, aridity index, water use efficiency, rain use efficiency and leaf area index), and the incorporation of the GIS-based model. This framework can then be applied effectively for water vulnerability assessment of other regions and countries.
Nguyen, TTQ, Loganathan, P, Nguyen, TV & Vigneswaran, S 2020, 'Removing arsenic from water with an original and modified natural manganese oxide ore: batch kinetic and equilibrium adsorption studies.', Environmental science and pollution research international, vol. 27, no. 5, pp. 5490-5502.
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Arsenic contamination of drinking water is a serious water quality problem in many parts of the world. In this study, a low-cost manganese oxide ore from Vietnam (Vietnamese manganese oxide (VMO)) was firstly evaluated for its performance in arsenate (As(V)) removal from water. This material contains both Mn (25.6%) and Fe (16.1%) mainly in the form of cryptomelane and goethite minerals. At the initial As(V) concentration of 0.5 mg/L, the adsorption capacity of original VMO determined using the Langmuir model was 0.11 mg/g. The modified VMOs produced by coating VMO with iron oxide (Fea-VMO) and zirconium oxide (Zra-VMO) at 110 °C and 550 °C achieved the highest As(V) adsorption capacity when compared to three other methods of VMO modifications. Langmuir maximum adsorption capacities of Fea-VMO and Zra-VMO at pH 7.0 were 2.19 mg/g and 1.94 mg/g, respectively, nearly twenty times higher than that of the original VMO. Batch equilibrium adsorption data fitted well to the Langmuir, Freundlich, and Temkin models and batch kinetics adsorption data to pseudo-first order, pseudo-second order, and Elovich models. The increase of pH progressively from 3 to 10 reduced As(V) adsorption with a maximum reduction of 50-60% at pH 10 for both original and modified VMOs. The co-existing oxyanions considerably weakened the As(V) removal efficiency because they competed with As(V) anions. The competition order was PO43- > SiO32- > CO32- > SO42-. The characteristics of the original and modified VMOs evaluated using SEM, FTIR, XRD, XRF, surface area, and zeta potential explained the As(V) adsorption behaviour.
Nguyen, TTQ, Loganathan, P, Nguyen, TV, Vigneswaran, S & Ngo, HH 2020, 'Iron and zirconium modified luffa fibre as an effective bioadsorbent to remove arsenic from drinking water', Chemosphere, vol. 258.
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© 2020 Porous luffa plant fibre (LF) was grafted with Fe and Zr, and the ability of the fabricated adsorbents to remove arsenate (As(V)) from water was investigated in batch and column adsorption experiments. The Langmuir adsorption capacity (mg g−1) at pH 7 of LF was found to be 0.035, which increased to 2.55 and 2.89 after being grafted with Fe (FLF-3) and Zr (ZLF-3), respectively. Grafting with Fe and Zr increased the zeta potential and zero point of charge (ZPC) of LF (from pH 3.9 to 7.4 for Fe grafting and to 7.6 for Zr grafting), due to chemical bonding of the metals, possibly with the hydroxyl and carboxylic groups in LF as indicated in FTIR peaks. Zeta potential and ZPC decreased after As adsorption owing to inner-sphere complexation mechanism of adsorption. The increase of pH from 3 to 10 progressively reduced the adsorbents’ adsorption capacity. Co-existing anions weakened the As(V) removal efficiency in the order, PO43− > SiO32− > CO32− > SO42−. Adsorption kinetics data fitted well to the Weber and Morris model, which revealed initial fast and subsequent slow rates of intra-particle As diffusion into the bigger pores and smaller pores, respectively. Column adsorption data fitted well to the Thomas model with the predicted adsorption capacities in the same order as in the batch adsorption experiment (ZLF-3 > FLF-3 > LF).
Nguyen, TV 2020, 'Toward the era of precision fracture risk assessment', The Journal of clinical endocrinology and metabolism, vol. 105, no. 7.
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Nguyen, XC, Tran, TCP, Hoan, H, Nguyen, TP, Chang, SW, Dinh, DN, Guo, W, Kumar, A, Duong, DL & Quang-Vu, B 2020, 'Combined biochar vertical flow and free-water surface constructed wetland system for dormitory sewage treatment and reuse', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 713.
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Ni, B-J, Zeng, S, Wei, W, Dai, X & Sun, J 2020, 'Impact of roxithromycin on waste activated sludge anaerobic digestion: Methane production, carbon transformation and antibiotic resistance genes', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 703.
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Ni, Z, Zhang, A, Yang, K, Gao, F & An, J 2020, 'Low-complexity Subarray-based RF Precoding for Wideband Multiuser Millimeter Wave Systems', IEEE Transactions on Vehicular Technology, pp. 1-1.
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Ni, Z, Zhang, JA, Yang, K, Gao, F & An, J 2020, 'Estimation of Multiple Angle-of-Arrivals with Localized Hybrid Subarrays for Millimeter Wave Systems', IEEE Transactions on Communications, vol. 68, no. 3, pp. 1897-1910.
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IEEE Angle of Arrival (AoA) estimation with localized hybrid arrays is challenging in millimeter-wave (mmWave) communication systems. Most existing solutions quantize AoAs into limited values with relatively low accuracy. This paper presents a multi-AoA estimation scheme which is capable of estimating multiple AoAs from multiple users with low complexity. Specifically, we design a path filter via combining the received signals for each subarray. Each path filter enables a certain range of AoAs to pass through while suppressing the rest. Then we can use low-complexity cross-correlation operations to obtain continuous AoA estimates. Association of paths to users is further achieved by a follow-up pseudo-random codes based correlation operation. The scheme is first presented for a narrowband system and then extended to wideband with frequency selectivity. We also introduce new metrics and derive the lower bound of mean square error for evaluating the accuracy of AoA estimates, as conventional metrics face difficulties in the presence of multiple closely located AoAs. Extensive simulation results are provided and validate the effectiveness of the proposed multi-AoA estimation scheme.
Niamir, L, Ivanova, O, Filatova, T, Voinov, A & Bressers, H 2020, 'Demand-side solutions for climate mitigation: Bottom-up drivers of household energy behavior change in the Netherlands and Spain', ENERGY RESEARCH & SOCIAL SCIENCE, vol. 62.
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Niamir, L, Kiesewetter, G, Wagner, F, Schöpp, W, Filatova, T, Voinov, A & Bressers, H 2020, 'Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions', Climatic Change.
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© 2019, The Author(s). In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally imp...
Niu, F, Qiu, X & Zhang, D 2020, 'Effects of active noise cancelling headphones on speech recognition', APPLIED ACOUSTICS, vol. 165.
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Niu, F, Zhao, S, Qiu, X & Zhang, D 2020, 'A note on wind velocity and pressure spectra inside compact spherical porous microphone windscreens', JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, vol. 147, no. 1, pp. EL43-EL49.
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Niu, K, Guo, J, Pan, Y, Gao, X, Peng, X, Li, N & Li, H 2020, 'Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data', COMPLEXITY, vol. 2020.
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Niu, T, Wang, J, Lu, H, Yang, W & Du, P 2020, 'Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting', EXPERT SYSTEMS WITH APPLICATIONS, vol. 148.
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Niwa, K, Chiba, H, Harada, N, Zhang, G & Kleijn, WB 2020, 'Microphone Array Wiener Post Filtering Using Monotone Operator Splitting', IEEE Transactions on Audio, Speech and Language Processing.
Nizami, MSH, Hossain, MJ & Fernandez, E 2020, 'Multiagent-Based Transactive Energy Management Systems for Residential Buildings with Distributed Energy Resources', IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1836-1847.
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© 2005-2012 IEEE. Proper management of building loads and distributed energy resources (DER) can offer grid assistance services in transactive energy (TE) frameworks besides providing cost savings for the consumer. However, most TE models require building loads and DER units to be managed by external entities (e.g., aggregators), and in some cases, consumers need to provide critical information related to their electricity demand and usage, which hampers their privacy. This article introduces a transactive energy management framework for the buildings in a residential neighborhood to address grid overloading and cost optimization of the buildings. The decentralized coordination for the energy management system is realized by using a multiagent system architecture, which provides the consumers with full decision-making authority and preserves their privacy. A new event-triggered transactive market algorithm is developed, where the buildings trade energy to maximize profits, while the regional grid operator procures energy-supply flexibility of active consumers to prevent transformer overloading. A two-stage energy management system is developed for the residential buildings that schedules building loads and DER units in day-ahead stage to minimize cost and inconveniences for the consumer while participating in the real-time transactive market to maximize profits. An optimal bidding model is developed for the buildings that incorporates the degradation of residential storage devices for energy trading. Case studies and analyses with actual Australian building data and electricity tariff structures indicate the efficacy of the proposed methodology for effective mitigation of transformer overloading at a negligible cost compared to transformer replacement cost. Results also indicate that the proposed system can provide 15-20% cost savings for the consumers while minimizing their inconveniences and degradation of storage devices.
Nizami, MSH, Hossain, MJ, Amin, BMR & Fernandez, E 2020, 'A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading', Applied Energy, vol. 261.
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© 2019 Elsevier Ltd Bi-directional electricity trading of demand response (DR) and transactive energy (TE) frameworks allows the traditionally passive end-users of electricity to play an active role in the local power balance of the grid. Appropriate building energy management systems (BEMSs), coupled with an optimized bidding strategy, can provide significant cost savings for prosumers (consumers with on-site power generation and/or storage facility) when they participate in such bi-directional trading. This paper presents a BEMS with an optimization-based scheduling and bidding strategy for small-scale residential prosumers to determine optimal day-ahead energy-quantity bids considering the expected cost of real-time imbalance trading under uncertainty. The proposed scheduling and bidding strategy is formulated as a stochastic bi-level minimization problem that determines the day-ahead energy-quantity bids by minimizing the energy cost in the upper level considering expected cost of uncertainty, whereas a number of lower-level sub-problems ensure optimal operation of building loads and distributed energy resources (DERs) for comfort reservation, minimization of consumers’ inconveniences and degradation of residential storage units. A modified decomposition method is used to reformulate the nonlinear bi-level problem as a mixed-integer linear programming (MILP) problem and solved using ‘of the shelf’ commercial software. The effectiveness of the proposed BEMS model is verified via case studies for a residential prosumer in Sydney, Australia with real measurement data for building energy demand. The efficacy of the proposed method for overall financial savings is also validated by comparing its performance with state-of-the-art day-ahead scheduling strategies. Case studies indicate that the proposed method can provide up to 51% and 22% cost savings compared to inflexible non-optimal scheduling strategies and deterministic optimization-based methods respectively. ...
Nobakht, M, Sui, Y, Seneviratne, A & Hu, W 2020, 'PGFIT: Static permission analysis of health and fitness apps in IoT programming frameworks', JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, vol. 152.
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Nosouhi, MR, Sood, K, Yu, S, Grobler, M & Zhang, J 2020, 'PASPORT: A Secure and Private Location Proof Generation and Verification Framework', IEEE Transactions on Computational Social Systems, vol. 7, no. 2, pp. 293-307.
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© 2014 IEEE. Recently, there has been a rapid growth in location-based systems and applications in which users submit their location information to service providers in order to gain access to a service, resource, or reward. We have seen that in these applications, dishonest users have an incentive to cheat on their location. Unfortunately, no effective protection mechanism has been adopted by service providers against these fake location submissions. This is a critical issue that causes severe consequences for these applications. Motivated by this, we propose the Privacy-Aware and Secure Proof Of pRoximiTy (PASPORT) scheme in this article to address the problem. Using PASPORT, users submit a location proof (LP) to service providers to prove that their submitted location is true. PASPORT has a decentralized architecture designed for ad hoc scenarios in which mobile users can act as witnesses and generate LPs for each other. It provides user privacy protection as well as security properties, such as unforgeability and nontransferability of LPs. Furthermore, the PASPORT scheme is resilient to prover-prover collusions and significantly reduces the success probability of Prover-Witness collusion attacks. To further make the proximity checking process private, we propose P-TREAD, a privacy-aware distance bounding protocol and integrate it into PASPORT. To validate our model, we implement a prototype of the proposed scheme on the Android platform. Extensive experiments indicate that the proposed method can efficiently protect location-based applications against fake submissions.
Nosouhi, MR, Yu, S, Zhou, W, Grobler, M & Keshtiar, H 2020, 'Blockchain for secure location verification', JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, vol. 136, pp. 40-51.
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Noushini, A, Castel, A, Aldred, J & Rawal, A 2020, 'Chloride diffusion resistance and chloride binding capacity of fly ash-based geopolymer concrete', Cement and Concrete Composites, vol. 105.
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© 2019 This study evaluated the chloride diffusion resistance of low-calcium fly ash-based geopolymer concrete through electrical and bulk diffusion techniques. The geopolymer concretes were prepared using 12 different heat curing conditions; three temperatures of 60, 75 and 90 °C and four curing durations of 8, 12, 18 and 24 h, as well as ambient curing. The mechanical and transport properties and microstructural characteristics of the geopolymer concretes were examined. NT BUILD 492 chloride migration and ASTM C1556 bulk diffusion tests were carried out. Results showed that the chloride diffusion resistance and the chloride binding capacity of fly ash-based geopolymer concrete is very low. The fly ash-based geopolymer concrete appears to be suitable for applications where there are little or no chloride-related durability concerns.
Nuruzzaman, M, Ren, J, Liu, Y, Rahman, MM, Shon, HK & Naidu, R 2020, 'Hollow Porous Silica Nanosphere with Single Large Pore Opening for Pesticide Loading and Delivery', ACS APPLIED NANO MATERIALS, vol. 3, no. 1, pp. 105-113.
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Ong, HC, Chen, W-H, Singh, Y, Gan, YY, Chen, C-Y & Show, PL 2020, 'A state-of-the-art review on thermochemical conversion of biomass for biofuel production: A TG-FTIR approach', ENERGY CONVERSION AND MANAGEMENT, vol. 209.
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Ong, HC, Mofijur, M, Silitonga, AS, Gumilang, D, Kusumo, F & Mahlia, TMI 2020, 'Physicochemical Properties of Biodiesel Synthesised from Grape Seed, Philippine Tung, Kesambi, and Palm Oils', ENERGIES, vol. 13, no. 6.
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Organ, B, Huang, Y, Zhou, JL, Yam, Y-S, Mok, W-C & Chan, EFC 2020, 'Simulation of engine faults and their impact on emissions and vehicle performance for a liquefied petroleum gas taxi', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 716.
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Otte, M, Sofge, D & Fitch, R 2020, 'Guest editorial: Special issue on robot communication challenges: real-world problems, systems, and methods', Autonomous Robots, vol. 44, no. 1.
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Ou, T, Wang, D, Xin, Z, Tan, J, Wu, C, Guo, Q & Zhang, Y 2020, 'Full-scale tests on the mechanical behaviour of a continuously welded stainless steel roof under wind excitation', Thin-Walled Structures, vol. 150.
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© 2020 Elsevier Ltd The wind uplift performance of the continuously welded stainless steel roof (CWSSR) system adopted in the Zhaoqing New District Sports (ZNDS) Center of China is investigated in this study. To determine the optimal welding program and examine the mechanical properties of the continuously welded stainless steel joints, uniaxial tensile testing is first conducted on 27 specimens with tension-shear and tension-bending types. Two CWSSR specimens, one that is square-shaped with a horizontal layout and one that is rectangular-shaped with an inclination layout of 10.71°, are further tested under dynamic and static ultimate wind uplift loadings to explore the wind uplift capacity. All specimens are full-size, and the corresponding materials, structural details and construction technologies are kept the same as the actual building to ensure the authenticity of the testing investigations. The testing results indicate that the integrated and sealed CWSSR system has a clear force transmission mechanism and a remarkable wind resistance performance. The welded joints achieve the best performance, and the mechanical behaviours are equivalent to those of the base material under the continuously welded conditions including an electric current of 65 A and a moving velocity of 750 mm/s. An excellent dynamic wind suction performance is achieved under 5000 five-level cumulative loading cycles with a maximum pressure of 5400 Pa. The static ultimate pressure reaches 9400 Pa for the square specimen and 10,400 Pa for the rectangular specimen. Damage observations show that no tearing or rupture failures are observed for the CWSSR system. The investigation results contribute the most to the safe design of the ZNDS Center and are expected to provide guidelines for future applications of the CWSSR system.
Ouyang, D, Yuan, L, Qin, L, Chang, L, Zhang, Y & Lin, X 2020, 'Efficient Shortest Path Index Maintenance on Dynamic Road Networks with Theoretical Guarantees.', Proc. VLDB Endow., vol. 13, pp. 602-615.
Pain, A, Nimbalkar, S & Hussain, M 2020, 'Applicability of Bouc-Wen Model to Capture Asymmetric Behavior of Sand at High Cyclic Shear Strain', INTERNATIONAL JOURNAL OF GEOMECHANICS, vol. 20, no. 6.
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Pan, Y, Tsang, IW, Singh, AK, Lin, C-T & Sugiyama, M 2020, 'Stochastic Multichannel Ranking with Brain Dynamics Preferences.', Neural computation, pp. 1-32.
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A driver's cognitive state of mental fatigue significantly affects his or her driving performance and more important, public safety. Previous studies have leveraged reaction time (RT) as the metric for mental fatigue and aim at estimating the exact value of RT using electroencephalogram (EEG) signals within a regression model. However, due to the easily corrupted and also nonsmooth properties of RTs during data collection, methods focusing on predicting the exact value of a noisy measurement, RT generally suffer from poor generalization performance. Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels.
Park, J, Lim, J, Park, Y, Han, DS, Shon, HK, Hoffmann, MR & Park, H 2020, 'In Situ-Generated Reactive Oxygen Species in Precharged Titania and Tungsten Trioxide Composite Catalyst Membrane Filters: Application to As(III) Oxidation in the Absence of Irradiation.', Environmental science & technology.
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This study demonstrates that in situ-generated reactive oxygen species (ROSs) in prephotocharged TiO2 and WO3 (TW) composite particle-embedded inorganic membrane filters oxidize arsenite (As(III)) into arsenate (As(V)) without any auxiliary chemical oxidants under