Publications

Ferrer-Comalat, JC, Linares-Mustarós, S, Merigó, JM & Kacprzyk, J 2020, Preface.

Jain, LC, Balas, VE, Tsihrintzis, GA & Sharma, DK 2020, Preface.

Chaczko, Z, Al-Doghman, F & Rakhi Ajayan, A 2020, 'Policy-Based Consensus Data Aggregation for the Internet of Things' in Klempous, R & Nikodem, J (eds), Smart Innovations in Engineering and Technology, Springer Nsture, Cham, Switzerland, pp. 119-130.
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The new trend of the Internet of Things brings a whole breed of opportunities and applications. Within it, a massive amount of data coming from heterogeneous sources travel in a bidirectional way. Data aggregation is one of the most efficient ways to mitigate Big Data. However, using one type of aggregation within a network at all times is not an optimal option. Various network situations require different aggregation functions at different times. We introduce a policy-based data aggregation framework that can handle this issue by referring to a policy when executing the aggregation strategy. An agreement process is used to reach consensus about the aggregation function that is to be applied on the network (or part of it) at a specific time. Participants are to negotiate the policy terms based on the current network status and the nature of the coming requests. The framework represents a promising scope for fully automated IoT.

Chaczko, Z, Rakhi Ajayan, A & Al-Doghman, F 2020, 'Tensor Decompositions in Multimodal Big Data: Studying Multiway Behavioral Patterns' in Klempous, R & Nikodem, J (eds), Smart Innovations in Engineering and Technology, Springer Nature, Cham, Switzerland, pp. 1-296.
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Preset day cyber-physical systems (CPS) are the confluence of very large data sets, tight time constraints, and heterogeneous hardware units, ridden with latency and volume constraints, demanding newer analytic perspectives. Their system logistics can be well-defined by the data-streams’ behavioural trends across various modalities, without numerical restrictions, favouring resource-saving over methods of investigating individual component features and operations. The aim of this paper is to demonstrate how behaviour patterns and related anomalies comprehensively define a CPS. Tensor decompositions are hypothesized as the solution in the context of multimodal smart-grid-originated Big Data analysis. Tensorial data representation is demonstrated to capture the complex knowledge encompassed in these data flows. The uniqueness of this approach is highlighted in the modified multiway anomaly patterns models. In addition, higher-order data preparation schemes, design and implementation of tensorial frameworks and experimental-analysis are final outcomes.

Chaczko, Z, Zhang, W, Christopher, C, Jan, S & Su, S 2020, 'Internet of Things Is Changing Teaching of Technical Subjects at UTS' in Klempous, R & Nikodem, J (eds), Smart Innovations in Engineering and Technology, Springer Nature AG Switzerland, Gewerbestrasse 11, 6330 Cham, Switzerland, pp. 131-296.
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The Internet of Things, involving the connection of intelligent devices (other than standard, already connected products such as computers and smartphones) to the Internet, is in the process of transforming numerous areas of lives. For many years, both academia and industry prepared the background theories and implementations for what is today's IoT, keeping changing and evolving. Students of universities, preparing to be the driving forces of innovation and progress have a very strong interest in IoT and related subjects. Teaching at universities, especially technical ones is strongly affected by the need to give students the required background and show the direction for self-study. In this paper we will provide a literature review to analyze how IoT inspired changes in teaching some technical subjects at the University of Technology Sydney (UTS) by describing, the recently undertaken projects in this area, providing some case studies and drawing conclusions.

Chilwesa, M, Akbarnezhad, A, Gharehchaei, M, Castel, A, Lloyd, R & Foster, S 2020, 'Using the Double Ring Test to Assess the Effect of Type of Aggregate Used on Thermal Cracking Potential of Concrete' in Lecture Notes in Civil Engineering, pp. 337-347.
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© 2020, Springer Nature Singapore Pte Ltd. Early age thermal cracking occurs when tensile stress generated from restrained deformations in young concrete reaches its tensile strength. The risk of early age cracking is especially high for elements involving large concrete placements such as bridge piers, foundations etc. and concrete with low water-to-binder (w/c) ratio. The research in literature has focused on finding ways to mitigate cracking through mix design approaches and devising test methods to determine restraint stresses in concrete. The most commonly used test methods, such as the Rigid Cracking Frame, require special equipment and calibration to function properly. There is need for a more simple but accurate test that can measure the effects of both early-age expansion and shrinkage, as well as account for the temperature changes that occur in a mass concrete element. The double ring test, which has been used to study effects of internal curing on early age cracking, has potential to measure effects of both shrinkage and expansion. To evaluate its viability as a reliable test to characterise the early age thermal cracking potential of concrete, the double ring test was used in this study to assess the effect of type of aggregate used on early age thermal cracking of concrete subjected to temperature history similar to that experienced in an actual mass concrete structural element. Three types of locally available aggregates were investigated. Preliminary results indicate that the double ring test can be used to study effects of thermal deformations on concrete subjected to a changing temperature history, and that type of aggregate used can influence the potential of cracking.

Dutta, S & Gandomi, AH 2020, 'Design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels' in Handbook of Probabilistic Models, Elsevier, pp. 369-381.
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Gowripalan, N 2020, 'Autogenous Shrinkage of Concrete at Early Ages' in Wang, CM, Ho, JCM & Kitipornchai, S (eds), ACMSM25, pp. 269-276.
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Holley, C, Mutongwizo, T, Pucci, S & Castilla Rho, J 2020, 'Groundwater regulation, compliance and enforcement: insights on regulators, regulated actors and frameworks in New South Wales, Australia' in Rinaudo, JD, Holley, C, Barnett, S & Montginoul, M (eds), Sustainable Groundwater Management: A Comparative Analysis of French and Australian Policies and Implications to Other Countries, Springer.
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Jaddoa, M, Gonzalez, L, Cuthbertson, H & Al-Jumaily, A 2020, 'Multi View Face Detection in Cattle Using Infrared Thermography' in Communications in Computer and Information Science, Springer International Publishing, pp. 223-236.
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Li, JT, Zhu, XQ & Samali, B 2020, 'Bridge Operational Modal Identification Using Sparse Blind Source Separation' in Lecture Notes in Civil Engineering, pp. 911-920.
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© 2020, Springer Nature Singapore Pte Ltd. The bridge infrastructures are subjected to continuous degradation due to ageing, environmental and excess loading. Monitoring of these structures is a key part of any maintenance strategy as it can give early warning if a bridge is becoming unsafe. Most of the current approaches are using direct measurements that the sensors are installed at different specific locations on the bridge to capture the dynamic characteristics of the structure under random input, such as wind loads, traffic loads and ground motions. Based on the assumption on the white noise characteristics of the random input, structural properties of the bridge could be extracted from the vibration responses only. However, the bridge is subjected to non-stationary traffic loads, and the frequency characteristics of vibrations are varied. Especially for short-span bridges, the non-stationary traffic excitation is significant and most of the existing output-only structural identification methods could not be used to assess the bridge condition. This study proposes a blind source separation (BSS) method using short time Fourier transform (STFT) for the analysis of non-stationary measurements in time frequency (TF) domain. The proposed method is capable of source component separation from response measurement for underdetermined problems when the number of independent measurements (sensors) is less than that of source component. The proposed method is applied to a cable-stayed bridge in the field for the operational modal identification under different traffic conditions.

Mohebali, B, Tahmassebi, A, Meyer-Baese, A & Gandomi, AH 2020, 'Probabilistic neural networks' in Handbook of Probabilistic Models, Elsevier, pp. 347-367.
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Tang, Z, Li, WG, Li, PR & Shah, SP 2020, 'Durability of Sustainable Construction Materials with Solid Wastes' in Lecture Notes in Civil Engineering, pp. 3-13.
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© 2020, Springer Nature Singapore Pte Ltd. Solid waste disposal has been emerging as a challenging issue worldwide following the enhanced economic activities and rapid urbanization. Considerable efforts have been made to use solid waste materials as partial cement replacement in concrete. This work investigates the sulfate resistance of concrete containing different solid waste materials—waste glass powder, coal gangue powder and fly ash, at various substitution levels (10, 20, and 30%). Two water to binder ratios were adopted as 0.41 and 0.54. The sulfate resistance was evaluated by exposing the prepared concrete specimens to a 5% sodium sulfate solution for a total period of 22 months and examining the mass change, compressive strength, splitting tensile strength, ultrasonic pulse velocity, mineralogical and microstructural properties. Results obtained show that the sulfate resistance of concrete can be improved substantially by the addition of solid waste materials. Additionally, waste glass powder is found to be the most effective, followed by gangue coal powder and fly ash. These studies reveal that in addition to helping solid waste management, reusing of these solid waste materials in concrete improves the resistance to sulfate attack.

Voinov, A, Castilla Rho, J, Perez, P & Kenny, D 2020, 'Integrated Ecological Economic Modeling: What is it good for?' in Creating Wellbeing Futures: A Research and Action Agenda for Ecological Economics.
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Climate disruption, overpopulation, biodiversity loss, the threats of financial collapse, large-scale damage to our natural and social environments and eroding democracy are all becoming critically important concerns. The editors of this timely book assert that these problems are not separate, but all stem from our overreliance on an out-dated approach to economics that puts growth of production and consumption above all else. Ecological economics can help create the future that most people want – a future that is prosperous, just, equitable and sustainable. This forward-thinking book lays out an alternative approach that places the sustainable wellbeing of humans and the rest of nature as the overarching goal. Each of the book’s chapters, written by a diverse collection of scholars and practitioners, outlines a research and action agenda for how this future can look and possible actions for its realization. Sustainable Wellbeing Futures will be of value to academics and students researching environmental and ecological economics, as well as individuals interested in gaining a greater understanding of the concept of a wellbeing future and how we might act to achieve it.

Wang, W & Wu, C 2020, 'Numerical Simulation of FRP-Concrete-Steel Double-Skin Tubular Column Under Lateral Impact Loading' in Lecture Notes in Civil Engineering, pp. 467-476.
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© 2020, Springer Nature Singapore Pte Ltd. Hybrid fiber reinforced polymer (FRP)-concrete-steel double-skin tubular column (DSTC) is a new form of composite column that consists of an outer FRP tube and an inner steel tube, with inner space filled with concrete. This study presents the first numerical investigation on the behavior of hybrid DSTC under lateral impact loading using LS-DYNA. The numerical models of hybrid DSTC were first developed and then verified by using existing test results. Afterwards, parametric analyses were conducted to investigate the influences of impactor shape and impact energy on the lateral impact behavior of hybrid DSTC. The impact force-time history and mid-span deflection-time history of hybrid DSTC were extracted from the simulation results. The simulation results show that hybrid DSTC behave in a very ductile manner under lateral impact loading. The impact energy can significantly influence the impact behavior of hybrid DSTC. However, the influence of impactor shape is insignificant.

Abdin, Z, Zafaranloo, A, Rafiee, A, Mérida, W, Lipiński, W & Khalilpour, KR 2020, 'Hydrogen as an energy vector', Renewable and Sustainable Energy Reviews, vol. 120, pp. 109620-109620.
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Abdo, P, 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.

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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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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© 2019 Compact heat exchangers have become an essential necessity for power production and multi other purposes on a daily basis. The corrugated plate heat exchangers (CPHEs) are well-known for their high thermal performance. This study proposes a unique CPHE with a simple modification that can boost its thermal performance significantly. The overall tests have been conducted on four CPHEs for two symmetric chevron angles (β) of 30°/30° and 60°/60° Two CPHEs belong to the newly CPHEs, and the other two belong to the well-known basic CPHE. Data are obtained for steady-state, single-phase (water-water), counter-current arrangements, and for Reynolds number (Re) ranges from 500 to 2500. Sophisticated mesh techniques have been adopted to develop the mesh for the plates and the fluids between the plates. An appropriate grid refinement test has been carried out for the accuracy of the numerical results. The results have been validated with benchmark experimental and numerical data. A realizable k−ε turbulence model with scalable wall treatment found to provide the most consistent and accurate prediction of the thermal performance of CPHE. The numerical results showed that the Nusselt number (Nu) and the effectiveness (ϵ) of the newly developed CPHEs are much higher than that of the basic one, which can be very useful when a heavy heat duty is required. The enhancement for Nu is up to 75% and for ϵ is up to 42%, and generally both exhibit a direct proportional relationship with Re. Based on the numerical result, a new correlation to predict Nu has been developed.

Al-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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© 2019 Elsevier Ltd This paper experimentally investigated how temperatures of the regenerated solution and spent solution together with other key governing factors influence the performance of electrodialysis (ED) used for liquid desiccant regeneration. A matrix of experiments based on Taguchi Orthogonal Arrays was designed and performed using a lab-scale ED system. Three different scenarios were designed to investigate the effect of solution temperature on the concentration increase and power consumption of the ED regeneration. The experimental design and data analysis were performed using Taguchi method and Analysis of Variance, respectively. The results showed that the highest improvement in concentration increase of the solution through the regenerated channels was around 19% and the highest saving in the power consumption was 19.3%, when the temperatures of the solutions at the inlet of the spent channels and regenerated channels were elevated from 30 to 45 °C and from 20 to 30 °C respectively, while maintaining the other parameters unchanged. The results obtained from this study could be used to guide the operation of the ED stack for liquid desiccant regeneration.

Alshahrani, AA, Algamdi, MS, Alsohaimi, IH, Nghiem, LD, Tu, KL, Al-Rawajfeh, AE & in het Panhuis, M 2020, 'The rejection of mono- and di-valent ions from aquatic environment by MWNT/chitosan buckypaper composite membranes: Influences of chitosan concentrations', Separation and Purification Technology, vol. 234.
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© 2019 Elsevier B.V. Owing to the scarcity of proper drinking water is an urgent problem, MWNT/Chitosan membrane is greeting to reject mono- and di-valent ions from water. MWNT/Chitosan membrane was fabricated through the dispersion of Multi-walled carbon nanotubes (MWNTs) in an aqueous solution containing different concentrations of chitosan. The influence of solution concentration on membrane salt rejection properties, as well as contact angle, electrical conductivity, water permeability, mechanical properties, zeta potential, surface area and internal pores morphologies has been investigated. The resulting buckypaper demonstrate that the contact angle (91° ± 4° to 124° ± 3°), electrical conductivity (17 ± 1 to 83 ± 3 S/cm), water permeability (0.59 ± 0.04 to 5.73 ± 0.3 L/m2 h bar), surface area and internal pores morphologies of the buckypaper membranes were decreased by increasing the concentration of chitosan. While, the mechanical properties (tensile strengths varied between 35 ± 2 and 75 ± 3 MPa) and zeta potential of these buckypaper membranes were found to increase with increasing the amounts of chitosan. A buckypaper fabricated from MWNTs and a high concentration of chitosan (0.4% w/v) showed a higher rejection efficiency for these salts, possibly due to their smaller internal pore volumes and lower specific surface area.

Awan, I, Younas, M & Hussain, F 2020, 'Emerging challenges and frontiers in cloud computing', Concurrency Computation, vol. 32, no. 1.
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Bao, T, Damtie, MM, Hosseinzadeh, A, Wei, W, Jin, J, Phong Vo, HN, Ye, JS, Liu, Y, Wang, XF, Yu, ZM, Chen, ZJ, Wu, K, Frost, RL & Ni, B-J 2020, 'Bentonite-supported nano zero-valent iron composite as a green catalyst for bisphenol A degradation: Preparation, performance, and mechanism of action', Journal of Environmental Management, vol. 260, pp. 110105-110105.
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Barthe, G, Hsu, J, Ying, M, Yu, N & Zhou, L 2020, 'Relational proofs for quantum programs', Proceedings of the ACM on Programming Languages, vol. 4, no. POPL, pp. 1-29.
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Barthe, G, Hsu, J, Ying, M, Yu, N & Zhou, L 2020, 'Relational proofs for quantum programs.', PACMPL, vol. 4, pp. 21:1-21:1.

Brodka, P, Musial, K & Jankowski, J 2020, 'Interacting spreading processes in multilayer networks: a systematic review', IEEE Access, pp. 1-1.
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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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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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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.

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', Science of the Total Environment, vol. 701.
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© 2019 Elsevier B.V. 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, Z, Duan, X, Wei, W, Wang, S, Zhang, Z & Ni, B-J 2020, 'Boride-based electrocatalysts: Emerging candidates for water splitting', NANO RESEARCH.
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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.

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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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.
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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, 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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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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© 2019 Elsevier Ltd This paper provides an analytical solution for consolidation problem of a stone column-improved soft soil layer subjected to an instantly applied loading under free strain condition. The radial and vertical consolidation equations are solved in a coupled fashion for both the stone column and its surrounding soil. A general solution of excess pore water pressure at any point of a unit cell model in terms of a Fourier-Bessel series was achieved using the combination of separation of variables method and orthogonal expansion technique. The obtained solution can capture the drain (well) resistance effect and the space-dependent distribution of total vertical stress induced by the external loading. Indeed, since the permeability and size of the stone column are directly utilised in the governing equations and the analytical solution, the drain resistance is directly captured. The capabilities of the proposed solution are exhibited through a comprehensive worked example, while the accuracy of the solution is verified against a finite element simulation and field measurements of a case history with good agreements. To examine the effect of various factors on consolidation behaviour of the composite ground, a parametric study involving column spacing, modulus and permeability of soft soil along with distribution pattern of total stress and thickness of soil layer is also conducted. A decrease in the column spacing or an increase in the modulus or permeability of soft soil led to the acceleration of the consolidation process of the soil region, while the variation of the total stress with depth and the thickness of soil deposit primarily affected the consolidation rate of stone column. Under the free strain condition, the average differential settlement between the stone column and encircling soil was indeed considerable during the consolidation process.

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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Dou, Y, Li, Y, Zhang, C, Yue, S & Zhu, J 2020, 'Effect of Uniaxial Stress along different directions on Alternating Magnetic Properties of Silicon Steel Sheet', IEEE Transactions on Magnetics, pp. 1-1.
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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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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.

Fanos, AM & Pradhan, B 2020, 'A Novel Hybrid Machine Learning-Based Model for Rockfall Source Identification in Presence of Other Landslide Types Using LiDAR and GIS', Earth Systems and Environment.
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© 2019, King Abdulaziz University and Springer Nature Switzerland AG. Abstract: Rockfall is a common phenomenon in mountainous and hilly areas worldwide, including Malaysia. Rockfall source identification is a challenging task in rockfall hazard assessment. The difficulty rise when the area of interest has other landslide types with nearly similar controlling factors. Therefore, this research presented and assessed a hybrid model for rockfall source identification based on the stacking ensemble model of random forest (RF), artificial neural network, Naive Bayes (NB), and logistic regression in addition to Gaussian mixture model (GMM) using high-resolution airborne laser scanning data (LiDAR). GMM was adopted to automatically compute the thresholds of slope angle for various landslide types. Chi square was utilised to rank and select the conditioning factors for each landslide type. The best fit ensemble model (RF–NB) was then used to produce probability maps, which were used to conduct rockfall source identification in combination with the reclassified slope raster based on the thresholds obtained by the GMM. Next, landslide potential area was structured to reduce the sensitivity and the noise of the model to the variations in different conditioning factors for improving its computation performance. The accuracy assessment of the developed model indicates that the model can efficiently identify probable rockfall sources with receiver operating characteristic curve accuracies of 0.945 and 0.923 on validation and training datasets, respectively. In general, the proposed hybrid model is an effective model for rockfall source identification in the presence of other landslide types with a reasonable generalisation performance. Graphic Abstract: [Figure not available: see fulltext.].

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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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.

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.

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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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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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.

H. Kong, F, 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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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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© 2019 Wiley Periodicals, Inc. 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.

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, H-T & Martelli, C 2020, '3-D Printed Smart Orthotic Insoles: Monitoring a Person's Gait Step by Step', IEEE Sensors Letters, vol. 4, no. 1, pp. 1-4.
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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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He, S, Huang, D, Feng, X, Deng, J, Li, J & Zhu, J 2020, 'Transient potential distribution on transformer winding considering the effect of core lamination stack', AIP Advances, vol. 10, no. 1, pp. 015024-015024.
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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, Z, Liu, S, Zhong, Y, Chen, D, Ding, H, Wang, J, Du, G, Yang, G & Hao, Q 2020, 'Preparation of BiPO4/graphene photoelectrode and its photoelectrocatalyitic performance', Chinese Journal of Catalysis, vol. 41, no. 2, pp. 302-311.
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© 2020 Dalian Institute of Chemical Physics, the Chinese Academy of Sciences In this work, a two-step electrodeposition method was employed to prepare BiPO4 nanorod/reduced graphene oxide/FTO composite electrodes (BiPO4/rGO/FTO). The BiPO4/rGO/FTO composite electrode showed the higher photoelectrocatalytic (PEC) activity for the removal of methyl orange than pure BiPO4, which was 2.8 times higher than that of BiPO4/FTO electrode. The effects of working voltage and BiPO4 deposition time on the degradation efficiency of methyl orange were investigated. The optimum BiPO4 deposition time was 45 min and the optimum working voltage was 1.2 V. The trapping experiments showed that hydroxyl radicals (•OH) and superoxide radicals (•O2) were the major reactive species in PEC degradation process. The BiPO4/rGO/FTO composite electrode showed the high stability and its methyl orange removal efficiency remained unchanged after four testing cycles. The reasons for the enhanced PEC efficiency of the BiPO4/rGO/FTO composite electrode was ascribed to the broad visible-light absorption range, the rapid transmission of photogenerated charges, and the mixed BiPO4 phase by the introduction of rGO in the composite electrode films.

Heidary, A, Radmanesh, H, Naqibi, H, Samandarpour, S, Rouzbehi, K & Shariati, N 2020, 'Distribution System Protection by Coordinated Fault Current Limiters', IET Energy Systems Integration.
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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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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.

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, 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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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.

Jafarizadeh, S, Tofigh, F, Lipman, J & Abolhasan, M 2020, 'Optimizing synchronizability in networks of coupled systems', Automatica, vol. 112.
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© 2019 Elsevier Ltd Of collective behaviors in networks of coupled systems, synchronization is of central importance and an extensively studied area. This is due to the fact that it is essential for the proper functioning of a wide variety of natural and engineered systems. Traditionally, uniform coupling strength has been the default choice and the synchronizability measure has been employed for analysis and enhancement of synchronizability. The main drawback of optimizing the synchronizability measure is that it can reach the Pareto frontier but not necessarily a unique point on the Pareto frontier. Additionally, the shortcoming of uniform coupling strength is that it can reach Pareto frontier in specific topologies including edge-transitive graphs. To achieve a unique optimal answer on the Pareto frontier, this paper takes a different approach and addresses the synchronizability in networks of coupled dynamical systems with nonuniform coupling strength and optimizing the synchronizability via maximizing the minimum distance between the nonzero eigenvalues of the Laplacian and the acceptable boundaries for the stability of the system. Furthermore, two solution methods, namely the concave–convex fractional programming and the Semidefinite Programming (SDP) formulations of the problem have been provided. The proposed solution methods have been compared over different topologies and branches of an arbitrary network, where the SDP based approach has shown to be less restricted and more suitable for a wider range of topologies.

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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Jin, P, Tian, Y, Lu, Y, Guo, Y, Lei, G & Zhu, J 2020, '3-D Analytical Magnetic Field Analysis of the Eddy Current Coupling With Halbach Magnets', IEEE Transactions on Magnetics, vol. 56, no. 1, pp. 1-4.
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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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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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Khan, MNH, Forouzesh, M, Siwakoti, Y, Li, L & Blaabjerg, F 2020, 'Switched Capacitor Integrated (2n+1)-Level Step-up Single-Phase Inverter', IEEE Transactions on Power Electronics, pp. 1-1.
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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.

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.

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, 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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Li, J, Qin, L, Yang, K, Ma, Z, Wang, Y, Cheng, L & Zhao, D 2020, 'Materials evolution of bone plates for internal fixation of bone fractures: A review', JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY, vol. 36, pp. 190-208.
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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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Li, Y, Wang, D, Yang, G, Yuan, X, Xu, Q, Yang, Q, Liu, Y, Wang, Q, Ni, BJ, 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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© 2019 Elsevier Ltd As the protonated form of nitrite, free nitrous acid (FNA) is a renewable chemical that can be produced on site from the anaerobic digestion liquor by nitritation, and has been widely employed to improve the fermentation of waste activated sludge (WAS). However, it is not clear whether and how FNA improves the dewaterability of anaerobically digested sludge (ADS). This work therefore aims to provide such supports through comparing the dewaterability of ADS treated by nitrite at different concentrations (0–250 mg/L) under three pH values (5.5, 6.3, or 7.2). Environmental results showed that nitrite was completely denitrified within 12 h, and its addition improved the dewaterability of ADS in all the cases. The optimal normalized capillary suction time of 18.0 ± 0.4 s L/g VSS was obtained at nitrite 50 mg/L and pH 5.5 (equivalent of 0.35 mg/L FNA) in comparison with corresponding value of 23.2 ± 0.4 s L/g·VSS at pH 5.5 (equivalent of 0 mg/L FNA). Under this scenario, 80.5% ± 2.0% of water content was obtained in the FNA-treated sample after press filtration while the corresponding value was 88.5% ± 1.7% in the control. The mechanism investigations showed that FNA treatment reduced surface negative charge of ADS flocs and caused disruption of extracellular polymeric substances and release of intracellular substances, which enhanced the flocculability, hydrophobicity, and flowability, but decreased the bound water content, fractal dimension, and viscosity of ADS. Additionally, FNA treatment altered the secondary structure of proteins through destroying the hydrogen bond, which led to a loose structure of protein, benefiting the exposure of hydrophobic sites or groups in EPS proteins. The findings obtained deepen our understanding of FNA affecting sludge dewatering and provide strong supports to sustainable operation of wastewater treatment plants.

Li, Y, Zhu, J, Zhu, L, Li, Y & Lei, G 2020, 'A Dynamic Magnetostriction Model of Grain-Oriented Sheet Steels Based on Becker–Döring Crystal Magnetization Model and Jiles-Atherton Theory of Magnetic Hysteresis', IEEE Transactions on Magnetics, pp. 1-1.
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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.

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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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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Liu, F, Nattestad, A, Naficy, S, Han, R, Cosillas, G, Angeloski, A, Sun, X & Huang, Z 2020, 'Fluorescent Carbon- and Oxygen-Doped Hexagonal Boron Nitride Powders as Printing Ink for Anticounterfeit Applications', ADVANCED OPTICAL MATERIALS.
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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, 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, 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, Ngo, HH, Guo, W, Wang, D, Peng, L, Wei, W & Ni, BJ 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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© 2019 Elsevier Ltd A common operational status of granule-based reactor is the inevitable coexistence of sludge flocs. Such hybrid system could have a profound impact on nitrous oxide (N2O) production in nitrifying process. In this work, a mathematical model is employed to evaluate the key role of the coexistence of sludge flocs on N2O production in a granule-based nitrifying system for the first time, by considering both nitrifier denitrification and hydroxylamine oxidation pathways. The modelling results show that the N2O production gradually decreases with the increase of the percentage of sludge flocs in the total biomass (10–60%). More N2O is tended to be generated in sludge flocs which has lower N2O production capacity compared to granular biomass, thus lowering the total N2O production. The relative contributions of two N2O production pathways are only affected by bulk dissolved oxygen (DO) for the sludge flocs in the hybrid system, whereas those are affected by both bulk DO and the fractions of sludge flocs for the granular biomass. The results reveal a substantial effect of the coexistence of sludge flocs on N2O production in granule-based nitrifying process, which should not be ignored in future design and operation.

Liu, 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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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.

M. Vanaki, S, Holmes, D, Saha, SC, Chen, J, Brown, RJ & Jayathilake, PG 2020, 'Muco-ciliary clearance: A review of modelling techniques', Journal of Biomechanics, vol. 99, pp. 109578-109578.
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Ma, 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.

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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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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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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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.

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.

Metia, S, Ha, QP, Duc, HN & Scorgie, Y 2020, 'Urban air pollution estimation using unscented Kalman filtered inverse modeling with scaled monitoring data', Sustainable Cities and Society, vol. 54, pp. 101970-101970.
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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, pp. 100942-100942.
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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.
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© 2019 Elsevier B.V. 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.

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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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.
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Nguyen, LN, Truong, M, Nguyen, AQ, Johir, MAH, Commault, AS, Ralph, PJ, Semblante, GU & Nghiem, LD 2020, 'A sequential membrane bioreactor followed by a membrane microalgal reactor for nutrient removal and algal biomass production', ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY, vol. 6, no. 1, pp. 189-196.
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Nguyen, XC, Tran, TCP, Hoang, VH, Nguyen, TP, Chang, SW, Nguyen, DD, Guo, W, Kumar, A, La, DD & Bach, Q-V 2020, 'Combined biochar vertical flow and free-water surface constructed wetland system for dormitory sewage treatment and reuse', Science of The Total Environment, pp. 136404-136404.
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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 and Social Science, vol. 62.
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© 2019 The Authors Households are responsible for 70% of CO2 emissions (directly and indirectly). While households as agents of change increasingly become a crucial element in energy transitions, bottom-up mechanisms facilitating behavioral change are not fully understood. A scientific understanding of individual energy use, requires eliciting factors that trigger or inhibit changes in energy behavior. This paper explores individual energy consumption practices and behavioral aspects that affect them. We quantitatively study the determinants of three energy actions: (1) investments in house insulation, solar panels and/or energy-efficient appliances, (2) conservation of energy by changing energy-use habits like switching off unused devices or adjusting house temperature, and (3) switching to green(er) electricity sources. To address this goal, we conduct a comprehensive survey among households (N = 1790) in two EU regions: Overijssel, the Netherlands and Navarre, Spain. We use probit regression to estimate how behavioral factors, households’ socioeconomic characteristics and structural attributes of dwellings influence energy related actions. Our analysis demonstrates that awareness and personal and social norms are as important as monetary factors. Moreover, education and structural dwelling factors significantly affect households’ actions. These results have implications for governmental policies aimed at reducing residential CO2 footprints and facilitating demand-side solutions in a transition to low-carbon economy.

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...

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.

Otte, M, Sofge, D & Fitch, R 2020, 'Guest editorial: Special issue on robot communication challenges: real-world problems, systems, and methods', Autonomous Robots.
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Pan, C, Bao, Y & Xu, B 2020, 'Seasonal variation of antibiotics in surface water of Pudong New Area of Shanghai, China and the occurrence in typical wastewater sources', CHEMOSPHERE, vol. 239.
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Pathak, N, Phuntsho, S, Van, HT, Johir, MAH, Ghaffour, N, Leiknes, T, Fujioka, T & Shon, HK 2020, 'Simultaneous nitrification-denitrification using baffled osmotic membrane bioreactor-microfiltration hybrid system at different oxic-anoxic conditions for wastewater treatment', JOURNAL OF ENVIRONMENTAL MANAGEMENT, vol. 253.
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Paull, NJ, Krix, D, Irga, PJ & Torpy, FR 2020, 'Airborne particulate matter accumulation on common green wall plants.', International journal of phytoremediation, pp. 1-13.
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In order to better design greening systems for effective particulate matter (PM) removal, it is important to understand the impact leaf traits have on PM deposition. There are however, inconsistences amongst the leaf traits that have previously been correlated with PM accumulation. The aim of this paper was to identify vegetation characteristics of green wall plants that were associated with the accumulation of particulate matter. To determine patterns associated with different leaf morphologies, eleven common ornamental plant species were sampled across 15 sites, over a 6 month duration. PM deposition was determined gravimetrically and its associated size fractions determined microscopically. Linear mixed models were used to identify statistical patterns relating to differences in PM deposition across plant species. PM deposition and the relative frequencies of particle size fractions were found to be statistically different among species, sites and months. Green wall plants were shown to be effective at PM accumulation as all of the assessed plant species had equivalent PM removal efficiency, with minimal evidence of influential leaf characteristics that could enhance PM removal.

Pham, TT, Ngo, HH, Tran, VS & Nguyen, MK 2020, 'Removal of As (V) from the aqueous solution by a modified granular ferric hydroxide adsorbent.', The Science of the total environment, vol. 706, p. 135947.
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A novel adsorbent was prepared in granular form from iron (III) hydroxide and other additives to remove arsenate (As (V)) from aqueous solution. Adsorption of As (V) onto the adsorbent in batch experiments was analyzed to understand the adsorption mechanism, affecting factors, and adsorption isotherms. The optimal working conditions for the developed adsorbent were at pH 3, 30 °C and 50 g/L. The adsorption of arsenate onto the adsorbent occurred rapidly in the first 10 min and reached equilibrium in 2 h. The Langmuir isotherm was found to be best fitted the adsorption. The pre- and post-adsorption adsorbents were characterized by SEM, BET, FTIR, XRD, and Zeta potential techniques. Experimental results clearly demonstrated the potential impact of elemental composition, crystallinity, surface morphology, and other physico-chemical properties of the adsorbent on the adsorption performance variety. The experimental results with the pilot scale treatment system revealed that the adsorbent can be applied successfully and lead to a very efficient drinking water treatment system, at a competitive cost compared to the water market in Hanoi, Vietnam.

Poostchi, H & Piccardi, M 2020, 'BiLSTM-SSVM: Training the BiLSTM with a Structured Hinge Loss for Named-Entity Recognition', IEEE Transactions on Big Data, pp. 1-1.
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Pramanik, BK, Long, DN & Hai, FI 2020, 'Extraction of strategically important elements from brines: Constraints and opportunities', WATER RESEARCH, vol. 168.
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Qu, X, Yu, Y, Zhou, M, Lin, CT & Wang, X 2020, 'Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach', Applied Energy, vol. 257.
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© 2019 Elsevier Ltd It has been well recognized that human driver's limits, heterogeneity, and selfishness substantially compromise the performance of our urban transport systems. In recent years, in order to deal with these deficiencies, our urban transport systems have been transforming with the blossom of key vehicle technology innovations, most notably, connected and automated vehicles. In this paper, we develop a car following model for electric, connected and automated vehicles based on reinforcement learning with the aim to dampen traffic oscillations (stop-and-go traffic waves) caused by human drivers and improve electric energy consumption. Compared to classical modelling approaches, the proposed reinforcement learning based model significantly reduces the modelling constraints and has the capability of self-learning and self-correction. Experiment results demonstrate that the proposed model is able to improve travel efficiency by reducing the negative impact of traffic oscillations, and it can also reduce the average electric energy consumption.

Rajabi, A, Eskandari, M, Ghadi, MJ, Li, L, Zhang, J & Siano, P 2020, 'A comparative study of clustering techniques for electrical load pattern segmentation', Renewable and Sustainable Energy Reviews.
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© 2019 Elsevier Ltd Smart meters have been widely deployed in power networks since the last decade. This trend has resulted in an enormous volume of data being collected from the electricity customers. To gain benefits for various stakeholders in power systems, proper data mining techniques, such as clustering, need to be employed to extract the underlying patterns from energy consumptions. In this paper, a comparative study of different techniques for load pattern clustering is carried out. Different parameters of the methods that affect the clustering results are evaluated and the clustering algorithms are compared for two data sets. In addition, the two suitable and commonly used data size reduction techniques and feature definition/extraction methods for load pattern clustering are analysed. Furthermore, the existing studies on clustering of electricity customers are reviewed and the main results are highlighted. Finally, the future trends and major applications of clustering consumption patterns are outlined to inform industry practitioners and academic researchers to optimize smart meter operational use and effectiveness.

Rasouli, H, Fatahi, B & Nimbalkar, S 2020, 'Liquefaction and Post-liquefaction Assessment of Lightly Cemented Sands', Canadian Geotechnical Journal.
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Razzak, I, Saris, RA, Blumenstein, M & Xu, G 2020, 'Integrating joint feature selection into subspace learning: A formulation of 2DPCA for outliers robust feature selection', Neural Networks, vol. 121, pp. 441-451.
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© 2019 Elsevier Ltd Since the principal component analysis and its variants are sensitive to outliers that affect their performance and applicability in real world, several variants have been proposed to improve the robustness. However, most of the existing methods are still sensitive to outliers and are unable to select useful features. To overcome the issue of sensitivity of PCA against outliers, in this paper, we introduce two-dimensional outliers-robust principal component analysis (ORPCA) by imposing the joint constraints on the objective function. ORPCA relaxes the orthogonal constraints and penalizes the regression coefficient, thus, it selects important features and ignores the same features that exist in other principal components. It is commonly known that square Frobenius norm is sensitive to outliers. To overcome this issue, we have devised an alternative way to derive objective function. Experimental results on four publicly available benchmark datasets show the effectiveness of joint feature selection and provide better performance as compared to state-of-the-art dimensionality-reduction methods.

Ren, LF, Ngo, HH, Bu, C, Ge, C, Ni, SQ, Shao, J & He, Y 2020, 'Novel external extractive membrane bioreactor (EMBR) using electrospun polydimethylsiloxane/polymethyl methacrylate membrane for phenol-laden saline wastewater', Chemical Engineering Journal, vol. 383.
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© 2019 Elsevier B.V. Phenol-laden saline wastewaters can adversely affect water, groundwater, soil, organisms and ecosystems. Given that frequently-used biodegradation process is generally inhibited by salinity, this work aims to solve the problem through a novel configuration of external extractive membrane bioreactor (EMBR) for the objective of simultaneous phenol permeation, salt rejection and biodegradation. Contact angles of 160.9 ± 2.2° (water) and 0.0° (phenol) were observed on the electrospun polydimethylsiloxane/polymethyl methacrylate (PDMS/PMMA) membrane, suggesting this superhydrophobic/superorganophilic membrane was suitable for separating phenol from water-soluble salt. Phenol ranging from 14.1 ± 2.7 to 290.7 ± 10.4 mg/L (stages 1 to 8) was continuously permeated and completely biodegraded in external EMBR under a hydraulic retention time (HRT) of 24 h, which corresponded with detoxification performance improving from 6.3% to 70.5%. After phenol exposure of 8 stages, Proteobacteria and Saccharibacteria became the main phyla for microorganisms. Enumeration of functional genes (phe, amoA, narG, nirS) confirmed that phenol was mainly consumed by denitrifiers and other heterotrophs as the sole carbon and energy source via oxidation and ring cleavage. As bacterial responses, these genes’ proliferation was promoted under low phenol concentrations but inhibited under high phenol concentrations. Meanwhile, results of extracellular polymeric substances revealed that protein was the key substance in toxicity resistance, phenol adsorption and transfer.

Ren, Y, Huu, HN, Guo, W, Wang, D, Peng, L, Ni, B-J, Wei, W & Liu, Y 2020, 'New perspectives on microbial communities and biological nitrogen removal processes in wastewater treatment systems', BIORESOURCE TECHNOLOGY, vol. 297.
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Richards, D, Ranjbartabar, H, Bilgin, A & Kutay, C 2020, 'First Impressions Count! The Role of the Human’s Emotional State on Rapport Established with an Empathic versus Neutral Virtual Therapist', IEEE Transactions on Affective Computing, pp. 1-3.
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Intelligent virtual agents are being endowed with empathic behaviours to perform roles such as virtual therapists. Studies often evaluate the level of rapport established, but do not measure the therapeutic benefit and the relative advantage of empathic versus neutral behaviours. We have created two virtual (empathic/neutral) therapists. Our experiment with 63 participants consisted of one within-subjects (empathic/neutral) and one between-subjects (order) factors. Regardless of the virtual therapist used, improvements in baseline emotion were reported after the first interaction (time one) and further improvement after the second interaction (time two). Our study reveals that if the human initially expresses strong emotional feeling for a problem they are facing, rapport will be higher for the empathic therapist and the level of rapport established at the first meeting will persist regardless of whether the second encounter used empathic or neutral dialogue. Conversely, participants experiencing low emotional feeling reported greater rapport with the neutral therapist, and that level of rapport persisted in the second encounter with the alternative therapist. This study shows that an empathic agent will not necessarily build more rapport or deliver better emotional outcomes than a neutral agent. Further studies are needed to determine when tailoring and complex behaviours are justified.

Saki, M, Abolhasan, M & Lipman, J 2020, 'A Novel Approach for Big Data Classification and Transportation in Rail Networks', IEEE Transactions on Intelligent Transportation Systems, pp. 1-11.
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Samaei, SM, Gato-Trinidad, S & Altaee, A 2020, 'Performance evaluation of reverse osmosis process in the post-treatment of mining wastewaters: Case study of Costerfield mining operations, Victoria, Australia', Journal of Water Process Engineering, vol. 34, pp. 101116-101116.
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Sameen, MI, Pradhan, B & Lee, S 2020, 'Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment', Catena, vol. 186.
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© 2019 Elsevier B.V. This study developed a deep learning based technique for the assessment of landslide susceptibility through a one-dimensional convolutional network (1D-CNN) and Bayesian optimisation in Southern Yangyang Province, South Korea. A total of 219 slide inventories and 17 slide conditioning variables were obtained for modelling. The data showed a complex scenario. Some past slides have spread over steep lands, while others have spread through flat terrain. Random forest (RF) served to keep only important factors for further analysis as a pre-processing measure. To select CNN hyperparameters, Bayesian optimization was used. Three methods contributed to overcoming the overfitting issue owing to small training data in our research. The selection of key factors by RF helped first of all to reduce information dimensionality. Second, the CNN model with 1D convolutions was intended to considerably decrease the number of its parameters. Third, a high rate of drop-out (0.66) helped reduce the CNN parameters. Overall accuracy, area under the receiver operating characteristics curve (AUROC) and 5-fold cross-validation were used to evaluate the models. CNN performance was compared to ANN and SVM. CNN achieved the highest accuracy on testing dataset (83.11%) and AUROC (0.880, 0.893, using testing and 5-fold CV, respectively). Bayesian optimization enhanced CNN accuracy by~3% (compared with default configuration). CNN could outperform ANN and SVM owing to its complicated architecture and handling of spatial correlations through convolution and pooling operations. In complex situations where some variables make a non-linear contribution to the occurrence of landslides, the method suggested could thus help develop landslide susceptibility maps.

Sameen, MI, Sarkar, R, Pradhan, B, Drukpa, D, Alamri, AM & Park, HJ 2020, 'Landslide spatial modelling using unsupervised factor optimisation and regularised greedy forests', Computers and Geosciences, vol. 134.
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© 2019 Elsevier Ltd This study evaluates the contribution of an unsupervised factor optimisation based on sparse autoencoders (SAEs) to spatial landslide modelling with regularised greedy forests (RGFs). A total of 952 landslides were identified by field surveys, equally divided and used for training and testing of the proposed model. Ten conditioning factors related to landslides, including geo-morphometrical (i.e. altitude, slope, aspect, curvature, slope length, topographic wetness index and sediment transport index) and geo-environmental (i.e. lithology, nearness to roads and nearness to streams), were used to investigate the spatial relationships between the variables and landslides. 1The steps of the modelling were twofold. First, the factors were optimised by SAE to reduce information redundancy and correlation in the data. Second, RGF was used to create landslide susceptibility maps with the optimised feature representations. The area under the receiver operating characteristic curve (AUROC) was used to assess the predictive ability of the proposed models. Experimental results show that the proposed SAE–RGF outperforms the RGF and random forest (RF) models in terms of prediction rate and is less sensitive to overfitting and underfitting. The highest prediction rate (AUROC = 0.892) was obtained with only seven features by the SAE–RGF model, which is better than the two other methods (RGF and RF). The unsupervised factor optimisation approach not only reduces computation time but also improves the prediction accuracy of tree-based models, including RGF. The generated landslide susceptibility maps can be implemented to mitigate landslide hazards and to designate land use by stakeholders (e.g. planners and engineers).

Shakor, P, Nejadi, S & Paul, G 2020, 'Investigation into the effect of delays between printed layers on the mechanical strength of inkjet 3DP mortar', Manufacturing Letters, vol. 23, pp. 19-22.
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Shakor, P, Nejadi, S, Paul, G & Sanjayan, J 2020, 'Dimensional accuracy, flowability, wettability, and porosity in inkjet 3DP for gypsum and cement mortar materials', Automation in Construction, vol. 110, pp. 102964-102964.
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Shan, Y, Hu, J, Liu, M, Zhu, J & Guerrero, JM 2020, 'Model Predictive Voltage and Power Control of Islanded PV-Battery Microgrids With Washout-Filter-Based Power Sharing Strategy', IEEE Transactions on Power Electronics, vol. 35, no. 2, pp. 1227-1238.
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Shanmugam, S, Ngo, HH & Wu, YR 2020, 'Advanced CRISPR/Cas-based genome editing tools for microbial biofuels production: A review', Renewable Energy.
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© 2019 Elsevier Ltd With rapid progress in the fields of synthetic biology and metabolic engineering, there are possible applications to generate a wide range of advanced biofuels with maximized yield and productivity to achieve a more sustainable bioprocess with reduced carbon footprints. Among the diverse molecular biology tools, clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins (CRISPR-Cas) technology stands out with potential targeted genome editing, exhibiting a more precise and accurate gene knock-out and knock-in system better than its predecessors, for example zinc finger nucleases (ZFN) and transcription activator-like effector nucleases (TALEN). There are reports involved in the advanced microbial genome engineering tools for the biofuels production; however, there is lack of a comprehensive review about the CRISPR-Cas based-techniques in improved biofuel production along with the strategies to reduce the off-target effect that ensures the success and safety of this method. Therefore, in this review we attempt to systematically comment on the mechanism of CRISPR-Cas and its application to microbial biofuels production. This includes bioethanol, biobutanol as well as other hydrocarbons that sequentially follow various suggestions on enhancing the efficiency of targeting genes. The role of inducible on/off genetic circuits in response to environmental stimuli in the regulation of targeted genome editing (TGE) by minimizing metabolic burden and maximizing fermentation efficiency is also discussed. The relevant stringent regulatory demands to ensure minimal off-target cleavage with maximum efficiency coupled with complete biosafety of this technology are considered. It can be concluded that the recent development of CRISPR-Cas technology should open a new avenue in creating microbial biorefineries for potentially enhanced biofuel production.

Sharifi, S, Abrishami, S & Gandomi, AH 2020, 'Consolidation assessment using Multi Expression Programming', APPLIED SOFT COMPUTING, vol. 86.
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Shen, F, Zhu, H, Zhu, G, Shi, Q & Schulte, J 2020, 'Enhanced negative-index collimation by overlapped second band of square lattice sonic crystals', Chinese Journal of Physics, vol. 63, pp. 201-206.
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© 2019 Acoustic wave collimation and enhancement are systematically investigated in the overlapped second and third bands of a two-dimensional square lattice sonic crystals by analyzing the band structure, equifrequency contour and field distribution. We find the collimation stems from a small negative-index of the second band, and an additional concave entrance can improve the collimation performance in the far field region. But for the third band, negative, zero, positive refraction and wave splitting may occur at the same time due to the simultaneous excitation of multiple Bloch modes, which ultimately destroy the collimation beam of the second band in the near field region. This work provides an insight into the collimation properties of the overlapped bands of sonic crystals with potential application in acoustic directional emission and wave manipulation.

Shi, Z, Sun, X, Lei, G, Yang, Z, Guo, Y & Zhu, J 2020, 'Analysis and Optimization of Radial Force of Permanent Magnet Synchronous Hub Motors', IEEE Transactions on Magnetics, pp. 1-1.
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Shirvani, F, Beydoun, G, Perez, P, Scott, W & Campbell, P 2020, 'An Architecture Framework Approach for Complex Transport Projects', INFORMATION SYSTEMS FRONTIERS.
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Shukla, N, Merigó, JM, Lammers, T & Miranda, L 2020, 'Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017', Computer Methods and Programs in Biomedicine, vol. 183.
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© 2019 Background and Objective: Computer Methods and Programs in Biomedicine (CMPB) is a leading international journal that presents developments about computing methods and their application in biomedical research. The journal published its first issue in 1970. In 2020, the journal celebrates the 50th anniversary. Motivated by this event, this article presents a bibliometric analysis of the publications of the journal during this period (1970–2017). Methods: The objective is to identify the leading trends occurring in the journal by analysing the most cited papers, keywords, authors, institutions and countries. For doing so, the study uses the Web of Science Core Collection database. Additionally, the work presents a graphical mapping of the bibliographic information by using the visualization of similarities (VOS) viewer software. This is done to analyze bibliographic coupling, co-citation and co-occurrence of keywords. Results: CMPB is identified as a leading and core journal for biomedical researchers. The journal is strongly connected to IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging. Paper from Wang, Jacques, Zheng (published in 1995) is its most cited document. The top author in this journal is James Geoffrey Chase and the top contributing institution is Uppsala U (Sweden). Most of the papers in CMPB are from the USA followed by the UK and Italy. China and Taiwan are the only Asian countries to appear in the top 10 publishing in CMPB. A keyword co-occurrences analysis revealed strong co-occurrences for classification, picture archiving and communication system (PACS), heart rate variability, survival analysis and simulation. Keywords analysis for the last decade revealed that machine learning for a variety of healthcare problems (including image processing and analysis) dominated other research fields in CMPB. Conclusions: It can be concluded that CMPB is a world-renowned publication outlet for biomedical researchers ...

Silitonga, AS, Shamsuddin, AH, Mahlia, TMI, Milano, J, Kusumo, F, Siswantoro, J, Dharma, S, Sebayang, AH, Masjuki, HH & Ong, HC 2020, 'Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization', Renewable Energy, vol. 146, pp. 1278-1291.
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© 2019 Elsevier Ltd In this study, microwave irradiation-assisted transesterification was used to produce Ceiba pentandra biodiesel, which accelerates the rate of reaction and temperature within a shorter period. The improvement of biodiesel production requires a reliable model that accurately reflects the effects of input variables on output variables. In this study, an extreme learning machine integrated with cuckoo search algorithm was developed to predict and optimize the process parameters. This model will be useful for virtual experimentations in order to enhance biodiesel research and development. The optimum parameters of the microwave irradiation-assisted transesterification process conditions were obtained as follows: (1) methanol/oil ratio: 60%, (2) potassium hydroxide catalyst concentration: 0.84%(w/w), (3) stirring speed: 800 rpm, and (4) reaction time: 388 s. The corresponding Ceiba pentandra biodiesel yield was 96.19%. Three independent experiments were conducted using the optimum process parameters and the average biodiesel yield was found to be 95.42%. In conclusion, microwave irradiation-assisted transesterification is an effective method for biodiesel production because it is more energy-efficient, which will reduce the overall cost of biodiesel production.

Song, B, Wang, X, Ni, W, Song, Y, Liu, RP, Jiang, G-P & Guo, YJ 2020, 'Reliability Analysis of Large-Scale Adaptive Weighted Networks', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 15, pp. 651-665.
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Sornalingam, K, McDonagh, A, Canning, J, Cook, K, Johir, MAH, Zhou, JL & Ahmed, MB 2020, 'Photocatalysis of 17α-ethynylestradiol and estriol in water using engineered immersible optical fibres and light emitting diodes', Journal of Water Process Engineering, vol. 33.
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© 2019 Elsevier Ltd This research aims to promote photocatalysis of endocrine disrupting chemicals (EDCs) in water. Two reactor setups with (i) modified air-clad optical fibres and (ii) waterproof LED strips were utilised to transmit light to photocatalysts P25 TiO2 and gold-modified TiO2 (Au-TiO2). The performances to photodegrade 17α-ethynylestradiol (EE2) and estriol (E3) under Cool White and UVA high efficacy LEDs were examined. Au-TiO2 showed superior photocatalytic activity for EE2 removal over P25 TiO2. The pseudo first-order rate constants for EE2 photocatalysis under UVA were 0.55 h−1 and 0.89 h−1 for TiO2 and Au-TiO2, respectively. E3 was effectively degraded by Au-TiO2 in the immersible LED strip reactor (0.13 h−1).

Sun, H-H, Ding, C, Zhu, H & Guo, YJ 2020, 'Dual-Polarized Multi-Resonance Antennas With Broad Bandwidths and Compact Sizes for Base Station Applications', IEEE Open Journal of Antennas and Propagation, vol. 1, pp. 11-19.
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Sun, X, Diao, K, Lei, G, Guo, Y & Zhu, J 2020, 'Real-Time HIL Emulation for a Segmented-Rotor Switched Reluctance Motor Using a New Magnetic Equivalent Circuit', IEEE Transactions on Power Electronics, vol. 35, no. 4, pp. 3841-3849.
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Sun, X, Hu, C, Lei, G, Guo, Y & Zhu, J 2020, 'State feedback control for a PM hub motor based on gray Wolf optimization algorithm', IEEE Transactions on Power Electronics, vol. 35, no. 1, pp. 1136-1146.
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© 1986-2012 IEEE. This paper presents an optimal control strategy for a permanent-magnet synchronous hub motor (PMSHM) drive using the state feedback control method plus the gray wolf optimization (GWO) algorithm. First, the linearized PMSHM mathematical model is obtained by voltage feedforward compensation. Second, to acquire satisfactory dynamics of speed response and zero d-axis current, the discretized state-space model of the PMSHM is augmented with the integral of rotor speed error and integral of d-axis current error. Then, the GWO algorithm is employed to acquire the weighting matrices Q and R in linear quadratic regulator optimization process. Moreover, a penalty term is introduced to the fitness index to suppress overshoots effectively. Finally, comparisons among the GWO-based state feedback controller (SFC) with and without the penalty term, the conventional SFC, and the genetic algorithm enhanced proportional-integral controllers are conducted in both simulations and experiments. The comparison results show the superiority of the proposed SFC with the penalty term in fast response.

Surawski, NC, Macdonald, LM, Baldock, JA, Sullivan, AL, Roxburgh, SH & Polglase, PJ 2020, 'Exploring how fire spread mode shapes the composition of pyrogenic carbon from burning forest litter fuels in a combustion wind tunnel', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 698.
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Taccola, G, Gad, P, Culaclii, S, Ichiyama, RM, Liu, W & Edgerton, VR 2020, 'Using EMG to deliver lumbar dynamic electrical stimulation to facilitate cortico-spinal excitability', Brain Stimulation, vol. 13, no. 1, pp. 20-34.
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© 2019 Elsevier Inc. Background: Potentiation of synaptic activity in spinal networks is reflected in the magnitude of modulation of motor responses evoked by spinal and cortical input. After spinal cord injury, motor evoked responses can be facilitated by pairing cortical and peripheral nerve stimuli. Objective: To facilitate synaptic potentiation of cortico-spinal input with epidural electrical stimulation, we designed a novel neuromodulation method called dynamic stimulation (DS), using patterns derived from hind limb EMG signal during stepping. Methods: DS was applied dorsally to the lumbar enlargement through a high-density epidural array composed of independent platinum-based micro-electrodes. Results: In fully anesthetized intact adult rats, at the interface array/spinal cord, the temporal and spatial features of DS neuromodulation affected the entire lumbosacral network, particularly the most rostral and caudal segments covered by the array. DS induced a transient (at least 1 min) increase in spinal cord excitability and, compared to tonic stimulation, generated a more robust potentiation of the motor output evoked by single pulses applied to the spinal cord. When sub-threshold pulses were selectively applied to a cortical motor area, EMG responses from the contralateral leg were facilitated by the delivery of DS to the lumbosacral cord. Finally, based on motor-evoked responses, DS was linked to a greater amplitude of motor output shortly after a calibrated spinal cord contusion. Conclusion: Compared to traditional tonic waveforms, DS amplifies both spinal and cortico-spinal input aimed at spinal networks, thus significantly increasing the potential and accelerating the rate of functional recovery after a severe spinal lesion.

Telikani, A, Gandomi, AH, Shahbahrami, A & Naderi Dehkordi, M 2020, 'Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony', Expert Systems with Applications, vol. 144, pp. 113097-113097.
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Teng, J, Kou, J, Yan, X, Zhang, S & Sheng, D 2020, 'Parameterization of soil freezing characteristic curve for unsaturated soils', Cold Regions Science and Technology, vol. 170.
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© 2019 The soil freezing characteristic curve (SFCC) describes the relationship between the temperature and unfrozen water content in a soil. The SFCC is indispensable in modelling the hydro-mechanical behaviour of frozen soils, but is less understood for the unsaturated soils. A series of SFCC tests of unsaturated silica sand, silt and red clay are preformed based on a newly developed nuclear magnetic resonance (NMR) apparatus, which can precisely control the sample temperature in the magnetic field. The experimental results show that the measured SFCC varies significantly for different initial water contents, and that a lower initial water content leads to a slower increase in unfrozen water content, proving that the SFCC is closely related to the initial unsaturated state. It is found that the thawing curve is better to represent the SFCC, in contrast the freezing curve is significantly affected by the supercooling phenomenon. A new parameterization of the SFCC is presented for unsaturated soils by combining the Clapeyron equation and the model for Soil Water Characteristic Curve (SWCC). A number of test results from the literature and this study are used to validate the new SFCC model. By inputting the parameters for the SWCC and initial state into the proposed model, the predicted SFCC can agree well with the measured results. The new model has a theoretical basis and simple form and is applicable to both saturated and unsaturated soils.

Thanh, HT, Li, J & Zhang, YX 2020, 'Numerical simulation of self-consolidating engineered cementitious composite flow with the V-funnel and U-box', Construction and Building Materials, vol. 236.
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© 2019 Elsevier Ltd A 3D numerical model is developed in this paper to simulate the flow of self-consolidating engineered cementitious composites (SC-ECC) through the V-funnel test and U-box test. The synthetic fibres utilized in SC-ECC are considered as flexible fibres and modelled as separated particles, virtually connected by a drag force between two adjacent fibre particles. The fibre particles and mortar particles possess similar continuum properties in the domain of SC-ECC. The constitutive equations of the flow of SC-ECC are solved using the mesh-free, weakly compressible smoothed particle hydrodynamics (WCSPH). The V-funnel model focuses on the flow rate characteristic and the correlation of the dispersion of synthetic fibres with the plastic viscosity of fresh mixtures. The U-box simulation is to confirm the self-consolidating capability and the passing ability of SC-ECC flow through reinforcing bars. The simulation results of the discharge time in the V-funnel model and the self-levelling index in the U-box model agree well with experimental results from the literature, which provide convincing evidence for the correctness and effectiveness of the developed model. The proposed model thus is capable of providing insight into the flow behaviour of SC-ECC in terms of filling, passing abilities and the dispersion/orientation of flexible synthetic fibres.

Thi, HN, Hai, NT, Hai, AV, Minh, VT, Tien, VN, Loganathan, P, Vigneswaran, S, Tuan, MN, Van, TT, Duc, LV & Thi, HHN 2020, 'Laterite as a low-cost adsorbent in a sustainable decentralized filtration system to remove arsenic from groundwater in Vietnam', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 699.
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Thu, TN, Huu, HN, Guo, W & Wang, XC 2020, 'A new model framework for sponge city implementation: Emerging challenges and future developments', JOURNAL OF ENVIRONMENTAL MANAGEMENT, vol. 253.
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Thuy, C-V, Suraski, N, Ristovski, Z, Yuan, C-S, Stevanovic, S, Rahman, SMA, Hossain, FM, Guo, Y, Rainey, T & Brown, RJ 2020, 'The effect of diesel fuel sulphur and vanadium on engine performance and emissions', FUEL, vol. 261.
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Tian, Y, Yang, H, Mo, W, Zhou, S, Zhang, N & Walker, PD 2020, 'Optimal coordinating gearshift control of a two-speed transmission for battery electric vehicles', Mechanical Systems and Signal Processing, vol. 136.
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© 2019 Elsevier Ltd With the sustained technology advancement in the power battery, battery electric vehicles (BEVs) are considered as the most advisable alternatives of their internal combustion engine (ICE) counterparts. Research reveals that the implementation of multispeed transmissions for BEVs can reduce the vehicle energy consumption, improve the vehicle dynamic performance and downsize the battery package and the driving motor volume. Considering the potential advantages of multispeed transmissions equipped BEVs, this paper presents an investigation into the optimal coordinating gearshift control of a seamless two-speed transmission for BEVs. Firstly, to achieve the gearshift transient behaviour, the mathematical model of the powertrain system is established. Next, model-based gearshift tactics are proposed for the torque phase and the inertia phase respectively. Subsequently, the proposed control tactics based optimal controllers are developed to achieve high-quality gearshift in the whole gear shifting process. To be more specific, in the torque phase, the controller aims at optimising dual objectives together, namely the vehicle jerk and friction work, and the control objective of the inertia phase only focuses on decreasing the friction work due to the coordinating gear shifting tactic. Finally, based on available sensor signals, sliding mode observers are employed to estimate unmeasurable torque as feedback information for the optimal controllers in different gear states. Simulation results demonstrate that the devised control approaches can promote gear change performance comprehensively.

Torghabeh, AK, Pradhan, B & Jahandari, A 2020, 'Assessment of geochemical and sedimentological characteristics of atmospheric dust in Shiraz, southwest Iran', Geoscience Frontiers.
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© 2019 China University of Geosciences (Beijing) and Peking University Geogenic dust is commonly believed to be one of the most important environmental problems in the Middle East. The present study investigated the geochemical characteristics of atmospheric dust particles in Shiraz City (south of Iran). Atmospheric dust samples were collected through a dry collector method by using glass trays at 10 location sites in May 2018. Elemental composition was analysed through inductively coupled plasma optical emission spectrometry. Meteorological data showed that the dustiest days were usually in spring and summer, particularly in April. X-ray diffraction analysis of atmospheric dust samples indicated that the mineralogical composition of atmospheric dust was calcite ​+ ​dolomite (24%)>palygorskite (18%)>quartz (14%)>muscovite (13%)>albite (11%)>kaolinite (7%)>gypsum (7%)>zircon ​= ​anatase (3%). The high occurrence of palygorskite (16%–23%) could serve as a tracer of the source areas of dust storms from the desert of Iraq and Saudi Arabia to the South of Iran. Scanning electron microscopy indicated that the sizes of the collected dust varied from 50 ​μm to 0.8 ​μm, but 10 ​μm was the predominant size. The atmospheric dust collected had prismatic trigonal–rhombohedral crystals and semi-rounded irregular shapes. Moreover, diatoms were detected in several samples, suggesting that emissions from dry-bed lakes, such as Hoor Al-Azim Wetland (located in the southwest of Iran), also contributed to the dust load. Backward trajectory simulations were performed at the date of sampling by using the NOAA HYSPLIT model. Results showed that the sources of atmospheric dust in the studied area were the eastern area of Iraq, eastern desert of Saudi Arabia, Kuwait and Khuzestan Province. The Ca/Al ratio of the collected samples (1.14) was different from the upper continental crust (UCC) value (UCC ​= ​0.37), whereas Mg/Al (0.29), K/Al (0.22) and Ti/Al (0.07) ratios were close to the UC...

Tran, HD, Manh, DP & Quang, PH 2020, 'Summit Navigator: A Novel Approach for Local Maxima Extraction', IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 29, pp. 551-564.
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Usman, M, Jan, MA, He, X & Chen, J 2020, 'A Mobile Multimedia Data Collection Scheme for Secured Wireless Multimedia Sensor Networks', IEEE Transactions on Network Science and Engineering.
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IEEE Wireless Multimedia Sensor Networks (WMSNs) produce an enormous amount of big multimedia data. Due to large size, Multimedia Sensor Nodes (MSNs) cannot store generated multimedia data for a long time. In this scenario, mobile sinks can be utilized for data collection. However, due to vulnerable nature of wireless networks, there is a need for an efficient security scheme to authenticate both MSNs and mobile sinks. In this paper, we propose a scheme to protect an underlying WMSN during mobile multimedia data collection. The proposed scheme is a two-layer scheme. At the first layer, all MSNs are distributed into small clusters, where each cluster is represented by a single Cluster Head (CH). At the second layer, all CHs verify identities of mobile sinks before sharing multimedia data. Authentication at both layers ensures a secure data exchange. We evaluate the performance of proposed scheme through extensive simulation results. The simulation results prove that the proposed scheme performs better as compared to existing state-of-the-art approaches in terms of resilience and handshake duration. The proposed scheme is also analyzed in terms of authentication rate, data freshness, and packet delivery ratio, and has shown a better performance.

Vakhshouri, B, Nejadi, S & Erkmen, E 2020, 'Advances in numerical analysis of creep effect in time-dependent deflection of light-weight concrete slabs', Mechanics of Advanced Materials and Structures.
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© 2018, © 2018 Taylor & Francis Group, LLC. Using a wide range of creep models, the experimental results of long-term deflection of lightweight concrete slabs subjected to two levels of early-age loading are investigated. Different creep models give considerably different estimation of the experimental deflection of slabs. The included factors in each creep model to simulate the experimental creep behavior of the concrete, and loading level on the slabs are the main causes of different results. Among the investigated models, the BP1 and FIBMC-2010 models including the aggregate type and concrete density is shown to be in good agreement with the experimental data in both loading levels.

Van, HT, Lim, S, Park, MJ, Han, DS, Phuntsho, S, Park, H, Matsuyama, H & Shon, HK 2020, 'Fouling and performance of outer selective hollow fiber membrane in osmotic membrane bioreactor: Cross flow and air scouring effects', BIORESOURCE TECHNOLOGY, vol. 295.
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Vu, HP, Nguyen, LN, Lesage, G & Nghiem, LD 2020, 'Synergistic effect of dual flocculation between inorganic salts and chitosan on harvesting microalgae Chlorella vulgaris', Environmental Technology & Innovation, vol. 17, pp. 100622-100622.
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Wan, G, Du, B, Pan, S & Wu, J 2020, 'Adaptive knowledge subgraph ensemble for robust and trustworthy knowledge graph completion', World Wide Web.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Knowledge graph (KG) embedding approaches are widely used to infer underlying missing facts based on intrinsic structure information. However, the presence of noisy facts in automatically extracted or crowdsourcing KGs significantly reduces the reliability of various embedding learners. In this paper, we thoroughly study the underlying reasons for the performance drop in dealing with noisy knowledge graphs, and we propose an ensemble framework, Adaptive Knowledge Subgraph Ensemble (AKSE), to enhance the robustness and trust of knowledge graph completion. By employing an effective knowledge subgraph extraction approach to re-sample the sub-components from the original knowledge graph, AKSE generates different representations for learning diversified base learners (e.g., TransE and DistMult), which substantially alleviates the noise effect of KG embedding. All embedding learners are integrated into a unified framework to reduce generalization errors via our simple or adaptive weighting schemes, where the weight is allocated based on each individual learner’s prediction capacity. Experimental results show that the robustness of our ensemble framework outperforms exiting knowledge graph embedding approaches on manually injected noise as well as inherent noisy extracted KGs.

Wang, J, Niu, T, Lu, HY, Yang, W & Du, P 2020, 'A Novel Framework of Reservoir Computing for Deterministic and Probabilistic Wind Power Forecasting', IEEE Transactions on Sustainable Energy, vol. 11, no. 1, pp. 337-349.
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Wang, P, Lin, Z & Qiu, X 2020, 'Influence of interaural cross-correlation coefficient and loudness level on auditory source width at different frequency', Applied Acoustics, vol. 162, pp. 107198-107198.
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Wang, S & Cao, L 2020, 'Inferring Implicit Rules by Learning Explicit and Hidden Item Dependency', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE Revealing complex relations between entities (e.g., items within or between transactions) is of great significance for business optimization, prediction, and decision making. Such relations include not only co-occurrence-based explicit relations but also nonco-occurrence-based implicit ones. Explicit relations have been substantially studied by rule mining-based approaches, including association rule mining and causal rule discovery. In contrast, implicit relations have received much less attention but could be more actionable. In this paper, we focus on the implicit relations between items which rarely or never co-occur while each of them co-occurs with other identical items (link items) with a high probability. A framework integrates both explicit and hidden item dependencies and a corresponding efficient algorithm IRRMiner captures such implicit relations with implicit rule inference. Experimental results show that IRRMiner not only infers implicit rules of various sizes consisting of both frequent and infrequent items effectively, it also runs at least four times faster than IARMiner, a typical indirect association rule mining algorithm which can only mine size-2 indirect association rules between frequent items. IRRMiner is applied to make recommendations and shows that the identified implicit rules can increase recommendation reliability.

Wang, X, Yang, Y, Liu, H, Ren, J, Xu, S, Wang, S & Yu, S 2020, 'Efficient measurement of round-trip link delays in software-defined networks', Journal of Network and Computer Applications, vol. 150.
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© 2019 Elsevier Ltd Round-trip link delay is an important indicator for network performance optimization and troubleshooting. The Software-Defined Networking (SDN) paradigm, which provides flexible and centralized control capability, paves the way for efficient round-trip link delay measurement. In this paper, we study the round-trip link delay measurement problem in SDN networks. We propose an efficient measurement scheme, which infers round-trip link delays from end-to-end delay of some measurement paths implemented with few flow rules in each SDN switch. Furthermore, to reduce measurement cost and meet measurement constraint, we address the Monitor Placement and Link Assignment (MPLA) problem involved in the measurement scheme. Specifically, we formulate the MPLA problem as a Mixed Integer Linear Programming (MILP) problem, prove that it is NP-hard, and propose an efficient algorithm called MPLA Algorithm based on Biding Strategy (MPLAA-BS) to solve the problem. The extensive simulation results on real network topologies reveal that the proposed scheme can efficiently and accurately measure round-trip link delays in SDN networks, and the MPLAA-BS can find feasible and resource-efficient solutions for the MPLA problem.

Wang, Y, Sayyadi, N, Zheng, X, Woods, TA, Leif, RC, Shi, B, Graves, SW, Piper, JA & Lu, Y 2020, 'Time-resolved microfluidic flow cytometer for decoding luminescence lifetimes in the microsecond region.', Lab on a chip.
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Time-resolved luminescence detection using long-lived probes with lifetimes in the microsecond region have shown great potential in ultrasensitive and multiplexed bioanalysis. In flow cytometry, however, the long lifetime poses a significant challenge to measure wherein the detection window is often too short to determine the decay characteristics. Here we report a time-resolved microfluidic flow cytometer (tr-mFCM) incorporating an acoustic-focusing chip, which allows slowing down of the flow while providing the same detection conditions for every target, achieving accurate lifetime measurement free of autofluorescence interference. Through configuration of the flow velocity and detection aperture with respect to the time-gating sequence, a multi-cycle luminescence decay profile is captured for every event under maximum excitation and detection efficiency. A custom fitting algorithm is then developed to resolve europium-stained polymer microspheres as well as leukemia cells against abundant fluorescent particles, achieving counting efficiency approaching 100% and lifetime CVs (coefficient of variation) around 2-6%. We further demonstrate lifetime-multiplexed detection of prostate and bladder cancer cells stained with different europium probes. Our acoustic-focusing tr-mFCM offers a practical technique for rapid screening of biofluidic samples containing multiple cell types, especially in resource-limited environments such as regional and/or underdeveloped areas as well as for point-of-care applications.

Wang, Y, Wang, D, Yi, N, Li, Y, Ni, BJ, Wang, Q, Wang, H & Li, X 2020, 'Insights into the toxicity of troclocarban to anaerobic digestion: Sludge characteristics and methane production', Journal of Hazardous Materials, vol. 385.
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© 2019 Elsevier B.V. Triclocarban (TCC), as the most typical antibacterial agent, is widely discovered in many ecological environment, especially in sludge. However, so far, no studies have reported the effect of TCC exposure on the properties of excess sludge. Therefore, in this study, TCC's toxicities to waste activated sludge (WAS) were analyzed by investigating the variation of physicochemical properties of sludge. It was found that TCC exposure has no effect on sludge pH, while it facilitated organic substances release from sludge, e.g. dissolved organic matter (DOM), protein and polysaccharide, which caused an increase of sludge reduction and changed the structure of functional groups and surface morphology of sludge. Moreover, we explored the effect of TCC on anaerobic digestion of WAS and found methane production was seriously inhibited by TCC. The related mechanism tests had illustrated that TCC exposure did not affect the hydrolysis process, but promoted the acidification and acetogenesis, and importantly inhibited the methanogenesis process. Methanogenic community was further evaluated and observed that the presence of TCC could vary the microbial community of methanogens with the abundance of aceticlastic methanogens increasing and hydrogenotrophic methanogens decreasing. These findings reached in this study would widen the understanding scope for TCC's toxicity to WAS.

Wei, W, Wu, L, Liu, X, Chen, Z, Hao, Q, Wang, D, Liu, Y, Peng, L & Ni, B-J 2020, 'How does synthetic musks affect methane production from the anaerobic digestion of waste activated sludge?', Science of The Total Environment, pp. 136594-136594.
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Wu, C, Fang, J, Zhang, Z, Entezari, A, Sun, G, Swain, MV & Li, Q 2020, 'Fracture modeling of brittle biomaterials by the phase-field method', Engineering Fracture Mechanics, vol. 224.
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© 2019 Elsevier Ltd Biomaterials have been extensively used in prosthetic applications for their proven biocompatibility and osseointegration characteristics. Nevertheless, one of the critical issues of some synthetic biomaterials is brittleness prone to experience fracture failure due to low tensile strength and low fracture toughness. This study aims to employ a recently-developed phase-field model to simulate the crack propagation in brittle biomaterials. Unlike discrete fracture modeling methods, the phase-field approach allows simulating crack path in a continuous manner, thereby avoiding remeshing that may not be trivial for complicated fracture surfaces and facilitate iterative procedure commonly required for structural optimization. The phase-field model is formulated to treat the fracture path as a localized region of diffusive damage that can be described in terms of a phase-field function, in which the discreteness in cracked materials is assumed to be smeared. In this study, three representative case studies from the biomedical context, namely a zirconia-based dental bridge (or namely fixed partial denture (FPD)), a ceramic tissue scaffold and an analog saw-bone femur, are employed as illustrative examples. The phase-field modeling results are compared with the in-house experimental tests, demonstrating the effectiveness of the phase-field technique for predicting brittle fracture failure in several typical biomedical case scenarios. The phase-field model provides a useful tool for the computational fracture analysis and design optimization of other brittle biomaterials.

Wu, C, Zheng, K, Fang, J, Steven, GP & Li, Q 2020, 'Time-dependent topology optimization of bone plates considering bone remodeling', Computer Methods in Applied Mechanics and Engineering, vol. 359.
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© 2019 Elsevier B.V. Bone plates have been widely used for the treatment of bone defects and trauma. These fixation plates can stabilize or replace bone tissue to restore appropriate load-bearing functionality. Nevertheless, the use of bone plates may lead to the stress shielding, thereby weakening prosthetic bone substitutes (e.g. bone graft or scaffolds) due to significant change in the biomechanical environment after implantation. To address this issue, we propose a time-dependent topology optimization procedure for the design of bone plates by taking into account bone remodeling. A solid isotropic material penalization (SIMP) model is used to interpolate design variables. The objective is to maximize total bone density within a reconstruction area at the final stage of bone remodeling, subject to a volume constraint of the bone plate and maximum allowable compliance of the prosthetic system. The sensitivity of bone density at the final stage is derived with respect to the topological variables of the plate in a step-wise manner. To facilitate sensitivity analysis, a bone remodeling rule is formulated in two different ways to accommodate a C1continuity. A jaw reconstruction problem is exemplified in this study to demonstrate the effectiveness of the proposed approach. Through this specific case, the non-differentiability issue due to the lazy zone of a remodeling rule is smoothed; and the proposed approach is also compared with that of a time-independent design. The effects of volume fraction and compliance constraints are also investigated to gain further insights into the design of prosthetic substitutes. Together with additive manufacturing technology, the proposed time-dependent topology optimization procedure is expected to form a useful tool for the design of implantable devices ensuring favorable long-term treatment outcomes.

Wu, K, Ni, W, Zhang, JA, Liu, RP & Guo, YJ 2020, 'Refinement of Optimal Interpolation Factor for DFT Interpolated Frequency Estimator', IEEE Communications Letters, pp. 1-1.
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Wu, SL, Sun, J, Chen, X, Wei, W, Song, L, Dai, X & Ni, BJ 2020, 'Unveiling the mechanisms of medium-chain fatty acid production from waste activated sludge alkaline fermentation liquor through physiological, thermodynamic and metagenomic investigations', Water Research, vol. 169.
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© 2019 Elsevier Ltd Effective sludge treatment with bioenergy production is attracting increasing interests as large quantities of waste activated sludge (WAS) are produced during the wastewater treatment. In this study, a new biotechnical process for converting the WAS alkaline fermentation liquor (WASAFL) into valuable, easy-separated medium chain fatty acids (MCFAs) through chain elongation (CE) was investigated, which may provide a new insight into sludge treatment. In the process, ethanol was served as the electron donor (EDs) and WASAFL were main electron acceptors (EAs). The MCFAs productions were investigated under three different ED to EA ratios (i.e., 1:2, 1:1 and 2:1). The result showed that MCFAs production was increased from 2.88 ± 0.01 to 5.28 ± 0.18 g COD/L with the increase of ED to EA ratio. However, the highest MCFA selectivity was achieved at 72.9% when the ED to EA ratio was 1:1. The decrease in the selectivity at high ED:EA ratio is mainly due to the production of higher alcohol (i.e., n-butanol and n-hexanol). The thermodynamic analysis confirmed all CE processes for MCFAs production from WASAFL were exothermic reactions, with the spontaneity and energy release of the reactions increased with the ethanol level. The microbial community analysis showed that the relative abundances of Clostridium, Oscillibacter, Leptolinea and Exilispira were positively correlated with the MCFAs production. The metagenomic analysis suggested that both the reverse β-oxidization pathway and fatty acid biosynthesis pathway contributed to the CE process in the studied system. The functional enzymes were mainly associated within Clostridium, with Clostridium Kluyveri, Clostridium botulinum and Clostridium magnum being likely the key species responsible for the CE process.

Xiao, T, Qiu, X & Halkon, B 2020, 'Ultra-broadband active noise cancellation at the ears via optical microphones', Scientific Reports.
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High frequency noise has generally been difficult to be cancelled actively at a person's ears, particularly for active headrest systems aiming to free the listener from noise cancellation headphones. One of the main challenges is to measure the noise precisely at the ears. Here we demonstrate a new error sensing methodology with an optical microphone arrangement for active noise cancellation (ANC). It can measure the noise accurately for ANC without any obstructions at the listener's ears. The demonstrated system, or virtual ANC headphone as we call it, is shown to provide more than 10 dB attenuation for ultra-broadband noise - up to 6000 Hz - inside the ears in a complex sound field. The bandwidth of the controllable noise significantly exceeds the results from the state-of-the-art system, which is below 1000 Hz. The proposed method leads to the next generation of personal hearing protection system and can open up a whole new area of sound control research.

Xu, B, Ahmed, MB, Zhou, JL & Altaee, A 2020, 'Visible and UV photocatalysis of aqueous perfluorooctanoic acid by TiO2 and peroxymonosulfate: Process kinetics and mechanistic insights', Chemosphere, vol. 243.
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© 2019 Elsevier Ltd The global occurrence and adverse environmental impacts of perfluorooctanoic acid (PFOA) have attracted wide attention. This study focused on the PFOA photodegradation by using photocatalyst TiO2 with peroxymonosulfate (PMS) activation. Aqueous PFOA (50 mg L−1) at the pH 3 was treated by TiO2/PMS under 300 W visible light (400–770 nm) or 32 W UV light (254 nm and 185 nm). The addition of PMS induced a significant degradation of PFOA under powerful visible light compared with sole TiO2. Under visible light, 0.25 g L−1 TiO2 and 0.75 g L−1 PMS in the solution with the initial pH 3 provided optimum condition which achieved 100% PFOA removal within 8 h. Under UV light irradiation at 254 nm and 185 nm wavelength, TiO2/PMS presented excellent performance of almost 100% removal of PFOA within 1.5 h, attributed to the high UV absorbance by the photocatalyst. The intermediates analysis showed that PFOA was degraded from a long carbon chain PFOA to shorter chain intermediates in a stepwise manner. Furthermore, scavenger experiments indicated that SO4•–radicals from PMS and photogenerated holes from TiO2 played an essential role in degrading PFOA. The presence of organic compounds in real wastewater reduced the degradation efficacy of PFOA by 18–35% in visible/TiO2/PMS system. In general, TiO2/PMS could be an ideal and effective photocatalysis system for the degradation of PFOA from wastewater using either visible or UV light source.

Xu, B, Zhou, JL, Altaee, A, Ahmed, MB, Johir, MAH, Ren, J & Li, X 2020, 'Improved photocatalysis of perfluorooctanoic acid in water and wastewater by Ga2O3/UV system assisted by peroxymonosulfate', CHEMOSPHERE, vol. 239.
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Yan, X, Sun, J, Kenjiahan, A, Dai, X, Ni, BJ & Yuan, Z 2020, 'Rapid and strong biocidal effect of ferrate on sulfidogenic and methanogenic sewer biofilms', Water Research, vol. 169.
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© 2019 Elsevier Ltd For the control of sulfide and methane in sewers, it is favorable to reduce their production rather than to remove them after generation. In this study, we revealed rapid and strong biocidal effect of ferrate (Fe(VI)) on sulfidogenic and methanogenic sewer biofilms, leading to control of sulfide and methane production in sewer. The inactivation of the microorganisms in sewer biofilms by Fe(VI) could be accomplished within 15 min for a single dosing event and the biocidal effect could be enhanced by applying pulse dosing strategy. The microbiological analysis showed that the key functional genes involved in sulfide and methane production, i.e. dsrA and mcrA, in the viable cells after Fe(VI) dosing were decreased substantially by 84.2% and 86.6%, respectively. Significant drops were also observed in the relative abundances of viable sulfide reducing bacteria (SRB) and methanogenic archaea (MA). The direct dosing of Fe(VI) into a sewer reactor led to instant and complete suppression of sulfidogenic and methanogenic activities, and the recovery of the activities resembled the regrowth of residual SRB and MA. The results of this study suggested the feasibility for developing an efficient and cost-effective sulfide and methane control strategy using Fe(VI).

Yang, G, Zhang, N, Yang, J, Fu, Q, Wang, Y, Wang, D, Tang, L, Xia, J, Liu, X, Li, X, Yang, Q, Liu, Y, Wang, Q & Ni, BJ 2020, 'Interaction between perfluorooctanoic acid and aerobic granular sludge', Water Research, vol. 169.
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© 2019 Elsevier Ltd The increasing use of perfluorooctanoic acid (PFOA) raises concerns about its potential toxicity to the environment. However, the interaction between PFOA and aerobic granular sludge has never been documented. This work therefore aims to provide such support through investigating the fate of PFOA at environmentally relevant levels in aerobic granular sludge systems and its impact on aerobic granular sludge. Experimental results showed that 32.0%∼36.4% of wastewater PFOA was removed by aerobic granular sludge in stable operation when PFOA concentration was ranged from 0.1 to 1.0 mg/L. Mass balance analyses and X-ray photoelectron spectroscopy survey scan revealed that the removal of PFOA was dominated by adsorption rather than biodegradation, and sorption kinetic analysis indicated that inhomogeneous multilayer adsorption was responsible for this removal. The adsorbed PFOA deteriorated the settleability of granular sludge and biological nitrogen and phosphorus removal significantly. Experimental results showed that 1.0 mg/L PFOA inhibited anaerobic phosphate release, aerobic phosphate uptake, nitrate reduction, and nitrite reduction processes by 60%, 50%, 13.1%, and 5.8%, respectively. It was observed that PFOA induced large amounts of filamentous villus growing on the surface and increased the extracellular polymeric substances of granular sludge. Fourier-transform infrared spectra and X-ray photoelectron spectroscopy spectrum showed that several function groups in extracellular polymeric substances such as hydroxyl groups, amides and polysaccharides were affected by PFOA. It was also found that PFOA inhibited the cyclic transformations of polyhydroxyalkanoates and glycogen. Microbial community analyses showed that PFOA decreased the abundances of Nitrosomonas, Nitrospira, Accumulibacter, and other function microbes such as Rhodospirillaceae, Thauera, and Azoarcus.

Yang, J, Liu, X, Liu, X, Xu, Q, Wang, W, Wang, D, Yang, G, Fu, Q, Kang, Z, Yang, Q, Liu, Y, Wang, Q & Ni, BJ 2020, 'Enhanced dark fermentative hydrogen production from waste activated sludge by combining potassium ferrate with alkaline pretreatment', Science of the Total Environment, vol. 707.
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© 2019 Alkaline pretreatment was demonstrated to be effective in the enhancement of hydrogen production. However, the sludge solubilization rate of alkaline pretreatment is still limited. This study reports a new strategy of K2FeO4 + pH 9.5 for sludge mesophilic anaerobic fermentation. Experimental results showed that the combination of K2FeO4/pH 9.5 pretreatment had a greater hydrogen yield than the individual K2FeO4 and pH 9.5. The maximum hydrogen yield was 19.2 mL per gram volatile suspended solids (VSS) under the optimal condition (0.02 g per gram total suspended solids K2FeO4 + pH 9.5). Kinetic analysis showed that the highest hydrogen production potential of 19.9 mL/g VSS was obtained in the combined reactor, which well fitted the first-order kinetic model (R2 = 0.9925). Besides, the fermentation type was mainly acetic and butyric in the combined reactor, which contributed to hydrogen production. Further analyses showed that the combined pretreatment reduced hydrogen sulfide yield, providing an environmentally friendly method to sludge treatment.

Yang, M & Sharma, D 2020, 'The Spatiality and Temporality of Electricity Reform: A Comparative and Critical Institutional Perspective', Energy Research and Social Science, vol. 60.
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© 2019 Elsevier Ltd The main objective of this paper is to develop an institutional perspective on the reasons for rising disparity between expected and actual outcomes of electricity reform. This objective is premised on our contention about the narrowness of the existing, industry-centric, approach to understanding the reasons for disparity, and the superiority of institutional perspective for facilitating such an understanding. The analytical framework employed in this paper is informed by core tenets of institutional economics. The reform experience of twelve countries1, over three distinctive time phases (namely, 1880s to 1930s, 1940s to 1970s, and 1980s to the present) provides the contextual backdrop for this paper. The results of the analysis in this paper suggest that the contours of electricity reform have historically been shaped by the underlying institutions for governing socio-economic development. Further, the underlying institutions of a time predominantly reflect the dominant, ‘politically-powerful’, socio-economic interests of the time. In such environments, issues of specific models of reform, processes of reform, and industry performance assume a dormant role. Significant disparity between expected and actual outcomes of reform is therefore a natural outcome. Clearly, existing policy discourse – which tends to ascribe disparity to flaws in the reform model and its implementation – is deficient. A much more insightful understanding of the reasons for the disparity can instead be gained through an institutional perspective on reform – this paper argues.

Yang, Y, Liu, Y, Fang, X, Miao, W, Chen, X, Sun, J, Ni, BJ & Mao, S 2020, 'Heterogeneous Electro-Fenton catalysis with HKUST-1-derived Cu@C decorated in 3D graphene network', Chemosphere, vol. 243.
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© 2019 Elsevier Ltd Transition metal and nanocarbon-based composites with high activity and stability draw great attention in electro-Fenton system for organic pollutants removal. In this study, HKUST-1-derived Cu@C nanoparticles embedded within three-dimensional reduced graphene oxide (rGO) network (denoted as 3DG/Cu@C) is synthesized through a simple strategy. The prepared catalyst shows ordered 3D porous carbon structure and Cu@C NPs are uniformly dispersed in the matrix. The 3DG/Cu@C is used as heterogeneous electro-Fenton (hetero-EF) catalyst and shows outstanding performance in various persistent organic pollutants removal. High concentration Rhodamine B (RhB) (40 mg L−1) can achieve a complete decolorization within 150 min with 25 mg L−1 3DG/Cu@C catalyst, which is one of the lowest catalyst dosages in hetero-EF for RhB removal. More importantly, the 3DG/Cu@C achieves high RhB mineralization efficiency of 81.5% and exhibits high catalytic performance in a wide pH window from 3 to 9. The 3DG/Cu@C also remains high efficiency after five successive reaction cycles. The working mechanism study shows that RhB is mainly oxidized by •OH and O2•- radicals through hetero-EF and anodic oxidation processes. The high stability and outstanding performance of 3DG/Cu@C provide new insights in organic pollutants removal by hetero-EF process with transition metal and nanocarbon-based catalysts.

Yang, Y, Zang, Y, Hu, Y, Wang, XC & Ngo, HH 2020, 'Upflow anaerobic dynamic membrane bioreactor (AnDMBR) for wastewater treatment at room temperature and short HRTs: Process characteristics and practical applicability', Chemical Engineering Journal, vol. 383.
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© 2019 Elsevier B.V. An upflow anaerobic dynamic membrane bioreactor (AnDMBR) was set up for real domestic wastewater treatment at room temperature (20–25 °C) and short hydraulic retention time (HRT = 8 h, 4 h, 2 h, and 1 h). Following continuous operation for 93 days with stepwise decreased HRT, stable average chemical oxygen demand (COD) removal was achieved (between 77.3% and 70.6%) when HRT was reduced from 8 h to 4 h, then 2 h with flux varying from 22.5 to 90 L/m2·h. At these three HRTs, the rate of increase in trans-membrane pressure (TMP) was 0.4, 0.38, and 0.57 kPa/d, and average methane (CH4) production was 0.12, 0.10, and 0.08 L/g CODremoved, respectively. Furthermore, decreasing the HRT to 1 h resulted in less COD being removed (60.4%) and lower CH4 production (0.05 L/g CODremoved) as well as a faster rate of TMP increase (2.11 kPa/d). Various analytical methods were applied to characterize the morphology and composition of the dynamic membrane (DM) layers. Organic components analysis revealed that, with reduced HRT, there were apparent increases in soluble microbial products in the liquid phase and accumulation of tryptophan protein-like substances and aromatic protein-like substances in the DM layer, especially when the HRT was shortened to 1 h. Whilst the upflow AnDMBR proved applicable to wastewater treatment at room temperature with short HRTs, 2 h could be the HRT limit for maintaining stable operation.

Yao, Z & Li, W 2020, 'Microstructure and thermal analysis of APS nano PYSZ coated aluminum alloy piston', Journal of Alloys and Compounds, vol. 812.
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© 2019 Elsevier B.V. Aluminium alloys in internal combustion (IC) engines may suffer from heat damage. Such heat damage can be mitigated using thermal barrier coatings (TBCs). In this study, the TBC Nano yttria partially stabilized zirconia (PYSZ) is applied as an aggregated powder to an aluminum alloy piston using an atmospheric plasma spray (APS) method. The preparation and application of the Nano PYSZ aggregated powder are critical to its effectiveness as a TBC. IC engine bench experiments were undertaken to provide a baseline against which the effectiveness of the TBC could be judged. The microstructure of the Nano PYSZ aggregated powder and thermal barrier coatings were examined using three instruments: scanning electron microscopy (SEM), field emission scanning electron microscopy (FESEM) and X-ray powder diffraction (XRD). Results from this study show that the Nano PYSZ ceramic TBCs, applied to the aluminum alloy piston using a plasma spraying technique, (a) has a high quality Nano-structure, (b) can effectively resist the thermal shock of high temperature gas in the cylinder and (c) maintains both stable macro characteristics and micro structure during the working cycle of the IC engine. The thermal insulation properties of TBCs were also examined. The thermal analyses describe the distribution of temperature across both the piston and the aluminum alloy substrate. Results desmonstrate the effectiveness of the TBCs in reducing the temperature of the aluminium alloy substrate at the top of piston. One benefit is that the piston can operate effectively at higher temperatures. Specifically, as the thickness of ceramic coating increased from 0.1 mm to 1.4 mm, the maximum temperature of the pistons coated with the TBCs increased from 399 °C to 665 °C. The maximum temperature of the aluminum alloy substrates simultaneously decreased from 336 °C to 241 °C. This study clearly demonstrates the excellent thermal insulation properties of the TBCs and shows that the ...

Yi, JH, Xing, LN, Wang, GG, Dong, J, Vasilakos, AV, Alavi, AH & Wang, L 2020, 'Behavior of crossover operators in NSGA-III for large-scale optimization problems', Information Sciences, vol. 509, pp. 470-487.
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© 2018 Elsevier Inc. Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algorithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.

Yong, B, Shen, J, Liu, X, Li, F, Chen, H & Zhou, Q 2020, 'An intelligent blockchain-based system for safe vaccine supply and supervision', International Journal of Information Management.
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© 2019 Elsevier Ltd Immunization is an indispensable mechanism for preventing infectious diseases in modern society, and vaccine safety is closely related to public health and national security. However, issues such as vaccine expiration and vaccine record fraud are still widespread in vaccine supply chains. Therefore, an effective management system for the supervision of vaccine supply chains is urgently required. As the next generation of core technology after the Internet, blockchain is designed to build trust mechanisms that can change current information management methods. Meanwhile, the development of machine learning technologies provides additional ways to analyze the data in information management systems. The main objective of this study is to develop a “vaccine blockchain” system based on blockchain and machine learning technologies. This vaccine blockchain system is designed to support vaccine traceability and smart contract functions, and can be used to address the problems of vaccine expiration and vaccine record fraud. Additionally, the use of machine learning models can provide valuable recommendations to immunization practitioners and recipients, allowing them to choose better immunization methods and vaccines.

Yu, G, Wang, X, Yu, K, Ni, W, Zhang, JA & Liu, RP 2020, 'Survey: Sharding in Blockchains', IEEE Access, pp. 1-1.
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Yu, Y, Wu, D & Gao, W 2020, 'Stochastic chemo-physical-mechanical degradation analysis on hydrated cement under acidic environments', Applied Mathematical Modelling, vol. 78, pp. 75-97.
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© 2019 Elsevier Inc. The accumulation of material degradation under contact with aggressive aqueous environments could lead to reduced structural reliability. In terms of hydrated cementitious materials, such interactions often result in the chemo-physical-mechanical (CPM) degradation, which represents a multiphysics process of high non-linearity and complexity. By further considering the inevitable uncertainties associated with both the materials and the serving conditions, solving such a process requires novel probabilistic approaches. This paper presents a stochastic chemo-physical-mechanical (SCPM) degradation analysis on the hydrated cement under acidic environment. The SCPM analysis consists of modelling the stochastic chemophysical degradation by finite element method, and assessing the mechanical deterioration through analytical micromechanics. The proposed modelling framework couples the conventional Monte Carlo Simulation with a novel support vector regression algorithm. The present method is able to not only address the detailed degradation mechanisms, but also ensure low computational costs for an accurate SCPM degradation assessment.

Yuan, J, Mostaan, A, Yang, Y, P. Siwakoti, Y & Blaabjerg, F 2020, 'A Modified Y-Source DC/DC Converter with High Voltage-Gains and Low Switch Stresses', IEEE Transactions on Power Electronics, pp. 1-1.
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Yuan, X, Feng, Z, Ni, W, Liu, RP, Zhang, JA & Xu, W 2020, 'Secrecy Performance of Terrestrial Radio Links Under Collaborative Aerial Eavesdropping', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 15, pp. 604-619.
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Zeng, J, Lv, T, Lin, Z, Liu, RP, Mei, J, Ni, W & Guo, YJ 2020, 'Achieving Ultra-Reliable and Low Latency Communications in IoT by FD-SCMA', IEEE Internet of Things Journal, pp. 1-1.
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Zhang, C, Qin, M, Zou, H & Qiu, X 2020, 'Secondary source and error sensing strategies for the active control of sound transmission through a small opening', JOURNAL OF SOUND AND VIBRATION, vol. 464.
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Zhang, J, Ding, F, Zhang, N, Chen, S & Zhang, B 2020, 'A New SSUKF Observer for Sliding Mode Force Tracking H∞ Control of Electrohydraulic Active Suspension', Asian Journal of Control.
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© 2018 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd Compared to passive and semi-active suspensions, active suspensions have the capacity to overcome the tradeoff design of vehicle ride comfort and handling performance. In this paper, a new control system with modified spherical simplex UKF (SSUKF) observer and sliding mode force tracker is proposed for electrohydraulic active suspensions. The estimated states obtained from the SSUKF observer are used to derive the mode energy of body motion and design the target demanded forces for suspension actuators. A sliding mode controller is presented to drive electrohydraulic actuators to track the demanded forces. Based on a robust H∞ control method, a new control strategy depending on body mode energy is proposed to restrict body motion. The performance comparisons between passive and active suspensions under three typical excitations are presented, and the obtained results indicate that the proposed control system can significantly depress body motion and decrease the mode energy to improve ride comfort and also effectively enhance the road hold abilities.

Zhang, S, Ly, QV, Nghiem, LD, Wang, J, Li, J & Hu, Y 2020, 'Optimization and organic fouling behavior of zwitterion-modified thin-film composite polyamide membrane for water reclamation: A comprehensive study', Journal of Membrane Science, vol. 596.
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© 2019 Membrane fouling can hinder the widespread application of thin film composite (TFC) reverse osmosis (RO) for water treatment. This study evaluated a novel zwitterion-grafted TFC RO as a mean to address organic fouling for water reclamation. The membrane exhibited the best permeability at the grafting condition of 45 °C in 1 h. This modified membrane consistently possessed improved antifouling ability irrespective of organic foulants. Among individual foulants, surfactant Dodecyl Trimethyl Ammonium Chloride (DTAC) posed the worst fouling potential due to its low molecular weight and positive charge, whereas fouling induced by other substances were relatively analogous and minor. In the mixture of DTAC and proteins, the former played a key role in governing the membrane fouling. While, their interplay affected membrane fouling, the fouling extent varied upon the membrane materials. The extended Derijaguin, Landau, Verwey and Overbeek (xDLVO) theory was unable to fully describe the interactions between surfactant foulants and the membrane materials. The complementary use of quartz crystal microbalance with dissipation (QCM-D), otherwise, concurred the fouling potential and gave the plausible interpretation for fouling mechanisms by providing insightful information of foulant layer on the polyamide-coated sensor. This study provided critical insights of organic foulants’ behavior on TFC RO membrane and offered the promising industrial implication of the novel membrane.

Zhang, X, Song, Z, Ngo, HH, Guo, W, Zhang, Z, Liu, Y, Zhang, D & Long, Z 2020, 'Impacts of typical pharmaceuticals and personal care products on the performance and microbial community of a sponge-based moving bed biofilm reactor', BIORESOURCE TECHNOLOGY, vol. 295.
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Zhao, E, Wu, C, Wang, S, Hu, J & Wang, W 2020, 'Seepage dissolution effect prediction on aging deformation of concrete dams by coupled chemo-mechanical model', Construction and Building Materials, vol. 237.
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© 2019 Elsevier Ltd Concrete dams undergo a seepage dissolution process because of huge reservoir water pressure and concrete permeability, and such dissolution weakens the mechanical properties of concrete and produces a certain amount of irreversible aging deformation. This study proposes a coupled chemo-mechanical model to predict the seepage dissolution effect on aging deformation of concrete dams. The interactions between elastic–plastic, chemical damage and mechanical damage are jointly explored combining with on-site inspection of long-term service performance of a concrete gravity dam firstly. Then an aging model of the seepage dissolution damage is digitized, and a novel model on chemo-mechanical coupled effect is put forward by introducing an overall chemo-mechanical damage scalar. The validity of the model is proved by a case study on the gravity dam through finite element simulation on its seepage dissolution and comparison with monitoring data. Finally, these methods are applied into an ultra-high arch dam to quantitatively calculate the irreversible aging deformation with the increase of the seepage dissolution degree. And the annual maximum aging deformation of the arch dam increases 0.65 mm after 100 years. The results indicate that the proposed model can effectively predict the aging deformation caused by the seepage dissolution during long-term operation of concrete dams.

Zhao, J, Zhang, H & Zhang, JA 2020, 'Gaussian kernel adaptive filters with adaptive kernel bandwidth', Signal Processing, vol. 166.
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© 2019 Gaussian kernel adaptive filters (GKAFs) have been successfully applied in functional approximation. The kernel bandwidth for GKAFs not only impacts on the smoothness of function approximation and the locality of training samples, but also affects the convergence rate and testing accuracy. However, in practice, it is hard to predesign an optimal one. In this paper, for practical applications, we propose a novel framework for kernel bandwidth adaptation in sparsification case. In this framework, we consider the latest K kernel bandwidths as free parameters, and sequentially update them using a gradient decent method to minimize the instantaneous squared error. Furthermore, we apply the proposed method to the quantized kernel least mean square (QKLMS) algorithm, and conduct convergence analysis for the algorithm. Extensive simulation results are provided and validate the superiority of our method compared to some state-of-the-art algorithms.

Zheng, J, Ji, J, Yin, S & Tong, V-C 2020, 'Internal loads and contact pressure distributions on the main shaft bearing in a modern gearless wind turbine', TRIBOLOGY INTERNATIONAL, vol. 141.
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Zheng, Y, Hu, R, Fung, SF, Yu, C, Long, G, Guo, T & Pan, S 2020, 'Clustering social audiences in business information networks', Pattern Recognition, vol. 100.
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© 2019 Elsevier Ltd Business information networks involve diverse users and rich content and have emerged as important platforms for enabling business intelligence and business decision making. A key step in an organizations business intelligence process is to cluster users with similar interests into social audiences and discover the roles they play within a business network. In this article, we propose a novel machine-learning approach, called CBIN, that co-clusters business information networks to discover and understand these audiences. The CBIN framework is based on co-factorization. The audience clusters are discovered from a combination of network structures and rich contextual information, such as node interactions and node-content correlations. Since what defines an audience cluster is data-driven, plus they often overlap, pre-determining the number of clusters is usually very difficult. Therefore, we have based CBIN on an overlapping clustering paradigm with a hold-out strategy to discover the optimal number of clusters given the underlying data. Experiments validate an outstanding performance by CBIN compared to other state-of-the-art algorithms on 13 real-world enterprise datasets.

Zhou, L, Ying, S, Yu, N & Ying, M 2020, 'Strassen's theorem for quantum couplings', Theoretical Computer Science, vol. 802, pp. 67-76.
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© 2019 Elsevier B.V. Strassen's theorem for probabilistic couplings is a fundamental theorem in probability theory that can be used to bound the probability of an event in a distribution by the probability of an event in another distribution coupled with the first. It has been widely applied in computer science for analysis of random algorithms, machine learning and verification of security and privacy protocols. We extend the coupling techniques in probability theory to quantum systems. A quantum generalisation of the notion of lifting, a coupling under certain constraints, is introduced. Several interesting examples and basic properties of quantum couplings and liftings are presented. Finally, a quantum extension of Strassen's theorem is established.

Zhou, L, Ying, S, Yu, N & Ying, M 2020, 'Strassen's theorem for quantum couplings', THEORETICAL COMPUTER SCIENCE, vol. 802, pp. 67-76.
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Zhu, Q, Qiu, X, Coleman, P & Burnett, I 2020, 'A comparison between two modal domain methods for personal audio reproduction', The Journal of the Acoustical Society of America, vol. 147, no. 1, pp. 161-173.
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Zhuang, Y, Sun, Y, Huang, Y, Teng, Q, He, B, Chen, W & Qian, Y 2020, 'Investigation of water injection benefits on downsized boosted direct injection spark ignition engine', Fuel, vol. 264.
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© 2019 Elsevier Ltd Engine downsizing and boosting are key technologies to achieve the increasingly stringent emissions standards for spark ignition (SI) engines. However, knock is a major obstacle inhibiting further downsizing of SI engines. Water injection is a promising technology that has regained attention recently to solve the knock problem. In this paper, a 1.5L turbocharged gasoline direct injection (GDI) engine was modified by installing a water port injection (WPI) system on the intake manifold. The WPI system was modified from a GDI system and deionized water was pressured to 50 bar in a water tank by compressed nitrogen. The effect of WPI on engine combustion and emissions performance were experimentally investigated under different water/gasoline volume percentages and WPI timings. The results show that WPI has great potential in suppressing engine knock. At original engine setting (without adjustment of spark timing), all the combustion indexes related to knock are decreased by WPI, including maximum in-cylinder pressure (Pmax) and maximum pressure rise rate (Rmax). The flame kernel formation process (CA0-5), initiation combustion duration (CA0-10), early combustion duration (CA0-50) and major combustion duration (CA0-90) are deteriorated, resulting in decreased indicated mean effective pressure (IMEP) and thermal efficiency. By properly advancing spark timing, the combustion process can be improved, allowing the engine to achieve higher Pmax and better combustion phases without occurrence of knock. It is also found that the water/gasoline volume percentage should be kept within a proper range (30% in this study) because over WPI can lead to deterioration of combustion and pollutant emissions. WPI can effectively reduce the production of NO and CO emissions, while HC emissions are increased with the rise of water/gasoline volume percentage.

Ziaei, M, Panahi, M, Fanaei, MA, Rafiee, A & Khalilpour, KR 2020, 'Maximizing the profitability of integrated Fischer-Tropsch GTL process with ammonia and urea synthesis using response surface methodology', JOURNAL OF CO2 UTILIZATION, vol. 35, pp. 14-27.
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Al-Hadhrami, Y & Hussain, FK 2020, 'A Machine Learning Architecture Towards Detecting Denial of Service Attack in IoT', Advances in Intelligent Systems and Computing, pp. 417-429.
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© 2020, Springer Nature Switzerland AG. Internet of thing is part of our everyday life nowadays. Where millions of devices contented to the internet to collect and share data. Although IoT devices are evolving quickly to the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators have been also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. With the rapid development of the Internet of Things in different areas, IoT is facing difficulty in securing overall availability of the network due to its heterogeneous nature. There are many types of vulnerability in IoT that can be mitigated with further research, however, in this paper, we have concentrated on distributed denial of Service attack (DDoS) on IoT. In this paper, we propose a machine learning architecture to detect DDoS attacks in IoT networks. The architecture collects IoT network traffic and analyzes the traffic through passing to machine learning model for attack detection. We propose the use of real-time data collection tool to dynamically monitor the network.

Al-Hadhrami, Y, Al-Hadhrami, N & Hussain, FK 2020, 'Data exportation framework for IoT simulation based devices', Advances in Intelligent Systems and Computing, pp. 212-222.
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© Springer Nature Switzerland AG 2020. Internet of things (IoT) is part of everyday life nowadays. Millions of devices are connected to the internet to collect and share data. Although IoT devices are evolving quickly in the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators are also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. A need for an IoT simulation tool is necessary for development purposes and testing before deployments. One of the widely used tools among IoT researchers is the open-source tool Cooja simulator. Cooja has limitations—one is the lack of a way to export collected data as a data set for further processing. Therefore, this study introduces an extension tool to present and export the data into different forms.

Almansor, EH & Hussain, FK 2020, 'Survey on Intelligent Chatbots: State-of-the-Art and Future Research Directions', Advances in Intelligent Systems and Computing, pp. 534-543.
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© 2020, Springer Nature Switzerland AG. Human-computer interaction (HCI) is an area of interest which plays a major role in understanding the interaction between humans and machines. Dialogue systems or conversational systems including chatbots, voice control interfaces and personal assistants are examples of HCI application that have been developed to interact with users using natural language. Chatbots can help customers find useful information for their needs. Thus, numerous organizations are using chatbots to automate their customer service. Thus, the needs for using artificial intelligence has been increasing due to the needs of automated services. However, devolving smart bots that can respond at the human level is challenging. In this paper, we survey the state-of-art chatbot approaches from based on the ability to generate appropriate responses perspective. After summarizing the review from this aspect, we identify the research issues and challenges in chatbots. The findings of this research will highlight directions for future work.

Almansor, EH, Al-Ani, A & Hussain, FK 2020, 'Transferring Informal Text in Arabic as Low Resource Languages: State-of-the-Art and Future Research Directions', Advances in Intelligent Systems and Computing, pp. 176-187.
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© 2020, Springer Nature Switzerland AG. Rapid growth in internet technology lead to increase the usage of social media platforms which make communication between users easier. Through the communication users used their daily languages which considered as non-standard language. The non-slandered text contains lots of noise, such as abbreviations, slang which used more in English languages and dialect words which are widely used in Arabic language. These texts face challenging using any natural language processing tools. Therefore, these texts need to be treated and transferred to be similar to their standard form. According to that the normalization and translation approach have been used to transfer the informal text. However, using these approach need large label or parallel datasets. While high resource languages such as English have enough parallel datasets, low resource languages such as Arabic is lack of enough parallel dataset. Therefore, in this paper we focus on the Arabic and Arabic dialects as a low resource language in the era of transferring non-stander text using normalization and translation approach.

Al-Tae, AA & Al-Jumaily, A 2020, 'Electrogastrogram Based Medical Applications an Overview and Processing Frame Work', Advances in Intelligent Systems and Computing, pp. 511-520.
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© 2020, Springer Nature Switzerland AG. Surface Electrogastrogram (sEGG) examination is a noninvasive method based on recording the electrical signals of stomach muscles that can be used for investigation of a stomach wave propagation. sEGG records by placing electrodes on the abdominal skin. The recording of gastric dysrhythmias is an important tool for the clinician when patients have symptoms that suggest gastric dysfunction such as unexplained nausea, bloating, postprandial fullness, and early satiety. On the other hand, these upper gastrointestinal (GI) symptoms are nonspecific, and diseases or disorders of other organ such as esophagus, gallbladder, small bowel, colon, and non-GI diseases also can be considered. The progress of the EGG based application is very slow as EGG still suffers from several limitations. The aim of this study is to review the Electrogastrogram detection, analyzing methods and its application to enter the clinical world with recommendations for future study.

Amirbagheri, K, Merigó, JM & Yang, JB 2020, 'A bibliometric analysis of leading countries in supply chain management research', Advances in Intelligent Systems and Computing, pp. 182-192.
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© 2020, Springer Nature Switzerland AG. Supply chain management as a newly comer discipline has attracted many attentions of the scholars to do an investigation based on its prominent level of importance for the economy and its influence on the management of the organizations. So, the key point is to understand the trends among the countries throughout the time to have a powerful insight about this issue. To this end, this work does a comprehensive analysis from 1990 to 2017. The purpose of this study is to analyze the leading countries and understand thoroughly their trends during the time. The work has dedicated to three sections. In the first one the countries have studied globally to give a comprehensive overview to academics. Next, the performance of the countries is studied in three periods to understand better the changes of each during the time. Finally, some individual journals and groups of journals are also investigated. The results show that the USA is the leader of the countries while China has experienced an enormous growth and it is predictable that with this trend can reach to the top of the list.

Cetindamar Kozanoglu, D, Shdifat, B & Erfani, S 2020, 'Assessing Big Data Analytics Capability and Sustainability in Supply Chains', Hawaii International Conference on System Sciences, Hawaii, USA, pp. 208-217.
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Cheng, Q, Shi, Z, Nguyen, D & Dutkiewicz, E 2020, 'Non-cooperative OFDM Spectrum Sensing Using Deep Learning', IEEE International Conference on Computing, Networking and Communications (ICNC), Hawaii.
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Dawson, NJ, Rizoiu, M-A, Johnston, B & Williams, M-A 2019, 'Adaptively selecting occupations to detect skill shortages from online job ads', IEEE International Conference on Big Data (IEEE Big Data 2019), IEEE International Conference on Big Data, Los Angeles, CA, USA, pp. 1-7.
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Labour demand and skill shortages have historically been difficult to assess given the high costs of conducting representative surveys and the inherent delays of these indicators. This is particularly consequential for fast developing skills and occupations, such as those relating to Data Science and Analytics (DSA). This paper develops a data-driven solution to detecting skill shortages from online job advertisements (ads) data. We first propose a method to generate sets of highly similar skills based on a set of seed skills from job ads. This provides researchers with a novel method to adaptively select occupations based on granular skills data. Next, we apply this adaptive skills similarity technique to a dataset of over 6.7 million Australian job ads in order to identify occupations with the highest proportions of DSA skills. This uncovers 306,577 DSA job ads across 23 occupational classes from 2012-2019. Finally, we propose five variables for detecting skill shortages from online job ads: (1) posting frequency; (2) salary levels; (3) education requirements; (4) experience demands; and (5) job ad posting predictability. This contributes further evidence to the goal of detecting skills shortages in real-time. In conducting this analysis, we also find strong evidence of skills shortages in Australia for highly technical DSA skills and occupations. These results provide insights to Data Science researchers, educators, and policy-makers from other advanced economies about the types of skills that should be cultivated to meet growing DSA labour demands in the future.

Gil-Doménech, D, Berbegal-Mirabent, J & Merigó, JM 2020, 'STEM education: A bibliometric overview', Advances in Intelligent Systems and Computing, pp. 193-205.
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© 2020, Springer Nature Switzerland AG. In the recent years, STEM (science, technology, engineering and mathematics) education has received increasing attention, and many calls for a fundamental change in this field have emerged. In this context, the analysis of the evolution and development of this scientific domain results crucial. In order to do so, the present study presents a bibliometric overview of the academic research developed in STEM education over the last years. The work uses the Web of Science database and a wide range of bibliometric indicators including the number of publications and citations, the h-index, and citation thresholds. The article also develops a graphical visualisation of the bibliographic data using the visualisation of similarities (VOS) viewer software. Results indicate that the amount of works addressing this topic has substantially increased in the recent years, although the number of citations has not experienced a similar growth rate. Also, when analysing journals and research areas, it can be deduced that the research on STEM education is diverse. The bibliometric analysis performed provides a rigorous and comprehensive view of research on STEM education that might be useful for those researchers interested in advancing future knowledge in this area.

Ikram, MA, Sharma, N, Raza, M & Hussain, FK 2020, 'Dynamic Ranking System of Cloud SaaS Based on Consumer Preferences - Find SaaS M2NFCP', Advances in Intelligent Systems and Computing, pp. 1000-1010.
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© 2020, Springer Nature Switzerland AG. Software as a Service (SaaS) is a type of software application that runs and operates over a cloud computing infrastructure. SaaS has grown more dramatically compared to other cloud services delivery models (i.e. PaaS and IaaS) in terms of the number of available services. This rapid growth in SaaS brings a lot of challenges for consumers in selecting the optimum services. The aim of this article is to propose a ranking system for SaaS based on consumer’s preferences called Find SaaS M2NFCP. The proposed ranking system is based on measuring the shortest distance to the minimum and maximum of the selected consumer’s non-functional preferences. In addition, linguistic terms are taken into account to weight the most important non-functional preferences. The proposed system is evaluated against traditional SaaS ranking systems using data collected from online CRM SaaS and achieved improved results.

Kalhori, H, Halkon, B, Abbasnejad, B, Li, B & Shooshtari, A 2019, 'Nonlinear Vibration of an Electrostatically Excited Capacitive Microplate', Springer, 2020, The 18th Asia-Pacific Vibration Conference, University of Technology Sydney UTS, Sydney, Australia.
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Khan, S & Hussain, FK 2020, 'A soa based sla negotiation and formulation architecture for personalized service delivery in sdn', Advances in Intelligent Systems and Computing, pp. 108-119.
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© Springer Nature Switzerland AG 2020. Supporting end-to-end personalized Quality of Services (QoS) delivery in existing network architecture is an ongoing issue. Software Defined Networking (SDN) model has emerged in response to the limitations of traditional network. Integrating Software Defined Network (SDN) architecture with Service Oriented Architecture (SOA) brings new concept for future service oriented delivery in SDN services. Researchers from both academic and industry are working to resolve the QoS limitations of service delivery, however; most of the proposed solutions are application oriented and unable to provide a reliable personalized QoS delivery in future service oriented SDN. This research propose a reliable Service Level Agreement (SLA) oriented Service Negotiation framework that would be able to provide reputation based personalized service delivery and assist in QoS management in SDN for informed decision making. Moreover, potential benefits of the proposed framework are also discussed in this paper in social, scientific and business aspects.

Masood, A & Al-Jumaily, A 2020, 'Orientation Sensitive Fuzzy C Means Based Fast Level Set Evolution for Segmentation of Histopathological Images to Detect Skin Cancer', Advances in Intelligent Systems and Computing, pp. 501-510.
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© 2020, Springer Nature Switzerland AG. Malignant Melanoma in one of the most deadly skin cancer. In latest cancer diagnosis, pathologists examine biopsies for analysing cell morphology and tissue distribution for making diagnostic assessments. However, this process is quite subjective and has considerable variability. Automated computational diagnostic tools based on quantitative measures can help the diagnosis done by pathologists. The first and foremost step in automated histopathological image analysis is to properly segment the tissue structures such as nest, irregular distribution and melanocytic cells which indicate some disorder or potential cancer. This paper presents a novel technique for automatic segmentation of histopathological skin images, without user intervention. It is based on an innovative approach for utilizing the concepts of clustering and level set evolution. Firstly, the image is pre-processed to enhance the differentiating structural details. Then a novel orientation sensitive Fuzzy C mean clustering is used to generate the initial coarse segmentation and to calculate the controlling parameters for level set evolution. Later, refined fast level set based algorithm is used to finalize the segmentation process. Experimental analysis on a database of 150 histopathological images display the accuracy of the proposed method for detecting the melanocytic areas of the image, with true detection rate of 87.66% and Dice similarity coefficient of 0.88, when segmentation results are compared with images marked by expert pathologists.

Mulet-Forteza, C, Mauleón-Méndez, E, Merigó, JM & Genovart-Balaguer, J 2020, 'Leading universities in tourism and hospitality research: A bibliometric overview', Advances in Intelligent Systems and Computing, pp. 142-152.
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© 2020, Springer Nature Switzerland AG. This study presents an overview of the most influential and productive institutions around the world in the tourism, leisure and hospitality field. The research uses a bibliometric approach to examine all the journals indexed in the Web of Science database in these fields. Results show a strong influence of the USA institutions, although institutions from other countries also achieve an important and significant position in these fields. Results also consider the temporal evolution of the leading universities and the most productive institutions in the tourism, leisure and hospitality field.

Munasinghe, N, Woods, M, Miles, L & Paul, G 2019, '3-D Printed Strain Sensor for Structural Health Monitoring', IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and the IEEE International Conference on Robotics, Automation and Mechatronics (RAM), IEEE, Bangkok.
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Additive manufacturing, or 3D printing, is evolving from a technology that can only aid rapid prototyping, to one that can be used to directly manufacture large-scale, real-world equipment. Gravity Separation Spirals (GSS) are vital to the mining industry for separating mineral-rich slurry into its different density components. In order to overcome inherent drawbacks of the traditional mould base manufacturing methods, including significant tooling costs, limited customisation and worker exposure to hazardous materials, a 3D printer is under development to directly print spirals. By embedding small Internet of Things (IoT) sensors inside the GSS, it is possible to remotely determine the operation conditions, predict faults, and use collected data to optimise production output. This work presents a 3D printed strain sensor, which can be directly printed into the GSS. This approach uses a carbon-based conductive filament to print a strain gauge on top of a Polylactic Acid (PLA) base material. Printed sensors have been tested using an Instron E10000 testing machine with an optical extensometer to improve accuracy. Testing was conducted by both loading and unloading conditions to understand the effect of hysteresis. Test results show a near-linear relationship between strain and measured resistance, and show a 6.05% increase in resistance after the test, which indicates minor hysteresis. Moreover, the impact of viscoelastic behaviour is identified, where the resistance response lags the strain. Results from both conductive and non-conductive material show the impact of the conductive carbon upon the tensile strength, which will help to inform future decisions about sensor placement.

Naji, M, Braytee, A, Anaissi, A, Sianaki, OA & Al-Ani, A 2020, 'Optimizing the Waiting Time for Airport Security Screening Using Multiple Queues and Servers', Advances in Intelligent Systems and Computing, pp. 496-507.
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© 2020, Springer Nature Switzerland AG. Airport security screening processes are essential to ensure the safety of passengers and the aviation industry. Security at airports has improved noticeably in recent years through the utilisation of state-of-the-art technologies and highly trained security officers. However, maintaining a high level of security can be costly to operate and implement and can cause delays for passengers and airlines. In optimising a security process it is essential to strike a balance between time delays, security and reduced operation cost. This paper uses queueing theory as a method to study the impact of queue formation and the size of the security area on the average waiting time for the case of multi-lane parallel servers. An experiment is conducted to validate the proposed approach.

Nguyen, H, Nguyen, D, Dinh, H, Dutkiewicz, E, Mueck, M & Srikanteswara, S 2020, 'Defeating Jamming Attacks with Ambient Backscatter Communications', IEEE International Conference on Computing, Networking and Communications (ICNC), Hawaii.
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Prestigiacomo, R, Hadgraft, R, Lockyer, L, Knight, S, van den Hoven, E, Martinez-Maldonado, R & Hunter, J 2020, 'Learning-centred Translucence: An Approach to Understand How Teachers Talk About Classroom Data', The 10th international Learning Analytics & Knowledge Conference, Frankfurt, Germany.
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Teachers are increasingly being encouraged to embrace evidence- based practices to improve their teaching. Learning analytics (LA) offer great promise in supporting these practices by providing evidence for teachers and learners to make informed decisions and transform the educational experience. However, LA limitations and their uptake by educators are also coming under critical scrutiny. This is in part due to the lack of involvement of teachers and learners in the design of LA tools to understand existing educational practices. In this paper, we propose a human-centred approach to generate understanding ofteachers’ data needs through the lens of three key principles of translucence: visibility, awareness and accountability. We illustrate our approach through a participatory design sprint to identify how teachers talk about classroom data. We describe teachers’ perspectives on the evidence they need for making better-informed decisions and discuss the implications of our approach for the design of human- centred LA in the next years.

Rehman, J, Sohaib, O, Hawryszkiewycz, I & Soomro, AM 2020, 'Developing Intellectual Capital in Professional Service Firms Using High Performance Work Practices as Toolkit', https://scholarspace.manoa.hawaii.edu/bitstream/10125/64355/0493.pdf, 53rd Hawaii International Conference on System Sciences, Hawaii, pp. 4983-4992.
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Torres-Martínez, A, Gil-Lafuente, AM & Merigó, JM 2020, 'The OWA operator with boxplot method in time series', Advances in Intelligent Systems and Computing, pp. 13-21.
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© 2020, Springer Nature Switzerland AG. This paper presents an aggregation model for time series with the OWA operator, using as arguments representative values calculated from the quantiles of a data set and particularly with the values of the boxplot graph. The application of the OWA operator in this model allows forecasting time series including the optimistic or pessimistic attitude of the analysts, through the weighting vector. To illustrate the boxplot-OWA operator model, results are presented with data from the IBEX 35 for different periods of time in 2017.

Ubaid, A, Dong, F & Hussain, FK 2020, 'Framework for Feature Selection in Health Assessment Systems', Advances in Intelligent Systems and Computing, pp. 313-324.
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© 2020, Springer Nature Switzerland AG. Anomaly detection in health assessment systems has gained much attention in the recent past. Various feature selection techniques have been proposed for successful anomaly detection. However, these methods do not cater for the need to select features in health assessment systems. Most of the present techniques are data dependent and do not offer an option for incorporating domain information. This paper proposes a novel domain knowledge-driven feature selection framework named domain-driven selective wrapping (DSW) that can help in the selection of a correlated feature subset. The proposed framework uses an expert’s domain knowledge for the selection of subsets. The framework uses a custom-designed logic-driven anomaly detection block (LDAB) as a wrapper. The experiment results show that the proposed framework is able to select feature subsets more efficiently than traditional sequential selection methods and is very successful in detecting anomalies.

Ulapane, N, Wickramanayake, S & Kodagoda, S 2019, 'Pulsed Eddy Current Sensing for Condition Assessment of Reinforced Concrete', 14th IEEE Conference on Industrial Electronics and Applications, China.
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Vicedo, P, Gil, H, Oltra-Badenes, R & Merigó, JM 2020, 'Critical success factors on ERP implementations: A bibliometric analysis', Advances in Intelligent Systems and Computing, pp. 169-181.
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© 2020, Springer Nature Switzerland AG. This work presents a bibliometric analysis of the influential authors, institutions, papers and countries on the field of Enterprise Resource Planning implementations and their Critical Success Factors based on the Web of Science database with 301 articles belonging to 86 Universities and Institutions from 48 different countries. The research has been conducted from 1999 to end of 2017.

Wang, X, Wu, Y, Zhu, L & Yang, Y 2020, 'Symbiotic Attention with Privileged Information for Egocentric Action Recognition', The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI.

Wedyan, M, Al-Jumaily, A & Crippa, A 2020, 'Early Diagnose of Autism Spectrum Disorder Using Machine Learning Based on Simple Upper Limb Movements', Advances in Intelligent Systems and Computing, pp. 491-500.
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© 2020, Springer Nature Switzerland AG. The importance of early diagnosis of autism that leads to early intervention such thing shall increase the results of treating it. The Autism Spectrum Disorder (ASD) affects the children activities and caused difficulties in interaction, impairments in communication, delayed speech, and weak eye contact. These activities used as the base for ASD diagnosis decision. Children move their upper limb before some of the other activities. Moving upper limb can be based for ASD diagnosis decision for autistic children. Such paper examines diagnosing the ASD that depends on motioning the children’s upper-limb aged between two and four years based on executing specific procedures and machine learning. The approach that such study utilized is both (LDA) Linear Discriminant Analysis in order to elicit the features and (SVM) Support Vector Machines for classifying thirty children such study selected fifteen autistic children out of fifteen non-autistic children by testing the collected data that are collected from doing an easy task. The results of such study have accomplished an optimal sortation accuracy of 100% and the average accuracy of 93.8%. Such outcomes provide more proof of simple brachium motioning that might be utilized in sorting poor performance of autistic children precisely.

Xiao, T, Qiu, X & Halkon, B 2019, 'Using a Laser Doppler Vibrometer to Estimate Sound Pressure in Air', Springer, The 18th Asia-Pacific Vibration Conference, Sydney.
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Zhong, Y, Huang, Y, Dutkiewicz, E, Wu, Q & Jiang, T 2020, 'Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing', Lecture Notes in Electrical Engineering, pp. 553-560.
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© 2020, Springer Nature Singapore Pte Ltd. Target classification in foliage environment is a challenging task in realistic due to the high-clutter background and unsettled weather. To detect a particular target, e.g., human, under such an environment, is an indispensable technique with significant application value. Traditional method such as computer vision techniques is hardly leveraged since the working condition is limited. Therefore, in this paper, we attempt to tackle human detection by using the radio frequency (RF) signal with a device-free sensing. To this end, we propose a differential evolution flower pollination algorithm support vector machine (DEFPA-SVM) approach to detect human among other targets, e.g., iron cupboard and wooden board. This task can be formally described as a target classification problem. In our experiment, the proposed DEFPA-SVM can effectively attain the best performance compared to other classical multi-target classification models and achieve a faster convergent speed than the traditional FPA-SVM.

Zhu, L, Sevilla-Lara, L, Tran, D, Feiszli, M, Yang, Y & Wang, H 2020, 'FASTER Recurrent Networks for Video Classification', The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI.