Author：Tabbara, H;Gu, S;McCartney, DG

Abstract:A warm spray system has been computationally investigated by introducing a centrally located mixing chamber into a HVOF thermal spray gun. The effects of injecting a cooling gas on the gas and particle dynamics are examined. The gas phase model incorporates liquid fuel droplets which heat, evaporate and then exothermically combust with oxygen within the combustion chamber producing a realistic compressible, supersonic and turbulent jet. The titanium powder is tracked using the Lagrangian approach including particle heating, melting and solidification. The results present an insight into the complex interrelations between the gas and particle phases, and highlight the advantage of warm spray, especially for the deposition of oxygen sensitive materials such as titanium. This work also demonstrates the effectiveness of a computational approach in aiding the development of thermal spray devices. (C) 2011 Elsevier Ltd. All rights reserved.

Author：Wang, X. ; Yuan, F. ; Chen, M. ; He, J. ; Wang, P. ; Yu, Y. ; Li, J.

Abstract:Compared with traditional structures, lattice components produced by additive manufacturing have an outstanding mechanical performance with ultra-low density per unit. This lattice structure consisting of periodic unit cells can carry loads in tension or compression. In this study, the numerical simulation is used to explore the effect of the metal coating on the lattice units, and the experiment has been conducted to validate the mechanical properties of metal-coated lattice structure. The results of the simulation indicate that the coating technology can enhance the relative stiffness to density for lattice structures. Nevertheless, the experimental results exhibit the controversial conclusion, which was possibly caused by plating technologies for lattice with thin rods. © 2020 Taylor & Francis Group, London.

Author：Liu, TQ;Lawluvy, Y;Shi, Y;Ighalo, JO;He, YD;Zhang, YJ;Yap, PS

Abstract:Cadmium (Cd) and lead (Pb) contaminations are disturbing environmental issues, which cause serious harm to aqueous systems and human health. Therefore, removing them from aqueous solution is essential to prevent their damage to the environment. Environmental adsorption research as one solution is promising and has been getting a lot of attention in the recent years. Using modified biochar has proved to be more suitable for the adsorption of cadmium and lead. In this review, the sources of cadmium and lead in the environment and their hazards have been elucidated. In addition, the preparation methods of modified biochar to remove Cd and Pb have been discussed. This review also presents the adsorption kinetics and isotherms results for the adsorption of Cd and Pb from aqueous solution using modified biochar. The effect of experimental parameters and adsorption mechanisms are also discussed in order to understand the adsorption performances of modified biochar in great detail. The adsorption mechanisms were surface precipitation, surface complexation, ion exchange, chelation, electrostatic attraction, inner sphere complexation, redox and physical adsorption. The adsorption was mainly endothermic and spontaneous. Additionally, the results on the regeneration of modified biochar are presented to provide a direction for sustainable improvement. Finally, this review article also provides the challenges, prospects and future perspectives of adsorption of cadmium and lead from aqueous solutions using the modified biochar.

Author：Yang, J;Jia, LM;Fu, YX;Lu, SF

Abstract:Based on the characteristics of freight train control, which are nonlinear, time-delay, with multi-constraint and multiobjective, this paper focuses on speed tracking problem. Firstly, in a gradual process, a multi-modal fuzzy PID (MM-FPID) control algorithm is presented on the basis of a brief analysis of PI and PID control, which is generally used to train control in active services. Secondly, in order to deal with the time-delay problem of freight train, the paper adopts an approach of traction force feed-forward, which greatly improves the dynamic performance of the controller. Thirdly, for the overspeed brake problem caused by speed overshoot, the strategy of adaptive traction force limitation is adopted, and we get satisfactory results without increasing the safety speed margin. Fourthly, inspired by the selflearning characteristic of neural networks (NNs), an integrated controller of MM-FPID and NNs is proposed. Finally, with the help of a computer simulation platform, the paper puts forward a set of simulations, comparing the MM-FPID and the integrated control method with classical PID and fuzzy control. The results show that both MM-FPID and the integrated controller has satisfactory control effect, and their multi-modal structure makes it easy to fit different applications well, while the integrated controller has more potential in self-learning.

Author：Wu, CL;Chung, H;Liu, ZY;Kim, I

Abstract:In recent years, many cities around the world have implemented bike-sharing programs. A number of studies on the relationship between the built environment and bike usage have provided important insights into understanding bike-sharing systems. However, the effects of the built environment on the structural properties of bike-sharing networks are seldom discussed in the literature. This research proposes a novel and interdisciplinary framework to explore how built environment factors affect the topological properties of bike-sharing networks. Firstly, this research applies a complex network approach to quantify the importance of bike stations in the network. Then, multisource data are utilized to identify comprehensive built environment attributes. Finally, spatial regression models are used to reveal the relationship between the importance of bike stations and built environment. In this study, the bike-sharing system in Suzhou, China, is taken as a case study. The empirical result shows that the importance of bike stations displays strong spatial dependence. Also, built environment attributes such as resident population, accessibility to subway stations, the capacity of bike stations, and the total length of main roads within a catchment area have different effects on the importance of bike stations. It should be noted that the floating population and the number of bus stops surrounding bike stations do not have strong correlations with the importance of bike stations. The findings of this study can guide urban planners and operators to improve the service quality and resilience of bike-sharing systems.

Author：Shi, HC;Wen, HQ;Chen, J;Hu, YH;Jiang, L;Chen, GP;Ma, JM

Abstract:As key component for the flexible dc distributed power system, the dual active bridge (DAB)-converter-based solid-state transformer (SST) with high efficiency for a wide operating range is essential. However, with the traditional phase-shift control, high backflow power and current stress will significantly affect the conversion efficiency. In this paper, the backflow power characteristics in both sides of DAB-based SST converters are comprehensively analyzed. On this basis, complete transmission power, backflow power, and peak current mathematical models are established. Then, a minimum-backflow-power-based extended-phase-shift control strategy is proposed with the determination of optimal phase-shift pairs by using the Karush-Kuhn-Tucker function for various scenarios. The backflow power and current stress curves with different algorithms are compared. It shows the proposed control can improve the output power regulation flexibility, minimize the backflow power, and improve the efficiency in wide operating range. Finally, a DAB-based SST prototype was developed and the experimental results verified the effectiveness of the proposed control strategy.

Author：Abid, F;Li, C;Alam, M

Abstract:Subjectivity detection in the text is essential for sentiment analysis, which requires many techniques to perceive unanticipated means of communication. Few accomplishments adapted to capture the syntactic, semantic, and contextual sentimental information via distributed word representations (DWRs)(1). This paper, concatenating the DWRs through a weighted mechanism on Recurrent Neural Network (RNN) variants joint with Convolutional Neural network (CNN) distinctively involving weighted attentive pooling (WAP)(2). Whereas, CNNs with traditional pooling operations comprise many layers merely able to capture enough features. Our considerations empower the sentiment analysis over DWRs contains Word2vec, FastText, and GloVe to produce dense efficient concatenated representation (DECR)(3) to hold long term dependencies on a single RNN layer acquired by Parts of Speech Tagging (POS) explicitly with verbs, adverbs, and noun only. Then use these representations gained in a way, inputted to CNN contain single convolution layer engaging WAP on multi-source social media data to handle the issues of syntactic and semantic regularities as well as out of vocabulary (OOV) words. Experimentations demonstrate that DWRs together with proposed concatenation qualified in resolving the mentioned issues by moderate hyper-parameter configurations. Our architecture devoid of stacking multiple layers achieved modest accuracy of 89.67%% by DECR-Bi-GRU-CNN (WAP) on IMDB as compared to random initialization 81.11%% on SST.

Author：Cha, J;Lee, S;Kim, KS

Abstract:Key issues of indoor localization is taking full advantages and overcoming its disadvantages. indoor localization based on Wi-Fi fingerprinting attracts researchers' attentions since it does not require new infrastructure and devices. Many devices such as smart phones and laptops, which have a function to capture Wi-Fi signals, can be used for Wi-Fi fingerprinting. However, due to unreliable Wi-Fi signals, there are still difficulty to achieve high positioning accuracy. The unreliable signal disturbs devices to find their locations. As a result, getting localization with devices sometimes makes a wrong decision in building classification. It is useless for people to find a destination floor if they are in different building. In this paper, we propose two consecutive multi-layer perceptrons to get more precise localization. With sumple structure, we get better performance and show precise decision results in building classification, which is critical in Wi-Fi fingerprinting. We use UJIndoorLoc dataset which is open dataset.

Author：Ni, K;Liu, YJ;Mei, ZB;Wu, TH;Hu, YH;Wen, HQ;Wang, YG

Abstract:This paper presents a review of the electrical and electronic technologies investigated in more-electric aircraft (MEA). In order to change the current situation of low power efficiency, serious pollution, and high operating cost in conventional aircraft, the concept of MEA is proposed. By converting some hydraulic, mechanical, and pneumatic power sources into electrical ones, the overall power efficiency is greatly increased, and more flexible power regulation is achieved. The main components in an MEA power system are electrical machines and power electronics devices. The design and control methods for electrical machines and various topologies and control strategies for power electronic converters have been widely researched. Besides, several studies are carried out regarding energy management strategies that intend to optimize the operation of MEA power distribution systems. Furthermore, it is necessary to investigate the system stability and reliability issues in an MEA, since they are directly related to the safety of passengers. In terms of machine technologies, power electronics techniques, energy management strategies, and the system stability and reliability, a review is carried out for the contributions in the literature to MEA.

Author：Zhang, T;Zhang, SW;Zhang, L;Meng, J

Abstract:Background: Methylated RNA immunoprecipitation sequencing (MeRIP-seq or m(6)A-seq) has been extensively used for profiling transcriptome-wide distribution of RNA N6-Methyl-Adnosine methylation. However, due to the intrinsic properties of RNA molecules and the intricate procedures of this technique, m(6)A-seq data often suffer from various flaws. A convenient and comprehensive tool is needed to assess the quality of m(6)A-seq data to ensure that they are suitable for subsequent analysis. Results: From a technical perspective, m(6)A-seq can be considered as a combination of ChIP-seq and RNA-seq; hence, by effectively combing the data quality assessment metrics of the two techniques, we developed the trumpet R package for evaluation of m(6)A-seq data quality. The trumpet package takes the aligned BAM files from m(6)A-seq data together with the transcriptome information as the inputs to generate a quality assessment report in the HTML format. Conclusions: The trumpet R package makes a valuable tool for assessing the data quality of m(6)A-seq, and it is also applicable to other fragmented RNA immunoprecipitation sequencing techniques, including m(1)A-seq, CeU-Seq, psi-seq, etc.

Author：Ding, H;Zhou, DQ;Liu, GQ;Zhou, P

Abstract:Government policies and investments in photovoltaic (PV) research and development (R&D) have contributed to the rapid development of a PV industry through technology push in most countries over the past decade. It is worth investigating the effectiveness of investment-drive R&D policies at a global level, particularly how they work in reducing the costs of PV technologies. This study constructs a learning curve model to assess the performance of PV R&D policies in China, Germany, the United States and Japan. Market information-for example, PV module production, PV installation and PV technology improvementis utilised to analyse how these policies take effect. The results show that PV- R&D investments are efficient in decreasing the production costs of PV modules, which positively affects the development of PV module markets. However, weak PV technology conditions (including conversion efficiency, reliability) and low PV electricity penetration levels have resulted in surpluses in PV module markets, as well as PV electricity curtailment around the world. It is suggested that future R&D policies should contribute more to improving conversion efficiencies (the structure of technology push power) and grid integration technologies (demand pull power) for PV systems.

Author：Chen, XC;Fan, RM;Wu, J;Song, XK;Liu, Q;Wang, YW;Wang, YJ;Tao, B

Abstract:Clear and focused pattern images are essential prerequisites for accurate feature detection in traditional camera calibration methods, which introduce numerous limitations in various areas, such as long-distance photogrammetry. A feature detection method robust against defocusing is proposed for extracting the centers or corners of a planar square periodic target. A Fourier transform is employed to calculate two wrapped phase maps from the periodic target images, which are then used to accurately extract the feature points. The calibration procedure is divided into two stages to obtain more accurate results. A rough calibration is performed to calculate the rotation angles between the target and the camera. If the tilt angle is larger than 12 degrees, the corresponding images are removed. Subsequently, the remaining images are used for precise calibration. The simulations and the experiments demonstrate that the proposed method can accurately calibrate a camera with a planar square periodic pattern, even in the case of severe defocusing.

Author：Yan, K;Wang, XD;Du, Y;Jin, N;Huang, HC;Zhou, HX

Abstract:Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household's personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.

Author：Liu, QH;Zhao, C;Mitrovic, IZ;Xu, WY;Yang, L;Zhao, CZ

Abstract:Solution-processed metal-oxide thin films have been widely studied in low-power and flexible electronics. However, the high temperature required to form a condensed and uniform film limits their applications in flexible and low-cost electronics. Here, a novel and environmental-friendly comproportionation reaction synthesis (CRS) is presented to obtain amorphous aluminum oxide (AlOx) thin films for solution-processed thin-film transistors (TFTs) employing water as the precursor solvent. The thermal decomposition of CRS-AlO(x)precursor is completed at approximate to 300 degrees C, which is 100 degrees C lower than that of the conventional water-induced AlOx. The morphological, optical, compositional, and electrical properties of CRS-AlO(x)dielectric films are studied systematically. Meanwhile, TFTs based on water-induced In(2)O(3)metal oxide semiconductor layers deposited on these dielectrics at low temperatures are formed and characterized. Compared with TFTs based on conventional AlO(x)showing low mobility and low clockwise hysteresis, In2O3TFTs based on CRS-AlO(x)exhibit improved electrical performance and counterclockwise hysteresis in the transfer curves. Water-induced TFTs fabricated on CRS-AlO(x)formed at a low temperature of 250 degrees C have average mobility of 98 cm(2)V(-1)s(-1). Through chemical composition characterization and electrical characterization, the high mobilities of TFTs based on CRS-AlO(x)dielectrics are correlated to trap states, which resulted in counterclockwise hysteresis in the transfer curves.

Author：Shi, Haochen ; Wen, Huiqing ; Cao, Zhenyan ; Hu, Yihua ; Jiang, Lin

Abstract:The derivation and implementation of multiple-phase-shift-based switching surfaces for a dual active bridge (DAB) converter is the main focus of this article. First, the mathematical models of multiple natural switching surfaces under different operation states of DAB converters are derived, which lays the foundation to achieve a fast transient response during startup, sudden voltage reference, and load changing conditions. Moreover, in order to improve the overall performance of DAB converters systematically, a minimum-current-stress boundary control (MBC) is proposed that can reduce the inductor peak current stress and achieve fast dynamic response simultaneously by using the multiple-phase-shift-based switching surfaces. The analytical derivation of the proposed MBC is presented together with the simulation and experimental evaluations, which shows the superior performance of the proposed MBC algorithm in terms of the efficiency and dynamic response improvement under various operating conditions. © 1982-2012 IEEE.

Author：Sun, MJ;Xiao, JM;Lim, EG

Abstract:In this paper, we are tackling the proposal-free referring expression grounding task, aiming at localizing the target object according to a query sentence, without relying on off-the-shelf object proposals. Existing proposal-free methods employ a query-image matching branch to select the highest-score point in the image feature map as the target box center, with its width and height predicted by another branch. Such methods, however, fail to utilize the contextual relation between the target and reference objects, and lack interpretability on its reasoning procedure. To solve these problems, we propose an iterative shrinking mechanism to localize the target, where the shrinking direction is decided by a reinforcement learning agent, with all contents within the current image patch comprehensively considered. Besides, the sequential shrinking processes enable to demonstrate the reasoning about how to iteratively find the target. Experiments show that the proposed method boosts the accuracy by 4.32%% against the previous state-of-the-art (SOTA) method on the RefCOCOg dataset, where query sentences are long and complex with many targets referred by other reference objects.

Author：Gupta, Jyoti ; Papadikis, Konstantinos ; Konysheva, Elena Yu. ; Lin, Yi ; Kozhevnikov, Ivan V. ; Li, Jingjing

Abstract:The impact of CaO on oakwood pyrolysis was explored by the Py-GC/MS at 500 °C. Ca(OH)2 presents on the CaO surface, indicating its partial hydration (noted as CaOOH). CaOOH promoted the ketonisation of carboxylic acids to aliphatic ketones, furfural to cyclopentanone/2-cyclopentenone, facilitated the elimination of the methoxy-phenolic compounds. The catalyst loading did not show any significant effect on the formation of acetone, while a noticeable reduction in the phenolics was revealed. Increasing catalyst loading almost eliminated CO2, acids, furans, aldehydes, ether groups, whilst the fractions of alcohols, esters, sugars, and alkoxybenzene decreased noticeably. The use of partially hydrated CaOOH in the CFP can create conditions where by-products generated during the ketonisation and phenolics upgrading reactions (CO2, H2O, CO) interact through the adsorption enhance water-gas-shift reaction and through coking reactions with formation of H2 used for hydrogenation during multistep conversion of furfural to cyclopentanone in a vapour phase. © 2020 Elsevier B.V.

Author：Syed, HI;Yap, PS

Abstract:Contamination of the aquatic ecosystem by heavy metals is a growing concern that has yet to be addressed with an efficient, cost-effective and environmentally-friendly solution. Heavy metals occur naturally in the earth's crust and also result from anthropogenic activities. Due to the rapid increase in industrialization, there is an increase in consumer demands across various industries such as metal processing, mining sector, agricultural activities, etc. and this has led to an increase in heavy metal concentrations in the aqueous environment. Cellulose based aerogels are a novel third-generation of aerogels that have recently attracted a lot of attention due to their high adsorption efficiency, eco-friendly prospects and cost effectiveness. In this review, recent literature on cellulose-based aerogel adsorbents used for the removal of heavy metals from aqueous solution has been compiled. The preparation of cellulose-based aerogels, adsorption mechanisms, effects of experimental factors such as pH, temperature, contact time, initial metal concentration and adsorbent dose have been discussed. In addition, cost analysis of cellulose-based adsorbents and some challenges in this research field along with recommendations of improvements have been presented. It can be concluded that functionalizing of cellulose-based aerogels with amine groups, thiol groups, other compounds such as nanobentonite and chitosan results in very high adsorption capacities. The adsorption studies revealed that pseudo-second-order kinetic model was the most commonly encountered adsorption kinetic model, and the most commonly encountered adsorption isotherm model was the Langmuir isotherm model. The main adsorption mechanisms were electrostatic interaction, complexation and ion exchange. (c) 2021 Elsevier B.V. All rights reserved.

Author：Yang, MY;Chen, L;Msigwa, G;Tang, KHD;Yap, PS

Abstract:The impacts of COVID-19 on global environmental pollution since its onset in December 2019 require special attention. The rapid spread of COVID-19 globally has led countries to lock down cities, restrict traffic travel and impose strict safety measures, all of which have implications on the environment. This review aims to systematically and comprehensively present and analyze the positive and negative impacts of COVID-19 on global environmental pollution and carbon emissions. It also aims to propose strategies to prolong the beneficial, while minimize the adverse environmental impacts of COVID-19. It systematically and comprehensively reviewed more than 100 peer-reviewed papers and publications related to the impacts of COVID-19 on air, water and soil pollution, carbon emissions as well as the sustainable strategies forward. It revealed that PM2.5, PM10, NO2, and CO levels reduced in most regions globally but SO2 and 0 3 levels increased or did not show significant changes. Surface water, coastal water and groundwater quality improved globally during COVID-19 lockdown except few reservoirs and coastal areas. Soil contamination worsened mainly due to waste from the use of personal protective equipment particularly masks and the packaging, besides household waste. Carbon emissions were reduced primarily due to travel restrictions and less usage of utilities though emissions from certain ships did not change significantly to maintain supply of the essentials. Sustainable strategies post-COVID-19 include the development and adoption of nanomaterial adsorption and microbial remediation technologies, integrated waste management measures, "sterilization wave" technology and energy-efficient technologies. This review provides important insight and novel coverage of the environmental implications of COVID-19 in more than 25 countries across different global regions to permit formulation of specific pollution control and sustainability strategies in the COVID-19 and postCOVID-19 eras for better environmental quality and human health. (C) 2021 Elsevier B.V. All rights reserved.

Author：Zhang, Q;Tao, SH;Du, J;He, A;Yang, Y;Tao, CY

Abstract:With regard to using the electric energy generated by renewable sources, the CO2 electroreduction reaction (CO2RR) for the production of fuels is helpful for creating an artificial carbon cycle. Herein, for the first time, we prepared in situ AgCo surface alloy electrocatalysts at room temperature by the cold H-2-plasma activation method; these electrocatalysts showed high activity for the CO2RR to ethanol with an excellent faradaic efficiency of ethanol (72.3%%) and current density (7.4 mA cm(-2) at -0.80 V). Based on experiments and DFT calculations, this high intrinsic activity was attributed to the selective suppression of hydrogen evolution and C1 production due to the distortion of the Ag lattice induced by the formation of a {111}Ag + Co surface alloy to reduce the energy barrier for *CO2 delta- formation; this increased the coverage of CO* and resulted in a C-C coupling reaction to form *OC-CO* on Ag atoms (CO* pool sites), which was further converted to CH3CH2OH. Thus, this result showed that promoting the Ag surface with small amounts of Co is a promising way to improve ethanol selectivity during the CO2RR.