bluuebunny/crossref_metadata_2025 huggingface dataset:
Electrochemical Sensors for Controlling Oxygen Content and Corrosion Processes in Lead-Bismuth Eutectic Coolant—State of the Art
Authors: Orlov Sergey N. , Bogachev Nikita A. , Mereshchenko Andrey S. , Zmitrodan Alexandr A. , Skripkin Mikhail Yu.
Published: January 2023

Controlling oxygen content in the primary circuit of nuclear reactors is one of the key tasks needed to ensure the safe operation of nuclear power plants where lead-bismuth eutectic alloy (LBE) is used as a coolant. If the oxygen concentration is low, active corrosion of structural materials takes place; upon increase in oxygen content, slag accumulates due to the formation of lead oxide. The generally accepted method of measuring the oxygen content in LBE is currently potentiometry. The sensors for measuring oxygen activity (electrochemical oxygen sensors) are galvanic cells with two electrodes (lead-bismuth coolant serves as working electrode) separated by a solid electrolyte. Control of corrosion and slag accumulation processes in circuits exploring LBE as a coolant is also based on data obtained by electrochemical oxygen sensors. The disadvantages of this approach are the low efficiency and low sensitivity of control. The alternative, Impedance Spectroscopy (EIS) Sensors, are proposed for Real-Time Corrosion Monitoring in LBE system. Currently their applicability in static LBE at temperatures up to 600 °C is shown.
A Low-Cost Hardware Architecture for EV Battery Cell Characterization Using an IoT-Based Platform
Authors: Martínez-Sánchez Rafael , Molina-García Ángel , Ramallo-González Alfonso P. , Sánchez-Valverde Juan , Úbeda-Miñarro Benito
Published: January 2023

Since 1997, when the first hybrid vehicle was launched on the market, until today, the number of NIMH batteries that have been discarded due to their obsolescence has not stopped increasing, with an even faster rate more recently due to the progressive disappearance of thermal vehicles on the market. The battery technologies used are mostly NIMH for hybrid vehicles and Li ion for pure electric vehicles, making recycling difficult due to the hazardous materials they contain. For this reason, and with the aim of extending the life of the batteries, even including a second life within electric vehicle applications, this paper describes and evaluates a low-cost system to characterize individual cells of commercial electric vehicle batteries by identifying such abnormally performing cells that are out of use, minimizing regeneration costs in a more sustainable manner. A platform based on the IoT technology is developed, allowing the automation of charging and discharging cycles of each independent cell according to some parameters given by the user, and monitoring the real-time data of such battery cells. A case study based on a commercial Toyota Prius battery is also included in the paper. The results show the suitability of the proposed solution as an alternative way to characterize individual cells for subsequent electric vehicle applications, decreasing operating costs and providing an autonomous, flexible, and reliable system.
An Overview of Flexible Sensors: Development, Application, and Challenges
Authors: Liu Enze , Cai Zhimin , Ye Yawei , Zhou Mingyue , Liao Hui , Yi Ying
Published: January 2023

The emergence and advancement of flexible electronics have great potential to lead development trends in many fields, such as “smart electronic skin” and wearable electronics. By acting as intermediates to detect a variety of external stimuli or physiological parameters, flexible sensors are regarded as a core component of flexible electronic systems and have been extensively studied. Unlike conventional rigid sensors requiring costly instruments and complicated fabrication processes, flexible sensors can be manufactured by simple procedures with excellent production efficiency, reliable output performance, and superior adaptability to the irregular surface of the surroundings where they are applied. Here, recent studies on flexible sensors for sensing humidity and strain/pressure are outlined, emphasizing their sensory materials, working mechanisms, structures, fabrication methods, and particular applications. Furthermore, a conclusion, including future perspectives and a short overview of the market share in this field, is given for further advancing this field of research.
Driving Activity Recognition Using UWB Radar and Deep Neural Networks
Authors: Brishtel Iuliia , Krauss Stephan , Chamseddine Mahdi , Rambach Jason Raphael , Stricker Didier
Published: January 2023

In-car activity monitoring is a key enabler of various automotive safety functions. Existing approaches are largely based on vision systems. Radar, however, can provide a low-cost, privacy-preserving alternative. To this day, such systems based on the radar are not widely researched. In our work, we introduce a novel approach that uses the Doppler signal of an ultra-wideband (UWB) radar as an input to deep neural networks for the classification of driving activities. In contrast to previous work in the domain, we focus on generalization to unseen persons and make a new radar driving activity dataset (RaDA) available to the scientific community to encourage comparison and the benchmarking of future methods.
Cost-Effective Network Reordering Using FPGA
Authors: Hoang Vinh Quoc , Chen Yuhua
Published: January 2023

The advancement of complex Internet of Things (IoT) devices in recent years has deepened their dependency on network connectivity, demanding low latency and high throughput. At the same time, expanding operating conditions for these devices have brought challenges that limit the design constraints and accessibility for future hardware or software upgrades. These limitations can result in data loss because of out-of-order packets if the design specification cannot keep up with network demands. In addition, existing network reordering solutions become less applicable due to the drastic changes in the type of network endpoints, as IoT devices typically have less memory and are likely to be power-constrained. One approach to address this problem is reordering packets using reconfigurable hardware to ease computation in other functions. Field Programmable Gate Array (FPGA) devices are ideal candidates for hardware implementations at the network endpoints due to their high performance and flexibility. Moreover, previous research on packet reordering using FPGAs has serious design flaws that can lead to unnecessary packet dropping due to blocking in memory. This research proposes a scalable hardware-focused method for packet reordering that can overcome the flaws from previous work while maintaining minimal resource usage and low time complexity. The design utilizes a pipelined approach to perform sorting in parallel and completes the operation within two clock cycles. FPGA resources are optimized using a two-layer memory management system that consumes minimal on-chip memory and registers. Furthermore, the design is scalable to support multi-flow applications with shared memories in a single FPGA chip.
A Robust Control via a Fuzzy System with PID for the ROV
Authors: Dong Junjie , Duan Xingguang
Published: January 2023

Uncertainty and nonlinearity in the depth control of remotely operated vehicles (ROVs) have been widely studied, especially in complex underwater environments. To improve the motion performance of ROVs and enhance their robustness, the model of ROV depth control in complex water environments was developed. The developed control scheme of interval type-2 fuzzy proportional–integral–derivative control (IT2FPID) is based on proportional–integral–derivative control (PID) and interval type-2 fuzzy logic control (IT2FLC). The performance indicators were used to evaluate the immunity of the controller type to external disturbances. The overshoot of 0.3% and settling time of 7.5 s of IT2FPID seem to be more robust compared to those of type-1 fuzzy proportional–integral–derivative (T1FPID) and PID.
A Video Sequence Face Expression Recognition Method Based on Squeeze-and-Excitation and 3DPCA Network
Authors: Li Chang , Wen Chenglin , Qiu Yiting
Published: January 2023

Expression recognition is a very important direction for computers to understand human emotions and human-computer interaction. However, for 3D data such as video sequences, the complex structure of traditional convolutional neural networks, which stretch the input 3D data into vectors, not only leads to a dimensional explosion, but also fails to retain structural information in 3D space, simultaneously leading to an increase in computational cost and a lower accuracy rate of expression recognition. This paper proposes a video sequence face expression recognition method based on Squeeze-and-Excitation and 3DPCA Network (SE-3DPCANet). The introduction of a 3DPCA algorithm in the convolution layer directly constructs tensor convolution kernels to extract the dynamic expression features of video sequences from the spatial and temporal dimensions, without weighting the convolution kernels of adjacent frames by shared weights. Squeeze-and-Excitation Network is introduced in the feature encoding layer, to automatically learn the weights of local channel features in the tensor features, thus increasing the representation capability of the model and further improving recognition accuracy. The proposed method is validated on three video face expression datasets. Comparisons were made with other common expression recognition methods, achieving higher recognition rates while significantly reducing the time required for training.
Machine Learning Models for the Automatic Detection of Exercise Thresholds in Cardiopulmonary Exercising Tests: From Regression to Generation to Explanation
Authors: Zignoli Andrea
Published: January 2023

The cardiopulmonary exercise test (CPET) constitutes a gold standard for the assessment of an individual’s cardiovascular fitness. A trend is emerging for the development of new machine-learning techniques applied to the automatic process of CPET data. Some of these focus on the precise task of detecting the exercise thresholds, which represent important physiological parameters. Three are the major challenges tackled by this contribution: (A) regression (i.e., the process of correctly identifying the exercise intensity domains and their crossing points); (B) generation (i.e., the process of artificially creating a CPET data file ex-novo); and (C) explanation (i.e., proving an interpretable explanation about the output of the machine learning model). The following methods were used for each challenge: (A) a convolutional neural network adapted for multi-variable time series; (B) a conditional generative adversarial neural network; and (C) visual explanations and calculations of model decisions have been conducted using cooperative game theory (Shapley’s values). The results for the regression, generation, and explanatory techniques for AI-assisted CPET interpretation are presented here in a unique framework for the first time: (A) machine learning techniques reported an expert-level accuracy in the classification of exercise intensity domains; (B) experts are not able to substantially differentiate between a real vs an artificially generated CPET; and (C) Shapley’s values can provide an explanation about the choices of the algorithms in terms of ventilatory variables. With the aim to increase their technology-readiness level, all the models discussed in this contribution have been incorporated into a free-to-use Python package called pyoxynet (ver. 12.1). This contribution should therefore be of interest to major players operating in the CPET device market and engineering.
Neural Network-Based Active Load-Sensing Scheme and Stiffness Adjustment for Pneumatic Soft Actuators for Minimally Invasive Surgery Support
Authors: Lu Yuxi , Zhou Zhongchao , Kokubu Shota , Qin Ruian , Tortós Vinocour Pablo E. , Yu Wenwei
Published: January 2023

To provide a stable surgical view in Minimally Invasive Surgery (MIS), it is necessary for a flexible endoscope applied in MIS to have adjustable stiffness to resist different external loads from surrounding organs and tissues. Pneumatic soft actuators are expected to fulfill this role, since they could feed the endoscope with an internal access channel and adjust their stiffness via an antagonistic mechanism. For that purpose, it is essential to estimate the external load. In this study, we proposed a neural network (NN)-based active load-sensing scheme and stiffness adjustment for a soft actuator for MIS support with antagonistic chambers for three degrees of freedom (DoFs) of control. To deal with the influence of the nonlinearity of the soft actuating system and uncertainty of the interaction between the soft actuator and its environment, an environment exploration strategy was studied for improving the robustness of sensing. Moreover, a NN-based inverse dynamics model for controlling the stiffness of the soft actuator with different flexible endoscopes was proposed too. The results showed that the exploration strategy with different sequence lengths improved the estimation accuracy of external loads in different conditions. The proposed method for external load exploration and inverse dynamics model could be used for in-depth studies of stiffness control of soft actuators for MIS support.
Intelligent Wearable Wrist Pulse Detection System Based on Piezoelectric Sensor Array
Authors: Liu Yan-Yun , Lv Yu-Xiang , Xue Hai-Bin
Published: January 2023

The human radial artery pulse carries a rich array of biomedical information. Accurate detection of pulse signal waveform and the identification of the corresponding pulse condition are helpful in understanding the health status of the human body. In the process of pulse detection, there are some problems, such as inaccurate location of radial artery key points, poor signal noise reduction effect and low accuracy of pulse recognition. In this system, the pulse signal waveform is collected by the main control circuit and the new piezoelectric sensor array combined with the wearable wristband, creating the hardware circuit. The key points of radial artery are located by an adaptive pulse finding algorithm. The pulse signal is denoised by wavelet transform, iterative sliding window and prediction reconstruction algorithm. The slippery pulse and the normal pulse are recognized by feature extraction and classification algorithm, so as to analyze the health status of the human body. The system has accurate pulse positioning, good noise reduction effect, and the accuracy of intelligent analysis is up to 98.4%, which can meet the needs of family health care.
An Innovative Compact System to Measure Skiing Ground Reaction Forces and Flexural Angles of Alpine and Touring Ski Boots
Authors: Zullo Giuseppe , Cibin Pierluigi , Bortolan Lorenzo , Botteon Michele , Petrone Nicola
Published: January 2023

Skiing is a popular winter activity spanning various subdisciplines. Key hardware are ski boots, bindings, and skis, which are designed to withstand loads generated during skiing. Obtaining service forces and moments has always been challenging to researchers in the past. The goal of the present study is to develop and test a lightweight and compact measurement system to obtain the Ground Reaction Forces and the kinematics for ski touring and alpine ski. To do so, we adapted two six-axis load cells to fit into ski touring and alpine skis adding 20 mm height and 500 g weight to the original ski. To measure kinematics, we created custom angular sensors from rotary potentiometers. The system was tested indoors using a force platform and motion capture system before a first set of field tests in which the sensors were used to measure ski touring and alpine skis kinetics and kinematics. Validation trials showed maximum errors of 10% for kinetics and 5% for kinematics. Field tests showed data in agreement with previous findings on the topic. The results of this study show the possibility of using our system to study biomechanics and equipment performances for ski touring, alpine skiing, and possibly other disciplines.
Electrochemical (Bio)Sensing Devices for Human-Microbiome-Related Biomarkers
Authors: Sánchez-Tirado Esther , Agüí Lourdes , González-Cortés Araceli , Campuzano Susana , Yáñez-Sedeño Paloma , Pingarrón José Manuel
Published: January 2023

The study of the human microbiome is a multidisciplinary area ranging from the field of technology to that of personalized medicine. The possibility of using microbiota biomarkers to improve the diagnosis and monitoring of diseases (e.g., cancer), health conditions (e.g., obesity) or relevant processes (e.g., aging) has raised great expectations, also in the field of bioelectroanalytical chemistry. The well-known advantages of electrochemical biosensors—high sensitivity, fast response, and the possibility of miniaturization, together with the potential for new nanomaterials to improve their design and performance—position them as unique tools to provide a better understanding of the entities of the human microbiome and raise the prospect of huge and important developments in the coming years. This review article compiles recent applications of electrochemical (bio)sensors for monitoring microbial metabolites and disease biomarkers related to different types of human microbiome, with a special focus on the gastrointestinal microbiome. Examples of electrochemical devices applied to real samples are critically discussed, as well as challenges to be faced and where future developments are expected to go.
Analysis and Design of a CLC/N Compensated CC-Type WPT System with Compact and Low-Cost Receiver
Authors: Yang Lin , Jiang Shuai , Wang Can , Zhang Li
Published: January 2023

Wireless power transfer (WPT) has been extensively studied by technicians for its advantages of safety, convenience and aesthetics. The load-independent constant current (CC) output is the focus of WPT research and has been initially applied in various fields, such as light-emitting diodes (LEDs) driving, CC charging of electric vehicles (EVs), etc. However, the existing CC-type WPT system has problems in that the output current is constrained by the loosely coupled transformer (LCT) parameters, the receiver is bulky, and the development cost is high. Therefore, this manuscript proposes a new CLC/None (CLC/N) compensated WPT system with a CC output function that eliminates the receiver-side compensation components, ensures the compactness of the receiver, and saves on production costs. The conditions for satisfying the CC output and zero-phase-angle (ZPA) operation of the proposed system are first discussed. Then, the detailed parameter design method is provided, and the characteristic that the output current is unconstrained by the LCT parameters is illustrated. In addition, the implementation of zero-voltage switching (ZVS) operation of the proposed system and the sensitivity of the changes of compensation components to the output current are analyzed in detail. Furthermore, to demonstrate the superiority of the proposed system, several other typical CC-type WPT systems are introduced for comparison. Finally, a confirmatory experimental prototype with an output current of 2 A is fabricated, and the experimental results are consistent with the theoretical analysis.
Catch Recognition in Automated American Football Training Using Machine Learning
Authors: Hollaus Bernhard , Reiter Bernhard , Volmer Jasper C.
Published: January 2023

In order to train receivers in American football in a targeted and individual manner, the strengths and weaknesses of the athletes must be evaluated precisely. As human resources are limited, it is beneficial to do it in an automated way. Automated passing machines are already given, therefore the motivation is to design a computer-based system that records and automatically evaluates the athlete’s catch attempts. The most fundamental evaluation would be whether the athlete has caught the pass successfully or not. An experiment was carried out to gain data about catch attempts that potentially contain information about the outcome of such. The experiment used a fully automated passing machine which can release passes on command. After a pass was released, an audio and a video sequence of the specific catch attempt was recorded. For this purpose, an audio-visual recording system was developed which was integrated into the passing machine. This system is used to create an audio and video dataset in the amount of 2276 recorded catch attempts. A Convolutional Neural Network (CNN) is used for feature extraction with downstream Long Short-Term Memory (LSTM) to classify the video data. Classification of the audio data is performed using a one-dimensional CNN. With the chosen neural network architecture, an accuracy of 92.19% was achieved in detecting whether a pass had been caught or not. The feasibility for automatic classification of catch attempts during automated catch training is confirmed with this result.
MD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction
Authors: Huang Xiaohui , Wang Junyang , Lan Yuanchun , Jiang Chaojie , Yuan Xinhua
Published: January 2023

The spatial–temporal prediction of traffic flow is very important for traffic management and planning. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. Due to the complex spatial correlation between different roads and the dynamic trend of time patterns, traditional forecasting methods still have limitations in obtaining spatial–temporal correlation, which makes it difficult to extract more valid information. In order to improve the accuracy of the forecasting, this paper proposes a multi-scale temporal dual graph convolution network for traffic flow prediction (MD-GCN). Firstly, we propose a gated temporal convolution based on a channel attention and inception structure to extract multi-scale temporal dependence. Then, aiming at the complexity of the traffic spatial structure, we develop a dual graph convolution module including the graph sampling and aggregation submodule (GraphSAGE) and the mix-hop propagation graph convolution submodule (MGCN) to extract the local correlation and global correlation between neighbor nodes. Finally, extensive experiments are carried out on several public traffic datasets, and the experimental results show that our proposed algorithm outperforms the existing methods.
Monocular Depth Estimation Using a Laplacian Image Pyramid with Local Planar Guidance Layers
Authors: Choi Youn-Ho , Kee Seok-Cheol
Published: January 2023

It is important to estimate the exact depth from 2D images, and many studies have been conducted for a long period of time to solve depth estimation problems. Recently, as research on estimating depth from monocular camera images based on deep learning is progressing, research for estimating accurate depths using various techniques is being conducted. However, depth estimation from 2D images has been a problem in predicting the boundary between objects. In this paper, we aim to predict sophisticated depths by emphasizing the precise boundaries between objects. We propose a depth estimation network with encoder–decoder structures using the Laplacian pyramid and local planar guidance method. In the process of upsampling the learned features using the encoder, the purpose of this step is to obtain a clearer depth map by guiding a more sophisticated boundary of an object using the Laplacian pyramid and local planar guidance techniques. We train and test our models with KITTI and NYU Depth V2 datasets. The proposed network constructs a DNN using only convolution and uses the ConvNext networks as a backbone. A trained model shows the performance of the absolute relative error (Abs_rel) 0.054 and root mean square error (RMSE) 2.252 based on the KITTI dataset and absolute relative error (Abs_rel) 0.102 and root mean square error 0.355 based on the NYU Depth V2 dataset. On the state-of-the-art monocular depth estimation, our network performance shows the fifth-best performance based on the KITTI Eigen split and the eighth-best performance based on the NYU Depth V2.
Split Ring Antennas and Their Application for Antenna Miniaturization
Authors: Liu Yanxia , Shafai Lotfollah , Isleifson Dustin , Shafai Cyrus
Published: January 2023

This paper investigates the miniaturization capability of split ring array antennas embedded in a low-permittivity dielectric substrate, in comparison with the same-sized high-permittivity dielectric resonator antennas (DRAs). In order to understand the miniaturization performance, a size-fixed dielectric substrate with different split ring arrays is studied. The simulation results show that the miniaturization capability increases with decreased unit cell resonant frequency and/or increased unit cell induced permeability. Miniaturizations as high as 25.54 times that of a high-permittivity DRA are obtained with split rings, etched on a dielectric substrate having a low permittivity of 2.2. Furthermore, this excessive miniaturization does not come at the expense of excessive deterioration of the antenna impedance bandwidth, gain, and radiation efficiency. Consequently, the miniaturized split ring arrays still provide high gains over wider bandwidths. This inference is further verified by comparing the miniaturization and other antenna performance parameters with three other modified split ring configurations. To experimentally verify this work, a split ring antenna was fabricated and tested, and good agreement between the simulated and measured results was observed. The results of this study indicate that adding resonant metallic inclusions into low- permittivity DRAs significantly increases their miniaturization capability, without overly deteriorating the performance.
Decision Method of Optimal Needle Insertion Angle for Dorsal Hand Intravenous Robot
Authors: Zhu Zihan , Li Kefeng , Zhang Guangyuan , Jin Hualei , Zhu Zhenfang , Wang Peng
Published: January 2023

In the context of COVID-19, the research on various aspects of the venipuncture robot field has become increasingly hot, but there has been little research on robotic needle insertion angles, primarily performed at a rough angle. This will increase the rate of puncture failure. Furthermore, there is sometimes significant pain due to the patients’ differences. This paper investigates the optimal needle entry angle decision for a dorsal hand intravenous injection robot. The dorsal plane of the hand was obtained by a linear structured light scan, which was used as a basis for calculating the needle entry angle. Simulation experiments were also designed to determine the optimal needle entry angle. Firstly, the linear structured optical system was calibrated and optimized, and the error function was constructed and solved iteratively by the optimization method to eliminate measurement error. Besides, the dorsal hand was scanned to obtain the spatial point clouds of the needle entry area, and the least squares method was used to fit it to obtain the dorsal hand plane. Then, the needle entry angle was calculated based on the needle entry area plane. Finally, the changes in the penetration force under different needle entry angles were analyzed to determine the optimal needle insertion angle. According to the experimental results, the average error of the optimized structured light plane position was about 0.1 mm, which meets the needs of the project, and a large angle should be properly selected for needle insertion during the intravenous injection.
Improved Method for the Detection of Highly Polar Pesticides and Their Main Metabolites in Foods of Animal Origin: Method Validation and Application to Monitoring Programme
Authors: Verdini Emanuela , Lattanzio Veronica , Ciasca Biancamaria , Fioroni Laura , Pecorelli Ivan
Published: January 2023

The application of polar pesticides in agricultural production has been of great interest due to their low costs and their high effectiveness. For this reason, the possibility of their transfer to foods of animal origin is of great concern for human health. The manuscript describes the implementation and validation of an analytical method to detect polar pesticides, at regulatory levels, in three foods of animal origin, including bovine fat, chicken eggs, and cow milk. The method was fully validated to detect glyphosate, glufosinate, and their respective metabolites in the above-mentioned foods obtaining fit-for-purpose sensitivity, recoveries (76–119%), repeatability (≤20%), within-laboratory reproducibility (≤20%), and experimental measurement uncertainty less than 50% as required by the SANTE/11312/2021 criteria. Given the satisfactory results, the applicability of the method to additional molecules belonging to the same category (AMPA, cyanuric acid, ethephon, fosetyl aluminum, HEPA, maleic hydrazide, and N-acetyl-glyphosate) was also evaluated in order to meet possible future requests. Finally, the implemented method was applied to analyse samples over the period of March 2021 to August 2022 from two Italian regions (Umbria and Marche) within the national monitoring programme. In agreement with previously available data, none of the samples analysed showed the presence of glyphosate and glufosinate at levels above the legal limit.
Engineering Learning Outcomes: The Possible Balance between the Passion and the Profession
Authors: Dias Diana
Published: January 2023

What is an engineering student expected to learn to become a competent engineer? Which is most desirable in higher education: hard skills or soft skills? Are there differences between master’s and bachelor’s learning outcomes, or between public or private schools? Previous works point out the relevance of hard rather than soft skills in engineering higher education. The implementation of learning outcomes (LOs) in higher-education curricula has been a common challenge for European educational institutions. Despite the efforts undertaken since the Bologna Declaration, the effective implementation of the learning outcomes paradigm is still in process. This research intends to analyse the LOs proposed in the scope of graduate and postgraduate electrical or computer electrical or computer engineering programs submitted to the Agency for Assessment and Accreditation of Higher Education (A3ES) in Portugal since 2004. Conducting a qualitative analysis, data documents were coded according to the Nusche typology of LOs. The results suggest that cognitive skills are a core dimension in electrical or computer engineering LOs. References to non-cognitive LOs are not representative. Different institution sectors (private vs. public), systems (universities vs. polytechnics), and study cycles (first vs. second vs. integrated master) highlight distinct cognitive and non-cognitive categories in their definition of LOs. The results are discussed in terms of a changing or a recycling paradigm in higher-education practices.
Active Learning and Professional Development: A Case of Thai Chinese Teachers
Authors: Duangmanee Katematu , Waluyo Budi
Published: January 2023

While Chinese has been taught as a school subject outside of China for decades, there is little research on how Chinese subjects are taught in secondary schools outside of China’s mainland with regard to the use of active learning and the impact of professional development events on teachers’ professional growth in classroom Chinese teaching. This article explored Thai Chinese teachers’ use of active learning methods at public and private secondary schools in the south of Thailand. It also examined the impact of a government-administered Chinese teaching seminar on their professional development. A sequential mixed-methods explanatory design was employed, involving focus group interviews and pre- and post-tests. The phenomenological approach with thematic analysis was used to analyze the qualitative data from the focus group interviews, while the Wilcoxon test was run to compare the pre- and post-test data. Findings revealed the application of active learning with discussion and role-playing activities involving audio-visual materials. The pedagogy of Thai Chinese secondary schools has shifted from knowledge transmission through lectures to knowledge generation and transformation via dynamic learning activities. After attending the seminar, teachers’ comprehension of active learning methods increased considerably (Z = 3.740, p < 0.001). However, teachers expressed concerns over the lack of innovative teaching techniques for teaching Chinese characters and the problems encountered by both students and teachers during the teaching and learning process. This study recommends that Hanban and the Thai Ministry of Education plan their seminars and workshops in a way that allows Thai Chinese instructors to continuously address their students’ and their own deficiencies while they are engaged in active learning activities, and it calls for additional research on the use of active CFL methods in secondary schools outside of China.
Analysing the Scope of Local Authorities (LAs) in Facilitating and Influencing the Sustainability of Foreign Direct Investment in Namibia: A Case Study of the Windhoek and Walvis Bay LAs
Authors: Marenga Ralph Vezembouua , Blaauw Lesley , Kakujaha-Matundu Omu
Published: January 2023

The negative and neutral developmental experiences of the Windhoek and Walvis Bay local authorities (LAs) with facilitating foreign direct investment (FDI) are disconcerting and contradict the bottom-up development approach in Namibia. The objective of this study is to analyse the legislative scope of LAs in facilitating and influencing FDI sustainability. A qualitative research method was followed by analysing secondary data and interviewing purposely selected key respondents. The thematic analysis of data was guided by two independent variables of interest: (1) decentralised functions of FDI facilitation in a multi-level governance (MLG) system; and (2) policy and legislative harmonisation in an MLG system. The key findings of this study indicate that the current scope of Namibian LA FDI facilitation functions are insufficient for influencing FDI sustainability for local development. The theoretical implications of this study are that it contributes to the sustenance of the MLG theory—which is inept at explaining the dynamics LAs experience in MLG systems. In the absence of an enabling legislative framework, the current study suggests the following practical implications: (1) bottom-up development using FDI cannot be achieved; (2) LAs cannot influence the sustainability of FDI for local development impact; and (3) LAs cannot protect local communities against the capitalist wrath and exploitative tendencies of FDI.
Review on Phytoremediation Potential of Floating Aquatic Plants for Heavy Metals: A Promising Approach
Authors: Pang Yean Ling , Quek Yen Ying , Lim Steven , Shuit Siew Hoong
Published: January 2023

Water pollution due to heavy metals has become a serious environmental concern due to their hazardous properties. Since conventional water remediation techniques are generally ineffective and non-environmentally friendly, phytoremediation has gained increasing attention from worldwide researchers and scientists due to its cost-effectiveness and environmental friendliness. Hence, this review first discussed soil and water remediations. Phytoremediation can be divided into five techniques to remove heavy metals from the polluted environment, namely, phytostabilization (phytosequestration), phytodegradation (phytotransformation), phytofiltration (rhizofiltration), phytoextraction (phytoaccumulation), and phytovolatilization. Four common floating aquatic plants (accumulator plants), such as duckweed (Lemna minor), water lettuce (Pistia stratiotes), water hyacinth (Eichhornia crassipes), and watermoss (Salvinia) were discussed in detail due to their great capability in absorbing the metal ions by their roots and further translocating the metal ions to the aerial parts. Furthermore, the parameter studies, such as optimum pH and temperature of the water, exposure duration, initial metal concentration, water salinity, and the addition of chelating agents, were evaluated. The absorption kinetics of the plants was discussed in detail. In short, phytoremediation is a promising green and sustainable water remediation approach. However, further research is necessary to enhance its practicability and performance at large-scale implementation.
Classification of Post-COVID-19 Emotions with Residual-Based Separable Convolution Networks and EEG Signals
Authors: Abbas Qaisar , Baig Abdul Rauf , Hussain Ayyaz
Published: January 2023

The COVID-19 epidemic has created highly unprocessed emotions that trigger stress, anxiety, or panic attacks. These attacks exhibit physical symptoms that may easily lead to misdiagnosis. Deep-learning (DL)-based classification approaches for emotion detection based on electroencephalography (EEG) signals are computationally costly. Nowadays, limiting memory potency, considerable training, and hyperparameter optimization are always needed for DL models. As a result, they are inappropriate for real-time applications, which require large computational resources to detect anxiety and stress through EEG signals. However, a two-dimensional residual separable convolution network (RCN) architecture can considerably enhance the efficiency of parameter use and calculation time. The primary aim of this study was to detect emotions in undergraduate students who had recently experienced COVID-19 by analyzing EEG signals. A novel separable convolution model that combines residual connection (RCN-L) and light gradient boosting machine (LightGBM) techniques was developed. To evaluate the performance, this paper used different statistical metrics. The RCN-L achieved an accuracy (ACC) of 0.9263, a sensitivity (SE) of 0.9246, a specificity (SP) of 0.9282, an F1-score of 0.9264, and an area under the curve (AUC) of 0.9263 when compared to other approaches. In the proposed RCN-L system, the network avoids the tedious detection and classification process for post-COVID-19 emotions while still achieving impressive network training performance and a significant reduction in learnable parameters. This paper also concludes that the emotions of students are highly impacted by COVID-19 scenarios.
The 21st-Century Empowering Wholeness Adaptive (EWA) Educational Model Transforming Learning Capacity and Human Capital through Wholeness Systems Thinking towards a Sustainable Future
Authors: Holman David , Švejdarová Eva
Published: January 2023

To meet the sustainability challenges in the 21st century, students need to develop a unique human learning capacity for creativity, responsibility, adaptation, meaningfulness, and lifelong learning. Furthermore, current changes in the societal environment have given rise to the need for a new learning strategy that guides learners in perceiving reality as an interrelated purposeful system with meaningful limits rather than collections of facts or systems without limits, as done so far. This paper aims to verify its hypothesis and introduce the 21st-century Empowering Wholeness Adaptive learning model (21st EWA Edu). This is a unique learning strategy that enables a meaningful transformation of learning capacity and creates a unifying learning system of dynamic content, didactics, knowledge, skills, competencies, understanding, values, and behaviors. Over three years, data from three cohorts of students at the School of Business Administration were collected. To assess the impact of the 21st EWA learning approach, both quantitative and qualitative methods of data collection and analysis were employed. The results suggest that the proposed model creates a learning system enabling the transformation of learning capacity while empowering students to become conscious and engaged lifelong learners, which is much needed for a truly sustainable future.
Digital Platforms and the Improvement of Learning Outcomes: Evidence Extracted from Meta-Analysis
Authors: Alshammary Farhan Mohammed , Alhalafawy Waleed Salim
Published: January 2023

Digital platforms are one of the educational resources that were used in education prior to Covid-19 pandemic. Nevertheless, the pandemic has led to a complete shift to learning via digital platforms, and therefore they have become a strategic alternative for sustainable education. Given that previous studies regarding the impact of digital platforms on learning outcomes have yielded conflicting results, the present study aims to determine whether or not digital platforms improve learning outcomes. To achieve this aim, meta-analysis approach was used through the examination of the overall effect size of these platforms on the learning outcomes besides the examination of the effect size of a set of mediating variables including study period, subject area, student rating, and publication type. Thirty studies published between 2015 and 2021 comparing learning via digital platforms and learning in traditional classrooms were accounted for. The focus on this period of time was because the fourth industrial revolution took place in that time where the use of digital platforms in teaching was prosperous. Findings showed that the overall effect size using the random effect model (g = 0.278; p < 0.001; α = 0.05) was small and positive, from (0.123–0.433) in favor of learning via digital platforms, and so no evidence regarding publication bias could be discovered in these data. The result of the current study may be useful to universities and e-learning centers on how to use digital platforms to improve learning outcomes.
Winter Bloom of Marine Cyanobacterium, Trichodesmium erythraeum and Its Relation to Environmental Factors
Authors: Shaika Nowrin Akter , Alhomaidi Eman , Sarker Md. Milon , An Nur Abdullah , Sadat Md. Ashfaq , Awal Sadiqul , Mostafa Golam , Hasan Shanur Jahedul , Mahmud Yahia , Khan Saleha
Published: January 2023

A winter bloom event of Trichodesmium erythraeum was monitored for the first time in the southeastern coastal parts of Bangladesh along the Bay of Bengal. This study presents the brownish to light pinkish bloom that appeared in mid-winter and disappeared abruptly during spring. Heavy blooms of T. erythraeum revealed the highest concentration of 91.47 ± 52.94 × 103 colonies/L in the Bakkhali River Estuary, and 66.93 ± 12.95 × 103 colonies/L in the Maheshkhali Channel of the Bay of Bengal. Three distinct morphological shapes, namely puffs, tufts and asymmetrical colonies, were depicted as major types. Several environmental factors, such as water temperature, salinity, pH, dissolved oxygen, NO3–N and PO4–P, were analyzed to determine their relationship with the occurrence, abundance and bloom formation of T. erythraeum. The abundance of the species showed a positive correlation with salinity and pH while exhibiting a negative correlation with temperature and DO. A cluster analysis revealed a clear indication of T. erythraeum bloom during winter. Thus, the prevalence of the highest density of the bloom in the present study area strongly suggests increased monitoring and research efforts in order to effectively manage or impede harmful algal blooms.
Deformation and Failure Laws of Surrounding Rocks of Coal Roadways under High Dynamic Load and Intelligent Prediction
Authors: Li Aoran , Cui Guangzhen , Wang Peng , Wang Xinjie , Hong Zhengtao , Kong Jiangrong , Kan Jiaguang
Published: January 2023

Under high dynamic load, roadway deformation and failure may occur, posing great challenges. As for now, few studies have been carried out on the impacts of various factors on the deformation of roadway surrounding rocks under high dynamic load, not to mention those on intelligent prediction of the deformation and failure laws. This paper fills these research gaps by studying the deformation and failure characteristics of roadway surrounding rocks and the intelligent prediction method under high dynamic load. The finite difference software Flac3D was used to analyze the influences of roadway buried depth, lithology, and side pressure coefficient on the stability of surrounding rocks and a model was constructed for deformation prediction under high dynamic load. Finally, the influence of various factors on the deformation and their weight was obtained and the deformation can be predicted in line with the BP neural network prediction theory. The results show that the prediction effect is good, with high accuracy.
Sustainable Education and Sustainability in Education: The Reality in the Era of Internationalisation and Commodification in Education—Is Higher Education Different?
Authors: Alam Gazi Mahabubul
Published: January 2023

Sustainable education refers to knowledge delivery that ensures a balanced national development which encompasses both economic and social development as well as a human needs perspective [...]
Combined Investigation of Indoor Environmental Conditions and Energy Performance of an Aquatic Center
Authors: Papadopoulos Giannis , Tolis Evangelos I. , Panaras Giorgos
Published: January 2023

This study presents a combined investigation of indoor environmental conditions and energy performance in a naturally ventilated aquatic center in Western Macedonia, Greece. The experimental analysis was conducted over nine days during the summer. The operative temperature exceeded the acceptable limits for most days, while the same can be stated for the PMV, demonstrating high indoor air and radiant temperature values. The weaknesses of applying the above thermal comfort models in this type of buildings are also discussed. Relative humidity presents generally acceptable values during operation time, indicating the contribution of natural ventilation; nevertheless, as demonstrated by the CO2 concentration values, the ventilation rate can be further increased. On the basis of the above findings, a renovation scenario has been formulated, considering the installation of an air-conditioning system, as well as specific interventions, towards the improvement of the building envelope and systems’ thermal performance. A dynamic energy analysis, based on Energy Plus software, had energy savings of 9%, noting the considerable upgrade of indoor conditions. Overall, the proposed combined investigation approach proved to be suitable for such a complicated problem, as the one of indoor aquatic centers, presenting a high generalization potential.
Assessment of Factors Determining Airline Consumer Loyalty: Case Study in Lithuania
Authors: Batarlienė Nijolė , Slavinskaitė Neringa
Published: January 2023

The aviation sector is constantly expanding and it is strongly influenced by external factors, which gives rise to significant competition between airlines. One of the most effective ways for an airline to gain a competitive advantage is to keep its customers as loyal users of the provided services, able to provide airlines with a stable revenue for the services they sell. The purpose of this paper was to identify the factors that have the greatest impact on airline passengers’ loyalty. This study analyzed the data from passengers of two classes: prestige (business) and economy. Prestige (business) passengers pay more attention to service quality (beverage and food selection, service and quality, especially accessibility, etc.), privacy, individual attention, and airline reliability, while economy class passengers focus on price and basic perception of service quality. Data were collected from 409 passengers who travelled from Lithuania airports (Vilnius, Kaunas, Palanga) to other countries. The results suggest that there are different factors of in-flight service quality that are important according to the customer seat class. The results indicate that 71% of passenger loyalty is explained by image and image is explained by satisfaction. In addition, service quality and price are found to have positive effects on satisfaction. Compared with price, service quality is found to be a stronger determinant of satisfaction.