bluuebunny/crossref_metadata_2025 huggingface dataset:
Parallel Processing of Sensor Data in a Distributed Rules Engine Environment through Clustering and Data Flow Reconfiguration
Authors: Alexandrescu Adrian
Published: February 2023

An emerging reality is the development of smart buildings and cities, which improve residents’ comfort. These environments employ multiple sensor networks, whose data must be acquired and processed in real time by multiple rule engines, which trigger events that enable specific actuators. The problem is how to handle those data in a scalable manner by using multiple processing instances to maximize the system throughput. This paper considers the types of sensors that are used in these scenarios and proposes a model for abstracting the information flow as a weighted dependency graph. Two parallel computing methods are then proposed for obtaining an efficient data flow: a variation of the parallel k-means clustering algorithm and a custom genetic algorithm. Simulation results show that the two proposed flow reconfiguration algorithms reduce the rule processing times and provide an efficient solution for increasing the scalability of the considered environment. Another aspect being discussed is using an open-source cloud solution to manage the system and how to use the two algorithms to increase efficiency. These methods allow for a seamless increase in the number of sensors in the environment by making smart use of the available resources.
Small Sample Coherent DOA Estimation Method Based on S2S Neural Network Meta Reinforcement Learning
Authors: Wu Zihan , Wang Jun
Published: February 2023

Aiming at the existing Direction of Arrival (DOA) methods based on neural network, a large number of samples are required to achieve signal-scene adaptation and accurate angle estimation. In the coherent signal environment, the problems of a larger amount of training sample data are required. In this paper, the DOA of coherent signal is converted into the DOA parameter estimation of the angle interval of incident signal. The accurate estimation of coherent DOA under the condition of small samples based on meta−reinforcement learning (MRL) is realized. The meta−reinforcement learning method in this paper models the process of angle interval estimation of coherent signals as a Markov decision process. In the inner loop layer, the sequence to sequence (S2S) neural network is used to express the angular interval feature sequence of the incident signal DOA. The strategy learning of the existence of angle interval under small samples is realized through making full use of the context relevance of spatial spectral sequence through S2S neural network. Thus, according to the optimal strategy, the output sequence is sequentially determined to give the angle interval of the incident signal. Finally, DOA is obtained through one-dimensional spectral peak search according to the angle interval obtained. The experiment shows that the meta−reinforcement learning algorithm based on S2S neural network can quickly converge to the optimal state by only updating the gradient of S2S neural network parameters with a small sample set when a new signal environment appears.
Validation of Inertial Sensors to Evaluate Gait Stability
Authors: Riek Paul M. , Best Aaron N. , Wu Amy R.
Published: February 2023

The portability of wearable inertial sensors makes them particularly suitable for measuring gait in real-world walking situations. However, it is unclear how well inertial sensors can measure and evaluate gait stability compared to traditional laboratory-based optical motion capture. This study investigated whether an inertial sensor-based motion-capture suit could accurately assess gait stability. Healthy adult participants were asked to walk normally, with eyes closed, with approximately twice their normal step width, and in tandem. Their motion was simultaneously measured by inertial measurement units (IMU) and optical motion capture (Optical). Gait stability was assessed by calculating the margin of stability (MoS), short-term Lyapunov exponents, and step variability, along with basic gait parameters, using each system. We found that IMUs were able to detect the same differences among conditions as Optical for all but one of the measures. Bland–Altman and intraclass correlation (ICC) analysis demonstrated that mediolateral parameters (step width and mediolateral MoS) were measured less accurately by IMUs compared to their anterior-posterior equivalents (step length and anterior-posterior MoS). Our results demonstrate that IMUs can be used to evaluate gait stability through detecting changes in stability-related measures, but that the magnitudes of these measures might not be accurate or reliable, especially in the mediolateral direction.
Framework for Generation and Removal of Multiple Types of Adverse Weather from Driving Scene Images
Authors: Yang Hanting , Carballo Alexander , Zhang Yuxiao , Takeda Kazuya
Published: February 2023

Weather variation in the distribution of image data can cause a decline in the performance of existing visual algorithms during evaluation. Adding additional samples of target domain to training data or using pre-trained image restoration methods such as de-hazing, de-raining, and de-snowing, to improve the quality of input images are two promising solutions. In this work, we propose Multiple Weather Translation GAN (MWTG), a CycleGAN-based, dual-purpose framework that simultaneously learns weather generation and its removal from image data. MWTG consists of four GANs constrained using cycle consistency that carry out domain translation tasks between hazy, rainy, snowy, and clear weather, using an asymmetric approach. To increase network capacity, we employ a spatial feature transform (SFT) layer to fuse the features extracted from the weather layer, which contains high-level domain information from the previous generators. Further, we collect an unpaired, real-world driving dataset recorded under various weather conditions called Realistic Driving Scenes under Bad Weather (RDSBW). We qualitatively and quantitatively evaluate MWTG using the RDSBW and the variation of Cityscapes that synthesize weather effects, eg., FoggyCityscape. Our experimental results suggest that MWTG can generate realistic weather in clear images and also accurately remove noise from weather images. Furthermore, the SOTA pedestrian detector ASCP is shown to achieve an impressive gain in detection precision after image restoration using the proposed MWTG method.
PA-Tran: Learning to Estimate 3D Hand Pose with Partial Annotation
Authors: Yu Tianze , Bidulka Luke , McKeown Martin J. , Wang Z. Jane
Published: February 2023

This paper tackles a novel and challenging problem—3D hand pose estimation (HPE) from a single RGB image using partial annotation. Most HPE methods ignore the fact that the keypoints could be partially visible (e.g., under occlusions). In contrast, we propose a deep-learning framework, PA-Tran, that jointly estimates the keypoints status and 3D hand pose from a single RGB image with two dependent branches. The regression branch consists of a Transformer encoder which is trained to predict a set of target keypoints, given an input set of status, position, and visual features embedding from a convolutional neural network (CNN); the classification branch adopts a CNN for estimating the keypoints status. One key idea of PA-Tran is a selective mask training (SMT) objective that uses a binary encoding scheme to represent the status of the keypoints as observed or unobserved during training. In addition, by explicitly encoding the label status (observed/unobserved), the proposed PA-Tran can efficiently handle the condition when only partial annotation is available. Investigating the annotation percentage ranging from 50–100%, we show that training with partial annotation is more efficient (e.g., achieving the best 6.0 PA-MPJPE when using about 85% annotations). Moreover, we provide two new datasets. APDM-Hand, is for synthetic hands with APDM sensor accessories, which is designed for a specific hand task. PD-APDM-Hand, is a real hand dataset collected from Parkinson’s Disease (PD) patients with partial annotation. The proposed PA-Tran can achieve higher estimation accuracy when evaluated on both proposed datasets and a more general hand dataset.
Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques
Authors: Astolfi Rodrigo S. , da Silva Daniel S. , Guedes Ingrid S. , Nascimento Caio S. , Damaševičius Robertas , Jagatheesaperumal Senthil K. , de Albuquerque Victor Hugo C. , Leite José Alberto D.
Published: February 2023

Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS–Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis.
Experimental Stand for Very-Low-Velocity Gas Flow Generation
Authors: Piga Tymoteusz , Wodziak Waldemar , Sobczyk Jacek , Rachalski Andrzej
Published: February 2023

This article presents an experimental stand for the generation of gas flows with very low velocities. The constant volume air flow was generated in the measurement area with the use of a Mariotte bottle and a water piston (water column in a tank). The air-flow velocity was determined from the change in the height of the water piston with time, which was measured via the difference between the pressure above the water surface and the pressure at the bottom of the water column. The tests were carried out in the mean velocity range of 0.35–66 cm/s. The five different velocities of air flowing out of the measurement pipe, measured with the particle image velocimetry method were compared with the results from the water piston method. The differences did not exceed 7%.
Pipeline Leakage Detection Based on Secondary Phase Transform Cross-Correlation
Authors: Liang Hetao , Gao Yan , Li Haibin , Huang Siyuan , Chen Minghui , Wang Baomin
Published: February 2023

Leaks from pipes and valves are a reputational issue in industry. Maintenance of pipeline integrity is becoming a growing challenge due to the serious socioeconomic consequences. This paper presents a secondary phase transform (PHAT) cross-correlation method to improve the performance of the acoustic methods based on cross-correlation for pipeline leakage detection. Acoustic emission signals generated by pipe leakage are first captured by the sensors at different locations, and are subsequently analyzed using the cross-correlation curve to determine whether leakage is occurring. When leakage occurs, time delay estimation (TDE) is further carried out by peak search in the cross-correlation curve between the two sensor signals. In the analysis, the proposed method calculates the secondary cross-correlation function before the PHAT operation. A sinc interpolation method is then introduced for automatic searching the peak value of the cross-correlation curve. Numerical simulations and experimental results confirm the improved performance of the proposed method for noise suppression and accurate TDE compared to the basic cross-correlation method, which may be beneficial in engineering applications.
A Classification Method for Workers’ Physical Risk
Authors: Tamantini Christian , Rondoni Cristiana , Cordella Francesca , Guglielmelli Eugenio , Zollo Loredana
Published: February 2023

In Industry 4.0 scenarios, wearable sensing allows the development of monitoring solutions for workers’ risk prevention. Current approaches aim to identify the presence of a risky event, such as falls, when it has already occurred. However, there is a need to develop methods capable of identifying the presence of a risk condition in order to prevent the occurrence of the damage itself. The measurement of vital and non-vital physiological parameters enables the worker’s complex state estimation to identify risk conditions preventing falls, slips and fainting, as a result of physical overexertion and heat stress exposure. This paper aims at investigating classification approaches to identify risk conditions with respect to normal physical activity by exploiting physiological measurements in different conditions of physical exertion and heat stress. Moreover, the role played in the risk identification by specific sensors and features was investigated. The obtained results evidenced that k-Nearest Neighbors is the best performing algorithm in all the experimental conditions exploiting only information coming from cardiorespiratory monitoring (mean accuracy 88.7±7.3% for the model trained with max(HR), std(RR) and std(HR)).
Fast and Enhanced MMW Imaging System Using a Simple Row Detector Circuit with GDDs as Sensor Elements and an FFT-Based Signal Acquisition System
Authors: Ramachandra Kurup Arun , Rozban Daniel , Abramovich Amir , Yitzhaky Yitzhak , Kopeika Natan
Published: February 2023

The relatively high atmospheric propagation of millimeter-waves (MMW) was found to be one of the most critical reasons for the development of reliable sensors for MMW detection. According to previous research works, it has been already shown that incident MMW radiation on a glow discharge detector (GDD) can increase the discharge current. Hence, the electrical mode of detection can be employed to detect the presence of MMW radiation. In this article, a new design of a row detector using GDDs as pixel elements, and the influence of MMW incidence on GDD’s discharge current, were acquired using an elementary data acquisition (DAQ) platform. The DAQ system computes the averaged Fast Fourier Transform (FFT) spectrum of the time signal and returns the FFT results as magnitude based on the level of detection. An FFT-based signal acquisition proved to be a better alternative to the lock-in detection that was commonly used in MMW detection systems. This improved detection circuit provides enhanced noise filtering, thereby resulting in better MMW images within a short time. The overhead expense of the entire system is very low, as it can avoid lock-in amplifier stages that were previously used for signal enhancement. A scanning mechanism using a motorized translation stage (step motor) is involved to place and align the row detector in the image plane. The scanning can be carried out vertically to perform the imaging, by configuring the step motor after selecting the desired step size and position. A simplified version of the MMW detection circuit with a dedicated over-voltage protection facility is presented here. This made the detection system more stable and reliable during its operation. The MMW detection circuit demonstrated in this work was found to be a milestone to develop larger focal plane arrays (FPA) with very inexpensive sensor elements.
Detection of Intermittent Claudication from Smartphone Inertial Data in Community Walks Using Machine Learning Classifiers
Authors: Pinto Bruno , Correia Miguel Velhote , Paredes Hugo , Silva Ivone
Published: February 2023

Peripheral arterial disease (PAD) causes blockage of the arteries, altering the blood flow to the lower limbs. This blockage can cause the individual with PAD to feel severe pain in the lower limbs. The main contribution of this research is the discovery of a solution that allows the automatic detection of the onset of claudication based on data analysis from patients’ smartphones. For the data-collection procedure, 40 patients were asked to walk with a smartphone on a thirty-meter path, back and forth, for six minutes. Each patient conducted the test twice on two different days. Several machine learning models were compared to detect the onset of claudication on two different datasets. The results suggest that we can identify the onset of claudication using inertial sensors with a best case accuracy of 92.25% for the Extreme Gradient Boosting model.
Digital Twins and Enabling Technologies in Museums and Cultural Heritage: An Overview
Authors: Luther Wolfram , Baloian Nelson , Biella Daniel , Sacher Daniel
Published: February 2023

This paper presents an overview of various types of virtual museums (ViM) as native artifacts or as digital twins (DT) of physical museums (PM). Depending on their mission and features, we discuss various enabling technologies and sensor equipment with their specific requirements and complexities, advantages and drawbacks in relation to each other at all stages of a DT’s life cycle. A DT is a virtual construct and embodies innovative concepts based on emerging technologies (ET) using adequate sensor configurations for (meta-)data import and exchange. Our keyword-based search for articles, conference papers, (chapters from) books and reviews yielded 43 contributions and 43 further important references from Industry 4.0, Tourism and Heritage 4.0. After closer examination, a reference corpus of 40 contributions was evaluated in detail and classified along with their variants of DT—content-, communication-, and collaboration-centric and risk-informed ViMs. Their system features correlate with different application areas (AA), new or improved technologies—mostly still under development—and sensors used. Our proposal suggests a template-based, generative approach to DTs using standardized metadata formats, expert/curator software and customers’/visitors’ engagement. It advocates for stakeholders’ collaboration as part of a comprehensive validation and verification assessment (V&VA) throughout the DT’s entire life cycle.
A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity
Authors: Ross Mindy K. , Tulabandhula Theja , Bennett Casey C. , Baek EuGene , Kim Dohyeon , Hussain Faraz , Demos Alexander P. , Ning Emma , Langenecker Scott A. , Ajilore Olusola , Leow Alex D.
Published: February 2023

The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input.
Structural Health Monitoring Using a New Type of Distributed Fiber Optic Smart Textiles in Combination with Optical Frequency Domain Reflectometry (OFDR): Taking a Pedestrian Bridge as Case Study
Authors: Abedin Sabrina , Biondi Andres M. , Wu Rui , Cao Lidan , Wang Xingwei
Published: February 2023

Distributed fiber optic sensors (DFOS) have become a new method for continuously monitoring infrastructure status. However, the fiber’s fragility and the installation’s complexity are some of the main drawbacks of this monitoring approach. This paper aims to overcome this limitation by embedding a fiber optic sensor into a textile for a faster and easier installation process. To demonstrate its feasibility, the smart textile was installed on a pedestrian bridge at the University of Massachusetts Lowell. In addition, dynamic strain data were collected for two different years (2021 and 2022) using Optical Frequency Domain Reflectometry (OFDR) and compared, to determine the variability of the data after one year of installation. We determined that no significant change was observed in the response pattern, and the difference between the amplitude of both datasets was 14% (one person jumping on the bridge) and 43% (two people jumping) at the first frequency band. This result shows the proposed system’s functionality after one year of installation, as well as its potential use for traffic monitoring.
Point Density Variations in Airborne Lidar Point Clouds
Authors: Petras Vaclav , Petrasova Anna , McCarter James B. , Mitasova Helena , Meentemeyer Ross K.
Published: February 2023

In spite of increasing point density and accuracy, airborne lidar point clouds often exhibit point density variations. Some of these density variations indicate issues with point clouds, potentially leading to errors in derived products. To highlight these issues, we provide an overview of point density variations and show examples in six airborne lidar point cloud datasets that we used in our topographic and geospatial modeling research. Using the published literature, we identified sources of point density variations and issues indicated or caused by these variations. Lastly, we discuss the reduction in point density variations using decimations, homogenizations, and their applicability.
Strong Noise Rejection in VLC Links under Realistic Conditions through a Real-Time SDR Front-End
Authors: Umair Muhammad Ali , Meucci Marco , Catani Jacopo
Published: February 2023

One of the main challenges in the deployment of visible light communication (VLC) in realistic application fields, such as intelligent transportation systems (ITSs), is represented by the presence of large background noise levels on top of the optical signal carrying the digital information. A versatile and effective digital filtering technique is, hence, crucial to face such an issue in an effective way. In this paper, we present an extensive experimental evaluation of a complete VLC system, embedding a software-defined-radio (SDR)-based digital signal processing (DSP) filter stage, which is tested either indoors, in the presence of strong artificial 100-Hz stray illumination, and outdoors, under direct sunlight. The system employs low-power automotive LED lamps, and it is tested for baud rates up to 1 Mbaud. We experimentally demonstrate that the use of the DSP technique improves 10× the performance of the VLC receiver over the original system without the filtering stage, reporting a very effective rejection of both 100-Hz and solar noise background. Indoors, the noise margin in the presence of strong 100-Hz noise is increased by up to 40 dB, whilst in the outdoor configuration, the system is capable of maintaining error-free communication in direct sunlight conditions, up to 7.5 m, improving the distance by a factor of 1.6 compared to the case without filtering. We believe that the proposed system is a very effective solution for the suppression of various types of noise effects in a large set of VLC applications.
Face Mask Identification Using Spatial and Frequency Features in Depth Image from Time-of-Flight Camera
Authors: Wang Xiaoyan , Xu Tianxu , An Dong , Sun Lei , Wang Qiang , Pan Zhongqi , Yue Yang
Published: February 2023

Face masks can effectively prevent the spread of viruses. It is necessary to determine the wearing condition of masks in various locations, such as traffic stations, hospitals, and other places with a risk of infection. Therefore, achieving fast and accurate identification in different application scenarios is an urgent problem to be solved. Contactless mask recognition can avoid the waste of human resources and the risk of exposure. We propose a novel method for face mask recognition, which is demonstrated using the spatial and frequency features from the 3D information. A ToF camera with a simple system and robust data are used to capture the depth images. The facial contour of the depth image is extracted accurately by the designed method, which can reduce the dimension of the depth data to improve the recognition speed. Additionally, the classification process is further divided into two parts. The wearing condition of the mask is first identified by features extracted from the facial contour. The types of masks are then classified by new features extracted from the spatial and frequency curves. With appropriate thresholds and a voting method, the total recall accuracy of the proposed algorithm can achieve 96.21%. Especially, the recall accuracy for images without mask can reach 99.21%.
Children’s Participatory Capability in Organized Leisure: The Mediation of Transactional Horizons
Authors: Stoecklin Daniel , Berchtold-Sedooka Ayuko , Bonvin Jean-Michel
Published: February 2023

This research conducted in Switzerland addresses the participatory capability of children regarding their organized leisure activities. Observations were made in 2016 in three French-speaking counties in Switzerland within 11 leisure facilities differentiated by their structural organization. Individual interviews were conducted with 34 children aged 13 to 16 as well as 11 managers of the leisure centers and three county-level child and youth policy-makers (Fribourg, Valais, Vaud). The findings are that children’s participatory capability in organized leisure facilities depends on a combination of factors that are both societal (economical, political, organizational) and personal. Three forms of participatory capability emerge around the opportunities for effective participation that are provided by the children’s social environment, corresponding to (1) the adaptive participation, (2) the innovative participation and (3) the cooperative participation. However, there is no strict correspondence between the types of organization of leisure structures and forms of participatory capability. This is due to the mediation of “transactional horizons”, acting as symbolic landscapes that are channeling social interactions and the negotiation of forms of participation. This confirms the relevance of an interpretive approach to children’s rights in order to better understand how they actually translate into practice.
How to Predict the Innovation to SMEs? Applying the Data Mining Process to the Spinner Innovation Model
Authors: Figueiredo Ronnie , Magalhães Carla , Huber Claudia
Published: February 2023

Despite the importance of small and medium-sized enterprises (SMEs) for the growth and development of companies, the high failure rate of these companies persists, and this correspondingly demands the attention of managers. Thus, to boost the company success rate, we may deploy certain approaches, for example predictive models, specifically for the SME innovation. This study aims to examine the variables that positively shape and contribute towards innovation of SMEs. Based on the Spinner innovation model, we explore how to predict the innovation of SMEs by applying the variables, namely knowledge creation, knowledge transfer, public knowledge management, private knowledge management and innovation. This study applied the data mining technique according to the cross industry standard process for data mining (CRISP-DM) method while the Statistical Package for the Social Sciences (SPSS_Version28) served to analyze the data collected from 208 SME employees in Oporto, Portugal. The results demonstrate how the Spinner innovation model positively influences the contributions of the SMEs. This SME-dedicated model fosters the creation of knowledge between internal and external interactions and increases the capacity to predict the SME innovation by 56%.
The Influence of Empowerment towards Agricultural Business Actors’ Ability in Surakarta, Indonesia
Authors: Suminah Suminah , Anantanyu Sapja , Suwarto Suwarto , Sugihardjo Sugihardjo , Padmaningrum Dwiningtyas
Published: February 2023

The situation of micro, small, and medium enterprises (MSMEs) in agriculture in Surakarta after the pandemic are still worrying; thus, effective empowerment efforts are needed to revive and develop MSMEs in agriculture. Empowerment will be maximally successful if various resources are available for its implementation. This study aimed to directly and indirectly analyze the effect of empowerment, human capital, economic capital, actors’ perceptions of their business, and financial capital on the ability of agricultural business actors. This research study used quantitative methods with survey techniques. The sample population of this research study included all agricultural business actors in Surakarta covering five districts, and the sample was taken randomly. The data collection techniques included direct interviews, observations, and focus group discussions. The results showed that empowerment has a significant direct effect on economic capital, increasing it by 21.6 percent, and a very significant positive direct effect on the ability of agricultural business actors, increasing it by 20.9 percent. Empowerment has no significant direct effect on human capital, perception, and financial capital. Economy capital and perceptions of business actors have very significant direct effects on the ability of business actors, at 23.1 percent and 37.2 percent, while human capital and financial capital have no direct effects on the ability of business actors. Indirect empowerment via human capital, economic capital, perception, and financial capital factors has no significant effect on the ability of agricultural business actors.
Quantitative Evaluation of Ecosystem Services of Urban Street Trees: A Case Study of Shengjing Historical and Cultural Block in Shenyang, China
Authors: Sui Qingyu , Jia Hongzuo , Zhao Meiyue , Zhou Yan , Fan Lei
Published: February 2023

Urban street trees are of great significance to the sustainable development of human settlements, and are key factors to enhance the service value of the urban environmental ecosystem. In this regard, it is necessary to improve and balance the benefit distribution of urban street trees for promoting the environmental quality of cities. In order to make clear the urban street tree benefits in a city, this paper presents the study on the ecosystem services from street trees in Shengjing Historical and Cultural Block (SHCB), Shenyang, China. By conducting a field survey on 1968 street trees and using the i-Tree model and the ENVI-met model to quantify the original data, this paper evaluated the ecosystem services distribution of eight streets and nine zones in the SHCB. The results showed that the co-creation annual ecological benefit and annual thermal comfort benefit of the street trees of SHCB were 163,965.62 and 233,533.48 dollars, respectively, totaling 397,499.10 dollars. It is obvious that the thermal comfort benefit is higher than the ecological benefit. Meanwhile, urban streets with high ecological benefit may not necessarily produce high thermal comfort benefit. Therefore, on the basis of ecological benefit analysis, the ecosystem services can be reflected more accurately by superimposing the thermal comfort benefit. The quantitative assessment system obtained in this study can provide a reference for future block planning and urban street tree allocation of SHCB and other regions in similar areas.
Teaching and Learning Approaches: Curriculum Framework for Sustainability Literacy for Technical and Vocational Teacher Training Programmes in Malaysia
Authors: Chinedu Caleb Chidozie , Saleem Atif , Wan Muda Wan Hanim Nadrah
Published: February 2023

Given the impact of unsustainable practices, work, and living patterns, it has become increasingly important to raise global citizens as stewards of the earth’s life support system. Thus, developing sustainability literacy has become very crucial. A sustainability-literate professional can make sound decisions and act in environmentally friendly, socially acceptable, and economically viable ways. Technical and vocational education and training (TVET) arguably has the potential to educate workers, graduates, and citizens alike in developing sustainability literacy since it is regarded in many nations as the supplier of skilled labour. TVET teacher training programmes in Malaysia have been vastly proficient in developing vocationally competent teachers and workers. Still, there remains uncertainty regarding the extent to which these vocational teaching professionals have been trained to develop sustainability competencies and literacies. The implication is that, when technical and vocational teachers responsible for training workers do not possess the requisite competencies to engage in sustainability education, the potential for TVET to become a viable tool for ESD diminishes. Given this backdrop, this study was undertaken to propose a curriculum framework for sustainability literacy for technical and vocational teacher training programmes. Using a modified Delphi method (MDM) consisting of 15 expert participants from Asian-Pacific countries, four important curricular elements for sustainability literacy were identified and defined. These include the learning outcomes for sustainability literacy, teaching competencies for sustainability literacy, pedagogical approaches to foster effective teaching and learning for sustainability, as well as ESD integration strategies. The specific indicators within this curriculum framework were also defined. The paper concludes with vivid implications for practice within TVET teacher training programmes in realising Goal 4 of the United Nations Sustainable Development Goals (SDGs).
Frequency Regulation for State-Space Model-Based Renewables Integrated to Multi-Area Microgrid Systems
Authors: Franklin Ruby Vincy Roy , Fathima A Peer
Published: February 2023

This paper focuses on effective frequency regulation of isolated multi-area microgrid structures using polynomial functions extracted from the modelling of renewable sources. This paper highlights the necessity of state-space (SS) formation for intermittent renewables such as solar and wind for specified quantities compared with the standard functions. The approximate gain and time constants for the variable generating sources used in the literature cannot be rationalized while carrying out frequency regulation since it does not epitomize the system dynamics. Thus, the article recommends non-linear modelling of renewables for executing frequency regulation in MATLAB/Simulink. The supremacy of the proposed work in the presence of a 3-degree of freedom (DOF)-based fractional PID controller aided with a recently evolved Artificial Hummingbird Algorithm (AHA) is determined through the transient and sensitivity analysis. The augmented results proved the outstanding response of AHA-optimized 3DOF-FOPID-based systems when compared with the existing controllers under variable operating conditions. Stringent evaluation is executed in the presence of commonly used heuristic approaches for witnessing the robustness of the optimized controller. The proposed system’s hardware-in-loop (HIL)-based implementation is also presented in the paper for a significant characterization of its working in the physical environment.
An Energy-Saving Regulation Framework of Central Air Conditioning Based on Cloud–Edge–Device Architecture
Authors: Luo Guofu , Sun Tianxing , Wang Haoqi , Li Hao , Wang Jiaqi , Miao Zhuang , Si Honglei , Che Fuliang , Liu Gen
Published: February 2023

As energy plays a fundamental role in our modern life and most of a building’s energy is used for air conditioning, understanding the sustainable regulation theory of central air conditioning remains a significant scientific issue. In view of three shortcomings of existing energy-saving regulation methods of central air conditioning: (1) few studies on low-latency, high-reliability, and safer energy-saving control operation modes, (2) lack of consideration for human comfort, and (3) insufficient analysis of the comprehensive impact of the human–machine–environment, this paper proposes an energy-saving control framework of central air conditioning based on cloud–edge–device architecture. The framework establishes a prediction model of human comfort based on recurrent neural network. An intelligent energy-saving control strategy is proposed to ensure indoor personnel’s thermal comfort, considering the human–machine–environment factors. This study provides a basis for better understanding the sustainable control theory of building central air conditioning. Finally, the experiment proves that the proposed method can effectively reduce the energy consumption of central air conditioning. Compared with traditional regulation approaches, the proposed real-time control strategy can save up to 91% of energy consumption, depending on the environment, and advance control strategies can save an average of 4%.
E-Health Research in Southeast Asia: A Bibliometric Review
Authors: Lwin Hnin Nu Nu , Punnakitikashem Prattana , Thananusak Trin
Published: February 2023

eHealth or digital health innovations expanded tremendously during the COVID-19 pandemic. Innovation and digitalization offer creative solutions to build up a healthy society. The eHealth technologies are quickly taken up by Southeast Asia countries and continue to flourish to alleviate the burden of healthcare challenges. This study is the first review exploring and analyzing the whole literature of eHealth, specifically in the Southeast Asia region. The objective of this study is to document the size, growth trajectory, and geographical distribution of eHealth in Southeast Asia research, identify high-impact authors and documents, explore the intellectual knowledge structure, and analyze the topical trends of the field. The bibliometric analysis was used to analyze a data set of 1405 Scopus-indexed documents between 1976 and 2021. Descriptive analysis, citation, co-citation, and keyword co-occurrence analyses were conducted to gain insights into eHealth in the Southeast Asia knowledge base. The growth rate of literature has rocketed up since 2018, reflecting the significant increase in demand for eHealth in Southeast Asia. Among the eleven Southeast Asian countries, Indonesia, Malaysia, Singapore, and Thailand were the top four countries where the most eHealth-related research was conducted. The intellectual structure of eHealth in Southeast Asia literature comprises four schools of thought (i.e., four groups of similar theoretical perspectives and research interests): (1) analysis and adoption of hospital information system/eHealth records, (2) user intention and acceptance of information technology, (3) technology for healthcare and disease management, and (4) mobile health technology (m-Health). Mobile applications, social networks, the COVID-19 pandemic, patient referral, follow-up, self-care, quality of life, psychology, diabetes mellitus, and hypertension are the recent emerging research themes in the field of study. The eHealth development should consider long-term sustainable management along with the rapid evolution of the field. Additionally, eHealth systems should be holistic and pay attention to technology adoption, data security, and ethical issues involved in medical practices. This bibliometric review delivers reference points for scholars interested in Southeast Asia eHealth, reveals the emerging intellectual structure of this interdisciplinary field, and provides guidance to future research on this domain.
A Social Media Analysis of the Experiences of Chinese Early Childhood Educators and Families with Young Children during COVID-19
Authors: Luo Wenwei , Berson Ilene R. , Berson Michael J.
Published: February 2023

This study used a small stories research narrative paradigm to examine social media posts that focused on efforts to sustain the delivery of early childhood education during COVID. Inductive and deductive content analysis focused on 1303 posts from 177 government officials and 1126 individual users (including preschools, kindergartens, teachers, and parents). The results include an analysis of conversational data that document implementation of the national policy to promote continuity of young children’s learning, including digital resources used, learning content, and teaching approaches. Actor-centric contextual factors determined the success of delivering instruction remotely; however, other contextual components created the conditions that necessitated adaptation of instruction. The COVID outbreak (chrono-level) led to shifts in education delivery and informed national policy (macro-level), influenced the teachers’ and parents’ work contexts (exo-level), enhanced home-school collaboration (meso-level), and required implementation of technological solutions to support children’s learning (micro-level). Contributions to theory, methodology, and practice are discussed.
Simulation of Corruption Decisions—An Agent-Based Approach
Authors: Kasa Richard , Réthi Gábor , Hauber György , Szegedi Krisztina
Published: February 2023

Corruption is one of the biggest barriers to sustainable development. Several objectives of the Sustainable Development Goals (SDGs) are directly linked with the fight against corruption, as it has an influence on the achievement of every single development goal. The aim of this research is to examine decision-making patterns in a typical corruption situation. The following research questions have been formulated: (1) what kind of decision-making approaches are effective against partners maintaining different strategies, and how do these relate to certain generations? (2) Is there a difference between the behavior patterns of employees of SMEs, large corporations, and the public sector in a situation that provides the opportunity for corruption? To answer these research questions, an agent-based corruption experiment was conducted, building on the prisoner’s dilemma. The relationship between cooperation and corruption was examined through the analysis of decision-making situations to uncover when and with which partners (artificial agents) the participants first start to cooperate or become corrupt. The results show that there is a significant difference in the propensity to cheat among different generations. Furthermore, the behavior patterns of employees of large corporations, SMEs, and the public sector also show deviation in a corruption situation.
Research on the Construction of Safety Information Ontology Knowledge Base and Accident Reasoning for Complex Hazardous Production Systems-Taking Methanol Production Process as an Example
Authors: Liu Meng , Huang Rui , Xu Fangting
Published: February 2023

Taking methanol production as an example, the concept of “ontology” is introduced to construct a safety knowledge ontology, and a safety information knowledge base is created with the help of the Protégé software. These can be used to efficiently handle the massive safety information data of dangerous chemical enterprises, associate all kinds of miscellaneous information, and improve the level of safety management. An accident tree reasoning model is designed to determine the cause of the accident using accident tree reasoning, and to mine the vast knowledge of safety information, according to safety information knowledge and accident tree analysis theory. Using these methods, the storage, processing, and reuse of safety information are realized, the efficiency of safety management can be improved, and the defects caused by incomplete personnel knowledge structure can be avoided.
Study on the Atmospheric Diffusion of Airborne Radionuclide under LOCA of Offshore Floating Nuclear Power Plants Based on CALPUFF
Authors: Huang Yan , Song Xiaoming , Zou Shuliang , Xu Shoulong , Zhao Fang , Liu Na
Published: February 2023

Studying the migration and diffusion of radionuclides plays an important role in emergency decision making and accident mitigation of floating nuclear power plants. Based on the CALPUFF model, this paper simulates the spatial distribution and concentration distribution of airborne radionuclides 131I diffusion under the conditions of sailing and power supply under LOCA (Loss-of-Coolant Accident) of the floating nuclear power plant, and the influence of four meteorological parameters, namely wind speed, cloudiness, temperature and air pressure, on the migration was analyzed using sensitivity analysis. The results show that the wind direction affects the diffusion direction of 131I, and the concentration of 131I decreases with the increase in the diffusion distance; under the same conditions, the radionuclides diffuses farther and the affected area is larger under the sailing condition. Wind speed is the dominant factor affecting the diffusion of radionuclides, followed by the cloud amount parameter, temperature parameter, and air pressure parameter. The research results can provide theoretical support for emergency responses to nuclear accidents in offshore floating nuclear power plants.
Experimental Research on a Solar Energy Phase Change Heat Storage Heating System Applied in the Rural Area
Authors: Lv Shilei , Zhu Jiawen , Wang Ran
Published: February 2023

Thermal energy storage technology can effectively promote the clean heating policy in northern China. Therefore, phase-change heat storage heating technology has been widely studied, both theoretically and experimentally, but there is still a lack of engineering application research. According to the characteristics of heating load in northern rural areas, a kind of solar heating system using phase-change materials (PCMs) for heat storage is proposed. Furthermore, a farmhouse is used to demonstrate the practical engineering applications of the heating system. The heating system consists of the phase-change heat storage device (PCHSD), solar thermal panels, and a floor radiant heating terminal, which can realize the effective utilization of solar energy. Considering solar power generation capacity, heating load characteristics of farm buildings, and the local electricity price model, four potential operation modes of the heating system are established. Then, the corresponding control strategies are proposed for the four operating modes. The actual operation data of the heating system under different operating modes were collected continuously, and the application effect of the heating system was evaluated from the aspects of thermal efficiency of the device, the renewable energy efficiency, thermal comfort level, and economy. The experimental results show that: (1) The thermal efficiency of the device is mainly affected by the heating load, which can reach more than 80% during the test period; (2) the renewable energy efficiency of the system is positively correlated with the solar radiation intensity, and the maximum can reach 100% when the solar radiation is sufficient; (3) the system maintains excellent thermal comfort in all conditions, with the average and the highest thermal comfort time accounting for 80% and 100%, respectively; (4) compared with the average level of existing clean heating technology, the annual operating cost of the system is reduced by 27.3%, and the economy is significant. The results show that the system achieves effective performance during the test period.