bluuebunny/crossref_metadata_2025 huggingface dataset:
A Sensor-Based Feedback Device Stimulating Daily Life Upper Extremity Activity in Stroke Patients: A Feasibility Study
Authors: Langerak Anthonia J. , Regterschot Gerrit Ruben Hendrik , Evers Marc , van Beijnum Bert-Jan F. , Meskers Carel G. M. , Selles Ruud W. , Ribbers Gerard M. , Bussmann Johannes B. J.
Published: June 2023

This study aims to evaluate the feasibility and explore the efficacy of the Arm Activity Tracker (AAT). The AAT is a device based on wrist-worn accelerometers that provides visual and tactile feedback to stimulate daily life upper extremity (UE) activity in stroke patients. Methods: A randomised, crossover within-subject study was conducted in sub-acute stroke patients admitted to a rehabilitation centre. Feasibility encompassed (1) adherence: the dropout rate and the number of participants with insufficient AAT data collection; (2) acceptance: the technology acceptance model (range: 7–112) and (3) usability: the system usability scale (range: 0–100). A two-way ANOVA was used to estimate the difference between the baseline, intervention and control conditions for (1) paretic UE activity and (2) UE activity ratio. Results: Seventeen stroke patients were included. A 29% dropout rate was observed, and two participants had insufficient data collection. Participants who adhered to the study reported good acceptance (median (IQR): 94 (77–111)) and usability (median (IQR): 77.5 (75–78.5)-). We found small to medium effect sizes favouring the intervention condition for paretic UE activity (η2G = 0.07, p = 0.04) and ratio (η2G = 0.11, p = 0.22). Conclusion: Participants who adhered to the study showed good acceptance and usability of the AAT and increased paretic UE activity. Dropouts should be further evaluated, and a sufficiently powered trial should be performed to analyse efficacy.
Remote Patient Activity Monitoring System by Integrating IoT Sensors and Artificial Intelligence Techniques
Authors: Palanisamy Preethi , Padmanabhan Amudhavalli , Ramasamy Asokan , Subramaniam Sakthivel
Published: June 2023

Even with the most cutting-edge tools, treating and monitoring patients—including children, elders, and suspected COVID-19 patients—remains a challenging activity. This study aimed to track multiple COVID-19-related vital indicators using a wearable monitoring device with an Internet of Things (IOT) focus. Additionally, the technology automatically alerts the appropriate medical authorities about any breaches of confinement for potentially contagious patients by tracking patients’ real-time GPS data. The wearable sensor is connected to a network edge in the Internet of Things cloud, where data are processed and analyzed to ascertain the state of body function. The proposed system is built with three tiers of functionalities: a cloud layer using an Application Peripheral Interface (API) for mobile devices, a layer of wearable IOT sensors, and a layer of Android web for mobile devices. Each layer performs a certain purpose. Data from the IoT perception layer are initially collected in order to identify the ailments. The following layer is used to store the information in the cloud database for preventative actions, notifications, and quick reactions. The Android mobile application layer notifies and alerts the families of the potentially impacted patients. In order to recognize human activities, this work suggests a novel integrated deep neural network model called CNN-UUGRU which mixes convolutional and updated gated recurrent subunits. The efficiency of this model, which was successfully evaluated on the Kaggle dataset, is significantly higher than that of other cutting-edge deep neural models and it surpassed existing products in local and public datasets, achieving accuracy of 97.7%, precision of 96.8%, and an F-measure of 97.75%.
Leakage Source Localization Method for Aerospace Composite Structures Based on U-Array Wave Velocity-Compensated Beamforming
Authors: Qi Lei , Xu Lixin , Sun Lichen , Rui Xiaobo , Cui Yuhao , He Xin , Zhang Yu
Published: June 2023

Composite materials have been widely used in spacecraft structures. Due to the harsh environment in space, gas leakage will occur in the structure, so it is necessary to locate the leakage position in time. In this paper, a beamforming localization method based on a U-shaped sensor array is studied. The array can be divided into two subarrays, which can orientate the direction of leakage sources, respectively. To solve the problem of uneven wave velocity caused by the anisotropy of composite materials, this method modifies the relationship between wave velocity and direction and combines it with the dispersion curve to select a filtering frequency band to reduce the influence of dispersion. The experiment simulates vacuum leakage by pumping holes with a diameter of 3 mm with a vacuum pump. The results show that the U-shaped array beamforming algorithm proposed in this paper can obtain a positioning error of 2.21 cm, which provides a new idea for the structural health detection of spacecraft.
Mechatronic Device Control by Artificial Intelligence
Authors: Bohušík Martin , Stenchlák Vladimír , Císar Miroslav , Bulej Vladimír , Kuric Ivan , Dodok Tomáš , Bencel Andrej
Published: June 2023

Nowadays, artificial intelligence is used everywhere in the world and is becoming a key factor for innovation and progress in many areas of human life. From medicine to industry to consumer electronics, its influence is ever-expanding and permeates all aspects of our modern society. This article presents the use of artificial intelligence (prediction) for the control of three motors used for effector control in a spherical parallel kinematic structure of a designed device. The kinematic model used was the “Agile eye” which can achieve high dynamics and has three degrees of freedom. A prototype of this device was designed and built, on which experiments were carried out in the framework of motor control. As the prototype was created through the means of the available equipment (3D printing and lathe), the clearances of the kinematic mechanism were made and then calibrated through prediction. The paper also presents a method for motor control calibration. On the one hand, using AI is an efficient way to achieve higher precision in positioning the optical axis of the effector. On the other hand, such calibration would be rendered unnecessary if the clearances and inaccuracies in the mechanism could be eliminated mechanically. The device was designed with imperfections such as clearances in mind so the effectiveness of the calibration could be tested and evaluated. The resulting control of the achieved movements of the axis of the device (effector) took place when obtaining the exact location of the tracked point. There are several methods for controlling the motors of mechatronic devices (e.g., Matlab-Simscape). This paper presents an experiment performed to verify the possibility of controlling the kinematic mechanism through neural networks and eliminating inaccuracies caused by imprecisely produced mechanical parts.
Wind Turbine Condition Monitoring Using the SSA-Optimized Self-Attention BiLSTM Network and Changepoint Detection Algorithm
Authors: Yan Junshuai , Liu Yongqian , Li Li , Ren Xiaoying
Published: June 2023

Condition-monitoring and anomaly-detection methods used for the assessment of wind turbines are key to reducing operation and maintenance (O&M) cost and improving their reliability. In this study, based on the sparrow search algorithm (SSA), bidirectional long short-term memory networks with a self-attention mechanism (SABiLSTM), and a binary segmentation changepoint detection algorithm (BinSegCPD), a condition-monitoring method (SSA-SABiLSTM-BinSegCPD, SSD) used for wind turbines is proposed. Specifically, the self-attention mechanism, which can mine the nonlinear dynamic characteristics and spatial–temporal features inherent in the SCADA time series, was introduced into a two-layer BiLSTM network to establish a normal-behavior model for wind turbine key components. Then, as a result of the advantages of searching precision and convergence rate methods, the sparrow search algorithm was employed to optimize the constructed SABiLSTM model. Moreover, the BinSegCPD algorithm was applied to the predicted residual sequence to achieve the automatic identification of deterioration conditions for wind turbines. Case studies conducted on multiple wind turbines located in south China showed that the established SSA-SABiLSTM model was superior to other contrast models, achieving a better prediction precision in terms of RMSE, MAE, MAPE, and R2. The MAE, RMSE, and MAPE of SSA-SABiLSTM were 0.2543 °C, 0.3412 °C, and 0.0069, which were 47.23%, 42.19%, and 53.38% lower than those of SABiLSTM, respectively. The R2 of SABiLSTM was 0.9731, which was 4.6% higher than that of SABiLSTM. The proposed SSD method can detect deterioration conditions 47–120 h in advance and trigger fault alarm signals approximately 36 h ahead of the actual failure time.
Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data
Authors: Tian Jinglian , Tian Yongzhong , Cao Yan , Wan Wenhao , Liu Kangning
Published: June 2023

To meet the challenge of food security, it is necessary to obtain information about rice fields accurately, quickly and conveniently. In this study, based on the analysis of existing rice fields extraction methods and the characteristics of intra-annual variation of normalized difference vegetation index (NDVI) in the different types of ground features, the NDVI difference method is used to extract rice fields using Sentinel data based on the unique feature of rice fields having large differences in vegetation between the pre-harvest and post-harvest periods. Firstly, partial correlation analysis is used to study the influencing factors of the rice harvesting period, and a simulation model of the rice harvesting period is constructed by multiple regression analysis with data from 32 sample points. Sentinel data of the pre-harvest and post-harvest periods of rice fields are determined based on the selected rice harvesting period. The NDVI values of the rice fields are calculated for both the pre-harvest and post-harvest periods, and 33 samples of the rice fields are selected from the high-resolution image. The threshold value for rice field extraction is determined through statistical analysis of the NDVI difference in the sample area. This threshold was then utilized to extract the initial extent of rice fields. Secondly, to address the phenomenon of the “water edge effect” in the initial data, the water extraction method based on the normalized difference water index (NDWI) is used to remove the pixels of water edges. Finally, the extraction results are verified and analyzed for accuracy. The study results show that: (1) The rice harvesting period is significantly correlated with altitude and latitude, with coefficients of 0.978 and 0.922, respectively, and the simulation model of the harvesting period can effectively determine the best period of remote sensing images needed to extract rice fields; (2) The NDVI difference method based on sentinel data for rice fields extraction is excellent; (3) The mixed pixels have a large impact on the accuracy of rice fields extraction, due to the water edge effect. Combining NDWI can effectively reduce the water edge effect and significantly improve the accuracy of rice field extraction.
Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors
Authors: Hoelzemann Alexander , Romero Julia Lee , Bock Marius , Laerhoven Kristof Van , Lv Qin
Published: June 2023

We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications of game analysis, guided training, and personal physical activity tracking. The dataset was recorded from two teams in separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist, during both a repetitive basketball training session and a game. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels since the participants were heterogeneous in terms of prior basketball experience. We illustrate the dataset’s features in several time-series analyses and report on a baseline classification performance study with two state-of-the-art deep learning architectures.
Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey
Authors: Nasralla Moustafa M. , Khattak Sohaib Bin Altaf , Ur Rehman Ikram , Iqbal Muddesar
Published: June 2023

Mobile-health (m-health) is described as the application of medical sensors and mobile computing to the healthcare provision. While 5G networks can support a variety of m-health services, applications such as telesurgery, holographic communications, and augmented/virtual reality are already emphasizing their limitations. These limitations apply to both the Quality of Service (QoS) and the Quality of Experience (QoE). However, 6G mobile networks are predicted to proliferate over the next decade in order to solve these limitations, enabling high QoS and QoE. Currently, academia and industry are concentrating their efforts on the 6G network, which is expected to be the next major game-changer in the telecom industry and will significantly impact all other related verticals. The exponential growth of m-health multimedia traffic (e.g., audio, video, and images) creates additional challenges for service providers in delivering a suitable QoE to their customers. As QoS is insufficient to represent the expectations of m-health end-users, the QoE of the services is critical. In recent years, QoE has attracted considerable attention and has established itself as a critical component of network service and operation evaluation. This article aims to provide the first thorough survey on a promising research subject that exists at the intersection of two well-established domains, i.e., QoE and m-health, and is driven by the continuing efforts to define 6G. This survey, in particular, creates a link between these two seemingly distinct domains by identifying and discussing the role of 6G in m-health applications from a QoE viewpoint. We start by exploring the vital role of QoE in m-health multimedia transmission. Moreover, we examine how m-health and QoE have evolved over the cellular network’s generations and then shed light on several critical 6G technologies that are projected to enable future m-health services and improve QoE, including reconfigurable intelligent surfaces, extended radio communications, terahertz communications, enormous ultra-reliable and low-latency communications, and blockchain. In contrast to earlier survey papers on the subject, we present an in-depth assessment of the functions of 6G in a variety of anticipated m-health applications via QoE. Multiple 6G-enabled m-health multimedia applications are reviewed, and various use cases are illustrated to demonstrate how 6G-enabled m-health applications are transforming human life. Finally, we discuss some of the intriguing research challenges associated with burgeoning multimedia m-health applications.
Design and Analysis of an Extended Simply Supported Beam Piezoelectric Energy Harvester
Authors: Su Wei-Jiun , Tseng Chu-Hsiang
Published: June 2023

The harvesting efficiency of a cantilevered piezoelectric energy harvester is limited by its uneven strain distribution. Moreover, a cantilevered harvester requires a large workspace due to the large displacement of its free end. To address these issues, a novel piezoelectric energy harvester based on an extended simply supported beam is proposed. The proposed design features a simply supported piezoelectric main beam with an extended beam attached to its roller end and a tip mass to reduce the resonant frequency. The theoretical model of the proposed piezoelectric energy harvester is developed based on the Euler–Bernoulli beam theory. The model has been experimentally validated through the fabrication of a prototype. The extended beam and tip mass are adjusted to see their influence on the performance of the harvester. The resonant frequency can be maintained by shortening the extended beam and increasing the tip mass simultaneously. A shorter extend beam leads to a more even strain distribution in the piezoelectric layer, resulting in an enhanced output voltage. Moreover, the simulation results show that a torsional spring is installed on the roller joint which greatly influences the voltage output. The strain distribution becomes more even when proper compressive preload is applied on the main beam. Experiments have shown that the proposed design enhances the output power by 86% and reduces tip displacement by 63.2% compared to a traditional cantilevered harvester.
Uncertainty of Standardized Track Insulation Measurement Methods for Stray Current Assessment
Authors: Bhagat Sahil , Bongiorno Jacopo , Mariscotti Andrea
Published: June 2023

Stray current is a relevant phenomenon in particular for DC electrified transportation systems, affecting track and infrastructure within the right of way and other structures and installations nearby. It worsens with time and the level of protection depends on timely maintenance, as well as correct design choices. The assessment of track insulation is the starting point for both stray current monitoring systems and at commissioning or upon major changes. Standardized methods (ref. EN 50122-2 or IEC 62128-2) have been almost unchanged in the last 20 years but suffer from accuracy issues and variability due to parameters and conditions not under the operator’s control. The uncertainty of test methods is increasingly important now that contractual specifications require a high level of insulation for new systems. A critical discussion and analysis of the sources of variability and practical constraints is proposed, followed by an evaluation of uncertainty, with the objective not only to assess the accuracy of the provided results, but also to foster research on innovative, more flexible and accurate methods.
Metasurface-Assisted Terahertz Sensing
Authors: Wang Qian , Chen Yuzi , Mao Jinxian , Yang Fengyuan , Wang Nan
Published: June 2023

Terahertz (THz) waves, which fall between microwaves and infrared bands, possess intriguing electromagnetic properties of non-ionizing radiation, low photon energy, being highly sensitive to weak resonances, and non-polar material penetrability. Therefore, THz waves are extremely suitable for sensing and detecting chemical, pharmaceutical, and biological molecules. However, the relatively long wavelength of THz waves (30~3000 μm) compared to the size of analytes (1~100 nm for biomolecules, <10 μm for microorganisms) constrains the development of THz-based sensors. To circumvent this problem, metasurface technology, by engineering subwavelength periodic resonators, has gained a great deal of attention to enhance the resonance response of THz waves. Those metasurface-based THz sensors exhibit high sensitivity for label-free sensing, making them appealing for a variety of applications in security, medical applications, and detection. The performance of metasurface-based THz sensors is controlled by geometric structure and material parameters. The operating mechanism is divided into two main categories, passive and active. To have a profound understanding of these metasurface-assisted THz sensing technologies, we review and categorize those THz sensors, based on their operating mechanisms, including resonators for frequency shift sensing, nanogaps for enhanced field confinement, chirality for handedness detection, and active elements (such as graphene and MEMS) for advanced tunable sensing. This comprehensive review can serve as a guideline for future metasurfaces design to assist THz sensing and detection.
Fruit Detection and Counting in Apple Orchards Based on Improved Yolov7 and Multi-Object Tracking Methods
Authors: Hu Jing , Fan Chuang , Wang Zhoupu , Ruan Jinglin , Wu Suyin
Published: June 2023

With the increasing popularity of online fruit sales, accurately predicting fruit yields has become crucial for optimizing logistics and storage strategies. However, existing manual vision-based systems and sensor methods have proven inadequate for solving the complex problem of fruit yield counting, as they struggle with issues such as crop overlap and variable lighting conditions. Recently CNN-based object detection models have emerged as a promising solution in the field of computer vision, but their effectiveness is limited in agricultural scenarios due to challenges such as occlusion and dissimilarity among the same fruits. To address this issue, we propose a novel variant model that combines the self-attentive mechanism of Vision Transform, a non-CNN network architecture, with Yolov7, a state-of-the-art object detection model. Our model utilizes two attention mechanisms, CBAM and CA, and is trained and tested on a dataset of apple images. In order to enable fruit counting across video frames in complex environments, we incorporate two multi-objective tracking methods based on Kalman filtering and motion trajectory prediction, namely SORT, and Cascade-SORT. Our results show that the Yolov7-CA model achieved a 91.3% mAP and 0.85 F1 score, representing a 4% improvement in mAP and 0.02 improvement in F1 score compared to using Yolov7 alone. Furthermore, three multi-object tracking methods demonstrated a significant improvement in MAE for inter-frame counting across all three test videos, with an 0.642 improvement over using yolov7 alone achieved using our multi-object tracking method. These findings suggest that our proposed model has the potential to improve fruit yield assessment methods and could have implications for decision-making in the fruit industry.
A Smart Image Encryption Technology via Applying Personal Information and Speaker-Verification System
Authors: Li Shih-Yu , Lee Chun-Hung , Tam Lap-Mou
Published: June 2023

In this paper, a framework for authorization and personal image protection that applies user accounts, passwords, and personal I-vectors as the keys for ciphering the image content was developed and connected. There were two main systems in this framework. The first involved a speaker verification system, wherein the user entered their account information and password to log into the system and provided a short voice sample for identification, and then the algorithm transferred the user’s voice (biometric) features, along with their account and password details, to a second image encryption system. For the image encryption process, the account name and password presented by the user were applied to produce the initial conditions for hyper-chaotic systems to generate private keys for image-shuffling and ciphering. In the final stage, the biometric features were also applied to protect the content of the image, so the encryption technology would be more robust. The final results of the encryption system were acceptable, as a lower correlation was obtained in the cipher images. The voice database we applied was the Pitch Tracking Database from the Graz University of Technology (PTDB-TUG), which provided the microphone and laryngoscope signals of 20 native English speakers. For image processing, four standard testing images from the University of Southern California–Signal and Image Processing Institute (USC-SIPI), including Lena, F-16, Mandrill, and Peppers, were presented to further demonstrate the effectiveness and efficiency of the smart image encryption algorithm.
Search Space Reduction for Localization and Tracking of an Acoustic Source
Authors: Rodríguez Orlando Camargo , Zhang Lilun , Cheng Xinghua
Published: June 2023

Experimental data from the SACLANTCEN 1993 Mediterranean Experiment are reviewed to assess the reduction of the search space for the localization and tracking of an acoustic source in a three-dimensional environment. Key to this goal is the availability of an initial estimate of source range and depth (called the 2D initial guess); an ambiguous estimate of source bearing can be obtained from the 2D initial guess through Environmental Signal Processing, and the ambiguity can be removed by searching for the source only in the range/bearing regions where bearing estimates are higher. This search provides a new estimate of source range and a single bearing, which together with the estimate for source depth constitute the center of the reduced search space for source localization and tracking. The suggested approach is tested on experimental data from the SACLANTCEN experiment considering different frequencies, as well as a stationary and a moving source.
Effects of Flywheel vs. Free-Weight Squats and Split Squats on Jumping Performance and Change of Direction Speed in Soccer Players
Authors: Jarosz Jakub , Królikowska Paulina , Matykiewicz Patryk , Aschenbrenner Piotr , Ewertowska Paulina , Krzysztofik Michał
Published: June 2023

The objective of this study was to compare (i) The effects of a flywheel and free-weight resistance training program; and (ii) The effects of performing lateral and frontal split squats as part of a flywheel-resistance training program on jumping performance, the 5–0–5 change of direction test time, and the one-repetition maximum (1RM) back squat in soccer players. Twenty-four male amateur soccer players participated in this study and were randomly and equally assigned to one of three different test groups: forward split-squat group (FSQ); lateral split-squat group (LSQ); and free-weight training group (TRAD). Athletes in the FSQ group performed a squat and a forward split squat on a flywheel device, while those in the LSQ group performed a squat and a lateral split squat (instead of a forward split squat) on a flywheel device. Each training lasted 4 weeks. The main finding was that all training groups, such as TRAD, FSQ, and LSQ, significantly improved broad jump length (p = 0.001; effect size [ES] = 0.36), 5–0–5 COD time with a turn on the dominant limb (p = 0.038; ES = 0.49), and 1RM back squat (p = 0.001; ES = 0.4). In turn, both flywheel-resistance training groups (FSQ and LSQ) significantly improved their counter-movement jump height (p = 0.001; ES = 0.8 and p = 0.002; ES = 0.58; respectively) with no effect in the TRAD (p = 0.676; ES = 0.07) training group. Both free-weight and flywheel-resistance training lasting 4 weeks performed in-season contributed to significant improvement in 1RM back squat, broad jump performance, and 5–0–5 change of direction testing time, while flywheel-resistance training might be superior in counter-movement jump height enhancement in soccer players. Moreover, the manner in which split squats were performed was not a factor influencing the obtained results.
Towards Optimal TMS Integration Scenario Based on HRM and SWOT Analysis
Authors: Rijavec Robert , Marsetič Rok , Strnad Irena
Published: June 2023

To meet the collective goal of providing an efficient, sustainable, and safe transportation system, motorway managers are oriented toward establishing an optimal traffic management system (TMS). However, there are no straightforward guidelines through which to deploy the optimal integration scenario for traffic control centers (TCCs). An important component of TMSs is traffic control decisions, implying that human resources represent a critical feature of a TMS. In this study, we introduce human resource management (HRM) into the process of selecting the most appropriate integration scenario, which is achieved by developing a model to determine the number of employees. We formulated the problem as the mixed integer nonlinear programming problem based on the analysis of traffic incidents and road infrastructure data, considering also the employee voice and some HRM specificities in incident situations. Combining these findings with a SWOT analysis of the possible integration scenarios in Slovenia, we propose the most suitable scenario, in which TMS is organized as a cloud service. Moreover, this study predicts TCCs’ space requirements and identifies threats of neglecting HRM in TMS design. This paper can serve as a guideline and a baseline of expertise for motorway managers to create an effective and sustainable TMS in their region.
Research on Energy Savings of an Air-Source Heat Pump Hot Water System in a College Student’s Dormitory Building
Authors: Zeng Yijiang , Li Shengyu , Lu Jun , Li Xiaodong , Xing Dingding , Xiao Jipan , Zhang Zhanhao , Li Leihong , Shi Xuhui
Published: June 2023

Centralized hot water systems are commonly installed in college student dormitories, representing a typical application for such systems. To achieve sustainable and environmentally friendly heating solutions, air-source heat pump hot water systems have gained attention for their high efficiency and energy-saving characteristics. By implementing heat pump technology, China could make significant progress towards achieving its carbon neutrality goals by reducing energy consumption and associated emissions. In this study, the heating performance of an air-source heat pump hot water system was tested in the field over the course of a year at a university in Chongqing, China. A simulation model was constructed using TRNSYS software, and a time-sharing control strategy was proposed to analyze the system’s operating characteristics and energy-saving performance. Results showed a 6% increase in unit annual average Coefficient of Performance (COP) and annual electricity savings of 10,027 kW·h, with an energy-saving rate of 8.77% after time-sharing control. The study highlights the significant economic and environmental benefits of adopting sustainable energy solutions, particularly in the context of increasing global greenhouse gas emissions.
A Corporate Social Responsibility (CSR) Model to Achieve Sustainable Business Performance (SBP) of SMEs in the South African Construction Industry
Authors: Wentzel Lance , Fapohunda Julius Ayodeji , Haldenwang Rainer
Published: June 2023

This paper purposes to develop a corporate social responsibility (CSR) model to guide small and medium enterprises (SMEs) in the South African construction industry (SACI) towards sustainable business performance (SBP). A theoretical CSR model was developed from the literature and validated through Partial Least Squares Structural Equation Modelling (PLS-SEM), using primary questionnaire data obtained from 110 SMEs in the SACI, who are registered on the construction industry development board (cidb) register of contractors between Grade 1 and 6 general building (GB) and/or civil engineering (CE). The PLS-SEM results indicate that CSR drivers influencing the CSR practices of SMEs, along with CSR implementation challenges experienced by SMEs, influence the perception of SMEs pertaining to the relationship between the integration of CSR and SBP all of which influences the CSR activities considered by SMEs to achieve SBP, thus implying that the holistic adaption of the PLS-SEM (CSR model) by SMEs in the SACI equates to more SBP. However, SMEs practicing CSR activities to achieve SBP are subjected to specific CSR: drivers and implementation challenges as well as SME owner perceptions.
Integrating “Neoliberal-Turn” and “Social-Turn” Constructs in Examining Sustainable Development and Happiness and Life Satisfaction: A Global-, Country Cluster-, and Country-Level Study
Authors: Canatay Arman , Prieto Leonel , Amin Muhammad Ruhul
Published: June 2023

This study explores several global and country cluster sustainable development (herein SD) models. It jointly examines constructs from the “Neoliberal-turn” ((e.g., Institutional Enhancers (e.g., business freedom, property rights, government integrity, and judicial effectiveness), Financial Enhancers (e.g., government spending and monetary, trade, investment, and financial freedoms), Foreign Direct Investment, Global Competitiveness-institutions, and Global Competitiveness-innovation-sophistication)), the “Social-turn” (five SD pillars (e.g., Planet, People, Peace, Prosperity, and Resources), which synthesize the 17 United Nations SD goals), and Happiness and Life Satisfaction (herein H&LS). This study examines a large and diverse set of relationships encompassing 11 years of data of 108 social, economic, and environmental indicators from 125 countries at the global, country cluster, and country levels. We analyze data using structural equation modeling. Remarkably, Planet, an SD pillar constituted by biophysical variables, is the least of the five SD pillars positively related to the “neoliberal-turn” constructs and H&LS. The results show model configurations and scale effects. Furthermore, the findings reveal both synergies between neoliberal and the five SD pillars, as well as both negative and nonsignificant relationships among them. The diversity of the results calls for further model integration and specificity. The diverse findings entail caution and difficulties in generalizing knowledge. Similarly, the results suggest that it is not advisable, since there may be many different and partly legitimate alternatives, to rely on just one perspective (e.g., giving primacy to economic-based analyses) and/or in analyses at only one scale. The perspective effects and the stakes involved in SD urgently call for more collaborative efforts at all levels.
The Evolution of the Collaborative Environmental Governance Network in Guizhou Province, China
Authors: Wang Zhanjie , Ma Yongfeng , Wang Shasha , Luo Chaoliang , Wang Yongjian
Published: June 2023

China has successively set goals of carbon peaking and carbon neutrality, aiming to transform to a green and low-carbon economy. Collaborative environmental governance (CEG) is an important way to achieve this transformation. To improve collaborative environmental governance, the study of the evolution of the collaborative environmental governance network (CEGN) is necessary. In this study, the CEGNs in different periods in Guizhou Province, China are established based on social network analysis. Then, the evolution of environment governance policy implementers’ functions in the network is investigated by two-dimensional (breadth and intensity) matrices. Moreover, the evolution of core implementers’ functions and its impacts on the operation of the network are analyzed based on the network stability. The results show that the network size, frequency of collaboration, and cohesion have gradually increased, and the network structure has changed from a loose pattern to an expanded and balanced system. Implementers such as the governments of municipalities (prefectures) (MGs) and counties (districts) (CGs), urban management committee (UMCs), and municipal (prefecture) ecological environment bureaus (MBEEs) have played leading and coordinating roles at different phases. However, the cohesion of the network is low, indicating a low level of collaboration. This study integrates the collaborative governance theory and social network analysis, which provides a new way for the study of CEG. In addition, the CEG effect is evaluated by analyzing the evolution of the CEGN, which provides a reference for the CEG policy making in developing regions.
Role of Cargo Owner in Logistic Chain Sustainability
Authors: Caldeirinha Vitor , Felício J. Augusto , Pinho Tiago
Published: June 2023

Understanding the factors and characteristics of the business and the influence of cargo owners on sustainable practices in maritime logistics chains is the main objective of this study. The sample consists of 141 valid responses from Portuguese companies that own cargo, freight forwarders and other maritime logistics service providers. Sustainable energy theory, green state theory, and shared value creation theory support the research. The SEM methodology was adopted. The sector’s structure, management characteristics, type of transport contract and the size of the cargo characterize and condition the business of cargo owners and influence the choice of green transport, the use of green fuel and corporate social responsibility. It is important to increase the knowledge and practice of cargo owners in order to understand their business constraints.
An Improved TOPSIS Model Based on Cumulative Prospect Theory: Application to ESG Performance Evaluation of State-Owned Mining Enterprises
Authors: Su Jiahui , Sun Yidi
Published: June 2023

The aim of this study is to provide a comprehensive decision-making method that can be applied to investment decisions based on the Environmental, Social and Governance (ESG) performance. The study contributes to the existing literature by introducing the CPT-TOPSIS model, a pioneering approach that incorporates the effect of non-rational factors on decision-making results in uncertain conditions by combining cumulative prospect theory (CPT) with the classic TOPSIS model. Moreover, by conducting an application to ESG evaluation on five state-owned mining enterprises in China, the study provides evidence of the effectiveness and improvement offered by the new model in comparison with the classic TOPSISI model and prospect theory TOPSIS (PT-TOPSIS) model. The results suggest that the CPT-TOPSIS model considers risk preferences and probability distortion in the decision-making process, narrows the gap between ESG scores, and makes ESG performance evaluation more realistic.
A Narrative of Urban Underground Pipeline System Disasters in China in 2021: Spatial and Temporal Distribution, Causal Analysis, and Response Strategies
Authors: Xie Zhiqiang , Jiang Fengshan , Xu Jiarui , Zhai Zhengang , He Jianglong , Zheng Daoyang , Lian Junyu , Hou Zhiqun , Zhao Lei , Wang Yanxia , Feng Yuyun
Published: June 2023

In recent years, there have been frequent disasters and accidents in the underground pipeline system of Chinese cities, posing a continuous threat to the safety of life and property of the public and the order of urban operations. This article uses statistical data on the spatiotemporal distribution of major underground pipeline disasters in various provinces of China in 2021. By using Pearson’s correlation coefficient and regression analysis, a causal analysis model for urban underground pipeline accidents is constructed, and the correlation between urban underground pipeline accident rate and economic and social indicators is analyzed. From the analysis, it has been found that the correlation coefficients of gross domestic product (GDP) per capita, the pipeline density in the built-up area, and the urban pipeline accident rate reach −0.4019 and −0.4275, respectively, showing a negative correlation. Further, the regression analysis results show that the underground pipeline accident rate shows a decreasing trend as the per capita GDP and the density of pipelines in the built-up areas increase. Among them, the results of the power function fitting model show that the GDP per capita accounts for 48.10% of the urban underground pipeline accidents, whereas the pipeline density of the built-up area accounts for 58.27% of these accidents. The construction of underground pipeline regulations is influential in reducing underground pipeline disaster accidents. In this study, the role and effectiveness of the factors, such as the construction of professional regulations in maintaining the safe operation of urban underground pipelines, has been discussed, and suggestions and methods to improve the problems that need to be solved, such as the construction of a municipal supporting emergency management system in the safe operation of underground pipelines in the future, have been proposed.
Critical Controlling for the Network Security and Privacy Based on Blockchain Technology: A Fuzzy DEMATEL Approach
Authors: Kamalov Firuz , Gheisari Mehdi , Liu Yang , Feylizadeh Mohammad Reza , Moussa Sherif
Published: June 2023

The Internet of Things (IoT) has been considered in various fields in the last decade. With the increasing number of IoT devices in the community, secure, accessible, and reliable infrastructure for processing and storing computed data has become necessary. Since traditional security protocols are unsuitable for IoT devices, IoT implementation is fraught with privacy and security challenges. Thus, blockchain technology has become an effective solution to the problems of IoT security. Blockchain is an empirical data distribution and storage model involving point-to-point transmission, consensus mechanism, asymmetric encryption, smart contract, and other computer technologies. Security and privacy are becoming increasingly important in using the IoT. Therefore, this study provides a comprehensive framework for classifying security criteria based on blockchain technology. Another goal of the present study is to identify causal relationship factors for the security issue using the Fuzzy Decision-Making Trial-and-Evaluation Laboratory (FDEMATEL) approach. In order to deal with uncertainty in human judgment, fuzzy logic is considered an effective tool. The present study’s results show the proposed approach’s efficiency. Authentication (CR6), intrusion detection (CR4), and availability (CR5) were also introduced as the most effective and essential criteria, respectively.
Knowledge Management and Firm Performance in Algerian F&B SMEs: The Role of Trust as a Moderating Variable
Authors: Dahinine Benameur , Chouayb Larabi , Bensahel Wassila
Published: June 2023

This study examines the influence of Knowledge Management (KM) on SMEs’ performance using data from a survey of 210 owners/managers of Algerian Small and Medium Enterprises (SMEs) operating in the Food and Beverage (F&B) industry. It highlights the moderating effect of trust as a facilitator of the potential acquisition, sharing, and application of the company’s knowledge to improve SMEs’ financial and non-financial performance. To analyze this moderating effect, we used the method of second-generation structural equations (PLS-SEM) using SmartPLS 3.2.7 statistical software. The results show a significant and positive effect of knowledge management on SMEs’ performance. On the other hand, these results indicate that the importance of the direct effect of KM on firm performance became very strong in the presence of a high level of trust. Implications of the study and future directions for research are discussed at the end of the study.
ACOGARE: Acoustic-Based Litter Garbage Recognition Utilizing Smartwatch
Authors: Tachibana Koki , Nakamura Yugo , Matsuda Yuki , Suwa Hirohiko , Yasumoto Keiichi
Published: June 2023

Litter has become a social problem. To prevent litter, we consider urban planning, the efficient placement of garbage bins, and interventions with litterers. In order to carry out these actions, we need to comprehensively grasp the types and locations of litter in advance. However, with the existing methods, collecting the types and locations of litter is very costly and has low privacy. In this research, we have proposed the conceptual design to estimate the types and locations of litter using only the sensor data from a smartwatch worn by the user. This system can record the types and locations of litter only when a user raps on the litter and picks it up. Also, we have constructed a sound recognition model to estimate the types of litter by using sound sensor data, and we have carried out experiments. We have confirmed that the model built with other people’s data enabled to estimate the F-measure of 80.2% in a noisy environment through the experiment with 12 participants.
Barriers to Achieving Sustainability in Highway Construction Projects: The Case of Jordan
Authors: Aljboor Ahmad , Imam Rana , Alawneh Rami
Published: June 2023

Despite the importance of sustainable highway construction projects in achieving sustainable development, unsustainable construction highway projects still prevail in Jordan. Therefore, this article aims to identify barriers affecting sustainability implementation in Jordan’s highway construction projects. These barriers were identified from the previous literature. Questionnaire surveys were developed and distributed to experts to rank the degree of importance of each barrier. Moreover, a case study was assessed to evaluate the sustainability level of highway projects in Jordan. The research found that the sustainability concept should be supported financially to protect the environment by using a legislative framework to implement sustainability principles. There are several barriers to implementing the concept, like the lack of sustainability education, improper communication amongst team members, and the lack of familiarity with the techniques and the necessary skills to employ them properly. The use of sustainability assessment tools is needed to evaluate the sustainability level for highway projects. For the case study, a sustainability assessment tool was applied to the most recent important highway project in Jordan, “Bus Rapid Transit project,” to determine its sustainability level and to show how the barriers affect the implementation of the sustainable highway project and lower the sustainability level in the project.
Intelligent Safety Risk Analysis and Decision-Making System for Underground Metal Mines Based on Big Data
Authors: Qiang Xingbang , Li Guoqing , Hou Jie , Zhang Xia , Liu Yujia
Published: June 2023

In view of the new situation faced by safety risk management in underground metal mines, based on a comprehensive analysis of the current situation of mine safety management business and system construction requirements, the main functional modules, overall architecture, and data interaction mode of the intelligent safety risk analysis and decision-making system were analyzed and designed. On the basis of elaborating the implementation process of the main functional modules of the system, such as multi-source safety information collection and governance, and safety risk intelligence analysis and visualization, a safety risk intelligence analysis and decision-making system was constructed, which provided efficient and real-time intelligent application and analysis services for safety in the production of underground metal mines and realized the whole process information management of collection, aggregation, processing, analysis, and visual display of multi-source mine safety risk information. The application of the system has made an essential change in the mode of mine safety risk management, realizing the active safety management goal of shifting safety risk management from post-analysis to pre-prevention, helping to improve the pertinence and efficiency of safety risk management, and greatly reducing the risk of mine safety accidents.
Thermal Performance of Green Façades: Research Trends Analysis Using a Science Mapping Approach
Authors: Blanco Ileana , Convertino Fabiana
Published: June 2023

Buildings consume a significant part of the world’s resources and energy. The growing environmental awareness and urgent need to reduce energy consumption have highlighted the importance of introducing innovative solutions as nature-based systems in new buildings’ construction and retrofitting. In this regard, green façades that integrate vegetation into building envelopes are attractive. This paper presents a bibliographic analysis, based on science mapping, of the available literature on green façades from 1999–2022 with a focus on the thermal effect on the building and on the surroundings. The objective of this study is to reveal the structure and the evolution of the research activity in the field, outlining the main research topics and the future research directions. The analysis was performed on a dataset of 270 documents. The results indicate a growing interest in this topic over the last six years and the multidisciplinary dimension of the studies. The keyword cluster analysis indicates the emergence of three main search topics: thermal behavior and energy modeling; urban design and large-scale effects; sustainable buildings management. A greater future dissemination of green façades could be enabled by further research results based on the application of a multidisciplinary approach and of standardized methods.