bluuebunny/crossref_metadata_2025 huggingface dataset:
Remote Sensing for Mapping Natura 2000 Habitats in the Brière Marshes: Setting Up a Long-Term Monitoring Strategy to Understand Changes
Authors: Lafitte Thomas , Robin Marc , Launeau Patrick , Debaine Françoise
Published: July 2024

On a global scale, wetlands are suffering from a steady decline in surface area and environmental quality. Protecting them is essential and requires a careful spatialisation of their natural habitats. Traditionally, in our study area, species discrimination for floristic mapping has been achieved through on-site field inventories, but this approach is very time-consuming in these difficult-to-access environments. Usually, the resulting maps are also not spatially exhaustive and are not frequently updated. In this paper, we propose to establish a complete map of the study area using remote sensors and set up a long-term and regular observatory of environmental changes to monitor the evolution of a major French wetland. This methodology combines three dataset acquisition technologies, airborne hyperspectral and WorldView-3 multispectral images, supplemented by LiDAR images, which we compared to evaluate the difference in performances. To do so, we applied the Random Forest supervised classification methods using ground reference areas and compared the out-of-bag score (OOB score) as well as the matrix of confusion resulting from each dataset. Thirteen habitats were discriminated at level 4 of the European Nature Information System (EUNIS) typology, at a spatial resolution of around 1.2 m. We first show that a multispectral image with 19 variables produces results which are almost as good as those produced by a hyperspectral image with 58 variables. The experiment with different features also demonstrates that the use of four bands derived from LiDAR datasets can improve the quality of the classification. Invasive alien species Ludwigia grandiflora and Crassula helmsii were also detected without error which is very interesting when applied to these endangered environments. Therefore, since WV-3 images provide very good results and are easier to acquire than airborne hyperspectral data, we propose to use them going forward for the regular observation of the Brière marshes habitat we initiated.
Trans-DCN: A High-Efficiency and Adaptive Deep Network for Bridge Cable Surface Defect Segmentation
Authors: Huang Zhihai , Guo Bo , Deng Xiaolong , Guo Wenchao , Min Xing
Published: July 2024

Cables are vital load-bearing components of cable-stayed bridges. Surface defects can lead to internal corrosion and fracturing, significantly impacting the stability of the bridge structure. The detection of surface defects from bridge cable images faces numerous challenges, including shadow disturbances due to uneven lighting and difficulties in addressing multiscale defect features. To address these challenges, this paper proposes a novel and cost-effective deep learning segmentation network, named Trans-DCN, to detect defects in the surface of the bridge cable. The network leverages an efficient Transformer-based encoder and integrates multiscale features to overcome the limitations associated with local feature inadequacy. The decoder implements an atrous Deformable Convolution (DCN) pyramid and dynamically fuses low-level feature information to perceive the complex distribution of defects. The effectiveness of Trans-DCN is evaluated by comparing it with state-of-the-art segmentation baseline models using a dataset comprising cable bridge defect images. Experimental results demonstrate that our network outperforms the state-of-the-art network SegFormer, achieving a 27.1% reduction in GFLOPs, a 1.2% increase in mean Intersection over Union, and a 1.5% increase in the F1 score. Ablation experiments confirmed the effectiveness of each module within our network, further substantiating the significant validity and advantages of Trans-DCN in the task of bridge cable defect segmentation. The network proposed in this paper provides an effective solution for downstream cable bridge image analysis.
Use of Landsat Imagery Time-Series and Random Forests Classifier to Reconstruct Eelgrass Bed Distribution Maps in Eeyou Istchee
Authors: Clyne Kevin , LaRocque Armand , Leblon Brigitte , Costa Maycira
Published: July 2024

The eastern coastline of James Bay is known to have been home to sizeable eelgrass beds (Zostera marina L.) which thrived in the bay’s shallow, subarctic waters. The region was subjected to substantial hydroelectric dams, large fires, and other human activities in the past half-century. To assess the impact of these factors on eelgrass beds, a historical reconstruction of eelgrass bed distribution was performed from images acquired by Landsat-5 Thematic Mapper (TM) in 1988, 1991, and 1996 and images of the Landsat-8 Operational Land Imager (OLI) in 2019. All the images were classified using the Random Forests classifier (RF) and assessed for accuracy each year on a bay-wide scale using an independent field validation dataset. The validation data were extracted from an eelgrass bed map established using aerial photos and field surveys in 1986, 1991, and 1995 and from a field survey in 2019. The overall validation accuracy of the classified images (between 72% and 85%) showed good agreement with the other datasets for most locations, providing reassurance about the reliability of the research. This makes it possible to use satellite imagery to detect past changes to eelgrass distribution within a bay. The classified images of 1988 and 1996 were also compared to aerial photos taken in years close to each other at ten sites to determine their ability to assess small eelgrass beds’ shape and presence. Such a comparison revealed that the classified images accurately portrayed eelgrass distribution even at finer scales.
Hyperspectral Characteristics and SPAD Estimation of Wheat Leaves under CO2 Microleakage Stress
Authors: Zhang Liuya , Yuan Debao , Fan Yuqing , Yang Renxu
Published: July 2024

To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO2 microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model’s accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO2 microleakage stress showed a “red shift” of the green peak and a “blue shift” of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO2 at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO2 trace-leakage stress and to dynamically monitor CO2 trace-leakage stress in crops.
The Effect of Annotation Quality on Wear Semantic Segmentation by CNN
Authors: Bilal Mühenad , Podishetti Ranadheer , Koval Leonid , Gaafar Mahmoud A. , Grossmann Daniel , Bregulla Markus
Published: July 2024

In this work, we investigate the impact of annotation quality and domain expertise on the performance of Convolutional Neural Networks (CNNs) for semantic segmentation of wear on titanium nitride (TiN) and titanium carbonitride (TiCN) coated end mills. Using an innovative measurement system and customized CNN architecture, we found that domain expertise significantly affects model performance. Annotator 1 achieved maximum mIoU scores of 0.8153 for abnormal wear and 0.7120 for normal wear on TiN datasets, whereas Annotator 3 with the lowest expertise achieved significantly lower scores. Sensitivity to annotation inconsistencies and model hyperparameters were examined, revealing that models for TiCN datasets showed a higher coefficient of variation (CV) of 16.32% compared to 8.6% for TiN due to the subtle wear characteristics, highlighting the need for optimized annotation policies and high-quality images to improve wear segmentation.
Optimizing Data Flow in Binary Neural Networks
Authors: Vorabbi Lorenzo , Maltoni Davide , Santi Stefano
Published: July 2024

Binary neural networks (BNNs) can substantially accelerate a neural network’s inference time by substituting its costly floating-point arithmetic with bit-wise operations. Nevertheless, state-of-the-art approaches reduce the efficiency of the data flow in the BNN layers by introducing intermediate conversions from 1 to 16/32 bits. We propose a novel training scheme, denoted as BNN-Clip, that can increase the parallelism and data flow of the BNN pipeline; specifically, we introduce a clipping block that reduces the data width from 32 bits to 8. Furthermore, we decrease the internal accumulator size of a binary layer, usually kept using 32 bits to prevent data overflow, with no accuracy loss. Moreover, we propose an optimization of the batch normalization layer that reduces latency and simplifies deployment. Finally, we present an optimized implementation of the binary direct convolution for ARM NEON instruction sets. Our experiments show a consistent inference latency speed-up (up to 1.3 and 2.4× compared to two state-of-the-art BNN frameworks) while reaching an accuracy comparable with state-of-the-art approaches on datasets like CIFAR-10, SVHN, and ImageNet.
Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention
Authors: Mäkynen Marko , Ng G. Andre , Li Xin , Schlindwein Fernando S. , Pearce Timothy C.
Published: July 2024

Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/PPG) data, yet deploying these on resource-constrained wearable devices remains challenging. This study proposes integrating a customized channel attention mechanism to compress DL neural networks for AF detection, allowing the model to focus only on the most salient time-series features. The results demonstrate that applying compression through channel attention significantly reduces the total number of model parameters and file size while minimizing loss in detection accuracy. Notably, after compression, performance increases for certain model variants in key AF databases (ADB and C2017DB). Moreover, analyzing the learned channel attention distributions after training enhances the explainability of the AF detection models by highlighting the salient temporal ECG/PPG features most important for its diagnosis. Overall, this research establishes that integrating attention mechanisms is an effective strategy for compressing large DL models, making them deployable on low-power wearable devices. We show that this approach yields compressed, accurate, and explainable AF detectors ideal for wearables. Incorporating channel attention enables simpler yet more accurate algorithms that have the potential to provide clinicians with valuable insights into the salient temporal biomarkers of AF. Our findings highlight that the use of attention is an important direction for the future development of efficient, high-performing, and interpretable AF screening tools for wearable technology.
Detection and Utilization of Reflections in LiDAR Scans through Plane Optimization and Plane SLAM
Authors: Li Yinjie , Zhao Xiting , Schwertfeger Sören
Published: July 2024

In LiDAR sensing, glass, mirrors and other materials often cause inconsistent data readings from reflections. This causes problems in robotics and 3D reconstruction, especially with respect to localization, mapping and, thus, navigation. Extending our previous work, we construct a global, optimized map of reflective planes, in order to then classify all LiDAR readings at the end. For this, we optimize the reflective plane parameters of the plane detection of multiple scans. In a further method, we apply the reflective plane estimation in a plane SLAM algorithm, highlighting the applicability of our method for robotics. As our experiments will show, this approach provides superior classification accuracy compared to the single scan approach. The code and data for this work are available as open source online.
Graph Attention Informer for Long-Term Traffic Flow Prediction under the Impact of Sports Events
Authors: Song Yaofeng , Luo Ruikang , Zhou Tianchen , Zhou Changgen , Su Rong
Published: July 2024

Traffic flow prediction is one of the challenges in the development of an Intelligent Transportation System (ITS). Accurate traffic flow prediction helps to alleviate urban traffic congestion and improve urban traffic efficiency, which is crucial for promoting the synergistic development of smart transportation and smart cities. With the development of deep learning, many deep neural networks have been proposed to address this problem. However, due to the complexity of traffic maps and external factors, such as sports events, these models cannot perform well in long-term prediction. In order to enhance the accuracy and robustness of the model on long-term time series prediction, a Graph Attention Informer (GAT-Informer) structure is proposed by combining the graph attention layer and informer layer to capture the intrinsic features and external factors in spatial–temporal correlation. The external factors are represented as sports events impact factors. The GAT-Informer model was tested on real-world data collected in London, and the experimental results showed that our model has better performance in long-term traffic flow prediction compared to other baseline models.
Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping
Authors: Rubio Arthur , Magnier Baptiste
Published: July 2024

This work presents a novel approach to enhancing iris recognition systems through a two-module approach focusing on low-level image preprocessing techniques and advanced feature extraction. The primary contributions of this paper include: (i) the development of a robust preprocessing module utilizing the Canny algorithm for edge detection and the circle-based Hough transform for precise iris extraction, and (ii) the implementation of Binary Statistical Image Features (BSIF) with domain-specific filters trained on iris-specific data for improved biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset, consisting of 1892 iris images, confirm the significant enhancements achieved. Moreover, this paper offers a comprehensive and reproducible research framework by providing source codes and access to the testing database through the Notre Dame University dataset website, thereby facilitating further application and study. Future research will focus on exploring adaptive algorithms and integrating machine learning techniques to improve performance across diverse and unpredictable real-world scenarios.
How the Degree of Anthropomorphism of Human-like Robots Affects Users’ Perceptual and Emotional Processing: Evidence from an EEG Study
Authors: Wu Jinchun , Du Xiaoxi , Liu Yixuan , Tang Wenzhe , Xue Chengqi
Published: July 2024

Anthropomorphized robots are increasingly integrated into human social life, playing vital roles across various fields. This study aimed to elucidate the neural dynamics underlying users’ perceptual and emotional responses to robots with varying levels of anthropomorphism. We investigated event-related potentials (ERPs) and event-related spectral perturbations (ERSPs) elicited while participants viewed, perceived, and rated the affection of robots with low (L-AR), medium (M-AR), and high (H-AR) levels of anthropomorphism. EEG data were recorded from 42 participants. Results revealed that H-AR induced a more negative N1 and increased frontal theta power, but decreased P2 in early time windows. Conversely, M-AR and L-AR elicited larger P2 compared to H-AR. In later time windows, M-AR generated greater late positive potential (LPP) and enhanced parietal-occipital theta oscillations than H-AR and L-AR. These findings suggest distinct neural processing phases: early feature detection and selective attention allocation, followed by later affective appraisal. Early detection of facial form and animacy, with P2 reflecting higher-order visual processing, appeared to correlate with anthropomorphism levels. This research advances the understanding of emotional processing in anthropomorphic robot design and provides valuable insights for robot designers and manufacturers regarding emotional and feature design, evaluation, and promotion of anthropomorphic robots.
Distributed Drive Electric Vehicle Handling Stability Coordination Control Framework Based on Adaptive Model Predictive Control
Authors: Guo Jianhua , Dai Zhiyuan , Liu Ming , Xie Zhihao , Jiang Yu , Yang Haochun , Xie Dong
Published: July 2024

Distributed drive electric vehicles improve steering response and enhance overall vehicle stability by independently controlling each motor. This paper introduces a control framework based on Adaptive Model Predictive Control (AMPC) for coordinating handling stability, consisting of three layers: the dynamic supervision layer, online optimization layer, and low-level control layer. The dynamic supervision layer considers the yaw rate and maneuverability limits when establishing the β−β˙ phase plane stability boundary and designs variable weight factors based on this stability boundary. The online optimization layer constructs the target weight-adaptive AMPC strategy, which can adjust the control weights for maneuverability and lateral stability in real time based on the variable weight factors provided by the dynamic supervision layer. The low-level control layer precisely allocates the driver’s requested driving force and additional yaw moment by using torque distribution error and tire utilization as the cost function. Finally, experiments are conducted on a Simulink-CarSim co-simulation platform to assess the performance of AMPC. Simulation results show that, compared to the traditional MPC strategy, this control strategy not only enhances maneuverability under normal conditions but also improves lateral stability control under extreme conditions.
A Wearable Personalised Sonification and Biofeedback Device to Enhance Movement Awareness
Authors: Pang Toh Yen , Connelly Thomas , Feltham Frank , Cheng Chi-Tsun , Rahman Azizur , Chan Jeffrey , McCarney Luke , Neville Katrina
Published: July 2024

Movement sonification has emerged as a promising approach for rehabilitation and motion control. Despite significant advancements in sensor technologies, challenges remain in developing cost-effective, user-friendly, and reliable systems for gait detection and sonification. This study introduces a novel wearable personalised sonification and biofeedback device to enhance movement awareness for individuals with irregular gait and posture. Through the integration of inertial measurement units (IMUs), MATLAB, and sophisticated audio feedback mechanisms, the device offers real-time, intuitive cues to facilitate gait correction and improve functional mobility. Utilising a single wearable sensor attached to the L4 vertebrae, the system captures kinematic parameters to generate auditory feedback through discrete and continuous tones corresponding to heel strike events and sagittal plane rotations. A preliminary test that involved 20 participants under various audio feedback conditions was conducted to assess the system’s accuracy, reliability, and user synchronisation. The results indicate a promising improvement in movement awareness facilitated by auditory cues. This suggests a potential for enhancing gait and balance, particularly beneficial for individuals with compromised gait or those undergoing a rehabilitation process. This paper details the development process, experimental setup, and initial findings, discussing the integration challenges and future research directions. It also presents a novel approach to providing real-time feedback to participants about their balance, potentially enabling them to make immediate adjustments to their posture and movement. Future research should evaluate this method in varied real-world settings and populations, including the elderly and individuals with Parkinson’s disease.
Impact of a Digital Growth Mindset on Enhancing the Motivation and Performance of Chemistry Students: A Non-Cognitive Approach
Authors: Sarwar Muhammad Naeem , Javed Zahida , Farooq Muhammad Shahid , Nazar Muhammad Faizan , Wasti Shahbaz Hassan , Butt Intzar Hussain , Ansari Ghulam Jillani , Basri Rabia , Kulsoom Sumaira , Ullah Zaka
Published: July 2024

The current study investigates the effects of a digital growth mindset on the motivation and success of chemistry students. The approach involves the use of technological tools that encourage students to face challenges and keep trying even when things become difficult. Students can achieve milestones by following this fruitful methodology. This study utilized a mixed-method design of an ordered–explanatory type, as identified in one of the categories of mixed-method approaches. The quantitative aspects of the research project were conducted using a matching-only pre-test–post-test control-group design. This was conducted because the study was carried out on secondary school students in Lahore, Pakistan, and the population included students up to the tenth grade. Only the experimental group participated in digital growth mindset activities. The control group was taught using traditional methods. The qualitative aspect of the study involved conducting focus group discussions with students in the experimental group. The results showed a significant improvement in motivation and chemistry achievement among the students in the experimental group, as evidenced by the higher mean scores from the pre-tests and the post-tests compared to the control group. The present research findings reveal that digital growth mindset interventions, when appropriately incorporated into chemistry curricula, possess the capacity to not only improve student engagement and subsequent performance but also to provide educators with valuable insights into instructional practices that are worth implementing in the digital era.
Afterword: Play, Personhood and Digital Mortality
Authors: Davies Douglas
Published: July 2024

This Afterword echoes our editors’ framing of this Special Issue as seeking to both sketch the ‘state of the art’ and indicate some ‘future avenues’ for research in digital death [...]
Insights from Portuguese LGBTQ+ Associations on Trans Population’s Professional Integration
Authors: Baptista José , Costa Dália
Published: July 2024

The need to understand the integration processes of trans individuals in the labor market is confirmed by the literature review in this paper, which supports the scarce existence of studies focused on their life experiences outside of pathological contexts or from perspectives of invisibility/violence (including in the professional dimension). This study aims to comprehend the professional experiences of the trans population and understand how mechanisms of discrimination operate in the labor market. Semi-structured interviews with representatives from Portuguese LGBTQ+ associations (n = 6) were conducted between 10 March and 26 April 2021 and analyzed with MAXQDA 2022. The results underscored the importance of the topic and the complex hurdles trans individuals encounter at work, emphasizing the vital support of LGBTQ+ associations in tackling these issues and stressing the urgency of comprehending the vulnerability and discrimination trans people face in employment. The implications of the study underscore the urgent need for concerted efforts from policymakers, employers, civil society organizations, and the broader community to address the systemic barriers faced by trans individuals in the workplace. Future research and advocacy efforts should continue to address the systemic barriers to workplace integration and strive toward creating more inclusive and supportive environments for the trans population.
Techno-Economic Analysis of a High-Rise Residential Building Adapted to Nearly Zero-Energy Building Standards
Authors: Jiang Di , Li Xueyan , Tian Zhiyong
Published: July 2024

Zero-energy buildings have attracted great attention in China. Limited research about typical high-rise, zero-energy residential buildings in China was found. To figure out the potential of zero-energy buildings in northern China, a techno-economic analysis of a typical residential building adapted to the nearly zero energy building (NZEB) standards in the cold region of China was carried out in detail in this paper. Firstly, the feasibility of different building energy efficiency technologies was figured out in the passive design level. Secondly, the annual energy balance of the nearly zero-energy building model was investigated. Finally, detailed economic and environmental analyses were performed. The results show that the energy consumption of space heating and cooling of a typical high-rise, nearly zero-energy building could decrease to 11.1 kWh/(m2·a) in Beijing. The conclusions could provide a reference and design basis for the development of zero-energy residential buildings in northern China in the near future.
Research on Dynamic Subsidy Based on Deep Reinforcement Learning for Non-Stationary Stochastic Demand in Ride-Hailing
Authors: Huang Xiangyu , Cheng Yan , Jin Jing , Kou Aiqing
Published: July 2024

The ride-hailing market often experiences significant fluctuations in traffic demand, resulting in supply-demand imbalances. In this regard, the dynamic subsidy strategy is frequently employed by ride-hailing platforms to incentivize drivers to relocate to zones with high demand. However, determining the appropriate amount of subsidy at the appropriate time remains challenging. First, traffic demand exhibits high non-stationarity, characterized by multi-context patterns with time-varying statistical features. Second, high-dimensional state/action spaces contain multiple spatiotemporal dimensions and context patterns. Third, decision-making should satisfy real-time requirements. To address the above challenges, we first construct a Non-Stationary Markov Decision Process (NSMDP) based on the assumption of ride-hailing service systems dynamics. Then, we develop a solution framework for the NSMDP. A change point detection method based on feature-enhanced LSTM within the framework can identify the changepoints and time-varying context patterns of stochastic demand. Moreover, the framework also includes a deterministic policy deep reinforcement learning algorithm to optimize. Finally, through simulated experiments with real-world historical data, we demonstrate the effectiveness of the proposed approach. It performs well in improving the platform’s profits and alleviating supply-demand imbalances under the dynamic subsidy strategy. The results also prove that a scientific dynamic subsidy strategy is particularly effective in the high-demand context pattern with more drastic fluctuations. Additionally, the profitability of dynamic subsidy strategy will increase with the increase of the non-stationary level.
A Comprehensive Review on Control Technique and Socio-Economic Analysis for Sustainable Dynamic Wireless Charging Applications
Authors: Ramesh Pabba , Komarasamy Pongiannan Rakkiya Goundar , Rajamanickam Narayanamoorthi , Alharthi Yahya Z. , Elrashidi Ali , Nureldeen Waleed
Published: July 2024

Dynamic wireless power transfer (DWPT) has garnered significant attention as a promising technology for electric vehicle (EV) charging, eliminating the need for physical connections between EVs and charging stations. However, the improvement in power transfer efficiency is a major challenge among the research community. Different techniques are investigated in the literature to maximize power transfer efficiency. The investigations include the power electronic circuit, magnetic coupler design, compensating capacitance and control technique. Also, the investigations are carried out based on the type of wireless charging system, which is either a static or dynamic scenario. There are a good number of review articles available on the power electronic circuit and compensator design aspects of WPT. However, studies on the controller design and tracking maximum efficiency are some of the important areas that need to be reviewed. This paper provides a comprehensive review of bibliometric analysis on the DWPT technology, design procedure, and control technique to increase the power transfer and socio-economic acceptance analysis. The manuscript also provides information on the challenges and future direction of research in the field of DWPT technology.
Are South Korea’s Environmental Policies Rational? An Analysis Focusing on Fine Dust Programs in the Seoul Metropolitan Area
Authors: Jin Sang-hyeon
Published: July 2024

Since 2018, the government of South Korea has strengthened its environmental policies to solve the problem of fine particulate matter in the air. Because of these strict regulations, diesel cars have been replaced with cleaner vehicles, and coal power plants have been shut down. Despite these government efforts, some researchers assert that fine dust programs have failed in Seoul, the capital of Korea. In other words, they conclude that the central and local governments designed and implemented the policies unreasonably. Despite these critics, this study attempts to prove that the government has thoroughly and meticulously prepared its policies on fine particles. Also, it tries to demonstrate that the policy scheme has been properly established. To attain these research goals, the theory of procedural rationality is adopted and utilized. As a result of the analysis, six steps of procedural rationality were identified in the Korean policy on fine dust: problem identification, goal setting, searching for alternatives, consequence prediction, comparison of alternatives, and policy decision. In conclusion, this study provides suggestions for environmental policies in other metropolitan cities, especially in developing countries that suffer from severe air pollution.
How to Choose Recycling Mode between Monopoly and Competition by Considering Blockchain Technology?
Authors: Zhang Xuemei , Zheng Haodong , Hang Tao , Meng Qiang
Published: July 2024

Enterprises adopting a circular economy approach can effectively solve the severe situation of resources and the environment, and recycling is considered an effective means to solve environmental issues. Simultaneously, blockchain technology (BT) has been used to enhance product quality trust. However, there is limited literature on how to choose between monopolistic and competitive recycling modes by considering BT. This paper uses a game involving a manufacturer, a retailer, and a third-party recycler (TPR) in a closed-loop supply chain (CLSC). The retailer can recycle on itself and compete with the TPR for recycling used products. The results show that BT adoption could increase the recycling rate and demand for remanufactured products. BT benefits the firms in the CLSC when they control usage costs, regardless of whether competitive recycling mode is used or not. In addition, whether BT is adopted or not, CLSC firms prefer competitive recycling mode only when the competitive intensity exceeds a specific threshold. Moreover, choosing an appropriate recycling mode can alleviate the negative impact of BT on the environment, then an all-win result can be obtained for CLSC firms, consumers, and society. These results can give suggestions for managers to optimize their supply chains regarding adopting BT and implementing recycling mode. In the future, we can expand our research on the transfer price of used products, the positive and negative effects of BT, and BT cost-sharing strategies.
Corporate Social Responsibility and Sustainability through Institutional Legitimacy in Police Forces
Authors: Briones-Peñalver Antonio-Juan , Olmo Fernandez Ignacio del , Fernández Cañavate Francisco-José , Santos José António C.
Published: July 2024

This paper analyses the effect of institutional legitimacy on Corporate Social Responsibility (CSR) and sustainability in police forces through their methods and procedures (procedural justice) that determine citizens’ trust in the police, which theoretically influences organised coexistence in human communities (social effectiveness). CSR can increase collective well-being through legitimacy, sustained by police action. An anonymous citizen survey was carried out to verify the theoretical proposal to inquire about their opinions on the legitimacy, methods, and community relations between Spanish police forces and the community. The hypotheses were analysed with a structural equation system. The practical implications aspire to know the citizens’ opinions about the methods and procedures used by the Spanish police and their relations with Spanish civilians. Finally, citizens consider that police actions and procedures are institutionally and legally regulated competencies, and, therefore, citizens cannot influence them.
Data-Driven Decision-Making: Leveraging the IoT for Real-Time Sustainability in Organizational Behavior
Authors: Malik Saadia
Published: July 2024

In today’s business environments, data-driven decision-making has played a crucial role in bringing real-time sustainability to organizational behavior. Furthermore, the Internet of Things (IoT) has been widely adopted recently, but studies investigating its impact on organizational dynamics and sustainability are limited. This study explores the IoT’s potential to improve the real-time decision-making and sustainability of organizations. A cross-sectional study was conducted on 250 respondents belonging to a diverse range of industries. Five variables were analyzed in the study—IoT implementation, real-time data analytics, decision-making, organizational behavior, and organizational performance. These variables were scored on a 5-point Likert-type scale, with responses ranging from 1 = Strongly Disagree to 5 = Strongly Agree. The data were collected with the help of a structured questionnaire. The data were analyzed using descriptive statistics, Pearson correlations, and structural equation modeling (SEM) to test the relationships among the study variables. The findings indicate that enhanced organizational behavior (r = 0.1101), decision-making (r = 0.269), and real-time data analytics (r = 0.1888) are all strongly associated with the IoT. Applying structural equation modeling further reveals a direct connection between IoT adoption and company performance. Moreover, it is observed that the coefficients for organizational behavior (β = 0.0707, p < 0.01) and real-time data analytics (β = 0.0851, p < 0.001) are statistically significant. This study demonstrates how the IoT can bring real-time sustainability to organizational behavior by improving decision-making and business operations. Although decision-making is enhanced by the IoT, organizations still need to optimize their resource usage and reduce waste to enhance sustainability. This study bridges a substantial gap in the existing literature by carefully investigating the relationship between IoT technology, organizational behavior, and decision-making techniques. Our findings conclude that leveraging the IoT changes the nature of digital innovation and brings real-time sustainability to organizational behavior.
Enhancing Business Incubator Performances from Knowledge-Based Perspectives
Authors: Fithri Prima , Hasan Alizar , Syafrizal Syafrizal , Games Donard
Published: July 2024

The rapid evolution of technology has reshaped the global economy, intensified competition, and prompted industry players to embrace innovation to maintain sustained competitiveness. In Indonesia, business incubators, overseen by universities and provincial/city governments, are pivotal. However, understanding the determinants of business incubator performance remains limited. This study investigates 24 statement items aiming to uncover the factors influencing technology business incubators in Indonesia, focusing on knowledge management as a mediating variable from a knowledge-based perspective. Employing a quantitative approach, structural equation modeling (SEM) is utilized to scrutinize these factors’ impact on business incubator performance. The findings reveal that knowledge management serves as a full and partial mediator among funding support, government assistance, incubator governance, and business incubator technology performance. This research offers valuable insights for entrepreneurs and stakeholders by emphasizing the significance of funding, governmental backing, incubator governance, and knowledge management in enhancing incubator business technology performance in Indonesia.
A Sustainable Development for Building Energy Consumption Based on Improved Rafflesia Optimization Algorithm with Feature Selection and Ensemble Deep Learning
Authors: Lee Zne-Jung , Pan Jeng-Shyang , Hwang Bor-Jiunn
Published: July 2024

Buildings emit a great deal of carbon dioxide and use a lot of energy. The study of building energy consumption is useful for the sustainable development of multi-energy planning and energy-saving strategies. Therefore, a sustainable development for building energy consumption based on the improved rafflesia optimization algorithm (ROA) with feature selection and ensemble deep learning is proposed in this paper. This method can explore data on building energy usage, assess prediction accuracy, and address concerns that building energy usage research must address. The proposed model first uses an improved self-organizing map with a new neighborhood function to select important features. After that, it uses ensemble deep learning to accurately anticipate the building’s energy usage. In addition, the improved ROA is used to fine-tune parameters for feature selection and ensemble deep learning. This research uses the dataset of the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) to compare the performance of several modeling approaches. It identifies the top five most important features based on the model’s results. Furthermore, the proposed model can be successfully applied to a real-world application. They both have the lowest root mean squared errors among the approaches examined. The proposed model indeed provides the benefits of feature selection and ensemble deep learning with the improved ROA for the prediction of building energy consumption.
Nematode Communities in Soils of the Same Volcanic Origin across a Gradient of Naturalization: From Intensive Agriculture to Forest
Authors: Landi Silvia , d’Errico Giada , Manachini Barbara
Published: July 2024

Nematodes play a key role in ecological environments. Biotic indices based on soil nematode community are effectively used for assessing soil health status. This work represents the opportunity to investigate three different management practices continuously maintained for 40 or more decades on soils with the same geological origin. This unique feature allows us to reduce variables and link biotic with abiotic factors. Therefore, the nematode communities of three neighboring volcanic soils under different managements were examined. The maximum values in soil biodiversity were found in the forest environment in which all the trophic groups were detected and well balanced. Instead, in permanent grassing and carnation crop greenhouse (CG), biodiversity indices progressively decreased with the intensification of agricultural practices. Furthermore, CG showed a stronger dominance of a specific plant-parasitic nematode identified as Heterodera daverti. Nematode indicators demonstrated that changes in the nematode community gradually varied from anthropic to natural environments whereas soil properties were feebly correlated to nematode community indicators. In conclusion, we demonstrated that biodiversity and ecological indices applied to the soil nematode community are effective at detecting alterations due to anthropogenic impact. Nematodes seem to be susceptible to perceiving the soil dynamics.
Characteristics and Influencing Factors of Landscape Pattern Gradient Transformation of Small-Scale Agroforestry Patches in Mountain Cities
Authors: Cheng Canhui , Xing Zhong , Ye Lin , Yang Junyue , Xie Zhuoming
Published: July 2024

Small-scale agroforestry patches possess irreplaceable value compared to large-scale patches. In southwestern mountainous cities of China, the complex terrain and urbanization have led to the presence of numerous small, fragmented agroforestry patches around urban areas. These patches serve as crucial habitats for endemic species and provide essential space for wild food sources, thereby contributing to a range of ecosystem services. Consequently, their proper conservation and utilization planning are of paramount importance. This study investigates the transformation characteristics of landscape patterns of mountainous small-scale agroforestry patches and their constituent elements across urban–rural gradients, identifying the driving factors behind these transformations to support conservation and utilization planning. From an urban–rural gradient perspective, four directional transects were selected and divided into uniform sample grids. Using Fragstats 4.3, landscape indices of small-scale agroforestry patches were calculated, analyzing the transformation characteristics of these patches and their elements across different gradients. Spearman correlation coefficients in SPSS were employed to assess the influence of terrain and relevant anthropogenic factors on the transformation of agroforestry patches. The findings reveal the following: (1) Small-scale agroforestry patches and their elements exhibit similar patterns in terms of size, fragmentation, dispersion, and connectivity, showing an “increasing trend in size and connectivity, decreasing fragmentation, and fluctuating dispersion” from urban centers to natural areas, with slight variations in orchard patches. However, patch cohesion and shape complexity display nonlinear differentiated transformation characteristics. (2) Overall, small-scale agroforestry patches are significantly influenced by anthropogenic construction factors, with the landscape pattern of forest patches notably affected by terrain factors. (3) Across urban–rural gradient zones, the landscape patterns of small-scale agroforestry patches in urban centers, suburbs, and rural natural areas are more affected by terrain factors, whereas those in urban construction zones are significantly influenced by anthropogenic construction factors. The findings of this study provide a scientific basis for the conservation and planning of mountainous small-scale agroforestry patches.
Rural Ecosystem Health Assessment and Spatial Divergence—A Case Study of Rural Areas around Qinling Mountain, Shaanxi Province, China
Authors: Xu Yuxia , Chen Qian , Zeng Hui
Published: July 2024

The rapid progress of urbanization and rural revitalization in developing countries has led to dramatic changes to the rural ecological environment. Assessing the rural ecosystem health (REH) is a crucial foundation for promoting sustainable development in rural areas. This study, focusing on rural areas around the Qinling Mountains in Shaanxi Province, establishes an evaluation system based on appropriate evaluation indicators for assessing the composite ecosystem. This evaluation system comprises four rural ecosystem subsystems: resource, environment, society, and economy. By employing a comprehensive indicator evaluation model and remote sensing image data, this study examines the health status of rural ecosystems in the 40 counties and districts across the study area, as well as their spatial differentiation characteristics, using ArcGIS (10.8) spatial analysis. The REH scores of these areas range from 0.6856 to 0.8818, with a fluctuating downward trend from north to south. This suggests that the rural ecosystems around the Qinling Mountains in Shaanxi Province are relatively healthy, with the northern area being notably healthier than the southern area. Spatial Gini coefficient analysis reveals a much smaller coefficient for the overall ecosystem compared to the subsystems in the study area, indicating that the distribution of health levels is dispersed and not concentrated. After establishing REH grades and quantity metrics, the 40 counties and districts are categorized into 13 types, followed by an analysis of the influencing factors for each type. Recommendations and management strategies are then proposed to enhance the health of rural ecosystems.
Analysis of Factors Affecting Emergency Response Linkage in Coal Mine Gas Explosion Accidents
Authors: Liu Jialin , Ye Qing , Jia Zhenzhen , Yang Yingqian , Xu Tingting
Published: July 2024

To analyze the influencing factors of the emergency linkage of gas explosion accidents and their causal relationships, a method for analyzing the influencing factors of the emergency linkage of gas explosion accidents is proposed based on a hierarchical holographic model and Bayesian networks. Firstly, based on the hierarchical holographic model to determine the main influencing factors of the accident emergency linkage, we constructed the topological structure model of accident control, secondary disaster, and emergency rescue, and used the triangular fuzzy number to assess the a priori probability and conditional probability of the influencing factors. Next, the most likely factors affecting the accident emergency linkage are quickly identified by combining Bayesian diagnostic reasoning. Finally, sensitivity analysis is conducted to identify the key factors affecting the accident emergency linkage. The results show that the probability of normal operation of gas explosion accident emergency linkage is 78.1%, but when the coal mine environment changes, especially when multiple influencing factors occur simultaneously, the probability of normal operation of accident emergency linkage decreases significantly. Through causal analysis, the degree of influence on the operation of the accident emergency linkage in different situations can be deduced. Through diagnostic analysis, it can be seen that the emergency linkage operation is more sensitive to the two factors of the ventilation and smoke extraction system response and gas over limit, so it is necessary to pay attention to its important role in accident treatment. Meanwhile, the sensitivity analysis shows that the response of the ventilation and smoke extraction system, the accuracy of disaster sharing, the gas over limit, the technical level of the operators, and the team rescue experience are the key factors affecting the emergency response linkage in accidents. This study can provide theoretical guidance for the improvement of the emergency response linkage mechanism of coal mine gas explosion accidents as well as the decision-making of the accidents, minimize the losses of the accidents, and promote the sustainable development of the coal mining industry.