bluuebunny/crossref_metadata_2025 huggingface dataset:
Considering the Effects of Horizontal Heterogeneities in Satellite-Based Large-Scale Statistics of Cloud Optical Properties
Authors: Várnai Tamás , Marshak Alexander
Published: September 2024

This paper explores a new approach to improving satellite measurements of cloud optical thickness and droplet size by considering the radiative impacts of horizontal heterogeneity in boundary-layer cumulus clouds. In contrast to the usual bottom-up approach that retrieves cloud properties for individual pixels and subsequently compiles large-scale statistics, the proposed top-down approach first determines the effect of 3D heterogeneity on large-scale cloud statistics and then distributes the overall effects to individual pixels. The potential of this approach is explored by applying a regression-based scheme to a simulated dataset containing over 3000 scenes generated through large eddy simulations. The results show that the new approach can greatly reduce the errors in widely used bispectral retrievals that assume horizontal homogeneity. Errors in large-scale mean values and cloud variability are typically reduced by factors of two to four for 1 km resolution retrievals—and the reductions remain significant even for a 4 km resolution. The calculations also reveal that over vegetation heterogeneity-caused droplet size retrieval biases are often opposite to the biases found over oceans. Ultimately, the proposed approach shows potential for improving the accuracy of both old and new satellite datasets.
Mapping Fruit-Tree Plantation Using Sentinel-1/2 Time Series Images with Multi-Index Entropy Weighting Dynamic Time Warping Method
Authors: Xu Weimeng , Li Zhenhong , Lin Hate , Shao Guowen , Zhao Fa , Wang Han , Cheng Jinpeng , Lei Lei , Chen Riqiang , Han Shaoyu , Yang Hao
Published: September 2024

Plantation distribution information is of great significance to the government’s macro-control, optimization of planting layout, and realization of efficient agricultural production. Existing studies primarily relied on high spatiotemporal resolution remote sensing data to address same-spectrum, different-object classification by extracting phenological information from temporal imagery. However, the classification problem of orchard or artificial forest, where the spectral and textural features are similar and their phenological characteristics are alike, still presents a substantial challenge. To address this challenge, we innovatively proposed a multi-index entropy weighting DTW method (ETW-DTW), building upon the traditional DTW method with single-feature inputs. In contrast to previous DTW classification approaches, this method introduces multi-band information and utilizes entropy weighting to increase the inter-class distances. This allowed for accurate classification of orchard categories, even in scenarios where the spectral textures were similar and the phenology was alike. We also investigated the impact of fusing optical and Synthetic Aperture Radar (SAR) data on the classification accuracy. By combining Sentinel-1 and Sentinel-2 time series imagery, we validated the enhanced classification effectiveness with the inclusion of SAR data. The experimental results demonstrated a noticeable improvement in orchard classification accuracy under conditions of similar spectral characteristics and phenological patterns, providing comprehensive information for orchard mapping. Additionally, we further explored the improvement in results based on two different parcel-based classification strategies compared to pixel-based classification methods. By comparing the classification results, we found that the parcel-based averaging method has advantages in clearly defining orchard boundaries and reducing noise interference. In conclusion, the introduction of the ETW-DTW method is of significant practical importance in addressing the challenge of same-spectrum, different-object classification. The obtained orchard distribution can provide valuable information for the government to optimize the planting structure and layout and regulate the macroeconomic benefits of the fruit industry.
Asymmetric Response of the Indonesian Throughflow to Co-Occurring El Niño–Southern Oscillation–Indian Ocean Dipole Events
Authors: Li Aojie , Zhang Yongchui , Hong Mei , Xu Tengfei , Wang Jing
Published: September 2024

The Indonesian Throughflow (ITF) is significantly modulated by Indo-Pacific climate forcing, especially the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). However, when ENSO and IOD occur concurrently, they tend to play different roles in the ITF volume transport. By employing an improved Constructed Circulation Analogue (CCA) method, the relative contributions of these climate events to the ITF inflow and outflow transport in the upper and lower layers were quantified. The results indicate that during co-occurring El Niño and positive IOD events, ENSO is the dominant influence, with ratio values of 5.5:1 (3.5:1) in the upper layer and 1.7:1 (1.6:1) in the lower layer of the inflow (outflow). Conversely, during co-occurring La Niña and negative IOD events, the IOD predominates, with ratio values of 1:6 (1:6.5) in the upper layer and 1:4 (1:3) in the lower layer of the inflow (outflow). The mechanisms underlying these variations in the upper and lower layers can be explained by the differences in sea level anomaly (SLA) and wave propagation, respectively. This study provides a new insight into distinct roles of climate forcing on the ITF volume transport during the simultaneous occurrence of multiple climate modes.
Summer Chukchi Sea Near-Surface Salinity Variability in Satellite Observations and Ocean Models
Authors: Grodsky Semyon A. , Reul Nicolas , Vandemark Douglas
Published: September 2024

The Chukchi Sea is an open estuary in the southwestern Arctic. Its near-surface salinities are higher than those of the surrounding open Arctic waters due to the key inflow of saltier and warmer Pacific waters through the Bering Strait. This salinity distribution may suggest that interannual changes in the Bering Strait mass transport are the sole and dominant factor shaping the salinity distribution in the downstream Chukchi Sea. Using satellite sea surface salinity (SSS) retrievals and altimetry-based estimates of the Bering Strait transport, the relationship between the Strait transport and Chukchi Sea SSS distributions is analyzed from 2010 onward, focusing on the ice-free summer to fall period. A comparison of five different satellite SSS products shows that anomalous SSS spatially averaged over the Chukchi Sea during the ice-free period is consistent among them. Observed interannual temporal change in satellite SSS is confirmed by comparison with collocated ship-based thermosalinograph transect datasets. Bering Strait transport variability is known to be driven by the local meridional wind stress and by the Pacific-to-Arctic sea level gradient (pressure head). This pressure head, in turn, is related to an Arctic Oscillation-like atmospheric mean sea level pattern over the high-latitude Arctic, which governs anomalous zonal winds over the Chukchi Sea and affects its sea level through Ekman dynamics. Satellite SSS anomalies averaged over the Chukchi Sea show a positive correlation with preceding months’ Strait transport anomalies. This correlation is confirmed using two longer (>40-year), separate ocean data assimilation models, with either higher- (0.1°) or lower-resolution (0.25°) spatial resolution. The relationship between the Strait transport and Chukchi Sea SSS anomalies is generally stronger in the low-resolution model. The area of SSS response correlated with the Strait transport is located along the northern coast of the Chukotka Peninsula in the Siberian Coastal Current and adjacent zones. The correlation between wind patterns governing Bering Strait variability and Siberian Coastal Current variability is driven by coastal sea level adjustments to changing winds, in turn driving the Strait transport. Due to the Chukotka coastline configuration, both zonal and meridional wind components contribute.
HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis
Authors: Ayuba Daniel La’ah , Guillemaut Jean-Yves , Marti-Cardona Belen , Mendez Oscar
Published: September 2024

The use of a pretrained image classification model (trained on cats and dogs, for example) as a perceptual loss function for hyperspectral super-resolution and pansharpening tasks is surprisingly effective. However, RGB-based networks do not take full advantage of the spectral information in hyperspectral data. This inspired the creation of HyperKon, a dedicated hyperspectral Convolutional Neural Network backbone built with self-supervised contrastive representation learning. HyperKon uniquely leverages the high spectral continuity, range, and resolution of hyperspectral data through a spectral attention mechanism. We also perform a thorough ablation study on different kinds of layers, showing their performance in understanding hyperspectral layers. Notably, HyperKon achieves a remarkable 98% Top-1 retrieval accuracy and surpasses traditional RGB-trained backbones in both pansharpening and image classification tasks. These results highlight the potential of hyperspectral-native backbones and herald a paradigm shift in hyperspectral image analysis.
Decimeter-Level Accuracy for Smartphone Real-Time Kinematic Positioning Implementing a Robust Kalman Filter Approach and Inertial Navigation System Infusion in Complex Urban Environments
Authors: Pourmina Amir Hossein , Alizadeh Mohamad Mahdi , Schuh Harald
Published: September 2024

New smartphones provide real-time access to GNSS pseudorange, Doppler, or carrier-phase measurement data at 1 Hz. Simultaneously, they can receive corrections broadcast by GNSS reference stations to perform real-time kinematic (RTK) positioning. This study aims at the real-time positioning capabilities of smartphones using raw GNSS measurements as a conventional method and proposes an improvement to the positioning through the integration of Inertial Navigation System (INS) measurements. A U-Blox GNSS receiver, model ZED-F9R, was used as a benchmark for comparison. We propose an enhanced ambiguity resolution algorithm that integrates the traditional LAMBDA method with an adaptive thresholding mechanism based on real-time quality metrics. The RTK/INS fusion method integrates RTK and INS measurements using an extended Kalman filter (EKF), where the state vector x includes the position, velocity, orientation, and their respective biases. The innovation here is the inclusion of a real-time weighting scheme that adjusts the contribution of the RTK and INS measurements based on their current estimated accuracy. Also, we use the tightly coupled (TC) RTK/INS fusion framework. By leveraging INS data, the system can maintain accurate positioning even when the GNSS data are unreliable, allowing for the detection and exclusion of abnormal GNSS measurements. However, in complex urban areas such as Qazvin City in Iran, the fusion method achieved positioning accuracies of approximately 0.380 m and 0.415 m for the Xiaomi Mi 8 and Samsung Galaxy S21 Ultra smartphones, respectively. The subsequent detailed analysis across different urban streets emphasized the significance of choosing the right positioning method based on the environmental conditions. In most cases, RTK positioning outperformed Single-Point Positioning (SPP), offering decimeter-level precision, while the fusion method bridged the gap between the two, showcasing improved stability accuracy. The comparative performance between the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 revealed minor differences, likely attributed to variations in the hardware design and software algorithms. The fusion method emerged as a valuable alternative when the RTK signals were unavailable or impractical. This demonstrates the potential of integrating RTK and INS measurements for enhanced real-time smartphone positioning, particularly in challenging urban environments.
Advanced Necklace for Real-Time PPG Monitoring in Drivers
Authors: Lo Grasso Anna , Zontone Pamela , Rinaldo Roberto , Affanni Antonio
Published: September 2024

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects’ movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver’s well-being by providing information about the driver’s physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace’s design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor’s performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer’s algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads.
Study of Global Navigation Satellite System Receivers’ Accuracy for Unmanned Vehicles
Authors: Miletiev Rosen , Petkov Peter Z. , Yordanov Rumen , Brusev Tihomir
Published: September 2024

The development of unmanned ground vehicles and unmanned aerial vehicles requires high-precision navigation due to the autonomous motion and higher traffic intensity. The existing L1 band GNSS receivers are a good and cheap decision for smartphones, vehicle navigation, fleet management systems, etc., but their accuracy is not good enough for many civilian purposes. At the same time, real-time kinematic (RTK) navigation allows for position precision in a sub-centimeter range, but the system cost significantly narrows this navigation to a very limited area of applications, such as geodesy. A practical solution includes the integration of dual-band GNSS receivers and inertial sensors to solve high-precision navigation tasks, but GNSS position accuracy may significantly affect IMU performance due to having a great impact on Kalman filter performance in unmanned vehicles. The estimation of dilution-of-precision (DOP) parameters is essential for the filter performance as the optimality of the estimation in the filter is closely connected to the quality of a priori information about the noise covariance matrix and measurement noise covariance. In this regard, the current paper analyzes the DOP parameters of the latest generation dual-band GNSS receivers and compares the results with the L1 ones. The study was accomplished using two types of antennas—L1/L5 band patch and wideband helix antennas, which were designed and assembled by the authors. In addition, the study is extended with a comparison of GNSS receivers from different generations but sold on the market by one of the world’s leading GNSS manufacturers. The analyses of dilution-of-precision (DOP) parameters show that the introduction of dual-band receivers may significantly increase the navigation precision in a sub-meter range, in addition to multi-constellation signal reception. The fast advances in the performance of the integrated CPU in GNSS receivers allow the number of correlations and tracking satellites to be increased from 8–10 to 24–30, which also significantly improves the position accuracy even of L1-band receivers.
Dual-Band Monopole MIMO Antenna Array for UAV Communication Systems
Authors: Raza Muhammad Usman , Ren Hongwei , Yan Sen
Published: September 2024

This study proposes a compact, low-profile, four-port dual-band monopole multiple-input-multiple-output (MIMO) antenna array for unmanned aerial vehicle (UAV) communication systems. Each monopole antenna of the array features a modified T-shaped radiator configuration and is printed on a Rogers RT5880 substrate with compact dimensions of 134.96 mm × 134.96 mm × 0.8 mm. A four-element square MIMO configuration with sequential 0°, 90°, 180°, and 270° rotations was integrated smoothly into the UAV body. A prototype of the MIMO array was fabricated and experimentally evaluated, with measured results showing a close correlation to simulated results. The proposed dual-band monopole antenna demonstrated one of the widest impedance bandwidths of 46.15% at 2.4 GHz (2.04 to 3.25 GHz) IEEE 802.11b and 31.85% at 5.8 GHz (5.37 to 7.38 GHz) IEEE 802.11a on a thin 0.0064 λo substrate while achieving high transmission efficiency. The isolation of the proposed four-port MIMO design was measured at 23 dB at 2.4 GHz and 19 dB at 5.8 GHz. The MIMO array’s total efficiency of each monopole antenna was measured at 96% at 2.4 GHz and 89% at 5.8 GHz. The design has measured diversity parameters such as an ECC below 0.01 and a DG of approximately 10. Based on these results, the proposed design suits the UAV communication system.
Lightweight Substation Equipment Defect Detection Algorithm for Small Targets
Authors: Wang Jianqiang , Sun Yiwei , Lin Ying , Zhang Ke
Published: September 2024

Substation equipment defect detection has always played an important role in equipment operation and maintenance. However, the task scenarios of substation equipment defect detection are complex and different. Recent studies have revealed issues such as a significant missed detection rate for small-sized targets and diminished detection precision. At the same time, the current mainstream detection algorithms are highly complex, which is not conducive to deployment on resource-constrained devices. In view of the above problems, a small target and lightweight substation main scene equipment defect detection algorithm is proposed: Efficient Attentional Lightweight-YOLO (EAL-YOLO), which detection accuracy exceeds the current mainstream model, and the number of parameters and floating point operations (FLOPs) are also advantageous. Firstly, the EfficientFormerV2 is used to optimize the model backbone, and the Large Separable Kernel Attention (LSKA) mechanism has been incorporated into the Spatial Pyramid Pooling Fast (SPPF) to enhance the model’s feature extraction capabilities; secondly, a small target neck network Attentional scale Sequence Fusion P2-Neck (ASF2-Neck) is proposed to enhance the model’s ability to detect small target defects; finally, in order to facilitate deployment on resource-constrained devices, a lightweight shared convolution detection head module Lightweight Shared Convolutional Head (LSCHead) is proposed. Experiments show that compared with YOLOv8n, EAL-YOLO has improved its accuracy by 2.93 percentage points, and the mAP50 of 12 types of typical equipment defects has reached 92.26%. Concurrently, the quantity of FLOPs and parameters has diminished by 46.5% and 61.17% respectively, in comparison with YOLOv8s, meeting the needs of substation defect detection.
Performance of a Novel Electronic Nose for the Detection of Volatile Organic Compounds Relating to Starvation or Human Decomposition Post-Mass Disaster
Authors: Sunnucks Emily J. , Thurn Bridget , Brown Amber O. , Zhang Wentian , Liu Taoping , Forbes Shari L. , Su Steven , Ueland Maiken
Published: September 2024

There has been a recent increase in the frequency of mass disaster events. Following these events, the rapid location of victims is paramount. Currently, the most reliable search method is scent detection dogs, which use their sense of smell to locate victims accurately and efficiently. Despite their efficacy, they have limited working times, can give false positive responses, and involve high costs. Therefore, alternative methods for detecting volatile compounds are needed, such as using electronic noses (e-noses). An e-nose named the ‘NOS.E’ was developed and has been used successfully to detect VOCs released from human remains in an open-air environment. However, the system’s full capabilities are currently unknown, and therefore, this work aimed to evaluate the NOS.E to determine the efficacy of detection and expected sensor response. This was achieved using analytical standards representative of known human ante-mortem and decomposition VOCs. Standards were air diluted in Tedlar gas sampling bags and sampled using the NOS.E. This study concluded that the e-nose could detect and differentiate a range of VOCs prevalent in ante-mortem and decomposition VOC profiles, with an average LOD of 7.9 ppm, across a range of different chemical classes. The NOS.E was then utilized in a simulated mass disaster scenario using donated human cadavers, where the system showed a significant difference between the known human donor and control samples from day 3 post-mortem. Overall, the NOS.E was advantageous: the system had low detection limits while offering portability, shorter sampling times, and lower costs than dogs and benchtop analytical instruments.
Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning
Authors: Lei Zhenwu , Zhang Yue , Wang Jing , Zhou Meng
Published: September 2024

Defect detection constitutes one of the most crucial processes in industrial production. With a continuous increase in the number of defect categories and samples, the defect detection model underpinned by deep learning finds it challenging to expand to new categories, and the accuracy and real-time performance of product defect detection are also confronted with severe challenges. This paper addresses the problem of insufficient detection accuracy of existing lightweight models on resource-constrained edge devices by presenting a new lightweight YoloV5 model, which integrates four modules, SCDown, GhostConv, RepNCSPELAN4, and ScalSeq. Here, this paper abbreviates it as SGRS-YoloV5n. Through the incorporation of these modules, the model notably enhances feature extraction and computational efficiency while reducing the model size and computational load, making it more conducive for deployment on edge devices. Furthermore, a cloud-edge collaborative defect detection system is constructed to improve detection accuracy and efficiency through initial detection by edge devices, followed by additional inspection by cloud servers. An incremental learning mechanism is also introduced, enabling the model to adapt promptly to new defect categories and update its parameters accordingly. Experimental results reveal that the SGRS-YoloV5n model exhibits superior detection accuracy and real-time performance, validating its value and stability for deployment in resource-constrained environments. This system presents a novel solution for achieving efficient and accurate real-time defect detection.
The Adaption of Recent New Concepts in Neural Radiance Fields and Their Role for High-Fidelity Volume Reconstruction in Medical Images
Authors: An Haill , Khan Jawad , Kim Suhyeon , Choi Junseo , Jung Younhyun
Published: September 2024

Volume reconstruction techniques are gaining increasing interest in medical domains due to their potential to learn complex 3D structural information from sparse 2D images. Recently, neural radiance fields (NeRF), which implicitly model continuous radiance fields based on multi-layer perceptrons to enable volume reconstruction of objects at arbitrary resolution, have gained traction in natural image volume reconstruction. However, the direct application of NeRF to medical volume reconstruction presents unique challenges due to differences in imaging principles, internal structure requirements, and boundary delineation. In this paper, we evaluate different NeRF techniques developed for natural images, including sampling strategies, feature encoding, and the use of complimentary features, by applying them to medical images. We evaluate three state-of-the-art NeRF techniques on four datasets of medical images of different complexity. Our goal is to identify the strengths, limitations, and future directions for integrating NeRF into the medical domain.
DIO-SLAM: A Dynamic RGB-D SLAM Method Combining Instance Segmentation and Optical Flow
Authors: He Lang , Li Shiyun , Qiu Junting , Zhang Chenhaomin
Published: September 2024

Feature points from moving objects can negatively impact the accuracy of Visual Simultaneous Localization and Mapping (VSLAM) algorithms, while detection or semantic segmentation-based VSLAM approaches often fail to accurately determine the true motion state of objects. To address this challenge, this paper introduces DIO-SLAM: Dynamic Instance Optical Flow SLAM, a VSLAM system specifically designed for dynamic environments. Initially, the detection thread employs YOLACT (You Only Look At CoefficienTs) to distinguish between rigid and non-rigid objects within the scene. Subsequently, the optical flow thread estimates optical flow and introduces a novel approach to capture the optical flow of moving objects by leveraging optical flow residuals. Following this, an optical flow consistency method is implemented to assess the dynamic nature of rigid object mask regions, classifying them as either moving or stationary rigid objects. To mitigate errors caused by missed detections or motion blur, a motion frame propagation method is employed. Lastly, a dense mapping thread is incorporated to filter out non-rigid objects using semantic information, track the point clouds of rigid objects, reconstruct the static background, and store the resulting map in an octree format. Experimental results demonstrate that the proposed method surpasses current mainstream dynamic VSLAM techniques in both localization accuracy and real-time performance.
Fog Density Analysis Based on the Alignment of an Airport Video and Visibility Data
Authors: Dai Mingrui , Li Guohua , Shi Weifeng
Published: September 2024

The density of fog is directly related to visibility and is one of the decision-making criteria for airport flight management and highway traffic management. Estimating fog density based on images and videos has been a popular research topic in recent years. However, the fog density estimated results based on images should be further evaluated and analyzed by combining weather information from other sensors. The data obtained by different sensors often need to be aligned in terms of time because of the difference in acquisition methods. In this paper, we propose a video and a visibility data alignment method based on temporal consistency for data alignment. After data alignment, the fog density estimation results based on images and videos can be analyzed, and the incorrect estimation results can be efficiently detected and corrected. The experimental results show that the new method effectively combines videos and visibility for fog density estimation.
Digital and Virtual Technologies for Work-Related Biomechanical Risk Assessment: A Scoping Review
Authors: Anacleto Filho Paulo C. , Colim Ana , Jesus Cristiano , Lopes Sérgio Ivan , Carneiro Paula
Published: September 2024

The field of ergonomics has been significantly shaped by the advent of evolving technologies linked to new industrial paradigms, often referred to as Industry 4.0 (I4.0) and, more recently, Industry 5.0 (I5.0). Consequently, several studies have reviewed the integration of advanced technologies for improved ergonomics in different industry sectors. However, studies often evaluate specific technologies, such as extended reality (XR), wearables, artificial intelligence (AI), and collaborative robot (cobot), and their advantages and problems. In this sense, there is a lack of research exploring the state of the art of I4.0 and I5.0 virtual and digital technologies in evaluating work-related biomechanical risks. Addressing this research gap, this study presents a comprehensive review of 24 commercial tools and 10 academic studies focusing on work-related biomechanical risk assessment using digital and virtual technologies. The analysis reveals that AI and digital human modelling (DHM) are the most commonly utilised technologies in commercial tools, followed by motion capture (MoCap) and virtual reality (VR). Discrepancies were found between commercial tools and academic studies. However, the study acknowledges limitations, including potential biases in sample selection and search methodology. Future research directions include enhancing transparency in commercial tool validation processes, examining the broader impact of emerging technologies on ergonomics, and considering human-centred design principles in technology integration. These findings contribute to a deeper understanding of the evolving landscape of biomechanical risk assessment.
Virtuality and Solidarity: Exploring the New Frontiers of Social Love in the Sign of Collective Wellbeing
Authors: Nardi Luisa
Published: September 2024

This paper aims to investigate the concept of love and solidarity in human relationships, especially in their manifestation within virtual communities. Solidarity, understood as the highest feeling of connection between individuals, finds new forms of expression in the digital age, where physical distances are overcome by the ability to communicate and share experiences in an immediate and simplified way. The aim is to analyze the renewed sociological perspective on how to explore human interactions as a practical implication of the transition from physical to digital space, which, in spite of divergences, does not seem to reduce the quality of social ties but rather offers new ways of connection and interaction. Indeed, digital technologies can positively influence social dynamics by fostering the construction of community networks that act as catalysts for collective intelligence and knowledge sharing in the pursuit of collective wellbeing.
Multidimensional Analysis of Physiological Entropy during Self-Paced Marathon Running
Authors: Palacin Florent , Poinsard Luc , Billat Véronique
Published: September 2024

The pacing of a marathon is arguably the most challenging aspect for runners, particularly in avoiding a sudden decline in speed, or what is colloquially termed a “wall”, occurring at approximately the 30 km mark. To gain further insight into the potential for optimizing self-paced marathon performance through the coding of comprehensive physiological data, this study investigates the complex physiological responses and pacing strategies during a marathon, with a focus on the application of Shannon entropy and principal component analysis (PCA) to quantify the variability and unpredictability of key cardiorespiratory measures. Nine recreational marathon runners were monitored throughout the marathon race, with continuous measurements of oxygen uptake (V˙O2), carbon dioxide output (V˙CO2), tidal volume (Vt), heart rate, respiratory frequency (Rf), and running speed. The PCA revealed that the entropy variance of V˙O2, V˙CO2, and Vt were captured along the F1 axis, while cadence and heart rate variances were primarily captured along the F2 axis. Notably, when distance and physiological responses were projected simultaneously on the PCA correlation circle, the first 26 km of the race were positioned on the same side of the F1 axis as the metabolic responses, whereas the final kilometers were distributed on the opposite side, indicating a shift in physiological state as fatigue set in. The separation of heart rate and cadence entropy variances from the metabolic parameters suggests that these responses are independent of distance, contrasting with the linear increase in heart rate and decrease in cadence typically observed. Additionally, Agglomerative Hierarchical Clustering further categorized runners’ physiological responses, revealing distinct clusters of entropy profiles. The analysis identified two to four classes of responses, representing different phases of the marathon for individual runners, with some clusters clearly distinguishing the beginning, middle, and end of the race. This variability emphasizes the personalized nature of physiological responses and pacing strategies, reinforcing the need for individualized approaches. These findings offer practical applications for optimizing pacing strategies, suggesting that real-time monitoring of entropy could enhance marathon performance by providing insights into a runner’s physiological state and helping to prevent the onset of hitting the wall.
Using Blockchain Technology for Sustainability and Secure Data Management in the Energy Industry: Implications and Future Research Directions
Authors: Lezzi Marianna , Del Vecchio Vito , Lazoi Mariangela
Published: September 2024

In the current era of digital transformation, among the plethora of technologies, blockchain (BC) technology has attracted attention, carrying the weight of enormous expectations in terms of its applicability and benefits. BC technology promises immutability, reliability, transparency, and security of transactions, using decentralized models to scale up existing Internet of Things (IoT) solutions while guaranteeing privacy. In the energy industry, BC technology is mainly used to secure distributed power grids, which have proven to be easily hackable by malicious users. Recognizing the need for a preliminary analysis of the literature investigating the role of BC technology for sustainability and secure data management in the energy industry, this study conducts a bibliometric analysis, identifying the implications and research directions in the field. Specifically, a performance analysis and scientific mapping are performed on 943 documents using the Scopus database and the VOSviewer software version 1.6.20. The result is the identification of seven thematic clusters and the most relevant implications as well as future research actions at the strategic, technical, regulatory, and social levels. This study extends the literature by suggesting potential sustainability opportunities regarding BC technology adoption in the energy industry; it also supports managers in identifying strategies to strengthen business sustainability by leveraging the development of new knowledge for secure asset management.
People Category of UN SDGs 2030 and Sustainable Economic Growth in Asia and the Pacific Region
Authors: Ashraf Muhammad Sajjad , Ahmed Farhan , Kousar Shazia , Ferreira Paulo Jorge Silveira , de Almeida Dora Maria Fortes
Published: September 2024

This study investigated the impact of the people category of the Sustainable Development Goals (SDGs) on sustainable and conventional economic growth in Asia and the Pacific region, using a sample of 52 selected countries between 2000 and 2023. Employing two distinct models, model A1 for conventional economic growth and model A2 for sustainable economic growth, we explained the relationships between five SDG indicators: employed poverty rate, stunted children, expenditure on health, expenditure of education, and % of women MNAs on economic growth. This study employed a fixed-effect model and random-effect model to investigate the impact of the people category SDGs on traditional and sustainable economic growth. The comparative analysis of each SDG in both models revealed valuable insights. SDG 1, “employed poverty rate”, has a positive impact on economic growth in both models, while SDG 2, “percentage of stunted child”, did not significantly influence economic growth in either model. Moreover, SDG 3 and SDG 4, relating to “government’s health expenditure per capita” and “government’s Education education expenditure per capita”, respectively, exhibited a positive impact on traditional and sustainable economic growth. Conversely, SDG 5, “percentage of women members of national parliament”, displayed an insignificant impact on traditional and sustainable economic growth models. In conclusion, this study suggests that policymakers should prioritize targeted interventions to alleviate employed poverty, enhance healthcare, and boost education spending. Moreover, promoting women’s representation in national parliaments should be approached with context-specific strategies to maximize its impact on economic growth.
Decoding the Characteristics of Ecosystem Services and the Scale Effect in the Middle Reaches of the Yangtze River Urban Agglomeration: Insights for Planning and Management
Authors: Zhang Ruiqi , Hu Chunguang , Sun Yucheng
Published: September 2024

A thorough exploration of Ecosystem Services (ESs) and their intricate interactions across time and space is a prerequisite for the sustainable management of multiple ESs. This study aimed to systematically evaluate the ESs of the middle reaches of the Yangtze River Urban Agglomeration (MRYRUA) across multiple spatial and temporal scales, thereby enhancing ecosystem management and informed scientific decision-making. Specifically, this study employed the InVEST model, hot spot analysis, a geographically weighted regression model, and self-organizing feature mapping combined with K-means clustering to systematically quantify the spatiotemporal characteristics, trade-offs, synergies, and ecosystem service clusters of habitat quality (HQ), water yield (WY), carbon storage (CS), soil conservation (SC), and landscape aesthetics (LA) at grid and county scales from 2000 to 2020. The results revealed the following: (1) There was significant spatial heterogeneity among various ESs, with an overall spatial pattern exhibiting layered and interwoven variations. (2) Trade-offs predominantly characterized the relationships among ESs in the MRYRUA, with the absolute values of correlation coefficients mostly reaching their nadir in 2010. The interaction strengths between HQ and CS, and between CS and SC, increased with scale, while the relationships and strengths between LA and other ESs were less affected by scale changes. (3) At the grid scale, five types of ecosystem service bundles (ESBs) were identified, whereas at the district scale, four types of ESBs were delineated, including three common types: the WY–LA synergy bundle, Ecological transition bundle, and Key synergetic bundle, and three distinct types: the HQ–CS synergy bundle, Integrated ecological bundle, and Key synergetic bundle. The transitions of these ESBs over the 20 year period generally exhibited fluctuating evolutionary characteristics, with more pronounced fluctuations as the scale expanded. The results improve our comprehension of how ESs are related across various scales and provide theoretical and scientific references for multi-scale sustainable ecosystem zoning management and ecological environment governance.
Development of an Alternative Sustainable Economy Index: Integrating Multidimensional Sustainability Features
Authors: Al-Thani Mishal J. , Koc Muammer
Published: September 2024

Many existing economic indicators focus on economic growth and overlook environmental and social impacts. This article presents the Sustainable Economy Index, an index created to assess the sustainability of economic systems by considering economic, environmental, and social aspects. The SEI incorporates indicators that highlight how these three pillars of sustainability interact to address this problem. By linking theory with application, the SEI aims to help plan and promote specific actions for sustainability at different levels. The process consists of five stages, starting with interviews to identify indicators, followed by data collection, standardization, and the development of a composite index. This index is then used to assess Qatar’s progress towards a Sustainable Economy and compare it with a set of comparator countries. Using Qatar as a benchmark, this research uses the Sustainable Economy Index to assess and contrast its sustainability performance with a set of comparative countries. The findings highlight Qatar’s strengths and weaknesses. The application of the Sustainable Economy Index underlines its ability to effectively inform, and guide formulated sustainable development strategies. The results emphasize the need for persistent improvement of environmental and mental sustainability indicators to encompass evolving international circumstances and facilitate policy modifications.
How Does New Media Shape the Sense of Belonging and Social Identity? The Social and Psychological Processes of Sustainable Successful Reintegration for Rehabilitated People
Authors: Xiao Liyao , Chu Fufeng , Mao Jingjing , Yang Jiaxin , Liu Ziyu
Published: September 2024

Objective: This study aims to explore the role of new media in shaping the social identity and reintegration of rehabilitated individuals (ex-offenders), given the increasing societal attention on their successful reintegration. The objective is to analyze how media dependency and social identity formation contribute to their sustainable reintegration into society. Methods: Conducted in 2022–2023, the study adopted a narrative research paradigm. It employed qualitative methods, including in-depth interviews and participant observation, to investigate the decision-making processes of rehabilitated people under social adaptation pressures. The analysis was framed by media dependency theory and the two-dimensional model of social identity (Evaluation-Relationship). Results: The findings reveal that media dependency significantly enhances the construction of rehabilitated individuals’ social identity, aiding in their successful reintegration. The process of media dependency facilitates emotional support, providing an important outlet for resolving ambivalences in identity during the reintegration process. Moreover, media dependency strengthens the positive impact of new media on identity formation, thereby supporting sustainable reintegration into society. Conclusions: This study underscores the practical importance of media in identity reconstruction for rehabilitated individuals. The findings offer insights for policymakers to design more inclusive media policies that reduce stigmatization. Additionally, the results provide valuable guidance for social workers and counselors in leveraging new media to offer emotional support and identity reconstruction. Future research should examine identity formation in diverse socio-cultural contexts to further understand how new media can support sustainable reintegration.
How Do Macroeconomic Cycles and Government Policies Influence Cash Holdings? Evidence from Listed Firms in China
Authors: Cui Fangnan , Tan Yue , Lu Bangwen
Published: September 2024

Cash holdings are vital for a firm’s resilience and ability to capitalize on investment opportunities amid economic fluctuations. In this study, the complex relationship between macroeconomic cycles, government policies, and the cash holdings of Chinese listed firms is investigated. By analyzing data from Shanghai and Shenzhen A-share listed firms from 2004 to 2019, this research uncovers the individual and combined effects of economic cycles and monetary policies on corporate cash management. Key findings include the following: (1) A significant negative correlation between cash holdings and economic cycle volatility indicates that firms tend to increase cash holdings during periods of instability and reduce them during economic stability. (2) There is a strong negative relationship between restrictive monetary policy and cash holdings, suggesting that firms accumulate more cash to safeguard against tighter financial conditions. (3) The interplay between economic policies and business cycles reveals that during recessions, restrictive monetary policy increases cash holdings, while economic policy uncertainty reduces them. In contrast, during economic prosperity, monetary policy has a minimal impact on cash holdings. These insights emphasize the need for firms to integrate both economic cycles and policy environments into their cash management strategies. The findings offer valuable guidance for policymakers and business leaders aiming to enhance financial stability and optimize cash holdings across different economic conditions.
Entrepreneurship and Corporate ESG Performance—A Case Study of China’s A-Share Listed Companies
Authors: Xie Hanjin , Qin Zilong , Li Jun
Published: September 2024

This paper examines the contemporary implications of entrepreneurship and utilizes panel data from Chinese A-share listed companies spanning 2011 to 2022. Based on the five aspects of Chinese entrepreneurship, namely “patriotism, courage to innovate, integrity and law-abiding, social responsibility, and international vision”, the findings suggest that fostering entrepreneurship enhances the environmental, social, and governance (ESG) performance of firms. Mechanism analysis indicates that green technology innovation, social performance enhancement, and governance capability optimization mediate this relationship. Furthermore, factors such as corporate market power, regional marketization processes, and advancements in artificial intelligence technology influence the link between entrepreneurship and ESG performance. Robust entrepreneurship equips firms to navigate environmental uncertainties, but entrepreneurship cannot improve corporate governance performance. This article elucidates the distinctive significance of entrepreneurship, expanding the institutional economics research perspective, offering practical insights for cultivating entrepreneurship and elucidating potential determinants of corporate ESG performance. This article also provides spiritual guidance for sustainable development.