bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiple-Band Electric Field Response to the Geomagnetic Storm on 4 November 2021
Authors: Zheng Jie , Huang Jianping , Li Zhong , Li Wenjing , Han Ying , Lu Hengxin , Zhima Zeren
Published: September 2024

This paper investigates the impact characteristics of the 4 November 2021 magnetic storm across different frequency bands based on the electric field data (EFD) from the China Seismo-Electromagnetic Satellite (CSES), categorized into four frequency bands: ULF (Ultra-Low-Frequency, DC to 16 Hz), ELF (Extremely Low-Frequency, 6 Hz to 2.2 kHz), VLF (Very Low-Frequency, 1.8 to 20 kHz), and HF (High-Frequency, 18 kHz to 3.5 MHz). The study reveals that in the ULF band, magnetic storm-induced electric field disturbances are primarily in the range of 0 to 5 Hz, with a significant disturbance frequency at 3.9 ± 1.0 Hz. Magnetic storms also enhance Schumann waves in the ULF band, with 8 Hz Schumann waves dominating in the southern hemisphere and 13 Hz Schumann waves dominating in the northern hemisphere. In the ELF band, the more pronounced anomalies occur at 300 Hz–900 Hz and above 1.8 kHz, with the 300 Hz–900 Hz band anomalies around 780 Hz being the most significant. In the VLF band, the electric field anomalies are mainly concentrated in the 3–15 kHz range. The ELF and VLF bands exhibit lower absolute and relative disturbance increments compared to the ULF band, with the relative perturbation growth rate in the ULF band being approximately 10% higher than in the ELF and VLF bands. Magnetic storm-induced electric field disturbances predominantly occur in the ULF, ELF, and VLF bands, with the most significant disturbances in the ULF band. The electric field perturbations in these three frequency bands exhibit hemispheric asymmetry, with strong perturbations in the northern hemisphere occurring earlier than in the southern hemisphere, corresponding to different Dst minima. No electric field disturbances were observed in the HF band (above 18 kHz). The conclusions of this paper are highly significant for future anti-jamming designs in spacecraft and communication equipment, as well as for the further study of magnetic storms.
Improving the Accuracy of mmWave Radar for Ethical Patient Monitoring in Mental Health Settings
Authors: Dowling Colm , Larijani Hadi , Mannion Mike , Marais Matt , Black Simon
Published: September 2024

Monitoring patient safety in high-risk mental health environments is a challenge for clinical staff. There has been a recent increase in the adoption of contactless sensing solutions for remote patient monitoring. mmWave radar is a technology that has high potential in this field due it its low cost and protection of privacy; however, it is prone to multipath reflections and other sources of environmental noise. This paper discusses some of the challenges in mmWave remote sensing applications for patient safety in mental health wards. In line with these challenges, we propose a novel low-data solution to mitigate the impact of multipath reflections and other sources of noise in mmWave sensing. Our solution uses an unscented Kalman filter for target tracking over time and analyses features of movement to determine whether targets are human or not. We chose a commercial off-the-shelf radar and compared the accuracy and reliability of sensor measurements before and after applying our solution. Our results show a marked decrease in false positives and false negatives during human target tracking, as well as an improvement in spatial location detection in a two-dimensional space. These improvements demonstrate how a simple low-data solution can improve existing mmWave sensors, making them more suitable for patient safety solutions in high-risk environments.
Adaptation of a Differential Scanning Calorimeter for Simultaneous Electromagnetic Measurements
Authors: Wilson John W. , Jolfaei Mohsen A. , Fletcher Adam D. , Slater Carl , Davis Claire , Peyton Anthony J.
Published: September 2024

Although much information can be gained about thermally induced microstructural changes in metals through the measurement of their thermophysical properties using a differential scanning calorimeter (DSC), due to competing influences on the signal, not all microstructural changes can be fully characterised this way. For example, accurate characterisation of recrystallisation, tempering, and changes in retained delta ferrite in alloyed steels becomes complex due to additional signal changes due to the Curie point, oxidation, and the rate (and therefore the magnitude) of transformation. However, these types of microstructural changes have been shown to invoke strong magnetic and electromagnetic (EM) responses; therefore, simultaneous EM measurements can provide additional complementary data which can help to emphasise or deconvolute these complex signals and develop a more complete understanding of certain metallurgical phenomena. This paper discusses how a DSC machine has been modified to incorporate an EM sensor consisting of two copper coils printed onto either side of a ceramic substrate, with one coil acting as a transmitter and the other as a receiver. The coil is interfaced with a custom-built data acquisition system, which provides current to the transmit coil, records signals from the receive coil, and is controlled by a graphical user interface which allows the user to select multiple excitation frequencies. The equipment has a useable frequency range of approximately 1–100 kHz and outputs phase and magnitude readings at a rate of approximately 50 samples per second. Simultaneous DSC-EM measurements were performed on a nickel sample up to a temperature of 600 °C, with the reversable ferromagnetic to paramagnetic transition in the nickel sample invoking a clear EM response. The results show that the combined DSC-EM apparatus has the potential to provide a powerful tool for the analysis of thermally induced microstructural changes in metals, feeding into research on steel production, development of magnetic and conductive materials, and many more areas.
Hybrid Multi-Access Method for Space-Based IoT: Adaptive Bandwidth Allocation and Beam Layout Based on User Distribution
Authors: Liu Qingquan , Chen Lihu , Li Songting , Xiang Yiran
Published: September 2024

The development of space-based Internet of Things is limited by insufficient allocable frequency resources and low spectrum utilization. To meet the demand for massive access users under the condition of restricted frequency resources, a multi-dimensional hybrid multiple-access method for space-time-frequency-code division based on user distribution (MHSTFC-UD) is established. It divides the beam cell of a low orbit satellite into the central and edge area and dynamically adjusts the radius of the central area and the allocation of frequency resources according to the distribution of users. The optimization model for the radius of the central area and the allocation of frequency resources is established and solved by the genetic algorithm. Also, it takes the edge area as the protection interval to realize the full-frequency multiplexing between the beam cells in the time domain, space domain and code domain. The simulation results show that compared with the traditional method of frequency reuse in two or three dimensions, the multi-dimensional hybrid multiple-access method can improve the maximum access capacity of a single satellite user by one to three orders of magnitude. Moreover, the MHSTFC-UD can increase users by an additional 11.5% to 33.1% compared to fixed area division and frequency resource allocation.
SMS Scam Detection Application Based on Optical Character Recognition for Image Data Using Unsupervised and Deep Semi-Supervised Learning
Authors: Shinde Anjali , Shahra Essa Q. , Basurra Shadi , Saeed Faisal , AlSewari Abdulrahman A. , Jabbar Waheb A.
Published: September 2024

The growing problem of unsolicited text messages (smishing) and data irregularities necessitates stronger spam detection solutions. This paper explores the development of a sophisticated model designed to identify smishing messages by understanding the complex relationships among words, images, and context-specific factors, areas that remain underexplored in existing research. To address this, we merge a UCI spam dataset of regular text messages with real-world spam data, leveraging OCR technology for comprehensive analysis. The study employs a combination of traditional machine learning models, including K-means, Non-Negative Matrix Factorization, and Gaussian Mixture Models, along with feature extraction techniques such as TF-IDF and PCA. Additionally, deep learning models like RNN-Flatten, LSTM, and Bi-LSTM are utilized. The selection of these models is driven by their complementary strengths in capturing both the linear and non-linear relationships inherent in smishing messages. Machine learning models are chosen for their efficiency in handling structured text data, while deep learning models are selected for their superior ability to capture sequential dependencies and contextual nuances. The performance of these models is rigorously evaluated using metrics like accuracy, precision, recall, and F1 score, enabling a comparative analysis between the machine learning and deep learning approaches. Notably, the K-means feature extraction with vectorizer achieved 91.01% accuracy, and the KNN-Flatten model reached 94.13% accuracy, emerging as the top performer. The rationale behind highlighting these models is their potential to significantly improve smishing detection rates. For instance, the high accuracy of the KNN-Flatten model suggests its applicability in real-time spam detection systems, but its computational complexity might limit scalability in large-scale deployments. Similarly, while K-means with vectorizer excels in accuracy, it may struggle with the dynamic and evolving nature of smishing attacks, necessitating continual retraining.
Enhancing the Minimum Awareness Failure Distance in V2X Communications: A Deep Reinforcement Learning Approach
Authors: Guzmán Leguel Anthony Kyung , Nguyen Hoa-Hung , Gómez Gutiérrez David , Yoo Jinwoo , Jeong Han-You
Published: September 2024

Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle’s ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept involving vehicle detection, tracking, and maintaining their safety distances. To enhance this awareness, we propose a deep reinforcement learning framework for the joint control of beacon rate and transmit power (DRL-JCBRTP). Our DRL−JCBRTP framework integrates LSTM-based actor networks and MLP-based critic networks within the Soft Actor-Critic (SAC) algorithm to effectively learn optimal policies. Leveraging local state information, the DRL-JCBRTP scheme uses an innovative reward function to increase the minimum awareness failure distance. Our SLMLab-Gym-VEINS simulations show that the DRL-JCBRTP scheme outperforms existing beaconing schemes in minimizing awareness failure probability and maximizing awareness distance, ultimately improving driving safety.
A Model-Based Systematic Innovative Design for Sonic Logging Instruments in Natural Gas Wells
Authors: Jiang Chong , Zhao Wu , Yu Miao , Zhang Kai
Published: September 2024

With the continued development of natural gas extraction technologies, the accurate determination of downhole temperature and pressure has become increasingly important. It is crucial for the optimization of gas well production and an important measure to prevent accidents. However, existing logging instruments have a series of deficiencies in measurement and cannot adequately monitor modern natural gas. In response to these problems, in this paper, we propose a new model-based systematic innovation design method for designing logging instruments and simulations using finite element software. Our research results confirm the theoretical and practical utility of this model-based design method and provide a novel approach to designing logging instruments.
Development of Lasing Silica Microsphere for High-Speed DNA Molecular Detection
Authors: Jun Chan Seok , Lee Wonsuk
Published: September 2024

Laser and molecular detection techniques that have been used to overcome the limitations of fluorescent DNA labeling have presented new challenges. To address some of these challenges, we developed a DNA laser that uses a solid-state silica microsphere as a ring resonator and a site for DNA-binding reactions, as well as a platform to detect and sequence target DNA molecules. We detected target DNA using laser emission from a DNA-labeling dye and a developed solid-state silica microsphere ring resonator. The microsphere was sensitive; a single base mismatch in the DNA resulted in the absence of an optical signal. As each individual microsphere can be utilized as a parallel DNA analysis chamber, this optical digital detection scheme allows for high-throughput and rapid analysis. More importantly, the solid-state DNA laser is free from deformation, which guarantees stable lasing characteristics, and can be manipulated freely outside the solution. Thus, this promising advanced DNA laser scheme can be implemented on platforms other than optofluidic chips.
Joint Spatio-Temporal-Frequency Representation Learning for Improved Sound Event Localization and Detection
Authors: Chen Baoqing , Wang Mei , Gu Yu
Published: September 2024

Sound event localization and detection (SELD) is a crucial component of machine listening that aims to simultaneously identify and localize sound events in multichannel audio recordings. This task demands an integrated analysis of spatial, temporal, and frequency domains to accurately characterize sound events. The spatial domain pertains to the varying acoustic signals captured by multichannel microphones, which are essential for determining the location of sound sources. However, the majority of recent studies have focused on time-frequency correlations and spatio-temporal correlations separately, leading to inadequate performance in real-life scenarios. In this paper, we propose a novel SELD method that utilizes the newly developed Spatio-Temporal-Frequency Fusion Network (STFF-Net) to jointly learn comprehensive features across spatial, temporal, and frequency domains of sound events. The backbone of our STFF-Net is the Enhanced-3D (E3D) residual block, which combines 3D convolutions with a parameter-free attention mechanism to capture and refine the intricate correlations among these domains. Furthermore, our method incorporates the multi-ACCDOA format to effectively handle homogeneous overlaps between sound events. During the evaluation, we conduct extensive experiments on three de facto benchmark datasets, and our results demonstrate that the proposed SELD method significantly outperforms current state-of-the-art approaches.
A Proposed Algorithm Based on Variance to Effectively Estimate Crack Source Localization in Solids
Authors: Choi Young-Chul , Chung Byunyoung , Jung Doyun
Published: September 2024

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNRs (signal-to-noise ratios) generated in rocks. The basic idea is that the variances in the crack wave and noise have different characteristics, depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The proposed algorithm for source localization can be used for evaluating and monitoring damage in tunnels or other underground facilities in real time.
An Adaptive Spatial Target Tracking Method Based on Unscented Kalman Filter
Authors: Rong Dandi , Wang Yi
Published: September 2024

The spatial target motion model exhibits a high degree of nonlinearity. This leads to the fact that it is easy to diverge when the conventional Kalman filter is used to track the spatial target. The Unscented Kalman filter can be a good solution to this problem. This is because it conveys the statistical properties of the state vector by selecting sampling points to be mapped through the nonlinear model. In practice, however, the measurement noise is often time-varying or unknown. In this case, the filtering accuracy of the Unscented Kalman filter will be reduced. In order to reduce the influence of time-varying measurement noise on the spatial target tracking, while accurately representing the a posteriori mean and covariance of the spatial target state vector, this paper proposes an adaptive noise factor method based on the Unscented Kalman filter to adaptively adjust the covariance matrix of the measurement noise. In this paper, numerical simulations are performed using measurement models from a space-based infrared satellite and a ground-based radar. It is experimentally demonstrated that the adaptive noise factor method can adapt to time-varying measurement noise and thus improve the accuracy of spatial target tracking compared to the Unscented Kalman filter.
Visual Feature-Guided Diamond Convolutional Network for Finger Vein Recognition
Authors: Yao Qiong , Song Dan , Xu Xiang , Zou Kun
Published: September 2024

Finger vein (FV) biometrics have garnered considerable attention due to their inherent non-contact nature and high security, exhibiting tremendous potential in identity authentication and beyond. Nevertheless, challenges pertaining to the scarcity of training data and inconsistent image quality continue to impede the effectiveness of finger vein recognition (FVR) systems. To tackle these challenges, we introduce the visual feature-guided diamond convolutional network (dubbed ‘VF-DCN’), a uniquely configured multi-scale and multi-orientation convolutional neural network. The VF-DCN showcases three pivotal innovations: Firstly, it meticulously tunes the convolutional kernels through multi-scale Log-Gabor filters. Secondly, it implements a distinctive diamond-shaped convolutional kernel architecture inspired by human visual perception. This design intelligently allocates more orientational filters to medium scales, which inherently carry richer information. In contrast, at extreme scales, the use of orientational filters is minimized to simulate the natural blurring of objects at extreme focal lengths. Thirdly, the network boasts a deliberate three-layer configuration and fully unsupervised training process, prioritizing simplicity and optimal performance. Extensive experiments are conducted on four FV databases, including MMCBNU_6000, FV_USM, HKPU, and ZSC_FV. The experimental results reveal that VF-DCN achieves remarkable improvement with equal error rates (EERs) of 0.17%, 0.19%, 2.11%, and 0.65%, respectively, and Accuracy Rates (ACC) of 100%, 99.97%, 98.92%, and 99.36%, respectively. These results indicate that, compared with some existing FVR approaches, the proposed VF-DCN not only achieves notable recognition accuracy but also shows fewer number of parameters and lower model complexity. Moreover, VF-DCN exhibits superior robustness across diverse FV databases.
Countermovement Jump Peak Power Changes with Age in Masters Weightlifters
Authors: Riemann Bryan L. , Johnson Matthew , Helms Matthew K. , Hatchett Andrew , Vondrasek Joseph D. , Watts Cullun Q. , Huebner Marianne
Published: September 2024

Aging is associated with decreased muscle strength and power. Power is particularly important for maintaining the independence of older adults when performing activities of daily living. The countermovement jump has been identified as a reliable and safe method to assess lower extremity power across the lifespan. The purpose of this investigation was to study sex differences and age-related changes in countermovement jump peak power among masters weightlifters with the secondary purpose of comparing results to previous reports of community and masters athletes. Female (n = 63, 39 to 70 yrs, med (56 yrs)) and male (n = 39, 35 to 86 yrs, med (59 yrs)) participants of the 2022 World Masters Championships completed three maximal effort countermovement jump repetitions following a dynamic warm-up. Vertical ground reaction forces were recorded, and peak power normalized to body mass was calculated. Results indicated significant age-related peak power among weightlifters, with the decline being significantly more pronounced in males than females. Female weightlifters exhibited less age-related decline compared to normative data as well as the other Master athlete comparison cohorts (short and long-distance runners), whereas the males demonstrated similar age-related declines as the comparison cohorts. While the female weightlifters in the current study generally demonstrated the least age-related declines in CMJ peak power of the comparative literature, the male weightlifters showed similar age-related decline rates.
Effect of Physical Activity Behaviors, Team Sports, and Sitting Time on Body Image and Exercise Dependence
Authors: Festino Emanuel , Papale Olga , Di Rocco Francesca , De Maio Marianna , Cortis Cristina , Fusco Andrea
Published: September 2024

This study aimed to evaluate whether the type and degree of physical activity commitment (i.e., team sport athletes, highly active individuals, sedentary behavior) influence body image and exercise behavior. A total of 96 participants (46 women and 50 men; age: 22.7 ± 2.7 years; height: 170 ± 8.6 cm; weight: 67.6 ± 10.8 kg) were divided in six groups: female volleyball and male football players (student athletes classified as Tier 2), highly physically active women and men, with high sitting time completed the Body Image Dimensional Assessment and the 21-item Exercise Dependence Scale to evaluate body dissatisfaction and level of dependency on exercise. The Body Image Dimensional Assessment is a silhouette-based scale, where three direct indices are derived from the participants’ responses: body dissatisfaction, sexual body dissatisfaction, and comparative body dissatisfaction. The Exercise Dependence Scale is a six-point Likert scale that evaluates seven dimensions of exercise dependence (tolerance, withdrawal, intention effects, lack of control, time, reductions in other activities, and continuance) and provides an overall score. A multivariate analysis of variance was used to examine the effects of different levels of physical activity, team sport participation (volleyball and football), and sedentary behavior (i.e., high sitting time) on participants’ body image indices and dimensions of exercise dependence according to sex. Volleyball players showed significantly higher body dissatisfaction than football players (d = 0.99) and the physically active men group (d = 2.31), who had lower values than sedentary women group (d = 1.68). Football players had lower comparative body dissatisfaction values than volleyball players (d = 1.70) and sedentary women (d = 1.69), who had higher values than sedentary men (d = 1.04). Sedentary women had a significantly lower exercise dependence scale score than volleyball players (d = 1.71), football players (d = 1.12), and physically active men (d = 1.21). The findings highlight the impact of regular physical activity on body dissatisfaction and the variance in body image perceptions between the sexes. Moreover, the high exercise dependence values found in volleyball and football players and physically active men suggest an effect of intense training and maladaptive exercise behaviors, underlining the need for comprehensive strategies to address exercise dependence.
The Use of Cryopreserved Umbilical Tissue as an Adjunctive Therapy in Immediate Breast-Reconstruction Patients at High Risk for Wound-Healing Complications: Case-Report Series and Preliminary Results
Authors: Ockerman Kyle M. , Trieu Nhan , Han Sabrina H. , Mardourian Markos , Spiguel Lisa , Dadireddy Kalyan , Sorice Virk Sarah
Published: September 2024

Introduction: Mastectomy skin necrosis (MSN) is a common complication occurring in up to 50% of patients. In patients with risk factors for poor wound healing such as immunosuppression, prior radiotherapy (XRT), and high body mass index (BMI > 30.0), this number is even higher. MSN can lead to infection, loss of reconstruction, poorer aesthetics, and most ominously, delay in adjuvant cancer therapy. Instead of forgoing reconstruction in these patients, adjunctive therapies to optimize wound healing are necessary. The purpose of this study is to introduce the use of cryopreserved umbilical tissue (vCUT) as an adjunct therapy for high-risk-wound-healing immediate breast reconstruction (IBR) patients. Methods: All patients who underwent breast reconstruction with vCUT as an adjunctive therapy were identified and retrospectively analyzed. Results: Seven patients who underwent breast reconstruction with vCUT placement were identified. These patients had risk factors for delayed healing, such as obesity, immunosuppression, and/or prior XRT. The mean post-operative follow-up was 252 days (range 183–287). Four out of seven patients demonstrated post-operative complications: two out of seven developed seromas, two out of seven developed wound dehiscence, two out of seven developed infection, two out of seven developed MSN, and two out of seven lost their reconstruction. Conclusion: As undergoing IBR leads to improved mental health and superior aesthetic outcomes, efforts to expand current indications for safe IBR to traditionally poorer reconstructive candidates are imperative. The results of this case series demonstrate vCUT as a promising novel adjunctive tool in the reconstructive surgeons’ armamentarium in managing the less ideal reconstructive breast candidate.
Development of an Agent-Based Model to Evaluate Rural Public Policies in Medellín, Colombia
Authors: Castillo Grisales Julian , Ceballos Yony , Bastidas-Orrego Lina , Jaramillo Gómez Natalia , Chaparro Cañola Elizabeth
Published: September 2024

Rural areas near large cities do not satisfy the food needs of the city’s population. In Medellín, Colombia, these areas satisfy only 2% of the city’s food needs, highlighting an urgent need to review and improve policies supporting agriculture. This study was conducted over a ten-year period since the release of the Medellín policy related to land use. The model uses agent-based modelling, geographic analysis and dichotomous variables, combining these structures to create a decision-making element and thus identify changes to examine in relation to current land use and detect properties with a potential for conversion to agricultural use. By evaluating post-processed geographic layers, land use in agricultural rural environments is prioritized, setting up clusters of homogeneous zones and finding new areas of rural influence. The implications of this study extend beyond Medellín, offering a model that can be applied to other regions facing similar challenges in agricultural productivity and land use. This research supports informed and effective decision-making in agricultural policy, contributing to improved food security and sustainable development. The results show that some properties are susceptible to policy changes and provide a framework for the revision of local regulations, serving as a support tool for decision-making in rural public policies by giving the local administration key factors to update in the current policies. The findings are relevant to local stakeholders, including policymakers and rural landowners, suggesting that several properties are susceptible to policy changes promoting agriculture and supporting informed decision-making in agricultural policy, contributing to food security and sustainable development. Also, this approach promotes efficient and sustainable agriculture, highlighting the importance of geographic analysis and agent-based modelling in policy planning and evaluation.
Improving Urban Cyclability and Perceived Bikeability: A Decision Support System for the City of Milan, Italy
Authors: Silvestri Fulvio , Babaei Seyed Hesam , Coppola Pierluigi
Published: September 2024

This paper presents a Decision Support System (DSS) designed to enhance cyclability and perceived bikeability in urban areas, with an application to the city of Milan, Italy, focusing on cycling toward the urban university campuses of Politecnico di Milano. Despite the increasing emphasis on sustainable urban mobility, research gaps remain in optimizing cycling infrastructure development based on both observable factors (e.g., availability and quality of cycleways) and latent factors (e.g., cyclists’ perceived safety and security). The objective of this study is to address these gaps by developing a DSS, based on a macroscopic multimodal transport simulation model, to facilitate an in-depth analysis and prioritization of cycling transport policies. Findings from the DSS simulations indicate that strategic enhancements to cycling infrastructure can shift user preferences toward safer and more dedicated cycling routes, despite potential increases in travel time and distance. This paper concludes that implementing a DSS not only supports more informed policymaking but also encourages sustainable urban development by improving the overall cycling experience in cities, highlighting the importance of addressing both tangible and intangible factors in the design and prioritization of cycling infrastructure projects.
Government Environmental Information Regulation and Corporate ESG Performance
Authors: Li Xianghua , Hu Ying , Guo Xiaodi , Wang Min
Published: September 2024

China’s environmental, social, and governance (ESG) actions are driven by multiple factors, among which the government is an indispensable key player. This paper empirically examines the impact of government environmental information regulation (GEIR) on corporate ESG performance using a sample of Chinese A-share listed companies in heavily polluting industries from 2011 to 2021, with a GEIR in 2014 as an exogenous shock. GEIR is found to significantly improve corporate ESG performance, which is mainly reflected in the environmental and social dimensions. Moreover, improvements in the quality of corporate information disclosure and the efficiency of green innovation are found to be the main paths through which GEIR enhances corporate ESG performance. Further research shows that the enhancement effect of GEIR is more obvious in firms with low political relevance, high investor attention, and low marketization in the region in which they are located. This work enriches the research on GEIR and corporate ESG performance and provides some references for promoting the government to play a key role in China’s ESG initiatives.
Infinite Affluence on a Finite Planet
Authors: Jacob Claus
Published: September 2024

The decades after the Second World War have seen economic growth and prosperity on an unprecedented scale. Yet this incredible turnover of raw materials and energy into goods and eventually waste has brought our planetary systems close to their limits, as witnessed most prominently, yet not exclusively, by climate change and mass extinctions. Changing towards a greener, more sustainable and circular economy without limiting our economic wealth is attractive—yet this change does not seem to be easy or speedy enough to save the planet, its eco- and social systems, and its inhabitants. In contrast, moving towards an economy less demanding on energy and raw materials, and focusing more on the pursuit of immaterial forms of satisfaction and happiness, requires an alternative form of hedonism. By cerishing quality time rather than heaps of money, and social interactions rather than material goods, even a finite planet may allow sustainable and indeed infinite forms and amounts of prosperity.
Mobile Platforms as the Alleged Culprit for Work–Life Imbalance: A Data-Driven Method Using Co-Occurrence Network and Explainable AI Framework
Authors: Wang Xizi , Ma Yakun , Hu Guangwei
Published: September 2024

The digital transformation of organizations has propelled the widespread adoption of mobile platforms. Extended availability and prolonged engagement with platform-mediated work have blurred boundaries, making it increasingly difficult for individuals to balance work and life. Criticism of mobile platforms has intensified, precluding digital transformation towards a sustainable future. This study examines the complex relationship between mobile platforms and work–life imbalance using a comprehensive data-driven methodology. We employed a co-occurrence network technique to extract relevant features based on previous findings. Subsequently, we applied an explainable AI framework to analyze the nonlinear relationships underlying technology-induced work–life imbalance and to detect behavior patterns. Our results indicate that there is a threshold for the beneficial effects of availability demands on integration behavior. Beyond this tolerance range, no further positive increase can be observed. For organizations aiming to either constrain or foster employees’ integration behavior, our findings provide tailored strategies to meet different needs. By extending the application of advanced machine learning algorithms to predict integration behaviors, this study offers nuanced insights that counter the alleged issue of technology-induced imbalance. This, in turn, promotes the sustainable success of digital transformation initiatives. This study has significant theoretical and practical implications for organizational digital transformation.
WSN Energy Control by Holonic Dynamic Reconfiguration: Application to the Sustainability of Communicating Materials
Authors: Derigent William , David Michaël , André Pascal , Cardin Olivier , Najjar Salma
Published: September 2024

Various works propose solutions addressing the sustainability of IoT technologies to reduce their energy consumption, especially in the domain of wireless sensor networks. The diversity of applications, as well as the variability of their long-term constraints, forces them to dynamically adapt the network through time. Accordingly, this study formalizes the SADHoA-WSN framework to tackle the reconfiguration process. This proposal is a dynamic Holonic Control Architecture, linking the physical network evolution to the decisions made by a virtual multi-agent control system. The potential of such an approach is demonstrated by applying this framework to the energy optimization of communicating materials, i.e., materials equipped with inner wireless sensor nodes. The first implemented components of SADHoA-WSN and their related experimental results validate it as a promising energy-efficient dynamic methodology. This work lays the groundwork for optimized energy control in IoT networks.
Influence of Climate Change on the Probability of Chloride-Induced Corrosion Initiation for RC Bridge Decks Made of Geopolymer Concrete
Authors: Amleh Lamya , Hassan Mostafa , Hussein Luaay
Published: September 2024

Climate change poses a significant threat to the durability of reinforced concrete (RC) bridges, which are particularly vulnerable to chloride-induced corrosion of steel reinforcements. The main problem for the current research is the increase in the projected maximum temperature values, especially for the high emission scenario in the future because of climate change, applied to the upper part of the RC bridge deck made of geopolymer concrete (GPC) composed of 50% fly ash and 50% slag. This will reduce the corrosion initiation time and the safety and durability of the RC bridge deck structure. Despite extensive research on chloride-induced corrosion, there is a scientific gap in understanding how future climate variations will influence the rate of corrosion in RC bridges. Specifically, comprehensive studies assessing the effect of maximum temperature on the probability of the corrosion initiation process in RC bridge decks made of GPC exposed to chloride environments are lacking. This study used the Monte Carlo simulation method to assess the probability of corrosion initiation (PCI) under various future climate scenarios for Toronto City, Canada. This research examines the impact of the maximum temperature and relative humidity on the diffusion coefficient of chloride ions in concrete. It assesses the PCI for different concrete cover thicknesses in RC decks made of geopolymer concrete composed of 50% fly ash and 50% slag over specified periods, dealing with the sensitivity analysis for this parameter among different parameters defined in the performance function. The results indicate a substantial increase in the PCI for a 40 mm concrete cover compared with a 50 mm cover in various years. Furthermore, maximum temperatures ranging from 40 °C to 45 °C significantly increase the PCI compared with temperatures between 25 °C and 35 °C for a 50 mm concrete cover. Finally, polynomial functions have been deduced to investigate the reliability index and PCI as a function of various coefficients of variations for mean concrete covers made of GPC at various maximum temperature values in different years. These findings provide important information for the design and maintenance of RC structures, ensuring their longevity in the face of climate change.
Unveiling the Age Factor: The Influence of Cabinet Members’ Age on Waste Electrical and Electronic Equipment Recycling Rates in European Nations
Authors: Arslan Erdal , Şanal Musa , Koyuncu Cuneyt , Yilmaz Rasim
Published: September 2024

Utilizing panel quantile regression on an unbalanced dataset for 30 European countries from 2008 to 2018, this article seeks to investigate how the age of cabinet members influences e-waste recycling rates in European countries, alongside other relevant factors. Prior research has overlooked the age of cabinet members as a determinant of e-waste recycling. By addressing this gap, this study introduces a novel factor that could impact e-waste recycling rates. Thus, this study provides insights into how the demographic characteristics of parliament members, particularly the age of cabinet members, impact environmental improvement, as indicated by e-waste recycling rates. Estimation results indicate the existence of a nonlinear relationship (i.e., an inverted U-shaped environmental Kuznets curve) between the age of cabinet members and the e-waste recycling rate, rather than a linear relationship. The calculated average turning point age is 49.087, indicating that the e-waste recycling rate increases as the age of cabinet members rises until reaching 49.087, after which the e-waste recycling rate declines. Overall, this study underscores the importance of the demographic characteristics of parliament members, particularly the age of cabinet members, in shaping e-waste recycling policies and environmental sustainability efforts. It emphasizes that the age of cabinet members and generational perspectives can influence their awareness, understanding, and commitment to addressing contemporary challenges such as e-waste.
Determinants of Solar Photovoltaic Adoption Intention among Households: A Meta-Analysis
Authors: Li Wenjie , Zhu Jiaolan , Li Yongchang , Li Yaning , Ding Zhikun
Published: September 2024

In recent years, research on the intention to adopt solar photovoltaic technology has yielded rich results. However, controversy still exists regarding the key antecedents of households’ intention to adopt solar photovoltaic technologies. To clarify the critical factors influencing the intention to adopt solar photovoltaic technology and potential moderating variables, this study utilized meta-analysis to perform a quantitative literature analysis on 29 empirical articles. It identified eight key influencing factors and tested the moderating effects of two variables: sample size and research area. The results show that “Attitude” and “Government Incentive” are moderately correlated with the intention to adopt. “Social Influence”, “Product Knowledge”, “Effort Expectancy”, “Perceived Cost-benefit”, “Performance Expectancy”, and “Perceived Behavioral Control” are weakly correlated with the adoption intention. The study also found that using the sample size and research area as moderating variables can partly reveal differences between various studies. Overall, the findings of this study offer theoretical guidance for subsequent in-depth studies and support for the practical promotion of solar photovoltaic technology.
Consistency Analysis of Collaborative Process Data Change Based on a Rule-Driven Method
Authors: Wang Qianqian , Shao Chifeng
Published: September 2024

In business process management, business process change analysis is the key link to ensure the flexibility and adaptability of the system. The existing methods mostly focus on the change analysis of a single business process from the perspective of control flow, ignoring the influence of data changes on collaborative processes with information interaction. In order to compensate for this deficiency, this paper proposes a rule-driven consistency analysis method for data changes in collaborative processes. Firstly, it analyzes the influence of data changes on other elements (such as activities, data, roles, and guards) in collaborative processes, and gives the definition of data influence. Secondly, the optimal alignment technology is used to explore how data changes interfere with the expected behavior of deviation activities, and decision rules are integrated into the Petri net model to accurately evaluate and screen out the effective expected behavior that conforms to business logic and established rules. Finally, the initial optimal alignment is repaired according to the screened effective expected behavior, and the consistency of business processes is recalculated. The experimental results show that the introduced rule constraint mechanism can effectively avoid the misjudgment of abnormal behavior. Compared with the traditional method, the average accuracy, recall rate, and F1-score of effective expected behavior are improved by 4%, 4.7%, and 4.3%, respectively. In addition, the repaired optimal alignment significantly enhances the system’s ability to respond quickly and self-adjust to data changes, providing a strong support for the intelligent and automated transformation of business process management.
Electromagnetic Field and Variable Inertia Analysis of a Dual Mass Flywheel Based on Electromagnetic Control
Authors: Niu Hongen , Zeng Liping , Wei Cuicui , Wan Zihao
Published: September 2024

The moment of inertia of the primary flywheel and the secondary flywheel in a dual mass flywheel (DMF) directly affects the vibration damping performance in an automotive driveline. To enable better minimization of vibration and noise by changing the moment of inertia of the DMF to adjust the frequency characteristics of the automotive driveline, a new variable inertia DMF structure is proposed by introducing electromagnetic devices. The finite element simulation model of the electromagnetic field of an electromagnetic device is established, the electromagnetic field characteristics in the structure are analyzed, and the variation in the electromagnetic force under different air gaps and current conditions is obtained. The electromagnetic force test system of the electromagnetic device is constructed, and the validity of the finite element simulation analysis of the electromagnetic field of the electromagnetic device is verified. A mechanical model of the electromagnetic device is established to analyze the characteristics of the displacement of the moving mass in the structure as well as the variation in the moment of inertia of the DMF at different rotational speeds and currents. The maximum adjustable proportion of its moment of inertia can reach 15.07%. A torsional model of the automotive driveline is established to analyze the effect of variable inertia DMF on the resonance frequency of the system under different currents. The results show that the electromagnetic device introduced in the DMF can realize the active adjustment of the moment of inertia and enable the resonance frequency to decrease with increasing rotational speed, which expands the idea of optimizing the vibration damping performance of the DMF and provides a reference for better control of the torsional vibration of the automobile or other mechanical transmission systems.
Some Aspects of Differential Topology of Subcartesian Spaces
Authors: Chen Liuyi , Xia Qianqian
Published: September 2024

In this paper, we investigate the differential topological properties of a large class of singular spaces: subcarteisan space. First, a minor further result on the partition of unity for differential spaces is derived. Second, the tubular neighborhood theorem for subcartesian spaces with constant structural dimensions is established. Third, the concept of Morse functions on smooth manifolds is generalized to differential spaces. For subcartesian space with constant structural dimension, a class of examples of Morse functions is provided. With the assumption that the subcartesian space can be embedded as a bounded subset of an Euclidean space, it is proved that any smooth bounded function on this space can be approximated by Morse functions. The infinitesimal stability of Morse functions on subcartesian spaces is studied. Classical results on Morse functions on smooth manifolds can be treated directly as corollaries of our results here.
Persistence Symmetric Kernels for Classification: A Comparative Study
Authors: Bandiziol Cinzia , De Marchi Stefano
Published: September 2024

The aim of the present work is a comparative study of different persistence kernels applied to various classification problems. After some necessary preliminaries on homology and persistence diagrams, we introduce five different kernels that are then used to compare their performances of classification on various datasets. We also provide the Python codes for the reproducibility of results and, thanks to the symmetry of kernels, we can reduce the computational costs of the Gram matrices.
Supply Chain Sustainability: Influencing Factors and Empirical Study from a Marxist Political Economy Perspective
Authors: Zhang Kun , He Mei , Yang Jimei , Hou Hanping
Published: September 2024

Marxist political economy provides a theoretical framework for sustainable supply chains, while the implementation of sustainable supply chains embodies and deepens the practical application of Marxist principles. This paper studies supply chain sustainability from the perspective of Marxist political economy, proposing a novel analytical framework to address sustainability challenges. The primary research focuses on (1) Identifying Influencing Factors: Influencing factors of Marxist political economy and supply chain sustainability are categorized into four main areas: society and government, environment, economy, and the supply chain itself. Through classification analysis, 16 key factors influencing sustainable supply chain implementation are identified. (2) DEMATEL Analysis (Decision-Making Trial and Evaluation Laboratory Method): Data are gathered through investigations and questionnaires to construct a direct influence matrix. Subsequently, a decision test method quantitatively analyzes the interactions among these factors, resulting in a comprehensive influence matrix and a cause–effect diagram. To enhance the overall benefits of supply chain sustainability and foster sustainable development.