bluuebunny/crossref_metadata_2025 huggingface dataset:
Vertical Total Electron Content Enhancements and Their Global Distribution in Relation to Tectonic Plate Boundaries
Authors: Wielgosz Paweł , Jarmołowski Wojciech , Mazur Stanisław , Milanowska Beata , Krypiak-Gregorczyk Anna
Published: February 2025

Atmospheric responses to earthquakes or volcanic eruptions have become an interesting topic and can potentially contribute to future forecasting of these events. Extensive anomalies of the total electron content (TEC) are most often linked with geomagnetic storms or Earth-dependent phenomena, like earthquakes, volcanic eruptions, or nuclear explosions. This study extends rarely discussed, but very frequent, interactions between tectonic plate boundaries and the ionosphere. Our investigations focus on the very frequent occurrence of TEC enhancements not exclusively linked with individual seismic phenomena but located over tectonic plate boundaries. The objective of this study is to provide a review of the global spatiotemporal distribution of TEC anomalies, facilitating the discussion of their potential relations with tectonic activity. We apply a Kriging-based UPC-IonSAT quarter-of-an-hour time resolution rapid global ionospheric map (UQRG) from the Polytechnic University of Catalonia (UPC) IonSAT group for the detection of relative vertical TEC (VTEC) changes. Our study describes global relative and normalized VTEC variations, which have spatial and temporal behaviours strongly indicating their relationship both with geomagnetic changes and the tectonic plate system. The variations in geomagnetic fields, including the storms, disturb the ionosphere and amplify TEC variations persisting for several hours over tectonic plate boundaries, mostly over the diverging ones. The seismic origin of the selected parts of these TEC enhancements and depletions and their link with tectonic plate edges are suspected from their duration, shape, and location. The changes in TEC originating from both sources can be observed separately or together, and therefore, there is an open question about the directions of the energy transfers. However, the importance of geomagnetic field lines seems to be probable, due to the frequent common occurrence of both types of TEC anomalies. This research also proves that permanent observation of global lithosphere–atmosphere–ionosphere coupling (LAIC) is also important in time periods without strong earthquake or volcanic events. The occurrence of TEC variations over diverging tectonic plate boundaries, sometimes combined with travelling anomalies of geomagnetic origin, can add to the studies on earthquake precursors and forecasting.
Analysis of Instantaneous Doppler Positioning Performance Based on LEO Satellite Ephemeris Errors
Authors: Shi Xingyu , Chen Mingjian , Li Wanli , Li Yuxing , Lv Wei , Zhou Wenlong , Shen Yang , Li Xueqing , Yu Jiashu
Published: February 2025

To address the limitations of Global Navigation Satellite Systems (GNSSs), such as vulnerability to electromagnetic interference and weak ground signal power, signal of opportunity (SOP) provided by low Earth orbit (LEO) satellites can serve as a backup positioning method. By simulating a LEO constellation, the impact of satellite visibility, Doppler geometric dilution of precision (DGDOP), and positioning accuracy was explored. Considering positioning errors such as satellite clock drift rate, ionospheric delay rate, tropospheric delay rate, and Earth rotation effects, the instantaneous positioning performance with satellite orbital errors and satellite velocity errors of different magnitudes was simulated. The results show that satellite visibility and DGDOP are negatively correlated. In a typical atmospheric environment with orbital errors of 10 m and satellite velocity errors of 0.1 m/s, positioning accuracy within 30 m can be achieved. This confirms that Doppler-based positioning with LEO satellites can be used as a backup method for GNSSs.
A Two-Stage Location-Allocation Optimization Method for Fixed UAV Nests in Power Inspection Considering Node Failure Scenarios
Authors: Huang Zheng , Wang Hongxing , Tang Yiming , Gao Feng , Du Biao , Wang Jia
Published: February 2025

This paper explores the configuration and deployment of UAV nests for power inspection operations, focusing on potential nest failures. It proposes a two-stage location-allocation method. The problem is divided into two subproblems, each modeled as an integer linear programming (ILP) problem. The first subproblem identifies the minimal set of nodes for nest construction using the commercial solver Gurobi. The second subproblem involves UAV nest type selection and task allocation, solved with an ILS-SA heuristic algorithm. A case study in China shows that our method reduces total costs by 33.9% and decreases the number of UAV nests by 32% compared to the current greedy deployment method used by the power grid company. These results demonstrate the effectiveness and practicality of our approach in improving the reliability and cost-efficiency of UAV-based power inspection systems.
A Diagnosis Method for Noise and Intermittent Faults in Analog Circuits Based on the Fusion of Multiscale Fuzzy Entropy Features and Amplitude Features
Authors: Shi Junyou , Hou Yilei , Wang Zili , Yang Zhilin , Lv Zhenyang
Published: February 2025

Intermittent faults occur randomly, last for short durations, and ultimately lead to permanent failures, threatening the safety and stability of analog circuits. Additionally, these faults are often hard to differentiate from noise-induced anomalies, resulting in incorrect disassembly and complicating circuit maintenance. To address these challenges, we propose a novel fault diagnosis method. The method uses an adjustable sliding window to extract multiscale fuzzy entropy features, mitigating the impact of normal data on entropy calculations for intermittent faults. The coarse granulation strategy of sliding point by point is applied to avoid information loss in short time series. The raw signal is then segmented and transformed into four statistical features, which are fused into comprehensive amplitude features via a self-attention mechanism. This comprehensive feature better captures amplitude variations than individual statistical features. Finally, the two features are fed into a convolutional neural network for diagnosis. The method is applied to two typical analog circuits. Ablation studies confirmed its effectiveness. Although the proposed method does not have the lowest diagnostic cost and the fastest detection time, the differences with state-of-the-art methods are minimal, and the proposed method achieves higher classification accuracy. Taken together, these findings demonstrate the superiority of the proposed method.
Impact Localization System of CFRP Structure Based on EFPI Sensors
Authors: Yu Junsong , Peng Zipeng , Gan Linghui , Liu Jun , Bai Yufang , Wan Shengpeng
Published: February 2025

Carbon fiber composites (CFRPs) are prone to impact loads during their production, transportation, and service life. These impacts can induce microscopic damage that is always undetectable to the naked eye, thereby posing a significant safety risk to the structural integrity of CFRP structures. In this study, we developed an impact localization system for CFRP structures using extrinsic Fabry–Perot interferometric (EFPI) sensors. The impact signals detected by EFPI sensors are demodulated at high speeds using an intensity modulation method. An impact localization method for the CFRP structure based on the energy–entropy ratio endpoint detection and CNN-BIGRU-Attention is proposed. The time difference of arrival (TDOA) between signals from different EFPI sensors is collected to characterize the impact location. The attention mechanism is integrated into the CNN-BIGRU model to enhance the significance of the TDOA of impact signals detected by proximal EFPI sensors. The model is trained using the training set, with its parameters optimized using the sand cat swarm optimization algorithm and validation set. The localization performance of different models is then evaluated and compared using the test set. The impact localization system based on the CNN-BIGRU-Attention model using EFPI sensors was validated on a CFRP plate with an experimental area of 400 mm × 400 mm. The average error in impact localization is 8.14 mm, and the experimental results demonstrate the effectiveness and satisfactory performance of the proposed method.
Hardware Acceleration of Division-Free Quadrature-Based Square Rooting Approach for Near-Lossless Compression of Hyperspectral Images
Authors: Altamimi Amal , Ben Youssef Belgacem Ben
Published: February 2025

Recent advancements in hyperspectral imaging have significantly increased the acquired data volume, creating a need for more efficient compression methods for handling the growing storage and transmission demands. These challenges are particularly critical for onboard satellite systems, where power and computational resources are limited, and real-time processing is essential. In this article, we present a novel FPGA-based hardware acceleration of a near-lossless compression technique for hyperspectral images by leveraging a division-free quadrature-based square rooting method. In this regard, the two division operations inherent in the original approach were replaced with pre-computed reciprocals, multiplications, and a geometric series expansion. Optimized for real-time applications, the synthesis results show that our approach achieves a high throughput of 1611.77 Mega Samples per second (MSps) and a low power requirement of 0.886 Watts on the economical Cyclone V FPGA. This results in an efficiency of 1819.15 MSps/Watt, which, to the best of our knowledge, surpasses recent state-of-the-art hardware implementations in the context of near-lossless compression of hyperspectral images.
Accuracy of the Instantaneous Breathing and Heart Rates Estimated by Smartphone Inertial Units
Authors: Cinotti Eliana , Centracchio Jessica , Parlato Salvatore , Esposito Daniele , Fratini Antonio , Bifulco Paolo , Andreozzi Emilio
Published: February 2025

Seismocardiography (SCG) and Gyrocardiography (GCG) use lightweight, miniaturized accelerometers and gyroscopes to record, respectively, cardiac-induced linear accelerations and angular velocities of the chest wall. These inertial sensors are also sensitive to thoracic movements with respiration, which cause baseline wanderings in SCG and GCG signals. Nowadays, accelerometers and gyroscopes are widely integrated into smartphones, thus increasing the potential of SCG and GCG as cardiorespiratory monitoring tools. This study investigates the accuracy of smartphone inertial sensors in simultaneously measuring instantaneous heart rates and breathing rates. Smartphone-derived SCG and GCG signals were acquired from 10 healthy subjects at rest. The performances of heartbeats and respiratory acts detection, as well as of inter-beat intervals (IBIs) and inter-breath intervals (IBrIs) estimation, were evaluated for both SCG and GCG via the comparison with simultaneous electrocardiography and respiration belt signals. Heartbeats were detected with a sensitivity and positive predictive value (PPV) of 89.3% and 93.3% in SCG signals and of 97.3% and 97.9% in GCG signals. Moreover, IBIs measurements reported strong linear relationships (R2 > 0.999), non-significant biases, and Bland–Altman limits of agreement (LoA) of ±7.33 ms for SCG and ±5.22 ms for GCG. On the other hand, respiratory acts detection scored a sensitivity and PPV of 95.6% and 94.7% for SCG and of 95.7% and 92.0% for GCG. Furthermore, high R2 values (0.976 and 0.968, respectively), non-significant biases, and an LoA of ±0.558 s for SCG and ±0.749 s for GCG were achieved for IBrIs estimates. The results of this study confirm that smartphone inertial sensors can provide accurate measurements of both instantaneous heart rate and breathing rate without the need for additional devices.
Thinned Linear Optical Phased Array Design Through a Pareto-Optimal Synthesis Strategy
Authors: Yang Xueqing , Anselmi Nicola , Rocca Paolo
Published: February 2025

The design of a thinned linear optical phased array (OPA) comprising a collection of waveguide grating antennas (WGAs) is addressed in this work. Given a fully populated linear OPA with antennas located in a uniform grid, the problem of selecting which elements have to be removed or retained is formulated as an optimization one. To this end, the definition of the optimal thinning architecture is produced through a multi-objective optimization strategy with the goal of minimizing the number of required antenna elements while maintaining a low sidelobe level and narrow beam width. A set of representative results is presented, also considering realistic WGA modeling to assess the capabilities and the potentialities of the proposed approach.
Point-Level Fusion and Channel Attention for 3D Object Detection in Autonomous Driving
Authors: Shen Juntao , Fang Zheng , Huang Jin
Published: February 2025

As autonomous driving technology progresses, LiDAR-based 3D object detection has emerged as a fundamental element of environmental perception systems. PointPillars transforms point cloud data into a two-dimensional pseudo-image and employs a 2D CNN for efficient and precise detection. Nevertheless, this approach encounters two primary challenges: (1) the sparsity and disorganization of raw point clouds hinder the model’s capacity to capture local features, thus impacting detection accuracy; and (2) existing models struggle to detect small objects within complex environments, particularly regarding orientation estimation. To address these issues, we propose two enhancements: (1) point-level fusion of LiDAR point clouds and RGB images, which integrates the semantic information of 2D images with the geometric features of 3D point clouds to improve model performance in intricate scenarios; (2) the incorporation of the Efficient Channel Attention mechanism to concentrate on essential features, particularly for small and sparse objects. Experimental results on the KITTI dataset indicate significant improvements, particularly in small object detection tasks, such as identifying pedestrians and cyclists. The enhanced model also demonstrates substantial gains in the Average Orientation Similarity (AOS) metric. These enhancements enhance the vehicle’s ability to track and predict object trajectories in dynamic environments, critical for reliable recognition and decision-making.
Modeling of Safe Braking Distance Considering Pedestrian Psychology and Vehicle Characteristics and the Design of an Active Safety Warning System for Pedestrian Crossings
Authors: Jia Yanfeng , Cui Shanning , Chen Xiufeng , Qu Dayi
Published: February 2025

Addressing the traffic safety issues caused by pedestrian–vehicle conflicts during street crossing, this study proposes optimization strategies from both theoretical and technical perspectives. A safety braking distance model is introduced, taking into account pedestrians’ psychological safety and vehicle braking processes. Additionally, an active safety warning system for crosswalks has been designed. This system features a modular design, including detection, control, alarm, and wireless communication modules. It can monitor, in real-time, the positions and speeds of pedestrians and vehicles, assess potential conflicts between them under various scenarios, and implement different warning strategies accordingly. Compared to mainstream variable message sign (VMS) warning systems, this proposed system shows significant advantages in terms of section-weighted total delay metrics. Through simulations involving 3000 pedestrian crossings and comparative analyses of vehicle speed, pedestrian speed, vehicle deceleration rate, and accident numbers before and after the application of the active safety warning system, it was found that the critical accident rate indicator decreased from 0.27% to 0.06%. The results demonstrate that the system effectively provides bidirectional warnings to pedestrians and vehicles, significantly enhancing the safety of pedestrian street crossings. This research offers new insights into addressing pedestrian crossing safety issues.
Albufera Lagoon Ecological State Study Through the Temporal Analysis Tools Developed with PerúSAT-1 Satellite
Authors: Alvado Bárbara , Saldarriaga Luis , Sòria-Perpinyà Xavier , Soria Juan Miguel , Vicent Jorge , Ruíz-Verdú Antonio , García-Martínez Clara , Vicente Eduardo , Delegido Jesus
Published: February 2025

The Albufera of Valencia (Spain) is a representative case of pressure on water quality, which caused the hypertrophic state of the lake to completely change the ecosystem that once featured crystal clear waters. PerúSAT-1 is the first Peruvian remote sensing satellite developed for natural disaster monitoring. Its high spatial resolution makes it an ideal sensor for capturing highly detailed products, which are useful for a variety of applications. The ability to change its acquisition geometry allows for an increase in revisit time. The main objective of this study is to assess the potential of PerúSAT-1′s multispectral images to develop multi-parameter algorithms to evaluate the ecological state of the Albufera lagoon. During five field campaigns, samples were taken, and measurements of ecological indicators (chlorophyll-a, Secchi disk depth, total suspended matter, and its organic-inorganic fraction) were made. All possible combinations of two bands were obtained and subsequently correlated with the biophysical variables by fitting a linear regression between the field data and the band combinations. The equations for estimating all the water variables result in the following R2 values: 0.76 for chlorophyll-a (NRMSE: 16%), 0.75 for Secchi disk depth (NRMSE: 15%), 0.84 for total suspended matter (NRMSE: 11%), 0.76 for the inorganic fraction (NRMSE: 15%), and 0.87 for the organic fraction (NRMSE: 9%). Finally, the equations were applied to the Albufera lagoon images to obtain thematic maps for all variables.
Neural Network-Assisted DPD of Wideband PA Nonlinearity for Sub-Nyquist Sampling Systems
Authors: Liu Mengqiu , Yang Xining , Gao Jian , Cao Sen , Liao Guisheng , Hou Gaopan , Gao Dawei
Published: February 2025

The design of conventional digital predistortion (DPD) requires an analogue-to-digital converter (ADC) with a sampling frequency that is multiple times the signal bandwidth, which is extremely challenging for sub-Nyquist sampling systems with undersampled signals. To address this, this paper proposes a neural network (NN)-assisted wideband power amplifier (PA) DPD method for sub-Nyquist sampling systems, wherein a dual-stage architecture is designed to handle the ambiguity caused by subsampled communications signals. In the first stage, the time-delayed polynomial reconstruction method is employed to estimate the wideband DPD nonlinearity coarsely with the undersampled signals with limited pilots. In the second stage, an NN-based DPD method is proposed for the virtual training of the DPD, which learns the up-sampled DPD behavior by taking advantage of the pre-estimated DPD model and the input data signals, which reduces the length of the training sequence significantly and refines the DPD behavior efficiently. Simulation results demonstrate the efficacy of the proposed method in tackling the wideband PA nonlinearity and its ability to outperform the conventional method in terms of power spectrum, error vector magnitude, and bit error rate.
Iatrogenic Small Intestine Perforation During Suprapubic Catheter Change
Authors: Kumar Naveen , Rizwi Kashif , Singh Saket , Kumar Shashikant
Published: February 2025

A gentleman in his 70s presented to the emergency department with diffuse abdominal pain, abdominal distention, and anuria of 3-days duration [...]
Review on the Management of Female Urinary Incontinence and Anterior Vaginal Prolapse
Authors: Mondesir Ronscardy F. , Gousse Angelica , Boaretto Daniel A. , Ajabshir Daniel , Gousse Angelo
Published: February 2025

Objectives: We aimed to evaluate management strategies for female urinary incontinence, specifically stress urinary incontinence (SUI), and anterior vaginal prolapse (pelvic organ prolapse, POP), emphasizing diagnostic methods, treatment options, and factors influencing surgical outcomes. Methods: We conducted a thorough literature review examining diagnostic tools, including physical examinations, urodynamic testing, and pessary evaluations, alongside treatment options for SUI and POP. Both surgical interventions, such as mid-urethral sling placement and anterior colporrhaphy, and non-surgical methods, including pelvic floor exercises, were analyzed. This review assesses these approaches’ efficacy, complications, and outcomes, incorporating current clinical guidelines and evidence-based practices. Results: Evidence indicates that SUI frequently coexists with POP, with a notable proportion of cases being occult until a prolapse is reduced. Diagnostic methods such as pessary testing and urodynamic evaluations are essential in identifying masked SUI, though their predictive accuracy varies. Surgical techniques such as using mid-urethral slings are highly effective but pose risks, including voiding dysfunction and lower urinary tract injury. Long-term data emphasize the need for personalized treatment strategies, with combined procedures showing superior outcomes for the concurrent management of POP and SUI in select cases. Conclusions: Effective management of SUI and POP requires a personalized approach, factoring in the severity of a prolapse and the likelihood of postoperative incontinence. While conservative treatments are practical initial options, surgical solutions, such as mid-urethral slings and apical suspension procedures, offer robust, lasting results for advanced cases. Preoperative diagnostics, collaborative decision-making, and tailored treatment plans are essential to optimize success and minimize complications. Future research should prioritize enhancing diagnostic precision and refining surgical methods to further advance patient care.
Barriers to Social Service Access for Ukrainian Refugees with Disabilities in Georgia: Outreach and Communication
Authors: Ihnatenko Kateryna , Sadzaglishvili Shorena
Published: February 2025

This paper aims to identify the barriers to accessing social services for persons with disabilities among Ukrainian refugees and to examine how stakeholders can reach them through communication channels. The article analyzes the challenges faced by disabled Ukrainian refugees in Georgia. We employed a mixed methods approach to address this objective, combining quantitative and qualitative research methods. We surveyed 114 Ukrainian refugees with disabilities, aged 18 to 60+ (n = 114), residing in Georgia, and conducted semi-structured interviews with 26 experts from civil society organizations assisting Ukrainian refugees, as well as 6 caregivers with diverse roles (e.g., mother, daughter, husband). The findings reveal significant difficulties faced by Ukrainian refugees with disabilities in accessing essential services such as healthcare, employment, psychological counseling, rehabilitation, early intervention services, legal aid, and information. The primary strategies employed by stakeholders providing humanitarian aid include individualized approaches and communication. However, while many nongovernmental organizations (NGOs) assist Ukrainian refugees, there are currently no NGOs specifically focused on supporting children and adults with disabilities in this population.
Gender Perspectives on Self-Employment Among Israeli Family Physicians: A Qualitative Study
Authors: Chudner Irit , Shnider Avi , Gluzman Omer , Keidar Hadas , Haimi Motti
Published: February 2025

Background: While previous studies have shown that occupational choices are influenced by traditional gender perceptions, little is known about gender’s role in choosing self-employment among family physicians. Family medicine, with its emphasis on holistic and integrative attitudes that align with independent practice characteristics, presents a unique context for examining gender-related decisions in choosing self-employment. Objectives: To examine the role of gender in family physicians’ transition to self-employed practice, focusing on gender differences in motivations and considerations for this career choice, and to analyze how traditional gender roles manifest in professional decision-making among highly educated medical professionals. Methods: A qualitative study based on in-depth interviews was conducted with 27 self-employed family physicians in Israel who recently chose to start independent practice rather than remaining salaried physicians in Health Maintenance Organizations (HMOs). Interviews were analyzed using thematic content analysis with a gender-sensitive approach. Results: Despite their advanced education and professional status, female family physicians tended to maintain traditional gender patterns in their professional choices. Women emphasized work–family flexibility as a primary consideration and expressed less confidence in financial management self-efficacy, often delegating these responsibilities to their spouses. In contrast, male family physicians displayed traditionally “feminine” characteristics in their professional approach, including emphasis on holistic care and family involvement. Male physicians also cited work–life balance and the opportunity for a more comprehensive, biopsychosocial approach to patient care as key factors in choosing family medicine and self-employment. Conclusions: The findings demonstrate the complexity of gender roles in family physicians’ professional choices, revealing both persistence of traditional gender roles among women and different patterns among men. While male physicians displayed characteristics traditionally identified as feminine, these patterns may reflect both gender role evolution and generational shifts towards work–family integration and collaborative patient care. This study highlights how gender and generational factors shape career decisions in primary care, with implications for medical education and healthcare organization policies.
Working with Cultural Heritage from the Local Environment: Analysis of Perceptions of Future Teachers
Authors: Suárez-Álvarez Patricia , Otero-Varela Alejandro
Published: February 2025

Cultural heritage refers to the set of tangible and intangible assets that a society inherits from its ancestors and transmits to future generations. The training of future teachers in this subject is vital, as they will be responsible for using resources and developing activities that promote the appreciation and conservation of cultural heritage by society. In this article, we conducted research among pre-service teachers at the University of Córdoba (Spain). Our goal was to explore, using primary sources, the conceptions that future teachers had about the cultural heritage of their local environment and to identify any difficulties students may have in recognizing and valuing it, as well as to promote critical thinking when addressing key concepts such as identity/otherness. We conducted a study among 189 students completing Early Childhood and Primary Education degrees which allowed us to draw conclusions about the identification, conception, and appreciation of heritage. These results will serve as a starting point for reflecting on their needs in this regard, with the aim of implementing future didactic improvements.
Impact of Technological Advances on Workers’ Health: Taking Robotics as an Example
Authors: Yang Wenhao , Zhong Changbiao , Wang Siyi , Fang Jiabin
Published: February 2025

Workers’ health is one of the key factors for sustainable economic development. In the new era of industrialization marked by the rise of automation, the impact of widespread robot use on workers’ health is a growing concern. We organize the robotics data released by the International Federation of Robotics to the prefecture and city level and further match them with the 2010–2015 China Comprehensive Social Survey database in the city–year dimension. Subsequently, we used fixed-order regression analysis with maximum likelihood estimation to explore the impact of robotics development on workers’ health indicators. Our findings reveal that the use of industrial robots generally improves workers’ health and releases the “health dividend” of workers, with each unit increase in urban robot penetration increasing the probability of worker health in that area by 4%. A mechanism analysis shows that robots enhance workers’ health primarily by reducing labor intensity. Further heterogeneity analysis indicates that the health benefits of industrial robots are more pronounced for low-skilled workers, female workers, and agricultural laborers. Our research offers valuable insights into protecting workers’ rights and well-being in the age of automation.
Consumer Sustainability: Is Knowledge Linked to Behavior in Recycling?
Authors: Xiao Jing Jian , Rafiee Parisa , Xia Feihong , Wu Jing
Published: February 2025

Sustainable consumer behavior encompasses any action that benefits both the environment and society. Recycling is a prime example of such behavior. However, there is a lack of research examining the connection between knowledge about recycling and recycling behavior. The purpose of this study was to examine factors associated with consumer recycling behavior under the guidance of an extended theory of planned behavior with an emphasis on the role of recycling knowledge. Using data collected from a national online survey in the U.S., results from the structural equation modeling showed that ascription of responsibility to others is negatively associated with recycling behavior, while behavior skill is positively associated with it. Also, both subjective and objective knowledge measures are positively associated with recycling behavior. In addition, subjective recycling knowledge moderates the relationships between attitude and recycling behavior.
Effect of Topographic Factors on Ecological Environment Quality in the Red Soil Region of Southern China: A Case from Changting County
Authors: Chen Junming , Lin Guangfa , Chen Zhibiao
Published: February 2025

The evaluation of ecological environment quality (EEQ) is an important method to determine regional eco-environment status, and topography, as one of the key factors affecting eco-environment, has an impact on the EEQ by influencing hydrothermal conditions. However, research on the effect of topography on the EEQ still needs to be strengthened, especially in the red soil region of southern China. Therefore, based on the evaluation of the EEQ for Changting County using the remote sensing ecological index (RSEI) combined with Landsat images from 2000 to 2019, the effects of topography on the EEQ were analyzed further. The main findings indicated, firstly, that the average values of topographic factors increased as the EEQ grade raised; secondly, the distribution of the EEQ gradually moved to the lower terrain factor categories as the EEQ grade declined for each study period on the whole; thirdly, the coupling effect of any two topographic factors on the EEQ was greater than the effect of a single topographic factor, and the coupling effect of the aspect with the elevation and topographic position index (TPI) on the EEQ was the most prominent. The main findings of the research can enhance the understanding of the variability of the EEQ and the effects of topography on the EEQ.
Exploring the Impact of Socioeconomic Status on Farmers’ Participation in Rural Living Environmental Governance Behavior—Evidence from Jiangsu Province, China
Authors: Yang Lisha , Tan Shuang , Yuan Rao
Published: February 2025

The participation of farmers in environmental governance is not simply the behavior choice of individuals but is also influenced by their socioeconomic status (SES). Using survey data from 2088 peasant households in Jiangsu Province, China, this study used the ordered probit model to identify the differential impacts of objective socioeconomic status (OSES) and subjective socioeconomic status (SSES) on farmers’ participation in rural living environmental governance behavior (FPLEB). The mediation effect model was also used to explore the mediating role of environmental cognition in the relationship between SES and FPLEB. The results reveal that (1) SES (i.e., OSES and SSES) is a positive factor affecting farmers’ involvement in improving their living environment, with SSES demonstrating a stronger correlation with efforts to enhance rural living conditions than OSES, (2) environmental knowledge and pollution cognition serve as mediating factors, bridging the effect of OSES on the level of participation in environmental governance, and (3) SES (i.e., OSES and SSES) is more effective in fostering future-oriented environmental governance behaviors among farmers than those with a present-oriented mindset.
Facilitation or Replacement: ICT Use in Leisure Constraints Negotiation During the Digital Transformation Era
Authors: Chun Sungbum , Lim Jinsun , Kang Heeyeob , Ryu Wonshick
Published: February 2025

This study explores how Information and Communications Technology (ICT) impacts active leisure activity participants in South Korea, focusing on leisure constraints and negotiation strategies. As ICT continues to transform leisure experiences, this research examines whether ICT serves as a facilitator or replacement for traditional active leisure. Using survey data from 285 adult participants, the study categorizes ICT’s influence based on framework. Key findings reveal that while time management and energy conservation strategies shape ICT use as a leisure facilitator or replacement, fitness level adjustments are associated with ICT’s facilitative role in active leisure. These insights highlight the nuanced ways digital tools impact leisure participation, especially among various demographic groups. The findings suggest that ICT’s dual role in either supporting or substituting leisure reflects broader trends in digital transformation and informs the development of digital tools aimed at enhancing well-being through active leisure engagement.
A Bibliometric and Systematic Review of Publications on Augmented Reality in Sustainable Tourism
Authors: Ercan Fatih , Sayın Kürşad , Dalgın Taner , Gençer Kansu
Published: February 2025

New tools and applications that can be used to develop the sustainability of tourism activities have emerged with developments in technology. In this context, augmented reality (AR), one of these cutting-edge technologies, has been studied in sustainable tourism applications in recent years, and studies have been carried out in the relevant fields that contribute to the literature on this subject. In this context, this study aims to reveal the current status and trends in published studies on AR technology in sustainable tourism through a bibliometric and systematic review. For this purpose, 61 publications on the subject were identified in the Web of Science (WoS) database, and bibliometric and systematic content analyses were performed using VOSviewer 1.6.20 software. As a result of these analyses, bibliometric data, such as the number of publications and citations by year, the most cited authors, countries, publications, their collaboration relationships, sustainable development goals, WoS categories, and content information regarding the study themes and aims, were obtained. Accordingly, it was determined that publications on AR in sustainable tourism emerged in 2014 and continued to develop into a new research area. The potential gaps suggested based on the findings of this study will guide future research and the development in the relevant sectors.
The Impacts of Revegetation on Ecosystem Services in the Extremely Degraded Alpine Grassland of Permafrost Regions
Authors: Du Juanjuan , Wei Peijie , Bahadur Ali , Chen Shengyun
Published: February 2025

Alpine grassland degradation in permafrost regions seriously affects the provision of ecosystem services, posing a threat to ecological security. Revegetation is a key strategy for the restoration of alpine grassland ecosystems on the Qinghai–Tibetan Plateau (QTP). However, there is a lack of comprehensive research evaluating ecosystem services after revegetation, especially in permafrost regions. In this study, we assessed the changes in ecosystem services following revegetation in the alpine permafrost regions of the QTP through on-site monitoring and sampling, using extremely degraded alpine grassland as a control. In addition, we analyzed trade-offs among ecosystem services and identified key drivers. Our results indicate that (1) revegetation significantly increased forage supply, carbon storage, and soil retention values (p < 0.05), while water retention and permafrost stability showed no significant changes (p > 0.05); (2) vegetation restoration effectively reduced the trade-offs among ecosystem services; and (3) the main drivers were vegetation coverage, precipitation, belowground biomass, and restoration duration. Overall, this study demonstrates that revegetation improves ecosystem services. The enhancement in these services provides valuable data for future research on ecosystem services in alpine grassland.
The Cooperativity and Spatial Network Relationship Between Regional Economic Quality Development and Higher Education Scale in China
Authors: Liu Miaomiao , Liu Shengbo , Xu Yinuo , Jin Jiahui , Liu Wanyu
Published: February 2025

The sustainable development of regional higher education is closely related to the level of regional economic development. There is a close interdependence between higher education and economic development. Based on data from 31 provinces in China in 2022, this study uses the entropy method to construct an evaluation index system to explore the coupling and coordination relationship between the regional economy and the development of higher education, as well as the social network effects presented by various regional cities. The results indicate the following: (1) there is a trend of a more coordinated relationship between economic development and the development of higher education in the eastern region compared to the western region, exhibiting a pattern of “higher in the east and lower in the west”; and (2) the economic development and the scale of higher education in East China and Central China are coordinated, and some provinces have played a role in bridging and internal and external linkages in the spatial network effect, economic development, and the scale of higher education in some provinces in Northwest and Southwest China to moderate the imbalance and weak internal and external linkages in the spatial network. Exploring the compatibility between the scale and structure of higher education and economic development is not only of guiding significance for promoting the regional layout and development of higher education in China, but also has an important reference value for economic structural adjustment and transformation.
Intelligent Assessment of Personal Credit Risk Based on Machine Learning
Authors: Wang Chuansheng , Yu Hang
Published: February 2025

In the 21st-century global economy, the rapid growth of the finance industry, particularly in personal credit, fuels economic growth and market prosperity. However, the rapid expansion of personal credit business has brought explosive growth in the amount of data, which puts forward higher requirements for the risk management of financial institutions. To solve this problem, this paper constructs an intelligent evaluation model of personal credit risk under the background of big data. Firstly, based on the forest optimization feature selection algorithm, combined with initialization based on chi-square check, adaptive global seeding, and greedy search strategies, key risk factors are accurately identified from high-dimensional data. Then, the XGBoost algorithm is used to evaluate the credit risk level of customers, and the traditional Sparrow Search Algorithm is improved by using Tent chaotic mapping, sine and cosine search, reverse learning, and Cauchy mutation strategy to improve the optimization performance of algorithm parameters. Finally, using the Lending Club dataset for empirical analysis, the experiment shows that the model improves the accuracy of personal credit risk assessment and enhances the ability of risk control.
Dynamic Surgical Prioritization: A Machine Learning and XAI-Based Strategy
Authors: Silva-Aravena Fabián , Morales Jenny , Jayabalan Manoj , Rana Muhammad Ehsan , Gutiérrez-Bahamondes Jimmy H.
Published: February 2025

Surgical waiting lists present significant challenges to healthcare systems, particularly in resource-constrained settings where equitable prioritization and efficient resource allocation are critical. We aim to address these issues by developing a novel, dynamic, and interpretable framework for prioritizing surgical patients. Our methodology integrates machine learning (ML), stochastic simulations, and explainable AI (XAI) to capture the temporal evolution of dynamic prioritization scores, qp(t), while ensuring transparency in decision making. Specifically, we employ the Light Gradient Boosting Machine (LightGBM) for predictive modeling, stochastic simulations to account for dynamic variables and competitive interactions, and SHapley Additive Explanations (SHAPs) to interpret model outputs at both the global and patient-specific levels. Our hybrid approach demonstrates strong predictive performance using a dataset of 205 patients from an otorhinolaryngology (ENT) unit of a high-complexity hospital in Chile. The LightGBM model achieved a mean squared error (MSE) of 0.00018 and a coefficient of determination (R2) value of 0.96282, underscoring its high accuracy in estimating qp(t). Stochastic simulations effectively captured temporal changes, illustrating that Patient 1’s qp(t) increased from 0.50 (at t=0) to 1.026 (at t=10) due to the significant growth of dynamic variables such as severity and urgency. SHAP analyses identified severity (Sever) as the most influential variable, contributing substantially to qp(t), while non-clinical factors, such as the capacity to participate in family activities (Lfam), exerted a moderating influence. Additionally, our methodology achieves a reduction in waiting times by up to 26%, demonstrating its effectiveness in optimizing surgical prioritization. Finally, our strategy effectively combines adaptability and interpretability, ensuring dynamic and transparent prioritization that aligns with evolving patient needs and resource constraints.
The Role of Artificial Intelligence in Optometric Diagnostics and Research: Deep Learning and Time-Series Forecasting Applications
Authors: Santos Luis F. F. M. , Sánchez-Tena Miguel Ángel , Alvarez-Peregrina Cristina , Sánchez-González José-María , Martinez-Perez Clara
Published: February 2025

This study introduces an Artificial Intelligence framework based on the Deep Learning model Bidirectional Encoder Representations from Transformers framework trained on a dataset from 2000–2023. The AI tool categorizes articles into six classes: Contactology, Low Vision, Refractive Surgery, Pediatrics, Myopia, and Dry Eye, with supervised learning enhancing classification accuracy, achieving F1-Scores averaging 86.4%, AUC at 0.98, Precision at 87%, and Accuracy at 86.8% via one-shot training, while Epoch training showed 85.9% Accuracy and 92.8% Precision. Utilizing the Artificial Intelligence model outputs, the Autoregressive Integrated Moving Average model provides forecasts from all classes through 2030, predicting decreases in research interest for Contactology, Low Vision, and Refractive Surgery but increases for Myopia and Dry Eye due to rising prevalence and lifestyle changes. Stability is expected in pediatric research, highlighting its focus on early detection and intervention. This study demonstrates the effectiveness of AI in enhancing diagnostic precision and strategic planning in optometry, with potential implications for broader clinical applications and improved accessibility to eye care.
Evaluation of Passive Silicone Samplers Compared to Active Sampling Methods for Polycyclic Aromatic Hydrocarbons During Fire Training
Authors: Sen Paro , Calkins Miriam , Stakes Keith , Neumann Danielle L. , Chen I-Chen , Horn Gavin P.
Published: February 2025

Firefighters are occupationally exposed to many chemicals, including polycyclic aromatic hydrocarbons (PAHs), which are formed by the incomplete combustion of organic matter during fire response and training activities. However, due to the harsh environments in which firefighters work, as well as consideration for time and physical safety while wearing bulky equipment, traditional active sampling methods may not be feasible to measure PAH exposures. Silicone passive samplers offer an alternative approach to assess exposure during fire responses and live fire training due to their heat resistance and ease of deployment in remote or time-limited environments. In this study, the primary objective was to investigate and determine the statistical strength of the relationship between active air sampling methods and passive silicone samplers for PAHs. In this study, silicone wristbands were paired with active sampling devices in a series of burn experiments to compare PAH measurements. Silicone-based measurements correlated strongly with active air samples for the dominant PAHs found, naphthalene and phenanthrene; however, detection was limited in the wristbands when air concentrations were low in active samples. In situations where PAH levels are expected to be high and the potential for contaminant loss via off-gassing is low, silicone samplers may be a useful tool for industrial hygienists to measure PAHs in fire and other emergency responses in extreme environments.
Non-Invasive Matrices for the Human Biomonitoring of PFAS: An Updated Review of the Scientific Literature
Authors: Nadal Martí , Domingo José L.
Published: February 2025

Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals widely used in consumer and industrial products due to their unique physicochemical properties. However, their persistence and bioaccumulative potential pose significant environmental and human health risks. This review focuses on the use of non-invasive matrices—urine, hair, and nails—for the human biomonitoring of PFAS, highlighting key findings from scientific studies. While urine offers a non-invasive and practical option, its limited sensitivity for long-chain PFAS requires further analytical advances. Hair and nails have demonstrated potential for use in biomonitoring, with higher detection frequencies and concentrations for certain PFAS compared to urine. The variability in PFAS levels across studies reflects differences in population characteristics, exposure sources, and geographic regions. This review emphasizes the need for standardized analytical methods, expanded population studies, and the use of complementary matrices to enhance the accuracy and reliability of PFAS exposure assessment.