bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrated Modeling and Target Classification Based on mmWave SAR and CNN Approach
Authors: Wadde Chandra , Routhu Gayatri , Clemente-Arenas Mark , Gummadi Surya Prakash , Kumar Rupesh
Published: December 2024

This study presents a numerical modeling approach that utilizes millimeter-wave (mm-Wave) Frequency-Modulated Continuous-Wave (FMCW) radar to reconstruct and classify five weapon types: grenades, knives, guns, iron rods, and wrenches. A dataset of 1000 images of these weapons was collected from various online sources and subsequently used to generate 3605 samples in the MATLAB (R2022b) environment for creating reflectivity-added images. Background reflectivity was considered to range from 0 to 0.3 (with 0 being a perfect absorber), while object reflectivity was set between 0.8 and 1 (with 1 representing a perfect electric conductor). These images were employed to reconstruct high-resolution weapon profiles using a monostatic two-dimensional (2D) Synthetic Aperture Radar (SAR) imaging technique. Subsequently, the reconstructed images were classified using a Convolutional Neural Network (CNN) algorithm in a Python (3.10.14) environment. The CNN architecture consists of 10 layers, including multiple convolutional, pooling, and fully connected layers, designed to effectively extract features and perform classification. The CNN model achieved high accuracy, with precision and recall values exceeding 98% across most categories, demonstrating the robustness and reliability of the model. This approach shows considerable promise for enhancing security screening technologies across a range of applications.
MFF-Net: A Lightweight Multi-Frequency Network for Measuring Heart Rhythm from Facial Videos
Authors: Yan Wenqin , Zhuang Jialiang , Chen Yuheng , Zhang Yun , Zheng Xiujuan
Published: December 2024

Remote photo-plethysmography (rPPG) is a useful camera-based health motioning method that can measure the heart rhythm from facial videos. Many well-established deep learning models can provide highly accurate and robust results in measuring heart rate (HR) and heart rate variability (HRV). However, these methods are unable to effectively eliminate illumination variation and motion artifact disturbances, and their substantial computational resource requirements significantly limit their applicability in real-world scenarios. Hence, we propose a lightweight multi-frequency network named MFF-Net to measure heart rhythm via facial videos in a short time. Firstly, we propose a multi-frequency mode signal fusion (MFF) mechanism, which can separate the characteristics of different modes of the original rPPG signals and send them to a processor with independent parameters, helping the network recover blood volume pulse (BVP) signals accurately under a complex noise environment. In addition, in order to help the network extract the characteristics of different modal signals effectively, we designed a temporal multiscale convolution module (TMSC-module) and spectrum self-attention module (SSA-module). The TMSC-module can expand the receptive field of the signal-refining network, obtain more abundant multiscale information, and transmit it to the signal reconstruction network. The SSA-module can help a signal reconstruction network locate the obvious inferior parts in the reconstruction process so as to make better decisions when merging multi-dimensional signals. Finally, in order to solve the over-fitting phenomenon that easily occurs in the network, we propose an over-fitting sampling training scheme to further improve the fitting ability of the network. Comprehensive experiments were conducted on three benchmark datasets, and we estimated HR and HRV based on the BVP signals derived by MFF-Net. Compared with state-of-the-art methods, our approach achieves better performance both on HR and HRV estimation with lower computational burden. We can conclude that the proposed MFF-Net has the opportunity to be applied in many real-world scenarios.
Research Progress on the Measurement Methods and Clinical Significance of Capillary Refill Time
Authors: Xia Yuxiang , Guo Zhe , Wang Xinrui , Wang Ziyi , Wang Xuesong , Wang Zhong
Published: December 2024

The monitoring of peripheral circulation, as indicated by the capillary refill time, is a sensitive and accurate method of assessing the microcirculatory status of the body. It is a widely used tool for the evaluation of critically ill patients, the guidance of therapeutic interventions, and the assessment of prognosis. In recent years, there has been a growing emphasis on microcirculation monitoring which has led to an increased focus on capillary refill time. The International Sepsis Guidelines, the American Academy of Pediatrics, the World Health Organization, and the American Heart Association all recommend its inclusion in the evaluation of the system in question. Furthermore, the methodology for its measurement has evolved from a traditional manual approach to semiautomatic and fully automatic techniques. This article presents a comprehensive overview of the current research on the measurement of capillary refill time, with a particular focus on its clinical significance. The aim is to provide a valuable reference for clinicians and researchers and further advance the development and application of microcirculation monitoring technology.
An Experimental Study of the Flexural Bearing Capacity of Reinforced Concrete Beams Damaged by Explosions Using Piezoelectric Smart Aggregates
Authors: Xu Kai , Sheng Shilong , Jiang Ronghui , Feng Qian
Published: December 2024

Two identically sized RC beams were fabricated to investigate the effects of explosive loads on the flexural behaviour of Reinforced Concrete (RC) beams. One of the beams was subjected to an explosive load to induce post-explosion damage, and subsequently, both beams underwent flexural capacity testing. Integrating piezoelectric smart aggregates (SAas) within the beams facilitated continuous observation of the damage conditions, allowing for the assessment of internal concrete deterioration from explosive impacts to bending failures. The internal crack development index R was established using the Wavelet Packet Energy Analysis method. Combined with the structure’s residual capacity-based damage assessment criterion, the relationship between R and component damage was found. This allowed us to identify the change in the bending capacity of RC beams after explosion damage and the quantitative damage assessment of the beam to be realised, providing valuable insights for structural engineers and researchers. Comparing the flexural test results between the explosively pre-damaged beam and the undamaged test beam, it was observed that the neutral axis of the damaged beam was significantly elevated, accompanied by a notable reduction in flexural capacity. By examining the variation curve of the internal crack development index R, it was noted that during the initial stage of the bending flexural test, due to bending deformation, cracks in the core region of the pre-damaged beam gradually healed, leading to a pseudo-decrease in the damage index. After reaching a minimum point, the damage progressed faster until failure occurred.
Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems
Authors: Liu Yang , Ren Shaochen , Wang Xuran , Zhou Mengjie
Published: December 2024

Detecting anomalies in distributed systems through log analysis remains challenging due to the complex temporal dependencies between log events, the diverse manifestation of system states, and the intricate causal relationships across distributed components. This paper introduces a TLAN (Temporal Logical Attention Network), a novel deep learning framework that integrates temporal sequence modeling with logical dependency analysis for robust anomaly detection in distributed system logs. Our approach makes three key contributions: (1) a temporal logical attention mechanism that explicitly models both time-series patterns and logical dependencies between log events across distributed components, (2) a multi-scale feature extraction module that captures system behaviors at different temporal granularities while preserving causal relationships, and (3) an adaptive threshold strategy that dynamically adjusts detection sensitivity based on system load and component interactions. Extensive experiments on a large-scale synthetic distributed system log dataset show that TLAN outperforms existing methods by achieving a 9.4% improvement in F1-score and reducing false alarms by 15.3% while maintaining low latency in real-time detection. The framework demonstrates particular effectiveness in identifying complex anomalies that involve multiple interacting components and cascading failures. Through comprehensive empirical analysis and case studies, we validate that TLAN can effectively capture both temporal patterns and logical correlations in log sequences, making it especially suitable for modern distributed architectures. Our approach also shows strong generalization capability across different system scales and deployment scenarios, supported by thorough ablation studies and performance evaluations.
An Improved Global and Local Fusion Path-Planning Algorithm for Mobile Robots
Authors: Shi Yongliang , Huang Shucheng , Li Mingxing
Published: December 2024

Path planning is a core technology for mobile robots. However, existing state-of-the-art methods suffer from issues such as excessive path redundancy, too many turning points, and poor environmental adaptability. To address these challenges, this paper proposes a novel global and local fusion path-planning algorithm. For global path planning, we reduce path redundancy and excessive turning points by designing a new heuristic function and constructing an improved path generation method. For local path planning, we propose an environment-aware dynamic parameter adjustment strategy, incorporating deviation and avoidance dynamic obstacle evaluation factors, thus addressing issues of local optima and timely avoidance of dynamic obstacles. Finally, we fuse those global and local path-planning improvements to form our fusion path-planning algorithm, which can enhance the robot’s adaptability to complex scenarios while reducing path redundancy and turning points. Simulation experiments demonstrate that the improved fusion path-planning algorithm not only effectively addresses existing issues but also operates with higher efficiency.
Attention-Based Malware Detection Model by Visualizing Latent Features Through Dynamic Residual Kernel Network
Authors: Basak Mainak , Kim Dong-Wook , Han Myung-Mook , Shin Gun-Yoon
Published: December 2024

In recent years, significant research has been directed towards the taxonomy of malware variants. Nevertheless, certain challenges persist, including the inadequate accuracy of sample classification within similar malware families, elevated false-negative rates, and significant processing time and resource consumption. Malware developers have effectively evaded signature-based detection methods. The predominant static analysis methodologies employ algorithms to convert the files. The analytic process is contingent upon the tool’s functionality; if the tool malfunctions, the entire process is obstructed. Most dynamic analysis methods necessitate the execution of a binary file within a sandboxed environment to examine its behavior. When executed within a virtual environment, the detrimental actions of the file might be easily concealed. This research examined a novel method for depicting malware as images. Subsequently, we trained a classifier to categorize new malware files into their respective classifications utilizing established neural network methodologies for detecting malware images. Through the process of transforming the file into an image representation, we have made our analytical procedure independent of any software, and it has also become more effective. To counter such adversaries, we employ a recognized technique called involution to extract location-specific and channel-agnostic features of malware data, utilizing a deep residual block. The proposed approach achieved remarkable accuracy of 99.5%, representing an absolute improvement of 95.65% over the equal probability benchmark.
Assessment and Management of Risks from Occupational Exposure to Electromagnetic Fields (0 Hz to 300 GHz): A Compass to Keep the Right Course Through European and Italian Regulations
Authors: Filosa Laura , Lopresto Vanni
Published: December 2024

This paper outlines the specific provisions of Italian legislation regarding workers’ exposure to electromagnetic fields (EMFs) from 0 Hz to 300 GHz compared to the minimum health and safety requirements set in European Directive 2013/35/EU. In particular, the path to be followed to assess and manage occupational exposure to EMFs is outlined in relation to the distinction between ‘professional’ and ‘non-professional’ exposure of workers, as well as to the precautionary limits regarding exposures from power lines (50 Hz) and broadcast and telecommunication fixed systems (100 kHz–300 GHz) established by Italian regulations. The reasons underlying such an approach—mainly relying on the intent to reconcile scientific evidence with risk perception in public opinion—are analysed and discussed with the aim of increasing the knowledge of national regulatory provisions on occupational risk assessment, which may be more stringent than the requirements envisaged by international guidelines and community regulations.
Perception of University Students Regarding Gender-Based Violence: Identification, Analysis and Detection
Authors: García-Rojas Antonio Daniel , Gómez Angel Hernando , Montero-Fernández Delia , Rodríguez-Vargas Susana
Published: December 2024

The general objective of the study is to study the degree of gender-based violence that exists among students at the University of Huelva. The sample of 678 students (540 women and 138 men) answered an anonymous and voluntary questionnaire, which was made up of several validated questionnaires. The results show that many of the students have rarely suffered or carried out any type of violent behavior in their relationships, although there is a very low percentage who claim to have suffered gender-based violence, so we can affirm that it is a phenomenon present to a greater or lesser extent. It is observed that students easily relate physical aggressions to gender-based violence while they hesitate to relate psychological aggressions to it. This difficulty can be supported by other studies that state that adolescents believe that some actions of psychological violence—such as being jealous—are simply signs of love. This is compounded by the large number of romantic myths that are increasingly spread through social networks, media, family patterns, etc. More educational interventions are therefore needed to prevent these outcomes from leading to unhealthy relationships disguised as excessive romanticism.
The Home as a Place of Work—Who Cares and Why?
Authors: Molitor Friederike
Published: December 2024

As demand for privately organized care and domestic work has grown, digital platforms have emerged as key intermediaries connecting prospective workers with clients. Drawing on unique survey data collected in Germany in 2019, this study offers a systematic analysis of the sociodemographic characteristics of platform-mediated care and domestic workers, with particular focus on their family and household compositions and their motivations for using a digital care-work platform. The study’s findings reveal similarities between these workers, traditional care and domestic workers, and other platform workers in the gig economy. This study also focuses on how this work serves as a strategy for reconciling paid work with unpaid family responsibilities. Importantly, a noticeable proportion of the workers have unpaid care responsibilities for children or other family members and friends while pursuing platform work. When asked about their reasons for using a digital platform, the workers mostly name the income potential, job flexibility, and independence that this platform-mediated work provides. However, the motivations of different groups of workers vary: those with children more often value the balance of paid work and family life that this work offers, while financial incentives and professional development are less of a priority.
The Risk of Disordered Eating in Fitness Club Members—A Cross-Sectional Study
Authors: Gjestvang Christina , Mathisen Therese F. , Bratland-Sanda Solfrid , Haakstad Lene A. H.
Published: December 2024

Fitness clubs may be environments where abnormal eating behaviors and excessive exercise are socially accepted, potentially putting individuals at risk for disordered eating (DE). This study examined the DE risk prevalence among gym members, comparing body appreciation, exercise motivation, frequency, BMI, and age across DE risk levels, and assessed the associated factors. A sample of 232 gym members (age: 39.6 ± 13.7) completed an online survey measuring DE risk (Eating Disorder Screen for Primary Care (ESP)), body appreciation (Body Appreciation Scale version 2), and exercise motivation (Behavioral Regulation in Exercise Questionnaire-2). One out of five (19.4%) were at risk of DE, while 15.5% and 11.6% reported having secretive eating behaviors and a history of eating disorders, respectively. Body weight impacted self-perception for 62.5% of the respondents. Those at DE risk had a higher mean BMI (26.23 ± 4.36 vs. 24.68 ± 3.61, p = 0.032) with a higher proportion of those with a BMI of 25–29.9 (46.67% vs. 29.41%, p = 0.027) compared to non-at-risk individuals. DE risk was associated with lower body appreciation (3.00 ± 3.60 versus 4.00 ± 3.70, p = ≤0.001), with body appreciation being the only factor associated with DE risk (OR = 0.24, 95% CI = 0.15, 0.39, p = ≤0.001).
ESG Ratings and Green Innovation
Authors: Lu Yu , Zhao Yaqi , Liu Lian , Shi Guoying
Published: December 2024

This study examines the impact of ESG ratings on corporate green innovation, selecting A-share listed companies in China from 2012 to 2022 as the research sample. Using a multiple-time-point difference-in-differences model, we analyze how ESG ratings influence both the output and efficiency of green innovation. The findings reveal that ESG ratings significantly promote green innovation, particularly by encouraging companies to increase investments in environmental protection, social responsibility, and corporate governance. Additionally, ESG ratings facilitate green innovation by reducing financing pressures, increasing media attention, and mitigating managerial myopia, with the effects most pronounced in highly polluting industries and firms with weaker corporate governance structures. These results offer valuable insights for companies pursuing sustainable development and for policymakers aiming to foster green innovation.
Fault-Tolerant Control Implemented for Sustainable Active and Reactive Regulation of a Wind Energy Generation System
Authors: Lopez Adolfo R. , Rumbo-Morales Jesse Y. , Ortiz-Torres Gerardo , Valdez-Resendiz Jesus E. , Vazquez Gerardo , Rosas-Caro Julio C.
Published: December 2024

This paper presents the design of a fault-tolerant control system based on fault estimation, aimed at enhancing the sustainability and efficiency of a wind energy conversion system using a doubly-fed induction generator. The control architecture comprises a rotor-side converter (RSC) and a grid-side converter (GSC). The RSC is responsible for regulating both active and reactive power, and its model incorporates two linear subsystem representations. A fault-tolerant control (FTC) scheme is developed using a state-feedback controller; this controller is applied to regulate stator and rotor currents. Additionally, for comparison purposes, Proportional–Integral (PI) and Sliding-Mode Controllers (SMCs) are designed to analyze the performance of each controller. Furthermore, a proportional integral observer is employed in the proposed fault-tolerant scheme for actuator fault estimation. Fault detection is achieved by comparing the fault estimation signal with a predefined threshold. The main contribution of this work is the design and validation of a comprehensive active FTC scheme that enhances system reliability and sustainability. It also includes a performance analysis comparing three controllers (PI, SMC, and state-feedback) applied to the RSC. These controllers are evaluated for their ability to regulate active and reactive power in a wind energy conversion system under conditions of non-constant actuator faults.
Performance Improvement of the LNG Regasification Process Based on Geothermal Energy Using a Thermoelectric Generator and Energy and Exergy Analyses
Authors: Mohammadi Amin , Maleki Akbar
Published: December 2024

In this paper, a new approach is proposed to improve the performance of the LNG regasification process in a geothermal-transcritical CO2–LNG cycle by using thermoelectric generators. Energy and exergy analyses were applied to the proposed system and the plant’s performance is compared with the conventional CO2–LNG cycle. To achieve the optimal solution for the system, a multi-objective optimization technique based on a genetic algorithm is used. This study’s findings revealed that in the conventional CO2–LNG cycle, the highest exergy destruction occurs in the preheater. However, integrating a thermoelectric generator allows a portion of this destroyed exergy to be converted into power. The proposed system demonstrated 2% less exergy destruction compared to the conventional system. Moreover, the TEG contributes additional power, increasing the net output power of the system by 24%. This improvement ultimately enhances the overall exergy efficiency of the system. The analysis also concluded that, although a lower LNG mass flow rate reduces the system’s net power output, it improves the exergy efficiency. Overall, the proposed system exhibits an 8.37% higher exergy efficiency and a 24.22% greater net output power compared to the conventional CO2–LNG cycle.
The Impact of Carbon Emissions Trading Pilots on the Low-Carbon Competitiveness of High-Carbon Industry-Listed Companies: An Empirical Analysis Based on Double Machine Learning
Authors: Yi Xiangfa , Liu Wanyi , Weng Diyao , Ma Ziyuan , Wei Jian , Dai Yongwu
Published: December 2024

Carbon emissions trading pilots are an essential environmental regulation tool for incentivizing companies to reduce carbon emissions and a critical initiative for achieving “dual carbon” targets. This study, based on 2366 observations of 169 high-carbon listed companies on the Shanghai and Shenzhen stock exchanges from 2009 to 2022, uses double machine learning analysis to examine the impact and mechanisms of pilot policy on the low-carbon competitiveness of high-carbon industry-listed companies. The empirical results show that, first, pilot policy significantly enhances the low-carbon competitiveness of high-carbon industry-listed companies, and this conclusion holds after considering a series of robustness checks. Second, mechanism analysis indicates that alleviating green financing constraints and enhancing total factor productivity are pathways through which pilot policy influences low-carbon competitiveness. Heterogeneity analysis shows that the policy effects are stronger for state-owned enterprises, small- and medium-sized enterprises, and companies in eastern regions. Further analysis reveals that pilot policy enhances low-carbon competitiveness and increase enterprise value. Based on the study’s conclusions, the government should ensure the incentivizing effect of pilot policy, promote expansion of the carbon emissions trading market, assist enterprises in overcoming green financing constraints, improve total factor productivity, and formulate tailored policies according to the development levels and resource endowments of regions and companies.
Sustainability Reporting in the University Context—A Review and Analysis of the Literature
Authors: Abello-Romero Juan , Mancilla Claudio , Restrepo Katherine , Sáez Walter , Durán-Seguel Ivette , Ganga-Contreras Francisco
Published: December 2024

This paper aims to determine the key themes addressed in sustainability reporting in higher education institutions (HEIs), focusing on papers indexed in the Scopus database until October 2024. The number of papers selected was fewer than the number of articles addressing sustainability reporting in corporate settings. We present the findings of this search by providing some metrics and an analysis of the selected texts through the construction of ten categories and 26 subcategories. Our study differs from previous works by providing an analysis of the content of some of the reviewed articles.
A Case Study on Sustainable Technologies in Residential Buildings from a Life Cycle Cost Analysis (LCC) Perspective
Authors: Vitkova Aneta , Vitasek Stanislav
Published: December 2024

The article mostly addresses the application of sustainable technologies in residential construction through life cycle cost analysis (LCC) using the net present value (NPV) calculation method. The authors rely on data obtained through their own research and information received from the market environment. The article outputs are in the form of conclusions based on a case study on a specific building (apartment building), elaborated in several versions with respect to the technologies used. In total, there are seven alternative versions divided into two groups, where a so-called reference technology representing a traditional (standard) technical solution is present in each group so that a relevant comparison can be made. The first group includes technologies related to heating and hot water, while the second group focuses on the application of recycled water (so-called grey water). The outputs obtained provide an interesting and fact-based view of sustainable technologies within the life cycle of a building drawing from currently available information sources. At the same time, the presented analysis has incorporated price predictions for key commodities, i.e., electricity, water, gas. The article’s specific conclusions indicate that the technologies utilizing renewable energy sources (RES) are typically less economically advantageous (in the absence of subsidy sources) compared to conventional (traditional) solutions, despite the significant savings in operating costs. The LCC indicator revealed a cost value per square meter of gross floor area (GFA) for a residential building ranging from EUR 43 to 68, contingent on the specific option under consideration. This cost value was determined over a 20-year follow-up period and a real discount rate of 4%.
Sustainable HR and Employee Psychological Well-Being in Shaping the Performance of a Business
Authors: Chomać-Pierzecka Ewa , Dyrka Stefan , Kokiel Andrzej , Urbańczyk Edward
Published: December 2024

Among the studies in the area of organisational performance analysis, little attention is paid to assessing the impact of employee well-being on business performance. Therefore, the aim of this study was to identify the sources of employees’ psychological well-being and assess its impact on business performance. The study was conducted in the form of a diagnostic survey among seven pharmaceutical entities operating in the market in Poland. The analysis and inference were based on the rules of economic and financial analysis, with particular emphasis on the cause–effect approach in the study of relationships. The sample of respondents was made up of seven pharmaceutical companies operating in the market in Poland, so the findings developed relate only to the companies accepted for the study and cannot be transferred to the general population of the country’s pharmaceutical sector. The objective of the study was made possible by appropriately selected methods of qualitative and quantitative nature. The study used the results of our own research (diagnostic survey), methods drawn from the instrumentarium of economic analysis (cause–effect analysis and dependency analysis) and statistical research techniques. The main findings of the paper indicate that the quality of organisational performance and its ability to shape the performance of activity-dependent firms is significantly dependent on the mental well-being of the workforce, as confirmed by the survey results. Firms with very good and good performance in operations demonstrate 88% of their staff achieving mental well-being. A key determinant of the above is job security (very important) and salary. The above leads to the fundamental conclusion that an important aspect of the management of a modern company is care for the comfort of working conditions, proper relations and determining the psychological safety and satisfaction of employees, which is unquestionably in the interest of both parties. The findings developed enrich the source material that can serve the management of organisations in shaping a sustainable HR strategy.
Does Adopting the Bean Technology Bundle Enhance Food Security and Resilience for Smallholder Farmers in Ethiopia?
Authors: Katungi Enid , Habte Endeshaw , Aseete Paul , Rubyogo Jean Claude
Published: December 2024

The analysis of the differential impacts of multiple improved technologies has largely accounted for selective adoption, considering either the full application of a bundle or its individual components. The impacts of adopting agricultural technology bundles on household welfare are less understood when considering a partial adoption of either the entire bundle or its individual components on a portion of crop area. We assess simultaneous adoption and the impacts of multiple improved technologies promoted as a bundle and recommended for legume intensification systems for smallholder farmers in Ethiopia. We use DNA fingerprinting data to precisely identify our key treatment—“adoption of improved bean varieties”—in this study. Using an endogenous multivariate treatment effects model, we found significant positive impacts of adopting bundled interventions on agricultural incomes and household food security but vulnerability to food insecurity persists for many households. We find that growing improved varieties with fertilizers increased household agricultural revenue, allowing for more legume consumption and enhancing their likelihood of achieving adequate food consumption and food security outcomes; however, the vulnerability to food insecurity of the adopters remains high due to pre-existing resource degradation issues. Given the similarity in production contexts in Sub-Saharan Africa, our results provide perspective for similar development interventions. We use the results of our analysis to discuss potential policy implications and programs to support technological intensification among smallholder farmers.
Evaluating the Integration of Education for Sustainable Development in the Life Sciences Curriculum: A Case Study of Grade 11 Teachers in Vhembe East District
Authors: Munasi Khathutshelo Ronald
Published: December 2024

This study examines the integration of Education for Sustainable Development (ESD) within the Grade 11 Life Sciences curriculum in the Vhembe East District. Despite a growing emphasis on ESD, teachers encounter significant barriers that impede effective implementation. Using a qualitative exploratory case study approach, data were gathered through classroom observations and semi-structured interviews with four purposively selected Grade 11 Life Sciences teachers. Thematic analysis revealed varying degrees of ESD integration, with challenges such as inadequate training, resource limitations, and a lack of diverse instructional materials, restricting teachers’ ability to effectively communicate complex environmental concepts. The study emphasizes the need for stronger curricular alignment of ESD, greater resource accessibility, and focused professional development to support teachers. These findings provide valuable insights into the status of ESD integration, emphasizing the necessity for systemic improvements to cultivate environmental literacy and sustainable behaviors that can benefit both educational communities and broader society.
Only Platformization? No, Community First!
Authors: Gagliardi Anna Roberta , Carrubbo Luca , Megaro Antonietta
Published: December 2024

This study explores the sharing economy through the lens of service ecosystem theory, focusing on how resource integration and value co-creation enhance competitiveness. Using a conceptual framework and case study analysis of platforms like Airbnb and Uber, the research examines the systemic interactions among actors within these ecosystems. The findings highlight the dual role of these platforms as drivers of innovation and instruments of platform capitalism. Practical and theoretical contributions include strategies for fostering resilience and sustainability in dynamic market environments.
Identifying the Characteristics of Sustainable Design System: A Survey Study
Authors: Basereh Taramsari Hossein , Hoffenson Steven , Lytle Ashley , Nilchiani Roshanak
Published: December 2024

To achieve triple bottom line sustainability, a system requires a balance of its social, economic, and environmental axioms. This multi-dimensional system has multiple stakeholders with different objectives acting within the system, leading to an increased level of complexity. Product design is an area with significant potential to achieve sustainable development, which is also influenced by policies. Product designers/managers and policymakers have been identified as critical stakeholders within this complex system, and their decisions directly affect the transition toward sustainable product design. However, these stakeholders have different perspectives on sustainability, and there is a lack of understanding of the main characteristics of a sustainable design system and its requirements. This research aims to find a detailed and unified understanding of these stakeholder’s perspectives, practices, and requirements. An online survey investigated the views of engineers/managers and policymakers in the United States to find their definitions of sustainability, their assessment methods, drivers, and barriers of sustainability. Finally, the participants were asked to identify their requirements for a sustainable design tool that can assist them effectively in designing a sustainable product. Considering the exploratory nature of this study, a targeted sample of 50 participants was selected to capture in-depth, qualitative insights, enabling a nuanced understanding of this complex system. The open-ended questions were designed to obtain detailed responses, which were analyzed qualitatively to develop a comprehensive view of the current state and future requirements for sustainable design tools. This targeted approach allowed the study to probe deeply into each stakeholder’s frame of reference, facilitating the identification of critical factors for a successful transition to sustainable design in both industry and policy. The results identified the critical factors that contribute to a successful transition toward sustainable product design in industry and policies while the requirements found in this study provided a road map to meet the diverse needs of these stakeholders.
A Health Risk Assessment of Workers Exposed to Organic Paint Solvents Used in the Korean Shipbuilding Industry
Authors: Seo Sue-Ji , Shin Sae-Mi , Yoon Wonsuck , Byeon Sang-Hoon
Published: December 2024

In the shipbuilding industry, during the painting process, workers are exposed to various substances in paint, including organic solvents that can adversely affect their health. Most workplace exposures to organic solvents involve mixtures of organic compounds. Therefore, in this study, the hazard quotient (HQ) and hazard index (HI) were derived using data from the Workplace Environmental Monitoring Program in Korea for six organic solvents (xylene, n-butanol, ethylbenzene, isobutyl alcohol, toluene, and methylisobutyl ketone [MIBK]) commonly used in the steel shipbuilding industry. The non-carcinogenic risk was assessed using Monte Carlo simulations, and sensitivity analysis was performed using the Spearman rank correlation coefficient with the R program. The HI for neurotoxicity and developmental toxicity exceeded 1 in the 25th and 75th percentile, respectively. According to the sensitivity analysis, the HI for neurotoxicity was correlated with the concentration of xylene and its exposure duration, whereas that for developmental toxicity was correlated with the concentration of ethylbenzene and MIBK and their exposure duration. This study investigated the health risks posed by organic solvents among workers involved in the painting process of shipbuilding. Additional research on percutaneous exposure to organic solvents and a detailed process analysis are needed.
Frequency Shift of Electromagnetic Radiation Around Charged Hayward Black Hole
Authors: Saidov Bakhodirkhon , Narzilloev Bakhtiyor , Abdujabbarov Ahmadjon , Khudoyberdieva Malika , Ahmedov Bobomurat
Published: December 2024

In this work, we investigate spacetime and photon dynamics around a charged Hayward black hole, focusing on the effects of electric charge Q and the length factor l. Our analysis shows that the maximum charge for black hole existence decreases as l increases, vanishing at l/M≃0.77. The black hole has both inner and outer horizons, with the outer horizon shrinking and the inner horizon expanding as spacetime parameters increase. The spacetime curvature, measured by the Kretschmann scalar, is most pronounced when both parameters are small, resembling the Schwarzschild black hole. The electric charge strongly influences the curvature and photon sphere size, while the effect of the length factor is less significant. Additionally, the gravitational redshift of photons is more sensitive to the electric charge of the compact object than the length factor, diminishing as Q increases and with greater radial distance from the black hole. Overall, while both spacetime parameters affect black hole properties, the electric charge has a slightly stronger impact, especially on gravitational redshift and photon behavior.
Estimating the Maximum Depth of Andean Lakes: A Comparative Analysis Using Machine Learning
Authors: Vázquez Raúl F. , Mejía Danilo , Mosquera Pablo V. , Hampel Henrietta
Published: December 2024

Multispectral modelling of 114 tropical Andean lakes in Southern Ecuador was implemented using observations of the maximum depth (Zmax). Five machine learning methods (MLMs), namely the multiple linear regression model (MLRM), generalised additive model (GAM), generalised linear model (GLM), multivariate adaptive regression splines (MARS), and random forest (RF), were applied on a LANDSAT 8 mosaic. Within the scope of a split-sample (SS) evaluation test, for each of the MLMs, a single model was developed for 70% (i.e., 80) of the studied lakes. Statistical measures and graphical inspection were used in the evaluation tests. An analysis of the absolute value of the model residuals (|res|) revealed that the MARS method outperformed the other MLMs. Nevertheless, a |res| > 10 m was observed for approximately 10% of the lakes. The worst predictions were produced by the GLM. These findings were confirmed in the model validation phase (SS test). With the exception of the GLM, the MLMs correctly predicted whether a lake was shallow or deep in more than 80% of the cases. In a more stringent multi-site (MS) test, the performance of the five Zmax models was assessed in predicting the bathymetry of 11,636 pixels that were not considered when fitting the models. Once more, MARS outperformed the other MLMs. However, a |res| > 10 m for 20% of the pixels was observed. Nevertheless, the quality of the predictions may still be regarded as acceptable for management purposes. Promising multispectral bathymetric predictions could be obtained, even with only a limited number of observations. The evaluation tests used in this pioneering study could be easily replicated elsewhere.
Impacts of Climate Change on Water Resources: Assessment and Modeling—First Edition
Authors: Sobkowiak Leszek , Wrzesiński Dariusz
Published: December 2024

The measurement and assessment of water resources are almost as old as the earliest civilizations: the Egyptians depended on the regular inundation of the Nile for their livelihood, and records of the Nile levels can be traced back to about 3000 to 3500 B [...]
Monthly Runoff Prediction Based on Stochastic Weighted Averaging-Improved Stacking Ensemble Model
Authors: Fu Kaixiang , Sun Xutong , Chen Kai , Mo Li , Xiao Wenjing , Liu Shuangquan
Published: December 2024

The accuracy of monthly runoff predictions is crucial for decision-making and efficiency in various areas, such as water resources management, flood control and disaster mitigation, hydraulic engineering scheduling, and agricultural irrigation. Therefore, in order to further improve the accuracy of monthly runoff prediction, aiming at the problem that the traditional Stacking ensemble method ignores (the base model correlation between different folds in the prediction process), this paper proposes a novel Stacking multi-scale ensemble learning model (SWA–FWWS) based on random weight averaging and a K-fold cross-validation weighted ensemble. Then, it is evaluated and compared with base models and other multi-model ensemble models in the runoff prediction of two upstream and downstream reservoirs in a certain river. The results show that the proposed model exhibits excellent performance and adaptability in monthly runoff prediction, with an average RMSE reduction of 6.44% compared to traditional Stacking models. This provides a new research direction for the application of ensemble models in reservoir monthly runoff prediction.
A Study on the Analysis and Prediction of the Evolution Path of China’s Electric Vehicle Industry Policy Based on Text Mining
Authors: Liu Zizheng , Xie Tao
Published: December 2024

In the 21st century, China’s electric vehicle (EV) industry has demonstrated remarkable growth, rapidly catching up with and surpassing other nations in scale and development. Understanding the policy mechanisms behind this rapid ascent is crucial for analyzing the evolution of China’s EV sector and making informed decisions for its future development. This study provides a comprehensive analysis of the evolution of China’s EV policies from 2009 to 2023, with projections through 2027, using a mixed-method approach that incorporates text mining, co-word network analysis, and BERT-based trajectory models to explore the operational logic of various policy frameworks and predict future policy directions. The study findings reveal distinct phases in the evolution of China’s EV policies. Initially, the focus was on building industrial capacity through supply-side measures, laying the foundation for growth. As the industry matured, policies expanded to include demand-side incentives and environmental regulations, reflecting a shift towards a balanced and sustainable approach. Our research shows that early policy decisions significantly influenced later adjustments, highlighting the role of path dependence. By mapping the trajectory of China’s EV policies, this study offers a framework for predicting future trends, providing guidance for Chinese policymakers and offering strategies that would allow other countries to effectively compete with China. Ultimately, this research underscores the importance of adaptive and coordinated policy strategies for fostering sustainable growth in strategic industries, providing valuable lessons for China and beyond.
NMPC-Based Path Tracking Control Through Cascaded Discretization Method Considering Handling Stability for 4WS Autonomous Vehicles Under Extreme Conditions
Authors: Zhu Guozhu , Hong Weirong
Published: December 2024

In the realm of autonomous driving, motion control generally involves two critical aspects—path following and stability control—and an inevitable mutual interference exists between them under extreme conditions. To tackle this challenge, this study proposes a collaborative approach of path tracking and stability control. When designing the path tracking control module, the effects of vertical tire load transfer and road surface adhesion coefficients on tire force calculations were taken into account to mitigate vehicle dynamics model mismatch. Leveraging the receding horizon optimization characteristic of nonlinear model predictive control (NMPC), a cascaded discretization approach was utilized to realize a balance between precision and real-time performance in numerical solutions. Then, a stability controller, which employs rear wheel steering, was designed to prevent excessive increases in the vehicle’s sideslip angle, thereby ensuring the vehicle’s lateral stability. The effectiveness of the proposed strategy is validated through CarSim 8.0/Simulink cosimulation. The outcomes demonstrate that the stability controller significantly enhances vehicle stability under high-speed and low-adhesion conditions. On the premise of stability, the proposed path tracking controller has exhibited significant enhancements in real-time performance, without compromising the accuracy of path tracking.
Turizm Rehberliği Öğrencileri Perspektifinden Staj Uygulamalarına Yönelik Bir Değerlendirme
Authors: Eker Nuray
Published: December 2024

Amaç ve Önem: Bu çalışmanın temel amacı lisans düzeyinde rehberlik eğitimi alan öğrencilerin staj uygulamalarına ilişkin görüşlerinin değerlendirilmesidir. Bu kapsamda çalışmada Sakarya Uygulamalı Bilimler Üniversitesi Turizm Rehberliği bölümü öğrencilerinin yapmış oldukları staj ve işletmede mesleki eğitim (dönem stajı) ile ilgili olumlu ve olumsuz değerlendirmeler üzerinde durulmuş ve çeşitli öneriler sunulmuştur.

Yöntem: Araştırmada nitel veri toplama yöntemi tercih edilmiş olup çalışmanın birincil verileri görüşme tekniği ile elde edilmiştir. Araştırma kapsamında staj veya işletmede mesleki eğitim uygulamalarını tamamlamış 14 öğrenci ile görüşme gerçekleştirilmiştir. Araştırma verilerinin değerlendirilmesinde içerik analizinden yararlanılmış; öğrenci görüşleri staj öncesi, staj süreci ve staj sonrası değerlendirmeler olmak üzere üç kategoride incelenmiştir.

Bulgular: Araştırma bulguları öğrencilerin staj sürecinde staj yeri bulma, çalışma saatleri ve ücret hakları konusunda sorunlar yaşadıklarını göstermektedir. Genel olarak stajların mesleği yakından tanıma, işi yaparak öğrenme, teorideki bilgilerini uygulamada pekiştirme, iletişim becerilerini geliştirme, analitik düşünme ve problem çözme kabiliyeti edinme, kariyer beklentilerini şekillendirme açısından önemli bir fırsat olduğu düşünülmektedir.

Özgünlük/Bilimsel Katkı: Turizm rehberliği bölümlerinde eğitim alan öğrencilerin staj uygulamalarına ilişkin olumlu ve olumsuz değerlendirmelerini içeren sınırlı sayıda çalışma bulunmaktadır. Bu kapsamda makalenin alana katkı sağlayacağı düşünülmektedir. Staj sürecinin etkinliği ve verimliliği, öğrencilerin sektöre entegrasyonlarını doğrudan etkileyen önemli bir faktördür. Staj programlarının tamamlanmasının ardından öğrencilerin staja ilişkin olumlu ve olumsuz değerlendirmelerinin incelenmesi ile üniversitelerin staj uygulamaları geliştirilebilir ve öğrencilerin stajlarda karşılaştıkları zorlukları hafifletmeye yönelik tedbirler alınabilir.

Sınırlılıklar: Bu araştırma Sakarya Uygulamalı Bilimler Üniversitesi Turizm Rehberliği bölümü öğrencileri ile kısıtlıdır. Farklı üniversite öğrencileri üzerine yapılacak karşılaştırmalı araştırmalar literatüre kayda değer katkılar sağlayacaktır.