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Found 6374 publications
Obstacles to Finding the Ideal Workplace: A Gender-Based Analysis Across the V4 Countries
Publication Name: Emerging Science Journal
Publication Date: 2025-08-01
Volume: 9
Issue: 4
Page Range: 2261-2274
Description:
This study explores gender-specific barriers to finding an ideal workplace in the Visegrád countries (Czech Republic, Hungary, Poland, and Slovakia), where similar historical and socioeconomic contexts shape labor market inequalities. Based on the relevant literature, women are disproportionately affected by challenges related to language proficiency, professional networks, and mobility. The research applied a quantitative methodology, including chi-square tests, multiple logistic regression, and cluster analysis, using SPSS Statistics software to analyze the survey data. Findings revealed significant gender disparities. Women report greater difficulties with language and mobility, particularly in Hungary and Slovakia, whereas men benefit more from strong professional connections. The cluster analysis identified three respondent groups: those hindered by language barriers, those with weak networks, and those facing limited mobility. International experience mitigates language challenges, and robust networks ease job search difficulties. In line with the ideals of a circular society, this study also explores how circularity, inclusiveness, and collaboration can help break down gender-based barriers in the labor market. The study’s novelty lies in its comparative regional focus and the integration of statistical methods to segment job-seeker profiles. These insights highlight the need for targeted policies that enhance language skills and foster professional networking opportunities, especially for women. By addressing these barriers, policymakers can better support gender equality in labor market access across Central Europe.
Open Access: Yes
Investigation of the synthesis and thermal insulation properties of K2Ti6O13 whisker-reinforced SiO2 micro powder composite coating fabrics
Publication Name: Energy
Publication Date: 2025-08-01
Volume: 328
Issue: Unknown
Page Range: Unknown
Description:
Developing functional textiles with thermal insulation and hydrophobic properties is of significant interest. This study successfully synthesizes K2 Ti6 O13 (potassium titanate, KTO) whiskers via the hydrothermal method and prepares KTO composite silica polyester fabric (KSP) through impregnation technology, exhibiting excellent thermal insulation and hydrophobic qualities. X-ray diffraction (XRD) analysis verifies the high purity and excellent crystallinity of KTO whiskers and SiO2 micro-powder. Scanning electron microscopy (SEM) images reveal that the KTO whiskers retain their original morphology and exhibit uniform size distribution, with an average length of around 1 μm and an aspect ratio of 40. Transmission electron microscopy (TEM) images further validate the planar growth properties of the whiskers. Raman spectroscopy research elucidates the vibrational modes of various chemical bonds in the KTO whiskers. The ultraviolet–visible–near-infrared spectrophotometer test results demonstrate that the KSP fabric reflects 43.5 % more light than standard polyester fabric and substantially lowers the temperature in the covered chamber under simulated sunlight exposure, achieving a maximum reduction of 7.4 °C. The KSP fabric has exceptional hydrophobic properties, completing a contact angle of 153.2° and maintaining reflectance stability, with a mere 5.92 % reduction after 20 days of outside exposure. This work offers substantial reference value for the advancement of practical textiles.
Open Access: Yes
The impact of domestic material consumption and energy mix on socioeconomic indicators—A global analysis from 1990 to 2022
Publication Name: Resources Policy
Publication Date: 2025-08-01
Volume: 107
Issue: Unknown
Page Range: Unknown
Description:
Relevance: Understanding the relationship between economic growth, resource consumption, and social development is crucial for sustainable policy-making. While economic expansion is often linked to improved well-being, its effects depend on material consumption patterns and energy dependency. Goals: This study aims to examine how domestic material consumption (DMC) and energy structure influence human development, particularly across different economic contexts. Methods: Using a panel data approach, the research applies econometric models to analyze the impact of DMC and energy dependency on social indicators. Results: Findings indicate that excessive DMC can hinder sustainable progress, while a higher share of renewable energy contributes to long-term social development and economic stability. Conclusion: The study highlights the need for resource-efficient policies and energy diversification, contributing to the literature on sustainable growth and development strategies.
Open Access: Yes
When Industry 5.0 Meets the Circular Economy: A Systematic Literature Review
Publication Name: Circular Economy and Sustainability
Publication Date: 2025-08-01
Volume: 5
Issue: 4
Page Range: 2621-2652
Description:
This paper examines the convergence of Industry 5.0 and the circular economy, emphasizing the role of emerging technologies in promoting sustainability via human-centric approaches. In contrast to Industry 4.0, which prioritizes automation and digitalization, Industry 5.0 stresses the synergistic integration of technology, environmental sustainability, and human collaboration to enhance resource efficiency and minimize waste. Using co-word analysis and BERTopic modeling on 283 journal articles extracted from the Scopus database, this research identifies key trends and themes linking Industry 5.0 and the circular economy. The study findings demonstrate the use of automation, machine learning, and 3D printing in sustainable manufacturing, which aligns with circular economy principles by optimizing resource efficiency and reducing waste. The topic modeling analysis further demonstrates the role of blockchain, cybersecurity, and human-centric AI in enabling closed-loop systems while assuring transparency and accountability in circular production models. The collaboration between humans and machines emerges as a crucial topic highlighting the need for adaptive manufacturing systems to balance productivity and environmental responsibility. The findings indicate that Industry 5.0 increasingly aligns with circular economy goals, paving the way to more sustainable, resilient, and human-centric industrial processes. This study offers valuable insights for academics and practitioners, indicating that the confluence of technology, sustainability, and human involvement will propel the future of industrial innovation.
Open Access: Yes
A Nonlinear Computational Framework for Optimizing Steel End-Plate Connections Using the Finite Element Method and Genetic Algorithms
Publication Name: Algorithms
Publication Date: 2025-08-01
Volume: 18
Issue: 8
Page Range: Unknown
Description:
The design of steel connections presents considerable complexity due to their inherently nonlinear behavior, cost constraints, and the necessity to comply with structural design codes. These factors highlight the need for advanced computational algorithms to identify optimal solutions. In this study, a comprehensive computational framework is presented in which the finite element method (FEM) is integrated with a genetic algorithm (GA) to optimize material usage in bolted steel end-plate joints, while structural safety is ensured based on multiple performance criteria. By incorporating both material and geometric nonlinearities, the mechanical response of the connections is accurately captured. The proposed approach is applied to a representative beam-to-column assembly, with numerical results verified against experimental data. By employing the framework, an optimized layout is obtained, yielding a (Formula presented.) improvement in the overall performance objective compared to the best-performing validated model and a (Formula presented.) reduction in material volume relative to the most efficient feasible alternative. Furthermore, a (Formula presented.) decrease in equivalent plastic strain is achieved compared to the configuration exhibiting the highest level of inelastic deformation. These findings demonstrate that the developed method is capable of enhancing design efficiency and precision, underscoring the potential of advanced computational tools in structural engineering applications.
Open Access: Yes
DOI: 10.3390/a18080460
Fake News in Tourism: A Systematic Literature Review
Publication Name: Social Sciences
Publication Date: 2025-08-01
Volume: 14
Issue: 8
Page Range: Unknown
Description:
In recent years, the number of fake news stories has significantly increased in the world of media, especially with the widespread use of social media. It has impacted several industries, including tourism. From a tourism point of view, the spread of fake news can contribute to the reduction of the popularity of a destination. It may influence travel decisions by discouraging tourists from visiting certain places and thus damage the reputation of the destination, contributing to economic loss. After a literature review on the communication aspect of fake news and a general introduction of fake news in the tourism and hospitality industry, we conducted a systematic literature review (SLR), a research methodology to collect, identify, and analyse available research studies through a systematic procedure. The current SLR is based on the Scopus, Web of Science, and Google Scholar databases of existing literature on the topic of fake news in the tourism and hospitality industry. The study identifies, lists, and examines existing papers and conference proceedings from a vast array of disciplines, in order to give a well-rounded view on the issue of fake news in the tourism and hospitality industry. After selecting a total of 54 previous studies from more than 20 thousand results for the keywords ‘fake news’ and ‘tourism,’ we have analysed 39 papers in total. The SLR aimed to highlight existing gaps in the literature and areas that may require further exploration in future primary research. We have found that there is relatively limited academic literature available on the subject of fake news affecting tourism destinations, compared to studies focused on hospitality services.
Open Access: Yes
Translational Pitfalls in SCI Bladder Research: The Hidden Role of Urinary Drainage Techniques in the Rat Model
Publication Name: Biology
Publication Date: 2025-08-01
Volume: 14
Issue: 8
Page Range: Unknown
Description:
Spinal cord injury (SCI) frequently leads to neurogenic lower urinary tract dysfunction, for which appropriate bladder management is essential. While clinical care relies on continuous low-pressure drainage in the acute phase, rat models commonly use twice-daily manual bladder expression—a method known to generate high intravesical pressures and retention. This study evaluated the impact of this standard practice on bladder tissue remodeling by comparing it to continuous drainage via high vesicostomy in a rat SCI model. 32 female Lewis rats underwent thoracic contusion SCI and were assigned to either manual expression or vesicostomy-based bladder management. Over eight weeks, locomotor recovery, wound healing, and bladder histology were assessed. Vesicostomy proved technically simple but required tailored wound care and calibration. Results showed significantly greater bladder wall thickness, detrusor muscle hypertrophy, urothelial thickening, collagen deposition, and mast cell infiltration in the manual expression group compared to both vesicostomy and controls. In contrast, vesicostomy animals exhibited near-control levels across most parameters. These findings highlight that commonly used bladder emptying protocols in rat SCI models may overestimate structural bladder changes and inflammatory responses. Refined drainage strategies such as vesicostomy can minimize secondary damage and improve the translational relevance of preclinical SCI research.
Open Access: Yes
Exploring Generation Z’s Acceptance of Artificial Intelligence in Higher Education: A TAM and UTAUT-Based PLS-SEM and Cluster Analysis
Publication Name: Education Sciences
Publication Date: 2025-08-01
Volume: 15
Issue: 8
Page Range: Unknown
Description:
In recent years, the rapid growth of artificial intelligence (AI) has significantly transformed higher education, particularly among Generation Z students who are more open to new technologies. Tools such as ChatGPT are increasingly being used for learning, yet empirical research on their acceptance, especially in Hungary, is limited. This study aims to explore the psychological, technological, and social factors that influence the acceptance of AI among Hungarian university students and to identify different user groups based on their attitudes. The methodological novelty lies in combining two approaches: partial least-squares structural equation modelling (PLS-SEM) and cluster analysis. The survey, based on the TAM and UTAUT models, involved 302 Hungarian students and examined six dimensions of AI acceptance: perceived usefulness, ease of use, attitude, social influence, enjoyment and behavioural intention. The PLS-SEM results show that enjoyment (β = 0.605) is the strongest predictor of the intention to use AI, followed by usefulness (β = 0.167). All other factors also had significant effects. Cluster analysis revealed four groups: AI sceptics, moderately open users, positive acceptors, and AI innovators. The findings highlight that the acceptance of AI is shaped not only by functionality but also by user experience. Educational institutions should, therefore, provide enjoyable and user-friendly AI tools and tailor support to students’ attitude profiles.
Open Access: Yes
Data-Driven Predictive Modeling for Investigating the Impact of Gear Manufacturing Parameters on Noise Levels in Electric Vehicle Drivetrains
Publication Name: World Electric Vehicle Journal
Publication Date: 2025-08-01
Volume: 16
Issue: 8
Page Range: Unknown
Description:
Reducing gear noise in electric vehicle (EV) drivetrains is crucial due to the absence of internal combustion engine noise, making even minor acoustic disturbances noticeable. Manufacturing parameters significantly influence gear-generated noise, yet traditional analytical methods often fail to predict these complex relationships accurately. This research addresses this gap by introducing a data-driven approach using machine learning (ML) to predict gear noise levels from manufacturing and sensor-derived data. The presented methodology encompasses systematic data collection from various production stages—including soft and hard machining, heat treatment, honing, rolling tests, and end-of-line (EOL) acoustic measurements. Predictive models employing Random Forest, Gradient Boosting (XGBoost), and Neural Network algorithms were developed and compared to traditional statistical approaches. The analysis identified critical manufacturing parameters, such as surface waviness, profile errors, and tooth geometry deviations, significantly influencing noise generation. Advanced ML models, specifically Random Forest, XGBoost, and deep neural networks, demonstrated superior prediction accuracy, providing early-stage identification of gear units likely to exceed acceptable noise thresholds. Integrating these data-driven models into manufacturing processes enables early detection of potential noise issues, reduces quality assurance costs, and supports sustainable manufacturing by minimizing prototype production and resource consumption. This research enhances the understanding of gear noise formation and offers practical solutions for real-time quality assurance.
Open Access: Yes
DOI: 10.3390/wevj16080426
