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Found 5157 publications
Estimating the air quality standard exceedance areas and the spatial representativeness of urban air quality stations applying microscale modelling
L. Tarrason
S. Janssen
L. Környei
J. L. Santiago
V. Rodrigues
G. Sousa Santos
P. Thunis
E. Rivas
C. Cuvelier
I. Sakellaris
M. G. Villani
S. Hamilton
N. Masey
F. Martín
N. Reiminger
X. Jurado
D. Barbero
J. L. Pérez-Camanyo
R. Jackson
J. Sousa
J. Bartzis
R. San José
G. Tinarelli
J. Stocker
Zoltán Horváth
F. Russo
Publication Name: Science of the Total Environment
Publication Date: 2025-08-01
Volume: 988
Issue: Unknown
Page Range: Unknown
Description:
This study builds upon the findings of a FAIRMODE intercomparison exercise conducted in a district of Antwerp, Belgium, where a comprehensive dataset of air pollutant measurements (air quality stations and passive samplers) was available. Long-term average NO2 concentrations at very high spatial resolution were estimated by several dispersion modelling systems (Martín et al., 2024) to investigate the ability of these to capture the detailed spatial distribution of NO2 concentrations at the microscale in urban environments. In this follow-up research, we extend the analysis by evaluating the capability of these modelling systems to predict the NO2 annual limit value exceedance areas (LVEAs) and spatial representativeness areas (SRAs) for NO₂ at two reference air quality stations. The different modelling approaches used are based on CFD, Lagrangian, Gaussian, and AI-driven models. The different modelling approaches are generally good at predicting the LVEA and SRAs of urban air quality stations, although a small SRA (corresponding to low concentration tolerances or the traffic station) is more difficult to predict correctly. However, there are notable differences in performance among the modelling systems. Those based on CFD models seem to provide more consistent results predicting LVEAs and SRAs. Then, lower accuracy is obtained with AI-based systems, Lagrangian models, and Gaussian models with street canyon parameterizations. The Gaussian models with street-canyon parametrizations show significantly better results than models using simply a Gaussian dispersion parametrization. Furthermore, little differences are observed in most of the statistical indicators corresponding to the LVEA and SRA estimates obtained from the unsteady full month CFD simulations compared to those from the scenario-based CFD simulation methodologies, but there are some noticeable differences in the LVEA or SRA (traffic station, 10 % tolerance) sizes. The number of scenarios does not seem to be relevant to the results. Different bias correction methodologies are explored.
Open Access: Yes
Five-day ski camp could enhance postural stability in young adults: A quasi-experimental study
Publication Name: Physiological Reports
Publication Date: 2025-08-01
Volume: 13
Issue: 16
Page Range: Unknown
Description:
This study investigated whether a 5-day ski camp could improve postural stability in young adults. It was hypothesized that skiing would reduce postural sway. In this quasi-experimental design, 43 undergraduate students who participated in a 5-day ski camp (approximately 20 h of skiing) were compared to 35 peers who did not attend. Postural stability was assessed using the modified Clinical Test of Sensory Integration and Balance protocol of the Balance Tracking System, which evaluates sway under four standing conditions: eyes open or closed, and on stable or unstable surfaces. Quade nonparametric ANCOVAs were used to compare percentage change scores between groups, controlling for age. No significant group differences emerged for standard, proprioceptive, or vestibular postural stability (p > 0.05). However, a statistically significant group effect was found for visual postural stability (p = 0.006), with improvement observed only in females (p = 0.003), not in males (p = 0.961). A 5-day ski camp significantly enhanced visual postural stability in females but did not affect males or other postural domains. These findings suggest a potential sex-specific adaptation to skiing and highlight the need for further research into the mechanisms underlying balance improvement.
Open Access: Yes
DOI: 10.14814/phy2.70501
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
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
Genetic Factors of Elite Wrestling Status: A Multi-Ethnic Comparative Study
Koichi Nakazato
Ildus I. Ahmetov
Seyrani Koncagul
Hirofumi Zempo
Mehmet A. Ergun
Andrey K. Larin
Naoki Kikuchi
Nikolay A. Kulemin
Hasan H. Kazan
Ryutaro Matsumoto
Ekaterina A. Semenova
Edward V. Generozov
Celal Bulgay
Mika Saito
Hiroki Homma
Ayumu Kozuma
Minoru Deguchi
Anıl Kasakolu
Shingo Matsumoto
Türker Bıyıklı
Giyasettin Baydaş
Takanobu Okamoto
Attila Szabo
Publication Name: Genes
Publication Date: 2025-08-01
Volume: 16
Issue: 8
Page Range: Unknown
Description:
Background: In recent years, comprehensive analyses using a genome-wide association study (GWAS) have been conducted to identify genetic factors related to athletic performance. In this study, we investigated the association between genetic variants and elite wrestling status across multiple ethnic groups using a genome-wide genotyping approach. Methods: This study included 168 elite wrestlers (64 Japanese, 67 Turkish, and 36 Russian), all of whom had competed in international tournaments, including the Olympic Games. Control groups consisted of 306 Japanese, 137 Turkish, and 173 Russian individuals without elite athletic backgrounds. We performed a GWAS comparing allele frequencies of single-nucleotide polymorphisms (SNPs) between elite wrestlers and controls in each ethnic cohort. Cross-population analysis comprised (1) identifying SNPs with nominal significance (p < 0.05) in all three groups, then (2) meta-analyzing overlapped SNPs to assess effect consistency and combined significance. Finally, we investigated whether the most significant SNPs were associated with gene expression in skeletal muscle in 23 physically active men. Results: The GWAS identified 328,388 (Japanese), 23,932 (Turkish), and 30,385 (Russian) SNPs reaching nominal significance. Meta-analysis revealed that the ATP2A3 rs6502758 and UNC5C rs265061 polymorphisms were associated (p < 0.0001) with elite wrestling status across all three populations. Both variants are located in intronic regions and influence the expression of their respective genes in skeletal muscle. Conclusions: This is the first study to investigate gene polymorphisms associated with elite wrestling status in a multi-ethnic cohort. ATP2A3 rs6502758 and UNC5C rs265061 polymorphisms may represent important genetic factors associated with achieving an elite status in wrestling, irrespective of ethnicity.
Open Access: Yes
Development Process of TGDI SI Engine Combustion Simulation Model Using Ethanol–Gasoline Blends as Fuel
Publication Name: Applied Sciences Switzerland
Publication Date: 2025-08-01
Volume: 15
Issue: 15
Page Range: Unknown
Description:
The Fit for 55 package introduced by the European Union aims to achieve a 55% reduction in greenhouse gas emissions by 2030. In parallel, increasingly stringent exhaust gas regulations have intensified research into alternative fuels. Ethanol presents a promising option due to its compatibility with gasoline, higher octane rating, and lower exhaust emissions compared to conventional gasoline. Additionally, ethanol can be derived from agricultural waste, further enhancing its sustainability. This study examines the impact of two ethanol–gasoline blends (E10, E20) on emissions and performance in a turbocharged gasoline direct injection (TGDI) spark-ignition (SI) engine. The investigation is conducted using three-dimensional computational fluid dynamics (3D CFD) simulations to minimize development time and costs. This paper details the model development process and presents the initial results. The boundary conditions for the simulations are derived from one-dimensional (1D) simulations, which have been validated against experimental data. Subsequently, the simulated performance and emissions results are compared with experimental measurements. The E10 simulations correlated well with experimental measurements, with the largest deviation in cylinder pressure being an RMSE of 1.42. In terms of emissions, HC was underpredicted, while CO was overpredicted compared to the experimental data. For E20, the IMEP was slightly higher at some operating points; however, the deviations were negligible. Regarding emissions, HC and CO emissions were higher with E20, whereas NOx and CO2 emissions were lower.
Open Access: Yes
DOI: 10.3390/app15158677
Regional Patterns in Weed Composition of Maize Fields in Eastern Hungary: The Balance of Environmental and Agricultural Factors
Publication Name: Agronomy
Publication Date: 2025-08-01
Volume: 15
Issue: 8
Page Range: Unknown
Description:
The primary aim of this study was to explore the influence of abiotic factors on weed development in maize fields, with the goal of informing more effective weed management practices. We focused on identifying key environmental, edaphic, and agricultural variables that contribute to weed infestations, particularly before the application of spring herbicide treatments. Field investigations were conducted from 2018 to 2021 across selected maize-growing regions in Hungary. Over the four-year period, a total of 51 weed species were recorded, with Echinochloa crus-galli, Chenopodium album, Portulaca oleracea, and Hibiscus trionum emerging as the most prevalent taxa. Collectively, these four species accounted for more than half (52%) of the total weed cover. Altogether, the 20 most dominant species contributed 95% of the overall weed coverage. The analysis revealed that weed cover, species richness, and weed diversity were significantly affected by soil properties, nutrient levels, geographic location, and tillage systems. The results confirm that the composition of weed species was influenced by several environmental and management-related factors, including soil parameters, geographical location, annual precipitation, tillage method, and fertilizer application. Environmental factors collectively explained a slightly higher proportion of the variance (13.37%) than farming factors (12.66%) at a 90% significance level. Seasonal dynamics and crop rotation history also played a notable role in species distribution. Nutrient inputs, particularly nitrogen, phosphorus, and potassium, influenced both species diversity and floristic composition. Deep tillage practices favored the proliferation of perennial species, whereas shallow cultivation tended to promote annual weeds. Overall, the composition of weed vegetation proved to be a valuable indicator of site-specific soil conditions and agricultural practices. These findings underscore the need to tailor weed management strategies to local environmental and soil contexts for sustainable crop production.
Open Access: Yes
Utilizing Different Crop Rotation Systems for Agricultural and Environmental Sustainability: A Review
Publication Name: Agronomy
Publication Date: 2025-08-01
Volume: 15
Issue: 8
Page Range: Unknown
Description:
Monoculture involves growing the same crop on the same land over at least two crop cycles. Continuous monoculture can increase the population density of pests and pathogens over time, thereby reducing agricultural yields and increasing dependence on chemical inputs. Crop rotation is an agricultural practice that involves systematically and sequentially planting different crops in the same field over multiple growing seasons. This review explores the advantages of crop rotation and its contribution to promoting sustainable farming practices, such as legume integration and cover cropping. It is based on a thematic literature review of peer-reviewed studies published between 1984 and 2025. We found that crop rotation can significantly improve soil structure and organic matter content and enhance nutrient cycling. Furthermore, soil organic carbon increased by up to 18% when legumes were included in rotations compared to monoculture systems in Europe, while also mitigating greenhouse gas emissions, enhancing carbon sequestration, and decreasing nutrient leaching and pesticide runoff. Farmers can adopt several strategies to optimise crop rotation benefits, such as diversification of various crops, legume integration, cultivation of cover crops, and rotational grazing. These practices ensure agricultural sustainability and food security and support climate resilience.
Open Access: Yes
The role of artificial intelligence in enhancing corporate environmental information disclosure: Implications for energy transition and sustainable development
Publication Name: Energy Economics
Publication Date: 2025-08-01
Volume: 148
Issue: Unknown
Page Range: Unknown
Description:
Global climate and environmental issues pose severe challenges to the sustainable development of human society. As major contributors to environmental pollution and carbon emissions, the quality of enterprises' environmental data has gained significant attention in academic and industrial circles. This study analyzes information from Chinese A-share companies spanning 2012 to 2023 to investigate the pathways through which artificial intelligence (AI) technology influences corporate environmental information disclosure (EID). The results indicate that AI significantly enhances the quality of corporate EID by optimising internal control levels and strengthening external supervision mechanisms. These conclusions have been validated through robustness and endogeneity tests. The heterogeneity analysis further reveals that the promoting effect of AI is more significant in large corporates, corporates in central cities, mature corporates, corporates audited by the Big Four international accounting firms, high-tech corporates, and heavily polluting industries. The study innovatively constructs a dual-path theoretical framework of ‘internal management optimisation–external supervision strengthening’ and integrates macro urban AI indicators with micro enterprise data, contributing new empirical support for the digital transformation and green governance of developing countries. Based on these findings, policymakers should promote the innovative application of AI technology in corporate environmental governance, improving internal control norms, optimising the external supervision system, and implementing a classified guidance strategy for different enterprise attributes, so as to help enterprises achieve low-carbon transformation and sustainable development.
Open Access: Yes
