Search Results (14,174)

Traffic violations represent a significant public health concern, with professional drivers substantially impacting road safety. This pilot study compared self-report questionnaires (general personality versus domain-specific), performance-based tests, and driving simulator measures to determine which assessment method best predicts traffic offences among professional truck drivers. Participants (N = 27) completed the Impulsiveness–Venturesomeness–Empathy Questionnaire (IVE), the Road Traffic Behaviours Questionnaire (KZD), and the Vienna Risk-Taking Test Traffic (WRBTV) and performed standardised driving scenarios in a truck simulator. Performance was assessed using speed variations in five validated decision-making situations. Drivers were classified into two groups based on relatively higher and relatively lower numbers of self-reported traffic offences. The KZD demonstrated the strongest group differentiation (p = 0.034, d = 0.76). Simulator performance was significantly different between the groups (p = 0.033, d = −0.68), with offence-reporting drivers showing smaller speed reductions. The WRBTV and the IVE empathy subscale approached significance (p = 0.056 and p = 0.059, respectively). Higher empathy characterised offence-free drivers, suggesting social–emotional factors may contribute to traffic safety. General impulsiveness and venturesomeness showed no group differences. The results indicate that domain-specific questionnaires and behavioural assessments offer superior predictive validity compared to general personality measures for identifying potentially unsafe drivers. ROC analysis revealed moderate predictive validity across significant measures (AUC: 0.64–0.70), with differential patterns of sensitivity and specificity among predictors. The findings suggest implementing tiered screening approaches using domain-specific questionnaires as initial cost-effective tools, followed by simulator assessment for at-risk drivers, enabling transport companies and regulatory bodies to identify high-risk drivers proactively. Full article

The United Nations Sustainable Development Goals (SDGs) have been one of the benchmarks for academic research. This study addresses global SDG-related challenges through top research in physical sciences (PS) at the University of Split (UNIST). UNIST was selected as a case study of a small and relatively new EU university, with moderate funding and research capacity. A bibliometric approach was applied, and articles related to SDGs were presented (with 1 to 50 authors) in the top 5% Q1 journals within the Web of Science Core Collection (WoSCC) for Physical Sciences. Sixty-three of the eighty-three articles are related to SDGs (subcategories: Astronomy & Astrophysics, Chemistry, Geoscience, Multidisciplinary, Mathematics, Meteorology & Atmospheric Science, Multidisciplinary Sciences, Oceanography, Physics, Thermodynamics, and Water Resources). The study presents the following: (a) UNIST articles in the top 5% Q1 journals and corresponding SDGs up to 2022; (b) their citations; (c) their contribution to SDG achievement; (d) drivers of top performance in PS; (e) comparison with other universities in Mediterranean Croatia; (f) policy recommendations. The SDG-related research output in PS at UNIST has expanded through interdisciplinary approaches and international collaborations addressing complex global challenges. The highest citation impacts were found for SDG13 (191.88), SDG14 (167.4), and SDG6 (145.56). Although 26 articles were related to SDG3, their citation impact was lower (not targeting core biomedical research). For 10 articles related to SDG14, the citation impact was very high (167.4). The drivers of the top 5% performance of UNIST in PS are identified and policy recommendations as well as lessons learned are mentioned to improve the participation of small universities in EU or national research programs and foster international cooperation across the European Research Area (ERA) to address the SDGs. Full article

The EPC model is currently the mainstream implementation approach for landscape projects, but fragmented management of the design–construction interface constrains project performance. Addressing issues such as cost overruns and schedule delays caused by ambiguous responsibility allocation, inefficient information transfer, and frequent design changes in EPC landscape projects, this paper focuses on the Xiaoyalong Wetland Park project in Kashi, Xinjiang, as a core case study. Combined with research on 12 representative projects, it identifies 16 interface management factors across four dimensions: contract management, organizational coordination, technical support, and ecological–artistic integration. Employing a mixed-methods approach combining questionnaire surveys (186 valid samples) and semi-structured interviews, validated through SPSS and structural equation modeling, this study confirms that early collaborative design serves as a core driver. Based on empirical findings, it derives and proposes a three-tiered optimization strategy: “foundation at the root layer, coordination at the transition layer, and assurance at the direct layer”. Pilot application of this strategy demonstrated significant effectiveness, reducing design change rates by 32%, shortening coordination time by 28%, and lowering cost overrun rates by 15%. This study enriches the theoretical framework of interface management in landscape engineering EPC projects and provides practical guidance for similar projects in arid regions. Full article

Global climate change is intensively altering precipitation regimes, with profound consequences for the structure and function of various terrestrial ecosystems. Soil microbes are a key driver of organic matter decomposition and nutrient cycling; however, their response mechanisms to precipitation variations in fragile ecosystems remain poorly understood. We conducted an in situ precipitation manipulation experiment in a savanna ecosystem within the Yuanjiang dry-hot valley of southwest China since January 2014. We established three treatments: a control plot with natural precipitation (NP), precipitation exclusion by 50% (PE50), and precipitation addition by 50% (PA50). Soil samples were collected in mid-April and mid-August 2025. Using high-throughput sequencing technology, we systematically examined how precipitation variations affected soil microbial community structure and the underlying environmental drivers. The study results showed that both PA50 and PE50 treatments significantly altered the α- and β-diversity of bacterial and fungal communities (PERMANOVA, p < 0.05), marking a clear separation in overall soil microbial community structure among treatments. The bacterial community response was more pronounced to precipitation variations than the fungal community, and exhibited a non-linear response pattern. Both PE50 and PA50 treatments increased bacterial richness. In contrast, shifts in fungal diversity were season-dependent. The analysis results of Linear discriminant analysis Effect Size (LEfSe) revealed that the PE50 treatment enriched drought-tolerant taxa, such as Actinobacteria and Ascomycota. Conversely, the PA50 treatment favored moisture-preferring taxa, including Acidobacteria and Basidiomycota. Redundancy analysis (RDA) identified soil moisture (SM), dissolved organic nitrogen (DON), and soil organic carbon (SOC) as the key factors driving these community shifts. The relative importance of these drivers varied seasonally: SM was dominant in the dry season, while SOC and nutrient-related factors prevailed during the rainy season. This study elucidates the non-linear and seasonally contingent response mechanisms of soil microbial communities to precipitation variations in a fragile savanna ecosystem. Our findings provide a critical theoretical framework for predicting how the structure and function of vulnerable ecosystems may evolve under future climate change. Full article

Tick-borne diseases account for a substantial proportion of the global incidence of infectious diseases, and their recent expansion has been increasingly associated with climate change. Nevertheless, previous studies have produced heterogeneous and often inconclusive results, largely due to differences in spatial scale, variable selection, and limited integration of climatic, ecological, and host-related drivers. Here, we assess the modeled impact of climate trends on the global distribution patterns of ticks parasitizing humans and livestock, rather than changes in tick abundance or pathogen transmission. This study is not an evaluation of human or animal contact rates with ticks. Using the largest curated compilation of georeferenced tick records available to date (213,513 records from 138 Ixodidae species), we adopt a global, climate-centered perspective based on the Holdridge life zones framework. The study characterized current climatic niches of tick genera and projected changes in suitability under future climate scenarios for 2040, 2060, 2080, and 2100. Our results reveal a strong association between tick occurrence patterns and large-scale gradients of temperature and atmospheric water balance, while precipitation plays a comparatively minor role. Projections indicate increasing climatic suitability for human-biting ticks at higher northern latitudes, concurrent with declining suitability across parts of central and southern Africa. By integrating modeled suitability with human population projections and livestock distributions, we estimated future changes in exposure risk. Although local processes such as tick abundance and pathogen prevalence are beyond the scope of this study, our findings provide a coherent global synthesis of how climate change may reshape tick distributions and associated risks. Full article

Digital transformation is a key driver of economic and social progress, and assessing its evolution is essential for guiding public policies. In the European Union (EU), until 2022 the European Commission published the quantitative values of the Digital Economy and Society Index (DESI); however, it is no longer being published, which makes it difficult to compare the digitalisation process between Member States. This study proposes a new composite index, the DESI-DP2, constructed using the distance P2 methodology (DP2), which provides a synthetic and up to date measurement of the digitalisation levels in the twenty-seven EU countries in 2025, both at an aggregate term and by dimensions. The results reveal notable stability in the ranking of countries, with Denmark, Finland, the Netherlands, and Sweden as persistent leaders, and Bulgaria and Romania among the most lagging countries. Moreover, although digitalisation is positively associated with human development, a high level of development alone is not sufficient to ensure strong digital performance. Finally, the study identifies a shift in the explanatory factors behind cross-country differences, from digital skills toward the digital transformation of the business sector, offering relevant insights for the design of public policies within the framework of the European Digital Decade. Full article

Accurate power-sector carbon emission data (PS-CED) are critical for ensuring sustainable practices in carbon trading and effective emission reductions. However, conventional centralized reporting systems are susceptible to data tampering, duplicate accounting, and inefficient manual verification, hindering the achievement of sustainability goals. Blockchain technology (BCT) provides transparency, immutability, and automated compliance, offering significant potential for improving the sustainability of PS-CED supervision. Despite this, its diffusion in the sector faces challenges such as data heterogeneity, security concerns, institutional differences, and resource limitations. This study integrates the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) to develop a diffusion framework for BCT adoption in PS-CED supervision with a focus on sustainability. Using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the study examines both linear effects and multiple adoption configurations. The results indicate that adoption willingness mediates the effects of perceived usefulness and ease of use, while perceived regulatory norms underscore policy pressure as a crucial external driver for fostering sustainability. Configurational analysis reveals heterogeneous diffusion patterns, with high adoption performance driven by technological capability combined with regulatory enforcement, and low performance linked to weak technological engagement and structural constraints. Based on these findings, a strategic framework is proposed to support differentiated and phased BCT adoption across organizational contexts to enhance sustainability in carbon emission supervision. This paper clarifies the diffusion mechanisms and provides practical guidance for scaling blockchain-based PS-CED supervision to promote sustainability. Full article

Resource-based cities face persistent challenges in reconciling economic growth with the transition to low-carbon development. This tension poses significant obstacles to sustainable regional development. Digital intelligence transformation (DIT) refers to the deep integration of digitalization and intelligent technologies. It offers a new pathway to enhance urban sustainability. Using panel data from 110 Chinese resource-based cities from 2013 to 2022, this study examines the impact of DIT on carbon emission efficiency (CEE). A comprehensive DIT index is constructed, and the SBM-GML approach is applied to measure CEE. A two-way fixed-effects model is employed to estimate the impact of DIT on CEE. The results show that DIT significantly improves CEE. A one–standard-deviation increase in DIT is associated with a 0.033 rise in CEE, which equals 3.96% of the sample mean. Mechanism analysis indicates that this effect is closely linked to lower resource misallocation and stronger green technological innovation. Heterogeneity analysis further suggests that DIT has a stronger impact in cities with advanced green finance, better digital infrastructure, and those at mature or regenerative development stages. Overall, the findings provide robust empirical evidence that digital intelligence technologies can serve as an effective driver of sustainable development in resource-based cities. Full article

Green innovation has been widely regarded as an important driver of sustainable development; however, its implications for biodiversity conservation remain insufficiently explored. Existing studies primarily focus on the roles of green innovation in pollution control and energy efficiency, leaving its relationship with biodiversity outcomes largely understudied. This gap is particularly pronounced in regions experiencing intense ecological pressure, such as the Yangtze River Economic Belt (YREB), where rapid industrialization and human activities have substantially altered ecosystems. Using panel data from 11 provinces in the YREB over the period 2017–2020, this study examines the impact of green innovation development on biodiversity. Employing a two-way fixed-effects model, the results indicate that green innovation development is positively associated with biodiversity conservation, and this association remains robust to a range of endogeneity checks and robustness tests. To further explore potential transmission channels, we conduct a mechanism analysis. The findings provide indicative evidence that green innovation is associated with biodiversity outcomes through carbon emission reduction and improvements in environmental governance. Overall, this study contributes to the literature by shedding light on the biodiversity implications of green innovation and offers policy-relevant insights for regions seeking to balance innovation-driven growth with ecological protection. Full article

Background: Cardiovascular disease (CVD) remains the leading global cause of mortality, with endothelial dysfunction as an early driver of pathology. Periodontal disease (PD) and its pathogen Porphyromonas gingivalis (Pg) are increasingly associated with metabolic disturbances and vascular injury, yet the combined impact of microbial and dietary stressors has not been mechanistically defined. Methods: In this 24-week study, mice were subjected to chronic Pg infection with or without a high-fat diet (HFD). Metabolic profiling, cytokine analyses, molecular signaling assessments, and ex vivo vascular reactivity studies were performed to evaluate systemic and vascular outcomes. Results: Pg infection induced metabolic alterations and vascular inflammation, while HFD alone caused obesity, insulin resistance, dyslipidemia, and impaired endothelial relaxation. Combined Pg infection and HFD produced the most severe phenotype, with synergistically elevated cytokines, heightened TLR4/NF-κB activation, marked suppression of PPARγ and Nrf2 signaling, reduced eNOS expression, and diminished nitric oxide bioavailability. Cinnamaldehyde (CNM) supplementation improved metabolic indices, reduced inflammatory cytokines, restored PPARγ and Nrf2 activation, enhanced Akt-mediated eNOS phosphorylation, and normalized endothelial-dependent vasorelaxation. Conclusions: Pg infection and HFD act as synergistic metabolic and vascular stressors that accelerate endothelial dysfunction through coordinated disruption of PPARγ/Akt/eNOS and Nrf2 pathways, while CNM provides substantial protective effects. Full article

Water–energy–food (WEF) is fundamental for human survival, with land use profoundly impacting their supply-demand relationships. Integrating land into the WEF nexus is crucial for sustainable development. This study used a pressure–state–response model to establish its water–energy–food–land (WEFL) evaluation indicator system. The entropy method and coupling coordination degree (CCD) were applied to assess the WEFL nexus of Shanxi Province during 2000−2023. The obstacle degree model and Geodetector were utilized to identify internal constraints and external drivers, while the ARIMA model was employed to predict future CCD trends. The results show that (1) the comprehensive evaluation index and CCD increased over time, but overall coordination remained limited (average CCD = 0.575). Most regions were at bare to primary coordination levels, indicating persistent subsystem constraints. The spatial pattern evolved from “high in north and south, low in central region” to “high in north and west, low in south and east.” (2) Energy and land subsystems were the main sources of constraints, while the obstacle degrees of the water and food subsystems increased. External drivers shifted from being dominated by government scale and economic growth to being led by technological innovation and urbanization, with growing interaction between anthropogenic and natural factors. (3) The ARIMA model predicted further CCD improvement to intermediate coordination by 2030, although regional disparities persisted. These results provided a scientific basis for resource management and sustainable development in Shanxi Province. Full article

The increasing occurrence of unfavorable meteorological conditions under global warming has significantly impacted urban atmospheric environments, particularly ozone (O₃) and fine particulate matter (PM_2.5) pollution in densely populated cities. Using nationwide air quality observations and reanalysis data from 2013 to 2022, we assessed the variations in three typical unfavorable meteorological conditions—heatwave (HW), atmospheric stagnation (AS), and temperature inversion (TI)—in Eastern China and their influences on air pollution, as well as the large-scale synoptic drivers behind them. Results indicate that HW and AS events have increased substantially by 9.61 and 1.72 days/decade, leading to remarkable rises in O₃ and PM_2.5 concentrations. Compound events (e.g., HW + AS and HW + TI) exhibit even stronger synergistic impacts, raising O₃ and PM_2.5 concentrations by more than 57.34% and 46.76%, respectively, compared to individual events. In addition, by applying the T-mode Principal Component Analysis (T-PCA), this study identified typical synoptic patterns favorable for such conditions and air pollution events. Synoptic patterns such as the northward displacement of Western Pacific Subtropical High (WPSH) were identified as critical large-scale drivers. These findings highlight linkages between unfavorable meteorological conditions and air quality, providing scientific support for air-quality management and pollution control in Eastern China. Full article

Gelling agents are widely used to solidify plant tissue culture media, yet differences among commercial products may influence the medium’s physical properties and in vitro development of explants. The aim of this study was to characterize the rheological behavior of six gelling agents (Daishin agar, Gelcarin, Gelrite, Microagar, Phytoagar, and Plant agar) and to examine it in parallel with in vitro performance in Hypericum perforatum, Mentha × piperita, and Stevia rebaudiana. Rheological measurements were performed under steady shear by recording apparent viscosity and shear stress across 5–300 s⁻¹. Daishin agar showed the highest apparent viscosity (49,028.95 ± 128 mPa·s), whereas Gelrite exhibited the lowest viscosity (7826.75 ± 98 mPa·s). Plant responses were evaluated after four weeks on PGR-free Driver and Kuniyuki Walnut (DKW) medium by assessing shoot growth, rooting parameters, and shoot water content. In H. perforatum, the longest shoots were obtained on Gelrite (3.92 ± 0.34 cm), accompanied by the highest rooting percentage (95%). In M. × piperita, Gelcarin produced the longest shoots (8.20 ± 0.55 cm) and the highest number of roots per explant (9.75). In S. rebaudiana, Gelcarin promoted superior root elongation (2.86 ± 0.16 cm) and enhanced shoot growth, while Plant agar also supported favorable shoot development. Shoot water content ranged between 74% and 90%, depending on species and gelling agent. These findings highlight the practical relevance of considering low-shear rheological properties when comparing gelling agents for improving the consistency of in vitro culture media. Full article

South Africa’s transport sector remains a major contributor to greenhouse gas emissions, yet limited empirical evidence exists on the environmental drivers of public transport emissions at the provincial level. This study applies an extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework within a dynamic panel setting to examine the determinants of provincial public transport emissions across nine South African provinces from 2015 to 2022. Rather than conducting economy-wide emissions accounting, the analysis focuses on transport-specific drivers relevant to public passenger mobility, including population, income, fuel consumption, infrastructure investment, and modal usage. A Bias-Corrected Method of Moments (BCMM) estimator is employed to address emission persistence, endogeneity, and small-sample bias, with pooled ordinary least squares and fixed-effects models used for robustness. Province fixed effects are used to control for unobserved regional heterogeneity, while common dynamic elasticities are estimated for key influencing factors. The results reveal strong dependence on emissions, indicating substantial structural persistence over time. GDP per capita emerges as the dominant and statistically significant driver of public transport emissions, while population, urbanisation, fuel consumption, transport infrastructure investment, and modal usage (road and rail) are statistically insignificant once dynamics and unobserved heterogeneity are controlled. These findings suggest that public transport emissions in South Africa are driven primarily by economic growth and entrenched structural factors rather than short-run changes in transport systems. Policy implications highlight the need for sustained low-carbon investment, technological transition, and integrated transport planning to decouple economic growth from emissions and support progress toward Sustainable Development Goals 11 and 13. Full article

This study evaluates the effectiveness of California’s homelessness assistance system by integrating national, state, and county-level trends with an analysis of structural barriers, policy implementation gaps, and service coordination challenges. Despite substantial public investment, homelessness in California has continued to rise, underscoring the need to assess not only system capacity but also the mechanisms through which individuals access and transition through services. To address this gap, this study examines sustained engagement as a potential driver of successful exits from homelessness. Using 24 months of Los Angeles County outreach data (N = 88,353), findings show that 30% of individuals exited to interim or permanent housing. A Pearson correlation analysis revealed a statistically significant, moderate positive association between engagement and housing exits (r(21) = 0.42, p = 0.045), indicating that higher engagement levels correspond to improved individual outcomes even within a constrained housing environment. These results highlight the importance of relationship-based service models, cross-sector coordination, and governance reforms to strengthen California’s homelessness response system. Full article