Search Results (5,198)

Education and post-licencing training programmes for novice drivers are widely implemented to improve road safety, yet their effectiveness remains debated. This study evaluates short-term attitudinal changes relating to participation in a mandatory post-licencing training programme for novice drivers in Slovenia. A within-subject pre–post survey methodology was used to evaluate self-reported driving attitudes across six safety-related domains among 225 novice drivers at a Slovenian driving training centre in 2024. Paired t-tests revealed minor yet statistically significant improvement following the programme in perceived support for the additional driver training, lowered overconfidence, heightened care in speeding and intersection behaviour, and enhanced attitudes towards vehicle operation and utilization of safety equipment. Attitudes regarding attention and adherence to traffic regulations showed negligible shifts, indicating a strong baseline attitude towards safe driving. The findings indicate a modest but fairly consistent short-term change in attitudes after programme participation. Due to the lack of a control group and dependence on self-reported data, the findings should be seen as evaluative rather than causative, necessitating more longitudinal and behavioural research to evaluate long-term and behavioural effects. Full article

This study investigates the valuation dynamics of 30 publicly listed fintech firms across six Asian economies from January 2021 to December 2025. It examines how intrinsic firm-level scale (market capitalization) and extrinsic macroeconomic conditions (GDP growth) jointly influence fintech valuation ratios, as reflected in price-to-earnings (P/E), price-to-book (P/B), and price-to-sales (P/S) measures. It also identifies significant structural heterogeneity and distributional asymmetries in valuation outcomes by implementing a multi-method empirical strategy that includes a Panel Autoregressive Distributed Lag (ARDL) framework, two-way fixed-effects models with interaction terms, and quantile regression. The findings reveal a robust, positive long-run relationship between market capitalization and valuation multiples across all ratios, confirming that firm-level scale as reflected in market capitalization is the primary driver of market value. Critically, the analysis identifies a dual-regime landscape in the Asian fintech sector: developed markets (South Korea, Japan, and Singapore) are fundamentally firm-scale driven, where intrinsic scale is the superior predictor of valuation. In contrast, developing markets (China, India, and Indonesia) are primarily macro-growth driven, exhibiting high sensitivity to GDP growth as a macroeconomic indicator of market expansion. The quantile regression results demonstrate a winner-takes-all effect, where the impact of scale on valuation is significantly more pronounced for highly valued firms in the 75th percentile. These results challenge the efficacy of universal valuation models and provide a context-dependent navigational framework for investors, analysts, and policymakers to distinguish between structural scale and cyclical growth in the rapidly evolving Asian fintech ecosystem. Full article

Landfills serve as a primary disposal method for municipal solid waste in China, with over 20,000 operational sites nationwide; however, long-term operations risk leachate leakage and groundwater contamination. Amid intensifying climate change and human activities, understanding contaminant evolution mechanisms in landfills has become critically urgent. Focusing on a representative plain-based landfill in North China, this study integrated field investigations and groundwater monitoring to establish a monthly coupled groundwater flow–solute transport model (using MODFLOW and MT3DMS codes) based on site-specific hydrogeological boundaries and multi-year monitoring data, analyzing spatiotemporal plume evolution under the coupled impacts of precipitation variability (climate change) and intensive groundwater extraction (human activities), spanning the historical period (2021–2024) and future projections (2025–2040). Historical simulations demonstrated robust model performance with satisfactory calibration against observed water levels and chloride concentrations, revealing that the current contamination plume exhibits a distinct distribution beneath the site. Future projections indicate nonlinear concentration increases: in the plume core zone, concentrations rise with precipitation, whereas at the advancing front, concentrations escalate with extraction intensity. Spatially, high-risk zones (>200 mg/L) emerge earlier under wetter conditions—under the baseline scenario (S0), such zones form by 2033 and exceed site boundaries by 2037. Plume expansion scales positively with extraction intensity, reaching its maximum advancement and coverage under the high-extraction scenario. These findings demonstrate dual drivers—precipitation accelerates contaminant accumulation through enhanced leaching, while groundwater extraction promotes plume expansion via heightened hydraulic gradients. This work elucidates coupled climate–human activity impacts on landfill contamination mechanisms, proposing a transferable numerical modeling framework that provides a quantitative scientific basis for post-closure supervision, risk assessment, and regional groundwater protection strategies, thereby aligning with China’s Standard for Pollution Control on the Landfill Site of Municipal Solid Waste and the Zero-Waste City initiative. Full article

Sustainable urban mobility necessitates traffic regimes that enhance operational efficiency and improve traffic safety and flow stability; the rise in intelligent connected vehicles (ICVs) provides a salient mechanism to meet this imperative. This paper aims to investigate the mixed traffic flow characteristics in an intelligent connected environment, using one-way single-lane, double-lane, and three-lane straight highways as modeling objects. Combining the different driving characteristics of human-driven vehicles (HDVs) and ICVs, a single-lane mixed traffic flow model and a multi-lane mixed traffic flow model are established based on the intelligent driver model (IDM) and flexible symmetric two-lane cellular automata model (FSTCAM). The mixed traffic flow in the intelligent connected environment is then simulated using MATLAB R2021a. The research results indicate that the integration of ICVs can improve the speed, flow, and critical density of traffic flow. The increase in the proportion of ICVs can reduce the congestion ratio and speed difference between front and rear vehicles at the same density. As the proportion of ICVs increases, the frequency of lane-changing for HDVs gradually increases, while the frequency of lane-changing for ICVs gradually decreases. The overall lane-changing frequency shows a trend of first increasing and then decreasing. In addition, with the continuous infiltration of ICVs, the area of road congestion gradually decreases, and congestion is significantly alleviated. The speed fluctuation of following vehicles gradually decreases. When the infiltration rate reaches a high level, vehicles travel at a stable speed and remain in a relatively steady state. The findings substantiate the potential of ICV-enabled operations to advance efficiency-oriented and stability-enhancing urban mobility and to inform evidence-based traffic management and policy design. Full article

Recent studies in the Romanian Western Carpathians have revealed increasing socio-demographic fragility in rural areas and small towns, driven by depopulation, population aging, and declining living standards. These trends stem from the legacy of forced collectivization and industrialization (1950–1990) and the post-1990 transition, which triggered extensive out-migration and the erosion of local socio-economic structures. This study examines the fragility of human communities in the Trascău Mountains in order to evaluate spatial, demographic, and economic recovery dynamics and to assess settlement vulnerability as a major obstacle to sustainable regional development. Fragility was measured using indicators of population density and change, age structure, accessibility, and socio-demographic dynamics, based on comparative data for the interval of 1977–2021. These variables were integrated into a composite development index (Id), derived from twelve indicators covering demography, economy, infrastructure, and living standards, enabling the hierarchical classification of settlements by degree of vulnerability. The methodological framework combines empirical and analytical methods, statistical, cartographic, bibliographic, and field-based analyses within evolutionary, structural–functional, and typological perspectives. The results identify the main drivers of decline, quantify their impacts, and outline development prospects and policy directions for reducing territorial disparities. Overall, fragile settlements emerge as critical pressure points that undermine sustainability, intensify regional instability, and increase risks related to migration and social cohesion. Full article

This study critically examines the sectoral dynamics of renewable energy (RE) adoption across the EU-27 from 1990 to 2023, addressing the persistent gap between electricity generation and end-use sectors. Utilizing Eurostat energy balance data, the research employs a robust multi-methodological framework. We apply the Logarithmic Mean Divisia Index (LMDI) decomposition to isolate driving factors, and the Self-Organizing Maps (SOM) of Kohonen to cluster countries with similar transition structures. Furthermore, the Method of Moments Quantile Regression (MMQR) is used to estimate heterogeneous drivers across the distribution of RE shares. The empirical findings reveal a sharp dichotomy: while the share of renewables in the electricity generation mix (RES-E-Renewable Energy Share in Electricity) reached approximately 53.8% in leading member states, the aggregated share in the transport sector (RES-T) remains significantly lower at 9.1%. This distinction highlights that while power generation is decarbonizing rapidly, end-use electrification lags behind. The MMQR analysis indicates that economic growth drives renewable adoption more effectively in countries with already high renewable shares (upper quantiles) due to established market mechanisms and grid flexibility. Conversely, in lower-quantile countries, regulatory stability and direct infrastructure investment prove more critical than market-based incentives, highlighting the need for differentiated policy instruments. While EU policy milestones (RED I–III-) align with progress in power generation, they have failed to accelerate transitions in lagging sectors. This study concludes that achieving climate neutrality requires moving beyond aggregate targets to implement distinct, sector-specific interventions that address the unique structural barriers in transport and thermal applications. Full article

The current-mode logic (CML) driver has evolved alongside integrated circuit (IC) technology. Its typical structure contains a tail current source, differential amplifying transistors, and load resistors. It is widely used in modern optical transceivers and other serial link transceivers, and is compatible with various processes, including CMOS, SiGe BiCMOS, and InP DHBT. The basic performance indicators of CML driver include gain, bandwidth, power, and total harmonic distortion (THD). For different application scenarios, different tail currents and load resistance are required. Nowadays, as the performance requirements for drivers in various applications continue to increase, more techniques need to be employed to balance high speed, high output amplitude, high linearity, and low power, such as bandwidth expansion techniques, linearity improvement techniques, and gain control techniques. In this review, the electrical characteristics of basic CML circuits are highlighted and compared with other interface level standards. The advancement of CML drivers is summarized. Emerging CML structures and performance enhancement technologies are introduced and analyzed. Design considerations are concluded in terms of the challenges faced by high-speed drivers. The review provides comparative study and comprehensive reference for designers. Full article

Climate change increasingly threatens global food security through disrupted precipitation patterns and extreme weather events, requiring resilient systems for assessing agricultural vulnerability. This study developed and compared machine learning approaches for predicting cropland vulnerability using Earth Observation data, operationalized through NDVI anomalies as a defensible biophysical metric. We employed both a dual-stream deep learning architecture and a Random Forest model to predict 2023 NDVI anomalies across Bangladesh croplands using a 22-year time series (2001–2023) of MODIS vegetation indices, ERA5 climate variables, and static environmental covariates. A spatially aware block cross-validation strategy ensured rigorous, independent performance evaluation. Results demonstrated that the Random Forest model (R² = 0.70, RMSE = 197.03) substantially outperformed the deep learning architecture (R² = 0.02, RMSE = 357.57), explaining 70% of cropland stress variance and enabling early detection of vulnerable areas three months before harvest. Feature importance analysis identified recent climate variables, March precipitation, February NDVI, and vapor pressure deficit as primary vulnerability drivers. Spatial analysis revealed distinct vulnerability patterns, with Natore and Magura districts exhibiting elevated stress consistent with 2023 drought conditions, threatening the productivity of the region’s critical irrigation-dependent rice cultivation. These findings demonstrate that simpler, interpretable models can sometimes outperform complex architectures while providing useful information for early warning systems and precision targeting of climate adaptation interventions. Full article

Mammalian placentation represents one of the most striking evolutionary innovations among vertebrates, and accumulating evidence indicates that virus-derived genes—particularly the metavirus-derived PEG10 and PEG11/RTL1—have played indispensable but distinct roles: PEG10 in the emergence of therian viviparity and PEG11/RTL1 in the subsequent differentiation between marsupial and eutherian placental types. Notably, the metavirus-derived SIRH/RTL gene group, which includes PEG10 and PEG11/RTL1, exhibits unique and diverse functions not only in placenta development but also within microglia of the brain. Because microglia originate from yolk sac progenitors, these findings suggest that extraembryonic tissues such as the placenta and yolk sac provided permissive environments that enabled the retention, expression and functional domestication of virus-derived sequences. Once the placenta itself was established through viral gene integration, it may in turn have acted as a powerful driver of eutherian evolution through recurrent acquisition and co-option of additional virus-derived genes—a process we refer to as “placenta-driven evolution.” This perspective offers a unified framework in which viral gene acquisition is viewed as a key driver of genomic innovation, tightly intertwined with the emergence of viviparity, subsequent divergence at the marsupial–eutherian split, and continued diversification of placental structure and function across eutherian lineages. Full article

The abrupt COVID-19 lockdown in early 2020 offered a unique natural experiment to examine vegetation productivity responses to sudden declines in human activity. Although vegetation often responds to environmental changes with time lags, how such lags operate under short-term, intensive disturbances remains unclear. This study combined multi-source environmental data with an interpretable machine learning framework (XGBoost-SHAP) to analyze spatiotemporal variations in net primary productivity (NPP) across the Beijing-Tianjin-Hebei region during the strict lockdown (March–May) and recovery (June–August) periods, using 2017–2019 as a baseline. Results indicate that: (1) NPP showed a significant increase during lockdown, with 88.4% of pixels showing positive changes, especially in central urban areas. During recovery, vegetation responses weakened (65.31% positive) and became more spatially heterogeneous. (2) Integrating lagged environmental variables improved model performance (R² increased by an average of 0.071). SHAP analysis identified climatic factors (temperature, precipitation, radiation) as dominant drivers of NPP, while aerosol optical depth (AOD) and nighttime light (NTL) had minimal influence and weak lagged effects. Importantly, under lockdown, vegetation exhibited stronger immediate responses to concurrent temperature, precipitation, and radiation (SHAP contribution increased by approximately 7.05% compared to the baseline), whereas lagged effects seen in baseline conditions were substantially reduced. Compared to the lockdown period, anthropogenic disturbances during the recovery phase showed a direct weakening of their impact (decreasing by 6.01%). However, the air quality improvements resulting from the spring lockdown exhibited a significant cross-seasonal lag effect. (3) Spatially, NPP response times showed an “urban-immediate, mountainous-delayed” pattern, reflecting both the ecological memory of mountain systems and the rapid adjustment capacity of urban vegetation. These findings demonstrate that short-term removal of anthropogenic disturbances shifted vegetation responses toward greater immediacy and sensitivity to environmental conditions. This offers new insights into a “green window period” for ecological management and supports evidence-based, adaptive regional climate and ecosystem policies. Full article

Chronic inflammation constitutes a well-established driver of colorectal carcinogenesis, yet the molecular circuitry linking inflammatory receptor signalling to tumour cell survival remains incompletely delineated. Here we demonstrate that the HMG-CoA reductase inhibitors atorvastatin and rosuvastatin modulate inflammatory survival pathways in colorectal cancer cells in a manner consistent with targeted interference with the protease-activated receptor 2 (PAR-2)–extracellular signal-regulated kinase (ERK)–tumour necrosis factor-α (TNF-α) signalling axis. Using lipopolysaccharide-stimulated HT-29 and Caco-2 cells as complementary models of inflammatory colorectal malignancy, we show that both statins selectively attenuate PAR-2 expression at the protein and transcript levels while leaving structurally related PAR-1 unaffected. This pattern of receptor modulation is accompanied by suppression of total ERK1/2 expression, ERK1/2 phosphorylation, and the transcriptional target DUSP6, together with attenuation of TNF-α secretion. Importantly, these signaling shifts are associated with dual apoptotic programs; the extrinsic pathway, reflected by transcriptional upregulation and proteolytic activation of caspase-8; and the intrinsic mitochondrial pathway, evidenced by reciprocal modulation of Bcl-2 family proteins favoring Bax over Bcl-2. Both pathways converge upon activation of executioner caspase-3 and an increase in Annexin V-defined apoptotic fractions, indicating re-engagement of programmed cell death under inflammatory stress. Notably, rosuvastatin consistently demonstrates superior potency across signaling endpoints, achieving comparable biological effects at lower concentrations than atorvastatin. Collectively, these data indicate that clinically deployed statins target the PAR-2–ERK axis and are associated with re-activation of apoptotic pathways in inflammatory colorectal cancer models, while leaving open the possibility that additional statin-responsive networks contribute to their pro-apoptotic effects. This mechanistic framework provides biological plausibility for epidemiologic observations linking statin use with reduced colorectal cancer risk and improved outcomes, and supports further translational evaluation of PAR-2-directed statin strategies in colorectal malignancy. Full article

Urban air mobility solutions such as drone taxi services are increasingly viewed as a promising response to congestion, sustainability, and smart-city mobility challenges. However, the large-scale adoption of such services depends on users’ perceptions of service experience, trust, and readiness to engage with emerging technologies. This study investigates the determinants of sustainable adoption of drone taxi services in the United Arab Emirates (UAE) by examining technology readiness and service experience factors, interpreted through conceptual alignment with the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to potential users, capturing perceptions related to optimism, innovation readiness, efficiency, control, privacy, insecurity, discomfort, inefficiency, and perceived operational risk, along with behavioral intention to adopt drone taxi services. Measurement reliability and validity were rigorously assessed using Cronbach’s alpha, composite reliability, average variance extracted (AVE), and the heterotrait–monotrait (HTMT) criterion. The validated latent construct scores were subsequently used to estimate a structural regression model examining the relative influence of each factor on adoption intention. The results indicate that privacy assurance and perceived control exert the strongest influence on behavioral intention, followed by optimism and innovation readiness, while negative readiness factors such as discomfort, insecurity, inefficiency, and perceived chaos demonstrate negligible effects. These findings suggest that in technologically progressive contexts such as the UAE, adoption intentions are primarily shaped by trust-building and empowerment-oriented perceptions rather than deterrence-based concerns. By positioning technology readiness and service experience constructs within established TAM and UTAUT theoretical perspectives, this study contributes a context-sensitive understanding of adoption drivers for emerging urban air mobility services. The findings offer practical insights for policy makers and service providers seeking to design user-centric, trustworthy, and sustainable drone taxi systems. Full article

This study examines the tension between “substance” and “form” in standardized sustainability reporting within an emerging market context. Using 21,964 firm-year observations from Chinese A-share listed companies (2018–2023), we investigate whether the adoption of the Global Reporting Initiative (GRI) framework enhances substantive Environmental, Social, and Governance (ESG) and creates firm value. While baseline regressions suggest a positive link between GRI and ESG performance, rigorously applying Propensity Score Matching (PSM) reveals a critical nuance: the effect of mere framework adoption attenuates after controlling for selection bias, whereas independent assurance remains a robust driver of substantive governance quality. Furthermore, mediation analysis using bootstrap resampling documents a distinct “Labeling Effect”: GRI adoption directly enhances market valuation (Tobin’s Q), yet the indirect path via ESG scores is statistically insignificant. This indicates that investors utilize GRI as a heuristic signal of legitimacy rather than pricing granular performance metrics. We also identify a “Valuation Latency”, where substantive ESG improvements significantly boost operational profitability (ROA) but are not yet fully incorporated into stock prices. Heterogeneity analysis shows these effects are stronger for non-state-owned enterprises (Non-SOEs), supporting the view that private firms leverage standardized reporting and verification to mitigate legitimacy deficits. These findings provide empirical evidence for regulators and investors to distinguish between the “label” of adoption and the “substance” of verification. Full article

CLARA-A3 currently provides the longest temporal coverage among available albedo products, with improvements in both retrieval algorithms and product coverage compared to earlier versions. This study first evaluates the performance of the CLARA-A3-SAL product over Greenland Ice Sheet (GrIS) and subsequently applies it to investigate spatiotemporal trends in summer albedo from 1979 to 2024. Validation against 32 in situ observation sites indicates negligible bias in the interior regions, with RMSE values ranging from 0.01 to 0.07. Although larger errors exist in the coastal ablation zone due to unresolved sub-grid surface heterogeneity, the product successfully captures observed spatiotemporal variability and long-term trends, demonstrating that CLARA-A3-SAL provides a generally reliable representation of surface albedo. Since 1979, the summer surface albedo averaged over the entire ice sheet has decreased at a rate of −0.24% decade⁻¹. Albedo in the dry snow area has remained relatively stable and showed no significant correlation with most climate variables, except for the North Atlantic Oscillation (NAO) and the Greenland Blocking Index (GBI). Conversely, the marginal zone has undergone substantial darkening (−0.66% decade⁻¹), which is strongly correlated with temperature, snowfall and melt, with meltwater showing the highest correlation (r = −0.90, p < 0.01). This suggests that meltwater-driven grain growth and exposure of bare ice are the primary drivers of albedo reduction over the non-dry snow zone. Large-scale atmospheric circulation also plays a key role: the GBI exhibits the strongest association with albedo (r = −0.63, p < 0.05), underscoring the importance of persistent blocking in amplifying surface warming and darkening. Furthermore, decadal-scale variability associated with the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO) modulates both the magnitude and spatial pattern of albedo changes across GrIS, with AMO+ generally linked to reduced albedo and PDO+ tending to enhance it. Full article

Gastronomic tourism has emerged as a significant driver of destination competitiveness, cultural preservation, and sustainable development. This comprehensive review synthesizes the scientific literature published between 2010 and 2025, examining the evolution of gastronomic tourism research, global trends, sustainability dimensions, and emerging challenges. Through systematic analysis of 150 peer-reviewed articles from major academic databases, this paper identifies key themes including cultural heritage preservation, sustainability practices, digital transformation, and educational innovations. The findings reveal a growing recognition of gastronomy as intangible cultural heritage, increased focus on farm-to-fork practices, and the transformative role of social media in destination branding. Despite the rapid growth of gastronomic tourism research, important gaps remain. The literature lacks standardized sustainability indicators, limiting comparability across studies, while cross-cultural comparative research and interdisciplinary integration remain insufficiently developed. Addressing these gaps is essential for advancing theoretical coherence and supporting evidence-based practice in gastronomic tourism. Full article