Search Results (3,363)

As the deployment of Automated Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS-L2) expands, understanding their real-world safety performance becomes essential. This study examines the severity and contributing factors of crashes involving vehicles equipped with ADS and ADAS-L2 technologies using NHTSA data. Using machine learning models on crash datasets from 2021 to 2024, this research identifies patterns and risk factors influencing injury outcomes. After data preprocessing and handling missing values for severity classification, four models were trained: logistic regression, random forest, SVM, and XGBoost. XGBoost outperformed the others for both ADS and ADAS-L2, achieving the highest accuracy and recall. Variable importance analysis showed that for ADS crashes, interactions with other road users and poor lighting were the strongest predictors of injury severity, while for ADAS-L2 crashes, fixed object collisions and low light conditions were most influential. From a policy and engineering perspective, this study highlights the need for standardized crash reporting and improved ADS object detection and pedestrian response. It also emphasizes effective human–machine interface design and driver training for partial automation. Unlike previous research, this study conducts comparative model-based evaluations of both ADS and ADAS-L2 using recent crash reports to inform safety standards and policy frameworks. Full article

The comprehensive evaluation of factors that increase the difficulty of autonomous driving in various complex traffic situations and diverse roadway geometries within living lab environments is of great interest, particularly in developing sustainable urban mobility systems. This study introduces a novel methodology for assessing autonomous driving vulnerabilities and identifying urban traffic segments susceptible to autonomous driving risks in mixed traffic situations where autonomous and manual vehicles coexist. A microscopic traffic simulation network that realistically represents conditions in a living lab demonstration area was used, and twelve safety indicators capturing longitudinal safety and vehicle interaction dynamics were employed to compute an integrated risk score (IRS). The promising weighting of each indicator was derived through decision tree method calibrated with real-world traffic accident data, allowing precise localization of vulnerability hotspots for autonomous driving. The analysis results indicate that an IRS-based hotspot was identified at an unsignalized intersection, with an IRS value of 0.845. In addition, analytical results were examined comprehensively from multiple perspectives to develop actionable improvement strategies that contribute to long-term sustainability, encompassing roadway and traffic facility enhancements, provision of infrastructure guidance information, autonomous vehicle route planning, and enforcement measures. Furthermore, this study categorized and analyzed the characteristics of high-risk road sections with similar geometric features to systematically derive effective traffic safety countermeasures. This research offers a systematic, practical framework for safety evaluation in autonomous driving living labs, delivering actionable guidelines to support infrastructure planning and validate sustainable autonomous mobility. Full article

Immersive methods and biometric tools provide a rigorous, context-rich way to study how people perceive and choose food. Immersive methods use extended reality, including virtual, augmented, mixed, and augmented virtual environments, to recreate settings such as homes, shops, and restaurants. They increase participants’ sense of presence and the ecological validity (realism of conditions) of experiments, while still tightly controlling sensory and social cues like lighting, sound, and surroundings. Biometric tools record objective signals linked to attention, emotion, and cognitive load via sensors such as eye-tracking, galvanic skin response (GSR), heart rate (and variability), facial electromyography, electroencephalography, and functional near-infrared spectroscopy. Researchers align stimuli presentation, gaze, and physiology on a common temporal reference and link these data to outcomes like liking, choice, or willingness-to-buy. This approach reveals implicit responses that self-reports may miss, clarifies how changes in context shift perception, and improves predictive power. It enables faster, lower-risk product and packaging development, better-informed labeling and retail design, and more targeted nutrition and health communication. Good practices emphasize careful system calibration, adequate statistical power, participant comfort and safety, robust data protection, and transparent analysis. In food science and consumer behavior, combining immersive environments with biometrics yields valid, reproducible evidence about what captures attention, creates value, and drives food choice. Full article

Public procurement in many European Union member states remains strongly price-oriented, often at the expense of delivery performance, innovation, and effective risk management. This study examines how the Best Value Approach (BVA) operates within a post-transition, legality-focused administrative environment through a document-based embedded case study of a major public construction contract in the Czech Republic. By analysing artefacts from the Selection, Clarification, and Execution phases, the study traces how BVA’s core governance mechanisms—expert signalling, vendor-led risk ownership, and information-centric oversight—functioned under locally constraining conditions. The findings show that BVA improved capability sorting, surfaced risks earlier, and enhanced transparency through structured reporting instruments such as Weekly Risk Reports (WRRs), Directors’ Reports (DRs), and Key Performance Indicators (KPI)s. However, the performance effects were partial. Three boundary conditions attenuated BVA’s mechanisms: a 40% price weighting that constrained qualitative differentiation, the omission of a formal Value-Added (VA) pathway for supplier-initiated optimisation, and the absence of continuous expert facilitation to support methodological fidelity. A documented execution-phase cost variance of approximately five percent further indicates residual volatility where key BVA complements are incomplete. The study integrates Principal–Agent theory, New Public Governance, and institutional isomorphism to explain why BVA’s governance architecture activated only in attenuated form and identifies the institutional conditions that moderate its effectiveness. While limited to a single revelatory case, the findings support analytical generalisation to similarly price-dominant, audit-driven procurement regimes in post-transition EU member states and offer practical guidance for evaluation design, innovation pathways, and facilitation models. Full article

The Veracruz Reef System National Park (VRSNP), located in the central Gulf of Mexico, is one of the country’s most ecologically and economically significant coral systems. Despite its high biodiversity and ecosystem functionality, it is affected by anthropogenic inputs such as fluvial discharges, urban effluents, and port and tourism activities that contribute organic and bacteriological loads. This study aimed to identify the presence of Enterococcus spp. and Vibrio spp. during three climatic seasons—dry, rainy, and north winds—at two water column depths (surface and bottom) across three reefs (Enmedio, Chopas, and Gallega) within the VRSNP during the 2022 annual cycle. Samples were analyzed according to national and international standards. Results showed that Vibrio spp. were influenced mainly by temporal factors, with higher values during north winds and the dry season (>1100 MPN/100 mL); otherwise, rainy conditions reported the lowest load (184.89 ± 15.00 MPN/100 mL). While Enterococcus spp. exhibited greater spatial influence, particularly in surface waters, Enmedio Reef recorded the highest load (478.34 ± 37.28 CFU/100 mL); in addition, Chopas Reef reported the lowest at the bottom (12.43 ± 1.26 CFU/100 mL). The findings highlight the need to strengthen microbiological monitoring protocols in marine coastal ecosystems to assess water quality, public health risks, and the ecological integrity of coral reef environments, as well as the implementation of molecular identification techniques. Full article

Understanding how urbanisation shapes species distributions and ecological interactions requires long-term, spatially structured data. Using an exceptionally rare 40-year dataset (1986–2025) from 150 habitat patches and 102 downtown grid cells in St. Petersburg, Russia, we examined patterns in birch (Betula pendula and B. pubescens) persistence, ground conditions, woody vegetation, and the occurrence of Eriocrania leaf-mining moths. Birch presence, birch abundance, and ground quality declined both toward the city centre and over time, whereas woody plant cover showed no clear spatial or temporal pattern. Eriocrania occurrence within birch-containing patches was influenced primarily by habitat type, artificial ground, and birch abundance, while distance to the city centre, year, and woody cover exerted no consistent effects. Habitat characteristics offered only moderate predictive power for local extinction risk in both birches and Eriocrania, indicating that multiple drivers interact to shape patch dynamics. Contrary to the widespread declines observed in many insect taxa, Eriocrania populations exhibited no directional density trend across four decades. This long-term stability highlights the resilience of specialised herbivores in heterogeneous urban landscapes and underscores the value of extended temporal datasets for detecting subtle or unexpected ecological responses to urbanisation. Full article

The aim of the research was to detect the existing microclimatic conditions of the environment in the Visitor Center of the Sirmium Imperial Palace and to determine whether they pose any potential risks to the preservation of the mosaics in room 34 (M34). In order to estimate the microclimatic conditions of the environment and examine their effects on the deterioration processes of the mosaic, the following research methods were applied: one-year microclimatic monitoring of air temperature and relative humidity, monitoring of physical processes in the mosaic and on its surface, determining the presence of soluble salts, the potential biological contamination by aerobiological sampling, and the present biological contamination by using adhesive tape and sterile swabs. The results of microclimatic monitoring indicate that the relative humidity values during January, February, November and December were constantly above 80%. The annual range of temperature values ranged from 0.4 °C to 31.5 °C, while the relative humidity values ranged from 38.9% to 93.9%. The results of microbiological analysis showed high biological contamination of the M34 mosaic, which could be expected because the conditions were favorable for fungal growth throughout the year (a_w > 0.6). Soluble salts, i.e., sulfates, nitrates and chlorides, were identified on the mentioned mosaic. It can be concluded that the existing conditions in the Visitor Center of the Sirmium Imperial Palace pose a risk to the preservation of the mosaic and that they need to be improved. Considering the interdependence of the microclimatic conditions of the environment and the physical, chemical and biological processes of mosaic deterioration, microclimatic monitoring must be introduced at archeological sites with mosaics as a mandatory procedure for the purpose of monitoring the microclimatic conditions of the environment and preventive protection. Full article

Urban river floodplains function not only as zones for flood regulation and ecological buffering but have increasingly been utilized as multifunctional spaces that support leisure, waterfront, and cultural activities. However, overlapping hydraulic and geomorphic factors such as channel meandering, vegetation distribution, and flood-induced flow redistribution have amplified environmental risks, including recurrent erosion deposition, vegetation disturbance, and infrastructure damage, yet quantitative assessment frameworks remain limited. This study systematically evaluates the environmental safety of an urban floodplain by estimating vegetation variability using Sentinel-2 derived NDVI time series and deriving SEDI and TEDI through FaSTMECH two-dimensional hydraulic modeling. NDVI response cases were identified for different rainfall intensities, and interpolation-based hazard maps were generated using spatial cross-validation. Results show that the left bank exhibits higher vegetation variability, indicating strong sensitivity to hydrological fluctuations, while outer meander bends repeatedly display elevated SEDI and TEDI values, revealing concentrated structural vulnerability. Integrated analyses across rainfall conditions indicate that overall safety remains high; however, low-safety zones expand in the upstream meander and several outer bends as rainfall intensity increases. Full article

Background/Objectives: Obesity and insulin resistance (IR) increase the risk of mood disorders, which often manifest during young adulthood. However, neuroelectrophysiological investigations of whether adiposity and IR modify electrocortical activity and emotional processing outcomes remain underexplored, particularly in young adults. Therefore, this study used electroencephalography (EEG) to investigate whether obesity and/or IR moderate the relationships between brain potentials and affective processing in younger adults. Methods: Thirty younger adults completed a passive picture-viewing task utilizing the International Affective Picture System while real-time electroencephalography was simultaneously recorded. Two event-related potentials—early posterior negativity (EPN) and late positive potential (LPP)—were quantified. Affective processing parameters included mean valence ratings and stimulus-to-response-onset reaction times in response to unpleasant, pleasant, and neutral images. Body fat percentage and Homeostatic Model Assessment for Insulin Resistance values were measured. Hierarchical moderated regression analysis was utilized to test the interrelationships between brain potentials, adiposity, IR, and affective processing. Results: In the Negative−Neutral condition, lean and insulin-sensitive participants gave less negative valence ratings to unpleasant versus neutral images when late-window LPP amplitudes were larger, whereas this relationship was reversed in participants with obesity and absent in those with IR. Contrariwise, neither obesity nor IR moderated LPP responses to affective processing parameters in the Positive−Neutral or Negative−Positive valence conditions. Additionally, obesity and IR did not moderate the links between EPN responses and affective processing parameters in any of the valence conditions. Conclusions: Lean, insulin-sensitive young adults showed attenuated affective processing of unpleasant stimuli through stronger neural responses, whereas neural responses to pleasant stimuli did not vary across levels of body fat or IR. These preliminary findings suggest that both obesity and IR increase the vulnerability to mood disorders in young adulthood. Full article

Conventional methods of nano-urea application in maize cultivation, such as tractor-operated gun sprayers, involve high water usage, labor intensity, and operator health risks due to chemical exposure. The drone spraying system ensures precise and automated application of nano-urea with minimal resource use, labor requirement, and operator intervention. However, the efficacy of the drone spraying system for nano-urea application was not evaluated and compared with traditional spraying systems in field conditions. There is a need to evaluate whether drone-based spraying systems can provide an equally effective and more resource-efficient alternative to conventional spraying techniques. Therefore, this study evaluated the agronomic efficacy of a drone-based spraying platform in comparison to conventional tractor-operated gun sprayers for the foliar spray application of nano-urea in the maize crop. Field experiments were conducted during the 2024 Kharif season to evaluate changes in SPAD, NDVI values, and grain yield due to two spray application methods. Both spraying methods showed statistically similar NDVI and SPAD values eight days after nano-urea application, indicating comparable effectiveness in nutrient delivery. Maize yield was also observed to be statistically indistinguishable between the two methods (t (8) = 0.025503, p = 0.9803), with 2912 ± 375 kg/ha (mean ± SE) for the gun sprayer and 2928 ± 503 kg/ha for the drone sprayer treatments. However, the drone system demonstrated significant operational advantages, including 95% water savings and decreased operational time. These findings support the use of drone spraying as a sustainable, safe, and scalable alternative to traditional fertilization application practices in precision agriculture. Full article

In the realm of financial management, optimizing the allocation of funds in banking companies is vital to their operational efficiency. Banks manage their funds by allocating them into reserve and credit funds as the main activities of banking. Optimizing these allocations ensures that all assets are effectively utilized. However, real-life optimization problems often involve uncertainty, making deterministic data assumptions insufficient. Robust Optimization is a methodology that addresses these uncertainties by incorporating computational tools to solve optimization problems with uncertain data. The uncertainty approach used in robust optimization is polyhedral sets. In the context of banking, uncertainties influencing the allocation of reserve and credit funds include financial risks and returns. These risks can be quantified using Conditional Value-at-Risk (CVaR), a suitable measure for banking fund allocation due to its ability to accommodate varying risk characteristics under different business conditions. This study focuses on developing an optimization model for reserve and credit fund allocation in banking companies using a Multi-objective Robust CVaR approach with lexicographic, informed by business risk data and credit instruments. The resulting optimization model yields optimal allocations for reserve and credit funds, ensuring efficient asset utilization to support banking operations. This approach offers new perspectives for banks to achieve fund allocations that are not only regulatory compliant but also optimal. The implications of such optimal allocations include mitigating risks associated with reserve fund imbalances and enhancing profitability through optimal credit returns. Full article

As a form of long-term asset allocation, pension fund investment necessitates accurate estimation of both asset returns and associated risks over extended time horizons. However, long-term asset returns are significantly influenced by macroeconomic factors, whereas variance-based risk measures cannot account for the directional nature of deviations from expected returns. To address these issues, we propose a novel CVaR-based Black–Litterman model incorporating macroeconomic cycle views (CVaR-BL-MCV) for optimal asset allocation of pension funds. This approach integrates macroeconomic cycle dynamics to quantify their impact on asset returns and utilizes Conditional Value-at-Risk (CVaR) as a coherent measure of downside risk. We employ a Markov-switching model to identify and forecast the phases of economic and monetary cycles. By analyzing the economic cycle with PMI and CPI, economic conditions are categorized into three distinct phases: stable, transitional, and overheating. Similarly, by analyzing the monetary cycle with M2 and SHIBOR, monetary conditions are classified into expansionary and contractionary phases. Based on historical asset return data across these cycles, view matrices are constructed for each cycle state. CVaR is used as the risk measure, and the posterior distribution of the Black–Litterman (BL) model is derived via generalized least squares (GLS), thereby extending the traditional BL framework to a CVaR-based approach. The experimental results demonstrate that the proposed CVaR-BL-MCV model outperforms the benchmark models. When the risk aversion coefficient is 1, 1.5, and 3, the Sharpe ratio of pension asset allocation using the CVaR-BL-MCV model is 21.7%, 18.4%, and 20.5% higher than that of the benchmark models, respectively. Moreover, the BL model incorporating CVaR improves the Sharpe ratio of pension asset allocation by an average of 19.7%, while the BL model with MCV achieves an average improvement of 14.4%. Full article

The accelerating impacts of climate change present critical challenges to cultural heritage, particularly in the Mediterranean region where hydroclimatic extremes are intensifying. Future estimates for the Sanctuary of Asklepios at Epidaurus, a UNESCO World Heritage Site, suggest more intense precipitation patterns, increased rainfall intensity and water-induced material degradation. This study aims to identify current and projected climate-related threats to the site and to inform adaptive strategies that safeguard both its physical integrity and its associated heritage values through a value-based approach. Opting for a heritage value-based risk assessment, the study employs a mixed-methods technical approach grounded in the Conceptual Framework for Disaster Risk Reduction of UNISDR and ICCROM’s “ABC Method” for the risk assessment of climatic threats that combines GIS-based hydrological modelling (HAND), field observations and existing material assessments with NARA Grids to link exposure, vulnerability and value loss. Results reveal intensified surface water runoff and localised water inundation threatening key monuments, particularly the Roman Odeion and the central part of the site’s ensemble, while frost-related risks are projected to decline towards 2100. The findings suggest the development of site-specific climate change adaptation that prioritises drainage enhancement, preventive conservation and continuous monitoring to preserve its Outstanding Universal Values under changing climatic conditions. Full article

Urban Heat Islands (UHIs) are urbanized areas that experience significantly higher temperatures than their surroundings, contributing to thermal discomfort, increased air pollution, heightened public health risks, and greater energy demand. In Bhutan, where urban expansion is concentrated within narrow valley systems, the formation and intensification of UHIs present emerging challenges for climate-resilient urban development. Thimphu, in particular, is experiencing rapid urban growth and densification, making it highly susceptible to UHI effects. Therefore, the aim of this study was to evaluate and simulate UHI conditions for Thimphu Thromde. We carried out the simulation using a GIS, multi-temporal Landsat imagery, and an Artificial Neural Network model. Land use and land cover classes were mapped through supervised classification in the GIS, and surface temperatures associated with each class were derived from thermal bands of Landsat data. These temperature values were normalized to identify existing UHI patterns. An Artificial Neural Network (ANN) model was then applied to simulate future UHI distribution under expected land use change scenarios. The results indicate that, by 2031, built-up areas in Thimphu Thromde are expected to increase to 72.82%, while vegetation cover is projected to decline to 23.52%. Correspondingly, both UHI and extreme UHI zones are projected to expand, accounting for approximately 14.26% and 6.08% of the total area, respectively. Existing hotspots, particularly dense residential areas, commercial centers, and major institutional or public spaces, are expected to intensify. In addition, new UHI zones are likely to develop along the urban fringe, where expansion is occurring around the current hotspots. These study findings will be useful for Thimphu Thromde authorities in deciding the mitigation measures and pre-emptive strategies required to reduce UHI effects. Full article

As life expectancy increases, the disease profile of the population also changes, with a higher prevalence of chronic diseases and reduced functional capacity, which increases the risk of social isolation and vulnerability. The aim of this study was to identify the association between functionality, depressive symptoms, and fragility in elderly adults in primary care. This is an exploratory, descriptive study with a quantitative approach and a cross-sectional design, carried out in a municipality in the interior of southwestern Bahia. The instruments used were the Mini-Mental State Examination (MMSE), a sociodemographic questionnaire on health conditions, the Edmonton Fragility Scale (EFS), and the Self-Reported Fragility Scale. The data were analyzed through descriptive analyses with absolute and relative frequencies and the application of the Chi-square test, adopting a value of p ≤ 0.05. Results: A statistically significant difference was found between elderly adults classified as frail and female gender (p = 0.019), marital status without a partner (p = 0.001), dependence in BADL (p = 0.008), dependence in IADL (p-value = 0.000), and the presence of depressive symptoms (p = 0.000). Conclusion: This study found an association between marital fragility related to being without a partner, dependence in IADL (instrumental activities of daily living), and the presence of depressive symptoms. Full article