Search Results (22,452)

Using International Waterbird Census data spanning 1993–2022, we analysed temporal trends in the abundance and community composition of wintering waterbirds in the Venice Lagoon (NE Italy). We examined total numbers, major lagoon macro-areas (fish farms, open lagoon, coastal littoral zone, minor wetlands), species-level and guild-level trends and assessed climate-related community changes through the Community Temperature Index (CTI). Total wintering waterbird abundance increased markedly over the study period, from 74,348 birds in 1993 to 445,350 in 2022. Fish farms (about 20% of the total area) hosted the largest number of individuals (about 83%) and accounted for most of the lagoon-wide increase, while open lagoon (15%) and coastal littoral (<2%) areas showed weaker and more variable dynamics. Species-level analyses revealed pronounced heterogeneity, with strong increases in several Anatidae, contrasted by stable or declining trends in other species. The CTI exhibited a significant long-term increase, indicating a progressive shift towards communities dominated by warm-affinity species. CTI decomposition nevertheless showed this signal was disproportionately driven by a limited number of highly abundant species. Our results indicate that wintering waterbird dynamics in the Venice Lagoon are shaped by the interaction between large-scale climatic processes and local habitat management, particularly within fish farms. While management practices can likely sustain exceptionally high wintering numbers and potentially buffer climate-driven redistribution, they may also promote strong species dominance and associated ecological risks. Integrating long-term census data with climate and functional indicators provides a robust framework for understanding and managing Mediterranean wetlands under ongoing climate change. Full article

Road safety barriers represent a core component of the road with relevant consequences on effective safety for users. Maintaining these components in adequate conditions, within the quality admissibility thresholds, in compliance with all economic and management constraints, is a primary need for road administrators. In this paper, the authors propose an original procedure to classify the state of efficiency of road safety barriers, at the network scale and relying on conventional administrative data, in an optimized BIM environment, to simplify evaluations and management procedures. Through purpose-built algorithms based on selected geometric and functional parameters of the different road barriers, the algorithm provides a preliminary classification of the various segments, evidencing attention class indicators, useful as preliminary alert signals and for anticipating detailed investigations that can ensure significant economic efficiencies. The method was tested on a 10 km long motorway segment in Italy, evidencing the potential advantages of such an innovative approach to support, as a final goal, a comprehensive infrastructure digital model for virtual inspections, evaluating road component “health” state and properly implementing maintenance strategies. This approach improves network-scale monitoring and maintenance-related activity prioritization phases for road safety barriers, leveraging administrative data. This methodology functions as a BIM-based asset screening tool, as it offers a digital decision support system that identifies critical segments, to optimize the allocation of physical resources and prioritize on-site inspections where they are most needed. Full article

Innovation system decision-making is a core component in promoting incentives and conditions necessary for the emergence of innovation. It also plays a critical role in guiding policy and modeling strategies that aim to promote science, technology, and entrepreneurship at national, regional, and local levels. Decision-makers often select innovation system models that do not align with contextual scope, data accessibility, or institutional conditions, undermining their implementation. The lack of alignment between innovation system model assumptions and contextual realities undermines analysis and policy design, particularly when trying to implement a regional model on a national scale without any sort of adaptation. This study presents a framework that aligns innovation system models to specific contexts by providing a decision-making system based on structural analysis. Using a comprehensive collection of relevant previous studies related to the theoretical evolution of innovation system models, this research provides insights regarding the most used types and techniques to compare innovation systems comprising national and regional ISs, helix models, and innovation and entrepreneurship ecosystems. For each model, explanatory potential via structural analysis is operationalized through five indicators derived from multilevel graphs: geopolitical scope, number of actors, vertical and horizontal density, and Shannon’s entropy. These indicators are then systematized into dimensions comprising two feasibility filters and three mechanism-related dimensions, forming the basis for a minimum viable innovation system model selection heuristic. This structural analysis shows that ecosystem lenses capture distributive and adaptive interaction structures; helix models emphasize coordination and governance; and national or regional innovation systems underscore policy reach and institutional boundaries. The results provide a numerical analysis of three different contexts—a national mission, a city entrepreneurship program, and a regional coordination upgrading effort—highlighting areas for improvement in planning, project implementation, and public policy design. Full article

The large-scale generation of asphalt dust waste (ADW) has raised increasing environmental concerns, while its high-value utilization in cementitious materials remains insufficiently explored, particularly in terms of mechanical performance, durability-related properties, and integrated sustainability evaluation. In this study, a graded utilization strategy based on particle size was proposed to incorporate ADW into alkali-activated concrete paving blocks, in which fine ADW fraction (<0.075 mm) was used as a partial replacement of blast furnace slag (BFS), while the coarser ADW fraction was used as a partial replacement of river sand, aiming at sustainable pavement applications. In addition, two types of ADW with different lithologies, namely limestone ADW and basalt ADW, along with their combined system, were investigated. The results show that the incorporation of ADW effectively enhances the engineering performance of paving blocks. The compressive strength increased from 45.3 MPa to 56.6 MPa, while water absorption decreased from 5.3% to 4.1%. All mixtures satisfied the requirements for abrasion resistance and slip resistance, demonstrating their compliance with the performance criteria for pedestrian pavement applications. Among all mixtures, the combined use of limestone ADW and basalt ADW exhibited the best overall performance. The improved performance may be attributed to the combined effects of graded particle utilization and the potential compositional complementarity between calcium-rich limestone ADW and silica–alumina-rich basalt ADW, which is consistent with the denser microstructure observed in SEM images. In addition, the proposed strategy contributes to improved solid waste utilization and reduced consumption of natural resources, as reflected in the quantitative sustainability assessment. Overall, this study demonstrates that graded utilization of ADW is a feasible approach for developing alkali-activated paving materials, with promising performance and sustainability potential. Full article

Sentiment expressions related to child maltreatment (CM) in public discourse are influenced by demographic, economic, and cultural factors and individual characteristics. Using 188,429 geotagged CM-related tweets during 2018–2022, we explored public sentiment expression about CM across the contiguous U.S. We applied multiscale geographically weighted regression (MGWR) to examine how contextual factors relate to the percentage of CM-related tweets with negative sentiment at the county level, revealing the spatial heterogeneity and varying geographic scales of these associations. Counties with higher male-to-female ratios and lower education levels tended to express negative sentiment in CM-related tweets, with consistent patterns observed nationwide. Five factors exhibited spatially varying associations by U.S. region, with higher levels of negative sentiment in the following contexts: a lower percentage of residents living in group quarters or a higher percentage of same-sex couples (Eastern and Central); fewer households lacking broadband access (Central); a higher percentage of single-parent households (New England and Southern Mississippi River); and areas where professionals are mandated to report CM (Great Lakes and Southern Appalachian Mountains). This study provides critical insights for policymakers to adjust policies, educators to design focused interventions, and the public to raise CM awareness. The methodology also provides a valuable framework for investigating public discourse on other social issues. Full article

Background: Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality despite major advances in diagnostics and therapies. The prognosis remains poor, mostly due to late-stage presentation and molecular heterogeneity. Epidermal growth factor receptor (EGFR) mutations are common drivers of NSCLC. The development of EGFR tyrosine kinase inhibitors (TKIs) has significantly improved outcomes in patients with EGFR mutations. Variant allele frequency (VAF) is a quantitative genomic measure representing the proportion of sequencing reads harboring a given mutation. In NSCLC tissue, the EGFR mutation VAF reflects tumor clonality and intratumoral heterogeneity, and accumulating evidence suggests an association between EGFR VAF and response to EGFR-targeted TKIs. Methods: To address the limited synthesis of data on the relevance of EGFR mutation VAF in NSCLC, we conducted a narrative review of the literature using PubMed/MEDLINE and Embase databases and current clinical guidelines, synthesizing available evidence on EGFR VAF, including its biological, molecular, and therapeutic implications in EGFR-mutated disease. The review was structured in accordance with the SANRA (Scale for the Assessment of Narrative Review Articles) checklist. Results: EGFR VAF and on-treatment VAF dynamics are consistently associated with treatment response, progression-free survival, and overall survival in osimertinib-treated NSCLC. Baseline VAF enables risk stratification, early clearance kinetics predict durable benefit, and longitudinal VAF monitoring facilitates early detection of resistance. Importantly, the prognostic implications of VAF differ fundamentally between tissue-based and plasma-based measurements: high tissue VAF reflects clonal homogeneity and predicts favorable TKI response, whereas high plasma VAF indicates elevated tumor burden and is associated with inferior outcomes. In the second-line setting, the T790M/activating mutation ratio serves as a surrogate for resistance clonality and independently predicts osimertinib efficacy. Conclusions: EGFR VAF represents a promising dynamic molecular biomarker for treatment monitoring and precision decision-making in EGFR-mutated NSCLC. Full article

Background: Ischemic stroke remains a major cause of disability and reduced quality of life (QoL). This study aimed to identify factors associated with QoL after ischemic stroke, with particular emphasis on independence, illness acceptance, social support, comorbidity status, and the timeliness of diagnosis and treatment. Methods: This single-center cross-sectional study included 100 consecutively recruited patients after ischemic stroke hospitalized in the Department of Neurology with the Stroke Unit at the S. Żeromski Specialist Hospital in Krakow. Data were collected using an author-designed questionnaire and standardized instruments: the World Health Organization Quality of Life-BREF (WHOQOL-BREF), the Multidimensional Scale of Perceived Social Support (MSPSS), the Lawton Instrumental Activities of Daily Living Scale (IADL), and the Acceptance of Illness Scale (AIS). Statistical analysis included Spearman’s rank correlation coefficient and the Mann–Whitney U, Friedman, and Kolmogorov–Smirnov tests. Results: Significant positive correlations were found between all WHOQOL-BREF domains and IADL, AIS, and MSPSS scores. The strongest correlations were observed between IADL and the physical and psychological QoL domains. A strong positive correlation was also found between IADL and AIS (rho = 0.88; p < 0.001). Better QoL and greater independence were observed in patients with fewer comorbidities. Patients who received timely diagnosis and treatment achieved better outcomes in terms of QoL, IADL, and AIS. Perceived social support was comparable across MSPSS subscales (p = 0.56) but positively correlated with all QoL domains (rho = 0.55–0.64; p < 0.001). Conclusions: Better QoL after ischemic stroke was associated with greater independence, higher illness acceptance, stronger perceived social support, and timely diagnosis and treatment, suggesting that post-stroke QoL is related to both functional and psychosocial factors. Full article

Large-scale constrained multi-objective optimization problems (LCMOPs) pose significant challenges due to the curse of dimensionality, complex constraint landscapes, and high computational overhead. In time-sensitive scenarios, existing large-scale constrained multi-objective evolutionary algorithms (LCMOEAs) often incur high computational costs and therefore struggle to meet efficiency requirements. This paper proposes a GPU-accelerated tensorized flexible differential evolution algorithm (TFDEMO) for LCMOPs. To address the curse of dimensionality and complex constraint landscapes in LCMOPs while maintaining GPU-level parallel efficiency, a tensorized flexible differential evolution operator (FlexDE) is developed. It utilizes a Bernoulli masking mechanism to switch between guided and random mutation modes in parallel on the GPU. The guidance probability is adaptively adjusted based on historical performance and the evolutionary state. Furthermore, a dual-population collaborative neighborhood selection mechanism is designed. For the main population, a Boolean mask tensor method is proposed, which constructs four Boolean mask tensors in parallel to encode feasibility states and dominance relations across all subproblems and their neighborhoods, and aggregates them via bitwise operations to produce the dominance tensor in a single pass. The auxiliary population performs constraint-ignoring neighborhood selection and shares its offspring with the main population to assist the main population in crossing large infeasible regions. The experimental results on the LIRCMOP and ZXH_CF benchmark suites with decision variable dimensions ranging from 100 to 800 demonstrate that TFDEMO achieves the best overall performance among the compared algorithms under both fixed-time and fixed function-evaluation settings. Additionally, a portfolio rebalancing problem with three objectives, five constraints, and scalable dimensions is designed to evaluate the performance of the proposed algorithm in time-sensitive application scenarios. Full article

Against the backdrop of rapid population aging, rural public spaces face growing challenges in meeting the everyday needs of older adults. Drawing on a place-based care perspective, this study develops age-friendly design strategies attuned to the spatial and cultural characteristics of rural environments. Using a mixed-methods approach that includes field observations, structured interviews, and questionnaire surveys, we identify the needs of older adults in rural public spaces. These needs are first clustered using the K-means algorithm and then analyzed using the FKANO model to extract core priorities. Their relative importance is quantified by an integrated procedure that combines ordinal relation diagrams and entropy weighting. Building on these results, we propose an age-friendly design framework and validate it with spatial simulation to assess scale, accessibility, and connectivity. The findings highlight five critical features prioritized by older adults in rural areas: non-slip surfaces, barrier-free access, safety railings, lighting systems, and public restrooms. The study provides a targeted and actionable pathway for the age-adaptive transformation of rural public spaces, offering both a theoretical foundation and a practical design paradigm for aging-friendly rural environments worldwide. Full article

Cities increasingly use experimental and participatory approaches to address sustainability challenges, yet knowledge about Urban Labs (ULs) and Urban Living Labs (ULLs) in Central and Eastern Europe remains limited. This article provides the first nationwide overview of ULs and ULLs in Poland and examines their role in sustainable urban transformation. Using desk research based on systematic searches of official websites, government reports, EU programme databases, academic sources, and social media, 34 ULs and 15 ULL initiatives operating as of July 2025 were identified. The cases were geolocated and analysed in terms of funding, collaboration models, and thematic orientations. ULs were classified into four functional types, while ULLs were grouped by their main areas of activity. The results indicate two development paths: a centralised model linked to national programmes and a bottom-up model driven by NGOs, universities, and civic initiatives. Most cases are concentrated in metropolitan areas, remain rare in towns with fewer than 60,000 inhabitants, and depend on short-term external funding. Despite organisational diversity, the initiatives consistently use co-creation, stakeholder engagement, and rapid prototyping. The study provides a first empirical evidence base for Poland and identifies challenges related to institutional durability and the scaling of urban innovation. Full article

I revisit Benjamin’s text ‘The Work of Art in the Age of Its Technological Reproducibility’ and find it both problematic and richly suggestive. Carefully reading it, both with and against, I search it for continuities and breaks with ‘reproducibility’ today and hence insights into AI and its relationship to art. In so doing, I sketch some tentative conclusions about how such an investigation might assist us towards understanding what art is and isn’t, how the practice of art relates to our humanity and finally, though a thorough settling of accounts with AI and its boosters will require political change on a grand scale, how Benjamin’s ‘aura’ might offer a small but significant locus of resistance to the commodification and dehumanising drive currently occasioned by AI in the field of art. Full article

The rapid advancement of unmanned aerial vehicle (UAV) technology has led to its widespread adoption in military reconnaissance, disaster monitoring, environmental inspection, and related fields. However, a single UAV often faces limitations when executing large-scale and complex missions. UAV swarm technology, which employs multi-agent collaboration, can significantly improve task execution efficiency and overall system performance, representing an area of considerable research importance. Current studies on task allocation and path planning for UAV swarms exhibit certain shortcomings, particularly the high computational complexity and insufficient real-time performance of existing path planning methods when applied to highly dynamic, multi-objective, and large-scale complex scenarios. To address the above challenge, this paper proposes a Gale-Shapley-based Genetic Algorithm (GSGA) for UAV swarm task allocation and path planning. First, a multi-UAV data inspection system model is formulated based on an energy consumption model, analyzing the influence of factors including geographical fairness, data utility, and energy consumption. The proposed GSGA integrates the Gale-Shapley stable matching algorithm for one-to-one task assignment between UAVs and sub-regions with a genetic algorithm optimized for intra-region path planning. Dynamic programming is further employed to refine the flight paths. The results show that the GSGA strategy can effectively improve the balance of task allocation, optimize path length and inspection quality. The proposed method demonstrated robust performance in complex scenarios characterized by numerous task targets and intricate regional partitions, consistently enabling UAVs to complete inspection tasks with high collaborative efficiency. Full article

Veterinary students’ perceptions of animal welfare are shaped by both educational exposure and individual background, with direct implications for future clinical decision-making. This study evaluated 157 ninth-semester veterinary students enrolled in a Deontology and Veterinary Legal course at the University of Las Palmas de Gran Canaria (Spain) across six academic years (2019/2020–2025/2026), excluding 2020/2021 due to pandemic-related teaching modifications. Participants completed a structured, previously published questionnaire assessing twelve common dog welfare issues on a 5-point Likert scale, subsequently grouped into five dog welfare dimensions. Students consistently prioritized overt and severe dog welfare concerns, including abuse or active cruelty, lack of treatment to prevent suffering, and malnutrition, while assigning lower importance to breed-related conditions and behavioral problems. Significant differences across academic years were identified for seven items and all dimensions, revealing temporal variability in dog welfare perception between cohorts. Gender and student background also influenced responses, with female and exchange students generally assigning higher scores to selected issues. These findings suggest that the perception of dog welfare is not static but varies between cohorts across academic years and is shaped by sociocultural factors. Strengthening veterinary curricula to address less visible and socially normalized dog welfare problems may be critical to ensuring comprehensive and ethically grounded professional practice. Full article

Large-scale entertainment venues are often positioned as anchors of mixed-use redevelopment, yet their surrounding effects may not unfold uniformly across indicators. This study examines differentiated urban effects around The O₂ and Greenwich Peninsula in London by comparing treatment and control Middle Layer Super Output Areas (MSOAs) within the Borough of Greenwich. Using Office for National Statistics housing data for 1995–2025 and NOMIS business-count data for 2016–2024, the analysis combines descriptive comparison, pre-2007 comparability assessment, static difference-in-differences, annual event-study estimation, and total and sector-level business analysis. The housing results show a persistent locational premium in the treatment areas. Static DID estimates were not statistically significant, but annual event-study estimates suggest relative strengthening in selected post-opening years. The business results show a substantially larger business base in the treatment areas but only a limited aggregate relative growth advantage. Sectoral patterns are uneven: retail and arts-related activities perform more strongly, accommodation and food services show a modest advantage, and office-oriented services show little relative difference. Overall, the findings indicate differentiated urban effects rather than uniform neighborhood transformation and suggest that venue-centered redevelopment should be evaluated through multiple indicators and with attention to sectoral composition. Full article

Climate change is increasingly recognized as a source of psychological distress, yet the prevalence, predictors, and behavioral implications of climate anxiety remain unevenly understood. This study examines climate anxiety, its key drivers, and associated behavioral responses in a multi-country sample of adults. A cross-sectional online survey was conducted across 21 countries using the Climate Change Anxiety Scale (CCAS), alongside measures of awareness, coping strategies, social support, and food-related behaviors, including food waste reduction, increased plant-based food consumption, and home or community gardening. Analyses included descriptive statistics, exploratory factor analysis (EFA), and multivariable regression. Given the uneven country-level representation, results are reported as pooled patterns with a few exploratory cross-country comparisons. Climate anxiety was widely reported, with over 60% of participants indicating that climate challenges were emotionally overwhelming. Regression analyses showed that climate awareness and frequency of climate-related thinking were positively associated with higher anxiety, although the effect sizes were small and explanatory power was limited (R² = 0.055). EFA identified two related dimensions: cognitive concern about future impacts and affective distress. Climate anxiety across countries showed modest variation (2.44–3.23) and no statistically significant differences, despite variation in awareness. A gap between concern and climate action was evident: only 39.1% reported environmentally motivated dietary changes. Cost, limited availability, and lack of information were the main barriers to climate action, and only 24.4% reported frequent social support. These findings indicate that climate anxiety is shaped by both psychological and structural factors, and that reducing it requires not only increasing awareness but also enabling conditions that support meaningful climate action. Full article