Search Results (1,545)

Sport events generate economic, social, and environmental impacts that shape residents’ perceptions and levels of support. In the context of sustainable urban development, understanding how residents evaluate these impacts provides valuable knowledge about community responses to tourism and event-led growth. Drawing on the Triple Bottom Line (TBL), Social Exchange Theory (SET), and Social Representations Theory (SRT), this study examines residents’ evaluations of the Rally Sierra Morena (RSM), a large-scale international motorsport event recently incorporated into the European Rally Championship (ERC). Data were collected shortly before the event using a self-administered questionnaire (n = 1529). An exploratory factor analysis (EFA) identified a multidimensional structure of perception, and a non-hierarchical k-means cluster analysis identified three clusters: Skeptics, who perceived stronger negative than positive impacts in economic and environmental dimensions; Pragmatists, who emphasized positive economic benefits while acknowledging environmental costs; and Enthusiasts, who consistently rated positive impacts higher across all dimensions and expressed the strongest support for the event. By integrating perceptual and sustainability-based approaches, this study connects residents’ evaluations of a motorsport event with broader discussions on urban resilience and sustainable community development. Full article

The large-scale integration of renewable energy sources has made power generation highly susceptible to climate variability, increasing operational risks within power systems. The growing frequency of extreme weather events has further intensified uncertainty and stochasticity, thereby elevating risks to supply security. To enhance the operational resilience of modern power systems under extreme weather conditions, this study proposes a multi-temporal and multi-spatial power supply planning model that explicitly incorporates the impacts of such events. First, the effects of extreme weather on the source–grid–load framework are analyzed, and a radiation attenuation model for the rainy season as well as a spatiotemporal evolution model for hurricanes are developed. Subsequently, a climate-dependent power output model is established, employing the Finkelstein–Schafer statistical method to construct a Typical Meteorological Year, which serves as input for the reliable power source modeling. Furthermore, a two-stage power supply planning model based on generation adequacy was established to optimize the location and capacity of various types of backup power sources. Case studies conducted on the IEEE 24-bus system demonstrate that optimized planning of thermal power units and energy storage systems can mitigate the overall power shortfall during extreme weather events, thereby improving the system’s ability to maintain a reliable electricity supply under adverse climate conditions. Full article

This study proposes a hybrid model that integrates Wavelet frequency decomposition, convolutional neural networks (CNNs), and Transformers to predict correlation structures among eight major cryptocurrencies. The Wavelet module decomposes asset time series into short-, medium-, and long-term components, enabling multi-scale trend analysis. CNNs capture localized correlation patterns across frequency bands, while the Transformer models long-term temporal dependencies and global relationships. Ablation studies with three baselines (Wavelet–CNN, Wavelet–Transformer, and CNN–Transformer) confirm that the proposed Wavelet–CNN–Transformer (WCT) consistently outperforms all alternatives across regression metrics (MSE, MAE, RMSE) and matrix similarity measures (Cosine Similarity and Frobenius Norm). The performance gap with the Wavelet–Transformer highlights CNN’s critical role in processing frequency-decomposed features, and WCT demonstrates stable accuracy even during periods of high market volatility. By improving correlation forecasts, the model enhances portfolio diversification and enables more effective risk-hedging strategies than volatility-based approaches. Moreover, it is capable of capturing the impact of major events such as policy announcements, geopolitical conflicts, and corporate earnings releases on market networks. This capability provides a powerful framework for monitoring structural transformations that are often overlooked by traditional price prediction models. Full article

The study is referenced by interdisciplinary theories, i.e., routine activity, and social cohesion, to investigate the impacts of sport clubs and events on London’s expressive crimes at varied geographical scales, by utilizing Geographical-temporally weighted regression model. It has identified the spatial patterns of effects from sport clubs’ onto local expressive crimes among London wards, with several boroughs standing out for their being significantly affected. The case study in the home borough of the Hotspur Football Club has further been conducted, by proving the seasonal influences of sports clubs on reducing youth violence within school terms. It was also found disproportional increases in expressive crimes on Premier League match days, especially when receiving the results of draw. The data-driven evidence has generated insights on localized policies and strategies on developing tailored sports to support local young people’s development; pinpointing the optimisation of police forces resources on stop and search practices during sports events in hot spot stadiums. The methodology and workflow had also been proved with high replicability into other UK cities. Full article

Prolonged stress caused by the COVID-19 pandemic and two concurrent earthquakes in 2020 increased Internet-based addictive behaviour, leading to decrease in mental health and quality of life (QoL) in the adult Croatian population. This study examined the association between Internet-based addictive behaviour and QoL during prolonged stress (pandemic and earthquakes). Specifically, it explored direct associations between QoL domains and overall/specific Internet use, problematic Internet use (PIU), and symptoms of anxiety, depression, and stress, as well as the indirect role of these symptoms in mediating the relationship between PIU and QoL. A cross-sectional online survey was conducted in autumn 2021 with a convenience sample (N = 1004; 82.2% women; M age = 34.98, SD = 12.24). Measures included increased overall and specific Internet use, PIU, stress (Impact of Event Scale), anxiety and depression symptoms (Hospital Anxiety and Depression Scale), and QoL (WHOQoL-BREF). Structural equation modelling showed that increased Internet use and PIU were directly associated with more severe symptoms of depression, anxiety, and stress, and with lower QoL. Significant indirect effects were also found: higher PIU, social media use, online shopping, and pornography viewing predicted greater depression, anxiety, and stress symptoms, which in turn predicted reduced QoL across multiple domains. These findings suggest that problematic and increased Internet use during periods associated with prolonged stress contribute to lower QoL through elevated psychological distress. Full article

Habitat condition and availability are fundamental for sustaining biodiversity and the ecosystem services that support human well-being. Achieving biodiversity-related sustainability goals, therefore, necessitates a focus on habitat itself. This study examines habitat dynamics in biodiversity “coldspots”, or relatively species-poor areas not currently under protection, to provide insights into their trends and patterns of habitat change. Using freely available remote sensing data and local environmental datasets, we analyze habitat changes across test sites from four European ecoregions between 2000 and 2018 and evaluate the impact of pressures driving these changes on local ecosystem functioning. The study identifies seven primary drivers of habitat change, with Range Shift and Regrowth emerging as the most widespread pressures, while Conversion, Degradation, and Deforestation exerted the strongest influence on ecosystem functions such as Aboveground Biomass and Water Yield. A consistent bimodal distribution of habitat changes was observed, with frequent small-scale events, fewer large-scale events, but a lack of intermediate-scale events. By drawing attention to conservation needs in biodiversity coldspots, these findings emphasize the importance of integrating such areas into sustainable land use planning and protected area network expansion, ensuring that efforts extend beyond species-rich regions to prevent the loss of irreplaceable habitats and safeguard long-term conservation goals. Full article

Addressing earthquake risk remains a significant global challenge, requiring rapid assessment of evacuation shelters for effective disaster response. Existing frameworks, such as FEMA’s Hazus, Copernicus EMS, and UNOSAT, offer valuable insights but are typically regional, static, and event-focused, lacking mechanisms for continuous shelter-level updates. This study introduces the Shelter Hazard Impact and Lifeline Outage Estimation (SHILOE) framework. SHILOE is a stepwise estimation approach integrating multisensor datasets for time-scaled assessments of shelter functionality and operability. These datasets include seismic intensity, liquefaction probability, tsunami inundation, IoT-derived power outage data, communication network disruptions, and social media. Application to the 2024 Noto Peninsula earthquake showed that ≥93.6% of designated and activated shelters were impacted by at least one hazard, with all experiencing at least one lifeline outage. The framework delivers estimates through three phases: immediate (within tens of minutes, e.g., simulation-based hazard models and lifeline data), intermediate (days, e.g., observation-based datasets), and refinement (ongoing, e.g., Social Networking Service and detailed field surveys). By progressively incorporating new data across these phases, SHILOE generates dynamic, facility-level insights that capture evolving hazard exposure and lifeline status. These outputs provide actionable information for emergency managers to prioritize resources, reinforce shelters, and sustain critical services, thereby advancing disaster resilience. Full article

Understanding global precipitation trends is critical for managing water resources, anticipating extreme events, and assessing the impacts of climate change. This study analyzes spatial and temporal patterns of precipitation from 1998 to 2024 using NASA’s Global Precipitation Measurement Mission (GPM) Integrated Multi-satellite Retrievals for (IMERG) Version 7, which merges satellite observations with rain-gauge data at 0.1° resolution. A total of 324 monthly datasets were aggregated into annual and seasonal composites to evaluate annual and seasonal trends in global precipitation. The non-parametric Mann–Kendall test was applied at the pixel scale to detect statistically significant monotonic trends, and Sen’s slope estimator method was used to quantify the magnitude of change in mean annual and seasonal global precipitation. Results reveal robust and geographically consistent patterns: significant wetting trends are evident in high-latitude regions, with the Arctic and Southern Oceans showing the strongest increases across multiple seasons, including +0.04 mm/day in December–January–February for the Arctic Ocean and +0.04 mm/day in June–July–August for the Southern Ocean. Northern China also demonstrates persistent increases, aligned with recent intensification of extreme late-season precipitation. In contrast, significant drying trends are detected in the tropical East Pacific (up to −0.02 mm/day), northern South America, and some areas in central-southern Africa, highlighting regions at risk of sustained hydroclimatic stress. The North Atlantic south of Greenland emerges as a summer drying hotspot, consistent with Greenland Ice Sheet melt enhancing stratification and reducing precipitation. Collectively, the findings underscore a dual pattern of wetting at high latitudes and drying in tropical belts, emphasizing the role of polar amplification, ocean–atmosphere interactions, and climate variability in shaping Earth’s precipitation dynamics. Full article

This study tackles the complex challenge of dynamic multi-objective vehicle routing optimization in large-scale equipment manufacturing, where routing operations significantly impact both economic performance and environmental sustainability. We develop an innovative Dynamic Multi-Objective Vehicle Routing Problem (DMOVRP) model that uniquely integrates three competing objectives: environmental impact reduction, delivery timeliness, and operational robustness. The proposed algorithm combines a dynamic event handler with the NSACOWDRL algorithm—an adaptive multi-objective optimization algorithm with dynamic event handling capability. The proposed system features adaptive mechanisms for handling real-time disruptions through specialized event classification and dynamic rescheduling protocols. Extensive computational experiments demonstrate the algorithm’s superior performance with statistically significant improvements using the Wilcoxon signed-rank test (p < 0.05, n = 30 runs per instance), achieving average relative gains of 15.2% in HV, 12.8% in IGD, and 8.9% in GD metrics compared to established methods. This research makes theoretical contributions through its feasibility quantification metric and practical advancements in routing schedule systems. By successfully reconciling traditionally conflicting objectives through dynamic JIT adjustments and robustness-aware optimization, this work provides manufacturers with a versatile decision-support tool that adapts to unpredictable workshop conditions while maintaining sustainable operations. Full article

This paper proposes a scalable serverless architecture for real-time natural language processing (NLP) on large datasets using Amazon Web Services (AWS). The framework integrates AWS Lambda, Step Functions, and S3 to enable fully parallel sentiment analysis with Transformer-based models such as DistilBERT, RoBERTa, and ClinicalBERT. By containerizing inference workloads and orchestrating parallel execution, the system eliminates the need for dedicated servers while dynamically scaling to workload demand. Experimental evaluation on the IMDb Reviews dataset demonstrates substantial efficiency gains: parallel execution achieved a 6.07× reduction in wall-clock duration, an 81.2% reduction in total computing time and energy consumption, and a 79.1% reduction in variable costs compared to sequential processing. These improvements directly translate into a smaller carbon footprint, highlighting the sustainability benefits of serverless architectures for AI workloads. The findings show that the proposed framework is model-independent and provides consistent advantages across diverse Transformer variants. This work illustrates how cloud-native, event-driven infrastructures can democratize access to large-scale NLP by reducing cost, processing time, and environmental impact while offering a reproducible pathway for real-world research and industrial applications. Full article

In recent years, the increasing frequency of global seismic events has imposed severe impacts on human society. Timely and accurate assessment of post-earthquake damage and recovery is essential for developing effective emergency response strategies and enhancing urban resilience. This study investigates 11 provinces in Turkey affected by the February 2023 Turkey–Syria earthquake, conducting a multidimensional evaluation of disaster loss and recovery. For loss assessment, existing studies typically focus on changes in the total value of nighttime lights at the regional level, overlooking variations at the pixel scale. In this study, we introduce a pixel-level NTL loss metric, which provides finer-grained insights and helps interpret outcomes driven by spatial heterogeneity. For recovery assessment, we propose a Composite Nighttime Light Index (CNLI) that integrates multiple recovery-phase indicators into a single quantitative measure, thus capturing more information than a one-dimensional metric. To account for complex interrelationships among indicators, a Bayesian network is employed, which moves beyond the conventional independence assumption. Moreover, an information gain (IG) approach is applied to optimize indicator weights, minimizing subjectivity and avoiding abnormal weight distributions compared with traditional methods, thereby ensuring a more objective construction of the Resilience Index (RI). Results show that Sanliurfa, Kilis, and Hatay suffered the most severe damage; Kahramanmaras and Malatya exhibited the lowest CNLI values, while Hatay, Kilis, and Gaziantep showed higher CNLI values. In contrast, Gaziantep and Adana obtained the highest RI values. Since CNLI reflects actual recovery performance while RI characterizes inherent resilience, accordingly, effectively linking CNLI and RI establishes a dual-perspective and novel framework, the 11 provinces are classified into four categories, and differentiated recovery strategies are suggested. This study contributes a refined quantitative framework for post-earthquake loss and recovery assessment and provides scientific evidence to support emergency response and targeted reconstruction. Full article

Climate change is disrupting nature, human lives, and infrastructure worldwide. Its effects are becoming more evident in every region, with IPCC reports warning of a warming world and an increase in extreme weather events. The scale and severity of climate change’s impacts exceed earlier estimates, leading to widespread disruption of ecosystems and societies. It threatens food production, clean water availability, and ultimately, the health and well-being of billions. The primary driver of these changes is rising global temperatures, which significantly influence climate patterns and hydrological conditions. This study analyzes time series of summer air temperature (at 500 hPa and 850 hPa) and total precipitation from NOAA records across ten major administrative regions of Greece over a 35-year period (1989–2024). Using a machine learning approach, the analysis identifies climate trends and extreme weather patterns while providing climate forecasts to support water management improvements and public health initiatives. Full article

This study explores the impact of assimilating Doppler radar data on short-term numerical weather forecasting for a heavy rainfall event in Southern China, focusing on different spatial scales. Results show that radar data assimilation significantly improves the initial analysis and enhances the accuracy of hourly precipitation forecasts by providing more detailed mesoscale system information, compared to assimilating only wind profiler radar data. The Barnes filter analysis reveals that radar data assimilation has a more pronounced effect on mesoscale systems, with improvements primarily concentrated in the first 2 h of the forecast. However, this improvement diminishes rapidly beyond the 2 h lead time, indicating the inherent predictability limits of mesoscale systems. In contrast, large-scale systems exhibit a greater stability and predictability, with radar data assimilation having a relatively smaller but still positive impact. The study emphasizes the importance of radar data assimilation for short-term forecasts at different spatial scales and suggests that future work prioritize extending mesoscale predictability. Full article

Given the high sensitivity of small estuaries to environmental changes, the present study aimed to investigate how climate-induced stressors—particularly rainfall and salinity—affect zooplankton community structure in the Amazonian Taperaçu estuary (northern Brazil), where limited spatial scale amplifies ecological responses. This study evaluated the effects of the extremely dry 2015–2016 El Niño period on hydrological patterns and zooplankton dynamics in this shallow tropical estuary. Eight sampling campaigns were conducted, with water and zooplankton samples analyzed using standard methods. Salinity, dissolved inorganic nutrients, and chlorophyll-a concentrations were affected by the marked decrease in rainfall caused by the El Niño event. These changes significantly impacted zooplankton community dynamics, especially the densities of marine-estuarine species Acartia lilljeborgii, Euterpina acutifrons, and Oikopleura dioica, which peaked during months of highest salinity. High recruitment of copepod larval stages was also observed, with peak densities coinciding with dominant adult forms. In contrast, coastal and estuarine species such as Acartia tonsa, Pseudodiaptomus marshi, Oithona oswaldocruzi, and Oithona hebes were negatively affected by reduced rainfall. Species richness, diversity, and evenness during the El Niño period were relatively high compared to previously reported values under normal conditions in the same ecosystem. Environmental and temporal variables accounted for over half the variance in predominant taxa density, indicating that El Niño–driven changes influenced zooplankton structure over time. This suggests that El Niño may have strong impacts at the secondary trophic level, likely to cascade throughout the estuarine food web, altering its dynamics and the flow of carbon and energy through the system. Full article

Background: Depression has been identified as an important determinant of outcomes in hip and knee arthroplasty, but its impact on total ankle arthroplasty (TAA) remains unclear. Given the growing use of TAA as a treatment for end-stage ankle arthritis, understanding psychosocial risk factors is critical for optimizing surgical outcomes. This study aims to assess the effect of preoperative depression on clinical and functional outcomes following total ankle arthroplasty. Methods: A systematic review was conducted in accordance with PRISMA guidelines and prospectively registered with the Open Science Framework. PubMed, Cochrane Library, and CINAHL were searched through August 2025 for studies reporting outcomes of TAA stratified by depression status. Eligible designs included randomized trials, cohort studies and case series. Risk of bias was assessed using the Newcastle–Ottawa Scale (NOS). Given heterogeneity in study designs, depression definitions, and outcome measures, findings were synthesized narratively and summarized using a revised effect-direction plot. Results: Six unique studies involving approximately 9000 patients met inclusion criteria. Five studies were rated as good quality on the Newcastle–Ottawa Scale, while one study was judged to be of moderate quality. Four studies assessing pain outcomes consistently demonstrated worse postoperative pain or less improvement in patients with depression. Three of five studies assessing functional or disability outcomes reported reduced improvement, while two studies found no independent association. Two studies evaluating complications showed higher risks of adverse events, including prolonged hospital stay, non-home discharge, osteophytosis, and implant subsidence, among depressed patients. Revised effect-direction synthesis confirmed a consistent trend toward poorer outcomes across pain, function, and complication domains. Conclusions: Depression is associated with worse pain and higher complication rates following TAA, while its influence on functional recovery was not demonstrated uniformly. These findings support the importance of routine preoperative screening and targeted management of depression. Further prospective, multicenter studies and interventional trials are needed to clarify causality and optimize perioperative care. Full article