Search Results (1,062)

We propose a strictly causal early–warning framework for financial crises based on topological signal extraction from multivariate return streams. Sliding windows of daily log–returns are mapped to point clouds, from which Vietoris–Rips persistence diagrams are computed and summarised by persistence landscapes. A single, interpretable indicator is obtained as the $L_2$ norm of the landscape and passed through a causal decision rule (with thresholds $\alpha ,\beta$ and run–length parameters $s,t$) that suppresses isolated spikes and collapses bursts to time–stamped warnings. On four major U.S. equity indices (S&P 500, NASDAQ, DJIA, Russell 2000) over 1999–2021, the method, at a fixed strictly causal operating point $(\alpha =\beta =3.1,s=57,t=16)$, attains a balanced precision–recall ($F_1\approx 0.50$) with an average lead time of about 34 days. It anticipates two of the four canonical crises and issues a contemporaneous signal for the 2008 global financial crisis. Sensitivity analyses confirm the qualitative robustness of the detector, while comparisons with permissive spike rules and volatility–based baselines demonstrate substantially fewer false alarms at comparable recall. The approach delivers interpretable topology–based warnings and provides a reproducible route to combining persistent homology with causal event detection in financial time series. Full article

This study examines the evolution of a beach formed along the coastline of a semi-enclosed, essentially tideless, embayment in the eastern Mediterranean Sea. The analysis revealed that the primary factors influencing its recent evolution are the terrestrial sediment influxes, current nearshore oceanographic conditions, and the existence of coastal constructions. The beach zone is exposed to waves approaching from the south with extreme values of height and period of 7 m and 4.3 s, respectively. Associated morphodynamic characteristics include a closure depth of 7 m, a breaking depth of 4.3 m, and a maximum run-up of 2.4 m. Since the mid-1900s, the shoreline has evolved through an accretional phase from 1960 to 1988, followed by a retreating phase from 1989 to 1997, except in the central part, where progradation has continued. The most recent period (1998–2017) has been relatively stable, though with a slight retreating trend. During storm events, changes to the beach are not uniform along-shore. Gross estimates of beach retreat due to sea level rise induced by climate change threaten the existence of the entire beach (for moderate and extreme IPCC Special Report Emissions Scenarios); however, this does not seem to be the case if riverine sediment influx continues. Full article

This study aimed to examine position-specific physical demands among elite U19 football players competing in a 4-3-3 formation, using data collected via STATSports GPS technology. A total of 23 players from a top-tier Bosnian club, FK “Sarajevo”, were monitored during 26 official matches in the 2024/2025 season. Match data included total distance, distance in six speed zones, high-speed running (HSR), sprint distance, number of sprints, maximum speed, and acceleration/deceleration events. One-way ANOVA and Bonferroni post hoc analyses revealed significant positional differences across all performance metrics (p < 0.05). Central midfielders (CMs) covered the greatest total distance and distance per minute, while side defenders (SD) and forwards (FWs) recorded the highest values in sprint distance, HSR, and sprint frequency. Central defenders (CDs) consistently demonstrated the lowest outputs in high-speed and sprint metrics. These findings highlight the distinct physical profiles required for each playing position in a 4-3-3 system and provide practical insights for designing position-specific training and load management strategies in elite youth football. Full article

The rapid expansion of ski tourism and climate change-induced snow shortages have led to intensified ski run maintenance, including extensive earthworks, artificial snowmaking, and regular snow grooming. While these activities are known to cause significant land degradation, quantitative geomorphological studies, specifically on the effects of snow grooming, are limited. This study addresses this knowledge gap by quantitatively assessing the impact of snow grooming on erosion processes and hillslope morphology by comparing them with natural landforms. We achieved this by determining the spatial distribution, morphometry, and long-term persistence of studied landforms. The study area consisted of a unique ski resort at Kasprowy Wierch, which does not use artificial snowmaking or extensive earthworks. We combined detailed field mapping with the analysis of multi-temporal Digital Elevation Models (DEMs) and orthophotos from 2012, 2019, 2020, and 2023. Our methodology also included the calculation of volumetric changes using the DEM of Difference (DoD) analysis. We distinguished two groups of eroded areas, natural landforms (e.g., shallow landslides, debris flow tracks, nivation niches) and snow groomer-induced forms, which were concentrated on ski runs. Natural landforms were elongated and deeper, with higher edges, clustered along debris flow tracks, and occurred on steeper slopes (mean 26.8°). They were more persistent and extensive, with a total area ranging from 3891 m² in 2012 to 3452 m² in 2023. In contrast, groomer-eroded landforms, located on gentler slopes (mean 23.4°), were smaller, more angular, less persistent, and concentrated on narrower, intensively used ski run sections. Their total area decreased from 2122.71 m² to 1762.25 m² over the same period, despite an increase in their count. The volumetric analysis revealed distinct dynamics: over the long term (2012–2023), natural forms showed a total deposition of +8.196 m³, while groomer-eroded forms experienced total erosion of −2.070 m³. During an extreme rainfall event in 2020, natural landforms experienced vast erosion of −163.651 m³, nearly five times greater than the −33.765 m³ observed on snow groomer-eroded landforms, demonstrating their greater susceptibility to high-magnitude events. Importantly, a comparison with other studies reveals that the scale of erosion from snow grooming is relatively small compared to the severe impacts of artificial snowmaking. Our findings are relevant for managing protected areas, such as Tatra National Park, where the focus should be on mitigating anthropogenic impacts to preserve natural processes, which in turn implies that the development of new ski infrastructure should be prohibited. Full article

Accurate precipitation estimates are essential for hydrological modeling and flood forecasting, particularly in regions like Ireland where rainfall patterns are highly variable and extreme events are becoming more frequent. This study evaluates the performance of two widely used gridded precipitation datasets, ERA5 reanalysis and GPM IMERG (Early, Late, and Final run) precipitation products, against ground-based observations from 25 synoptic stations operated by Met Éireann, Ireland’s national meteorological service, over the period of 2014–2021. A grid-to-point matching method was applied to ensure spatial alignment between gridded and point-based data. The datasets were assessed using seven statistical and categorical metrics across hourly and daily timescales, meteorological seasons, and rainfall intensity classes. Results show that ERA5 consistently outperforms IMERG across most evaluation metrics, particularly for low-to-moderate intensity rainfall associated with winter frontal systems, and demonstrates strong temporal agreement and low bias in coastal regions. However, it tends to underestimate short-duration, high-intensity events and displays higher false alarm rates at the hourly scale. In contrast, IMERG-Final exhibits improved detection of extreme rainfall events, especially during summer, and performs more reliably at daily resolution. Its spatial performance is stronger than the Early and Late runs but still limited in Ireland’s western regions due to complex climatological settings. IMERG-Early and Late generally follow similar trends but tend to overestimate rainfall in mountainous regions. This study provides the first systematic intercomparison of ERA5 and IMERG datasets over Ireland and supports the recommendation of adopting a hybrid approach of combining ERA5’s seasonal consistency with IMERG-Final’s event responsiveness for enhanced rainfall monitoring and hydrological applications. Full article

Bridge Structural Health Monitoring (BSHM) is vital for assessing structural integrity and operational safety. Traditional wired systems are limited by high installation costs and complexity, while existing wireless systems still face issues with cost, synchronization, and reliability. Moreover, cloud-based methods for extreme event detection struggle to meet real-time and bandwidth constraints in edge environments. To address these challenges, this study proposes a lightweight wireless BSHM system based on edge computing, enabling local data acquisition and real-time intelligent detection of extreme events. The system consists of wireless sensor nodes for front-end acceleration data collection and an intelligent hub for data storage, visualization, and earthquake recognition. Acceleration data are converted into time–frequency images to train a MobileNetV2-based model. With model quantization and Neural Processing Unit (NPU) acceleration, efficient on-device inference is achieved. Experiments on a laboratory steel bridge verify the system’s high acquisition accuracy, precise clock synchronization, and strong anti-interference performance. Compared with inference on a general-purpose ARM CPU running the unquantized model, the quantized model deployed on the NPU achieves a 26× speedup in inference, a 35% reduction in power consumption, and less than 1% accuracy loss. This solution provides a cost-effective, reliable BSHM framework for small-to-medium-sized bridges, offering local intelligence and rapid response with strong potential for real-world applications. Full article

Introduction: Marathon runs conducted in tropical environments can result in high injury rates. This study was conducted to provide information about the burden of injuries in such environments, to aid planning for similar mass events, enhance medical support, and improve participant safety. Methods: This was a retrospective review of casualty data from the Singapore Marathon races from 2013 to 2016. Patient Presentation Rate (PPR) and Transport to Hospital Rate (THR) were calculated and correlated with heat index, derived from weather information. Injury types were also reviewed. The negative binomial regression was performed to investigate impact of heat index on casualty rates. The medical response plan is briefly described. Results: During the four-year period covered, heat index increased from 29° to 35°. There were more casualties amongst the participants from the full marathon than other race categories. The THR was 0.3 to 0.68 per 1000 participants. Two participants had cardiac arrest. Negative binomial regression showed significant impact of heat index on casualty rate. Incidence rate ratio was 1.22 for severe casualties, which indicated that every 1 unit increase in heat index resulted in 22% rise in severe casualty numbers. Compared with 10 km racers, half marathon racers experienced 1.58 times greater likelihood of all injuries and full marathon racers, a 3.87 times greater risk. Conclusions: Adverse weather conditions with high-heat index can increase injury rates during strenuous physical activities such as the marathon. Applying careful measures to minimise the impact of heat and high humidity may help minimise such injuries. Full article

The COVID-19 pandemic has caused market turmoil and economic distress. To understand the effect of the pandemic on the U.S. financial systemic risk, we analyze the explanatory power of detailed COVID-19 data on three market-based systemic risk measures (SRMs): Conditional Value at Risk, Distress Insurance Premium, and SRISK. In the time-series dimension, we use the Dynamic OLS model and find that financial variables, such as credit default swap spreads, equity correlation, and firm size, significantly affect the SRMs, but the COVID-19 variables do not appear to drive the SRMs. However, if we focus on the first wave of the COVID-19 pandemic in March 2020, we find a positive and significant COVID-19 effect, especially before the government interventions. In the cross-sectional dimension, we run fixed-effect and event-study regressions with clustered variance-covariance matrices. We find that market capitalization helps to reduce a firm’s contribution to the SRMs, while firm size significantly predicts the surge in a firm’s SRM contribution when the pandemic first hits the system. The policy implications include that proper market interventions can help to mitigate the negative pandemic effect, and policymakers should continue the current regulation of required capital holding and consider size when designating systemically important financial institutions. Full article

The recent worldwide turbulence of events from the pandemic lockdown through increased industrial digitization to geopolitical unease shifted towards new primary targets for the latest generation of DDoS threats. Although certain characteristics of current DDoS attack patterns existed before the pandemic or the cloud platform boom, they have now gained prominence and reached their current level of sophistication. In addition to employing innovative methods and tools, the frequency, scale, and complexity of these attacks have also experienced a significant surge. The amalgamation of diverse attack vectors has paved the way for multi-vector attacks, incorporating a distinctive combination of L3–L7 attacking profiles. The integration of the hit-and-run strategy with the multi-vector approach has notably bolstered the success rate. This paper centers around two main aspects. Firstly, it explores the characteristics of the most recent DDoS attacks identified within actual data center infrastructures. To underscore the changes in attack profiles, we reference samples collected recently from diverse data center networks. Secondly, it offers an extensive overview of the cutting-edge methods and techniques for detecting and mitigating recent attacks. The paper places particular emphasis on the precision and speed of these detection and mitigation approaches, predominantly those related to networking. Additionally, we establish criteria, both quantitative and qualitative, to aid in the development of detection methods capable of addressing the latest threat profiles. Full article

Rio Grande do Sul accounts for 22% of Brazil’s losses from extreme events, mainly droughts and floods. The state had the second-worst economic performance in the country between 2000 and 2022. This study quantifies the impacts of major events such as droughts, floods, and the COVID-19 pandemic on economic sectors. Three methods were applied: structural breaks, recovery time, and sector-specific loss estimates. The analysis covers 15,365,123 observations of monthly invoice values from January 2017 to April 2025, involving 357,001 companies paying value-added tax on consumption. The results indicate that negative structural breaks occurred in a few sectors, which account for 5% of the state’s economy. The recovery time followed a similar trajectory between droughts and COVID-19. On average, sectors took 12 months to recover from COVID-19, compared with about 6 months for natural hazards. The sectors most impacted were travel, artistic activities, machinery and equipment industry, accommodation, and domestic services. Aggregated loss estimates were highest during the COVID-19 pandemic (−8%), followed by floods (−1%) and droughts (0%). The results indicate remarkable overall short-run economic resilience. Furthermore, sectors such as information technology, consulting, business services, and healthcare performed exceptionally well. Full article

This study aimed to analyze and quantify the relative importance of each shooting series (Series 1 through 4) to variations in total shooting performance during the laser run event in Modern Pentathlon using machine learning (ML) models and interpretability methods. Individual shooting times (n = 1453) were collected from six international competitions hosted by UIPM in 2024, and 2-ML models, Random Forest and XGBoost, were trained to predict total shooting time. To interpret model results, two interpretability methods—Permutation Importance and SHAP (SHapley Additive exPlanations)—were applied to the trained models, and their performance was evaluated using Mean Absolute Error (MAE) and R-squared (R²) with 5-fold cross-validation. Series 4 shooting consistently exhibited the highest importance in explaining variations in total shooting time for both male and female athletes, with the XGBoost model interpreted using SHAP achieving the highest predictive accuracy (R² = 0.97). Additionally, Permutation Importance identified Series 1 as particularly influential among male athletes, addressing potential biases in Random Forest’s Mean Decrease Impurity measure. These findings highlight the importance of managing concentration, rapid recovery, and psychological pressure during the final shooting stage, alongside the significance of stability in earlier series, providing actionable, data-driven strategies for training and psychological preparation in Modern Pentathlon athletes. Full article

The precise estimation of precipitation is key to understanding and mitigating the effects of extreme weather conditions, especially in areas susceptible to Mediterranean cyclones. This work assesses the performance of the integrated multi-satellite retrievals for GPM (IMERG) precipitation products during the extreme Mediterranean cyclone “Medicane Daniel” that affected the Thessaly Plain in Central Greece in early September 2023. Three IMERG versions (final run (FR), early run (ER), and late run (LR)) were inter-compared with gauge-based interpolated rainfall estimates using inverse distance weighting (IDW) and ordinary kriging techniques. Pixel-wise and categorical verification metrics, such as the probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and Peirce skill score (PSS), were calculated for rainfall thresholds between 50 mm and 400 mm. It was found that the IMERG final run agreed most with the ground observations, with a correlation coefficient (R) of 0.87, RMSE of 138.8 mm, and CSI up to 0.995 at the 100 mm threshold when the IDW interpolation was used. Kriging produced slightly better spatial accuracy overall, as indicated by a lower RMSE (14.5 mm) and higher correlation (R = 0.99). The results indicate the benefit of combining satellite precipitation data with ground-based observations through spatial interpolation for the enhanced monitoring of extreme weather events over complex terrain. Kriging is suggested when greater spatial reliability is needed, while IMERG-FR is found to be a reliable satellite product for quick response analysis during heavy precipitation events. The study emphasizes the importance of blending satellite precipitation estimates and ground observations via spatial interpolation methods, i.e., kriging and IDW, allowing for a more localized and precise validation of intense weather events. Full article

Background/Objectives: Although nutrition is widely recognized as a key factor in post-event recovery in sports, little attention has been given to how its cultural and social dimensions—embodied in local cuisine—intersect with the needs of traveling athletes, for whom food often also serves as a medium of cultural immersion and sensory exploration. Poland, with its rich regional culinary traditions and numerous international running events, offers a compelling context in which to explore these interactions. This study aims to understand the role of local cuisine in the experiences of foreign runners participating in the Poznan Half Marathon 2025, with particular attention on cultural engagement, tourist motivations, and post-exercise recovery processes. Methods: This study was based on a qualitative approach, utilizing semi-structured in-depth interviews conducted with 12 international runners from the United Kingdom, Germany, and Ukraine. The participants possessed a minimum of two years’ experience in traveling for sports. Results: The findings identified three main areas of the significance of food: (1) food as an element of cultural exploration, (2) local cuisine as a motivator or barrier when choosing a race, (3) food as a symbolic reward and structured recovery practice supporting nutritional and psychological processes. Approaches varied by nationality—British participants preferred spontaneous taste discovery, Ukrainians valued culinary comfort similar to home, and Germans planned their culinary experiences with greater awareness. Conclusions: Local cuisine plays a multifaceted role in international running events, serving not only nutritional needs but also emotional and cultural functions that shape the overall participant experience. Both event organizers and local restaurants should consider offering diverse and culturally sensitive food options to enhance recovery, satisfaction, and the appeal of sports tourism destinations. Full article

This work aims to highlight the effects of nonlinearity on the crest shape of large directional water wave events. To simulate such events, we chose to focus frequencies on a pre-determined time step over a wavefield with randomised phases, running the simulations with HOS-ocean, a fully nonlinear potential flow solver. By also applying a phase separation scheme, we were able to identify the contributions of the various orders of nonlinearity to the formation of these large wave events. The findings show a significant change in the shape of these large water waves compared to linear theory, particularly in shallower water depth. In addition, the phase separation reveals the increased significance of high-order harmonics in finite water depths compared to deep water. Full article

The Lidia cattle breed is featured in several traditional popular bullfighting festivals throughout Spain, including the “Toro de Cuerda” event, in which the animals are subjected to intense physical exercise. However, the physiological impact and welfare implications of these activities remain poorly characterized. This study aimed to evaluate the stress response and muscle damage in Lidia breed bulls during roping bull celebrations through comprehensive blood analysis. Blood samples were collected from 53 adult male Lidia bulls before and after a standardized 45 min continuous running exercise during traditional roping bull events in four Spanish autonomous regions. Hematological parameters, muscle enzymes (creatine kinase, lactate dehydrogenase, lactate), and stress hormones (cortisol and ACTH) were analyzed. Significant increases (p < 0.05) were observed in leukocytes, lymphocytes, monocytes, eosinophils, neutrophils, erythrocytes, hematocrit, hemoglobin, and post-exercise platelets. Muscle enzymes showed marked elevations, with creatine kinase increasing up to 10-fold above baseline values. Stress hormones, cortisol and ACTH, also demonstrated significant increases. Despite the magnitude of these changes, all parameters remained within established reference ranges for the bovine species. This study provides the first physiological assessment of Lidia cattle during popular bullfighting celebrations, establishing baseline data for evidence-based welfare evaluation and management protocols. Full article