Search Results (59)

We present a robust, data-efficient framework for early outbreak assessment using multiscale analysis and Computational Singular Perturbation (CSP). This framework overcomes the shortcomings of the standard compartmental epidemiological models, which often struggle with parameter identifiability during the early stages of a pandemic, limiting their predictive utility considerably when data is sparse. Rather than relying on curve-fitting population profiles, which are sensitive to uncertainty, our approach isolates the dominant “explosive time scale that characterizes the outbreak’s intensity and duration. Using a calibrated SEIRD model, CSP allows for the identification of the paths that drive the process during the outbreak phase and the critical transition from accelerating to decelerating growth, which serves as a reliable precursor to the epidemic peak. This framework is assessed against the 4th, 5th, and 6th waves of the COVID-19 pandemic in Greece during 2021, covering periods dominated by the Delta and Omicron variants. Using only early-stage data from short calibration windows, CSP diagnostic tools revealed distinct dynamical drivers for each wave; e.g., a transition from the 4th wave that was driven by transmission intensity (Delta variant dominance) to the 6th wave that was driven by rapid exposure-to-infection turnover and reduced opposition from recovery mechanisms (Omicron variant dominance). Furthermore, it is demonstrated that the timing of the outbreak’s weakening can be accurately predicted, demonstrating robustness with results obtained from longer observation windows. These findings position multiscale analysis as a powerful, pathogen-agnostic early-warning system, capable of disentangling complex epidemic mechanisms and assessing intervention efficacy in real-time. Full article

Background: Body mass index (BMI) is widely used as a simple anthropometric indicator, but its functional relevance to physical fitness in physically active populations, such as Physical Education students, remains debated. Aim: This study examined the association between BMI and selected components of physical fitness in Physical Education students, considering sex and country differences. Methods: A cross-sectional study was conducted among undergraduate Physical Education students from Poland and Romania (n = 515; mean age: 21.64 ± 1.34 years). BMI was calculated from measured height and body mass and analyzed as both a continuous and categorical variable. Physical fitness was assessed using three Eurofit tests evaluating upper-limb movement speed, trunk muscular endurance, and lower-limb explosive power. Analyses included correlation methods and multiple linear regression models with subgroup analyses, interaction terms, and quadratic BMI terms to assess nonlinearity. Results: Associations between BMI and fitness components were small in magnitude and inconsistent (r = −0.28 to 0.143; β = −1.614 to 0.005) and varied across tests and subgroups. No significant interaction effects by sex or country were observed, as interaction terms were not statistically significant, and no clear nonlinear relationships were identified. Sex and country were significantly associated with performance levels, whereas BMI contributed only marginally to explaining variability (ΔR² = 0.005–0.011). Conclusions: BMI showed limited and inconsistent associations with the assessed fitness components in this relatively homogeneous group of Physical Education students. It should be interpreted cautiously as a functional indicator and complemented with more precise measures of body composition and physical fitness. Full article

The Moroccan locust (Dociostaurus maroccanus) is one of the most economically significant locust species in the Caucasus and Central Asia. In the past, the Mediterranean region also experienced severe damage to crops and pastures, until widespread grassland conversion to cropland began in the second half of the 20th century. However, climate change, environmental shifts, land-use changes, cropland abandonment, and overgrazing are likely to alter the spatial distribution and outbreak patterns of this pest. Understanding potential changes and geographic shifts is essential for proactive pest management, including effective monitoring and control strategies. In this study, we apply Ecological Niche Modelling (ENM) using 12 machine learning algorithms, historical survey data covering the species’ full distribution range, and relevant abiotic variables to identify the most suitable areas for potential mass breeding during 1991–2020 and the near future (2021–2040), based on the “middle-of-the-road” Shared Socioeconomic Pathway (SSP2-4.5) scenario. Our results indicate significant regional shifts. Notably, breeding suitability is projected to increase in parts of Greece, Turkey, Armenia, Georgia, Kyrgyzstan, and Tajikistan. In contrast, countries such as Turkmenistan, Afghanistan, Pakistan, and Spain are likely to experience a decline in optimal breeding areas. The forecast results support field observations of a geographical shift northward and toward higher altitudes. Additionally, higher temperatures in suitable areas suggest more drought-like conditions, which typically promote locust population explosions and outbreaks. If left unaddressed, such outbreaks can cause severe economic damage to affected regions. Full article

In many densely populated towns and semi-urban areas, masonry buildings often stand close to busy roads, exposing them to blasts from improvised explosives or other localized sources. Such structures are rarely designed to resist sudden explosive forces, making severe damage or even progressive collapse likely. Even moderate-intensity blasts can weaken walls, endanger occupants, and cause significant property loss. Unlike reinforced concrete, masonry is highly susceptible to explosive impact. Therefore, understanding how these buildings behave under blast loads and developing affordable protection methods is crucial. Low-rise unreinforced masonry (URM) structures, usually up to about 13 m in height (roughly 2–4 stories), common in villages, semi-urban regions, and conflict-prone zones, are particularly at risk. In many areas, these poorly constructed buildings lack proper engineering design and are therefore highly vulnerable to blast damage. Non-load-bearing internal dividers and perimeter enclosures are especially prone to lateral displacement, which can initiate instability and, in severe cases, lead to overall structural failure. This research focuses on reducing catastrophic damage in URM walls when exposed to close-proximity blast forces using concrete-based protective coatings, both with and without embedded steel-welded wire mesh. The study references a previously tested laterally supported clay brick wall built with cement–sand mortar as the baseline model, with its behavior validated against experimental findings from existing literature. Two blast cases were considered corresponding to scaled stand-off distances of 2.19 m/kg^1/3 and 1.83 m/kg^1/3, representing moderate flexural-shear cracking and full structural failure, respectively. To replicate the observed behavior, a comprehensive 3D numerical simulation was developed using the ABAQUS/Explicit 2020 solver. The model’s predictions were benchmarked and verified through comparison with reported test data. While both blast intensities were used to confirm computational accuracy, the effectiveness of UHPC and UHPFRC protective coatings with and without embedded wire mesh was specifically evaluated under the more severe collapse scenario (Z = 1.83 m/kg^1/3). Results indicated that at a scaled distance of 1.83 m/kg^1/3, the uncoated URM wall could not withstand the blast because of poor tensile and bending capacity. In contrast, the UHPC- and UHPFRC-coatings provided improved confinement and better stress distribution. When welded wire mesh was embedded, crack control improved further, the interface bond strengthened, and a larger portion of blast energy was absorbed and dissipated. Full article

Background: Beach volleyball is a high-intensity, intermittent sport requiring repeated explosive actions and rapid changes of direction performed on an unstable sand surface. Creatine monohydrate (CrM) supplementation has consistently been shown to enhance short-duration, high-intensity performance; however, evidence in female athletes and sport-specific contexts in beach volleyball remains limited. The purpose of this study was to examine the effects of CrM supplementation delivered in gummy form on physical performance outcomes, body composition, and reaction time in female beach volleyball athletes. Methods: Thirty-two female collegiate and professional beach volleyball athletes completed a 10-week randomized controlled trial and were assigned to either CrM, 5 g·day⁻¹ group (n = 17) or control group (n = 15). Countermovement jump (CMJ) height, change-of-direction speed (CODS), body composition, and reaction time were assessed before and after the intervention. Outcomes were analyzed using mixed-model analyses of variance. Results: Significant Group × Time interactions were observed for CMJ height and CODS, with the CrM group demonstrating improvements in jump height (p < 0.001, ηp² = 0.34) and faster change-of-direction performance (p = 0.009, ηp² = 0.21), while the control group showed no improvement or performance declines. Significant Group × Time interactions were also observed for body fat mass (p = 0.024, ηp² = 0.16), body fat percentage (p = 0.015, ηp² = 0.18), and total body water (p = 0.038, ηp² = 0.14). No significant interactions were observed for lean body mass, skeletal muscle mass, total body mass, or reaction time. Conclusions: CrM supplementation delivered in gummy form enhanced selected performance outcomes and helped maintain body composition in female beach volleyball athletes. These findings support creatine gummies as a practical supplementation strategy in this population. Full article

Partially ionized plasma physics has attracted increased attention recently due to numerous technological applications made possible by the increased sophistication of computer modelling, the depth of the theoretical analysis, and the technological applications to a vast field of manufacturing for computer components. Partially ionized plasma is characterized by a significant presence of neutral particles in contrast to the fully ionized plasma. The theoretical analysis is based upon solutions of the kinetic Boltzmann equation, yielding the non-Maxwellian electron energy distribution function (EEDF), thereby emphasizing the difference with a fully ionized plasma. The impact of the effect on discharges in inert and molecular gases is described in detail, yielding the complex nonlinear phenomena resulting in plasma selforganization. A few examples of such phenomena are given, including the non-monotonic EEDFs in the discharge afterglow in a mixture of argon with the molecular gas NF₃; the explosive generation of cold electron populations in capacitive discharges, hysteresis of EEDF in inductively coupled plasmas. Recently, highly advanced computer codes were developed in order to address the outstanding challenges in plasma technology. These developments are briefly described in general terms. Full article

Wire explosion (WE) inherently generates particle ensembles spanning the nano- to microscale, posing challenges for conventional characterization methods in terms of capturing the full particle population. To address this issue, spectrophotometric analysis combined with algorithmic spectrum reconstruction based on Mie theory and constrained distribution models were employed to characterize copper WE products formed in aqueous surroundings within the 4–12 kV discharge voltage range. Three independent fitting strategies, specifically a semimanual fitting, an evolutionary algorithm, and a grid search, were applied to retrieve the size distributions and relative shares of copper and copper oxide particles as a function of discharge voltage. Based on experimental and theoretical findings, lognormal and normal distributions across the 10–300 nm diameter range were assumed as constraints for oxide and metallic fractions, respectively. The reconstructed metallic copper population exhibited mean diameters ranging from 123 to 181 nm, while oxidized fractions followed lognormal distributions centred near 10 nm mode diameters. Voltage-dependent trends revealed an optimal discharge regime between 6 kV and 8 kV, where the exploded fraction reached approximately 63% and the metallic mass share exceeded 80%. These results confirmed that spectrophotometry represents an essential tool for the quantitative characterization of such complex, wide-range systems. Full article

Modeling and visualizing zones within the spread of toxic clouds from fires and explosions during accidents at industrial facilities located near residential areas is of high practical value. This tool is critical for the rapid planning of population evacuation measures and emergency response. Of particular importance is the development of computer software that can quickly model the hazard zone of toxic cloud spread and superimpose it on a terrain map to determine the potential impact on residential areas. This software should be based on a mathematical model that can accurately predict the parameters of the hazard zone both near the industrial facility and beyond it, at a distance of more than 1 km. The objective of this study is to create algorithms for modeling the hazard zone during a fire or explosion at an industrial facility using a cellular automaton method and to develop a software tool for its visualization. The software must display the hazard zone for the population of a nearby residential area on a map in real time, which is necessary for assessing potential harm to residents’ health and in planning their rapid evacuation. To achieve this objective, this article presents a model for determining the boundaries and main parameters of a hazard zone based on the cellular automaton method (frontal and probabilistic). The proposed model takes into account both constants (properties of chemical substances, building parameters, population size, etc.) and variables (the mass of the substance at each explosion and fire, wind speed and direction, air temperature, etc.). The FireSoft III software, developed by the authors and based on the cellular automaton model, provides more rapid calculation of the parameters and delineation of the hazard zone boundaries compared to similar software, which was tested in cases of an ammonia tank explosion and a prolonged fire in a warehouse containing polyvinyl chloride at an enterprise. This makes FireSoft III promising for use in a fire and explosion response at enterprises. Full article

Despite its global distribution, the impacts of wild pigs on the environment are poorly understood. However, wild boar (Sus scrofa) is recognized as a pest species, causes extensive damage to agriculture, biodiversity, and forests, and contributes to motor vehicle accidents. This study investigates the causes and mechanisms underlying the demographic explosion of wild boar in Italy. The analysis is based exclusively on official datasets from Italian governmental institutes, allowing quantitative correlations between population dynamics, culling rates, and economic impacts. By integrating historical data, population biology, reproductive physiology, and chemical communication, the study reveals that anthropogenic pressures, counterintuitively driven by wildlife management practices, have significantly contributed to population growth. A shift from a K-strategy to an r-strategy in reproductive behavior, induced by sustained control pressure, has led to increased birth rates and accelerated expansion. Disruptions in species homeostasis trigger harmful changes in ecosystem structure and functionality, delineating a model of environmental damage. These findings highlight the urgency of adopting an integrated wildlife management approach that combines conservation biology and physiological principles with targeted operational interventions to prevent further degradation affecting both the species and the ecosystem. Full article

Non-suicidal self-injury (NSSI) has become an increasingly salient mental health concern among adolescents, and it commonly co-occurs with depression, anxiety, borderline personality disorder, substance use, and childhood maltreatment, forming a complex psychological risk structure. Despite a growing body of literature, a systematic understanding of the structural links between NSSI and psychiatric comorbidities remains limited. This study uses bibliometric and visualization methods to map the developmental trajectory and knowledge structure of the field and to identify research hotspots and frontiers. Drawing on the Web of Science Core Collection, we screened 1562 papers published between 2005 and 2024 on adolescent NSSI and comorbid psychological problems. Using CiteSpace 6.3.R1, VOSviewer 1.6.20, and R 4.3.3, we constructed knowledge graphs from keyword co-occurrence, clustering, burst-term detection, and co-citation analyses. The results show an explosive growth of research in recent years. Hotspots center on comorbidity mechanisms of mood disorders, the impact of childhood trauma, and advances in dynamic assessment. Research has evolved from describing behavioral features toward integrative mechanisms, with five current emphases: risk factor modeling, diagnostic standard optimization, cultural sensitivity, stratified intervention strategies, and psychological risks in special populations. With big data and AI applications, the field is moving toward dynamic prediction and precision intervention. Future work should strengthen cross-cultural comparisons, refine comorbidity network theory, and develop biomarker-informed differentiated interventions to advance both theory and clinical practice. Full article

We present a freeze-out approach for describing the formation of heavy elements in expanding nuclear matter. Applying concepts used in modeling heavy-ion collisions or ternary fission, we determine the abundances of heavy elements taking into account in-medium effects such as Pauli blocking and the Mott effect, which describes the dissolution of nuclei at high densities of nuclear matter. With this approach, we search for a universal initial distribution in a quasi-equilibrium state from which the coarse-grained pattern of the solar abundances of heavy elements freezes out and evolves by radioactive decay of the excited states. The universal initial state is characterized by the Lagrange parameters, which are related to temperature and chemical potentials of neutrons and protons. We show that such a state exists and determine a temperature of 5.266 MeV, a neutron chemical potential of 940.317 MeV and a proton chemical potential of 845.069 MeV, with a baryon number density of 0.013 fm⁻³ and a proton fraction of $0.13$. Heavy neutron-rich nuclei such as the hypothetical double-magic nucleus ³⁵⁸Sn appear in the initial distribution and contribute to the observed abundances after fission. We discuss astrophysical scenarios for the realization of this universal initial distribution for heavy-element nucleosynthesis, including supernova explosions, neutron star mergers and the inhomogeneous Big Bang. The latter scenario may be of interest in the light of early massive objects observed with the James Webb Space Telescope and opens new perspectives on the universality of the observed r-process patterns and the lack of observations of population III stars. Full article

Background: Cardiometabolic risk in adolescents represents a growing public health concern that is closely linked to modifiable factors such as physical fitness. Traditional statistical approaches often fail to capture complex, nonlinear relationships among anthropometric and fitness-related variables. Objective: To develop and evaluate supervised machine learning algorithms, including artificial neural networks and ensemble methods, for classifying cardiometabolic risk levels among Chilean adolescents based on standardized physical fitness assessments. Methods: A cross-sectional analysis was conducted using a large representative sample of school-aged adolescents. Field-based physical fitness tests, such as cardiorespiratory fitness (in terms of estimated maximal oxygen consumption [VO_2max]), muscular strength (push-ups), and explosive power (horizontal jump) testing, were used as input variables. A cardiometabolic risk index was derived using international criteria. Various supervised machine learning models were trained and compared regarding accuracy, F1 score, recall, and area under the receiver operating characteristic curve (AUC-ROC). Results: Among all the models tested, the gradient boosting classifier achieved the best overall performance, with an accuracy of 77.0%, an F1 score of 67.3%, and the highest AUC-ROC (0.601). These results indicate a strong balance between sensitivity and specificity in classifying adolescents at cardiometabolic risk. Horizontal jumps and push-ups emerged as the most influential predictive variables. Conclusions: Gradient boosting proved to be the most effective model for predicting cardiometabolic risk based on physical fitness data. This approach offers a practical, data-driven tool for early risk detection in adolescent populations and may support scalable screening efforts in educational and clinical settings. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

In the domain of financial markets, deep learning techniques have emerged as a significant tool for the development of investment strategies. The present study investigates the potential of time series forecasting (TSF) in financial application scenarios, aiming to predict future spreads and inform investment decisions more effectively. However, the inherent nonlinearity and high volatility of financial time series pose significant challenges for accurate forecasting. To address these issues, this paper proposes the IGWO-MALSTM model, a hybrid framework that integrates Improved Grey Wolf Optimization (IGWO) for hyperparameter tuning and a multi-head attention (MA) mechanism to enhance long-term sequence modeling within the long short-term memory (LSTM) architecture. The IGWO algorithm improves population diversity during initialization using the Mersenne Twister, thereby enhancing the convergence speed and search capability of the optimizer. Simultaneously, the MA mechanism mitigates gradient vanishing and explosion problems, enabling the model to better capture long-range dependencies in financial sequences. Experimental results on real futures market data demonstrate that the proposed model reduces Mean Square Error (MSE) by up to 61.45% and Mean Absolute Error (MAE) by 44.53%, and increases the R² score by 0.83% compared to existing benchmark models. These findings confirm that IGWO-MALSTM offers improved predictive accuracy and stability for financial time series forecasting tasks. Full article

This work investigates the impact of sea level rise (SLR) of up to 3 m on flood protection and freshwater availability in the Netherlands. We applied an exploratory modeling approach to consider the large degree of uncertainty associated with SLR. The results show the current degree of flood protection can be technically and financially maintained for up to three meters of SLR. A primary finding of this work is that a similar degree of safety against floods can be maintained. There are, however, several challenges: First, maintaining this degree of safety against floods requires considerable spatial allocations to maintain and upgrade flood defenses, often in populated areas with limited space. Second, the supply of sand for coastal nourishments will be challenging due to other functions in the North Sea (wind energy, shipping) and explosive remnants of war. Third, an acceleration in the rate of SLR may impact the overall feasibility of maintaining flood defenses. Maintaining the freshwater strategy will be challenging due to SLR-induced salt intrusion, which aggravates climate impacts including droughts. Continued flushing of salinized areas of regional water systems and polders with fresh river water will increasingly compete with other demands. Our analysis highlights the vulnerabilities of the flood protection and freshwater strategies and gives input to follow-up analyses on societal impact and perspectives of actions for adaptation. Full article