Search Results (326)

Coastal zones are dynamic interfaces where land, ocean, and atmosphere interact, making them sensitive indicators of environmental change. However, quantifying shoreline movement across long distances and over multi-year timescales remains challenging using traditional ground-based methods alone. We conducted an analysis of environmental factors and shoreline dynamics along a 58 km stretch of the arid Cabo Pulmo shoreline in Mexico from 2020 to 2026 using the CoastSat tool. The landscape is characterized by a diverse array of geographical features, including sandy beaches, granite cliffs, estuarine systems, and various anthropogenic structures. Results indicated a sea-level rise of 2 mm/year over the last 27 years, which is consistent with the reported range for the Pacific (1.8 to 3.8 mm/year). Notably, we observed an increasing trend of Category 4 and 5 hurricanes in the Mexican Pacific, with an average of 1 additional hurricane per decade (1950–2023). A total of 457 Sentinel-2 satellite images were used for automated analysis using the CoastSat platform, all of which were acquired under tidal conditions not exceeding 1 m. Our findings indicate that the granite cliffs show no detectable horizontal changes in the satellite images; however, their minimal vertical erosion contributes sediment to adjacent beaches. The most significant shoreline erosion was observed north of a marina breakwater, measuring −19.7 m, attributed to the disruption of littoral transport toward the southeast. In contrast, sandy beaches located in front of streams and estuaries—characterized by a lack of infrastructure (houses and breakwaters) and gentle slopes of 2° to 4°—demonstrated positive accretion of up to 5.9 m. According to the autoregressive distributed lag model, wave energy and hurricane-driven wind gusts are the primary agents of shoreline retreat, displacing sediment seaward to the continental shelf. Sea level rise exacerbates this retreat, while rainfall plays a minor but contributing role by transporting sediment during hurricanes in this arid region. This study highlights the effectiveness of CoastSat as a neural network-based tool for analyzing shoreline changes; however, we faced certain limitations, such as the absence of in situ beach profiles due to restricted access. Full article

Empty-vehicle repositioning is a persistent challenge in long-haul road freight because carriers must reduce empty mileage without sacrificing service reliability under lead times, appointment windows, and uncertain load realization. This paper formulates empty-vehicle repositioning on freight corridors as a stochastic control problem with explicit space–time feasibility and a stated within-epoch event order. Lead times couple current dispatch decisions to future capacity, pickup windows impose reachability constraints, and stochastic match feasibility captures information and market frictions. We develop dynamic lower bounds from time-expanded relaxations, showing that dual prices of inventory-balance constraints can be interpreted as space–time scarcity values. We further introduce an order-dependent nested friction decomposition that separates excess empty movement into spatial imbalance, temporal mismatch induced by lead times and time windows, and information frictions. Guided by this structure, we propose price-guided rolling-horizon and generalized-cost policies and evaluate them on synthetic corridor experiments organized around the three friction families. The results reveal service–empty-mileage trade-offs, a pronounced knee in the Pareto frontier, lower service loss under widened tight pickup windows, and strong sensitivity to match feasibility. The PG-RH policy reduces empty-distance exposure and total cost relative to static balancing in the main scenarios while maintaining comparable, but not uniformly dominant, service performance. The framework provides a diagnostic basis for identifying the sources of deadhead and for designing operational interventions that reduce empty mileage without undermining reliability. Full article

Rift Valley fever (RVF) is a viral zoonosis endemic in Madagascar, threatening human and animal health as well as the economy. Trade-related livestock movements are a major factor in the spread of RVF virus. While previous RVF research in Madagascar has focused on farmers or general ecology, this study is the first to specifically target livestock traders, the primary drivers for long-distance viral spread, in the Alaotra Mangoro endemic hotspot. This study aimed to assess the level of knowledge, prevailing attitudes and current practices regarding RVF among people engaged in livestock trade in the Alaotra Mangoro region, as well as the factors associated with these KAPs. A descriptive and analytical cross-sectional survey was conducted among 406 livestock traders in five districts of the Alaotra Mangoro region, using a structured questionnaire. A multi-stage sampling approach was employed, utilising purposive selection of markets followed by snowball sampling to reach informal traders often missed by traditional surveys. Generalised linear mixed models were used to analyse factors associated with KAPs regarding RVF. Awareness of RVF was very low (only 18.5% respondents had heard of it), with significant regional disparities (0% in Anosibe An’Ala versus 51.6% in Moramanga). Veterinarians (15.5%), family (12.8%), radio (9.6%) and neighbours (9.6%) were the main sources of information. Understanding of symptoms and modes of transmission (particularly mosquito bites) was limited. Higher levels of education (OR = 181.6; 95% CI: 29.9–1123.7; p < 0.001) and older age (50–60 years) were associated with better knowledge. Proactive attitudes were scarce (21.4%), although more than half (53.4%) believed that RVF is a real disease. Perception of personal risk and the contribution of livestock trade to the spread of the disease was low. However, confidence in animal vaccination was relatively high (60.3%). Preventive practices were highly inadequate. The majority did not wear protective equipment when handling sick animals (94.6%) and rarely avoided touching aborted foetuses (12.6%). Less than half (48.3%) expressed a willingness to report sick or dead animals, and nearly half admitted to having sold or purchased sick livestock (49.5%). Cooking meat (95.1%) and using mosquito nets (74.1%) were the only well-established practices. More than half of respondents (57.9%) lived more than 5 km from veterinary services, and cost was the most frequently cited barrier to consultation. Participation in awareness campaigns was virtually non-existent (5.4%). Results revealed critical gaps in KAP that may contribute to the persistence of RVF. A “One Health” approach is imperative, integrating human, animal and environmental health. Full article

Bird migration is the regular, long-distance movement of birds between breeding and wintering grounds, influenced by climate change and human activities. The East Asia–Australasia Flyway (EAAF) is one of the largest migratory routes in the world, covering various species such as waders and waterfowl, with the eastern coastal areas of China serving as important stopover and wintering grounds. This paper selects the Qingdao area as the research object, and based on weather radar and meteorological data, explores the spatiotemporal characteristics of bird migration patterns in this region, discusses changes in regional bird activity and their causes, and investigates the influence of weather factors on migration altitude. By analyzing weather radar data from spring 2023, the peak migration period was found to occur mainly from mid-April to mid-May, with multiple large-scale migrations in late April exhibiting alternating peaks and troughs. Migration activity peaked between 8 p.m. and midnight, with altitudes below 600 m serving as the primary migration height range. Using correlation analysis, linear regression, and generalized additive models, the study further analyzed the contribution of various weather factors to birds’ altitude selection. Results showed that wind conditions, temperature, and humidity had significant effects on migration altitude. Full article

Bull sharks (Carcharhinus leucas) are highly mobile coastal predators, whose broad movements complicate conservation planning, particularly in the Western Indian Ocean, where key habitats remain poorly understood. Using passive acoustic telemetry, this study assessed the habitat use, residency, and regional connectivity of adult bull sharks within the Bazaruto Seascape, Mozambique, evaluating the area’s ecological role within a wider movement network. Sharks tagged around Bazaruto exhibited high residency (overall R_max = 0.45) and year-round presence, with core habitat concentrated along seaward reef systems and inshore areas off the San Sebastian Peninsula. Although most core habitat areas (82%) overlapped with existing marine protected areas, important habitats extended beyond protected area boundaries. In addition to supporting resident individuals, the seascape functioned as a seasonal waypoint within a regional movement corridor, with transient sharks exhibiting short periods (<5 days) of seasonal residency (primarily between May and November). Movement patterns revealed strong connectivity with other aggregation sites along the southeast African coast and occasional long-distance movements across the Mozambique Channel to Madagascar and beyond. These findings demonstrate that the Bazaruto Seascape provides both critical habitat and acts as a connectivity node for resident and transient bull sharks, highlighting the need for conservation strategies that combine local spatial protection with broader regional and transboundary management frameworks. Full article

The assessment of art learning outcomes has long relied on teachers’ subjective judgment, facing challenges such as inconsistent evaluation criteria and difficulty in multi-dimensional quantitative analysis. To address these issues, this study proposes a framework for the automatic assessment of art learning outcomes based on symmetry analysis of multi-dimensional aesthetic features. The model quantifies the symmetry between student works and instructional exemplars across three aesthetic dimensions: color distribution features (HSV color space histograms and dominant color composition), compositional features (visual center distribution and structural symmetry), and art movement style features (multi-layer Gram matrices from VGG-19 with PCA dimensionality reduction). Using publicly available artwork datasets, this study constructed Temporal Evolution Pairs (early and late works by the same artist) and Stylistic Inheritance Pairs (works by different artists within the same movement) to validate the model’s effectiveness. The experimental results demonstrate that the proposed multi-dimensional feature fusion strategy achieves 87.6% accuracy in artist style evolution trajectory recognition and 82.3% accuracy in art movement style inheritance quantification, significantly outperforming baseline methods including SSIM (52.3%), VGG-fc features (68.9%), and single style loss (76.4%). Two in-depth case studies further validate the model’s quantitative capability: in analyzing Picasso’s stylistic evolution, the Mastery Index and the Creativity Divergence Index successfully captured the stylistic continuity of adjacent periods (Blue Period to Rose Period: the Mastery Index = 73.6) and the breakthrough innovation of cross-period transformations (Rose Period to Cubism: the Creativity Divergence Index = 82.7). t-SNE visualization of the feature space further revealed that deep style features can clearly distinguish different art movements and individual artists, with spatial distances between artists closely corresponding to stylistic affinities. This research provides new perspectives and tools for a computational framework with the potential for art education assessment practice. It is important to emphasize that the reported performance demonstrates the model’s ability to quantify stylistic relationships between artworks but does not yet demonstrate its validity for assessing student learning outcomes in real classroom settings. As noted, the current validation is based on art-historical consensus, and direct application to educational contexts will require further validation with actual student works and expert evaluation, which we plan to address in future work. Full article

Thisarticle develops and analyzes a fractional-order Leslie–Gower prey–predator–parasite system incorporating two discrete delays and nonlocal spatial diffusion. The model’s central novelty lies in the simultaneous integration of three biologically realistic features that have not previously been combined: (i) fractional-order memory effects via a Caputo derivative of order $\alpha \in (0,1]$, (ii) two distinct biological delays—an infection transmission delay ${\tau }_1$ and a predator handling delay ${\tau }_2$—and (iii) nonlocal spatial dispersal modeled through fractional Laplacian operators ${(-\Delta )}^{\gamma /2}$. This triple integration enables the model to capture long-range temporal memory, delayed biological responses, and nonlocal spatial interactions simultaneously, offering insights into dynamics that are challenging to capture with classical integer-order or single-delay formulations. The fractional Laplacian generalizes classical diffusion by allowing long-range dispersal events (Lévy flights), where individuals can occasionally move over large distances with heavy-tailed step-size distributions—a phenomenon observed in many animal movement patterns but absent from standard diffusion models. We provide rigorous proofs of solution existence, uniqueness, non-negativity, and boundedness in both temporal and spatiotemporal settings. Local asymptotic stability conditions are derived for all feasible equilibrium states via characteristic equation analysis. The coexistence equilibrium undergoes a Hopf bifurcation when either delay crosses a critical threshold, with fractional order $\alpha$ modulating the bifurcation point and post-bifurcation oscillation frequency. A Lyapunov functional demonstrates global asymptotic stability of the infection-free equilibrium under biologically interpretable conditions. Turing instability analysis reveals conditions for spontaneous pattern formation, with the fractional exponent $\gamma$ controlling pattern wavelength and correlation length. Numerical simulations validate theoretical predictions, including spatial patterns, traveling waves, and chaos. To bridge theory with potential applications, we outline a statistical framework for parameter estimation and uncertainty quantification, suggesting that $\beta$, $\alpha$, and ${\tau }_1$ may be priority targets for parameter estimation. Full article

Kazakhstan is a major migration corridor for raptors in Central Asia, yet the migratory connectivity of these species remains poorly documented. We analysed 60 years of ringing data (1966–2025) for three species: Eurasian Sparrowhawk (Accipiter nisus), Northern Goshawk (Astur gentilis), and Shikra (Tachyspiza badia). In total, 5785 individuals were ringed, and 38 recoveries of Kazakhstan-ringed birds were obtained (0.66%). Because recoveries for Goshawk (n = 2) and Shikra (n = 1) are extremely limited, quantitative analyses were restricted to Sparrowhawk recoveries (n = 35), while the other two species are reported descriptively as case records. For Sparrowhawks, migration distances reached 1947 km (mean = 975 km) and did not differ detectably among age classes. Most ringing effort occurred at Shakpak Pass (94.7%), and recoveries indicate connectivity between Kazakhstan, Western Siberia and wintering areas in Central Asia and northern India. Among recovered dead birds (n = 25), shooting (n = 10) and powerline electrocution or collision (n = 3) were frequently reported causes. These long-term ring recoveries provide baseline information on movement connectivity and threats for Central Asian accipiters and highlight the value of sustained monitoring at migration bottlenecks. Full article

This study evaluates the landslide susceptibility of Zonguldak Province, Türkiye, by integrating the Analytical Hierarchy Process (AHP), artificial intelligence (AI) algorithms, and SBAS-InSAR deformation data. Eight environmental and geological parameters—elevation, slope, aspect, lithology, hydrogeology, land use, and distances to rivers and roads—were weighted using AHP and analyzed through 25 AI models. Among them, the Ensemble Bagged Trees (EBT) algorithm achieved the highest predictive accuracy (84%), demonstrating strong adaptability to complex geological datasets. The resulting susceptibility maps were validated using both traditional landslide inventories and InSAR-derived deformation maps, achieving an overall agreement of 83.05%. This dual-validation approach allows for the identification of unrecorded or active slope movements not captured in existing inventories. The combined use of AHP and AI significantly improves model reliability by incorporating both expert judgment and data-driven learning. The study introduces a novel hybrid framework for landslide susceptibility mapping and provides a valuable reference for disaster risk management and spatial planning in regions with complex topography. This study also contributes to sustainability by supporting risk-informed land-use planning, reducing potential economic losses, and enhancing environmental resilience in landslide-prone regions. The proposed framework aligns with sustainable development goals by integrating geospatial technologies and data-driven approaches for long-term hazard mitigation. Full article

African swine fever (ASF) has become one of the most significant transboundary animal diseases affecting countries worldwide. Wild boars play a major role in virus persistence and in local spread through geographical contiguity, while long-distance and transboundary dissemination is more commonly associated with human-mediated activities, particularly the movement of contaminated pork products and materials. Hunting is frequently considered a central tool for disease control; however, its epidemiological impact remains debated. This review comparatively analyses the approaches adopted by countries of European continent affected by ASF, paying particular attention to the role of hunting within integrated management strategies. The review examines country-specific control measures, including surveillance systems, carcass search and removal, fencing, zoning, and population reduction policies. The analysis shows that successful eradication was associated with early detection, temporary suspension of hunting in infected core areas, rapid spatial containment through fencing, and intensive passive surveillance based on systematic carcass removal. Hunting was reintroduced only after containment, as a regulated depopulation tool under strict biosecurity supervision. In contrast, where the virus was already widespread at detection and containment measures were delayed or fragmented, intensified hunting alone did not prevent endemic persistence. These findings indicate that hunting is neither inherently protective nor detrimental; rather, its effectiveness depends on timing, coordination, and integration within a structured epidemiological framework. Effective control requires combining wildlife management with surveillance, biosecurity, and clear governance. Full article

Martial arts have evolved from self-defense practices into structured competitive sports that demand high levels of neuromotor control, where improper execution remains a major source of injury. This study evaluates lower-limb control during the execution of the karate lateral kick using videogrammetry biomechanical analysis. Three participants were recorded during regular training sessions and selected according to their level of expertise. Each participant performed lateral kicks at three predefined distances (close, comfortable, and long), selected based on common training practice and individual biomechanical considerations. Videogrammetry data were generated using Kinovea version 0.9.5 software to extract sagittal ankle trajectories. Statistical analyses were carried out in MATLAB version 2025b using spatial coordinates to obtain kinematic data on the practitioner’s performance. The results revealed skill-dependent differences in movement control, characterized by temporal evolution of kinematic variables and their corresponding time–frequency representations. Novice practitioners exhibited limited control during the raising and recovery phases, despite reaching the target. In contrast, expert practitioners demonstrated consistent posture, controlled acceleration during impact, and stable limb trajectories during descent. These observations provide a foundation for data-driven classification of kick execution quality and outline potential applications in supervised learning, real-time feedback systems, and injury risk reduction during karate training. Full article

Understanding the motion parameters of floating ice is very important for characterizing the ice water dynamics of rivers during freezing periods. Due to the low spatiotemporal resolution of satellite images, limited observation range of unmanned aerial vehicles, and deformation of shore-based camera images, it is difficult to simultaneously quantify the translational and rotational motion characteristics of floating ice and long-distance transportation. This study used the unmanned aerial vehicle GPS joint observation method to observe and obtain various motion parameters such as local translation, rotation, and long-distance transportation in the curved section of the upper reaches of the Yellow River and the straight section of the middle reaches of the Yellow River during the winter of 2024–2025 under conditions of ice density of 50–90%. The velocity field obtained by the drone shows an average ice velocity of 1.27 m/s at the bend and 1.18 m/s in the straight section, with lateral velocity gradients of −0.245 to 0.050 s⁻¹ and −0.141 to 0.222 s⁻¹, respectively. The angular velocity of a single floating ice block is 0.008–0.016 rad/s at bends and 0.010–0.036 rad/s in straight sections. The angular velocity is positively correlated with the local shear strength, and the rotation direction is consistent with the sign of the velocity gradient. GPS tracking provides long-distance transportation trajectories, and the average difference between the speeds obtained by GPS and drones is 0.10 m/s, confirming the reliability of speed estimation based on drones. These results indicate that integrated unmanned aerial vehicle GPS observation can quantitatively characterize local floating ice movement and long-distance floating ice transport behavior, providing on-site parameters for river ice water dynamics research and hazard assessment, and has the potential to be applied to rivers in other cold regions. Full article

Magnetic nanoparticles, with their excellent biocompatibility and biodegradability, serve as ideal materials for constructing targeted drug delivery systems. Iron oxide (Fe₃O₄) nanoparticles, controllably prepared via methods such as solvothermal synthesis, can be combined with mesoporous silica to construct magnetically steerable nanorobots. Such robots enable efficient drug loading and precise delivery. To address challenges in the treatment of Inflammatory Bowel Disease (IBD), including the significant side effects of systemic drugs and the low oral bioavailability and poor colonic targeting of novel food-derived drugs (e.g., capsaicin with anti-inflammatory activity), this study designed capsaicin-loaded iron oxide-mesoporous silica composite nanorobots (Cap-M@mSbots). Driven by a rotating gradient magnetic field of up to 80 mT, Cap-M@mSbots achieve large-scale emergent collective locomotion, with a maximum collective locomotion velocity reaching 180.7 μm/s, and are capable of long-distance movement overcoming millimeter-scale obstacles. This system can be actively propelled to colonic lesion sites under magnetic guidance, achieving targeted drug enrichment and sustained release, thereby offering a novel strategy for the targeted therapy of IBD. Full article

In this paper, we show that the sphere-in-contact model can predict long-range surface adsorption phenomena based on adsorbate-adsorbate repulsions and their geometric distance, assuming that their negative surface-induced charge is smeared on the surface of the adsorbate atoms. Additionally, it can be used to model collective surface diffusion mechanisms such as the domino-type surface diffusion of adsorbate rows on close-packed metal HCP and FCC surfaces. We have recently shown that the sphere-in-contact model can be used as an educational and research tool in various contexts, such as the visualization of carbon structures (e.g., graphene, carbon nanotubes, carbon nanocones, and graphite), heterogeneous catalysts, metal nanoparticles, and organic molecules. Here we present how it can be used to model the adsorbate structure of monoatomic elements on the hexagonal close-packed surface of HCP and FCC metals to study long-range ordering phenomena of monoatomic adsorbates on metals. We have used atoms of varying radius and color to represent the metal surface atoms and the adsorbate atoms. The study reveals that many surface configurations are possible for a fixed adsorbate coverage (θ) by the movement of the adsorbate atoms in response to surface adsorbate-adsorbate repulsions. The movement of the particles (e.g., particle diffusion) can be seen directly in the model, and this is caused by the user intervention. This has great educational and research value, as one can directly see how the adsorbate atoms reorder on the surface of a metal and therefore study diffusion mechanisms. We calculate the repulsive interaction energy of adsorbates using the sphere-in-contact model and can identify which surface-adsorbed configuration is the lowest energy. We find that at a surface coverage of 1/3 (0.333 ML), the most stable adsorbate configuration places adsorbates at the third nearest neighbor 3-fold hollow sites, forming a hexagonal pattern. We find that this model will be useful in the rational design of catalytic materials and material coatings with new technological applications where long-range ordering of surface adsorbates is essential and adsorbate interactions are mainly repulsive interatomic interactions. Full article

Long-distance water conveyance systems often experience free-surface-pressurized flow transitions and air pocket entrapment during filling, which may trigger hazardous phenomena such as air explosions and geysering. Existing models typically lack sufficient predictive accuracy due to oversimplified descriptions of dynamic air exchange and multi-shaft ventilation coupling mechanisms. To resolve this limitation, we propose an enhanced AirSWMM model integrated with a comprehensive ventilation calculation module. The model adopts a unified air pocket formulation and simulates real-time air exchange via predefined ventilation areas along the pipeline. Experimental validation confirms its reliability in predicting key hydraulic parameters, including filling duration, pressure variation, and flow rates. When applied to a prototype project, the model classifies the filling process into four distinct phases based on gas release characteristics and air–water interface movement: initial pressurization, advancing pressurized flow with free venting, system-wide pressurized flow with intermittent venting, and full-pipe flow with terminal intermittent venting. This study provides a robust numerical tool for the safety-oriented management of filling operations in multi-shaft water conveyance systems, delivering practical insights for engineering design and operational optimization. Full article