Search Results (4,075)

Federated learning enables multiple organizations to collaboratively analyze data while retaining local control over their datasets, making it attractive for applications such as healthcare. Distributed quantum computing provides a natural framework for such workflows, allowing geographically separated quantum processors to execute joint computations using shared entanglement while preserving data privacy. In this work, we investigate the feasibility of satellite-enabled distributed quantum computing for federated quantum learning. As a representative application, we consider a distributed distance-based quantum classifier in which multiple parties contribute local data through quantum operations. To support this application, we develop a hybrid space-ground quantum network architecture in which satellites distribute entanglement between distant ground stations. The communication layer is combined with a noise-aware neutral-atom processor model, enabling a system-level analysis that captures both network and hardware constraints. Simulation results across varying network sizes, feature dimensions, and coherence regimes show that classifier performance is jointly determined by communication resources, processor noise, and data representation. In low-coherence regimes, decoherence destroys the classifier’s discriminative signal, whereas high-coherence regimes reveal limitations arising from feature-space conditioning and feature redundancy. These results demonstrate that satellite quantum networks could support distributed quantum learning over long distances, while highlighting the importance of coherence time, entanglement-distribution performance, and learning-aware data encoding for future large-scale deployments. Full article

Background: Increasing demand for emergency medical services (EMS) challenges prehospital resource availability, particularly for time-critical emergencies requiring advanced life support (ALS). Although tiered BLS and ALS response models are established in many non-physician-led EMS systems, evidence regarding qualification-based dispatch stratification within physician-led EMS systems remains limited. This study evaluated the clinical and operational characteristics of an expanded BLS dispatch segment in Berlin EMS. Methods: A retrospective observational study analysed 2,132,246 EMS missions in Berlin, Germany, between 2020 and 2024. Missions were retrospectively classified according to dispatch codes included in the expanded BLS segment, designed to allocate incidents with lower expected requirements for ALS-level interventions to appropriately qualified EMS personnel. Analyses included dispatch characteristics, clinical findings from electronic patient care records, observed safety-related indicators, and ALS response intervals. Results: Overall, 28.7% of EMS missions were classified within the expanded BLS segment. Traumatic and psychiatric presentations represented the most frequent diagnostic groups. Based on predefined clinical indicators, no immediately life-threatening condition was documented in more than 95% of missions. Cardiopulmonary resuscitation occurred in 0.03% of cases, and emergency physician involvement was documented in approximately 3% of cases. Conclusions: A substantial proportion of EMS missions represented a population with predominantly lower expected prehospital treatment complexity within a qualification-based dispatch framework. Structured emergency call interrogation combined with dispatch classification and linked clinical data enabled retrospective evaluation of this approach. Further validation against independent clinical reference standards and linkage with downstream outcomes are required to determine broader applicability. Full article

Background/Objectives: Childhood cancer outcomes are influenced by clinical, socioeconomic, and territorial factors, particularly in resource-limited settings. This study characterized pediatric cancer patients from Córdoba and Sucre, Colombia, and evaluated factors associated with clinical outcomes in a regional referral center. Methods: A retrospective hospital-based study included 434 patients aged 0–18 years diagnosed between 2018 and 2024. Sociodemographic, clinical, and territorial socioeconomic variables were analyzed using the Multidimensional Poverty Index (MPI). Clinical outcomes were classified as favorable, non-favorable, or death. Firth-penalized logistic regression models were used to assess adjusted associations. Results: Hematologic malignancies predominated (65.9%), with acute lymphoblastic leukemia as the most frequent diagnosis. Most patients came from municipalities with moderate or high multidimensional poverty (85.2%), and the rural territories of origin showed greater deprivation in education, employment, housing, water access, and sanitation. Favorable outcomes occurred in 82.9% of patients, while 8.5% had non-favorable outcomes and 8.5% died. Older age and non-hematologic malignancies were associated with non-favorable outcomes. Mortality was associated with older age, subsidized health insurance, and non-hematologic malignancies. MPI and rural residence were not independently associated with outcomes after adjustment. Conclusions: Although MPI was not independently associated with clinical outcomes, it identified substantial territorial deprivation. Integrating clinical and territorial indicators may support equity-oriented surveillance and pediatric oncology interventions. Full article

Rapid and unplanned urbanization in Dhaka is reshaping land use, intensifying peripheral expansion, and increasing pressure on urban and ecological resources. Understanding these growth dynamics is essential for effective urban growth boundary delineation and sustainable planning, yet integrated assessments of historical and future urban growth remain limited. The current study evaluates spatiotemporal urban expansion from 2010 to 2025, delineates the urban growth boundary using a morphological framework, and simulates future growth for 2030 through a coupled cellular automaton-neural network model. Multi-temporal Landsat imagery (2010, 2015, 2020, and 2025) was classified in Google Earth Engine using supervised Maximum Likelihood Classification. Urban growth patterns were quantified using the urban expansion intensity index (UEII), annual urban expansion rate (AUER), and landscape expansion index (LEI). The urban largest continuous patch index (ULCPI) approach was applied to extract functional urban boundaries. Model performance was validated using the Chi-square (χ²) goodness-of-fit test. Results show a substantial increase in built-up land from 115.85 km² to 171.42 km² between 2010 and 2025, accompanied by a decline of approximately 60 km² in urban green spaces. LEI results demonstrate a transition from compact infilling growth (2010–2015) to dominant edge and outlying expansion (2015–2020), indicating progressive peri-urbanization. The urban largest continuous patch (ULCP) nearly doubled from 78.58 km² to 152.13 km² over the same period, accentuating rapid spatial consolidation. The 2030 projection anticipates continued corridor-oriented expansion, particularly toward the northern and eastern peripheries, with predictive agreement from the CA–ANN model (χ² = 0.03 < 7.8). The study identifies a clear transition from monocentric compactness to polycentric expansion, emphasizing the necessity for enforceable growth containment, transit-oriented development, and ecologically responsive planning strategies to ensure long-term urban sustainability. Full article

National and international frameworks provide strategic direction for antimicrobial stewardship (AMS), while their translation into operational practice remains inconsistent. Building on ARCO Step I findings, this study aimed to establish an interregional, multidisciplinary consensus on sustainable AMS implementation strategies across hospital and community settings in Northeastern Italy. A modified Delphi methodology engaged 33 interdisciplinary professionals from Northeastern Italy. Eighteen operational statements across three domains (Governance, Hospital AMS, Community AMS) underwent two quantitative scoring rounds (relevance and feasibility; 1–10 scale) separated by a structured in-person workshop with thematic round-table discussions. Consensus threshold was defined as median score ≥ 8.0; high-priority statements required combined score ≥ 8.5. All 18 statements achieved consensus for relevance, whereas 12 statements achieved consensus for feasibility. Post-discussion median scores feasibility showed a numerically larger increase for feasibility (Δ +0.78) compared to relevance (Δ +0.44), indicating that structured deliberation reduced perceived implementation barriers. Furthermore, 14 statements were classified as high-priority. Qualitative analysis identified key enablers (CEO performance mandates, multidisciplinary teams, standardized indicators) and barriers (digital infrastructure gaps, resource constraints). The participatory approach helped translate strategic policy into a set of practice-oriented recommendations, offering a potentially transferable model for regional health authorities implementing AMR action plans, pending evaluation of actual implementation. Full article

Carbonate formations host abundant hydrocarbon resources and constitute a key target for current and future petroleum explorations. A massive Paleocene-Eocene carbonate platform system overlies Late Cretaceous Deccan volcanic rocks within the Indus Fan Offshore Basin. Low geophysical exploration maturity and insufficient targeted research on carbonate reef identification and platform evolution hinder hydrocarbon discoveries in this region. Using 2D seismic data, this study employs multiple seismic attribute methods to characterize carbonate reef-related anomalies. An innovative reef identification method based on a weighted superposition of spectrally decomposed amplitude spectra is proposed to delineate reef-prone sedimentary facies and characterize the multi-stage evolutionary patterns of the carbonate platforms. Based on quantitative constraints from seismic reflection and amplitude differences, platform evolution is classified into four clear phases: initial development, platform expansion, gradual decline and platform drowning. The initial development phase shows prominent high-amplitude and low-frequency seismic responses, and intervals with such high-amplitude responses may indicate favorable reservoir potential. Platform margin zones show the most prominent amplitude anomalies and are considered priority exploration targets, suggesting excellent hydrocarbon potential within strata formed during the initial platform stage. This study provides quantitative geophysical support for the stage classification of Paleocene–Eocene carbonate platforms in the Indus Fan Offshore Basin, and offers practical guidance for carbonate reservoir exploration and the prospect evaluation of analogous basins worldwide. Full article

Against the backdrop of accelerating globalization and the reconfiguration of ecological security patterns, border regions—serving as critical carriers of national spatial governance—exhibit pronounced spatial heterogeneity and stage-dependent characteristics in land use evolution that warrant systematic investigation. This study focuses on global border zones to reveal the trends and evolutionary pathways of land use change, thereby providing a scientific basis for border spatial governance. Using the period of 1992–2020, four phases of global land cover data from the European Space Agency (ESA) were employed. Buffer zones of 50 km, 100 km, and 150 km were established along global national boundaries, and the study period was divided into three decadal stages. Border types were classified into terrestrial borders, coastal borders, and island borders. An integrated indicator system was constructed, including the Single Land Use Dynamic Degree (SLUDD), Integrated Land Use Dynamic Degree (ILUDD), and Weighted Composite Transition Intensity. A multi-scale nested analytical framework coupled with transition matrices was applied to assess land use evolution across global, continental, and bilateral national scales. The results indicate the following: (1) Distinct evolutionary pathways exist among border types. Terrestrial borders exhibit a combination of development and ecological restoration, with staged peaks in urban expansion; coastal borders are dominated by a unidirectional conversion from cropland to built-up land; and island borders demonstrate concurrent agricultural decline, ecological recovery, and built-up expansion. (2) From 1992 to 2020, global border land use underwent a transition from high-intensity transformations to low-intensity steady adjustments. Major changes were centered on the expansion of production space, outward diffusion of built-up areas, and vegetation restructuring. High-intensity changes were concentrated near boundary zones and attenuated significantly with increasing buffer distance. (3) At the continental scale, stable hierarchical patterns emerge in terms of change intensity, spatial gradients, and transition structures. Borders in Eurasia and North America show higher activity, Africa exhibits widespread diffusion, and South America and Oceania are characterized by forest boundary reshaping and vegetation adjustments, respectively. (4) Typical border regions can be categorized into three evolutionary types: forest ecological frontiers, agricultural expansion zones, and land–sea composite corridors. Regional trajectories vary significantly, with Southeast Asia transitioning rapidly toward stabilization, West Africa retaining periodic high-intensity disturbances, and South America remaining in a low-intensity adjustment state. This study clarifies the evolutionary patterns and boundary effect ranges of global border land use, providing theoretical support and empirical references for resource allocation and ecological regulation in border regions. It also lays the groundwork for refining border typologies, quantifying driving mechanisms, and developing differentiated cross-border governance strategies. Full article

Automatic sleep staging is fundamental for diagnosing sleep disorders and enabling long-term sleep monitoring with wearable devices. Although Deep Learning has significantly improved classification performance, balancing accuracy with computational efficiency remains challenging, particularly for resource-constrained systems. This paper proposes a lightweight two-stage Deep Learning framework for five-class sleep staging based on optimized multimodal physiological features extracted from electroencephalogram (EEG), electrooculogram (EOG), and photoplethysmography (PPG) signals. The framework is trained and tested using the Bitbrain Open Access Sleep (BOAS) database, considering a 31-subject dataset partitioned into training (24 subjects) and independent test (7 subjects) sets. Feature selection is performed using the minimum Redundancy Maximum Relevance (mRMR) algorithm, followed by Principal Component Analysis (PCA) for EEG and EOG features, while respiratory and cardiac features derived from PPG are directly incorporated into the multimodal representation. A hierarchical Long Short-Term Memory (LSTM) architecture first classifies sleep into Wake, REM, and NREM, then further distinguishes the N1, N2, and N3 stages. On an independent test set, the classifier achieves 88.2% five-class accuracy on the multimodal feature set (EEG + EOG + PPG) with a model size of 3.14 MB, and 87.1% accuracy on the EEG-only feature set using only 2.95 MB of memory. Leave-One-Subject-Out (LOSO) cross-validation yields 86.9% accuracy, supporting subject-independent generalization. Inference latency ranged from 2.55 ms (EEG-only) to 4.02 ms (multimodal), with measured energy per inference of 2.88–10.9 mJ across feature sets. Additional validation on the RichSleep and ISRUC datasets demonstrates robustness across different recording conditions, achieving mean accuracies of 78.2% and 77.3%, respectively. The proposed framework provides a favorable trade-off among classification performance, complexity, and memory footprint, suggesting its potential suitability for wearable and edge-based sleep-monitoring systems. Full article

Typhoons often cause severe casualties, property losses, and infrastructure damage, and high-resolution spatial risk assessment is an important basis for developing effective disaster prevention and mitigation strategies. However, most existing typhoon risk assessments are conducted at relatively coarse spatial scales and provide limited representation of intra-urban differences in mitigation capacity. To address this gap, this study takes Haikou, China, as the study area and develops a spatially detailed mitigation-capacity indicator system. Mitigation capacity is incorporated as a key dimension into the conventional hazard–exposure–vulnerability framework. Based on multi-source geospatial data, data mining, and spatial analysis, a risk assessment system comprising 23 indicators was established. Indicator weights were determined using the analytic hierarchy process, and all indicator layers were harmonized to generate a typhoon risk map on a 30 m analytical grid. The results show that the high-resolution risk maps can effectively characterize the spatial extent and level differentiation of typhoon risk while revealing significant spatial heterogeneity in risk at the fine grid scale. In Haikou, 18.99% of the area is classified as being at high and very high risk levels, mainly distributed along the coastal zones of Shishan Town, Xixiu Town, Changliu Town, Lingshan Town, and Yanfeng Town. A preliminary plausibility check was conducted using eight georeferenced typhoon-related fatality locations recorded from 2015 to 2024. Five were located in high- or very-high-risk zones. Given the limited sample size, this comparison does not constitute formal statistical validation, but the observed spatial correspondence provides preliminary support for the plausibility of the assessment results. This study provides spatially explicit decision support for identifying intra-urban variations in typhoon risk, delineating priority areas for disaster mitigation, and optimizing the allocation of mitigation resources. Full article

The growing demand for renewable biomass energy has driven in-depth research into pyrolysis, in which particle size has emerged as a key factor influencing reaction kinetics and gas release. In this study, beech wood with four different sizes were prepared. A thermogravimetric analyzer (TGA 4000) and a Fourier transform infrared spectrometer (FTIR) were used to analyze the thermal behavior of the biomass under a high-purity N₂ atmosphere at heating rates of 10, 20, and 40 K/min. Conversion rates and activation energies were calculated from the thermogravimetric data using two model-free methods, while infrared spectroscopy was employed to analyze gas composition and release characteristics. The experimental results indicate that changes in particle size significantly affect the DTG curves: as particle size increases, the maximum rate of weight loss gradually rises. In terms of pyrolysis kinetic parameters, the activation energy of the biomass samples increased from 166.42 kJ/mol to 176.07 kJ/mol. Gas release peaks also exhibited a trend of shifting toward higher temperature regions. The primary gaseous products were classified into six functional group/gas categories, with their yields ranked in descending order as follows: CO₂ > CH₂O > CH₃OH > H₂O > CH₄ > CO. Except for CO₂, the yields of all other components increased with increasing particle size. These research findings provide data and guidance for the recovery and reuse of biomass resources, as well as for the modeling of biomass pyrolysis reactors, and the classification, pretreatment, and process optimization of biomass materials, thereby accelerating their practical application. Full article

Against the backdrop of climate change and compounded by human activities, increasing water scarcity has triggered a series of drought disasters, which have already severely impacted both ecological environments and socioeconomic production. The SWAT model, recognized for its strong portability and superior spatial heterogeneity, has gained widespread acceptance in fields such as hydrology and environmental science, and is extensively applied in hydrological simulation studies across large-scale river basins. Hydrological models of the study area can be constructed in the SWAT model to simulate changes in hydrological variables by conducting spatial discretization, parameter specification, and boundary condition definition. Standardized drought index can effectively reflect the spatiotemporal variations in drought disasters, holding significant importance for clarifying and predicting drought characteristics. This study took the Luanhe River Basin as the research area, constructed a watershed hydrological model based on SWAT, and projected changes in the basin’s hydrological processes for the period 2030–2060. Based on the model’s projected data, we calculated drought indices and extracted drought events for the basin. The results indicate the following: (1) During the simulation period, only 30% of the years in the Luanhe River basin had annual runoff above the long-term average, with a range of 228.18 mm. The range of mean annual runoff across sub-basins was 173.32 mm. Drought and uneven water resource allocation over both spatial and temporal scales coexisted, and this issue is expected to intensify under future climate warming and drying. (2) The mid-reaches of the Luanhe River are more prone to drought compared to the upper reaches for its higher water demand. However, due to a stronger capacity for ecological restoration, droughts there are mostly of low intensity in the mid-reaches. In contrast, the upper reaches experience more periods classified as severe or extreme drought, and the drought events encountered are generally more intense than those in the mid-reaches. (3) The method proposed in this study can screen extreme drought events based on outliers in the characteristic values of drought events. Taking the simulation from this study as an illustration, anomalies in drought event characteristic values suggest a potential basin-scale, prolonged extreme drought event in the Luanhe River Basin from June 2038 to July 2042. Proactive drought prevention policies should be formulated for this period. The findings of this study provide guiding significance and practical value for drought assessment, risk management, and policy application in the Luanhe River Basin. This study methodologically combines hydrological model predictions with drought event responses, providing a novel method for predicting basin-scale drought conditions and issuing early warnings for extreme drought events. Full article

In the digital era, the rapid proliferation of fake news poses critical challenges to information credibility and public trust, particularly in Arabic news ecosystems where linguistic complexity and limited annotated resources exacerbate detection difficulties. This study proposes a framework for Arabic fake news detection based on a weighted-distance ensemble learning method (WDELM). The WDELM framework combines posterior-probability estimates from six heterogeneous base classifiers: extreme gradient boosting (XGBoost), LightGBM (LGBM), random forest (RF), adaptive boosting (AdaBoost), gradient boosting (GB), and logistic regression (LR). The classifier outputs are integrated through a distance-aware adaptive weighting strategy based on cosine distance in the prediction space. Unlike conventional ensemble techniques, the proposed framework employs normalised distance-aware adaptive weighting to adapt classifier contributions while preserving the probabilistic interpretation of the final ensemble output. Experiments were conducted on an Arabic fake news dataset comprising 2538 manually annotated instances. The model was evaluated using multiple performance metrics, including precision, recall, F1-score, Cohen’s kappa, ROC-AUC, and accuracy, together with explainability analyses. Using stratified ten-fold cross-validation, the proposed WDELM achieved a mean accuracy of $92.120\pm 1.097\%$ and a mean ROC-AUC of $97.125\pm 0.686\%$. Analysis of the concatenated predictions generated across the ten outer-validation folds yielded an F1-score of 91.357% for the Fake class, an F1-score of 92.759% for the Real class, and a pooled macro-F1 score of 92.058%, indicating balanced classification performance across both classes. The results indicate that the proposed weighted-distance ensemble strategy provides improved empirical performance within the evaluated dataset and offers a transparent mechanism for combining heterogeneous classifiers. The framework is further assessed through statistical validation, error analysis, and explainability analysis, supporting its potential use as an auxiliary decision-support tool for Arabic fake news screening rather than as a fully automated replacement for professional fact-checking. Full article

Background/Objectives: Perinatal care is essential for the health and well-being of women and adolescent girls from conception through the first year postpartum. The World Health Organization (WHO) provides evidence-based recommendations aimed at improving the quality of antenatal, intrapartum, and postnatal care. This study mapped 51 WHO perinatal care recommendations against Zambian guidelines to identify areas of alignment, as well as gaps and challenges related to their implementation. Methods: The review was conducted following Joanna Briggs Institute (JBI) guidance. Published recommendations and relevant documents issued from 2016 onwards were included. National guidelines, policy documents, professional standards, and literature related to perinatal care in Zambia were identified through searches of PubMed, Google Scholar, WHO resources, and relevant institutional websites. WHO recommendations were mapped against Zambian guidelines and classified according to their level of alignment. Results: The findings demonstrated significant alignment between WHO recommendations and Zambian guidelines in perinatal healthcare. However, important implementation gaps remain; for example, only 4.7% of pregnant women attended all eight recommended antenatal care visits. Intrapartum care largely aligns with WHO standards, though there are differences in pain-relief choices. Furthermore, difficulties in adopting WHO recommendations are mostly due to health system constraints. Conclusions: Most of the Zambian guidelines align with the WHO recommendations. However, ten were classified as partly aligned, and one did not align. This small proportion of non-alignment may reflect adaptation for a national context and require further evidence-based evaluation. In addition, consolidating existing recommendations, strengthening health-system capacity, and addressing implementation gaps may support the delivery of standardized, high-quality perinatal care and contribute to improved maternal and newborn health outcomes. Full article

As the global population ages rapidly, demand for safe and effective strategies to promote healthy aging grows markedly. Natural bioactive compounds, with favorable biosafety profiles and multi-target regulatory properties, have become a core focus in developing anti-aging functional foods and nutraceuticals. Amid the search for novel sustainable bioresources, insects emerge as promising anti-aging candidates for their rich species diversity, scalable production, low environmental footprint, and abundant unique bioactive components such as functional proteins and bioactive peptides. Based on bibliometric analysis of 500 eligible publications spanning two decades, this review systematically identifies 32 anti-aging insect species across seven orders, classifies their bioactive components into five major categories, summarizes green extraction technologies, and evaluates their in vitro and in vivo anti-aging activities centered on oxidative stress and inflammatory regulation, while outlining the field’s trajectory from basic mechanistic research to functional application. It further highlights core challenges including fragmented research frameworks and insufficient robust in vivo validation. Finally, it recommends integrating established food science and medical methodologies with emerging technologies such as omics, artificial intelligence, and advanced delivery systems to advance future research paradigms. These efforts could provide a strong theoretical foundation for the efficient and sustainable use of insect resources in anti-aging applications. Full article

Weeds create a significant challenge in agricultural production by competing with crops for essential resources such as nutrients, sunlight, and water. This competition leads to reduced crop yields and quality, resulting in substantial economic losses. Consequently, there is a critical need for effective weed control strategies to mitigate the impact of unwanted plant growth and ensure sustainable agricultural practices. In precision agriculture, enabling autonomous navigation between crop rows during tasks such as weeding and harvesting presents a significant research challenge, particularly when leveraging cost-effective technological solutions. Effective robot navigation requires adaptive traffic management strategies and robust object recognition capabilities to distinguish between cultivated and uncultivated areas. In fact, the integration of computer vision techniques into these systems is essential for optimizing trafficability in cropping fields and enhancing robots’ dynamics for better working efficiency in agricultural environments. This review addresses the challenge of enhancing inter-row navigation in field crops and delivering reliable guidance for autonomous agricultural robots. A PRISMA-based systematic review methodology was adopted to identify, screen, and analyze 38 relevant studies selected from the Scopus and Web of Science databases. The selected studies are classified according to their target platform (Unmanned Ground Vehicles and Unmanned Aerial Vehicles) and grouped into three methodological categories: conventional computer vision, deep learning architectures, and hybrid approaches. The findings provide practical guidance for selecting appropriate vision-based crop row detection technologies according to the application requirements and highlight key research directions toward more robust, cost-effective, and adaptable autonomous navigation systems. This article presents an outline of artificial-intelligence-based row detection methods used in agricultural fields and a classification of related semantic segmentation approaches. Unlike previous surveys, it provides an overview of the technological progress in agricultural robots and navigation based on systems vision for crop row detection, with a focus on comparisons balancing technical performance with economic and practical constraints. Full article