Search Results (115,351)

Swarm-based UAV cooperative ground target detection faces critical challenges including sensitivity to small targets, susceptibility to occlusion, and data heterogeneity across distributed platforms. To address these issues, we propose FedGTD-UAVs—a privacy-preserving federated transfer learning (FTL) framework optimized for real-time swarm perception tasks. Our solution integrates three key innovations: (1) an FTL paradigm employing centralized pre-training on public datasets followed by federated fine-tuning of sparse parameter subsets—under severe non-Independent and Identically Distributed (non-IID) data distributions, this paradigm ensures data privacy while maintaining over 98% performance; (2) an Space-to-Depth Convolution (SPD-Conv) backbone that replaces lossy downsampling with lossless space-to-depth operations, preserving fine-grained spatial features critical for small targets; (3) a lightweight Global Context Network (GCNet) module leverages contextual reasoning to effectively capture long-range dependencies, thereby enhancing robustness against occluded objects while maintaining real-time inference at 217 FPS. Extensive validation on VisDrone2019 and CARPK benchmarks demonstrates state-of-the-art performance: 44.2% mAP@0.5 (surpassing YOLOv8s by 12.1%) with 3.2× superior accuracy-efficiency trade-off. Compared to traditional centralized learning methods that rely on global data sharing and pose privacy risks, as well as the significant performance degradation of standard federated learning under non-IID data, this framework successfully resolves the core conflict between data privacy protection and detection performance maintenance, providing a secure and efficient solution for real-world deployment in complex dynamic environments. Full article

Coal resources serve as a fundamental pillar for global economic development and remain the dominant energy source in China. To improve coal resource utilization and assess the impact hazards related to strip filling mining, this study selects the No. 3-3 coal seam of a mine in Tuokexun as its engineering context. By integrating theoretical investigation and numerical modeling, a comparative evaluation was performed between the conventional mining approach and the strip filling mining technique in terms of impact hazard. The results reveal that during the first phase of strip filling mining—characterized by a high filling ratio—the level of impact hazard remains minimal. Relative to the traditional method, the peak advance abutment pressure during the second phase of strip filling mining is reduced by as much as 17.8%. Moreover, significant reductions are observed in stress concentration, deformation intensity, and the extent of plastic zone propagation along the retreat roadway. Under the conventional method, the influence range is approximately 70 m, whereas under strip filling mining, it decreases to about 60 m. These insights confirm that strip filling mining can effectively diminish impact-related hazards and enhance the safety of underground coal extraction operations. Full article

Objectives: Pulmonary embolism (PE) is an uncommon but potentially fatal complication of acute ischaemic stroke (AIS). Its global burden and prevention remain incompletely defined. We performed a systematic review and meta-analysis (PEARL-AIS) to estimate prevalence, risk factors, outcomes, and prophylactic efficacy, with GRADE evidence appraisal. Methods: Following PRISMA 2020 and MOOSE guidelines, five databases (PubMed, Embase, Cochrane, Scopus, Web of Science) were searched (1995–2024). The protocol was prospectively registered (OSF s25ny). Random-effects models (DerSimonian–Laird; REML sensitivity) were used to pool prevalence and odds ratios; heterogeneity was evaluated with I², Cochran’s Q, and τ². Influence (leave-one-out) and subgroup analyses for prevalence and mortality of PE in AIS were explored. Bias was assessed using the Modified Jadad Scale; overall certainty was graded with the GRADE framework. Results: Twenty-four studies met the inclusion criteria (n = 25,666,067), of which seventeen studies (n = 23,637,708) contributed to pooled prevalence analyses. The pooled prevalence of PE after AIS was 0.40% (95% CI 0.33–0.49), approximately six-fold higher than in the general population, with considerable heterogeneity (I² > 90%, Cochrane classification). The pooled mortality among AIS patients with PE was 12.9% (95% CI 1.6–31.7). Mortality risk was significantly higher in AIS patients with PE (OR 4.96, 95% CI 2.98–8.24). Atrial fibrillation (29%), cancer (19%), and smoking (23%) were common; hypertension (54%) and diabetes (23%) were prevalent but not predictive, with diabetes showing a paradoxical protective association (OR 0.88, 95% CI 0.84–0.92). Pharmacological prophylaxis was associated with a reduced risk of PE (OR 0.64, 95% CI 0.46–0.90; I² = 0%), supported by moderate-certainty evidence. Conclusions: PE is an uncommon but often fatal complication of AIS. Traditional venous thromboembolism predictors underperform in this context, suggesting a stroke-specific thromboinflammatory mechanism linking the brain and lung axis. Despite considerable heterogeneity and low-to-moderate certainty of evidence, pharmacological prophylaxis demonstrates a consistent protective effect. Systematic PE surveillance and tailored prophylactic strategies should be integral to contemporary stroke care, while future studies should refine risk stratification and elucidate the mechanistic underpinnings of this brain–lung thromboinflammatory continuum. Full article

Due to the growing global population, rising energy demands, and the environmental impacts of fossil fuel use, there is an urgent need for sustainable energy sources. Biomass conversion technologies have emerged as a promising solution, particularly supercritical water gasification (SCWG), which enables efficient energy recovery from wet and dry biomass. This systematic review, following PRISMA 2020 guidelines, analyzed 51 peer-reviewed studies published between 2015 and 2025. The number of publications has increased over the decade, reflecting rising interest in SCWG for energy production. Research has focused on six biomass feedstock categories, with lignocellulosic and wet biomasses most widely studied. Reported energy efficiencies ranged from ~20% to >80%, strongly influenced by operating conditions and system integration. Integrating SCWG with solid oxide fuel cells, organic Rankine cycles, carbon capture and storage, or solar input enhanced both energy recovery and environmental performance. While SCWG demonstrates lower greenhouse gas emissions than conventional methods, many studies lacked comprehensive life cycle or economic analyses. Common limitations include high energy demand, modeling simplifications, and scalability challenges. These trends highlight both the potential and the barriers to advancing SCWG as a viable biomass-to-energy technology. Full article

Air pollution is associated with many adverse health outcomes, including kidney diseases. Kidney diseases, especially chronic kidney disease, are a significant public health issue globally. The burden of kidney disease is expected to rise due to population aging and the growing prevalence of diabetes and hypertension. End-stage kidney disease is associated with significant healthcare costs, morbidity, and mortality. Long-term exposure to air pollution was associated with increased risk for chronic kidney disease progression to kidney replacement therapy. Evidence on the effect of short-term exposure to air pollution on renal function is rather limited. Kidney transplant patients are likely to be even more susceptible to detrimental effects of air pollutants. Exposure to air pollution results in a higher risk for delayed graft function, acute rejection, and mortality. In this review we would like to summarize the state of knowledge on the influence of air pollution on outcomes in end-stage kidney failure and kidney transplantation. Full article

Precision agriculture technologies (PATs) are revolutionizing the agricultural sector by minimizing the reliance on plant protection products (PPPs) in crop management. This approach integrates a broad range of advanced solutions employed to help farmers in optimizing PPP application, while minimizing input and maintaining effective crop protection. These technologies include sensors, drones, robotics, variable rate systems, and artificial intelligence (AI) tools that support site-specific pesticide applications. The objective of this review was to perform a bibliometric analysis to identify scientific trends and gaps in this field. The analysis was conducted using Scopus and Web of Science databases for the period of 2015–2024, by applying a data filtering process to ensure a clean and reliable dataset. The methodology involved citation, co-authorship, co-citation, and co-occurrence analysis. VOSviewer software (version 1.6.20) was used to generate maps and assess global research developments. Results identified AI, sensor, and data processing categories as the most central and interconnected scientific topics, emphasizing their vital role in the evolution of precision spraying technology. Bibliometric analysis highlighted that China, the United States, and India were the most productive countries, with strong collaborations within Europe. The co-occurrence and co-citation analyses revealed increasing interdisciplinarity and the integration of AI tools across various technologies. These findings help identify key experts and research leaders in the precision agriculture domain, thus underscoring the shift toward a more sustainable, data-driven, and synergistic approach in crop protection. Full article

The deterioration of the global climate and accelerated urbanization have led to intense pressure on surface space resources. As a strategic development field, deep underground space has become a crucial direction for alleviating human habitation pressure. However, current research on deep underground space mostly focuses on fields such as geology and medicine, while the design of habitable environments lacks interdisciplinary integration and systematic approaches. Taking deep underground space as the research object, this study first clarifies the interdisciplinary research context through bibliometric analysis. Then, combined with geological data (ground temperature, groundwater, and ground stress, etc.) from major cities in China, it defines the characteristics of the in situ environment and the characteristics of the development and utilization of deep underground space. By comparing the habitable design experiences of extreme environments, such as space stations, Moon habitats, and desert survival modules, the study extracts five categories of design elements: natural conditions, construction status, social economy, users, and existing resources. Ultimately, it establishes a demand-oriented, five-dimensional habitable design methodology covering in situ environment adaptation, living support, medical and health services, resilience and flexibility, and existing space renovation. This research clarifies the differentiated design strategies for hundred-meter-level and kilometer-level deep underground spaces, providing theoretical support for the scientific development of deep underground space and serving as a reference for habitable design in other extreme environments. Full article

Wind energy is a critical renewable resource in the global effort toward sustainable development and climate change mitigation. This paper introduces a hybrid forecasting framework that integrates multistep gated recurrent unit (GRU) modeling with Weibull distribution analysis to assess wind energy potential and predict long-term wind speed dynamics. The approach combines deterministic and probabilistic components, improving robustness against seasonal variability and uncertainties. To demonstrate its effectiveness, the framework was applied to hourly wind data collected from multiple stations across diverse geographical regions in Turkey. Weibull parameters, wind power density, capacity factor, and annual energy production were estimated, while five machine learning models were compared for forecasting accuracy. The GRU model outperformed alternative methods, and the hybrid GRU–Weibull approach produced highly consistent forecasts aligned with historical patterns. Results highlight that the proposed framework offers a reliable and transferable methodology for evaluating wind energy resources, with applicability beyond the case study region. Full article

Background: Isometric handgrip (IHG) exercise reduces blood pressure (BP) in both normotensive and hypertensive individuals. However, there are few studies specifically addressing its effects in hypertensive patients with ischemic heart disease (IHD). This research aimed to compare acute hemodynamic responses and post-exercise hypotension to single bouts of IHG handgrip performed at two different intensities in patients with IHD. Methods: Fifty-four sedentary patients were enrolled and randomly assigned to one of three groups: (1) high-intensity isometric handgrip performed at 70% of maximal voluntary contraction (MVC) (IHG-70%); (2) low-intensity isometric handgrip performed at 30% of MVC (IHG-30%); (3) control group (no exercise). Heart rate and BP were measured, and transthoracic echocardiography was performed at baseline, during exercise (lasting 3 min), and after 15 min post-exercise. BP was also measured at 30, 45, and 60 min of recovery. Results: No significant changes in systolic BP occurred during the exercise phase between the three study groups. Systolic BP decreased significantly in IHG-70% compared to the control at 30 (−7.7 ± 1.9; p = 0.035) and 45 min (−8.1 ± 2.3; p = 0.021) post-exercise, while there were no significant differences between IHG-70% and IHG-30% at different time-points. There were no significant changes in diastolic BP between the two active groups and between IHG-70 and IHG-30 versus control at different time-points (repeated-measures ANOVA p = 0.257). Global work efficiency was unchanged in IHG-70% (−4%) and IHG-30% (+1%) compared to control (ANOVA p = 0.154). Conclusions: High-intensity and low-intensity isometric handgrip exercises did not cause hemodynamic impairment in IHD. High-intensity exercise was more effective than low-intensity in reducing post-exercise systolic BP. Full article

Greenhouse automation has become increasingly important in facility agriculture, yet multi-span glass greenhouses pose both scientific and practical challenges for autonomous mobile robots. Scientifically, solid-state LiDAR is vulnerable to glass-induced reflections, sparse geometric features, and narrow vertical fields of view, all of which undermine Simultaneous Localization and Mapping (SLAM)-based localization and mapping. Practically, large-scale crop production demands accurate inter-row navigation and efficient rail switching to reduce labor intensity and ensure stable operations. To address these challenges, this study presents an integrated localization-navigation framework for mobile robots in multi-span glass greenhouses. In the intralogistics area, the LiDAR Inertial Odometry-Simultaneous Localization and Mapping (LIO-SAM) pipeline was enhanced with reflection filtering, adaptive feature-extraction thresholds, and improved loop-closure detection, generating high-fidelity three-dimensional maps that were converted into two-dimensional occupancy grids for A-Star global path planning and Dynamic Window Approach (DWA) local control. In the cultivation area, where rails intersect with internal corridors, YOLOv8n-based rail-center detection combined with a pure-pursuit controller established a vision-servo framework for lateral rail switching and inter-row navigation. Field experiments demonstrated that the optimized mapping reduced the mean relative error by 15%. At a navigation speed of 0.2 m/s, the robot achieved a mean lateral deviation of 4.12 cm and a heading offset of 1.79°, while the vision-servo rail-switching system improved efficiency by 25.2%. These findings confirm the proposed framework’s accuracy, robustness, and practical applicability, providing strong support for intelligent facility-agriculture operations. Full article

Background: Diabetes-related foot diseases represent a global health problem because of the associated complications, the risk of amputation, and the economic burden on health systems. Negative pressure wound therapy (NPWT) is a technique that uses sub-atmospheric pressure to help promote wound healing by reducing the inflammatory exudate while keeping the wound moist, inhibiting bacterial growth, and promoting the formation of granulation tissue. Objective: This study aimed to assess the effectiveness of NPWT in preventing major amputation in diabetic patients with complicated foot or lower limb infections and to contextualize the results through a review of the existing literature. Materials and methods: We conducted a retrospective study at the First Surgical Department of “Dr. I. Cantacuzino” Clinical Hospital in Bucharest, Romania, over a 15-year period, including 30 consecutive adult patients with diabetes and soft tissue foot or lower limb infections treated with NPWT. Patients with non-diabetic ulcers, incomplete medical data, or aged under 18 were excluded. All patients underwent initial surgical debridement, minor amputation, or drainage procedures, followed by the application of NPWT using a standard protocol. Dressings were changed every 2–4 days for a total of 7–10 days. Antibiotic therapy was adapted according to the culture results. The primary outcome was limb preservation, defined as avoidance of major amputation. Secondary outcomes included in-hospital mortality and wound status at discharge. Results: NPWT was associated with a favorable outcome in 24 patients (80%), defined by wound granulation or healing without the need for major amputation. Five patients (16.6%) underwent major amputation because of failure of the primary lesion treatment, and one patient died. No statistically significant association was observed between the outcomes and standard classification scores (WIFI, IWGDF, and TPI). A comprehensive literature review helped to integrate these findings into the existing pool of knowledge. Conclusions: NPWT may support limb preservation in selected diabetic foot cases. While the retrospective design and the small sample size of the study limit generalizability, these results reinforce the need for further controlled studies to evaluate NPWT in real-life clinical settings. The correct use of NPWT combined with etiological treatment may offer a maximum chance to avoid major amputation in patients with diabetes-related foot diseases. Full article

Reliability assessment of power systems is essential for ensuring the secure and stable operation of power grids, and identifying weak links constitutes a critical step in enhancing system reliability. Traditional deterministic methods are limited in their ability to capture the complex, nonlinear relationships between component failures and overall system risk. To overcome this limitation, this paper proposes an explainable machine learning-based approach for identifying weak components in power systems. Specifically, a set of contingency scenarios is constructed through enumeration, and a random forest regression model is trained to map transmission line outage events to the amount of system load curtailment. The trained model is then interpreted using SHapley Additive exPlanations (SHAP) values. By aggregating these values, the global reliability contribution of each component is quantified. The proposed method is validated on the IEEE 57-bus system, and the results demonstrate its effectiveness and feasibility. This research offers a data-driven framework for translating system-level reliability metrics into device-level quantitative attributions, thereby enabling interpretable identification of weak links. Full article

Background/Objectives: The COVID-19 pandemic has significantly intensified global concerns surrounding antimicrobial resistance (AMR), particularly in relation to tuberculosis (TB). In the European Union (EU), the reallocation of healthcare resources towards managing COVID-19 led to a de-prioritization of TB surveillance and control. This shift contributed to delays in TB diagnosis and treatment, creating conditions favorable for the emergence and spread of drug-resistant TB strains. This meta-analysis aims to assess epidemiological trends of drug-resistant TB across EU countries before, during, and after the pandemic and quantify the impact of COVID-19 on Mycobacterium tuberculosis resistance patterns. Methods: Data were obtained from the European Centre for Disease Prevention and Control (ECDC) covering 2015 to 2022. Following the TB incidence, the multidrug-resistant TB (MDR-TB) and rifampicin-resistant/MDR-TB (RR/MDR-TB) cases, as well as treatment success rates over 12- and 24-month periods, were analyzed. The analysis included 31 EU countries across three-time frames: pre-pandemic (2015–2019), pandemic onset (2020), and post-pandemic transition (2020–2022). Results: The pandemic was associated with a decrease in reported TB cases but a simultaneous increase in the proportion of MDR and RR/MDR cases. Treatment success rates showed a modest rise for 24-month regimens, while outcomes declined for 12-month therapies. Conclusions: These findings underscore the pandemic’s disruptive impact on TB control and highlight the need for renewed investment in diagnostic capacity, treatment access, and antimicrobial stewardship, in order to reduce antimicrobial resistance occurrence. Continued monitoring beyond 2022 is essential to fully understand long-term effects and inform future public health strategies. Full article

One of the leading causes of climate change and global warming is the rise in carbon dioxide (${\mathrm{CO}}_2$) levels. For a precise assessment of ${\mathrm{CO}}_2$’s impact on the climate and the creation of successful mitigation methods, it is essential to comprehend its distribution by analyzing ${\mathrm{CO}}_2$ sources and sinks, which is a challenging task using sparsely available ground monitoring stations and airborne platforms. Therefore, the data retrieved by the Orbiting Carbon Observatory-2 (OCO-2) satellite can be useful due to its extensive spatial and temporal coverage. Sparse and missed retrievals in the satellite make it challenging to perform a thorough analysis. This work trains machine learning models using the Orbiting Carbon Observatory-2 (OCO-2) ${\mathrm{XCO}}_2$ retrievals and auxiliary features to obtain a monthly, high-spatial-resolution, gap-filled ${\mathrm{CO}}_2$ concentration distribution. It uses a multi-source aggregated (MSD) dataset and the generalized stacked ensemble model to predict country-level high-resolution (1 ${\mathrm{km}}^2$) ${\mathrm{XCO}}_2$. When evaluated with TCCON, this country-level model can achieve an RMSE of 1.42 ppm, a MAE of 0.84 ppm, and ${\mathrm{R}}^2$ of 0.90. Full article

Background: Cardiovascular disease (CVD) is the leading global cause of death and is shaped by interacting biological, environmental, lifestyle, and social factors. Traditional models often treat risk factors in isolation and may miss dependencies among exposures and biomarkers. Objective: To map interdependencies among environmental, social, behavioral, and biological predictors of CVD risk using Bayesian network models. Methods: A cross-sectional analysis was conducted using NHANES 2017–2018 data. After complete-case procedures, the analytic sample included 601 adults and 22 variables: outcomes (systolic/diastolic blood pressure, total/LDL/HDL cholesterol, triglycerides) and predictors (BMI, C-reactive protein (CRP), allostatic load, Dietary Inflammatory Index, income, education, age, gender, race, smoking, alcohol, and serum lead, cadmium, mercury, and PFOA). Spearman’s correlations summarized pairwise associations. Bayesian networks were learned with two approaches: Grow–Shrink (constraint-based) and Hill-Climbing (score-based, Bayesian Gaussian equivalent score). Network size metrics included number of nodes, directed edges, average neighborhood size, and Markov blanket size. Results: Correlation screening reproduced expected patterns, including very high systolic–diastolic concordance (p ≈ 1.00), strong LDL–total cholesterol correlation (p = 0.90), inverse HDL–triglycerides association, and positive BMI–CRP association. The final Hill-Climbing network contained 22 nodes and 44 directed edges, with an average neighborhood size of ~4 and an average Markov blanket size of ~6.1, indicating multiple indirect dependencies. Across both learning algorithms, BMI, CRP, and allostatic load emerged as central nodes. Environmental toxicants (lead, cadmium, mercury, PFOS, PFOA) showed connections to sociodemographic variables (income, education, race) and to inflammatory and lipid markers, suggesting patterned exposure linked to socioeconomic position. Diet and stress measures were positioned upstream of blood pressure and triglycerides in the score-based model, consistent with stress-inflammation–metabolic pathways. Agreement across algorithms on key hubs (BMI, CRP, allostatic load) supported network robustness for central structures. Conclusions: Bayesian network modeling identified interconnected pathways linking obesity, systemic inflammation, chronic stress, and environmental toxicant burden with cardiovascular risk indicators. Findings are consistent with the view that biological dysregulation is linked with CVD and environmental or social stresses. Full article