Search Results (98,823)

Rolling bearings represent one of the core functional components of rotating machinery, with their application scope continuously expanding into various sectors of modern social production and life, making the research on fault diagnosis of rolling bearings increasingly significant. Effective vibration feature extraction and improved classification models are crucial to achieving accurate and automated fault diagnosis of rolling bearings. We proposed a fault diagnosis approach based on a Swin Transformer–Improved ResNet module. In the data preprocessing stage, the frequency-domain features and time-domain multi-scale features of fault signals are extracted using FFT and VMD methods, respectively. And then, dual-channel feature extraction is employed using both the Swin Transformer and Improved ResNet module, followed by feature fusion through an ECA module, thereby enhancing diagnostic accuracy and model robustness. The architecture retains shallow-level feature details while incorporating global contextual information, improving feature representation and detection precision. Extensive experiments were carried out on data collected from an SEU bearing dataset, including model validation, ablation analysis, comparative evaluation and simulated noise testing. An average classification accuracy of 99.41% was achieved by the proposed model under uniform experimental conditions, as evidenced by the obtained experimental results, outperforming other models by at least 0.96%. Even under severe noise interference with a signal-to-noise ratio of -4, the model maintained an average accuracy of 91.92%, exceeding that of noise-resistant counterparts. Moreover, generalization experiments on the CWRU bearing dataset under varying load conditions revealed an average fault diagnosis accuracy exceeding 98%, confirming the model’s strong cross-domain adaptability. Full article

Object detection in Unmanned Aerial Vehicle (UAV) imagery faces severe challenges, including small target scales, dense spatial distributions, and complex backgrounds. To address the feature attenuation and noise interference inherent in existing deep learning models, this paper proposes DOL-DETR, an efficient small object detection algorithm based on the Real-Time DEtection TRansformer (RT-DETR) architecture. Our model introduces three key innovations. First, the DAttention-based Intra-scale Feature Interaction (DAIFI) module reconstructs intra-scale feature interactions using deformable attention to focus on salient regions with linear complexity. Second, the Omni-Modulated Feature Fusion (OMFF) mechanism adaptively captures multi-scale features and dynamically suppresses background noise. Finally, Linear De-redundancy Convolution (LDConv) replaces standard downsampling to dynamically adapt to object deformations. While introducing a complex dynamic resampling mechanism, it strategically optimizes parameter allocation, significantly enhancing localization precision without introducing excessive computational overhead. Extensive experiments on the VisDrone2019 benchmark demonstrate that DOL-DETR achieves an mAP@0.5 of 52.4% (a 4.2% improvement over the baseline) while maintaining a real-time inference speed of 120.1 FPS with only 20.1M parameters. Furthermore, generalization experiments on the large-scale DOTA dataset yield a 76.1% mAP@0.5, outperforming the baseline by 3.8%. These results indicate that DOL-DETR provides a better trade-off between detection accuracy, inference efficiency, and cross-domain generalization in UAV remote sensing scenarios. Full article

Dogs are key reservoirs of zoonotic infections, including Toxocara canis, a widely distributed parasite of major public health concern. In Vietnam, the parasite is highly prevalent in dog populations and humans. Epidemiological studies assessing infection and associated factors are essential to better understand transmission and to inform effective control strategies. We conducted a cross-sectional baseline survey to assess Toxocara infection in dogs in rural Vietnam. Fecal samples from 371 dogs were examined using centrifugal flotation (Sheather’s solution, specific gravity 1.2) and conventional polymerase chain reaction (PCR), alongside structured questionnaires on dog demographics and management. Using combined copromicroscopic and molecular methods, the overall prevalence of Toxocara infection was 44.7% (95% CI: 39.6–50.0). By microscopy alone, 29.9% (95% CI: 25.4–34.9) of samples were positive, while PCR detected Toxocara DNA in 41.2% (95% CI: 36.2–46.5) of dogs. Molecular analysis identified T. canis in 35.9% (95% CI: 31.0–41.0) and T. cati in 10.5% (95% CI: 7.7–14.2) of tested dogs. Dog age and deworming status were independently associated with PCR-detected T. canis infection. The elevated likelihood of infection among dogs that have never been dewormed highlights the importance of canine deworming. Questionnaire findings indicating suboptimal dog care and management highlight the need for community public health education to promote responsible ownership and reduce transmission risk. This baseline assessment provides essential evidence to inform targeted interventions and improve understanding of Toxocara transmission in endemic settings. Full article

The California Bearing Ratio (CBR) is a critical parameter in pavement design and building foundation assessment; however, it requires labor intensive laboratory testing, including a 96 h soaking period. This study evaluated nine machine learning algorithms for predicting CBR from soil index properties: Extra Trees, Support Vector Regression (SVR), Random Forest, Gaussian Process Regression (GPR), CatBoost, LightGBM, XGBoost, Artificial Neural Network (ANN), and ElasticNet. Using 236 soil samples characterized by eight features, we conducted repeated stratified 10-fold cross validation (100 iterations). Extra Trees achieved the highest cross validation R² of 0.789 ± 0.095 (RMSE = 2.064 ± 0.481, MAE = 1.482 ± 0.294), followed by SVR (R² = 0.783 ± 0.102, RMSE = 2.090 ± 0.511, MAE = 1.446 ± 0.300) and Random Forest (R² = 0.777 ± 0.104, RMSE = 2.117 ± 0.460, MAE = 1.518 ± 0.299). The Friedman statistical test confirmed significant performance differences (χ² = 191.97, p < 10⁻³⁷), and Nemenyi post hoc analysis identified Extra Trees, SVR, Random Forest, and GPR as statistically equivalent superior groups. SHAP analysis highlighted gravel content (29.0%), maximum dry density (23.8%), and fines content (14.8%), which is consistent with geotechnical principles. Systematic noise injection (20% perturbation) demonstrated model stability, with less than 7% performance degradation at 15% noise. On this heterogeneous compiled dataset, which extends beyond the calibration domain of the empirical equations, all six empirical methods yielded negative R² (range: −0.803 to −22.639), while all ML models achieved positive R² (≥0.655 to 0.789). Extra Trees achieved a 3.1× lower RMSE than the best empirical equation, confirming substantially better predictive performance in this out-of-calibration setting. The framework provides a practical five step implementation workflow that may reduce the need for preliminary CBR tests under project specific accuracy thresholds. Full article

Background/Objectives: The Nutrition Care Process (NCP) is a standardized model designed to improve the quality and consistency of nutrition care. However, its implementation remains variable across settings, influenced by factors such as time constraints, training, peer support, and technological infrastructure. This systematic review aims to synthesize the available evidence on barriers and facilitators influencing the implementation of the NCP/NCPT and to explore how different documentation formats may influence its adoption. Methods: This systematic review was conducted in accordance with PRISMA 2020 guidelines and included peer-reviewed studies published between 2009 and 2024 in English or Greek. Searches were conducted in MEDLINE, EMBASE, Scopus, CINAHL, and the Cochrane Library. Study quality was assessed using the National Heart, Lung, and Blood Institute (NIH) tool. A total of 11 reports representing eight studies were included, comprising cross-sectional, cohort, qualitative, and pilot designs. Results: The most commonly reported barriers to NCP implementation were lack of training, time constraints, and limited technological infrastructure. Key facilitators included support from national dietetic associations, peer collaboration, and access to electronic health records (EHRs). Electronic formats were more frequently described as supporting improved documentation practices, practitioner confidence, and workflow efficiency, whereas manual approaches were commonly reported as time-consuming and less structured. Conclusions: Digital integration of the NCP may support more consistent documentation practices and improved workflow processes; however, the current evidence is largely observational and heterogeneous. Evidence regarding patient-level outcomes remains limited, and definitive conclusions regarding the comparative effectiveness of implementation formats cannot be drawn. Further high-quality research is needed to evaluate the long-term clinical impact of NCP implementation. Full article

With the rapid evolution of digital payment systems and financial services, the number of fraudulent transactions is increasing, and risks are becoming increasingly critical. Although several fraud detection approaches have been proposed, they remain hampered by certain limitations, including confidentiality constraints on cross-institutional data sharing and class imbalance in fraud datasets. To address these challenges, we propose a new hybrid fraud detection framework that integrates federated learning with ensemble learning, enabling collaborative and efficient model training across distributed financial institutions without sharing raw data. The framework leverages heterogeneous machine learning models (XGBoost, CatBoost, and MLP) trained distributedly in a federated architecture, coordinated by a central aggregation server. The three federated models are combined using an ensemble learning method to improve predictive performance and generate more accurate decisions. This solution can help to effectively detect fraud in distributed environments while reducing the need for direct data sharing. Experimental results demonstrate that the proposed federated framework offers competitive performance in terms of recall, F1-score, and AUC-PR similar to, or even superior to, centralized models in certain federated configurations. Full article

Background: Family caregivers of older adults with cognitive impairment commonly encounter heavy care burdens and elevated mental health risks, particularly depressive symptoms. This study aimed to explore the association between telehealth guidance service use and depressive symptoms among family caregivers of older adults with cognitive impairment, and to further examine the mediating role of caregiving competence and the moderating role of psychological resilience. Methods: A cross-sectional survey of 491 family caregivers of older adults with cognitive impairment was conducted from August to October 2023. Descriptive statistics, correlation analysis, linear regression analysis, and moderated-mediating-effect analysis were employed. Results: Among the participants, only 17.31% reported using telehealth guidance services. Mean scores for caregiving competence, psychological resilience, and depressive symptoms were 3.04 ± 0.48, 27.11 ± 7.54, and 9.69 ± 1.46, respectively. Telehealth service use was positively associated with caregiving competence, and both telehealth service use and caregiving competence were negatively associated with depressive symptoms. The interaction between psychological resilience and caregiving competence was also significantly negatively associated with depressive symptoms (p < 0.01). Conclusions: Telehealth guidance service use is directly and indirectly negatively associated with depressive symptoms via caregiving competence. Psychological resilience moderates the relationship between caregiving competence and depressive symptoms. These findings contribute to a better understanding of factors linked to mental health among family caregivers of older adults with cognitive impairment. Full article

Methane (CH₄) is the second most important greenhouse gas after carbon dioxide (CO₂), and its atmospheric concentration is on the rise. Soil CH₄ consumption (=absorption) capacity is declining due to reduced forests and green spaces, as well as other environmental factors and anaerobic stresses. Environmental and stand structure parameters were cross-referenced with publicly available international ecosystem databases, such as FLUXNET, ICOS, NEON, AmeriFlux, the TRY plant trait database and the Oak Ridge FACE site. Searches were conducted using keywords such as region, water level, and stand density. The data indicate that under high-CO₂ conditions, the increase of forest canopy density leads to increased litter accumulation on the forest floor and reduced sunlight penetration, creating anaerobic conditions. This can cause forests to shift from CH₄ consumption to CH₄ release. Based on these findings, we discussed methods to maintain and enhance the CH₄-absorbing capacity of forest soils. This can be achieved through management practices that improve environmental conditions and increase soil fauna’s activity, such as those associated with thinning operations in overmature forest stands across various regions. This ecological manipulation through thinning practices promotes ground-level temperature increases and the activities of soil fauna, as well as maintaining aerobic conditions near the soil surface. Full article

Background/Objectives: Exclusive breastfeeding offers optimal benefits for infant nutrition and health and increases maternal involvement, bonding and interactions. This study aimed to explore breastfeeding practices among mothers in Romania and identify risk factors associated with low exclusive breastfeeding rates. Methods: A cross-sectional online survey was conducted between September and December 2025, targeting mothers in Romania via social media platforms. The questionnaire, developed specifically for this study, collected data on sociodemographic characteristics, birth and neonatology variables, hospital practices, feeding intentions, community influences, and breastfeeding outcomes. Responses were analyzed using Fisher’s exact tests and multivariable logistic regression. Results: A total of 357 complete questionnaires were analyzed. Cesarean section was the most frequent mode of delivery (54.6%), while immediate mother–infant contact after birth was reported by only 35.6% of mothers, and breastfeeding initiation within the first hour occurred in 10.6% of cases. Overall, 49.3% of mothers reported exclusive breastfeeding, 35.3% mixed feeding, and 15.4% exclusive formula feeding. Women who delivered in private hospitals reported earlier mother–infant contact, more frequent encouragement to initiate breastfeeding, and earlier breastfeeding initiation compared with those delivering in public hospitals. Preterm birth was associated with delayed breastfeeding initiation, reduced rooming-in, and lower rates of exclusive breastfeeding up to six months. In multivariable logistic regression, rooming-in was independently associated with higher odds of exclusive breastfeeding (aOR = 2.798, 95% CI: 1.779–4.401), while lack of lactation support was associated with lower odds (aOR = 0.546, 95% CI: 0.302–0.987). No significant associations were observed for timing of initial maternal–infant contact (aOR = 1.084, 95% CI: 0.679–1.733) or encouragement from medical staff to initiate breastfeeding (aOR = 1.207, 95% CI: 0.721–2.020). Conclusions: Our study highlights current breastfeeding practices and associated hospital factors in Romania. However, significant challenges remain in supporting and encouraging mothers to optimally feed their infants. Additional investment and bold policy action are needed to promote and support breastfeeding from the first hour of life, for both term and preterm infants, in all maternity hospitals in Romania. Full article

Background and Objectives: The inner ear has traditionally been regarded as an immunoprivileged and anatomically isolated organ. However, growing interest in neuro-lymphatic interactions has raised the hypothesis that glymphatic and lymphatic mechanisms may contribute to auditory pathology and its association with cognitive dysfunction. This systematic review aimed to synthesize current human evidence regarding anatomical, imaging, and clinical correlates of glymphatic mechanisms in the inner ear and audiological pathologies, and to quantitatively evaluate currently available biomarkers. Materials and Methods: A structured search of PubMed, Scopus, and Cochrane databases was performed from inception through March 2026. Eligible studies included human investigations reporting anatomical, histopathological, or MRI-based glymphatic assessments related to inner ear disorders. Risk of bias was assessed using the Newcastle–Ottawa Scale and Joanna Briggs Institute tools. Meta-analysis was conducted for diffusion tensor image analysis along the perivascular space (DTI-ALPS) indices comparing auditory disorders with healthy controls. Results: Six studies met inclusion criteria (five cross-sectional imaging studies and one surgical histopathological case series). Histopathology demonstrated lymphatic capillaries in advanced Ménière disease. MRI studies consistently reported reduced ALPS indices and/or increased choroid plexus volume and enlarged perivascular spaces in tinnitus, congenital sensorineural hearing loss, and age-related hearing loss. Meta-analysis of five studies showed a significant reduction of ALPS index in auditory disorders compared with controls (SMD = −0.73, 95% CI −0.90 to −0.55; p < 0.001), with no heterogeneity. Glymphatic markers were frequently associated with audiological data, cognitive performance and inflammatory biomarkers. Conclusions: Human evidence supports the presence of altered central glymphatic function across diverse auditory phenotypes. Although predominantly based on indirect MRI proxies and cross-sectional data, the meta-analytic findings strengthen the biological plausibility of an auditory–glymphatic interaction. Prospective longitudinal studies are warranted to clarify causality and therapeutic implications. Full article

This study investigates the key determinants of firm profitability in the global automotive sector, examining whether superior returns on assets (ROA) stem from operational efficiency, strategic leverage, or innovation intensity, and highlighting the potential trade-off between efficiency and investment in capital-intensive industries. Analysing a global panel dataset of 192 automotive firms from 38 countries/regions over 2010–2024, a fixed effects regression model with Driscoll–Kraay standard errors was applied to control for unobserved heterogeneity, heteroskedasticity, and cross-sectional dependence across 11 financial and strategic variables. The findings reveal that firm size and inventory turnover are significant positive drivers of profitability, while research and development (R&D) intensity exerts a strong negative impact. The positive association with the effective tax rate reflects reverse causality, where more profitable firms incur higher tax burdens, rather than a causal effect of taxation on performance. Notably, working capital management, leverage, sales growth, and capital expenditure showed no statistically significant effects after controlling for firm and time effects. Temporal fluctuations, including a marked profitability decline in 2024, underscore the sector’s sensitivity to macroeconomic shocks. This study contributes robust, large-scale empirical evidence on the short-term profitability trade-off associated with R&D intensity in a globally integrated industry, addressing cross-sectional dependence through its methodological approach. Full article

Although canine parvovirus (CPV) vaccination has been widely implemented, CPV continues to circulate in dog populations and poses a potential cross-species transmission risk to wildlife, including giant pandas. Recent increases in CPV-2c detection in China highlight the need for molecular surveillance and standardized immunoreagents for diagnosis and epitope mapping. This study aimed to isolate a representative CPV-2c strain from China and develop VP2-targeted nanobody-based recognition molecules to support antigen monitoring and detection optimization. Canine and giant panda samples were collected in Sichuan Province, and CPV was isolated in F81 cells, followed by VP2 gene sequencing and phylogenetic analysis. A secretion expression system in Bacillus subtilis was established to produce VP2-targeting nanobodies, and a canine Fc-fused format of Nb10 (Nb10-Fc) was constructed. Immunoreactivity was evaluated via immunoassays, and structural modeling and molecular docking were performed to predict binding interfaces. The results showed that CPV-2c was the dominant genotype in Sichuan, with CPV L4 being a representative strain that exhibited 100% identity in VP2 with a giant panda-derived CPV-2c strain. Nb10 and Nb10-Fc demonstrated strong reactivity in Western blotting and immunofluorescence assays. The Fc-fusion improved detection sensitivity, offering potential in vivo application benefits. This study provides a standardized VP2-specific nanobody and molecular system for CPV-2c surveillance, antigenic studies, and diagnostic optimization. Full article

Driven by the global energy transition and carbon neutrality goals, hybrid power systems have become a core technical path for energy conservation and carbon reduction in the transportation and power sectors, and the performance of energy management strategies directly determines the system’s overall energy efficiency. Traditional energy management methods have inherent bottlenecks of high model dependence and poor adaptability, making it difficult to satisfy real-time decision-making requirements under complex operating conditions. Deep Reinforcement Learning (DRL) provides an innovative solution to this technical bottleneck, and has become a cutting-edge research direction in this field. However, existing reviews have not yet constructed a full-chain analysis framework covering its algorithms, applications, verification, challenges and prospects. Focusing on the engineering application of DRL in the real-time energy management of hybrid power systems, this paper systematically sorts out domestic and international research results up to the first quarter of 2026. The core quantitative findings of this review are as follows: (1) DRL-based strategies can achieve 93–99.5% of the Dynamic Programming (DP) theoretical global optimum in fuel economy, which is 5–25% higher than rule-based methods; (2) DRL strategies only have 3.1–4.8% performance degradation under unseen operating conditions, which is significantly better than the 10.3–14.7% degradation of the Equivalent Consumption Minimization Strategy (ECMS); (3) Actor–Critic (AC) algorithms (Twin Delayed Deep Deterministic Policy Gradient (TD3)/Soft Actor–Critic (SAC)) have become the mainstream in this field, with a 3–5 times higher sample efficiency than value function-based algorithms; and (4) offline DRL and transfer learning can reduce the training time of DRL strategies by more than 80% while maintaining equivalent optimization performance. This paper first analyzes the essential attributes and core technical challenges of hybrid power system energy management; second, classifies DRL algorithms from the perspective of control engineering and analyzes their technical characteristics; third, disassembles the application design logic of DRL around four major scenarios: land vehicles, water vessels, aerial vehicles and fixed microgrids; fourth, summarizes the mainstream verification platforms and evaluation systems; fifth, analyzes core bottlenecks and cutting-edge solutions; and finally, prospects the development trends of next-generation intelligent energy management systems combined with cross-fusion technologies. This paper aims to build a complete technical system map for this field and promote the engineering deployment and practical application of intelligent energy management technologies integrating data and knowledge. Full article

This study evaluates mission-long inter-sensor radiometric calibration biases in Sensor Data Record (SDR) and/or Temperature Data Record (TDR) radiances from NOAA microwave sounders, including Advanced Technology Microwave Sounder (ATMS) (Suomi National Polar-orbiting Partnership or SNPP, NOAA-20, NOAA-21) and Advanced Microwave Sounding Unit-A (AMSU-A) (NOAA-19). Using four complementary validation techniques within the Inter-Sensor Radiometric Bias Assessment (iSensor-RCBA) system—32-day averaging, Community Radiative Transfer Model (CRTM) Double Difference (DD), Simultaneously Nadir Overpass (SNO), and sensor-DD via SNO—we characterize long-term performance. Results indicate that the SDR/TDR radiance quality remains stable and generally meets scientific requirements throughout their operational lifetimes with minimal anomalies; observed anomalies were infrequent and primarily correlated with calibration-table updates or spacecraft events or instrument degradation. Moreover, this research examines how radiometric calibration biases for the three ATMS instruments vary with Earth scene radiance or temperatures using the CRTM and SNO methods, as well as the radiance-dependency of inter-sensor calibration biases across the three instruments. Notably, due to its exceptional stability over 14 years, despite an approximate two-month data gap, the SNPP ATMS TDR and SDR datasets are recommended as the ideal reference to link legacy AMSU-A and Microwave Humidity Sounder (MHS) with Joint Polar Satellite System (JPSS), QuickSounder, and MetOp-Second Generation (MetOp-SG) microwave instruments. Beyond quantifying data quality, our multi-method framework with iSensor-RCBA effectively diagnosed critical issues, including a simulation error for CRTM ATMS radiance related to the CRTM spectral-response approximation and a NOAA-19 AMSU-A channel-8 performance anomaly. These findings confirm the long-term integrity of NOAA microwave sounder records and reinforce the value of integrated cross-sensor calibration assessments. Full article

Knowledge management platforms are increasingly important for strengthening governance, scientific collaboration, and evidence-based decision making in complex regional networks. This study analyses the prospective intention to adopt two strategic digital mechanisms of the Amazon Cooperation Treaty Organization (OCTA): the Amazon Regional Observatory (ORA) and the Network of Amazonian Research Centres (REDCIA). Adapting the Unified Theory of Acceptance and Use of Technology (UTAUT) to a pre-implementation context, the study focuses on performance expectancy, social influence, and facilitating conditions, while operationalizing these constructs through a Knowledge, Attitudes, and Practices (KAP) survey. Using a quantitative, non-experimental, cross-sectional design, penalized ordinal logistic regression models were estimated from 162 responses collected from institutional actors and experts across eight Amazonian jurisdictions. The results show that social influence is the only statistically significant predictor of intention to use in both mechanisms, whereas performance expectancy and facilitating conditions are not significant in the estimated models. These findings suggest that, in the Amazonian cooperation context, adoption is driven less by individual evaluations of utility or technical feasibility than by institutional legitimacy, peer expectations, and collaborative norms. The study contributes to the information systems literature by providing an ex ante analytical approach for assessing technology acceptance in the absence of an operational artefact. It also offers practical guidance for OCTA by highlighting the importance of change management, political endorsement, and network-based incentives to support future implementation. Full article