Search Results (9,030)

As the scale of shipboard power systems expands, their vulnerability becomes increasingly prominent. Identifying vulnerable points in ship power grids is essential for enhancing system stability, optimizing overall performance, and ensuring safe navigation. To address this issue, this paper proposes an algorithm based on fuzzy local dimension and topology (FLDT). The algorithm distinguishes contributions from nodes at different radii and within the same radius to a central node using fuzzy sets, and then derives the final importance value of each node by combining the local dimension and topology. Experimental results on nine datasets demonstrate that the FLDT algorithm outperforms degree centrality (DC), closeness centrality (CC), local dimension (LD), fuzzy local dimension (FLD), local link similarity (LLS), and mixed degree decomposition (MDD) algorithms in three metrics: network efficiency (NE), largest connected component (LCC), and monotonicity. Furthermore, in a ship power grid experiment, when 40% of the most important nodes were removed, FLDT caused a network efficiency drop of 99.78% and reduced the LCC to 2.17%, significantly outperforming traditional methods. Additional experiments under topological perturbations—including edge addition, removal, and rewiring—also show that FLDT maintains superior performance, highlighting its robustness to structural changes. This indicates that the FLDT algorithm is more effective in identifying and evaluating vulnerable points and distinguishing nodes with varying levels of importance. Full article

Background/Objectives: Acrylamide is a probably carcinogenic to humans that naturally forms during the thermal processing of foods. An individual’s lifestyle—especially dietary habits and physical activity—may influence the severity of acrylamide’s adverse health effects. This study aimed to examine the relationship between adolescents’ dietary and exercise behaviors and their dietary acrylamide exposure and associated health risks. Methods: This descriptive and cross-sectional study was conducted with 370 high school students in Türkiye. Data were collected using the Nutrition Exercise Behavior Scale (NEBS) and a retrospective 24-h dietary recall questionnaire. Acrylamide exposure was calculated based on food intake to estimate carcinogenic (CR) and non-corcinogenic (target hazard quotient: THQ) health risks and analyzed in relation to NEBS scores. Results: Findings indicated that while adolescents are beginning to adopt healthy eating and exercise habits, these behaviors are not yet consistent. Emotional eating and unhealthy food choices still occur. Higher acrylamide exposure and risk values were observed in boys and underweight individuals. This can be explained mainly by the fact that boys consume more of certain foods—especially bread, which contains relatively higher levels of acrylamide—than girls do, and that underweight individuals have lower body weights despite consuming similar amounts of food as other groups. Bread products emerged as the primary source of daily acrylamide intake. Positive correlations were found between NEBS total and subscale scores and acrylamide exposure and health risk values. Conclusions: The study demonstrates a significant association between adolescents’ health behaviors and acrylamide exposure. These results underscore potential public health concerns regarding acrylamide intake during adolescence and emphasize the need for targeted nutritional interventions to reduce risk and promote sustainable healthy behaviors. Full article

The right to development, an inherent human right for all, emphasizes that all individuals and communities have the right to participate in, contribute to, and benefit from development that ensures the full realization of human rights. In Kenya, where a significant portion of the population faces poverty and vulnerability to climate change, access to rights-based needs such as clean water, healthcare, and education still remains a critical challenge. This study explored the implementation of a Human Rights-Based approach to community development through a Public–Private Development Partnership model (PPDP), with a focus on alleviating poverty and improving access to rights-based services at the community level in Narok and Nakuru counties. The research aimed to identify critical success factors for scaling the PPDP model and explore its effects on socio-economic empowerment. The study employed a mixed-methods approach for data collection, using questionnaires to obtain quantitative data, focus group discussions, and key informant interviews with community members, local leaders, and stakeholders to gather qualitative data. We cleaned and analyzed all our data in R (version 4.4.3) and used the chi-square to establish the significance of differences between areas where the PPDP model was implemented and control areas where it was not. Results reveal that communities with the PPDP model experienced statistically significant improvements in employment, income levels, and access to rights-based services compared to control areas. The outcomes underscore the potential of the PPDP model to address inclusive and sustainable development. This study therefore proposes a scalable pathway beginning with access to rights-based needs, followed by improved service delivery, and culminating in economic empowerment. These findings offer valuable insights for governments, development practitioners, investment agencies, and researchers seeking community-driven developments in similar socio-economic contexts across Africa. For the first time, it can be adopted in the design and implementation of development projects in rural and local communities across Africa bringing into focus the need to integrate rights-based needs at the core of the project. Full article

Enhancing osseointegration is a common goal for many titanium implant coatings, since the naturally forming oxides are often bioinert and exhibit less than ideal bone-to-implant contact. Oxide coating surface topographies, chemistries, and crystallinities are known to play key roles in enhancing bone–implant interactions. In the present study, two novel anodization processes were developed in electrolytes based on juiced navel oranges to create bioactive oxide coatings on commercially pure titanium (CPTi) surfaces. Both oxide groups revealed multi-scaled micro and nano surface topographies, significant Ca and P-dopant incorporation exhibiting Ca/P ratios similar to human bone (1.7 and 1.8), and physiologically relevant Mg uptake levels of <0.1% and 1.4 at%. XRD and FTIR analyses of each oxide revealed a combination of tricalcium phosphate and hydroxyapatite phases that showed carbonate substitutions indicative of bone-like apatite formation. Finally, VDI indentation testing revealed good adhesion strengths, minimal cracking, and no visible delamination for both oxides. In summary, the anodization processes in the present study were shown to produce carbonated tricalcium phosphate and apatite containing oxides with contrasting levels of Mg uptake that show much promise to improve future implant clinical outcomes. Full article

Smart meters play a significant role in power systems, but their condition assessment faces challenges such as inconsistent evaluation criteria and inaccurate assessment results. This paper proposes feature engineering including feature construction and feature selection for smart meter group failure rate prediction. First, the basic structure and common fault types of smart meters are introduced. Smart meters are grouped by batch and distribution area. Next, 25 condition features are constructed based on failure mechanisms and technical specifications. Then, an evolutionary multi-objective feature selection algorithm combining NSGA-II, Jaccard similarity, and XGBoost is developed, where feature subsets are encoded as binary individuals optimized for three objectives: MSE, 1 − R², and the number of features. The experimental results demonstrate that the proposed method not only reduces the number of features (25→7) but also improves the prediction accuracy (MSE: 0.0049 → 0.0042, R²: 0.6638 → 0.7228) of smart meter group failure rates. Comparative studies with other feature selection methods further confirm the superiority of our approach. The optimized features enhance interpretability and computational efficiency, providing a practical solution for large-scale smart meter condition assessment in power systems. Full article

Boda boda motorbike taxis are a primary mode of transport in Nairobi, Kenya, and a major source of urban air pollution. This study investigates the environmental and electrical grid impacts of electrifying Nairobi’s boda boda fleet. Using real-world tracking data from 118 motorbikes, we simulated the effects of a full-scale transition from internal combustion engine (ICE) vehicles to electric motorbikes. We analysed various scenarios, including different battery charging strategies (swapping and home charging), motor efficiencies, battery capacities, charging rates, and the potential for solar power offsetting. The results indicate that electrification could reduce daily ${\mathrm{CO}}_2$ emissions by approximately 85% and eliminate tailpipe particulate matter emissions. However, transitioning the entire country’s fleet would increase the national daily energy demand by up to 6.85 GWh and could introduce peak grid loads as high as 2.40 GW, depending on the charging approach and vehicle efficiency. Battery swapping was found to distribute the grid load more evenly and better complement solar power integration compared to home charging, which concentrates demand in the evening. This research provides a scalable, data-driven framework for policymakers to assess the impacts of transport electrification in similar urban contexts, highlighting the critical trade-offs between environmental benefits and grid infrastructure requirements. Full article

Three-dimensional printing is commonly used to fabricate provisional dental restorations. Studies have reported that changes in printing orientation affect the physical and mechanical properties of 3D-printed polymeric provisional restorations; however the findings have been inconsistent. Therefore, this systematic review and meta-analysis aims to analyze the articles evaluating the influence of printing orientation on the physical and mechanical properties of 3D-printed polymeric provisional dental restorations. Recommendations provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to structure and compose the review. The PICO (Participant, Intervention, Comparison, Outcome) question ordered was: ‘Do 3D-printed provisional dental restorations (P) printed at various orientations (except 0°) (I) exhibit similar physical and mechanical properties (O) when compared to those printed at a 0° orientation (C)?’. An electronic search was conducted on 28 and 29 April 2025, by two independent researchers across four databases (MEDLINE/PubMed, Scopus, Cochrane Library, and Web of Science) to systematically collect relevant articles published up to March 2025. After removing duplicate articles and applying predefined inclusion and exclusion criteria, twenty-one articles were incorporated into this review. Self-designed Performa’s were used to tabulate all relevant information. For the quality analysis, the modified CONSORT scale was utilized. The quantitative analysis was performed on only fifteen out of twenty-one articles. It can be concluded that the printing orientation affects some of the tested properties, which include fracture strength (significantly higher for specimens printed at 0° when compared to 90°), wear resistance (significantly higher for specimens printed at 90° when compared to 0°), microhardness (significantly higher for specimens printed at 90°and 45° when compared to 0°), color stability (high at 0°), and surface roughness (significantly higher for specimens printed at 45° and 90° when compared to 0°). There were varied outcomes in terms of flexural strength and elastic modulus. Full article

Multi-organ medical image segmentation is essential for accurate clinical diagnosis, effective treatment planning, and reliable prognosis, yet it remains challenging due to complex backgrounds, irrelevant noise, unclear organ boundaries, and wide variations in organ size. To address these challenges, the boundary feature learning and enhancement network is proposed. This model integrates a dedicated boundary learning module combined with an auxiliary loss function to strengthen the semantic correlations between boundary pixels and regional features, thus reducing category mis-segmentation. Additionally, channel and positional compound attention mechanisms are employed to selectively filter features and minimize background interference. To further enhance multi-scale representation capabilities, the dynamic scale-aware context module dynamically selects and fuses multi-scale features, significantly improving the model’s adaptability. The model achieves average Dice similarity coefficients of 81.67% on synapse and 90.55% on ACDC datasets, outperforming state-of-the-art methods. This network significantly improves segmentation by emphasizing boundary accuracy, noise reduction, and multi-scale adaptability, enhancing clinical diagnostics and treatment planning. Full article

Purpose: Exercise is recommended for survivors of breast cancer to alleviate adverse reactions and reduce the psychological burden. In recent years, however, environmental factors (e.g., pandemics and climate change) have made it difficult to exercise outdoors. Therefore, this study focused on the COVID-19 pandemic in Japan and evaluated the relationship between changes in physical activity (PA) and mental and physical health in breast cancer survivors. Methods: A questionnaire survey was conducted among 345 outpatient female breast cancer survivors aged between 29 and 69 years. The questionnaire was based on the International Physical Activity Questionnaire, the Patient Health Questionnaire-9, the European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire, and the Fear of COVID-19 Scale and included patient characteristics, changes in PA during pandemic restrictions, and needs for exercise support. The analysis categorized PA changes into two groups according to activity levels. The relationship between changes in PA and physical and mental health was evaluated using logistic regression analysis. Results: Patients with decreased PA accounted for 65.5% of the study population. Regardless of their activity level, these patients were aware of an increased susceptibility to COVID-19, showed a fear of the disease and a tendency for depression, and reported low life satisfaction and declined physical function. Of the patients who stopped exercising, 82.9% reported a decline in PA. Compared with those who had never exercised, those who stopped exercising saw their risk of depression increase by 15.6%. There was a high demand for personalized exercise support from healthcare professionals. Conclusions: Regardless of their activity level, decreasing PA during the pandemic decreased mental health and physical function in breast cancer survivors. There was a higher risk of depression among patients who stopped exercising. Because it is possible that similar situations may occur in the future, interventions by healthcare professionals must be considered in order to continue exercise. Full article

Background: Automated surgical navigation can be separated into three stages: (1) organ identification and localization, (2) identification of the organs requiring further surgery, and (3) automated planning of the operation path and steps. With its ideal visual and operating system, the da Vinci surgical system provides a promising platform for automated surgical navigation. This study focuses on the first step in automated surgical navigation by identifying organs in gynecological surgery. Methods: Due to the difficulty of collecting da Vinci gynecological endoscopy data, we propose DeepVinci, a novel end-to-end high-performance encoder–decoder network based on convolutional neural networks (CNNs) for pixel-level organ semantic segmentation. Specifically, to overcome the drawback of a limited field of view, we incorporate a densely multi-scale pyramid module and feature fusion module, which can also enhance the global context information. In addition, the system integrates an edge supervision network to refine the segmented results on the decoding side. Results: Experimental results show that DeepVinci can achieve state-of-the-art accuracy, obtaining dice similarity coefficient and mean pixel accuracy values of 0.684 and 0.700, respectively. Conclusions: The proposed DeepVinci network presents a practical and competitive semantic segmentation solution for da Vinci gynecological surgery. Full article

Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with each other, which makes wheat ear detection work face a lot of challenges. At the same time, the increasing demand for high accuracy and fast response in wheat spike detection has led to the need for models to be lightweight function with reduced the hardware costs. Therefore, this study proposes a lightweight wheat ear detection model, CML-RTDETR, for efficient and accurate detection of wheat ears in real complex farmland environments. In the model construction, the lightweight network CSPDarknet is firstly introduced as the backbone network of CML-RTDETR to enhance the feature extraction efficiency. In addition, the FM module is cleverly introduced to modify the bottleneck layer in the C2f component, and hybrid feature extraction is realized by spatial and frequency domain splicing to enhance the feature extraction capability of wheat to be tested in complex scenes. Secondly, to improve the model’s detection capability for targets of different scales, a multi-scale feature enhancement pyramid (MFEP) is designed, consisting of GHSDConv, for efficiently obtaining low-level detail information and CSPDWOK for constructing a multi-scale semantic fusion structure. Finally, channel pruning based on Layer-Adaptive Magnitude Pruning (LAMP) scoring is performed to reduce model parameters and runtime memory. The experimental results on the GWHD2021 dataset show that the ${\mathrm{AP}}_{50}$ of CML-RTDETR reaches 90.5%, which is an improvement of 1.2% compared to the baseline RTDETR-R18 model. Meanwhile, the parameters and GFLOPs have been decreased to 11.03 M and 37.8 G, respectively, resulting in a reduction of 42% and 34%, respectively. Finally, the real-time frame rate reaches 73 fps, significantly achieving parameter simplification and speed improvement. Full article

Tornado outbreaks can cause substantial damage, injuries, and fatalities, highlighting the need to understand their characteristics for assessing present and future risks. However, global climate models (GCMs) lack the resolution to explicitly simulate tornado outbreaks. As an alternative, researchers examine large-scale atmospheric ingredients that approximate tornado-conducive environments. Building on this approach, we tested whether patterns of covariability between WMAXSHEAR and 500-hPa geopotential height anomalies, previously identified in ERA5 reanalysis, could approximate major U.S. May tornado outbreaks in a GCM. We developed a proxy-based methodology by systematically testing pairs of thresholds for both variables to identify the combination that best reproduced the leading pattern selected for analysis. These thresholds were then applied to simulations from the high-resolution MPI-ESM1.2-HR model to assess its ability to reproduce the original pattern. Results show that the model closely mirrored the observed tornado outbreak pattern, as indicated by a low normalized root mean square error, high spatial correlation, and similar distributions. This study demonstrates a replicable approach for approximating tornado outbreak patterns, applied here to the leading pattern, within a GCM, providing a foundation for future research on how such environments might evolve in a warming climate. Full article

Temporomandibular disorders (TMDs) encompass a heterogeneous group of musculoskeletal and neuromuscular conditions affecting the temporomandibular joint (TMJ) and masticatory system. Due to academic stress and parafunctional habits, dental students may be particularly vulnerable to TMD. Objective: To determine the prevalence of TMD symptoms and their psychosocial and functional correlates among students at the Faculty of Dental Medicine, UMPh Iasi, Romania, using the diagnostic criteria for TMD (DC/TMD) self-report axis and axis II instruments. Methods: In this cross-sectional survey, 356 volunteer students (66.0% female; mean age, 22.9 ± 3.6 years) out of a total population of 1874 completed an online DC/TMD–based questionnaire. Axis I assessed orofacial pain, joint noises, and mandibular locking. Axis II instruments included the Graded Chronic Pain Scale (GCPS), Jaw Functional Limitation Scale (JFLS-20), Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Oral Behaviors Checklist (OBC). Descriptive statistics summarized frequencies, means, and standard deviations; χ² tests and t-tests compared subgroups by sex; Pearson correlations explored relationships among continuous measures (α = 0.05). Results: A total of 5% of respondents reported orofacial pain in the past 30 days; 41.6% observed TMJ noises; 19.7% experienced locking episodes. Mean JFLS score was 28.3 ± 30.5, with 4.8% scoring > 80 (severe limitation). Mean PHQ-9 was 5.96 ± 5.37 (mild depression); 15.5% scored ≥ 10. Mean GAD-7 was 5.20 ± 4.95 (mild anxiety); 16.0% scored ≥ 10. Mean OBC score was 12.3 ± 8.5; 30.1% scored ≥ 16, indicating frequent parafunctional habits. Symptom prevalence was similar by sex, except temporal headache (43.4% females vs. 24.3% males; p = 0.0008). Females reported higher mean scores for pain intensity (2.09 vs. 1.55; p = 0.0013), JFLS (32.5 vs. 18.0; p < 0.001), PHQ-9 (6.43 vs. 5.16; p = 0.048), and OBC (13.9 vs. 9.7; p = 0.0014). Strong correlation was observed between PHQ-9 and GAD-7 (r = 0.74; p < 0.001); moderate correlations were observed between pain intensity and PHQ-9 (r = 0.31) or GAD-7 (r = 0.30), between JFLS and pain intensity (r = 0.33), and between OBC and PHQ-9 (r = 0.39) (all p < 0.001). Conclusions: Nearly half of dental students reported TMD symptoms, with appreciable functional limitation and psychosocial impact. Parafunctional behaviors and psychological distress were significantly associated with pain and dysfunction. These findings underscore the need for early screening, stress-management interventions, and interdisciplinary care strategies in the dental student population. Full article

Background: Intracerebral hemorrhage (ICH) is a severe form of stroke associated with high morbidity and mortality. While neurosurgical evacuation may offer theoretical benefits, its impact on survival and hospital course remains debated. We aimed to compare the outcomes of surgical versus conservative management in patients with lobar, capsulo-lenticular, and thalamic ICH and to identify factors influencing mortality and the surgical decision. Methods: This single-center, retrospective cohort study included adult patients admitted to the County Clinical Emergency Hospital of Sibiu (2017–2023) with spontaneous supratentorial ICH confirmed via CT (deepest affected structure determining lobar, capsulo-lenticular, or thalamic location). We collected data on demographics, clinical presentation (Glasgow Coma Scale [GCS], anticoagulant use), hematoma characteristics (volume, extension), treatment modality (surgical vs. conservative), and in-hospital outcomes (mortality, length of stay). Statistical analyses included t-tests, χ², correlation tests, and logistic regression to identify independent predictors of mortality and surgery. Results: A total of 445 patients were analyzed: 144 lobar, 150 capsulo-lenticular, and 151 thalamic. Surgical intervention was more common in patients with larger volumes and lower GCS. Overall, in-hospital mortality varied by location, reaching 13% in the lobar group, 20.7% in the capsulo-lenticular group, and 35.1% in the thalamic group. Within each location, surgical intervention did not significantly reduce overall in-hospital mortality despite the more severe baseline presentation in surgical patients. In lobar ICH specifically, no clear survival advantage emerged, although surgery may still benefit those most severely compromised. For capsulo-lenticular hematomas > 30 mL, surgery was associated with lower mortality (39.4% vs. 61.5%). In patients with large lobar ICH, surgical intervention was associated with mortality rates similar to those seen in less severe, conservatively managed cohorts. Multivariable adjustment confirmed GCS and hematoma volume as independent mortality predictors; age and volume predicted the likelihood of surgical intervention. Conclusions: Despite targeting more severe cases, neurosurgical evacuation did not uniformly lower in-hospital mortality. In lobar ICH, surgical patients with larger hematomas (~48 mL) and lower GCS (~11.6) had mortality rates (~13%) comparable to less severe, conservative cohorts, indicating that surgical intervention was associated with similar mortality rates despite higher baseline risk. However, these findings do not establish a causal survival benefit and should be interpreted in the context of non-randomized patient selection. For capsulo-lenticular hematomas > 30 mL, surgery was associated with lower observed mortality (39.4% vs. 61.5%). Thalamic ICH remained most lethal, highlighting the difficulty of deep-brain bleeds and frequent ventricular extension. Across locations, hematoma volume and GCS were the primary outcome predictors, indicating the need for timely intervention, better patient selection, and possibly minimally invasive approaches. Future prospective multicenter research is necessary to refine surgical indications and validate these findings. To our knowledge, this investigation represents the largest and most contemporary single-center cohort study of supratentorial intracerebral hemorrhage conducted in Romania. Full article

The increasing complexity and scale of electronic health records (EHRs) demand advanced tools for efficient data retrieval, summarization, and comparative analysis in clinical practice. MERA (Medical Electronic Records Assistant) is a Retrieval-Augmented Generation (RAG)-based AI system that addresses these needs by integrating domain-specific retrieval with large language models (LLMs) to deliver robust question answering, similarity search, and report summarization functionalities. MERA is designed to overcome key limitations of conventional LLMs in healthcare, such as hallucinations, outdated knowledge, and limited explainability. To ensure both privacy compliance and model robustness, we constructed a large synthetic dataset using state-of-the-art LLMs, including Mistral v0.3, Qwen 2.5, and Llama 3, and further validated MERA on de-identified real-world EHRs from the MIMIC-IV-Note dataset. Comprehensive evaluation demonstrates MERA’s high accuracy in medical question answering (correctness: 0.91; relevance: 0.98; groundedness: 0.89; retrieval relevance: 0.92), strong summarization performance (ROUGE-1 F1-score: 0.70; Jaccard similarity: 0.73), and effective similarity search (METEOR: 0.7–1.0 across diagnoses), with consistent results on real EHRs. The similarity search module empowers clinicians to efficiently identify and compare analogous patient cases, supporting differential diagnosis and personalized treatment planning. By generating concise, contextually relevant, and explainable insights, MERA reduces clinician workload and enhances decision-making. To our knowledge, this is the first system to integrate clinical question answering, summarization, and similarity search within a unified RAG-based framework. Full article