Search Results (5,956)

Background/Objectives: Labor pain is a multidimensional experience associated with physiological, cultural, psychological, and contextual factors. This study aimed to examine the association of personality-related characteristics, cultural beliefs, obstetric characteristics, and proxy indicators of post-disaster context with labor pain perception among women giving birth in Hatay after the 2023 Türkiye earthquakes. Methods: This prospective observational study was conducted with 314 women admitted to Hatay Training and Research Hospital between February and June 2025. Participants were between 38 and 42 gestational weeks, had a singleton healthy fetus, were admitted in active labor, and were expected to give birth vaginally. Data were collected using a researcher-developed questionnaire, the Ten-Item Personality Inventory, and the Visual Analog Scale. Labor pain was assessed at 6 cm, 8 cm, and full cervical dilatation (10 cm). Results: VAS scores increased significantly across cervical dilatation points, from 5.04 ± 0.81 at 6 cm to 7.01 ± 0.82 at 8 cm and 8.06 ± 0.93 at full cervical dilatation (10 cm). Repeated-measures ANOVA showed a significant within-person increase in pain intensity across the three assessment points, F(2, 626) = 996.444, p < 0.001, partial η² = 0.761. Age was not significantly correlated with VAS pain score at full cervical dilatation. In exploratory unadjusted comparisons, VAS scores at full cervical dilatation differed according to education level, official marriage status, previous birth history and mode, attendance at antenatal education, and praying to relieve labor pain. In the multivariable regression model, higher Extraversion and higher education level were associated with lower VAS scores, whereas attendance at antenatal education, greater importance given to traditional rules, previous assisted vaginal/cesarean birth, and current place of residence were independently associated with VAS scores. Conscientiousness was not significantly associated with VAS scores in the adjusted model. Earthquake experience was not significantly associated with VAS scores. Conclusions: Labor pain perception was associated with selected sociodemographic, obstetric, and cultural characteristics. The findings support the importance of individualized, culturally sensitive, and trauma-informed midwifery care in disaster-affected regions. Personality-related findings should be interpreted cautiously because the corrected reliability analysis showed low internal consistency for Agreeableness, Emotional Stability, and Openness to Experience, although Extraversion showed high internal consistency and Conscientiousness showed relatively better but still limited internal consistency. Disaster-related findings should also be interpreted cautiously because post-disaster context was assessed using only limited proxy indicators; current place of residence was independently associated with VAS scores in the adjusted model, whereas earthquake experience was not. Because of the observational design, causal interpretations cannot be made. Full article

Maintenance management increasingly depends on effective information flow and coordination between internal teams and external service providers. This study investigates the use of Business Process Model and Notation (BPMN) to support the formalization of Computerized Maintenance Management System (CMMS) workflows and improve transparency, decision-making, and interorganizational coordination. A single case study was conducted in the maintenance department of an electricity distribution company characterized by tacit knowledge, informal communication practices, and limited process formalization. Existing corrective maintenance workflows were analyzed and modeled using BPMN to identify inefficiencies, decision points, and opportunities for improvement. The proposed BPMN models were aligned with CMMS operational states associated with anomaly management and work-order execution processes and supported by a procedural manual. Results obtained during a three-month observation period suggest reductions in training time, email communications, and dependence on individual decision-makers, together with increased use of CMMS workflow functionalities and improved process traceability. These findings provide preliminary evidence, derived from operational indicators within a single case study, that BPMN-supported process formalization may contribute to workflow standardization, operational clarity, and knowledge management in maintenance-intensive environments. Given the single-case design and limited observation period, the results should be interpreted as context-specific and not directly generalizable to the broader energy sector. Full article

With the adoption of more ambitious emission reduction strategies in the shipping industry by the International Maritime Organization and the resulting stricter greenhouse gas emission reduction requirements, it is particularly important for all stakeholders in the global maritime shipping industry to assess the energy efficiency of shipping vessels. Forming predictive capabilities for ship fuel consumption and Carbon Intensity Indicator (CII) annual ratings, for example, are two important works. This article adopted 14 different algorithms in three categories of data-driven approaches, i.e., statistics, machine learning and deep learning, including polynomial regression, ridge regression, adaptive boosting, categorical boosting, elastic net, etc., and built the ship fuel consumption prediction model using ship noon report as the data source. The prediction accuracy and computational efficiency of model training were compared based on metrics of coefficient of determination, mean absolute percentage error and floating-point operations per amount of training data. Cross-validations were performed for all 14 algorithms to analyze their sensitivities to their respective tuned parameters. Comparisons indicated that algorithms of the statistics approach were sensitive to the quality of the data source, compared with the machine learning and the deep learning approaches. The accuracy of the elastic net algorithm was sensitive to the tuned parameters. Two algorithms, light gradient boosting machine and random forest, were selected based on their performances of prediction accuracy and computational efficiency of model training. Then, the selected algorithms were separately combined with long short-term memory as the time-series prediction algorithm to form their respective coupled framework. Both of the coupled frameworks achieved successful prediction of the CII annual discriminant and rating of the studied ships. The prediction accuracy was validated to be sufficient. Full article

Plastic-covered greenhouses (PCGs) are an important form of intensive agricultural land use, but their long-term spatial dynamics are difficult to summarize from annual maps alone. This study mapped PCGs in Weifang, China, from 2016 to 2025 using Sentinel-2 imagery processed in Google Earth Engine. A Random Forest model trained with pooled multi-year samples was used to generate annual probability maps, which were converted to binary maps using a fixed threshold (T = 0.45) to improve cross-year comparability. Pixel-wise annual sequences were then summarized into four process classes: stable, gain, loss, and flip. These process classes, together with annual greenhouse coverage, were aggregated to a 16 km² hexagon grid. Current coverage, long-term change, and process composition were further combined to produce an exploratory rule-based zoning interpretation. Independent year-specific validation showed overall accuracies of 0.969–0.983 and Kappa values of 0.740–0.841. Greenhouse precision remained high, while recall was lower, indicating a conservative detection tendency. From 2016 to 2025, mapped greenhouse area increased by 21.3%, reaching 752 km². Shouguang, Qingzhou, and Changle accounted for 77.7% of the 2025 total. The results show a persistent high-intensity core and more dynamic marginal areas, providing spatial evidence for differentiated monitoring and targeted verification. Full article

This study investigated the effects of dietary sodium butyrate supplementation on blood gas profiles, biochemical parameters, and untargeted plasma metabolomics in tumbler pigeons. Eighty tumbler pigeons of similar age, body weight, and training intensity were randomly allocated into four groups: a control group without sodium butyrate supplementation (CON) and three sodium butyrate-supplemented groups receiving 6 mg/d (T1), 12 mg/d (T2), and 18 mg/d (T3), respectively. All birds were maintained under identical husbandry conditions and fed the same basal diet throughout a 40-day experimental period consisting of a 10-day acclimation phase and a 30-day feeding trial. Results showed that dietary sodium butyrate supplementation significantly improved dry matter digestibility, with T2 and T3 exhibiting higher values than CON (p < 0.05), while metabolizable energy utilization was significantly increased in T3 compared with CON (p < 0.05). Sodium butyrate supplementation also significantly altered several blood gas parameters associated with acid–base balance and gas exchange. In addition, antioxidant enzyme activities were enhanced, with SOD, CAT, GSH-Px, and T-AOC activities significantly increased in supplemented groups compared with CON (p < 0.01). Furthermore, sodium butyrate supplementation significantly modulated inflammatory responses, increasing IL-10 concentrations (p < 0.01) while decreasing IL-6 and IL-8 levels (p < 0.01). Untargeted metabolomic analysis revealed significant alterations in pathways related to lipid metabolism, amino acid metabolism, and inflammatory regulation. In conclusion, dietary sodium butyrate supplementation influenced nutrient utilization, blood physiological parameters, antioxidant capacity, inflammatory status, and plasma metabolic profiles in tumbler pigeons. Among the tested supplementation levels, 18 mg/d sodium butyrate was associated with the most favorable overall physiological responses. These findings provide a basis for future investigations into the physiological and metabolic effects of sodium butyrate supplementation in competitive pigeons. Full article

To enhance drive performance, this paper introduces an advanced speed controller architecture intended for a brushless DC motor (BLDCM) operating under field-oriented control (FOC). This newly developed controller integrates decision tree theory (DTT) with sliding mode theory (SMT). Initially, the regression algorithm from the classification and regression tree (CART) framework is applied to partition the deviation between the actual motor speed and the target command into 10 distinct error zones. These intervals serve as the basis for configuring three critical parameters of a standard exponential reaching law sliding mode controller (ERLSMC): namely, the sliding mode dynamic trajectory control gain, the exponential reaching gain, and the constant speed reaching gain. Following each split, the mean squared error (MSE) of the respective nodes is evaluated to determine the root node. The dataset is recursively bifurcated into dual subsets using the chosen split variables and thresholds, establishing a structured decision pathway through each successive child node. As a result, the sliding mode speed controller receives dynamically optimized modifications for its three key gains in real time during BLDCM operation. In addition, the controller continuously computes an updated sliding mode dynamic trajectory control gain by tracking the derivative of the speed error. Tuning these three operational gains effectively mitigates the transient overshoot typically induced by the conventional exponential reaching law (ERL) across diverse running states. This mechanism ensures that the speed response of the BLDCM drive system dynamically and accurately follows target commands under fluctuating conditions. Advantageously, the introduced control strategy avoids intensive computational routines and eliminates the need for extensive training datasets, ensuring straightforward implementation. To validate this approach, the proposed methodology is applied to the BLDCM drive system using the Matlab/Simulink environment. Its execution is benchmarked against conventional sliding mode controllers (SMCs) configured with three distinct control strategies: the constant speed reaching law (CSRL), the standard ERL, and the extension theory combined with exponential reaching law (ETERL). The resulting simulation data confirms that the proposed adaptive controller delivers superior performance over the alternative three reaching laws regarding both transient command tracking and robustness in load regulation. Full article

Manually counting sunflower seeds on capitula is labor-intensive, requiring approximately one person-hour per head, and can be inconsistent for densely packed heads. Existing phenotyping approaches often depend on laboratory-based equipment, limiting their accessibility. In this study, we developed a benchtop image-based pipeline for rapid, non-destructive estimation of developed and aborted seeds on intact dried sunflower heads. A dataset of 1093 sunflower capitula was imaged under fixed indoor lighting, and individual seeds were annotated as developed or aborted. A YOLOv8m one-stage object detector was trained and evaluated using a counting-focused protocol, in which a single confidence threshold was selected on the validation set and then applied unchanged to an independent test set of 109 images. The baseline model was compared with recent YOLO variants and different augmentation strategies. On the test set, the model achieved a mean absolute count error of 61.3 seeds per image, a mean relative error of 12.0%, and an mAP50 of 0.18 at the locked confidence threshold of 0.15. Only 13.8% of test images had relative errors below 2%. Larger YOLO models and augmentation variants did not improve performance. These findings show that the proposed system provides approximate, non-destructive seed-count estimation under controlled imaging conditions, while highlighting the need for improved localization in dense regions and domain adaptation for fresh heads or field conditions. The annotated dataset and trained model weights are made available to support reproducible research. Full article

Background/Objectives: Postoperative monitoring of microvascular free flaps is critical for early detection of vascular complications and flap survival. Nurses play a central role in this process; however, qualitative evidence on their experiences and challenges remains limited. This study explored nurses’ experiences in free tissue flap care to identify clinical practices, challenges, and improvement needs. Methods: A phenomenological qualitative design was used. Data were collected through semi-structured interviews with nine nurses experienced in free tissue flap care, recruited via purposive and snowball sampling. Interviews were conducted online and lasted 30–45 min. Data were analyzed using content analysis with MAXQDA 2025. Inter-researcher reliability was 97%. Results: The findings were categorized into four main themes and seventeen subthemes: (1) clinical monitoring and evaluation in the care process, (2) challenges and difficulties, (3) emotional and professional reflections, and (4) suggestions for improving care. Nurses reported that flap care requires intensive monitoring, rapid decision-making, and close collaboration with physicians, especially within the first 24–48 h. Monitoring was largely based on observation and experience due to the lack of standardized protocols. Major challenges included high workload, frequent assessments, and donor site management. Emotional burden, stress, and responsibility were also prominent. Conclusions: Free flap care is a complex and demanding process for nurses. The lack of standardized monitoring tools and protocols is a key gap. Developing structured tools, improving training, and strengthening multidisciplinary collaboration may enhance patient safety and care quality. Full article

Background: Patients with prolonged mechanical ventilation (PMV) frequently require tracheostomy due to failure to wean, yet the pathway from ventilator dependence to successful decannulation remains complex and poorly standardised. Comprehensive respiratory rehabilitation is recognised as a core strategy for improving decannulation outcomes, but no unified, evidence-based guidelines currently exist for this population. This review addresses that gap by synthesising current evidence on respiratory rehabilitation and decannulation strategies for tracheostomized PMV patients. Methods: A narrative review was conducted through a systematic search of PubMed/MEDLINE covering publications indexed from May 2019 to February 2026, supplemented by targeted searches of Embase and the Cochrane Library. The search combined free-text keywords and Medical Subject Headings (MeSH) terms across eight search string combinations. Following title and abstract screening of 830 deduplicated records, 51 studies met eligibility criteria and were included in the final narrative synthesis. Results: Six core rehabilitation intervention domains were identified: respiratory muscle training, physical rehabilitation and nutritional optimisation, sedation and delirium management, speaking valve use, airway complication management, and ventilator mode optimisation. High-intensity inspiratory muscle training at no less than 50% of maximal inspiratory pressure is currently supported by the strongest available evidence among the interventions reviewed, although this threshold derives primarily from general ICU populations and has not been specifically validated in heterogeneous tracheostomized PMV cohorts. Decannulation readiness assessment may benefit from evaluating five core domains—neurological readiness, secretion management capacity (suctioning ≤ 4 times/24 h), cough efficacy (peak cough flow > 160 L/min), safe swallowing confirmed by instrumental assessment, and upper airway patency confirmed by fiberoptic bronchoscopy—using a structured multidisciplinary framework. Conclusions: Successful decannulation in tracheostomized PMV patients requires integration of evidence-based rehabilitation interventions, structured multidisciplinary assessment, and a patient-centred outcome framework that extends beyond physiological endpoints to encompass voice restoration, psychological well-being, and social reintegration. Significant evidence gaps remain—particularly for expiratory muscle training, population-specific decannulation protocols, and adapted rehabilitation models for resource-limited settings—representing priority areas for future research. Full article

Ventilator-associated pneumonia (VAP) is linked to high mortality rates, especially in developing countries. This cross-sectional survey study was conducted across three central hospitals in the Hail region of Saudi Arabia, King Salman Specialist Hospital, Hail General Hospital, and King Khalid Hospital, to assess the knowledge and adherence of intensive care unit (ICU) healthcare practitioners to the ventilator bundle (VB) for VAP prevention. It also looked at the practitioners’ perceived barriers to effective VB deployment. The study (n = 86) revealed significant disparities in VAP prevention knowledge across educational levels regarding the recommended degree of head-of-bed (HOB) elevation (p < 0.001), the use of endotracheal tubes with extra lumens for subglottic drainage (p < 0.001), and the protective effects of 0.12% chlorhexidine gluconate antiseptic oral rinse (p = 0.019). Professional experience significantly influenced knowledge of non-standard VB components (p < 0.001), the recommended frequency of awakening and spontaneous breathing trials (SBTs) (p < 0.001), and knowledge of extra-lumen tubes (p = 0.038) and kinetic beds vs. standard beds (p = 0.005). Significant differences were found between professional categories regarding knowledge of hand hygiene performance (p = 0.032), the correct degree of HOB elevation (p = 0.007), and patient positioning (semi-recumbent vs. supine) (p = 0.023). Years of experience significantly impacted reported compliance with institutional VB (p = 0.013), adherence to oral care protocols (p = 0.035), and the assessment of sedation depth (p = 0.002). While basic measures like HOB elevation practice and DVT prophylaxis showed universal reported compliance (100%), significant performance gaps were identified in more complex tasks, such as interrupting continuous sedative infusions and performing SBTs as recommended (p < 0.001), particularly among novice practitioners. The primary implementation barrier preventing full compliance with the VB was identified as educational deficit, which was prioritized as the most important area for quality improvement, highlighting the need for targeted training for newly hired ICU staff. Full article

Solar power plants are among the most widely used renewable energy sources today. Varying radiation levels from region to region, and similarly varying consumption depending on the user within a given region, make the optimal sizing of these plants challenging. In this study, a machine learning-based surrogate model for the real-time sizing optimization of solar power plants, trained with a completely original dataset, has been developed. In the first stage, 500 different solar power plant installation scenarios were synthetically generated and evaluated in HOMER, and the obtained optimal sizing outputs were used as training targets for the proposed surrogate model rather than real operational data. The results obtained by applying various machine learning methods to the generated dataset are presented comparatively. Among 7 different machine learning models, XGBoost, Gradient Boosting, and LightGBM demonstrated the best performance. The developed model achieved an average R² score of 0.9425 for a total of 3 targets, while target-specific performance showed R² scores of 0.9747 for inverters, 0.9365 for PV panels, and 0.9165 for batteries. This model serves as a computationally efficient surrogate of the HOMER optimization process, enabling high-accuracy real-time predictions while significantly reducing the computational burden associated with intensive mathematical calculations, iterative procedures, and complex search spaces. Full article

Accurate and wide-area estimation of seaweed biomass is essential for evaluating blue carbon. Conventional diver surveys and two-dimensional (2D) aerial imagery analysis face challenges such as intensive labor and biomass underestimation. While Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-LiDAR) provides dense 3D spatial data, classifying point clouds in extremely shallow coastal waters with dense kelp and artificial structures remains difficult. This study establishes a high-accuracy biomass estimation method using UAV-LiDAR and PointNet. A heuristic hybrid filtering approach combining physical constraints and local statistics was developed to automatically generate high-quality reference data. The trained PointNet successfully segmented complex point clouds into four classes with an overall accuracy of 94.2%. To calculate biomass, we introduced a volume correction model based on point cloud density (coverage) to mitigate overestimation caused by internal canopy gaps. This correction yielded estimated wet weights nearly identical to the in situ measurements (an approximate 3% difference), confirming highly accurate biomass reproduction. Furthermore, while the conventional 2D maximum likelihood method underestimated total biomass, our 3D point cloud analysis successfully quantified the dense, overlapping canopy. This framework significantly improves the efficiency and accuracy of blue carbon monitoring. Full article

Background/Objectives: Telomeres are critical biomarkers of biological aging, with shortened leukocyte telomere length strongly linked to all-cause mortality and age-related disease risk. Although exercise modulates telomere dynamics, the field’s evolution from descriptive measurements to mechanistic inquiries involving redox biology and epigenetics remains incompletely mapped. This study systematically characterized the global research landscape of telomere–exercise science over 25 years to establish a strategic evidence base for precision exercise prescription. Methods: A bibliometric analysis was conducted on 858 publications from the Web of Science Core Collection (2000–2025). CiteSpace and VOSviewer were used for keyword co-occurrence analysis, strategic thematic mapping, and citation burst detection to visualize global research trends and identify emerging frontiers. Results: Annual publication volume grew from 2 (2000) to 71 (2025), with a compound annual growth rate of 15.4%. China emerged as one of the leading global contributors. Thematic analysis revealed a paradigm shift from descriptive leukocyte telomere length studies toward mechanistic investigations of oxidative stress, mitochondrial homeostasis, and epigenetic clocks. Keyword network analysis confirmed oxidative stress and inflammation as central hubs, mediating telomere protection via redox regulation and non-canonical telomerase functions. Conclusions: Exercise preserves telomere integrity primarily through redox–mitochondrial homeostasis, hormesis-driven antioxidant upregulation, and non-canonical telomerase activation. For aging populations and individuals at metabolic risk, aerobic training and high-intensity interval training (HIIT) are recommended as first-line non-pharmacological interventions for healthspan extension. Leukocyte telomere length and telomerase activity should be integrated as biomarkers in preventive medicine practice. Future large-scale randomized controlled trials incorporating multi-omics approaches and sex-stratified analyses are warranted to establish individualized dose–response guidelines for precision exercise prescription. Full article

Introduction: College student-athletes must often balance academic responsibilities with intensive training and competition, placing them under considerable pressure and potentially increasing their risk of mental health difficulties. Against this background, the present study focused on the link between mindfulness and psychological distress and examined whether cognitive reappraisal and subjective vitality were statistically involved in this association as indirect associations. Methods: Participants were 430 college student-athletes recruited from five universities in Hunan Province, China. Using a cross-sectional survey design, the hypothesized model was tested using structural equation modeling in AMOS 23.0, and indirect associations were examined with bootstrap analysis based on 5000 resamples. Results: Mindfulness was positively associated with both cognitive reappraisal and subjective vitality. Cognitive reappraisal was positively associated with subjective vitality but negatively associated with psychological distress. Subjective vitality also showed a negative association with distress. Moreover, mindfulness showed an indirect association with lower distress through cognitive reappraisal and subjective vitality. Discussion: The findings may contribute to a better understanding of the psychological correlates associated with mental health in college student-athletes. They also suggest that mindfulness-related psychological resources may be associated with lower distress and may help guide future longitudinal and intervention research in this group. Full article

This study aims to empirically analyze the patterns, intensity, and perceived barriers to physical activity among Polish university students during the COVID-19 pandemic. The research utilized a diagnostic survey method, employing a questionnaire. The online survey was conducted from December 2020 to May 2022 via the Webankieta.pl platform. The minimum sample size, calculated using the standard formula for estimating a proportion in a large population, was set at 1100 participants and was exceeded, with 1260 students providing valid responses. The results show that over half (55.8%, mainly women) of the respondents did not participate in regular physical activity during the pandemic. Participants cited lack of desire, fatigue, and low motivation—not pandemic restrictions—as primary reasons. Conversely, 44.2% of respondents, mostly men, reported engaging in regular physical activity. Most engaged in moderate-intensity activities two to five times a week, with vigorous activities performed slightly less often. Women were more likely to do both types, while men favored strength training. The most common activities included walking (61.6%), simple gymnastic exercises (43.1%), strength training with equipment (35.0%), cycling (34.5%), and calisthenics (30.2%). The majority (81.3%) exercised at home or nearby (33.4%). Reported barriers, especially among those who exercised regularly, were pandemic-related, such as limited or closed access to gyms, fitness centers, and pools (59.1%), along with time constraints (44.7%) and low motivation or determination (32.0%). The findings emphasize the importance of targeted interventions to boost physical activity among university students, particularly women and those with fewer financial resources. Universities should consider implementing programs that promote accessible, regular activity and initiatives to enhance motivation and foster long-term, health-promoting habits. Full article