Search Results (3,518)

Under increasing cropland constraints and pressure to secure oilseed supplies, using winter–fallow cropland for winter rapeseed production can improve annual cropland-use efficiency. Taking the Wanjiang Plain, China, as the study area, this study integrated 10 m winter–fallow cropland maps (2019–2024), 30 m winter rapeseed maps (2000–2022), annual Satellite Embedding features, cropland data, and administrative boundaries. Multi-year occurrence frequency and Getis–Ord Gi* statistics characterized temporal persistence and spatial clustering. A MaxEnt model calibrated with 394 occurrence records from long-term high-frequency rapeseed areas and 26 screened embedding dimensions delineated cropland with present-day land-surface characteristics similar to historically persistent rapeseed locations. This layer was progressively intersected with the historical winter–fallow union and stable winter–fallow cropland. Stable winter–fallow cropland covered approximately 4437 km², whereas long-term high-frequency winter rapeseed covered only 568 km², revealing a marked spatial mismatch. Under five-fold spatial cross-validation, the selected linear-feature model with a regularization multiplier of 4 achieved a mean test AUC of 0.904 ± 0.015 and a 10% training-omission rate of 0.108 ± 0.022. Using the model-specific threshold of 0.3096, the final estimates were 6017 km² of potentially suitable cropland, 2943 km² of general expansion potential, and 1137 km² of candidate spatial priority areas for field verification. The last tier was concentrated mainly in Xuanzhou District, Lujiang County, urban Wuhu, Wuwei City, Nanling County, and He County. The framework provides a cautious spatial-screening basis for optimizing winter–fallow cropland use and guiding subsequent field and feasibility assessments. Full article

This study aimed to explore the maternal experiences of Korean migrant working women living in the United Arab Emirates (UAE) and to gain an in-depth understanding of the meaning and essence of these experiences. In-depth interviews were conducted with 10 Korean migrant working women who had experienced pregnancy while living in the UAE and had experience raising their children while living and working in the UAE. The collected data were analyzed using Colaizzi’s phenomenological method. A total of 13 themes were identified from the participants’ maternal experiences and organized into five theme clusters. The five theme clusters were “Transition to Pregnancy and Motherhood in a Foreign Land,” “The Dual Challenges of Childbirth and Recovery in an Unfamiliar Environment,” “Struggling at the Intersection of Work and Motherhood,” “Becoming a Mother in a Foreign Country,” and “Growing into Motherhood Through Diverse Sources of Support.” The findings of this study provide an in-depth understanding of how Korean migrant working women experience and reconstruct motherhood within a migration context and provide foundational evidence for developing culturally appropriate maternal healthcare services and nursing interventions. Full article

Objective feedback is important for improving welding training and reducing dependence on experienced instructors. This study proposes an image-based sensing framework for extracting motion and geometric features from videos acquired using a simple visualization system mounted inside a welding helmet during semi-automatic gas metal arc welding (GMAW) and manual gas tungsten arc welding (GTAW). The welding videos were acquired in real time, whereas the subsequent image-processing and quantitative feature-extraction procedures were performed offline. This visualization system consists of an instructor-side unit and a welder-side unit equipped with a prototype compact camera. Welding images were trimmed to 288 × 288 pixels and classified into eight regions for GMAW and six regions for GTAW using a U-Net-based semantic segmentation model. The segmented regions in GMAW included the arc, molten pool, groove, torch, wire, overlap regions, and background, whereas those in GTAW included the arc, bead, groove, electrode, filler wire, and background. Groove edges were detected using Canny edge detection and the Hough transform. The wire-tip position in GMAW and the electrode-tip position in GTAW were estimated using the arc centroid as an image-based positional proxy and normalized by the detected groove width to obtain normalized wire-tip and electrode-tip motion, respectively. In addition, the molten-pool width in GMAW and the bead width in GTAW were extracted and normalized using the same procedure to obtain groove-normalized geometric features. For the GMAW dataset, the segmentation model achieved a validation accuracy of 95.2% and a validation mean intersection over union (mIoU) of approximately 67.1%. For the GTAW dataset, the corresponding validation accuracy and validation mIoU were 96.6% and approximately 85.9%, respectively. The results showed that the proposed framework can successfully extract normalized wire-tip motion and molten-pool width in GMAW, as well as normalized electrode-tip motion and bead width in GTAW, from welding videos acquired from different welders. These findings demonstrate the potential of the proposed framework to extract quantitative motion and geometric features from welding videos, providing useful data for future welding training support and welding behavior analysis. Full article

In adaptive signal control, the upstream observation range at intersections is typically determined by empirical detector placement, lacking a data-driven adaptive mechanism. This paper addresses the question of how far upstream is sufficient for timing decisions and proposes a sensing boundary identification method based on Bayesian online learning. A microscopic simulation model coupling all signal phases is first constructed based on the Intelligent Driver Model, with a green split optimization function minimizing the total queued vehicles at cycle end. The prefix length is then modeled as a probabilistic variable, and the class separability score serves as the likelihood basis. Through recursive Bayesian posterior updates, the optimal prefix length is automatically determined. Based on the identified sensing boundary, K-nearest neighbors weighted regression enables online prediction of the green split. Experiments on 10,368 simulated scenarios demonstrate that the posterior converges to a prefix length consistent with the core queue dissipation region. Far-end free-flow vehicles contribute limited information and degrade class separability when included, validating that a longer observation range does not necessarily improve performance. The identified prefix length uses only a small fraction of the full feature dimensions while maintaining favorable prediction accuracy, achieving a good trade-off among accuracy, computational efficiency, and online adaptability. The proposed method shifts sensing boundary determination from empirical setting to data-driven identification, offering a reference for detector placement and edge controller deployment. Full article

This study examines the intersection of multinational enterprise (MNE) strategy and international financial management, focusing on how the geographic diversification of tangible assets, intangible assets, and growth opportunities is associated with MNEs’ cash holdings, an important component of their financial strategy. Panel regression models were estimated using data from 576 MNEs headquartered in 28 countries. The findings suggest that the geographic diversification of intangible assets and growth opportunities attenuates the positive associations of these assets with cash holdings. In contrast, the geographic diversification of tangible assets attenuates the negative association between tangible assets and cash holdings. Overall, the findings suggest that MNEs’ cash holdings cannot be fully understood by examining only firm-level determinants, such as growth opportunities and multinationality, but also by considering the geographic diversification of different types of assets. Full article

Background: Negative aortic remodeling after thoracic endovascular aortic repair (TEVAR) remains a major challenge in patients with type B aortic dissection (TBAD). This exploratory study aimed to integrate human aortic tissue transcriptomic data with serum proteomic profiles from a remodeling-stratified TEVAR cohort to prioritize candidate molecules associated with TBAD and negative aortic remodeling. Methods: Public human aortic tissue transcriptomic data from GSE190635 were analyzed to identify differentially expressed genes between aortic dissection and control aortic tissues. Serum tandem mass tag (TMT)-based untargeted proteomic profiling was performed in a remodeling-stratified TEVAR cohort, including patients with negative remodeling and those with stable or positive remodeling. Cross-omics integration was conducted by intersecting differentially expressed genes and differentially abundant serum proteins. The prioritized candidate molecule was further validated using serum ELISA, quantitative real-time PCR, Western blotting, hematoxylin and eosin staining, immunohistochemistry, and immunofluorescence co-staining in human serum and aortic tissue samples. Results: Transcriptomic analysis identified 1534 differentially expressed genes in aortic dissection tissues, while serum proteomic profiling identified 82 differentially abundant proteins associated with postoperative remodeling status. Cross-omics integration revealed seven overlapping molecules, among which PCSK9 was the only candidate showing concordant upregulation in both aortic dissection tissues and serum samples from patients with negative remodeling after TEVAR. Serum PCSK9 levels were significantly higher in patients with aortic dissection than in normal controls. PCSK9 mRNA and protein expression were also increased in human aortic dissection tissues. Histological and immunostaining analyses showed enhanced PCSK9 accumulation in the dissected aortic wall, particularly in medial regions with partial overlap with α-SMA-positive areas. Conclusions: PCSK9 was identified as a cross-omics prioritized candidate molecule associated with aortic dissection and negative remodeling after TEVAR. Its elevation in serum and aortic tissues, together with its enrichment in α-SMA-positive medial regions, suggests a potential link between circulating proteomic alterations and local aortic wall pathology. These findings support further investigation of PCSK9 as a candidate biomarker for adverse remodeling in TBAD. Full article

Methods for object localization and classification in images acquired from unmanned aerial vehicles (UAVs) quickly develop and find new applications. Pre-trained convolutional neural networks (CNNs) play the key role in solving these tasks. However, there are many factors that degrade the quality of acquired images and make the performance of methods intended for object detection and classification worse. One such factor is lossy compression of acquired images or video data widely used to pass them from on-board sensors and devices of preliminary data processing to on-land centers that perform further data processing for retrieval of valuable information. Both CNNs applied for localization and classification, and lossy compression techniques used to reduce the transferred data size have an impact on final results. To study this impact, we analyze the performance of several modifications of YOLO (You Only Look Once) CNNs applied to color images compressed by JPEG, which continues to be one of the basic compression tools. The quality factor is varied within wide limits to detect the situation when distortions due to lossy compression start to become too large and have a considerable negative effect on the localization and classification of objects of different types and sizes. Analysis is carried out using several traditional criteria, including Intersection over Union, F1, and mAP metrics, as well as some others. Dependence of localization and classification characteristics on the object size is performed. The datasets VisDrone and TAI are employed in training and verification. Full article

Background: Nutrition counselling during the perinatal period is an important component of midwifery care and health promotion. Existing competency frameworks recognize nutrition-related education and counselling as part of midwifery practice, while several studies have assessed midwives’ nutrition knowledge, attitudes, confidence, and self-reported practices. However, it remains unclear to what extent existing assessment approaches capture the quality of nutrition counselling as experienced by women receiving midwife-led care. Objective: This scoping review aimed to map existing tools, questionnaires, competency frameworks, checklists, and other assessment approaches related to nutrition counselling, nutrition education, and nutrition-related competencies in midwifery and perinatal care; to compare the measurement domains represented across these approaches; and to identify remaining measurement gaps. Methods: The review was conducted according to established scoping review methodology and reported in accordance with PRISMA-ScR. PubMed/MEDLINE, Scopus, Web of Science Core Collection, CINAHL, and Embase were searched from database inception to 1 July 2026 using two complementary search strategies targeting both nutrition-related assessment approaches and patient-experience/counselling-quality concepts. Eligible sources included studies or documents describing instruments, competency frameworks, questionnaires, screening tools, checklists, or other assessment approaches related to nutrition counselling within midwifery or perinatal care. Data were charted according to assessment type, target population, intended purpose, domains assessed, development methodology, validation evidence, and relevance to midwife-led nutrition counselling, and were synthesized descriptively and conceptually across assessment domains. Results: The database searches identified 186 records, of which 42 duplicates were removed, leaving 144 records for title and abstract screening. Thirty-four reports underwent full-text assessment, of which 25 were excluded, resulting in nine included reports. These reports represented a smaller number of distinct assessment tools, frameworks, and approaches because three reports evaluated or applied the same underlying instrument, the FIGO Nutrition Checklist. The included evidence comprised nutritional-risk screening, provider-focused knowledge-, attitude-, and practice-based assessment, observational approaches to counselling delivery, and a professional competency framework. Cross-cutting synthesis identified four conceptually distinct measurement levels: nutritional risk and dietary status, professional knowledge and competence, counselling delivery and implementation, and women’s experience of nutrition counselling. No eligible nutrition-specific patient-reported experience measure was identified, leaving the intersection between nutrition-specific content and woman-reported counselling experience insufficiently represented. Conclusions: Existing approaches provide fragmented assessment of nutrition-related care and do not comprehensively capture women’s experiences of midwife-led nutrition counselling. Further research should determine whether existing measures can be adapted or combined or whether a new patient-reported experience measure (PREM) is warranted. Full article

Large language models (LLMs) and time-series foundation models have advanced traffic forecasting, but almost exclusively on freeway sensors at five-minute, aggregate-flow resolution; their behavior on minute-resolution, movement-level counts at signalized arterials is unknown. This study assembled a real corridor dataset—6449 gap-free one-minute turning-movement records (255,364 counted vehicles) at 20 signalized intersections along 13 miles of Nolensville Pike, Nashville, Tennessee—and ran a controlled benchmark across three model families (classical, deep, and LLM/foundation models). We evaluate in-sample accuracy at 5/10/15 min horizons (RQ1), cross-date leave-intersection-out transfer to morning-only sites (RQ2). On the full-day sites, a per-series ARIMA attains the best short-horizon accuracy (MASE 0.87 at h = 5), while a zero-shot foundation model (Chronos) is the most stable across horizons; under leave-intersection-out transfer the ordering reverses—foundation models transfer best (MASE ≈ 0.61, improving to ≈0.56 with few-shot adaptation) whereas globally trained deep networks fail catastrophically (MASE > 4). All benchmark values reported here are measured on the corridor data; foundation-model families that we could not execute end-to-end in this environment are discussed qualitatively and are not included in the quantitative comparison. The dataset and protocol establish an honest, cost-aware basis for judging whether language and foundation models genuinely forecast an urban corridor at minute resolution. Full article

Background: This narrative concept-generating review proposes a theoretical and heuristic framework for conceptualizing established and emerging interventions for posttraumatic stress disorder (PTSD) including three categories: top-down/conscious (e.g., cognitive processing therapy, CPT), bottom-up/non-conscious (e.g., pharmacotherapy) and hybrid/conscious and non-conscious integration (e.g., eye movement desensitization and reprocessing, EMDR). The categorization offers a preliminary novel classification of well established and emerging interventions, while highlighting gaps in current treatment approaches. Methods: Clinical guidelines and review articles from 2008 through 2026 were used to create the framework; PubMed and Google Scholar databases were searched for relevant meta-analyses, systematic reviews, narrative reviews, and randomized clinical trials. A total of 27 treatment modalities were included presenting effect size data from 43 published articles. Results: Treatments across categories present mixed results for efficacy. While guideline recommended treatments offer more robust evidence, many understudied emerging interventions exhibit preliminary efficacy. At the intersection of conscious and non-conscious approaches, hybrid modalities may present unique potential benefits. However, superiority determinations between modalities cannot be made and further empirical validation of categorization is needed. Conclusions: The proposed framework highlights the need for increased investigation of treatments that address PTSD as an interconnected whole-body disorder to potentially expand care options. Full article

Parenting practices involving violent discipline are a global health concern, particularly among low-income families. While the current literature offers general prevalence data, it lacks a comprehensive global perspective evaluating how diverse sociodemographic, cultural, and historical contexts intersect within disadvantaged households. This scoping review protocol aims to map the global evidence on parenting practices related to physical and psychological violence used as disciplinary practices against children within low-income contexts. Following the JBI Manual for Evidence Synthesis and PRISMA-ScR guidelines, we will conduct a comprehensive search across databases, including MEDLINE/PubMed (via the National Library of Medicine), CINAHL (EBSCO), APA PsycINFO (APA), Embase (Elsevier), ERIC, IBECS (via the VHL) and the WHO Global Index Medicus to capture global evidence published in recent years. Studies published in English, Portuguese, and Spanish between January 2015 and June 2026 that report violent disciplinary practices by parents or caregivers of children aged 1–8 years will be included. Rayyan software will assist with study selection. Mapping these practices across diverse socioeconomic settings may clarify current research gaps and synthesize the characteristics of families globally, shifting the interpretation of violent discipline from isolated parental choices to broader understanding of its social determinants and providing a clearer baseline to support future child protection research. Full article

Background: The global healthcare landscape is being increasingly shaped by two pivotal trends: the adoption of international quality frameworks, such as Joint Commission International (JCI) accreditation, and the strategic shift towards resource-efficient models like outpatient surgery. While both areas have been extensively addressed in the healthcare literature, empirical evidence concerning their intersection remains limited. This scoping review aimed to systematically map the available literature, identify critical evidence gaps at the intersection of JCI accreditation and outpatient surgery, and examine implications for healthcare risk management and policy development. Methods: The scoping review was performed in accordance with the PRISMA-ScR guidelines. A dual-track evidence-mapping strategy was implemented across seven electronic databases and platforms (PubMed/MEDLINE, Scopus, Web of Science, Cochrane Library, eLIBRARY.ru, CyberLeninka, and Google Scholar) to search for publications published between January 2000 and May 2026. The final database searches were conducted on 15 May 2026. Data extraction was centered on study design, interventions, and key findings related to quality and risk management. Results: A total of 80 publications were included. The evidence comprised two complementary streams: literature concerning JCI accreditation and healthcare quality and safety, predominantly in hospital and general healthcare settings, and literature concerning outpatient, ambulatory, and day-surgery quality, safety, and hospital-substituting technologies. No empirical study directly evaluating the implementation or impact of JCI accreditation within outpatient surgery centers was identified. Conclusions: This scoping review identified a critical empirical evidence gap concerning the integration of accreditation and quality and risk-management frameworks in outpatient surgical settings. A hypothesis-generating conceptual framework is proposed to guide future empirical research evaluating whether and how JCI accreditation influences quality, safety, organizational, and economic outcomes in outpatient surgical settings. Full article

Automatic reseeding in spoon-chain potato planters requires reliable classification of miss-seeding, normal, and multiple-seeding events. Oblique viewing and continuous spoon motion cause scale changes, boundary truncation, blur, and occlusion, which destabilize single-frame predictions. The proposed system treats each seed-spoon passage as one control event. It combines lightweight detection, track-level learning, and multi-frame classification with air-blow clearing and missed-seed reseeding. Based on the target-size distribution, the detector removes the P3 head from YOLOv8n and restores shallow details through space-to-depth rearrangement and gated addition. Frame quality combines boundary distance, sharpness, adjacent-box intersection over union, and box-area stability. High-quality frames form a track prototype, while low-quality frames receive stronger constraints. A quality-gated temporal network classifies each spoon as containing 0, 1, or at least 2 seed potatoes. Each model was trained with three random seeds under the same data split. Compared with YOLOv8n, the detector reduced the parameter count and computational cost by 35.8% and 48.3%, respectively. Track-level learning improved event accuracy by 3.67 percentage points over frame-level training. The seven-frame model achieved 93.41% event accuracy and 93.80% macro-F1. At 0.5 m/s, the closed-loop recognition, air-blow, and reseeding success rates were 93.7%, 96.4%, and 97.1%, respectively. These results provide a practical basis for real-time, event-level planting-quality control in spoon-chain potato planters. Full article

Non-inferiority (NI) clinical trials have gained immense popularity within the last decades, especially in cancer and cardiovascular studies. These trials are designed to establish the non-inferiority of a new experimental treatment as compared to the existing active control. In other words, NI trials are required to demonstrate that the efficacy of an experimental treatment is not unacceptably worse than that of an active control by more than a pre-specified small margin. We consider three-arm NI trials that have been widely acknowledged as the Gold Standard. Three-arm NI trials aim to simultaneously establish both NI and the assay sensitivity (AS). Hence, the analysis of three-arm NI trials involves multiple hypothesis testing. The existing literature on the Bayesian modeling of three-arm NI trials suggests implementing a test procedure based on the joint posterior probability of the NI and AS hypotheses. This joint testing of NI with AS resembles the framework of intersection-union (IU) testing, which may result in a very conservative test. In this article we propose a novel Bayesian testing based on an isotonic transformation in conjunction with Bayes factors. Bayes factors for assessing NI with AS are computed based on Gibbs Sampling. The performance of the proposed testing is evaluated through simulated data sets under varying scenarios. Empirical results show that the proposed Bayesian method gives better control in terms of Type-I error rates, and more powers than existing Bayesian tests. The usefulness of our test is illustrated by synthetic data from the Mildly Asthmatic Study. Full article

Urban traffic environments can elevate physiological stress, yet most existing studies assess this indirectly through infrastructural or traffic-related proxies rather than direct physiological measurement. This pilot study proposes a geographic information system (GIS)–Analytical Hierarchy Process (AHP) framework that integrates wearable heart-rate sensing with spatial analysis to identify localized physiological activation patterns at urban intersections. The proposed framework is presented as a methodological proof-of-concept and is not yet validated as a decision-support tool; application to urban health assessment or smart-city planning would require testing on a substantially larger and independently sampled spatial dataset. Data were collected from ten participants across 118 repeated commuting passes by private automobile at six intersections in Kragujevac, Serbia. An AHP-weighted urban stress index combining heart rate, the traffic-intensity proxy, time of day, and acceleration events (CR = 0.0115) was computed and mapped using inverse-distance-weighted interpolation. A linear mixed-effects model showed a significant positive association between an ordinal, time-of-day-based traffic-intensity proxy and heart rate across the 118 passes (8.90 bpm per ordinal unit, p < 0.001); because this proxy is derived from time-of-day categories, the association is best interpreted as an exploratory time-of-day–heart-rate relationship rather than a validated causal effect of traffic, and a sensitivity analysis confirmed that the same three intersections ranked highest across alternative weighting scenarios. The results indicate a consistent spatial relationship between intersections associated with higher traffic-intensity proxy values and elevated physiological activation. Although based on a limited pilot-scale dataset, the proposed framework demonstrates the feasibility of combining wearable physiological sensing with GIS–AHP spatial analysis and offers a methodological proof-of-concept for smart-city and urban-health research in medium-sized cities, pending validation on larger, independently sampled spatial datasets. Full article