Search Results (6,829)

The undergraduate nursing students’ final year project, commonly called a ‘dissertation’ is an important component of the bachelor’s nursing programme. It can take the form of a literature review and proposal for a research or service improvement project. While crucial for developing research competence and evidence-based practice skills in preparation for their future careers, nursing students often find the dissertation process highly stressful. An online qualitative survey comprising open-ended questions was used to elicit nursing students’ rich, reflective accounts of the dissertation process at a university in the Northeast of England (hereafter referred to as the study site) from those who have recently completed their dissertations. The data obtained from 24 pre-registration nursing students who responded to the survey were thematically analysed. The findings revealed that critical relationships and essential support systems were key mediators of the challenges students faced, particularly a lack of readiness for the dissertation module, but they ultimately achieved transformative outcomes of an effective learning experience. Their navigational challenges can inform curriculum design and practices to better support students in their dissertation journey. Full article

Feature-based visual–inertial odometry (VIO) often suffers from initialization failures and tracking drift under degraded visual conditions, such as low-texture regions, abrupt illumination changes, and scenes with a high ratio of dynamic correspondences. We present RISE-VIO, a real-time inertial-navigation-system-centric (INS-centric) visual–inertial odometry system that improves robustness by introducing GNC-style robustification into two failure-critical stages: initialization and per-frame pose estimation. For robust initialization, we develop a GNC-based decoupled rotation–translation initialization module with a two-stage observability gate, consisting of (i) rotation-compensated parallax-rate screening and (ii) a spectral-stability test on the linear global translation (LiGT) system. For online robustness, we design an IMU-prior-guided GNC-EPnP module to selectively downweight or reject outlier correspondences during pose estimation. Experiments on public benchmark datasets show that RISE-VIO achieves more reliable initialization and more stable trajectory estimation in challenging visual conditions while maintaining real-time performance. Additional Monte Carlo perspective-n-point (PnP) evaluations further support the robustness of the proposed pose estimation module under severe outlier contamination. Full article

This study examines how lean manufacturing practices are adopted in Singapore’s SME precision and electronics manufacturing industry. Its main goal is to assess the extent of lean manufacturing method adoption and the challenges involved. The study analyzed 36 responses from 150 surveys distributed online. The results show that about 50% of manufacturers find it difficult to implement lean manufacturing practices. Our research reveals that most SMEs face significant challenges when applying lean manufacturing techniques. The findings identify barriers such as a lack of experience, skills, and knowledge, which significantly slow progress. Additionally, the study emphasizes that management support is vital for successful lean implementation. Key factors include employee training, goal alignment, and the creation of a supportive environment. While tools and external expertise are helpful, internal resources and organizational culture are considered more critical. Full article

This paper proposes a hybrid non-personalized temporal recommendation framework integrating Sliding-Window Weighted Popularity (SWWP) with Elite Evolutionary Discrete Particle Swarm Optimization (EEDPSO) to address the challenges of extreme data sparsity and temporal dynamics in global popularity-based recommendation. We first formally prove the NP hardness of the temporal-constrained recommendation problem, justifying the adoption of a metaheuristic approach. The proposed SWWP model employs a dual-scale sliding-window mechanism to balance short-term trend adaptation with long-term periodicity capture. A novel deep integration mechanism couples SWWP with EEDPSO through a “purchase heat” indicator, which guides temporal-aware particle initialization, position updates, and fitness evaluation. Extensive experiments on the Amazon Reviews dataset with extreme sparsity (density < 0.0005%) demonstrate that SWWP achieves an NDCG@20 of 0.245, outperforming nine temporal baselines by at least 13%. Furthermore, under a unified fitness function incorporating temporal prediction accuracy, the SWWP-EEDPSO framework achieves 5.95% higher fitness compared to vanilla EEDPSO, while significantly outperforming Differential Evolution and Genetic Algorithms. The temporally informed search strategy enables SWWP-EEDPSO to discover recommendations that better align with future user behavior, while maintaining sub-millisecond online query latency (0.52 ms) through offline precomputation and caching, demonstrating practical feasibility for deployment scenarios where periodic offline updates are acceptable. Full article

Virtual try-on is a key enabling technology for online fashion retail and digital garment visualization. It aims to realistically render a target garment on a person while preserving geometric alignment and fine texture details. Appearance flow-based approaches provide explicit deformation modeling but often suffer from texture squeezing and boundary artifacts in challenging scenarios, such as long sleeves and tucked-in garments, especially under high-resolution settings. In this work, we propose StageAttn-VTON (Stage-wise Attentive Virtual Try-On), an appearance flow-based framework that improves structural coherence and visual fidelity through stage-wise deformation modeling. Specifically, garment warping is decomposed into three stages—coarse alignment, local refinement, and non-target region removal—which mitigates the coupling between competing objectives, such as smooth texture preservation and accurate structural alignment. Furthermore, we introduce a self-attention module in the image synthesis stage to enhance global dependency modeling and capture long-range garment–body interactions. Experiments on VITON-HD and the upper-body subset of DressCode demonstrate that StageAttn-VTON achieves consistently strong performance against representative warping-based and diffusion-based baselines. In addition, qualitative comparisons show that the proposed method better alleviates deformation artifacts in challenging regions such as sleeves and waist areas. Full article

Accurate trajectory tracking is challenging for tracked agricultural vehicles in orchards. Uneven terrain, track slip, and vehicle posture variations are the main causes, often leading to model mismatch and degraded control performance. To address these issues, this paper proposes an improved nonlinear model predictive control (NMPC) strategy integrated with curvature feedforward compensation for trajectory tracking of tracked agricultural vehicles under uneven terrain conditions. An enhanced kinematic model based on the instantaneous center of rotation is developed by incorporating vehicle roll and pitch angles, and track slip parameters are estimated online using a Levenberg–Marquardt optimization method to improve prediction accuracy. Furthermore, curvature feedforward information derived from the reference trajectory is embedded into the NMPC objective function to provide anticipatory control inputs and reduce computational burden. Simulation results demonstrate that compared to conventional NMPC, the proposed method reduces the mean and standard deviation of tracking error by 30.28% and 32.46% respectively, while decreasing the mean and standard deviation of heading error by 37.27% and 35.05%. Concurrently, the maximum of optimize solution time is significantly reduced, effectively resolving tracking accuracy degradation caused by system solution timeouts. Field experiments conducted under different load conditions further validate that the proposed control strategy significantly reduces lateral, longitudinal, and heading tracking errors compared with conventional NMPC, confirming its effectiveness and robustness for tracked agricultural vehicle trajectory tracking in complex orchard environments. Full article

Cooperative multi-UAV pursuit–evasion under occlusions and sensor noise is challenged by intermittent observability of the evader, varying observation-window lengths, and non-stationary evader tactics, all of which destabilize prediction and undermine safety-constrained cooperation. To address these challenges, we propose a safe decision-making framework that uses behavior mode and subgoal inference as intermediate representations for interpretable, uncertainty-aware cooperation. Specifically, an observation-driven generative intent–subgoal model infers the evader’s behavior mode and subgoal from short observation windows. Building on this model, a length-agnostic trajectory predictor is trained via multi-window knowledge distillation and consistency regularization to produce future trajectory predictions with calibrated uncertainty for arbitrary observation-window lengths, thereby reducing cross-window inference inconsistency and lowering online computational cost. Based on these predictions, we derive belief and risk features and develop a belief–risk-gated hierarchical multi-agent policy based on soft actor-critic with a safety projection layer, enabling adaptive strategy switching and a controllable trade-off between efficiency and safety. Experiments in obstacle-rich pursuit–evasion environments with randomized layouts and diverse obstacle configurations demonstrate more stable cooperative capture, safer maneuvering, and lower decision variance than representative baselines, indicating strong robustness and real-time feasibility. Specifically, across different observation-window settings, the proposed method improves the normalized expected return by approximately 5–7% over the strongest baseline and reduces pursuer losses by roughly 22–25%. Moreover, its end-to-end decision latency consistently remains within the 50 ms control cycle. Full article

Background: Retrograde cricopharyngeus dysfunction (RCPD) is a recently described upper esophageal sphincter motility disorder caused by the inability of the cricopharyngeus muscle to relax, prohibiting belching. While clinical features and treatment have been reported, early-life experiences remain unclear. This study aimed to explore childhood experiences, comorbidities, and family history in adults reporting symptoms consistent with RCPD. Methods: This cross-sectional survey included adults recruited through an online community focused on RCPD who reported cardinal symptoms consistent with RCPD. The survey collected and descriptively analyzed demographics, symptom profile, family history, neonatal and childhood experiences, psychological factors, and physician visits. Results: Of 225 respondents, 207 met inclusion criteria (mean age 32 years; 69% female). Nearly all experienced abdominal bloating (98%), gurgling noises (98%), flatulence (90%), and inability to belch (100%). Painful hiccupping, a newer described symptom, was reported by 80%. Symptoms began before age 25 in 97%, and 29% reported a first-degree relative affected. Common early-life experiences included emetophobia (39%), anxiety (38%), and difficulty being burped as an infant (20%). No statistically significant crude differences were detected in symptom severity, frequency, gender, or age of onset by presence of experiences. Only 36% felt that any physician understood their condition, and 18% reported their gastroenterologist improved their symptoms. Conclusions: Psychological early-life experiences and family history were common, but exploratory analyses did not detect statistically significant differences in symptom burden by their presence. These findings provide a foundation for future studies investigating the disorder’s pathophysiology. Limited physician recognition highlights the need for greater clinical awareness of this emerging esophageal motility disorder. Full article

Online reviews are widely used to evaluate tourism performance, but it remains unclear whether platform ratings adequately reflect the underlying tourist experience. This study uses 67,744 cleaned Ctrip reviews from 112 A-level scenic spots in Liaoning Province, China, to examine what online reviews reveal beyond conventional satisfaction metrics. The final analytical sample comprises 106 threshold-qualified attractions with at least 100 reviews, supplemented by six highly reviewed sub-attractions that were listed separately on the platform but belonged to officially recognized A-level scenic systems. We combine topic modelling, sentiment analysis, and a rating–sentiment analytical framework to identify experiential dimensions, emotional patterns, and attraction-level sentiment risk. The results reveal a five-dimensional structure of tourist experience, including accessibility and ticketing, natural landscape imagery, cultural heritage interpretation, service-process quality, and overall affective appraisal. Positive sentiment is concentrated in landscape, heritage, and holistic appraisal themes, whereas negative sentiment is more prominent in accessibility and service-process dimensions. Quadrant-based analysis further shows that favourable ratings may coexist with relatively negative textual sentiment, suggesting that platform ratings and review-text sentiment do not fully converge. To extend review-level evidence to the attraction level, the study develops an attraction-level sentiment-risk indicator that captures the concentration of sentiment-negative reviews within each scenic spot. The findings suggest that online reviews function as a dual-channel evaluative system and can support sustainable destination management through more sensitive monitoring of operational friction and experiential risk. Full article

Student attrition remains a persistent challenge in higher education and is shaped by interacting socioeconomic, academic, institutional, and wellbeing-related mechanisms. Although learning analytics and educational data mining increasingly support early-warning and intervention workflows, dataset reuse is often limited by incomplete documentation and inconsistent variable definitions. This Data Descriptor presents a structured cross-sectional survey dataset on factors influencing student persistence at a Colombian public university campus (La Paz). Data were collected between August and December 2025 through an online questionnaire and subsequently cleaned to remove duplicate entries and personally identifiable information. The released dataset contains 333 student records and 33 variables covering demographics (e.g., age, gender, first-generation status), socioeconomic conditions (e.g., residential stratum, housing, financial aid), academic experience and satisfaction (multiple 1–5 Likert items), perceived dropout intention across personal/socioeconomic/academic domains, thematically coded open-ended items describing challenges and motives, and a self-allocation of 0–100 weights across three dropout-factor domains. We provide a machine-readable codebook, a transparent preprocessing description, and technical validation checks (value ranges, category consistency, and composite-score integrity). The dataset is intended to support reproducible retention research, equity-oriented analyses, and benchmarking of predictive models, while encouraging responsible reuse through privacy-preserving release practices and FAIR-aligned metadata, repository deposition, and versioning. Full article

Semantic segmentation for autonomous driving demands balancing high-fidelity perception with real-time latency. While Transformers achieve state-of-the-art results, their quadratic complexity bottlenecks high-resolution processing. State Space Models (SSMs) like Mamba offer linear complexity but often suffer from local detail loss and inefficient scanning strategies. We introduce AutoMamba, a tailored Hybrid-SSM architecture. We propose a Hybrid-SSM block incorporating Depthwise Convolutions to inject local spatial priors and a Stage-Adaptive Mixed-Scanning strategy. This strategy prioritizes horizontal context in early stages for road layouts while only activating vertical scanning in deep layers to preserve anisotropic structures like poles. Furthermore, we reveal that unlike Transformers, Mamba architectures require Auxiliary Supervision and Online Hard Example Mining (OHEM) to address “long-tail forgetting.” Experiments on Cityscapes and BDD100K under a training-from-scratch setting demonstrate AutoMamba’s superiority. Notably, AutoMamba-B0 achieves 67.79% mIoU on Cityscapes with 31.3% fewer FLOPs than SegFormer-B0. Moreover, while the larger SegFormer-B2 fails with Out-Of-Memory errors at $2048\times 2048$ resolution, AutoMamba-B2 scales efficiently, validating its linear complexity advantage for next-generation perception systems. Full article

Background: Black transgender women experience disproportionately high rates of violent victimization rooted in intersecting systems of oppression, including cisgenderism and anti-Black racism. Although victimization is linked to psychological distress, the mental health impacts of intersectional violence, which targets overlapping marginalized identities, remain understudied. Objectives: To examine the associations between anticipated and experienced intersectional victimization and psychological distress among Black transgender women. Methods: Online survey data from 151 Black transgender women (age ≥ 18) in the United States (US) between October 2021 and February 2024 were analyzed using t-tests and multivariate linear regressions. Results: In models controlling for age, employment, and US region, experienced sexual, physical, and threats of intersectional violence, as well as anticipated intersectional violence, were associated with increased post-traumatic stress disorder (PTSD) symptom severity, in separate models. Conversely, only experienced sexual intersectional violence and anticipated intersectional violence were associated with greater depressive symptom severity. When all violence variables were included simultaneously, experienced intersectional sexual violence and anticipated violence remained significantly associated with PTSD and depressive symptoms in separate models. Conclusions: Service providers who work with Black transgender women should routinely assess for anticipated and experienced intersectional victimization to guide person-centered interventions. Further research is needed to distinguish the effects of intersectional victimization from opportunistic victimization and to inform the adaptation of targeted mental health interventions. Full article

The rapid growth of online learning platforms has intensified the need for privacy-aware methods that can analyze learner behavior without centralizing sensitive activity logs. This study presents a Federated Learning-Based Graph Representation Learning (FL-GRL) framework for online student performance analysis in distributed learning environments. Each learner is represented through a local Student Learning Knowledge Graph (SLKG) that captures typed interactions with courses, lessons, webinars, challenges, and forum activities. Graph Neural Networks (GNNs) are used to derive relation-aware embeddings from these local graphs, while federated learning supports collaborative model optimization without sharing raw data. A federated clustering stage is then used to identify soft learner groups with partially overlapping behavioral patterns that may support exploratory personalization and confidence-aware educational follow-up. The current experiments focus on the feasibility of privacy-aware graph-based analysis rather than on a complete supervised prediction benchmark. Results across the evaluated graph-based variants indicate that the proposed framework is operationally viable, preserves relational structure better than flat-feature formulations, and provides an interpretable basis for learner-group discovery in privacy-sensitive online education settings. Full article

Background: As digital healthcare ecosystems evolve, doctors are no longer confined to a single platform for online medical services. Instead, a new omni-channel service mode has emerged, integrating professional medical platforms with widely used social platforms. This shift raises important questions about how doctors adopt and navigate multi-platform environments. The purpose of this study is to explore the behavioral mechanisms that shape doctors’ adoption of omni-channel online medical services in the post-pandemic era. Methods: Drawing on the Unified Theory of Acceptance and Use of Technology, complemented by channel effect and technology transfer perspectives, we conducted a survey of 958 Chinese doctors. A structural equation model was employed to test the relationships among effort expectancy, social influence, patient volume, habitual use, platform experience, future expectancy, and adoption intention. Results: The analysis revealed that effort expectancy, social influence, patient volume, and habitual use exert significant positive effects on adoption intention. Platform experience enhances doctors’ perceptions of ease of use and usefulness, while future expectancy indirectly shapes adoption intention through these perceptions. In contrast, performance expectancy no longer emerges as a decisive factor in the post-pandemic context, suggesting that external motivations may be overshadowed by practical experience and social dynamics. Moreover, doctors’ engagement with social platforms positively influences their use of professional platforms, highlighting cross-channel spillover effects that reinforce adoption across service types. Conclusions: This study extends technology adoption theory by situating doctors within dynamic, multi-platform service environments and demonstrating the importance of cross-channel influences. The findings provide practical guidance for platform designers and policymakers on how to effectively integrate professional and social platforms to enhance digital healthcare delivery. Full article

With the rapid growth of e-commerce, recommender systems have been widely adopted across diverse online services by presenting products aligned with user preferences. Moreover, review-based recommender systems have been studied to alleviate the sparsity of interaction data. However, many studies directly use full review texts, which may contain redundant semantics or noise that is irrelevant to recommendations, thereby degrading data quality and recommendation performance. To address this limitation, this study proposes summarized reviews fusion for adaptive recommendation (SuReFAR), which predicts ratings by summarizing reviews into key information using a multi-summarization strategy. Specifically, SuReFAR utilizes TextRank and bidirectional and auto-regressive transformers (BART) to generate extractive and abstractive summaries of user and item review sets, respectively. Subsequently, we apply an attention mechanism to emphasize salient information within each summary representation and fuse multiple summary representations by adaptively controlling their contributions through a gated multimodal unit (GMU) to predict ratings. We conducted experiments on Amazon and Yelp review datasets, demonstrating that the proposed model consistently outperforms baseline models and captures user preferences more effectively via personalized summary representations. Full article