Search Results (3,293)

Background: Digital transformation in healthcare has advanced rapidly in hospitals and primary care, while long-term care facilities have often lagged behind. In nursing homes, nurses play a central role in coordinating care and accessing medical expertise, yet digital tools to support these tasks remain inconsistently implemented. The CareConnect study, funded under the German Model Program for Telecare (§ 125a SGB XI), aimed to develop and implement a multiprofessional telecare system tailored to nursing home care. Objective: This implementation study examined the feasibility, acceptability, and early adoption of a multiprofessional telecare system in nursing homes, focusing on implementation processes, contextual influences, and facilitators and barriers to integration into routine nursing workflows. Methods: A participatory implementation design was employed over 15 months (June 2024–August 2025), involving a university hospital, two nursing homes (NHs), and four medical practices in an urban region in Germany. The telecare intervention consisted of scheduled video-based teleconsultations and interdisciplinary case discussions supported by diagnostic devices (e.g., otoscopes, dermatoscopes, ECGs). The implementation strategy followed the Standards for Reporting Implementation Studies (StaRI) and was informed by the Consolidated Framework for Implementation Research (CFIR). Data sources included telecare documentation, nurse surveys, researcher observations, and structured feedback discussions. Quantitative and qualitative data were analyzed descriptively and triangulated to assess implementation outcomes and mechanisms. Results: A total of 152 documented telecare contacts were conducted with 69 participating residents. Most interactions occurred with general practitioners (48.7%) and dermatologists (23%). Across all contacts, in 79% of cases, there was no need for an in-person visit or transportation. Physicians rated most cases as suitable for digital management, as indicated by a mean of 4.09 (SD = 1.00) on a 5-point Likert scale. Nurses reported improved communication, time savings, and enhanced technical and diagnostic skills. Key challenges included delayed technical integration, interoperability issues, and varying interpretations of data protection requirements across facilities. Conclusions: This pilot study suggests that telecare can be feasibly introduced and accepted in nursing home settings when implemented through context-sensitive, participatory strategies. Implementation science approaches are essential for understanding how telecare can be sustainably embedded into routine nursing home practice. Full article

The present work explores the effects of dental splints from a mechanical standpoint, aiming to provide a practical tool for the surgical decision-making process regarding splint cross-section dimensions. Our investigation centers on the anatomical structure of a pentamorphic dental arch encompassing central and lateral incisors and one canine on each side. Using parametric in silico models built up by means of an ad-hoc procedure, geometry, material properties, and boundary conditions are defined on a parametric anatomical model that can be tailored using RX-derived geometrical information. Two general cases have been considered, one with the splint and the other splintless, and a sensitivity analysis has been performed by varying the splint section height and thickness. The results show the diminishing mobility at the apex and basis of the diseased incisors, demonstrating the effectiveness of the periodontal treatment. Moreover, the stress due to physiological loads moves away from diseased teeth toward the healthy ones due to the splint effects, focusing on the splint–glue–canine contact zone and highlighting the crucial role played by the canine in fixing the entire dental structure. To establish a preliminary mechanical assessment of the dental structure’s safety and to confine its actual value within a mechanically reasonable range, a synthetic “traffic-light” indicator of stress-based failure risk is proposed. It is felt that the tool proposed in this study can help surgeons assess the pre-operative patient-specific mechanical effects of the splint treatment, driving the design and choice of periodontal splints. By linking splint geometry to mechanical safety via a stress-based indicator, the method supports the optimized design and selection of splints, improving treatment reliability while preserving comfort and clinical effectiveness. Full article

Precise identification of weeds at the maize seedling stage is pivotal for implementing Site-Specific Weed Management and minimizing herbicide environmental pollution. However, the performance of existing lightweight detectors is severely bottlenecked by unstructured field environments, characterized by the “green-on-green” spectral similarity between crops and weeds, diminutive seedling targets, and complex mutual occlusion of leaves. To address these challenges, this study proposes SLD-YOLO11, a topology-reconstructed lightweight detection model tailored for complex field environments. First, to mitigate the feature loss of tiny targets, a Lossless Downsampling Topology based on Space-to-Depth Convolution (SPD-Conv) is constructed, transforming spatial information into depth channels to preserve fine-grained features. Second, a Decomposed Large Kernel Attention (D-LKA) mechanism is designed to mimic the wide receptive field of human vision. By modeling long-range spatial dependencies with decomposed large-kernel attention, it enhances discrimination under severe occlusion by leveraging global structural context. Third, the DySample operator is introduced to replace static interpolation, enabling content-aware feature flow reconstruction. Experimental results demonstrate that SLD-YOLO11 achieves an mAP@0.5 of 97.4% on a self-collected maize field dataset, significantly outperforming YOLOv8n, YOLOv10n, YOLOv11n, and mainstream lightweight variants. Notably, the model achieves Zero Inter-class Misclassification between maize and weeds, establishing high safety standards for weeding operations. To further bridge the gap between visual perception and precision operations, a Visual Weed-Crop Competition Index (VWCI) is innovatively proposed. By integrating detection bounding boxes with species-specific morphological correction coefficients, the VWCI quantifies field weed pressure with low cost and high throughput. Regression analysis reveals a high consistency (R² = 0.70) between the automated VWCI and manual ground-truth coverage. This study not only provides a robust detector but also offers a reliable decision-making basis for real-time variable-rate spraying by intelligent weeding robots. Full article

Extended Kalman Filters (EKFs) are widely employed for attitude estimation using Magnetic and Inertial Measurement Units (MIMUs) in battery-powered sensing systems. In such applications, energy availability influences system operation, yet battery state information is commonly treated by external supervisory mechanisms rather than being integrated into the estimation process. This work presents an EKF-based formulation in which the battery State of Charge (SOC) is explicitly included as a state variable, allowing joint estimation of attitude and energy state within a single filtering framework. SOC dynamics are modeled using a low-complexity estimator based on terminal voltage and current measurements, while attitude estimation is performed using a Simplified Extended Kalman Filter (SEKF) tailored for embedded MIMU-based applications. The proposed approach was evaluated through numerical simulations under constant and time-varying load profiles representative of low-power electronic devices. The results indicate that the inclusion of SOC estimation does not affect the attitude estimation performance of the original SEKF, while SOC estimation errors remain below 8% for the evaluated load conditions with power consumption of approximately 0.1 W, consistent with wearable and small autonomous electronic platforms. By incorporating energy state estimation directly into the filtering structure, rather than treating it as an external supervisory task, the proposed formulation offers a unified estimation approach suitable for embedded MIMU-based systems with limited computational and energy resources. Full article

This article analyses the professional routines of health journalists in Spain, and their role in tackling disinformation in health reporting targeted at people over the age of 74. It is based on the premise that this age group, being highly exposed to health issues and particularly vulnerable to health-related misinformation, requires content that is tailored, reliable and easy to understand. The research adopts an exploratory-descriptive approach through a self-administered questionnaire addressed to health journalists belonging to professional associations and working in both general and specialist media outlets. As this is an ongoing study, the preliminary results indicate that these professionals report applying rigorous verification mechanisms, which suggests a trend within the surveyed group towards the consolidation of practices against disinformation. The findings also reveal a preference for informative styles that avoid sensationalism and prioritise clarity, although there remains a tendency towards high-impact topics and those linked to media figures. In contrast, attention to the informational needs of older adults is limited and addressed only occasionally. The study concludes that, although the interviewed professionals consider that health journalism in Spain maintains high standards of rigor, it still faces the challenge of systematically adapting its communicative practices to the needs of vulnerable audiences. Full article

With the rapid advancement of autonomous driving technology, Traffic Sign Detection and Recognition (TSDR) has become a critical component for ensuring vehicle safety. However, existing TSDR systems still face significant challenges in accurately detecting partially occluded traffic signs, which poses a substantial risk in real-world applications. To address this issue, this study proposes a comprehensive solution from three perspectives: data augmentation, model architecture enhancement, and dataset construction. We propose an innovative network framework tailored for occluded traffic sign detection. The framework enhances feature representation through a dual-path convolutional mechanism (DualConv) that preserves information flow even when parts of the sign are blocked, and employs a spatial attention module (SEAM) that helps the model focus on visible sign regions while ignoring occluded areas. Finally, we construct the Jinzhou Traffic Sign (JZTS) occlusion dataset to provide targeted training and evaluation samples. Extensive experiments on the public Tsinghua-Tencent 100K (TT-100K) dataset and our JZTS dataset demonstrate the superior performance and strong generalisation capability of our model under occlusion conditions. This work not only advances the robustness of TSDR systems for autonomous driving but also provides a valuable benchmark for future research. Full article

People living with and beyond cancer often face ongoing challenges related to nutrition, wellbeing, and long-term health. Many individuals express a need for evidence-based, tailored dietary support, yet practical approaches to sustaining healthy eating behaviours remain limited. Culinary nutrition interventions, which integrate nutrition education with hands-on culinary skills, may help address these needs; however, their effects have not been systematically synthesised. This systematic review evaluates the impact of culinary nutrition interventions, delivered alone or in combination with physical activity or mental health components, on dietary intake, psychosocial and health-related outcomes, anthropometric measures, clinical and metabolic markers, and feasibility among individuals living with or beyond cancer. Following PRISMA guidelines, 18 studies were identified across PubMed, Scopus, EMBASE, CINAHL, and Web of Science (last searched in April 2025) and narratively synthesised. A total of 1173 participants were included, with sample sizes ranging from 4 to 190 participants per intervention. Interventions were well received and rated as highly acceptable, with strong engagement and minimal adverse effects. Across studies, statistically significant improvements were reported in dietary intake (7/13 studies), quality of life (4/5), mental health (5/6), self-efficacy (2/3), symptom management (3/4), self-reported cognitive health (1/1), food-related behaviours (2/2), selected anthropometric measures (4/8), and selected metabolic biomarkers (4/6). The evidence suggests that culinary nutrition interventions hold promise as supportive, behaviour-focused strategies aligned with oncology nutrition guidelines and responsive to patient needs. However, due to heterogeneity across interventions and outcomes, and variability in methodological quality as assessed using the Cochrane risk of bias tool, quantification of effects was not possible, limiting interpretation of the evidence. Further high-quality studies using comparable outcome measures and longer-term follow-up are needed to quantify the magnitude of effects, assess their durability over time, and inform the integration of culinary nutrition programmes into cancer care. This systematic review is registered under the PROSPERO ID CRD42024567041 and was funded by the RCSI Research Summer School Fund. Full article

The management of rheumatoid arthritis (RA) has undergone several practice-defining evolutions, beginning with the approval of low-dose methotrexate and continuing through the introduction of numerous disease-modifying antirheumatic drugs (DMARDs). With increasing capability to target pro-inflammatory pathways, successive therapeutics have carried the promise of improved disease control for patients with RA; however, many patients still fail to meet treatment objectives, leading to the recognition of clinical phenotypes that remain therapeutically challenging under the current treat-to-target standard of care, including preclinical inflammatory arthritis, late-onset RA, and treatment-resistant RA. Precision medicine approaches are beginning to characterize the pathogenesis of RA in such populations, and to inform effective tailoring of DMARD therapy to individual patients. Simultaneously, observational data derived from clinical practice are increasingly being used to understand the risks and benefits of long-term DMARD therapy under real-world conditions of use, with registries and other observational sources confirming long-term effectiveness, revising safety profiles, and estimating the costs of treatment for approved therapies. Together, these strategies offer opportunities to address unmet needs in the care of patients with RA. In this review of peer-reviewed clinical and translational research in RA, we identify several clinical phenotypes that demonstrate inadequate response to guideline-directed therapy and review frontiers in clinical research in RA emerging over the last decade, highlighting the use of precision medicine and real-world evidence-based approaches to advance individualized, patient-centered care. Full article

Background/Objectives: The Thriving at Work model proposes that organisations have a responsibility to provide supportive work environments that identify individual health outcomes, which organisations can use to determine where workforce support is needed. The aims of this study are to (1) identify and compare the predictors of early-career nurses’ thriving at work in New Zealand and Australia and (2) provide innovative and theory-informed recommendations to improve organisational support of early-career nurses to increase retention in the profession. Design: A multinational cross-sectional study design was followed. Methods: The methods include a sub-study of an international action research programme to support the thriving of early-career nurses, which evaluates and compares results from surveys of nurses at approximately three months post-registration in 2024 and 2025. A theory-informed survey assesses predictors and outcomes of thriving at work. Results: Early-career nurses (N = 320) from New Zealand (n = 277) and Australia (n = 43) completed the survey. New Zealand early-career nurses experience greater quality of care and authenticity at work; however, they also report greater burnout. For Australian early-career nurses, authenticity at work is the greatest predictor of thriving. In New Zealand, thriving is linked to burnout and colleague support. Conclusions: New Zealand must focus on reducing burnout and fostering workplaces that value social connection if it wants to mitigate early-career nurse attrition to Australia for better working conditions. In Australia, the value of authenticity at work highlights the importance of organisational cultures that enable nurses to express their true selves and professional identity. The findings highlight the need for tailored approaches in each country to strengthen workforce sustainability and improve nurse wellbeing. Implications for the Profession: In New Zealand, additional funding to bolster the recruitment and retention of the nursing workforce is crucial to improve patient ratios and reduce workloads. The remuneration of nurses must also remain competitive with Australia. Additionally, workplaces should incorporate Māori values and practices into workplace policies to strengthen social connections. Australian organisations should include authentic management training, psychological safety initiatives, and policies that value diversity and encourage open communication. Full article

To address the challenges of accurately extracting features from elongated scratches, irregular defects, and small-scale surface flaws on high-precision linear guide rails, this paper proposes a novel instance segmentation algorithm tailored for guide rail surface defect detection. The algorithm integrates the YOLOv8 instance segmentation framework with deformable convolutional networks and multi-scale feature fusion to enhance defect feature extraction and segmentation performance. A dedicated guide rail surface Defect (GSD) segmentation dataset is constructed to support model training and evaluation. In the backbone, the DCNv3 module is incorporated to strengthen the extraction of elongated and irregular defect features while simultaneously reducing model parameters. In the feature fusion network, a multi-scale feature fusion module and a triple-feature encoding module are introduced to jointly capture global contextual information and preserve fine-grained local defect details. Furthermore, a Channel and Position Attention Module (CPAM) is employed to integrate global and local features, improving the model’s sensitivity to channel and positional cues of small-target defects and thereby enhancing segmentation accuracy. Experimental results show that, compared with the original YOLOv8n-Seg, the proposed method achieves improvements of 3.9% and 3.8% in Box and Mask mAP₅₀, while maintaining a real-time inference speed of 148 FPS. Additional evaluations on the public MSD dataset further demonstrate the model’s strong versatility and robustness. Full article

The World Health Organization (WHO) and International Consensus Classification (ICC) systems have classified EBV-positive NK/T-cell neoplasms in adults and EBV-positive T/NK-cell lymphoid lymphoproliferative disorders (LPD) in children. Recent molecular profiling techniques have revealed the pathogenesis of these disorders, showing interactions among EBV-encoded proteins, host immune responses, and genetic alterations. Extranodal NK/T-cell lymphoma (ENKTL) shows molecular diversity, with various subtypes (TSIM, MB, and HEA) identified through a multiomics approach. Aggressive NK-cell leukemia (ANKL) has mutations in JAK/STAT, epigenetic regulators, and TP53 pathways. EBV-positive nodal T- and NK-cell lymphoma (ENTNKL) is a new entity, distinguished by primary nodal presentation and a unique molecular profile. Severe mosquito bite allergy (SMBA), hydroa vacciniforme lymphoproliferative disorder (HVLPD), and systemic chronic active EBV disease (CAEBV) are rare childhood EBV-driven LPDs defined by clinico-pathologic criteria, with largely unexplored genomic landscapes. Studies of CAEBV samples have found ENKTL-like driver mutations, including DDX3X and KMT2D, in EBV-infected NK/T cells, while KMT2D and chromatin modifier mutations were common in HVLPD. Comprehensive molecular sequencing of SMBA and Systemic EBV-positive T-cell lymphoma of childhood remains lacking. These findings suggest all EBV⁺ NK/T-cell LPDs exist on a biological continuum of viral oncogenesis. The integration of clinical, pathological, and molecular information aims to create a more accurate classification system, enabling better risk evaluation and tailored treatment strategies for patients with these complex disorders. Full article

This study examines how different programming paradigms are associated with learning experiences and cognitive challenges as encountered by non-computer science novice learners. Using a case-study approach situated within specific instructional contexts, we integrate survey data from undergraduate students with large-scale public question-and-answer data from Stack Overflow to explore paradigm-related difficulty patterns. Four instructional contexts—C, Java, Python, and Prolog—were examined as pedagogical instantiations of imperative, object-oriented, functional-style, and logic-based paradigms using text clustering, word embedding models, and interaction-informed complexity metrics. The analysis identifies distinct patterns of learning challenges across paradigmatic contexts, including difficulties related to low-level memory management in C-based instruction, abstraction and design reasoning in object-oriented contexts, inference-driven reasoning in Prolog-based instruction, and recursion-related challenges in functional-style programming tasks. Survey responses exhibit tendencies that are broadly consistent with patterns observed in public Q&A data, supporting the use of large-scale community-generated content as a complementary source for learner-centered educational analysis. Based on these findings, the study discusses paradigm-aware instructional implications for programming education tailored to non-major learners within comparable educational settings. The results provide empirical support for differentiated instructional approaches and offer evidence-informed insights relevant to curriculum-oriented teaching and future research on adaptive learning systems. Full article

Bananas underpin Uganda’s food security and rural economy, but productivity is declining due to emerging pests, diseases, and declining soil fertility. To address these challenges, hybrid stress-tolerant banana varieties (HBVs) have been developed and released, but their adoption remains uneven across the country. This study analyzes the spatial distribution and determinants of HBV adoption and intensity in Uganda, providing new insights to inform scaling strategies. A cross-sectional survey of 624 banana-farming households was conducted across 24 districts in both traditional and non-traditional banana-growing regions. Data were analyzed using descriptive statistics and a Tobit regression model to capture both the binary decision to adopt and the intensity of adoption, measured as the number of HBV mats planted. Results showed significant regional variation; adoption was highest in Northern Uganda (73.9%) and lowest in Central and Southwestern regions (≈24%). Education and land size positively influenced adoption, while reliance on planting materials from fellow farmers consistently reduced adoption intensity across all regions. Gender and household structure also shaped adoption patterns, with male and married farmers more likely to plant larger areas of HBVs. The findings highlight the need for regionally tailored interventions, including strengthening formal seed systems, enhancing farmer knowledge, and addressing gender gaps in technology access. Strengthening institutional seed channels and extension support can accelerate HBV scaling and contribute to resilient banana production in Uganda. Full article

Two novel spacetimes are introduced in this work as anisotropic preferred reference frames tailored for applications in relativistic quantum information systems. The resulting anisotropic geometry arises intrinsically from the underlying algebraic structure of spin matrices rather than being imposed through external prescriptions, background fields, or perturbative approximations. The associated Lorentz factors, symmetry group structure and metric-preserving transformations are systematically analyzed within the context of relativistic quantum information theory. Full article

Physics-informed neural networks (PINNs) have recently attracted considerable attention as a framework for solving partial differential equations. Underwater sound-field prediction fundamentally relies on solving acoustic wave equations, making PINNs a natural candidate for this application. This paper reviews recent developments in PINN-based modeling of underwater acoustic propagation, which we group into two main lines of research. The first introduces mathematically motivated simplifications of the governing equations and then employs PINNs as efficient solvers; examples include ray-based PINNs and PINN estimators of modal wavenumbers. The second focuses on improving computational performance by tailoring network architectures and hyperparameters, such as spatial domain-decomposition strategies. While PINNs demonstrate significant potential, challenges persist regarding computational efficiency and convergence in high-frequency regimes. Future research directions are identified, emphasizing a multi-faceted strategy that systematically addresses limitations at both the physical formulation level and the neural network architecture level. By integrating advanced hybrid physics-data modeling and scalable training algorithms, this review highlights the pathway toward bridging the gap between theoretical frameworks and realistic ocean applications. Full article