Search Results (172)

The agrarian sector, as the key source of livelihood in Sub-Saharan Africa (SSA), has become highly vulnerable to changes in extension service deliveries. Farmers mainly lack access to technical advice, financial credits, farming inputs and mechanization tools while environmental challenges reinforce the adaptation of sustainable management practices. Therefore, an understanding how multi-functional actor relationships determine agricultural knowledge and information (AKI) sharing is required. This study contributes to filling this gap by characterizing horizontal and vertical interactions. By applying a social network analysis, we mapped actor relations along public–private-community co-operations to provide insights into structural dependencies at different administrative levels. Related to three sites distributed over Burkina Faso and Ghana, local perceptions were collected in stakeholder workshops to generate social network narratives. These narratives were analyzed by various metrics to identify patterns of partnerships and key actors. Study results reveal for Burkina Faso a slight shared network topology, while both sites in Ghana reflect a top-down flow of AKI. The statistical findings indicate that agricultural extension services are primarily delivered to farmers through a few key actors such as NGOs and farm-based organizations/cooperatives. Especially at the community level, the results show many reciprocal links between farmers, business actors and NGOs. This highlights a shift toward a pluralistic agricultural extension service system and underpins the demand for policies to support the long-term viability of these actors, in particular for regions where public extension agents are under-represented. Full article

YouTube is a de facto learning environment for athletes seeking fast, actionable nutritional guidance, yet platform dynamics may favor simplified or testimonial narratives over evidence-aligned messages. This study maps Spanish-language sports-nutrition videos to clarify who is most visible, how advice is framed, and what users encounter first. We conducted a cross-sectional, mixed-methods study of 558 YouTube videos on pre/post-exercise nutrition and supplementation. Data was coded for video types (divulgation/testimonial), claim presence, evidence links, and creator status (professional/non-professional). Exposure-adjusted metrics (View Ratio, Viewer Interaction) and nonparametric tests summarized distributions. An undirected network generated centrality rankings to select qualitative samples. Thematic analysis of titles and descriptions identified recurring rhetorical patterns and discourse modes. Divulgation videos predominated (97.3%). Evidence links were rare (0.2%). Exposure and interaction were right-skewed, indicating concentrated visibility. Non-professionals produced most videos, with older uploads and higher daily view accrual; however, interaction per view was similar across groups. Qualitative synthesis revealed two dominant discourse modes, scientific–cautious and experience–testimonial. Oversimplification and motivational cues clustered in testimonial/non-professional items; instructional language and scarce evidence links concentrated in professional/divulgation items. In Spanish sports-nutrition content, visibility is concentrated, and creator identity shapes advice framing. Evidence-aligned messages can compete when expressed with clear athletic framing, explicit caveats, and links to trustworthy sources. Full article

English argumentative writing is a cornerstone of academic and professional communication, yet it remains a significant challenge for second-language (L2) learners. While Large Language Models (LLMs) show promise as components in automated feedback systems, their responses are often generic and lack the structural insight necessary for meaningful improvement. Existing Automated Essay Scoring (AES) systems, conversely, typically provide holistic scores without the kind of actionable, fine-grained advice that can guide concrete revisions. To bridge this systemic gap, we introduce ARGUS (Argument Understanding and Structured-feedback), a novel neuro-symbolic system that synergizes the semantic understanding of LLMs with the structured reasoning of Graph Neural Networks (GNNs). The ARGUS system architecture comprises three integrated modules: (1) an LLM-based parser transforms an essay into a structured argument graph; (2) a Relational Graph Convolutional Network (R-GCN) analyzes this symbolic structure to identify specific logical and structural flaws; and (3) this flaw analysis directly guides a conditional LLM to generate feedback that is not only contextually relevant but also pinpoints precise weaknesses in the student’s reasoning. We evaluate ARGUS on the Argument Annotated Essays corpus and on an additional set of 150 L2 persuasive essays collected from the same population to augment training of both the parser and the structural flaw detector. Our argument parsing module achieves a component identification F1-score of 90.4% and a relation identification F1-score of 86.1%. The R-GCN-based structural flaw detector attains a macro-averaged F1-score of 0.83 across the seven flaw categories, indicating that the enriched training data substantially improves its generalization. Most importantly, in a human evaluation study, feedback generated by the ARGUS system was rated as consistently and significantly more specific, accurate, actionable, and helpful than that from strong baselines, including a fine-tuned LLM and a zero-shot GPT-4. Our work demonstrates a robust systems engineering approach, grounding LLM-based feedback in GNN-driven structural analysis to create an intelligent teaching system that provides targeted, pedagogically valuable guidance for L2 student writers engaging with persuasive essays. Full article

Background/Objectives: Early melanoma management depends on rapid triage and definitive treatment. Image-enabled teledermatology, particularly teledermoscopy, may accelerate pathways without compromising safety. This review evaluated effects on timeliness, diagnostic performance, and initial prognostic severity at diagnosis. Methods: Following a preregistered PRISMA-2020 protocol, PubMed/MEDLINE, Embase, and Scopus were searched from inception to 1 September 2025. Comparative clinical studies and service evaluations reporting melanoma-specific outcomes were eligible. Dual screening, duplicate data extraction, and risk-of-bias appraisal were performed; substantial clinical and methodological heterogeneity precluded quantitative meta-analysis, so findings were synthesized narratively. Results: Twelve studies met criteria. Front-end intervals consistently shortened: time to expert advice was ≈1 day and median time to first clinic was reduced by ~10 days versus conventional referral. Downstream milestones showed mixed translation, with time to excision and histology in several programs remaining ≈45–67 days, indicating capacity constraints beyond triage. Diagnostic safety was high where quantified: melanoma triage sensitivity approached ~99% with a false-negative rate near ~1.1%, and positive predictive value for suspected-melanoma referrals was ~45–50% in mature services. Selected observational comparisons suggested a ≈0.6 mm lower mean Breslow thickness and ~13-percentage-point higher in situ/T1 proportion in one TD network, although these findings are susceptible to selection and lead-time bias and are not consistent across all settings. Overall, teledermoscopy functioned as a “front-door accelerator,” with pathway gains being largest before surgical and pathology steps. Conclusions: Teledermatology/teledermoscopy reliably compresses referral-to-decision intervals and maintains high triage safety, with indications of stage migration toward thinner tumors at diagnosis. Integrating surgical and pathology capacity is essential to convert early advice into earlier definitive treatment. Full article

This review will focus on the structure and role of the ECM in physiological conditions and pathological alterations, based on a cardiological case. The patient has a particular case of connective tissue disease (CTD), specifically bicuspid aortic valve type (BAV). The presented clinical case is as follows: a 34-year-old patient has been diagnosed with BAV. The subject is concerned about how his condition may affect his daily life. The subject is worried about passing the disease on to his children. He asked experts for advice on the causes, possible consequences and treatments. BAV is a major congenital heart defect, affecting 1–2% of the global population. This review provides an overview of the structure and function of the ECM, which plays an important role in the architecture of heart valves and vascular structures associated with connective tissue disease. The BAV has been observed to affect the connective tissue, although the underlying causes remain unclear. ECM is a 3-dimensional network of macromolecules that provides structural support for cells and tissues. Extensive research has established the regulatory functions of ECM, given its role in orchestrating cell signalling, functions, properties and morphology. Extracellular and cell-bound factors represent a substantial proportion of the major constituents of the ECM. The following proteins and glycoproteins are of particular interest: collagen, elastin, laminins, tenascins, proteoglycans, glycosaminoglycans and hyaluronan. Relevant cell receptors include CD44. Full article

Background and Objectives: Evaluating osteoarthritis (OA) care quality is increasingly relevant for service improvement and benchmarking purposes. The Osteoarthritis Quality Indicator questionnaire (OA-QI) measures patient-reported guideline-concordant care; however, no version has been tested in Italian primary care or bilingual contexts. This study aimed to introduce the OA-QI version 3 (OA-QI v3) in German and Italian, assess its applicability in practice, and examine its acceptability and reliability. Materials and Methods: A cross-sectional survey was conducted using the South Tyrolean General Practice Research Network. Thirty-eight general practitioners recruited 266 patients with hip or knee OA. Patients completed the OA-QI v3 in German or Italian, with subsamples for comprehensibility testing (n = 38) and retest reliability after 14 days (n = 36). Test–retest reliability was analyzed using percent agreement, Cohen’s κ, intraclass correlation coefficients (ICC), and standard error of measurement. The smallest detectable change was analyzed to estimate factual change. Results: Response rate reached 95% of the targeted patients. Patient feedback showed good comprehensibility and ease of use in both languages. Adherence to recommended quality indicators varied, with strengths in physical activity advice, NSAID prescription, and pain assessment, but gaps in weight management, occupational counseling, and assistive devices. Test–retest reliability ranged from fair to substantial at the item level (κ = 0.33–0.69) and was moderate for the total score (ICC = 0.55, 95% CI 0.28–0.74). While measurement error restricted individual-level interpretation, reliability at the practice or institutional level supports application for benchmarking and quality monitoring. Conclusions: The OA-QI v3 was feasible, acceptable, and reliable for group-level assessments in South Tyrol. These findings position OA-QI v3 as a practical tool for identifying care gaps and guiding quality improvement, while providing important lessons for the full validation of the German and Italian versions in larger cross-national samples. Full article

Agricultural management in Europe faces increasing pressure to reduce its environmental footprint. Implementing precision agriculture for weed management could offer a solution and minimize the use of chemical products. High spatial resolution imagery from real time kinematic (RTK) unmanned aerial vehicles (UAV) in combination with supervised convolutional neural network (CNNs) models have proven successful in making location specific treatments. This site-specific advice limits the amount of herbicide applied to the field to areas that require action, thereby reducing the environmental impact and inputs for the farmer. To develop performant CNN models, there is a need for sufficient high-quality labelled data. To reduce the labelling effort and time, a new labelling method is proposed whereby image subsection pairs are labelled based on their relative differences in weed pressure to train a CNN ordinal regression model. The model is evaluated on detecting weed pressure in potato (Solanum tuberosum L.). Model performance was evaluated on different levels: pairwise accuracy, linearity (Pearson correlation coefficient), rank consistency (Spearman’s (r_s) and Kendal (τ) rank correlations coefficients) and binary accuracy. After hyperparameter tuning, a pairwise accuracy of 85.2%, significant linearity (r_s = 0.81) and significant rank consistency (r_s = 0.87 and τ = 0.69) were found. This suggests that the model is capable of correctly detecting the gradient in weed pressure for the dataset. A maximum binary accuracy and F1-score of 92% and 88% were found for the dataset after thresholding the predicted weed scores into weed versus non-weed images. The model architecture allows us to visualize the intermediate features of the last convolutional block. This allows data analysts to better evaluate if the model “sees” the features of interest (in this case weeds). The results indicate the potential of ordinal regression with relative labels as a fast, lightweight model that predicts weed pressure gradients. Experts have the freedom to decide which threshold value(s) can be used on predicted weed scores depending on the weed, crop and treatment that they want to use for flexible weed control management. Full article

In the context of digital school development, the leadership practices of school leadership teams play a significant role. If leadership teams want to enact leadership practices effectively, they require strong connections to the entire teaching staff as well as close contact with other key actors in the digital process. Since little is known about these connection patterns of school leadership teams, this study aims to uncover them. The aim is to provide practical advice to school administrators and schools regarding digital school development, and to derive concrete recommendations for action concerning their relationships and management. To this end, we examined the social networks of the teaching staff of 13 German secondary schools (N = 817 teachers) by asking all the teachers to complete a questionnaire about their contacts in relation to digital school development. We conducted a social network analysis and extracted various network metrics pertaining to the school leadership teams of these institutions, considering not only their integration within the overall network but also their connections with a pivotal stakeholder: the digital coordinator. To contextualize our findings, we compared the network metrics of the two different professional target groups using t-tests. The results reveal significant variability in the connection patterns of school leadership teams across different schools. Furthermore, our analysis indicates that digital coordinators consistently exhibit higher levels of connectedness within the realm of digital school development than the members of the school leadership teams. These findings highlight the importance of close collaboration between school leadership teams and the digital coordinator in order to advance digital school development. It is also suggested that school leadership teams should consider delegating more responsibilities to the digital coordinator, particularly those necessitating close collaboration with the teaching staff. Full article

This study examines industrial digitalization, with a particular focus on the transformation from Industry 4.0 to Industry 5.0. The research is based on a database of 1441 Scopus-indexed articles, which forms the basis of a systematic literature review and bibliometric network analysis. The articles were ranked using Global Citation Score (GCS), followed by Co-Coupling Network (CCN) within VosViewer, the method to create arrays. The arrays were analyzed based on the connection strengths of the citations in them. Next, we performed Burst Detection using the CiteSpace app. Finally, the most relevant keywords, determined in the Burst Detection, were used for Co-Occurrence Network (CONK), with which we could create new arrays and analyze them. By connecting the various, fragmented scientific findings, our results highlight that digital twins, artificial intelligence, supply chain resilience and the Internet of Things are the focus of Industry 4.0, i.e., the technological side is dominant. In contrast, Industry 5.0 places employees at the center. It also emphasizes the analysis of human–machine interaction and the importance of green digital sustainability. The results provide a comprehensive picture of how decision-makers, researchers, and professionals can interpret a changing mindset and apply it as practical advice. Full article

Based on SWOT and TOWS analyses and combined with expert interviews, this study proposes a series of marketing strategies to enhance consumers’ willingness to purchase used electric vehicles (UEVs). In terms of the strengths and opportunities (SO) strategy, it is recommended that enterprises strengthen marketing and brand building, customize services and special features, use price advantages and environmental awareness to attract specific groups, provide convenient charging services, give full play to technical support advantages, and expand channels through cooperation with the government and manufacturers. The strategies for the strengths and threats (ST) scenario include establishing a government relations department, improving product quality and brand image, enhancing information transparency and quality assurance, and building a partner network and customer relationships. In terms of weaknesses and opportunities (WO), it is proposed to transform corporate weaknesses into opportunities by investing in evaluation technology and expanding charging facilities, strengthening market promotion and consumer education, and providing personalized car purchase advice and high-quality after-sales services. In the face of weaknesses and threats (WT), the emphasis is on reducing risks and improving competitiveness by improving quality management, internal management, and providing long-term after-sales and warranty services. The main innovation of this study lies in integrating SWOT-TOWS strategic frameworks with qualitative expert insights to develop actionable and scenario-specific marketing strategies for the UEV market—an area previously underexplored in existing literature. The comprehensive strategy proposed in this study provides a practical path for UEV companies to enhance consumer trust and purchase willingness and promote the industry’s sustainable development. Full article

As veganism becomes more popular, the number of vegan pregnant women and children is steadily increasing. During vegan pregnancy and early childhood, there is a high risk for nutrient deficiencies that may impair child development. External factors, such as healthcare advice, social networks, and social environments, that affect the diet of vegan pregnant women, parents, and their children, as well as their approach towards dietary supplementation, have not yet been investigated. Various sources of information, combined with a lack of expertise, sparse food and nutritional health literacy, and qualitatively heterogeneous information provision by medical experts, unsettle vegan pregnant women and parents and affect their dietary choices and potentially the health of their children. The VedieS study aims to investigate potential connections between external influences and associated impacts on a vegan diet and the intake of dietary supplements (DS) of pregnant women and children. Two surveys are being conducted within the study: one targeting 1000 vegan pregnant women and parents, and another targeting 60 experts in each of five healthcare groups: gynecologists, pediatricians, general practitioners, pharmacists, and dietitians. This study is the first to examine how socio-economic, social, and further informational factors influence dietary practices during vegan pregnancy and childhood. It highlights the need for reliable, expert-led guidance, as current information sources are often inconsistent and may put these vulnerable groups at risk. Full article

In recent years, DC grids have gained traction, and several proposals regarding measuring strategies and several Power Quality (PQ) parameters have been defined to be used in such networks that differ from traditional AC power grids. As a complement to all this preliminary work, this study on the effect of modifying the sampling frequency on some of those parameters has been conducted. For time series evaluation of mean and RMS voltage values, the Dynamic Time Warping (DTW) algorithm has been used. Additionally, the consequence of varying the sampling rate in voltage event detection has also been analysed. As a result, relevant advice regarding sampling frequency is presented in this paper for an effective and optimum evaluation of RMS or mean voltage values and its implementation in detecting voltage events (dips or swells). At least for the parameters in the monitored DC microgrid, a clue for the minimum sampling rate that guarantees accurate measurements is found. Full article

Sport-related injuries are common presentations to primary care and hospital settings. Australian osteopaths practice mainly in private clinical settings in which the frequency of sport-related injury presentations, and how these injuries are managed, is unknown. The objective of the study was to describe the demographic, practice, and clinical management characteristics of Australian osteopaths who report often treating sport-related injuries. The study is a secondary analysis of data derived from the Australian osteopathy practice-based research network. Respondents indicated the frequency treating sports-related injuries in addition to other demographic, practice, and patient management characteristics. Backward logistic regression identified significant characteristics associated with often treating sport injuries. Over half (51%) of a nationally representative sample of Australian osteopaths reported treating sport-related injuries often. Those osteopaths who treat sports injuries often were likely to be male (p < 0.01) and utilise exercise prescription (OR2.34) and sports taping (OR5.99). Australian osteopaths who often treat sports-related injuries provide advice to patients and use exercise prescription more frequently than osteopaths who do not treat these injuries often. The data in the current work begin to explore how osteopaths manage sports-related injuries and highlights how they may be able to provide sports injury care for both recreational and elite sport populations. Full article

As the cradle of Jiangnan culture, Suzhou is home to a dense concentration of historical architectural heritage that is currently facing existential threats from rapid urbanization. This study aims to develop a spatial heritage corridor network for conservation and sustainable utilization. Using kernel density estimation, this study identifies 15 kernel density groups, along with the Analytic Hierarchy Process (AHP), to pinpoint clusters of historical architectural heritage and assess the involved resistance factors. Current Effective Conductance (CEC) theory is further applied to model spatial flow relationships among heritage nodes, leading to the delineation of 27 heritage corridors and revealing a spatial structure characterized by one primary core, one secondary core, and multiple peripheral zones. Based on 15 source points, six cultural relics-themed routes are proposed—three land-based and three waterfront routes—connecting historical sites, towns, and ecological areas. The study further recommends a resource management strategy centered on departmental collaboration, digital integration, and community co-governance. By integrating historical architectural types, settlement forms, and ecological patterns, the research builds a multi-scale narrative and experience system that addresses fragmentation while improving coordination and sustainability. This framework delivers practical advice on heritage conservation and cultural tourism development in Suzhou and the broader Jiangnan region. Full article

Mammography is the main method for early detection of breast cancer, which is still a major global health concern. However, inter-reader variability and the inherent difficulty of interpreting subtle radiographic features frequently limit the accuracy of diagnosis. A thorough assessment of deep convolutional neural networks (CNNs) for automated mammogram classification is presented in this work, along with the introduction of two innovative multi-view integration techniques: Dual-Branch Ensemble (DBE) and Merged Dual-View (MDV). By setting aside two datasets for out-of-sample testing, we evaluate the generalizability of the model using six different mammography datasets that represent various populations and imaging systems. We compare a number of cutting-edge architectures on both individual and combined datasets, including ResNet, DenseNet, EfficientNet, MobileNet, Vision Transformers, and VGG19. Both MDV and DBE strategies improve classification performance, according to experimental results. VGG19 and DenseNet both obtained high ROC AUC scores of 0.9051 and 0.7960 under the MDV approach. DenseNet demonstrated strong performance in the DBE setting, achieving a ROC AUC of 0.8033, while ResNet50 recorded a ROC AUC of 0.8042. These enhancements demonstrate how beneficial multi-view fusion is for boosting model robustness. The impact of domain shift is further highlighted by generalization tests, which emphasize the need for diverse datasets in training. These results offer practical advice for improving CNN architectures and integration tactics, which will aid in the creation of trustworthy, broadly applicable AI-assisted breast cancer screening tools. Full article