Search Results (337)

This study investigates how participants conceptualise sustainability and sustainability citizenship, as well as how these conceptualisations relate to perceived agency. Drawing on two open-ended prompts, it analyses participants’ visions of a sustainable future and the roles they would like to play within it. The dataset was based on 1714 coded response segments from 164 participants. Methodologically, the study combines qualitative content analysis, independent human-AI double coding, manual validation, inter-rater reliability assessment, and residual-based co-occurrence analysis within a qualitatively grounded mixed-methods design. The results show that sustainability is predominantly framed in civic, symbolic, and ecological terms, whereas strategic competence and professionally articulated agency remain less visible. Sustainability meanings and role conceptions also vary systematically across disciplinary contexts. In addition, the analyses reveal patterned gaps between participants’ future visions and their self-attributed roles in sustainability transformations. The study contributes empirical insights into sustainability meaning-making and perceived agency and shows how LLM-assisted coding can be embedded in a transparent mixed-methods workflow. For sustainability education, the findings underline the importance of strengthening strategic and systemic dimensions of competence and linking civic engagement more closely to professional pathways of action. Full article

Reinforcement learning (RL) agents are increasingly deployed for voltage control in power distribution networks. However, their opaque decision-making creates a significant trust barrier, limiting their adoption in safety-sensitive operational settings. This paper presents XRL-LLM, a novel framework that generates natural language explanations for RL control decisions by combining game-theoretic feature attribution (KernelSHAP) with large language model (LLM) reasoning grounded in power systems domain knowledge. We deployed a Proximal Policy Optimization (PPO) agent on an IEEE 33-bus network to coordinate capacitor banks and on-load tap changers, successfully reducing voltage violations by 90.5% across diverse loading conditions. To make these decisions interpretable, KernelSHAP identifies the most influential state features. These features are then processed by a domain-context-engineered LLM prompt that explicitly encodes network topology, device specifications, and ANSI C84.1 voltage limits.Evaluated via G-Eval across 30 scenarios, XRL-LLM achieves an explanation quality score of 4.13/5. This represents a 33.7% improvement over template-based generation and a 67.9% improvement over raw SHAP outputs, delivering statistically significant gains in accuracy, actionability, and completeness ($p<0.001$, Cohen’s d values up to 4.07). Additionally, a physics-grounded counterfactual verification procedure, which perturbs the underlying power flow model, confirms a causal faithfulness of 0.81 under critical loading. Finally, five ablation studies yield three broader insights. First, structured domain context engineering produces synergistic quality gains that exceed any single knowledge component, demonstrating that prompt composition matters more than the choice of foundational model. Second, even an open source 8B-parameter model outperforms templates given the same prompt, confirming the framework’s backbone-agnostic value. Most importantly, counterfactual faithfulness increases alongside load severity, indicating that post hoc attributions are most reliable in the high-stakes regimes where trustworthy explanations matter most. Full article

Background: Liberia modernized vaccination data systems in 2023–2025 by piloting a District Health Information System (DHIS2)-based Digital Vaccination Registry (Electronic Immunization Registry, EIR) to address the limitations of paper-based workflows and of a proprietary COVID-19 electronic platform (offline gaps, lack of unique identifiers, performance issues and cost). Objective: To assess a pilot platform by evaluating training, registry use and device management, utility for routine immunization, vaccine logistics and Adverse Events Following Immunization (AEFI) data, and routine immunization data quality in the DHIS2 mobile application compared with paper registers. Methods: Using the Public Health Informatics Institute’s Collaborative Requirements Development Methodology, stakeholders defined requirements, trained users and implemented a pilot. Mixed methods were used; a mini data audit was performed, and qualitative data were collected across 19 facilities in Montserrado, Gbarpolu and Grand Bassa. Seventy-eight health workers were trained to use the DHIS2 mobile application. Results: The future state design replaces paper aggregation steps with real-time mobile entry to a national registry and dashboard. Dual entry persisted during high-volume periods. The mini data audit found discrepancies between facility paper registers and DHIS2-EIR entries for child enrollment data and, Bacillus Calmette Guérin and Diphtheria–Pertussis–Tetanus dose administration records Participants attributed these discrepancies to internet and device problems and challenges navigating the system. Participants requested a training manual, improved connectivity at point of service, integration with supportive supervision, additional staff and system features (field to record hospital number, automated next visit date, and vaccination status prompts). Conclusions: Lessons from the pilot will inform country-wide implementation, including planned linkage with electronic birth and death registration to enable a unique child identifier and reduce manual errors and delays. Full article

This work addresses critical challenges to academic integrity, including plagiarism, fabrication, and verification of authorship of educational content, by proposing a Natural Language Processing (NLP)-based framework for authenticating students’ content through author attribution and style change detection. Despite some initial efforts, several aspects of the topic are yet to be explored. In contrast to existing solutions, the paper provides a comprehensive analyzis of the topic by targeting four relevant tasks, including (i) classification of human and machine text, (ii) differentiating in single and multi-authored documents, (iii) author change detection within multi-authored documents, and (iv) author recognition in collaboratively produced documents. The solutions proposed for the tasks are evaluated on two datasets generated with Gemini using two different prompts, including a normal and a strict set of instructions. During experiments, some performance reduction is observed for the proposed solutions on the dataset generated by the strict prompt, demonstrating the complexities involved in detecting machine-generated text with cleverly crafted prompts. The generated datasets, code, and other relevant materials are made publicly available on GitHub, which are expected to provide a baseline for future research in the domain. Full article

Cardiologists consider degenerative or infectious causes when evaluating valvular heart disease. However, the role of autoimmune disorders, though less frequent, remains clinically significant. This report describes a young male patient presenting with persistent coronary disease and a suspected valvular cusp perforation initially attributed to infective endocarditis, which ultimately proved to be a manifestation of IgG4-related disease. IgG4-related disease is a rare condition, more prevalent in Asian populations, that typically affects the pancreas, salivary glands, lacrimal glands, and the retroperitoneum. Cardiac involvement, although uncommon, can present in various ways, including pericarditis, pulmonary arterial hypertension, valve dysfunction, cardiac pseudotumor, and coronary disease. Diagnosing and managing IgG4-related cardiac involvement requires heightened clinical suspicion, serological and histopathological assessment, and prompt interdisciplinary collaboration. Notably, involving rheumatologists in the management of these rare yet impactful autoimmune cardiac diseases is essential. Full article

We propose Trust Triangle, a Bridging Methodology that establishes evidential reliability through multi-attribution consensus, ensures external validity via statistical hypothesis testing, and enables controlled generation with RAG-anchored LLMs to transform black-box predictions into trustworthy, auditable explanations. This framework is instantiated for credit card fraud detection by integrating multi-method feature attributions with rigorous statistical validation. The resulting reliability-validity-verified insights are synthesized with high-relevance domain knowledge (relevance score > 0.7) retrieved from a real-world corpus via Retrieval-Augmented Generation (RAG). A structured Chain-of-Thought (CoT) prompt then guides an LLM to produce coherent, audit-ready case reports. Our contributions are threefold: (1) a verifiable framework for quantifying attribution reliability and validity, (2) a demonstrated end-to-end pipeline from robust prediction to semantically grounded explanation, and (3) a generalizable paradigm for Trustworthy ML in high-stakes domains. Experiments on a highly imbalanced dataset (fraud rate: 8.74%) demonstrate robust performance (PR-AUC = 0.7867), successfully identify statistically significant predictive features, and generate audit-ready reports, thereby advancing a rigorous, evidence-based pathway from model output to decision-ready support. Full article

Flood control briefings are critical emergency response documents that provide timely decision support for urban safety and regional development under climate change challenges. However, existing large language models (LLMs) face significant difficulties in domain-specific adaptation, content controllability, and logical consistency when processing complex water conservancy data. This study aims to develop a robust automated text generation method that ensures high accuracy and logical rigor for flood prevention in the Lixiahe region. We propose an IP-CoT method that integrates Chain-of-Thought (CoT) reasoning for structured information extraction and an Inverse Prompting (IP) mechanism with beam search to optimize content relevance using the DeepSeek-R1 model. Validated on a constructed dataset comprising flood control records from the Lixia River network from 2010 to 2024, the proposed method achieved an accuracy rate of 95.32% in the verification of emotional attributes, which is 2% to 15% higher than most traditional models. Additionally, in the verification of thematic attributes, fluency and diversity were improved, showing significant enhancements compared to the baseline model. This approach significantly enhances the quality and efficiency of domain-specific text generation, providing a reliable intelligent solution for modernizing regional flood control decision-making systems. Full article

Road safety assessment is essential for reducing traffic fatalities, with road infrastructure contributing to a substantial proportion of crashes worldwide. International frameworks such as the International Road Assessment Program (iRAP) define standardized attributes for infrastructure auditing; however, many of these attributes remain challenging to automate using imagery alone. This study evaluates V-RoAst (visual question answering for road assessment), a public dataset of road images that are annotated with iRAP-style attributes, using state-of-the-art multimodal large language models (MLLMs), specifically Gemini 2.0 and Gemini 2.5. The analysis focuses on how prompt design influences the accuracy and stability of single image iRAP inference. A token-efficient reduced prompt is developed that preserves the iRAP schema while removing single-class constants, hard-coded administrative fields, and derived or non-visual codes, retaining only visually interpretable attributes. Performance is compared with the original full multi-attribute prompt and single attribute prompts using a fixed evaluation protocol incorporating majority voting, bootstrap 95% confidence intervals, and per-code sample-size checks. Results indicate only minor performance differences between Gemini 2.0 and Gemini 2.5, while prompt optimization produces the most consistent gains, improving macro-F1 scores and tightening confidence intervals for visually grounded attributes such as roadside severity, intersection channelization, and service-road presence. Token analysis shows an approximate 30% reduction in prompt length, reducing computational cost and truncation risk. Overall, the findings demonstrate that prompt scope has a greater impact than model version in image-only iRAP coding, offering practical guidance for scalable infrastructure assessment. Full article

Background and Clinical Significance: Sarcoidosis is a systemic inflammatory disorder characterized by noncaseating granulomas. While pulmonary involvement is common, isolated renal involvement is rare and diagnostically challenging. We report a case emphasizing the utility of urinary tubular markers for early detection. Case Presentation: A 60-year-old woman with a history of suspected ocular sarcoidosis presented with progressive renal impairment and constitutional symptoms. Initial workup for systemic sarcoidosis was negative, leading to a misdiagnosis of chronic fatigue syndrome. Her rising serum creatinine was initially attributed to dehydration. However, a marked elevation in urinary β2-microglobulin (33,736 μg/L) prompted a renal biopsy, which revealed granulomatous tubulointerstitial nephritis. Following prednisolone therapy, her renal function improved, and her fatigue resolved completely. Conclusions: This case demonstrates that the kidney can be the primary site for histological diagnosis in the absence of pulmonary lesions. Incorporating urinary β2-microglobulin into routine monitoring may facilitate the early detection of renal sarcoidosis, preventing diagnostic delays. Full article

Alzheimer’s disease (AD) is a rapidly escalating global health crisis with limited effective treatments. Emerging research underscores the pivotal role of the microbiota–gut–brain axis in AD pathogenesis, prompting the exploration into gut microbiota-targeted interventions. This narrative review aimed to comprehensively synthesize the latest epidemiological, experimental, and clinical evidence regarding the effects and mechanisms of probiotics, prebiotics, synbiotics, and postbiotics (PPSPs) in AD prevention and management. We conducted a narrative review of relevant literature from the Web of Science and PubMed databases. The search focused on articles published within the last 5 years using keywords such as “Alzheimer’s disease”, “AD”, “gut-brain axis”, “gut microbiota”, “probiotics”, “prebiotics”, “synbiotics”, and “postbiotics”. The findings suggest that PPSPs mitigate AD pathology and improve cognitive performance by modulating gut microbiota, strengthening intestinal barrier integrity, decreasing amyloid-beta (Aβ) deposition and tau hyperphosphorylation, reducing neuroinflammation and oxidative stress, regulating neurotransmitter metabolism, and promoting synaptic plasticity. Some studies also report varied outcomes, attributable to factors like strain specificity, dosage, intervention duration, patient heterogeneity, and methodological differences. In conclusion, targeting the microbiota–gut–brain axis with PPSPs offers a promising, mechanism-based strategy for AD, though further research is essential to optimize specific interventions for clinical application. Full article

Through theorists like M. Jacqui Alexander, Édouard Glissant, Saidiya Hartman, Carolyn Cooper, and Michelle Wright, this project reconsiders the “vulgarity” attributed to Caribbean musical genres, like dancehall, dembow, and reguetón, as a pedagogical practice: an embodied, sensorial way of knowing that challenges colonial and racialized modes of aesthetics, morality, and order. Through an examination of Vybz Kartel’s “Fever,” Tokischa’s “Sistema de Patio,” and Bad Bunny’s “El Apagón,” I examine how sound, image, and movement converge to create what Alexander calls “pedagogies,” which simultaneously disturb and instruct. These pedagogies of the vulgar illuminate the ongoing impact of colonialism and plantation slavery in the Caribbean, particularly the gendered extraction of labor and capital that continues to shape daily life. In this context, vulgarity is not simply performed but inverted, prompting us to ask what is truly vulgar: Caribbean music and dance, or the systemic violence of Western modernity? These pedagogies foreground the paradoxical beauty of violence and survival, revealing how Caribbean peoples reconfigure “vulgarity” to craft pleasure and freedom amidst constraint. Embracing Michelle Wright’s concept of “epiphenomenal time,” this study invites readers to watch, listen, and feel, reminding us that the pedagogy of the vulgar must be embodied to be understood. Full article

The growing demand for functional and value-added foods has prompted interest in integrating nutrient-rich ingredients and novel encapsulated systems into traditional pasta products. This study aimed to develop and optimize a ravioli dough formulated with elephant foot yam flour (EFYF), wheat flour (WF) and amaranth flour (AF) using mixture design in response surface methodology and to create an innovative filling using encapsulated edible pearls produced from okra mucilage and moringa leaf powder through ionotropic gelation. The pearls and ravioli dough were analyzed for physicochemical, textural, color and nutritional characteristics. Cooked ravioli was investigated for cooking quality and sensory attributes. The optimized dough formulation (46.67 g EFYF, 43.32 g WF, 10 g AF) exhibited desirable hardness (4.64 ± 0.28 N), chewiness (0.40 ± 0.02 N), nutritional, physicochemical and color attributes. The edible pearls demonstrated moderate moisture content (21.18 ± 0.26%), high protein (26.25 ± 0.02%), crude fiber (2.60 ± 0.01%), dietary fiber (8.60 ± 0.52%), high ash content (14 ± 0.62%) and soft gel-like texture. The cooked ravioli showed a cooking time of 8 ± 1 min, high water absorption capacity (209.9 ± 0.34%), minimal solid loss (1.30 ± 0.21%) and favorable sensory scores across appearance, taste, texture and overall acceptability. The study concludes that incorporating encapsulated pearls and nutrient-dense flours can produce a functional, nutritionally enriched ravioli with good technological performance and consumer appeal. Full article

Urban mental health burdens are increasing, prompting interest in how nearby green spaces aid emotional restoration. Bamboo-dominant green spaces are widespread in East Asia, but evidence connecting their management and structural features to restorative experiences is limited. This study conducted a controlled photo-exposure experiment in Ya’an, China, to examine how bamboo space typology and structural attributes relate to visual attention, affective responses, and short-term physiological recovery. One hundred and twenty participants viewed 50 photographs representing five bamboo space types (ecological conservation, productive–economic, protective–greenbelt, landscape–recreational, and understory–composite). Each image was linked to a matched field plot, enabling integration of structural indicators with eye tracking, EEG β/α, and repeated ratings of relaxation, pleasure, and preference. Results showed that landscape–recreational spaces received the highest affective ratings, while understory–composite spaces had longer fixations, indicating higher visual processing demands. Vertical stratification and groundcover coverage were robust predictors of affect beyond typology. Eye-movement metrics did not mediate structure–affect associations, and EEG β/α, as an auxiliary and context-dependent indicator under brief photo-based exposure, showed limited sensitivity. These findings offer insights into structural elements that can inform the design and management of bamboo green spaces for improved emotional restoration. Full article

Background: Inguinal hernia repair (IHR) is frequently performed in infants, often under general anesthesia. Preterm infants are routinely monitored for 24 h postoperatively, due to high reported rates of respiratory complications. However, recent data suggest a decline in these events, prompting a reevaluation of the existing monitoring protocols. This study assesses the detection of (possible) anesthesia-related complications within 24 h after IHR in infants under 3 months of age and aims to identify risk factors for these complications. Methods: This retrospective cohort study included consecutive patients aged ≤ 3 months who underwent IHR between November 2015 and August 2023. All underwent IHR under general anesthesia. Subjects were compared based on whether they experienced possible anesthesia-related complications within 24 h after surgery or not. A logistic regression model was constructed and the number needed to monitor was calculated. Results: 306 patients were included, of which 36.3% were prematurely born (gestational age < 37 weeks) and the mean postconceptional age at surgery was 47.7 ± 4.8 weeks. Possible anesthesia-related complications were detected in 10 patients (3.3%), but only 8 (2.6%) were likely attributable to anesthesia. Events included desaturations, convulsions, fever, and a choking incident. Significant differences were found in patients experiencing complications when they had pre-existing respiratory (p = 0.013) or circulatory (p = 0.016) comorbidities. The postconceptional age (PCA) and gestational age (GA) were not significantly different between groups. Univariate logistic regression did not show a significant correlation between anesthesia-related complications and PCA or GA. Conclusions: Our data corroborates the suggestion that prematurity and PCA alone are not the main characteristics upon which postoperative monitoring protocols should be based. We hypothesize that an individualized approach based on comorbidities and clinical history could be more accurate. These findings point toward the necessity of more (prospective) research to support the refinement of postoperative monitoring guidelines to optimize healthcare resource allocation, while maintaining patient safety. Full article

Background and Objectives: Cotesting combines cervical cytology and HPV testing and usually identifies HSIL/ASC-H in association with HPV positivity; however, a small subset shows discordant results with high-grade cytology but negative HPV testing. We evaluated the clinicopathologic significance and histologic outcomes of HPV-negative HSIL or ASC-H cytology in a tertiary referral setting. Materials and Methods: We retrospectively reviewed women referred to a tertiary colposcopy unit (January 2019–October 2025) with HPV-negative HSIL or ASC-H on cotesting. Clinical findings, colposcopy, histology, excisional procedures, and follow-up were abstracted. Cytology and histology were reviewed by an expert gynecologic pathologist, and p16 immunohistochemistry was performed in all cases. Results: Among 92 women with HSIL/ASC-H cytology who underwent cotesting, 84 were HPV-positive (35 HSIL, 49 ASC-H). Eight cases (8.7%) remained HPV-negative after cytology review: 2/37 (5.4%) HSIL and 6/55 (10.9%) ASC-H. On histology, 4/8 (50%) had HSIL (CIN3) and 4/8 had LSIL; all CIN3 cases showed diffuse block-type p16 positivity. Two of six HPV-negative ASC-H cases (33.3%) were CIN3. One patient had persistent high-grade disease requiring two excisional procedures during follow-up. Conclusions: HPV-negative HSIL/ASC-H cytology is uncommon but associated with a substantial risk of CIN3. The consistent p16 positivity in tissue-confirmed HSIL supports HPV-attributable disease and suggests that most discordant cases reflect false-negative HPV testing rather than HPV-independent pathology. High-grade cytology should prompt colposcopic evaluation regardless of HPV status, and management should not be de-escalated solely on the basis of a negative HPV test. Full article