Search Results (263)

Background: Long-acting monoclonal antibodies have become an important strategy for preventing respiratory syncytial virus (RSV) disease in infants. Nirsevimab is the first product for which substantial post-licensure implementation data are available, whereas real-world evidence on clesrovimab remains limited. Although nirsevimab has demonstrated high efficacy, its uptake varies considerably across countries, healthcare systems, delivery settings, and population subgroups. This World Association for Infectious Diseases and Immunological Disorders (WAidid) consensus document examines the policy, organizational, economic, and equity-related determinants that shape real-world implementation of long-acting monoclonal antibodies for infant RSV prevention. Methods: This study was conducted as a structured narrative review and WAidid expert consensus document. A structured literature search was performed in PubMed and Embase for English-language publications relevant to nirsevimab uptake and implementation, complemented by targeted review of surveillance reports, policy documents, and public health guidance from the ECDC, UKHSA, and CDC, as well as reference lists of selected publications. Eligible sources included observational and real-world implementation studies, systematic reviews and meta-analyses, economic evaluations, guidelines, policy statements, surveillance reports, and relevant narrative reviews. Evidence was synthesized qualitatively according to policy frameworks, financing and reimbursement, delivery pathways, demographic and socioeconomic determinants, and healthcare-system factors influencing uptake. No statistical software was used because no quantitative re-analysis or meta-analysis was performed. Results: Nirsevimab uptake was strongly influenced by national RSV prevention policies, particularly whether countries adopted universal infant monoclonal antibody programs, maternal RSV vaccination strategies, dual maternal–infant approaches, or targeted risk-based models. Universal, publicly funded programs integrated into neonatal care achieved the highest and most homogeneous coverage, especially when administration occurred before hospital discharge and was supported by registry-based recall systems for infants born outside the RSV season. In contrast, fragmented, outpatient-only, insurance-dependent, or partially reimbursed models were associated with lower, delayed, or more variable uptake. Additional determinants included product cost, reimbursement pathways, provider practices, caregiver awareness and health literacy, insurance status, income, race and ethnicity, geographic deprivation, and access to primary pediatric care. Most available evidence comes from high-income countries, limiting generalizability to low- and middle-income settings, where RSV burden is greatest and implementation constraints may differ. Conclusions: Successful implementation of long-acting monoclonal antibodies for infant RSV prevention requires more than regulatory approval and demonstrated efficacy. Equitable uptake depends on clear national recommendations, sustainable public financing, reliable product supply, integration into neonatal and primary pediatric care, proactive identification and recall of eligible infants, and targeted strategies to reduce socioeconomic and geographic disparities. Although many determinants identified in high-income settings are likely relevant globally, their feasibility, relative importance, and impact require dedicated evaluation in low- and middle-income countries. Full article

We explore the capability of a Large Language Model (LLM) to perform specific computations in mathematical physics: the task is to compute the coordinate Bethe Ansatz solution of selected integrable spin chain models. We select three integrable Hamiltonians for which the solutions were unpublished; two of the Hamiltonians are actually new. We observed that the LLM semi-autonomously solved the task in all cases, with a few mistakes along the way. These were corrected after the human researchers spotted them. The results of the LLM were checked against exact diagonalization (performed by separate programs), and the derivations were also checked by the authors. The Bethe Ansatz solutions are interesting in themselves. Our second model manifestly breaks left–right invariance, but it is PT-symmetric; therefore its solution could be interesting for applications in Generalized Hydrodynamics. And our third model is solved by a special form of the nested Bethe Ansatz, where the model is interacting, but the nesting level has a free fermionic structure lacking $U\left(1\right)$-invariance. This structure appears to be unique and it was found by the LLM. We used ChatGPT 5.2 Pro and 5.4 Pro by OpenAI. Full article

Earth Observation (EO) analysis increasingly relies on large and heterogeneous satellite datasets, yet developing EO workflows often requires specialized expertise in data selection, geospatial programming, and cloud-based processing. Recent advances in Large Language Models (LLMs) offer new opportunities for natural-language interaction with EO systems, although challenges related to transparency, reproducibility, and domain-specific reasoning remain. This study presents SeaScope, an explainable AI framework that integrates LLMs, Retrieval-Augmented Generation (RAG), scientific knowledge retrieval, and Google Earth Engine (GEE) to transform natural-language requests into transparent and executable EO workflows. The framework combines knowledge retrieval, code generation, cloud execution, provenance tracking, and interactive visualization within a unified environment. A pilot implementation is demonstrated through maritime and coastal monitoring applications, including oil spill detection, vessel monitoring, water quality assessment, floating debris detection, and air quality analysis. Multiple state-of-the-art LLMs are evaluated under both RAG and non-RAG configurations using representative EO case studies. The results indicate substantial differences among model families and show that retrieval augmentation can significantly improve workflow generation quality and reliability for capable models, while providing more limited benefits for smaller models. The proposed framework demonstrates the potential of explainable AI agents to support transparent, reproducible, and scalable EO analysis. Full article

Large language model-enabled virtual patients may expand access to clinical skills practice by supporting explicitly defined practice tasks. This program evaluation examined medical students’ awareness, use, and perceptions of the McMaster Virtual SP Tool, a custom generative artificial intelligence tool developed to supplement clinical skills practice. We conducted an anonymous, single-institution cross-sectional survey of students across three cohorts at McMaster University. Quantitative responses were summarized descriptively, and free-text responses were analyzed using qualitative content analysis informed by task-aligned fidelity, deliberate practice, learner-centred feedback, and simulation instructional design. Thirty-five students responded; 23 (65.7%) were aware of the tool and 16 (45.7%) had used it. Among the 16 users, 15/16 (93.8%) found the tool at least somewhat easy to navigate; 13 (81.3%) would use it again; and 13 (81.3%) would recommend it. Across all respondents, 14/35 (40.0%) reported using AI-enabled virtual patients for OSCE preparation. Users valued its accessibility, independent low-stakes rehearsal, and usefulness for focused history-taking, question wording, and clinical reasoning. Perceived limitations included reduced human connection, nonverbal and emotional realism, physical examination practice, and feedback specificity. AI-enabled virtual patients may therefore be considered as adjuncts for selected cognitive and structural rehearsal tasks. Objective educational outcomes were not assessed. Full article

Programs for adults with visual impairment are diverse; however, the methods used to evaluate them have not been systematically mapped. We conducted a scoping review following Arksey and O’Malley’s framework and the PRISMA-ScR guideline. We searched peer-reviewed, English-language articles in PsycINFO, ERIC, PubMed, CINAHL, Scopus, and Web of Science Core Collection, with an original search on 1 October 2025 and an updated search on 1 May 2026. Two independent reviewers screened records in EPPI-Reviewer; disagreements were resolved through discussion. A total of 56 studies published between 1989 and 2024 were included. Most studies relied on summative pre-post assessment. Only two studies incorporated formative evaluation, and none used a logic model or an explicit theoretical framework. Sixteen studies included follow-up assessments, with limited variation in timing and frequency. Studies used both generic and vision-specific standardized instruments; however, vision-specific tools appeared in only three of five outcome domains. Accessibility accommodations were common, most often involving item read-aloud, with proxy reporting used in a small subset. Only four studies addressed the potential measurement bias associated with these accommodations. These findings highlight gaps in evaluation design, indicator selection, and the balance between accessibility and measurement validity, identifying priorities for future research. Full article

LiDAR (Light Detection and Ranging) and RGB-D camera imaging have emerged as essential tools in agricultural applications, particularly for plant size and distance measurements, enabling non-destructive, cost-effective, and precise estimation. The objective of this study was to measure the plant canopy dimensions and distance between apples using commercial LiDAR, and an RGB-D camera with a speed sprayer platform was used to determine whether LiDAR provides a higher measurement accuracy under field conditions. Data were collected in an apple orchard in Muju, Republic of Korea. Commercial 3D LiDAR, a terminal box, an RGB-D camera, a microcontroller, a power supply, and individual display monitors were integrated into a customized data acquisition (DAQ) box for LiDAR point cloud (PCD), RGB, and depth imagery data collection. Commercial software was used for data acquisition, data conversion (pcap to PCD), segmentation of regions of interest (ROI), and pre-processing of data. PCD processing and measurement consisted of data frame selection, data conversion, outlier removal, downsampling, denoising, ground point removal by filtering, voxelization, and density map generation using an open access programming language script. Depth image processing included importing raw data, shaping metadata using intrinsic camera parameters, visualizing depth images, extracting depth points, and measuring the plant canopy at the pixel level. RGB image analysis involved grayscale conversion, thresholding, segmentation of ROI, contour preparation, noise removal, and binary masking for eliminating the background. Estimated results were compared to measured results. LiDAR measurements showed the closest agreement with the measured results for plant height, canopy volume, plant spacing, and row distance, outperforming both RGB and depth imaging. Under field conditions, plant spacing and row distance were estimated with accuracies of 97.5% and 94.7%, respectively, exhibiting higher measurement accuracies than RGB and depth imagery data results. Despite some discrepancies due to complex plant geometry and dynamic data collection, the results support data collection strategies critical for precision horticulture. Full article

This paper presents a fully native optimization framework, implemented within DIgSILENT PowerFactory, which is aimed at solving the optimal conductor sizing problem (OCSP) in electrical distribution systems under realistic operating conditions. Our methodology integrates a tabu search algorithm (TSA) directly with the three-phase power flow routines and database objects available in the DigSILENT programming language (DPL), thereby eliminating the need for external data exchange and synchronization between independent optimization and network simulation environments. Our framework considers balanced and unbalanced operating conditions while incorporating peak demand, multilevel demand, and hourly demand load profiles. The optimization process minimizes annual investment and operating costs while satisfying voltage regulation and conductor ampacity constraints. The methodology was validated using a 27-bus benchmark system and the IEEE 33- and 123-bus distribution systems under different operating scenarios. The numerical results indicate that chronological demand scenarios significantly influence conductor allocation decisions and annual operating costs. Compared to the conventional peak demand load profile, the multilevel and hourly load profiles produced lower annual costs by distributing conductor sizing decisions across multiple operating states instead of considering worst-case loading conditions. Additionally, the unbalanced scenarios increased the operating losses and modified the conductor selection patterns due to unequal phase loading and current asymmetries. The proposed TSA-DPL implementation maintained stable convergence behavior and low statistical dispersion under all the evaluated benchmark systems and operating conditions. Even for the IEEE 123-bus feeder under unbalanced hourly operating conditions, the standard deviation remained below 0.70% of the average annual cost, confirming the robustness and repeatability of the methodology. Although the detailed three-phase chronological simulations increased the computational requirements, the proposed implementation demonstrated computational applicability to the evaluated benchmark systems. Overall, the proposed TSA-DPL framework constitutes a robust native implementation for realistic conductor sizing studies in modern three-phase distribution systems. Full article

Young breast cancer survivors often develop post-breast surgery pain syndrome (PBSPS). Despite potential benefit from physical activity, access to in-person programs is often limited by competing work and caregiving responsibilities. We assessed the feasibility and acceptability of a remotely delivered physical activity intervention for young Hispanic breast cancer survivors with PBSPS. Our single-arm, 12-week feasibility study enrolled Hispanic women aged ≤60, at least 3 months post-treatment for invasive breast cancer or ductal carcinoma in situ (DCIS), with PBSPS. Using a Fitbit Inspire 3 activity tracker and health coaching, the program encouraged whole-of-day movement to increase activity. Feasibility outcomes included recruitment, retention, and adherence. Secondary outcomes assessed pain measures, daily steps, and quality of life (QOL). Twenty-five breast cancer survivors enrolled (mean age 47.4 years [SD 6.3]), with 76% selecting Spanish-language health coaching. Retention at 12 weeks was 79% (19/24). Engagement with health coaching was high, with 95% completing at least 4 of 5 pre-planned calls. The mean change in daily step count was 1502 (−348, 3353), with twelve (66.7%) increasing in step count (median 2334.5, range: 14, 13,422). Each additional 1000-step increase was associated with a 2.67-point increase in pain self-efficacy scores (β = 2.669, 95% CI: 0.884 to 4.454, p = 0.006). A remotely delivered, health coaching physical activity intervention was feasible and acceptable for young Hispanic breast cancer survivors with PBSPS. This supports further evaluation in a larger trial. Accessible survivorship interventions for young breast cancer patients in their preferred language are needed. Full article

Aging-related decline involves changes in oxidative stress, inflammation, mitochondrial function, telomere maintenance, metabolic regulation, and extracellular matrix homeostasis. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement and searched PubMed, Scopus, Embase, and Web of Science for English-language studies published from 1993 to 2025. Twelve primary studies met the eligibility criteria: four randomized clinical studies, four animal studies, one in vitro mechanistic study, and three delivery-system/formulation studies. Risk of bias was assessed using Cochrane Risk of Bias 2 (RoB 2), the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) tool, and a review-specific domain-based checklist informed by the National Toxicology Program/Office of Health Assessment and Translation framework. Representative compounds, including resveratrol, astragaloside IV, Ganoderma lucidum polysaccharide, epigallocatechin-3-gallate (EGCG) and betulinic acid, were associated with mitochondrial regulation, antioxidant defense, cytoskeletal stability, gut–brain signaling, extracellular-matrix protection, and longevity-related signaling. Nanoliposomes and poly(lactic-co-glycolic acid) (PLGA) nanoparticles improved selected stability, penetration, and pharmacokinetic outcomes. However, the evidence base was heterogeneous and predominantly preclinical; human evidence was limited to four relatively small studies, and certainty ranged from moderate to very low. The findings support mechanistic and translational potential but do not establish definitive anti-aging efficacy in humans. Full article

Background/Objectives: Satisfaction with health and psychosocial services is a key indicator of perceived quality and acceptability, but it does not show whether services improve parenting, family functioning or youth outcomes. It is rarely assessed systematically in community-based addiction prevention and psychosocial health promotion services. This primarily descriptive cross-sectional survey assessed parent-reported satisfaction with family-based addiction prevention and psychosocial health promotion services delivered by a Greek community prevention center. Methods: An anonymous cross-sectional online survey was completed by 110 of 250 invited parents (44%) who had participated in family-based programs delivered by the Prevention Center of the Regional Unit of Achaia (“Kallipolis”) during the 3 years immediately preceding data collection. Satisfaction was measured with a licensed Greek-language version of the Client Satisfaction Questionnaire (CSQ-8). The primary analyses were descriptive, and sample-specific internal consistency was assessed. Because only six respondents had scores below the upper descriptive range, a two-predictor Firth penalized logistic regression was fitted solely for exploratory, hypothesis-generating purposes. Results: The mean CSQ-8 score was 29.35 (SD = 2.84), the median was 30 (interquartile range = 28–31.75), and 104 respondents (94.5%) scored 25–32. Internal consistency was good (Cronbach’s alpha = 0.82), although the distribution showed a pronounced ceiling effect. The exploratory model suggested an association between complete attendance and an upper-range score; however, the reference attendance group contained only three participants, producing an extremely imprecise estimate and precluding reliable interpretation of the magnitude of the association. Conclusions: Respondents reported very high perceived satisfaction and acceptability. The findings are primarily descriptive, may be affected by nonresponse, selection, and recall bias, and do not demonstrate an improvement in parenting, family functioning or youth outcomes. Satisfaction monitoring should be combined with measures of reach, retention, implementation fidelity, and participant, family and youth outcomes. Full article

One-semester exchange programs place students in university courses within compressed, credit-bearing mobility timelines. This qualitative case study examines how eight Korean undergraduate exchange students described academic learning and adjustment during a one-semester exchange at a U.S. university. Drawing on online written interviews, the study analyzes students’ reported classroom experiences rather than formal course design or enacted instructional practice. The analysis focuses on how students navigated unfamiliar classroom expectations at the level of reported classroom practice. The findings show that participation became visible as academic work when students prepared texts, engaged in discussions, managed language risk, and judged what counted as an appropriate contribution. Feedback and academic help-seeking served as interpretive resources through peer review, instructor clarification, writing tools, and efforts to turn uncertainty into next steps. Across the accounts, adjustment appeared as selective and uneven additions to students’ academic strategies, most visible in discussion, peer feedback, open-ended writing, instructor clarification, and resource assembly. This study contributes to higher education and international student mobility research by showing how temporary exchange students made classroom expectations workable through targeted strategy additions rather than through a shift from one national learning culture to another. It offers implications for participation scaffolding, feedback design, and pre-departure preparation. Full article

Long-term analyses of year changes in preschoolers’ cognitive and affective development are limited. This field-based study examined yearly trends in a multimedia-assisted, theme-based English and Chinese program in South Korean daycare centers (2022–2024). Participants included 112, 120, and 124 children aged 3–5 annually. Changes were analyzed for cognitive (vocabulary/sentence comprehension and expression) and affective (learning interest, confidence, and motivation) domains. Group differences based on duration of enrollment (three years vs. less than three years) were examined among five-year-old children. Significant cognitive gains occurred for both languages across all three years. Affective development was significant for both languages in 2022 and 2023; however, in 2024, significant positive changes persisted for English across all subdomains, while for Chinese, only confidence improved. Correlation patterns varied by year and language; notably, English motivation was significantly associated with selected cognitive domains in the third year. Furthermore, group comparisons indicated that children enrolled for three years demonstrated better performance in both English and Chinese than those enrolled for less than three years, with statistically significant differences particularly evident in sentence expression. These findings underscore the value of developmentally appropriate and engaging learning contexts that foster both the cognitive and affective dimensions of early learning. Full article

How can an LLM-based agent recognize the limits of its own self-knowledge and improve that self-knowledge over time? This paper proposes the metacognitive evolutionary system (MES), a functional framework for studying metacognitive evolution at the prompt level in large language model (LLM)-based agents. MES does not modify model weights; it evolves the prompt program around a fixed base model, keeping the system readable, auditable, and easier for humans to inspect. The framework introduces a recursive metacognitive tower, $M^n\left(P\right)=\mathrm{LLM}\left(⟨M^{n-1}\left(P\right)⟩\right)$, to model layered self-evaluation. The fixed-point behavior of this tower is interpreted as a Strange Loop, with a formal analogy to Gödelian incompleteness used to describe its epistemic limits. The system is built using five primitive functions: inference, grounding, awareness, adaptive self-replication, and population-level selection. A grounded fitness function guides prompt evolution across generations, evaluating uncertainty calibration, error detection, strategy adaptation, and epistemic boundedness. Through Quine-style prompt rewriting, MES studies how agents can revise their own prompt-level structure while remaining constrained by grounded evaluation. The paper presents the formal architecture, analyzes tower dynamics using Markov chains, discusses convergence results, identifies six application domains, and proposes QuineBench as an evaluation design. Numerical case studies serve as illustrative analytical examples rather than empirical experiments, and QuineBench is presented as a structured protocol for future validation, providing a theoretical foundation and a clear path toward empirical evaluation. Full article

Quartile rankings of journals have become shorthand for research quality in many national evaluation systems. This Commentary offers a non-systematic documentary analysis of this phenomenon in Spain and selected Latin American systems. It conceptualizes these arrangements as quartile regimes: configurations of rules, indicators, organizational routines, and incentives that make the Journal Citation Reports or SCImago quartile position of a journal function as a high-stakes proxy for research quality. The article draws on legal and policy texts, agency criteria, reform documents, peer-reviewed literature, and selected integrity cases used as illustrative vignettes rather than prevalence evidence. Spain is analyzed as an early and influential case in which sexenios and accreditation made journal indicators central to individual careers, although the 2024 sexenio criteria now move explicitly toward qualitative narratives, broader outputs, and responsible indicators. Mexico, Brazil, Colombia, Argentina, and Peru are treated as purposive Latin American cases that show distinct pathways through individual recognition schemes, graduate-program evaluation, journal-indexing systems, career committees, and publication bonuses. The article argues that quartile regimes reshape publication language, research agendas, disciplinary hierarchies, authorship practices, and integrity risks, with particularly strong effects in the social sciences and humanities and in regional journal ecosystems. Current reform efforts, including the Agreement on Reforming Research Assessment, the Coalition for Advancing Research Assessment, FOLEC-CLACSO, and the ALAEC manifesto, show that quartiles can be repositioned as weak contextual signals within broader, field-sensitive frameworks that value quality, bibliodiversity, multilingual communication, open science, and societal relevance. Full article

Large language models (LLMs) are increasingly used in programming education to provide on-demand conceptual clarification, yet how students actually use this feature in mastery learning systems (in which learners must demonstrate conceptual competence before progressing)—and whether clarification interactions relate to subsequent learning—has received limited empirical study. This paper analyzes 732 student remediation episodes (366 students, 43 assignments) to examine how students move through the remediation branch of an LLM-assisted programming course, whether their behavioral pathway choices are associated with subsequent coding challenge efficiency, and what theoretical role the clarification function plays. The results show that 78.0% of remediation episodes follow a pure retesting strategy, with only 22.0% involving any clarification interaction. Clarification is highly concentrated on conceptual questions (84.7%) and occurs mostly in the first remediation round (86.3%). An effect size analysis reveals a large difference in remediation rounds between single immediate and single delayed clarifiers (Cliff’s $\delta =-0.912$), suggesting that the timing of clarification is more strongly associated with remediation efficiency than its occurrence alone. mixed-effect linear models show no significant pathway effects on coding challenge process efficiency (active time and number of code snapshots; all $p>0.05$), a null result that is further examined through code-variability subgroup analyses. We argue that the clarification feature acts as a selective process-support mechanism: its observable value appears to lie in a shorter remediation process rather than in improved subsequent task efficiency, and this association is clearest when clarification occurs early. The findings have practical implications for the design of clarification features in AI-assisted learning systems and for instructional intervention strategies. Full article