Search Results (329)

Handwriting is a fundamental fine motor skill in early childhood development, yet between 10% and 30% of school-age children experience significant difficulties in its acquisition. Existing automated assessment approaches predominantly classify whether the correct character was produced, rather than evaluating the morphological quality of the stroke itself—the level at which handwriting difficulties are believed to manifest. This article addresses this gap by presenting the design, implementation, and technical evaluation of an interactive social robot shaped like a capybara, developed to support Spanish-language handwriting learning in preschool children through stroke-level, rather than character-level, assessment. The system integrates a Raspberry Pi 5, a 15.6-inch touchscreen, and a stroke morphological comparison algorithm implemented with the Open Source Computer Vision Library. The evaluation engine performs preprocessing, region-of-interest masking, and pixel-level coverage analysis based on the standard recall formulation, translated into 1-to-5-star multimodal feedback. A controlled technical evaluation of 360 trials, conducted by four trained adult evaluators, yielded an overall recognition rate of 82.78% (95% CI: 78.54–86.33%) and a mean response time of 0.68 s, well below the threshold identified in the literature as critical for sustaining engagement in preschool children. Recognition was statistically equivalent across character categories (p = 0.547) but differed markedly across stroke-quality levels (p < 0.001), evidencing the formative sensitivity of the algorithm. Exploratory observations in two Peruvian preschools indicated operational stability and children’s spontaneous engagement with the system. These results position the prototype as a technically validated, replicable foundation—based on general-purpose embedded hardware—for future pedagogically oriented research on child–robot interaction in handwriting instruction. Full article

Background: Although advances in critical care have improved short-term outcomes, sepsis survivors continue to face substantial chronic morbidity and impaired long-term survival. Conventional threshold-based tools such as the Sequential Organ Failure Assessment (SOFA) and Modified Early Warning Score (MEWS) show moderate and variable discrimination across cohorts. Reported areas under the receiver operating characteristic curve (AUROCs) must therefore be interpreted in relation to the population, prediction horizon, and outcome used in each study rather than as direct head-to-head comparisons. Objectives: This review evaluates how artificial intelligence (AI) could be linked with dynamic plasma metabolites, particularly citrulline and β-hydroxybutyrate (3-HB), to support biologically informed sepsis phenotyping, while critically examining mechanistic evidence, clinical limitations, and translational readiness. Data Synthesis: Machine-learning and natural language processing architectures have shown promising discrimination in many early-detection studies, with pooled AUROCs near 0.87 and reported prediction windows extending to 48 h. However, performance estimates vary with cohort composition, outcome definition, and validation design, and they should not be ranked against unrelated biomarker studies. Human sepsis studies generally associate low or persistently low citrulline with impaired intestinal function and organ injury, but no sepsis-specific decision cutoff has been externally validated. For 3-HB, an AUROC of 0.8429 for septic liver injury was derived from a cohort of 57 patients and has not been shown to add value beyond routine liver tests or illness-severity measures. Murine experiments provide mechanistic hypotheses for ketone-mediated organ protection, but model-specific and sometimes opposing nutritional effects limit direct translation. These metabolites are therefore best considered candidate longitudinal features for multimodal phenotyping rather than stand-alone clinical triggers. Conclusions: Biologically informed algorithmic surveillance is a promising direction, but clinical implementation requires prospective serial sampling, explicit adjustment for renal, hepatic and nutritional confounders, head-to-head comparison with routine markers, and external validation of calibration and clinical utility. Until these requirements are met, citrulline and 3-HB should support research phenotyping rather than direct treatment selection. Full article

Background: MSC–hydrogel composite systems were developed to overcome the poor cell retention and limited durability of conventional marrow stimulation and suspension-based MSC delivery. A rigorous synthesis of the clinical evidence is lacking. Objectives: To evaluate the safety and efficacy of MSC–hydrogel composite therapy for focal knee cartilage defects and knee osteoarthritis, and to assess the certainty of evidence using the GRADE framework. Methods: PROSPERO-registered systematic review (CRD420261393525), conducted and reported per PRISMA 2020 and SWiM. Five databases (Embase, PubMed/MEDLINE, Cochrane CENTRAL, Scopus, Web of Science) were searched in May 2026 without date, language, or design restrictions. Adults receiving MSCs co-delivered in a hydrogel carrier for knee cartilage pathology were eligible. Risk of bias was assessed using RoB 2 (RCTs) and ROBINS-I (non-randomized studies). Narrative synthesis following Popay et al. was the primary method; GRADE certainty was assessed per outcome domain. Results: Ten studies (N = 521) were identified. Eight studies fulfilled the predefined eligibility criteria for MSC–hydrogel composite interventions and formed the primary evidence synthesis. Two additional studies were retained as contextual comparators because they evaluated either hydrogel-based therapy without MSC administration or MSC therapy without a structured hydrogel carrier. Surgical MSC–hydrogel implantation was associated with improvements in cartilage repair. Intra-articular injection without a hydrogel scaffold produced synovitis reduction but no detectable structural regeneration at six months. No serious treatment-related adverse events were recorded. Risk of bias was serious or critical in seven of nine assessable studies; GRADE certainty was low to very low across all outcome domains. Conclusions: MSC–hydrogel composite implantation may provide favorable safety signals and directionally positive effects on cartilage repair, pain, and function, with seven-year follow-up data suggesting a possible durability advantage over marrow stimulation. However, adverse-event reporting was inconsistent, and certainty of evidence remains low or very low because most studies were non-randomized, single-center, and concentrated around one commercial platform. These findings are relevant to international cartilage-regeneration research because they identify key methodological limitations and trial-design priorities for translating MSC–hydrogel systems across different clinical and regulatory settings. Full article

This paper proposes METMAP, a comprehensive 6D evaluation framework designed to assess large language models in meteorological applications. The framework encompasses six critical capabilities including meteorological knowledge comprehension, expert-level meteorological content summarization, multilingual translation of meteorological information, geospatial mapping context understanding, alignment with authoritative meteorological standards, and professional meteorological service communication. To operationalize this framework, the paper further introduces MetSEval-1k, a high-quality benchmark comprising 1083 expert-curated questions spanning operational and public-facing meteorological services. The benchmark integrates both objective multiple-choice items and subjective open-ended tasks to enable holistic model assessment. This paper conducts systematic evaluations of multiple state-of-the-art large language models using MetSEval-1k, revealing substantial performance disparities across the six dimensions. The results highlight the critical necessity for domain-specific adaptation and rigorous validation before deploying large language models in operational meteorological contexts. MetSEval-1k is released as a foundational benchmark to advance research and development of trustworthy, service-oriented artificial intelligence application in meteorology. Full article

The University Student Engagement Inventory (USEI) is a multidimensional self-report instrument designed to assess student engagement in higher education through three interrelated dimensions: behavioral, emotional, and cognitive engagement. Developed within a hierarchical framework, the USEI provides both dimension-specific scores and an overall indicator of student engagement, supporting research, institutional assessment, and educational quality evaluation across diverse cultural and educational contexts. Student engagement is widely recognized as a key determinant of academic success, persistence, and educational quality in higher education. The University Student Engagement Inventory (USEI) was developed to provide a theoretically grounded and psychometrically sound measure of engagement that captures students’ behavioral, emotional, and cognitive involvement in learning activities. This encyclopedia entry reviews the conceptual foundations, development process, factorial structure, psychometric properties, and applications of the USEI. Evidence from multiple studies indicates that the instrument exhibits satisfactory reliability, construct validity, predictive validity, and measurement invariance across different countries, languages, and educational settings. The USEI has been translated and adapted for use in diverse cultural contexts and has been employed extensively in research examining academic performance, well-being, persistence, and learning environments, including online and blended education. Its brevity, multidimensional structure, and cross-cultural validation have supported its use in educational research and institutional assessment. Overall, the USEI represents a robust and versatile instrument for evaluating student engagement and supporting evidence-based improvements in higher education. Full article

As electric vehicles become widespread, home EV chargers have emerged as a key interface between household energy use and daily mobility. However, their design optimization remains insufficiently informed by large-scale user feedback. This study develops a review-driven, interpretable machine learning framework to identify design priorities for home EV chargers. Of the 26,763 reviews collected from the JD e-commerce platform, 23,893 were retained after cleaning. BERTopic extracted raw topics, which were consolidated into ten design dimensions through independent coding, inter-coder agreement assessment, and consensus adjudication. A structured large language model protocol then transformed the reviews into evidence-constrained, aspect-level semantic proxy variables representing evaluative direction and intensity. Coding reliability was evaluated against dual-coder annotations, while a matched absence-as-zero specification examined sensitivity to the treatment of unmentioned dimensions. Platform ratings were subsequently introduced as the prediction target, and repeated data partitions and cross-model SHAP comparisons were used to assess partition- and model-level stability. Charging Performance, Operational Stability, Perceived Product Quality, and Operational Convenience and Portability consistently ranked as the most important factors associated with platform-rated satisfaction. In contrast, Installation Friendliness and After-sales Service showed asymmetric attribution patterns characterized by stronger low-value penalties than high-value gains. The framework supports translating online review evidence into product-level design priorities, while emphasizing that SHAP identifies predictive associations rather than causal effects. Full article

The misalignment between workforce competences and the requirements of digitally evolving occupations is a critical barrier to SME competitiveness. This study’s primary contribution is theoretical and methodological: it reconceptualizes the workforce skill gap as a firm-level human-capital–technology complementarity constraint rendered observable and commensurable through the ESCO taxonomy, and abstracts four transferable design principles—commensurability, macro–micro integration, a transferable metric, and modular extraction. Drawing on human capital theory, the knowledge-based view, and skill-biased technical change, the framework maps anonymized employee CVs to ESCO occupational requirements through a deterministic natural language processing procedure and computes a Skill Gap Indicator as the complement of evidenced competence coverage. A prototype Intelligent Learning Management System, developed within the LUCE project, instantiates the framework as a proof of concept, translating identified gaps into targeted training recommendations. Applied to a convenience sample of publicly available professional profiles, the indicator has a mean of 0.956, interpreted as a conservative upper-bound estimate rather than a literal deficit. The empirical results are an exploratory demonstration that motivates, rather than confirms, the posited link between skill gaps and firm performance; a cross-sectional test found no significant association, which the design cannot adjudicate. Confirmatory testing would require sample expansion, employer-provided workforce records, and a longitudinal design, identified as priorities for future research. The study thus contributes a standardised, interoperable, and transferable approach to measuring and comparing workforce skill gaps in SMEs. Full article

Background: Visceral obesity is increasingly recognised not as a simple excess of adipose tissue, but as a systemic pathological condition characterised by profound metabolic, endocrine, and immune dysregulation. Visceral adipose tissue (VAT) operates as an autonomous neuro-immune-endocrine organ whose dysfunctional expansion drives insulin resistance, atherogenesis, and accelerated cellular ageing through mechanisms converging on chronic low-grade sterile inflammation, referred to as inflammaging. Objectives: This narrative review integrates evidence across four domains: (1) the multi-system clinical complications of visceral obesity and the methodological controversies surrounding its measurement; (2) the cellular heterogeneity, immunometabolic reprogramming, and molecular mechanisms through which excess VAT accelerates biological ageing, with a focus on genomic instability, mitochondrial dysfunction, the NAD⁺/sirtuin regulatory axis, cellular senescence, and inter-organ communication; (3) the role of bioelectrical impedance analysis (BIA)—particularly phase angle—as a non-invasive functional biomarker of biological age and longevity, positioned critically against alternative assessment methods; and (4) current knowledge gaps and priorities for future research. Methods: A narrative review of PubMed/MEDLINE, Google Scholar, and the Cochrane Library was conducted using MeSH terms and free-text keywords including visceral obesity, bioelectrical impedance analysis, phase angle, sarcopenia, inflammaging, mitochondrial dysfunction, cellular senescence, epigenetic clocks, NAD⁺, sirtuin, and longevity, supplemented by citation-tracking of retrieved reviews. English-language articles published up to April 2025 were considered, prioritising systematic reviews, meta-analyses, and prospective cohort studies; formal risk-of-bias tools and quantitative synthesis were not applied, consistent with a narrative review design. Results and Discussion: BIA-derived phase angle constitutes a macroscopic electrobiological correlate of inflammaging: low phase angle values in visceral obese subjects overlap with those of frail elderly individuals, reflecting impaired membrane integrity, loss of active cell mass, and altered ICW/ECW balance. However, this evidence base remains largely cross-sectional and correlative; the directionality and population-specific calibration of BIA-derived indices constitute the principal unresolved methodological questions. Integration with epigenetic clocks, circulating NAD⁺ levels, and gut microbiome indices offers a framework for dynamic biological age assessment, though prospective interventional validation is still lacking. Sarcopenic obesity, evaluated through EWGSOP2 combined with BIA-derived skeletal muscle mass index and handgrip dynamometry, represents a critical comorbidity demanding integrated therapeutic targeting. Conclusions: BIA provides a quantitative, accessible correlate for translating cellular metabolic health into clinically actionable parameters, complementary to rather than a replacement for anthropometric and imaging-based methods. Optimising phase angle and reducing VAT through anti-inflammatory nutrition, exercise, and nutraceutical strategies targeting the NAD⁺/sirtuin and mTOR/AMPK axes constitutes a measurable objective for the promotion of healthy longevity, contingent on the longitudinal, mechanistic studies identified as priorities in this review. Full article

Pediatric oncology nursing is among the highest-intensity care specialties, exposing nurses to repeated child death and sustained emotional investment in family relationships. This occupational burden is a recognized psychosocial risk factor and a driver of compassion fatigue, burnout, and reported intention to leave, yet the organizational and educational determinants of this risk remain poorly captured by existing assessment approaches. This study investigated the lived experiences of nurses caring for terminally ill children in a pediatric onco-hematology unit and hospice ward in central Italy (N = 15), using a qualitative descriptive design, informed by a phenomenological sensibility to lived experience, supported by computer-assisted textual analysis. Structured face-to-face interviews were analyzed through thematic content analysis with stepwise replication, while quantitative textual analysis was performed using T-Lab software to map co-occurrence patterns among key lemmas, providing a frequency-based, semantically grounded picture of nurses’ representations of occupational risk. Seven themes emerged, including quality of nursing care, parental relationships, coping with a child’s death, and impact on personal and professional life. Findings showed that reported intention to leave was associated, in participants’ accounts, with absent psychological debriefing, cumulative emotional burden, inadequate end-of-life training, and a near-total absence of organizational vocabulary for peer-level support—a gap directly visible in the co-occurrence structure of nurses’ language. As a secondary aim, by combining qualitative depth with complementary lexical analysis of thematic patterns, this study also offers a methodological example of how psychosocial risk in high-intensity care settings can be assessed and translated into actionable indicators for organizational climate and workforce well-being. The findings inform concrete prevention strategies and policy recommendations for nursing education and occupational health, consistent with this Special Issue’s focus on methodological innovation for psychosocial risk assessment and public health policy design. Full article

Text-to-Cypher generator systems translate natural language questions into Cypher queries, enabling intuitive interactions with graph databases such as Neo4j and Amazon Neptune. Despite recent advancements in LLM-based Cypher query generation, the vulnerabilities of the known methods—such as prompt injection attacks—are not discussed in detail. In this paper, we employ a robust Retrieval-Augmented Generation (RAG) architecture tailored specifically for text-to-Cypher tasks, leveraging dense vector retrieval to enhance query generation accuracy. We propose a dynamic and self-corrective procedure with feedback-loop-based AI architecture with Large Language Models (LLMs) for near real-time validation and correction of generated queries. We create a systematic procedure for generating datasets specifically designed to assess prompt injection robustness. Comprehensive evaluations are conducted using a diverse set of LLMs, including GPT-4o, DeepSeek R1, Claude 3.5 Sonnet and Qwen 2.5 Coder 32B Instruct. Our evaluation results indicate substantial improvements in resiliency against prompt injection attacks compared to various benchmarks. It is demonstrated that the proposed solution outperforms various training-free prompt injection defense methods. Full article

Large language models (LLMs) such as ChatGPT have advanced machine translation, but their quality on low-resource language pairs remains uneven and is typically assessed with automatic metrics alone. This study examines how prompting strategy and source-text type jointly affect LLM translation quality for the low-resource Chinese–Vietnamese pair and whether automatic and human assessments agree. Using a 2 × 3 mixed factorial design, we compared an English-mediated pivot strategy with a direct strategy across informative, expressive, and operative texts, evaluating 60 ChatGPT translations with COMET and with 15 bilingual readers who rated adequacy, fluency, faithfulness, trustworthiness, willingness to use, and need for revision. On COMET, pivoting significantly improved overall quality (p < 0.001), text type was the dominant factor (η² = 0.91; informative > operative > expressive), and strategy interacted with text type, with the largest pivot gain for expressive texts. Human ratings reproduced this ordering but diverged sharply for expressive texts: although COMET favoured the pivot output, readers reported that pivoting raised fluency yet substantially reduced faithfulness (4.10 → 1.77 on a 7-point scale) and were unwilling to accept it. These results show that the value of a prompting strategy is text-type-dependent and that fluent LLM output is not necessarily faithful, underscoring the need to pair automatic metrics with human evaluation when benchmarking low-resource translation. Full article

Immune checkpoint inhibitors (ICIs) have translated the scope of cancer therapy, but their immune-restorative mechanism can lead to immune-related adverse events (irAEs), which can affect several organs with varying severity. Early identification of patients at risk of irAEs is prudent to inform clinical decisions in precision oncology and remains a challenge as current clinical and biomarker methods still lack predictive accuracy. Artificial Intelligence (AI) presents a promising strategy for improving early detection, risk stratification as well as monitoring of irAEs. This narrative review summarizes the current AI modalities for detecting and predicting irAEs risk occurring with ICI therapy, including clinical Machine Learning models, radiomics-based approaches, natural language processing (NLP) systems and the integration of modalities with multimodal AI frameworks. Clinical Machine Learning models demonstrate moderate predictive performance whereas radiomics-derived modeling appears promising for pneumonitis. NLP and language models have achieved higher accuracy for retrospective irAEs detection. Multimodal AI applications offer theoretical potential through diverse data integration that captures the complex biology of irAEs; however, current evidence is limited. All these modalities face limitations of inadequate sample sizes, retrospective design, heterogeneous outcome definitions, class imbalance, and insufficient external validation. AI-based models have significant potential for personalized immunotherapy monitoring but require prospective multicenter validation, standardized datasets and clinically interpretable frameworks prior to implementation. Future advances in multimodal modeling can also enable precise prediction and early detection of irAEs, ultimately improving the safety and effectiveness of cancer immunotherapy. Full article

The growing presence of artificial intelligence (AI) in everyday life has generated debate regarding its impact on language services, where tools such as Large Language Models (LLMs) are increasingly being integrated. Framed as an exploratory and preliminary study, this article examines how a self-selected sample of 60 language-service professionals working in European contexts perceive the adoption of AI, particularly LLMs, in relation to professional practices, quality, ethical concerns, and emerging competence requirements. An embedded mixed-methods design was adopted, combining descriptive quantitative analysis with the thematic analysis of open-ended responses. The findings suggest a cautious and selective adoption of LLMs. While respondents recognise potential advantages related to speed, productivity, and support for specific tasks, they also identify persistent limitations concerning quality, terminology, contextual adequacy, cultural sensitivity, and the need for human revision. Respondents also report concerns about professional devaluation, changing work conditions, and the need for reskilling, particularly in relation to general translation and AI-assisted workflows. At the same time, some participants identify opportunities for innovation, enhanced human oversight, and the revaluation of specialised expertise. Overall, the study suggests that, from the perspective of the surveyed professionals, AI is contributing to the reconfiguration of language-service practices, while reinforcing the continued importance of human judgement, linguistic expertise, ethical responsibility, and critical engagement with AI-generated outputs. Full article

Caused by the SARS-CoV-2 virus, the COVID-19 pandemic is still considered a complex challenge, with manifestations not only of respiratory issues, but also conditions related to chronic cerebrovascular damage. Endothelial biomarkers, neuropathological and neuroimaging findings indicate endothelial dysfunction, microthrombosis, and disruption of the blood–brain barrier (BBB) are central mechanisms for acute and chronic ischemic and hemorrhagic cerebral events. Understanding these mechanisms is vital to reducing their impact on population health, whether through treatment or prevention of adverse outcomes. The objective of this study is to perform a review of the scientific literature on the post-infection effects of SARS-CoV-2 affecting the cerebral endothelium and the BBB, correlating them with potential clinical outcomes. Material and Methods: Analysis of studies extracted from the PubMed database using the following terms: Long-COVID “AND” SARS-CoV-2 “AND” blood–brain barrier. Inclusion criteria: keywords, publications related to the topic, and primary studies published after peer review. Exclusion criteria: preprint studies, publication outside of the timeframe 2020–2025, study design not compatible with this research, and full text not available. Results: An initial 121 studies were identified, of which 105 were excluded due to not meeting all inclusion criteria and 6 studies were inaccessible due to not being in the English language and full-text access limitations. Fifteen articles were included in the analysis, for topics as expression of viral receptors in the endothelium, markers of their activation, cerebral microvascular injury and coagulopathies. To clarify the pathogenic cascade, the evidence was stratified by biological model where in vitro evidence demonstrates that the Spike protein induces direct endothelial toxicity and platelet aggregation, establishing the primary molecular insult. Animal models confirm the translation of this insult into structural degradation of the BBB and pericyte loss. Clinically, infection-phase findings, characterized by multifocal microthrombosis and permeability spikes, act as the determining event that predisposes to the persistent neuroinflammatory environment. Biomarkers of BBB disruption and neuronal damage were consistently reported, with persistence of BBB dysfunction modifying risk stratification and rehabilitation efforts. Reported cases of Long-COVID demonstrated normalization of BBB markers without correlation with long-term symptoms, suggesting that other mechanisms are involved in Long-COVID. Conclusion: Cerebral endotheliopathies and BBB dysfunction in patients with COVID-19 continue to impact the health of the population. The scientific literature indicates that SARS-CoV-2 induces cerebral endothelial injury, BBB disruption, and an increased risk of vascular events related to endotheliopathy, inflammation, and hypercoagulability. Understanding the impact of COVID-19 pathology on the population and developing prospective studies is essential to quantify the prevalence and mechanisms of brain injury. The identification of molecular targets and infection pathways are promising toward defining both preventive and therapeutic strategies to improve outcomes in the Long-COVID population. Full article

Despite the rapid growth of AI-based digital language learning platforms, prevailing approaches to vocabulary instruction have remained relatively unchanged. Flashcards, translation drills, and isolated quizzes continue to dominate, and while they may support short-term retention, they tend to strip away the contextual, physical, and spatial dimensions that research identifies as important for durable language acquisition. This paper proposes spatial experiential vocabulary learning as a pedagogically grounded alternative. Drawing on situated learning theory, embodied cognition, and experiential learning theory, it argues that lasting vocabulary acquisition may depend not only on repeated exposure but also on learners engaging actively with language in meaningful, spatially situated contexts. The central theoretical contribution is the EARR framework, a four-phase learning cycle comprising Encounter, Act, Respond, and Reuse, in which vocabulary is embedded within object-rich spatial environments that invite purposeful interaction rather than passive recognition. The paper develops this framework theoretically and draws out its implications for platform design. It concludes by identifying priorities for future empirical research and by considering how digital language learning environments might be redesigned to better support the conditions under which vocabulary acquisition appears to occur. Full article