Search Results (57,833)

Educational sustainability has emerged as a critical framework for achieving the Sustainable Development Goals, yet the systematic measurement and management of sustainability features in schools remain underexplored, particularly regarding the application of advanced data-driven technologies. This study addresses the research question: How is Business Intelligence being utilized in measuring and managing sustainability features within educational contexts? A systematic literature review was conducted across Web of Science and Scopus databases, employing rigorous inclusion and exclusion criteria. From an initial identification of 4317 records, 36 articles published between 2021 and 2025 were selected for comprehensive analysis. Bibliometric analysis revealed an annual growth rate of 60.69%, indicating rapid emergence of this research domain. Qualitative content analysis identified four principal dimensions structuring the intersection between sustainability and data-driven technologies in education: environmental (energy efficiency, waste management), social (academic performance, educational equity), economic (cost–benefit analysis, return on investment), and governance and management based on Business Intelligence and information technologies. The findings confirm the convergence of Business Intelligence and educational sustainability as a promising field, while revealing a critical absence of empirical investigations validating these concepts within Brazilian contexts, particularly in technical secondary schools. This gap establishes both the foundation and scientific justification for advancing research in this domain. Full article

Currently, the non-uniformity of girth weld positions makes their limit state a crucial determinant of pipeline safety. The design method based on the limit state is pivotal in ensuring the integrity and reliability of the pipeline system. Challenges often emerge when determining the limit states of girth welds using semi-empirical formula methods, primarily due to difficulties in accurately identifying influential factors. The quantitative impact of each influence parameter on the crack driving force and the results determined by the semi-empirical formula remain unclear. This study utilizes numerical simulation methods to systematically analyze the quantitative sensitivity laws of critical factors such as crack depth on the crack driving force to address this challenge. The findings revealed that the strength matching coefficient, crack depth, and misalignment are the most significant factors influencing the crack driving force, followed by crack length, softening rate, yield-to-strength ratio, internal pressure, and wall thickness. The effects of tensile strength and outer diameter are relatively minor. A comprehensive database of crack driving forces is constructed using a parameter matrix approach. Combined with the LightGBM machine learning algorithm, a full-scale prediction model for the strain capacity of pipeline girth welds is developed. Predictions for 18 sets of wide-plate test results from the literature confirm the high accuracy of the prediction model, with a prediction accuracy of 6.48%. This research provides a robust reference for accurately determining the limit state of pipeline girth welds and effectively meets the demands of rapidly advancing welding technologies and increasingly complex service environments. Full article

Ion-releasing restorative biomaterials have gained increasing attention in minimally invasive dentistry due to their potential to combine mechanical reliability with therapeutic functionality. Cention^® N is an alkasite-based restorative material designed to release fluoride, calcium, and hydroxyl ions while exhibiting mechanical properties comparable to resin-based composites. The present study aimed to systematically evaluate the clinical performance of this ion-releasing restorative material in comparison with conventional resin composites and glass ionomer cements. A comprehensive systematic search was conducted in PubMed (MEDLINE), Cochrane Library, Web of Science, Scopus, EMBASE, and SciELO databases up to 31 October 2024, following the PRISMA guidelines. Clinical studies assessing restorative performance outcomes were included. Meta-analyses were performed using Review Manager software (version 5.1). Fourteen studies met the inclusion criteria for qualitative synthesis, of which ten were eligible for quantitative analysis. The pooled results demonstrated comparable clinical performance between alkasite restoratives and resin-based composites regarding retention and secondary caries incidence, while superior outcomes were observed when compared with glass ionomer cements. Within the limitations of the available evidence, ion-releasing alkasite restorative materials represent a clinically acceptable alternative to conventional restorative options, combining functional biomaterial properties with reliable clinical performance. The conclusions should be interpreted within the context of the included studies, which exhibited clinical heterogeneity and, in several cases, a moderate risk of bias. Full article

Maritime operations are increasingly reliant on sensor data to drive efficiency and enhance decision-making. However, despite rapid advances in large language models, including expanded context windows and stronger generative capabilities, critical industrial settings still require secure, role-constrained access to enterprise data and explicit limitation of model context. Retrieval-Augmented Generation (RAG) remains essential to enforce data minimization, preserve privacy, support verifiability, and meet regulatory obligations by retrieving only permissioned, provenance-tracked slices of information at query time. However, current RAG solutions lack robust validation protocols for numerical accuracy for high-stakes industrial applications. This paper introduces Lighthouse Bot, a novel Agentic RAG system specifically designed to provide natural-language access to complex maritime sensor data, including time-series and relational sensor data. The system addresses a critical need for verifiable autonomous data analysis within the Artificial Intelligence of Things (AIoT) domain, which we explore through a case study on optimizing ferry operations. We present a detailed architecture that integrates a Large Language Model with a specialized database and coding agents to transform natural language into executable tasks, enabling core AIoT capabilities such as generating Python code for time-series analysis, executing complex SQL queries on relational sensor databases, and automating workflows, while keeping sensitive data outside the prompt and ensuring auditable, policy-aligned tool use. To evaluate performance, we designed a test suite of 24 questions with ground-truth answers, categorized by query complexity (simple, moderate, complex) and data interaction type (retrieval, aggregation, analysis). Our results show robust, controlled data access with high factual fidelity: the proprietary Claude 3.7 achieved close to 90% overall factual correctness, while the open-source Qwen 72B achieved 66% overall and 99% on simple retrieval and aggregation queries. These findings underscore the need for a secure limited-context RAG in maritime AIoT and the potential for cost-effective automation of routine exploratory analyses. Full article

Background: Poultry-associated Enterococcus spp. are widespread commensals but may serve as One Health indicators when virulence-associated determinants and antimicrobial resistance co-occur. We characterized paired phenotypic and genomic profiles to delineate species-stratified virulome and resistome patterns. Methods: Isolates originated from a previously established poultry collection with MIC testing. Genotype–phenotype analyses were restricted to the whole-genome sequenced subset (n = 31). The acquired antimicrobial resistance genes were identified using the Comprehensive Antibiotic Resistance Database (CARD), and virulence-associated determinants were screened using the Virulence Factors Database (VFDB). Results were summarized as isolate-level presence/absence matrices and integrated with MIC-derived susceptible/intermediate/resistant categories. Results: The WGS subset comprised E. faecalis (n = 23) and E. faecium (n = 8) with diverse sequence types. Virulome architecture was strongly species-dependent: E. faecalis carried a broad repertoire of adhesion/biofilm-associated determinants, whereas E. faecium showed a limited set of high-confidence virulence-associated hits. Acquired resistance determinants were common across isolates, and resistome profiles displayed structured co-occurrence. Integrated analyses suggested only a modest overall association between virulence-gene burden and acquired resistome size, largely driven by species-level differences. Genotype–phenotype concordance was class-dependent, with incomplete alignment in several antimicrobial classes, consistent with mechanisms beyond the screened acquired gene set. The acquired resistance determinants detected in the WGS subset predominantly mapped to antimicrobial classes commonly used in food-producing animals (e.g., tetracyclines, macrolides, lincosamides, aminoglycosides, and phenicols), supporting interpretation in the context of production-associated antimicrobial selection rather than implying last-line clinical resistance by default. Conclusions: Poultry-derived enterococci may combine genetic features compatible with persistence/colonization and acquired antimicrobial resistance, with co-occurrence patterns shaped primarily by species/lineage background. These findings support risk-stratified One Health surveillance and targeted functional and mechanism-focused follow-up. This integrated virulome–resistome view highlights species-specific risk signatures in poultry-associated Enterococcus and identifies discordant high-level phenotypes that merit targeted mechanistic follow-up. Full article

Bone remodeling is a dynamic process involving bone resorption and formation that is regulated on a cellular level and impacted by mechanical stress. A variety of Orthopedic surgery treatment strategies can affect bone remodeling, which can in turn may have long-term impacts on skeletal stress tolerance and function. This review provides a comprehensive overview of bone remodeling involved in Orthopedic surgery. Materials related to bone remodeling principles across Orthopedic surgery domains were selected and compiled using databases including PubMed, MEDLINE, AccessMedicine, and CINAHL; case studies were not included. Relevant literature was summarized for a general review of bone remodeling and as it relates to treatment principles in trauma, arthroplasty, and amputation with the aim of providing a relevant, comprehensive review. Overall, the purpose of this review is to provide an overview of bone remodeling principles that are implicated in various techniques within Orthopedic surgery. Full article

This study aimed to characterize the site-specific variation in skin traits of Shenxian pigs and to identify key genetic loci regulating skin thickness. A total of 50 Shenxian pigs were selected, and skin samples were collected from nine different anatomical sites. Total skin thickness was precisely measured, and collagen content was determined for each site. Based on literature review and database screening, TAF11 was identified as a candidate gene. Genotyping of the g.35543837 locus was performed using Sanger sequencing and KASP, followed by association analysis between different genotypes and skin thickness traits. The results showed significant site-specific variations in skin thickness (1.26–7.20 mm) and collagen content (7.01–24.54 g/100 g) in Shenxian pigs. Association analysis revealed that the TAF11 g.35543837 C > G variant was significantly associated with increased skin thickness, with the effect being particularly evident in gilt. Individuals with the CG genotype exhibited greater skin thickness at multiple anatomical sites compared with those carrying the CC genotype. This study preliminarily identified a potential locus associated with skin thickness in Shenxian pigs within the TAF11 gene. The sex-dependent effect observed at this locus provides a new clue for understanding the genetic basis of this complex trait and offers valuable information for the genetic improvement of skin-related traits in Shenxian pigs. Full article

The escalation of thermal runaway in lithium-ion batteries presents severe safety hazards that necessitate advanced monitoring protocols to ensure early warning of potential failures. Carbon dioxide (CO₂) is released during preliminary decomposition well before catastrophic failure occurs, thereby providing a strategic advantage for early-stage warning. Consequently, identifying materials with high-selective CO₂ recognition is an essential prerequisite for developing reliable sensing platforms. This study integrates Grand Canonical Monte Carlo simulations with Random Forest (RF) models to systematically screen 1470 MOFs from the CoRE-MOF 2019 database. The screening process evaluates selective CO₂ recognition under multicomponent competitive adsorption conditions involving CO₂, C₂H₄, and O₂. The performance evaluation is based on working capacity, selectivity, and the trade-off between working capacity and selectivity (TSN). The RF model achieves high predictive accuracy, with tested R² exceeding 0.92 on the test samples. Shapley Additive Explanations (SHAP) interpretability analysis identifies Q⁰_st(CO₂), Q⁰_st(C₂H₄), WEPA, K_H(C₂H₄), and ETR as key performance drivers. The results indicate that CO₂ selectivity is constrained by the binding strength of competing C₂H₄. Optimal materials tend to have hard Lewis acid centers and polar inorganic clusters to minimize non-specific π-interactions with interfering species. Top-performing MOFs require balanced structural features, concentrating in moderate surface areas (965–1975 m²/g), narrow pore windows (PLD ≈ 4–7 Å, LCD ≈ 5.5–9.6 Å), high void fractions above 0.6, and low densities below 1.3 g/cm³. AJOTEY emerges as the optimal candidate with a TSN of 6.43 mol/kg, combining substantial working capacity (4.57 mol/kg) with strong selectivity (25.52). These results will accelerate the discovery of sensing materials and provide a practical pathway for MOF-based CO₂ sensor development to enhance lithium-ion battery safety. Full article

The doctor–patient relationship is a central factor in healthcare delivery. Artificial Intelligence (AI) represents an emerging technological frontier whose implications remain to be fully clarified. Evidence-based studies provide reliable analyses of effects and offer a deeper understanding of both limits and benefits. This narrative review aimed to explore the role of AI in modern clinical practice, with particular reference to its effects on the doctor–patient relationship. Scopus and Web of Science databases were searched between 1 and 10 December 2025 to identify suitable studies. Inclusion criteria comprised English-language articles published in the last 10 years, with a direct focus on the doctor–patient relationship and exclusively employing empirical research designs. A total of 21 studies published between 2021 and 2025 were identified as eligible. The most common AI applications were conceptual systems discussed at a perceptual level (thirteen studies), followed by simulated AI decision-making scenarios (two studies). Implemented AI applications were less frequent and mainly included AI-based clinical decision support systems, administrative and documentation-focused tools, and a small number of conversational or relational AI applications (six studies in total). These studies focused on patients, healthcare professionals, and medical students preparing for future clinical roles. Results highlighted generally positive patient attitudes toward AI, often mediated by educational level, technological familiarity, and risk awareness. Among healthcare professionals, positive attitudes also emerged, although concerns regarding epistemic and professional values were noted. Greater involvement of clinicians in its development was consistently recommended. Findings from academic samples aligned with those of patients and clinicians, showing that integrating AI with traditional clinical practices was consistently preferred. Empathy, compassion, effective communication, accuracy, ethics, and trust were highlighted as fundamental values essential for mitigating risks. These elements are fundamental to the effective implementation of technologies aimed at improving clinical practice, while an integrative perspective is needed to safeguard the doctor–patient relationship. Overall, the use of AI in medical practice emerged as promising. Further studies should strengthen the empirical basis of the field to support an evidence-based approach to AI integration in healthcare. Full article

Background: The carcinogenic potential of cadmium has been suggested, but its association with endometrial cancer risk remains uncertain. This meta-analysis aimed to evaluate whether cadmium exposure is associated with the risk of endometrial cancer. Methods: A thorough search of seven databases was conducted to identify observational studies published up to September 2025. The Newcastle-Ottawa Scale (NOS) and the Agency for Healthcare Research and Quality (AHRQ) tool were utilized to evaluate the quality of observational studies. The I² statistic was calculated to assess heterogeneity among studies. Pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were estimated using a random-effects model. Furthermore, sensitivity analysis, subgroup analysis, and an assessment of publication bias were performed. Results: Eight studies involving 196,456 participants were included. Study quality assessment indicated that all included studies were of moderate or high quality. Overall, cadmium exposure was associated with an increased risk of endometrial cancer (OR = 1.27, 95% CI: 1.07–1.50, I² = 64.1%). Stronger associations were observed in case–control studies, European populations, and studies using blood or urinary cadmium biomarkers. The association remained significant in high-quality and adjusted analyses. Conclusions: The findings of this meta-analysis suggest a possible association between cadmium exposure and endometrial cancer risk. However, given the observational nature of the included studies, causality cannot be established. Further large-scale, well-designed prospective studies with standardized exposure assessment are needed to clarify this relationship. Full article

Manufacturing decarbonization and sustainability improvement require decision-support methods that can prioritise actions across multiple, often conflicting dimensions, including product quality, process stability, resource efficiency, and environmental performance. In industrial practice, such decisions are further complicated by stochastic variability and the presence of dominant drivers, which limit the usefulness of conventional linear, weighted-sum scoring approaches. This paper proposes a non-compensatory decision framework with explicit stochastic uncertainty propagation that integrates quality function deployment (QFD) with life cycle assessment (LCA) to support robust, value-driven prioritisation of manufacturing improvement actions under uncertainty. The approach combines QFD-style influence factor modelling with LCA-based environmental indicators and employs a nonlinear, non-compensatory aggregation scheme to reduce sensitivity to arbitrary weighting and to better capture dominant and tail-risk effects. Uncertainty is propagated using Monte Carlo simulation, and the stability of prioritisation outcomes is analysed using sensitivity measures. The framework is demonstrated on an industrial old corrugated container (OCC) paper machine line using operational data from plant information systems, including quality, process control, laboratory, and maintenance databases. Results show that the proposed integration yields more stable and interpretable prioritisation of improvement actions than conventional compensatory scoring methods, particularly under variable operating conditions. The proposed approach enables practical, data-driven sustainability decision-making in complex manufacturing processes under variable operating conditions and alternative process configurations. Full article

Data science has become central to contemporary social, civic, and professional life, yet its integration into initial teacher education remains fragmented and undertheorised. This paper addresses the need to support teacher educators in designing learning experiences that develop pre-service teachers, who are non-data science specialists, competence in data science. A systematic scoping review of the literature was conducted across major academic databases and complemented by an expert-informed literature identification strategy. The review examined how data science is described conceptually, how it is structured within school curricula and teacher education, and what knowledge and practices are emphasised for teachers. Findings indicate that while core processes and practices of data science, such as problem formulation, data preparation, exploratory analysis, modelling, visualisation, and ethical engagement, are widely recognised, their translation into teacher education is inconsistent and often lacks coherence. In response, the paper presents a conceptual framework designed to support pre-service teachers in engaging with the processes and practices of doing data science. The framework offers a flexible, practice-informed structure that is accessible to non-specialist teachers and aligned with pedagogical decision-making in educational settings. The paper concludes by discussing how the framework, alongside practical considerations for enactment, can support the preparation of data-literate teachers capable of fostering critical, ethical, and inquiry-based engagements with data in schools. Full article

Manual code review is essential for software quality, but often slows down development cycles due to the high time demands on developers. In this study, we propose an automated solution for Python (version 3.13) projects that generates code review comments by combining Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG). To achieve this, we first curated a dataset from GitHub pull requests (PRs) using the GitHub REST Application Programming Interface (API) (version 2022-11-28) and classified comments into semantic categories using a semi-supervised Support Vector Machine (SVM) model. During the review process, our system uses a vector database to retrieve the top-k most relevant historical comments, providing context for a diverse spectrum of open-weights LLMs, including DeepSeek-Coder-33B, Qwen2.5-Coder-32B, Codestral-22B, CodeLlama-13B, Mistral-Instruct-7B, and Phi-3-Mini. We evaluated the system using a multi-step validation that combined standard metrics (BLEU-4, ROUGE-L, cosine similarity) with an LLM-as-a-Judge approach, and verified the results through targeted human review to ensure consistency with expert standards. The findings show that retrieval augmentation improves feedback relevance for larger models, with DeepSeek-Coder’s alignment score increasing by 17.9% at a retrieval depth of k = 3. In contrast, smaller models such as Phi-3-Mini suffered from context collapse, where too much context reduced accuracy. To manage this trade-off, we built a hybrid expert system that routes each task to the most suitable model. Our results indicate that the proposed approach improved performance by 13.2% compared to the zero-shot baseline (k = 0). In addition, our proposed system reduces hallucinations and generates comments that closely align with the standards expected from the experts. Full article

In this work, we use an Echo State Network (ESN) model, which is essentially a recurrent neural network (RNN) operating according to the reservoir computing (RC) paradigm, to classify individual ECG heartbeats using the MIT-BIH arrhythmia database. The aim is to evaluate the performance of ESN in a challenging task that involves classification of complex, unprocessed one-dimensional signals, distributed into five classes. Moreover, we investigate the performance of the ESN in the presence of (i) noise in the dynamics of the internal variables of the hidden (reservoir) layer and (ii) random variability in the activation functions of the hidden layer cells (neurons). The overall accuracy of the best-performing ESN, without noise and variability, exceeded 96% with per-class accuracies ranging from 90.2% to 99.1%, which is higher than previous studies using CNNs and more complex machine learning approaches. The top-performing ESN required only 40 min of training on a CPU (Intel i5-1235U@1.3 GHz) HP laptop. Notably, an alternative ESN configuration that matched the accuracy of a prior CNN-based study (93.4%) required only 6 min of training, whereas a CNN would typically require an estimated training time of 2–3 days. Surprisingly, ESN performance proved to be very robust when Gaussian noise was added to the dynamics of the reservoir hidden variables, even for high noise amplitudes. Moreover, the success rates remained essentially the same when random variability was imposed in the activation functions of the hidden layer cells. The stability of ESN performance under noisy conditions and random variability in the hidden layer (reservoir) cells demonstrates the potential of analog hardware implementations of ESNs to be robust in time-series classification tasks. Full article

Background: With the rapid advancement of artificial intelligence (AI), its applications in andrology are expanding across diagnostic assessment, preoperative planning, intraoperative assistance, and postoperative management. This narrative review aims to synthesize current evidence regarding AI applications across the spectrum of andrological surgery. Methods: A comprehensive literature search was conducted using the PubMed, Scopus and Web of Science databases to identify relevant studies published between January 2020 and October 2025. The search strategy utilized combinations of keywords including “artificial intelligence,” “andrology,” “erectile dysfunction,” “male infertility,” “microsurgery,” and “robotic-assisted surgery.” Original research and review articles published in English were selected based on their clinical relevance to surgical practice. Results: AI has shown promise in the evaluation and management of erectile dysfunction (ED), male infertility-related microsurgery, and complex reconstructive procedures. AI-based models can improve risk prediction and diagnosis of ED, standardize semen analysis, support individualized selection of surgical candidates for varicocele repair and other interventions, and augment microsurgery through enhanced visualization and decision support. In the postoperative phase, AI-driven tools are being explored for complication prediction, functional recovery monitoring, and long-term quality-of-life follow-up, enabling more patient-centered, continuous care. Conclusions: AI holds significant promise for advancing precision medicine in andrological surgery by enhancing objective assessment and intraoperative guidance. However, large-scale, standardized datasets and rigorous multi-institutional validation are needed. Establishing robust ethical and legal frameworks will be essential to ensure the safe and effective integration of AI into routine andrological care. Full article