Search Results (1,186)

The rapid expansion of artificial intelligence in the educational field has configured a broad, dynamic, and constantly evolving research domain. Nevertheless, there remains a need to systematically analyze the evolution of its pedagogical approaches and to identify the conceptual dimensions that structure recent scientific production. For this purpose, a systematic literature review was conducted following the PRISMA protocol, based on searches in Web of Science and Scopus. The final corpus consisted of 235 articles, analyzed using bibliometric and semantic techniques in R, including bibliometrix, tidyverse, and ggplot2, complemented by co-occurrence maps developed with VOSviewer. The thematic classification was carried out through an inductive analysis based on clusters and emerging patterns. The results reveal a progressive transition from technocentric approaches toward more complex and integrative pedagogical perspectives. The semantic analysis made it possible to identify four structuring dimensions of the field: critical, ethical, literacy-oriented, and humanistic. Recent literature also shows a growing emphasis on teacher education, academic integrity, and cognitive coexistence between humans and intelligent systems. These findings indicate that artificial intelligence not only introduces technological innovations but is also reconfiguring the epistemological and pedagogical foundations of contemporary education, demanding conceptual frameworks capable of articulating its ethical, cognitive, and formative implications. Full article

This study investigated undergraduates’ conceptualization of systems thinking (ST). An open-ended question was administered pre- and post-course. Pre-test findings revealed limited conceptualization, with most students unable to articulate core ST attributes. Post-course responses showed reasonable improvement, with seven key attributes—interconnectedness, feedback, causality, systems boundary, mapping, emergent behaviour, and synthesis—emerging to varying extents in their responses. While nearly all students indicated interconnectedness and mapping, fewer mentioned feedback and systems boundary, indicating these as higher-order cognitive skills. A continuum was also developed to categorize students’ conceptualization from inadequate to canonical; this also indicated that only a few students demonstrated engagement with the key attributes of ST. Novel analytical approaches such as attributes prevalence tables, attributes continuum, and evolution of threshold concepts have contributed to different modes for exploring ST in the responses. Findings underscore the complexity of ST and the challenges in fostering holistic conceptualization. Overall, the study highlights a nuanced engagement with the attributes of ST from the intervention and suggests that further work is necessary to better foster these among the students. Full article

Educational information systems have evolved into highly interconnected digital landscapes that support learning management platforms, student information systems, institutional repositories, and online assessment environments. As these systems increasingly operate across cloud infrastructures and mobile devices, ensuring the confidentiality, integrity, and availability (CIA Triad) of educational data is critical for safeguarding institutional operations and maintaining trust in digital education services. This paper investigates how next-generation security protocols, such as adaptive multi-factor authentication and advanced access control and data protection mechanisms, can reinforce ISO/IEC 27001:2022 requirements within contemporary educational information systems. The analysis maps emerging protocol capabilities to relevant new ISO/IEC 27001:2022 control domains, illustrating how they mitigate threats associated with unauthorized access, data manipulation, and service disruption. The proposed framework is further supported by an implementation-oriented mapping and an illustrative operational architecture that demonstrates the feasibility of translating prioritized security determinants into practical mechanisms. The FAHP analysis identifies access control mechanisms, backup and recovery, and data validation as the three highest-weighted determinants, with aggregate weights of 0.061, 0.059, and 0.057, respectively. These determinants are translated into a determinant-driven Security Operationalization Matrix that connects ISO/IEC 27001:2022 control domains, CIA dimensions, and technology recommendations, and is complemented by implementation feasibility considerations tailored to the budgetary, infrastructural, and resource constraints characteristic of educational institutions. Based on the prioritization results and conceptual operationalization, the proposed integrative approach provides a structured and progressively adoptable foundation for CIA-oriented security governance in digital educational environments. Full article

The mineral system model formalizes the critical geological processes and mappable parameters that control ore formation, which can then be translated into spatial predictors used as input features in machine learning (ML)-based mineral prospectivity mapping (MPM). In most MPM studies, exploration evidence features are indeed derived from the mineral system model of the targeted deposit type. However, not all features produced in this way are necessarily informative or favorable for prospectivity analysis. This challenge can be addressed by using feature selection frameworks to identify the most relevant features before applying ML and deep learning (DL) algorithms for mathematical integration. To address this need, this study employs an unsupervised variational autoencoder (VAE) framework to evaluate and rank exploration evidence layers. The VAE quantifies feature importance through a systematic strategy that measures the sensitivity of reconstruction-error components, mean squared error (MSE), mean absolute error (MAE), and Kullback–Leibler (KL) divergence, to individual feature variations. In this way, the VAE ranks the exploration features and helps to identify those that are the most useful for prospectivity mapping. The proposed approach was applied to a real geo-dataset from a porphyry copper district in Iran. Based on the conceptual model of porphyry copper mineralization, 15 evidence layers were generated, including proximity to phyllic, argillic, propylitic, iron oxide, and silicification alteration zones; proximity to intrusive rocks, faults, and fault intersections; and geochemical maps of Cu, Mo, Sb, Pb, Zn, As, and W. The VAE-based ranking indicated that evidence layers related to hydrothermal alterations, intrusive rocks, and faults were the most influential exploration features, whereas geochemical evidence layers showed lower relative importance. Based on this evaluation, two modeling scenarios were considered: in the first, all available features were used, and in the second, only the features selected by the VAE framework were included. In both cases, the final prospectivity model was produced by an autoencoder (AE). For comparison, the prediction-area (P–A) plots of the two prospectivity models were generated using 14 known mineral occurrences as positive ground-truth labels, indicating that the model based on the selected features achieved a higher prediction rate (80%) than the model based on all features (72%). These results demonstrate that the evidence layers derived from the mineral system approach can benefit from unsupervised VAE-based evaluation, leading to improved performance of the prospectivity modeling. Full article

In spite of plausible biology, the majority of oncology trials involving drug repurposing have failed to demonstrate any efficacy. Numerous factors can potentially cause failure, including issues with dosing, drug strength, the trial design itself, and patient diversity. A major, potentially correctable contributor is the absence of pharmacogenomic eligibility criteria. Here, we propose the Pharmacogenomic Stratification Framework for Drug Repurposing (PSDR), a novel framework for drug response that encompasses the triad of Exposure (E, pharmacokinetic adequacy), Target engagement (T, somatic tumor genomics), and Context competence (C, tumor microenvironment). These domains are represented as R = E × T × C, an eligibility model capturing the necessary, though not sufficient, conditions for anticancer drug activity. The model is not presented as an empirically validated quantitative law but as a conceptual framework to guide biomarker-stratified trial design. We derive five testable pharmacogenomic hypotheses for metformin, statins, beta-blockers, NSAIDs, and SSRIs, and propose a three-point PSDR eligibility scoring system. Prospective validation of each hypothesis in appropriately stratified cohorts is required before clinical implementation. The PSDR framework complements rather than replaces existing precision oncology resources (OncoKB, CIViC, PharmGKB, CPIC, DepMap, GDSC) by integrating germline pharmacokinetics, somatic genomics, and microenvironmental profiling for repurposed agents. If validated, PSDR-guided enrichment designs could substantially improve the efficiency and interpretability of repurposing trials. The PSDR framework should be considered a conceptual and hypothesis-generating model that requires prospective validation before clinical implementation. Full article

Background: Type 2 Diabetes Mellitus (T2DM) is associated with a vascular-executive cognitive decline profile that early impacts complex daily tasks. Despite the increased risk of Mild Cognitive Impairment (MCI) in this population, there is a critical shortage of instruments specifically validated for this group. This scoping review aims to identify the instruments used to assess functionality in individuals with T2DM and MCI and to map their psychometric properties. Methods: We conducted a scoping review based on the JBI methodology and PRISMA-ScR guidelines. The search was performed across several electronic databases (PubMed, Cochrane Library, Web of Science, Scopus and SciELO), up to March 2026, focusing on the intersection of T2DM, mild cognitive impairment, and the psychometric properties of functional scales. Results: Our search identified only three studies meeting the eligibility criteria. The functional instruments evaluated across these publications were the ADCS-ADL scale, the A-FAQ, and a predictive nomogram including the Lawton-Brody scale. Methodological approaches, sample configurations and reported outcomes varied substantially within the included literature, with no comparative validation studies conducted among homogeneous T2DM cohorts. Conclusions: The notable scarcity and marked heterogeneity of the available literature prevent any definitive conclusions regarding the comparative diagnostic superiority of current functional scales. While gradated instruments show conceptual compatibility with the executive-vascular cognitive decline profile of T2DM, their psychometric properties remain unvalidated in this specific population. Future research should prioritize longitudinal validation designs in homogeneous diabetic cohorts to standardize screening protocols calibrated to metabolic and vascular variations. Full article

Background/Objectives: Grief following the death of a partner is a complex psychosocial process associated with an increased risk of prolonged grief, depression and suicidal ideation. Among lesbian, gay, bisexual, transgender, intersex, and queer (LGBTIQ+) individuals, these risks are exacerbated by stigma, relational invisibility and family rejection, often resulting in unrecognized or disenfranchised grief. This scoping review aimed to map the available evidence on the experiences of bereavement following the death of a partner among LGBTIQ+ individuals between 2016 and 2026, identifying study types, recurring themes and knowledge gaps relevant to nursing practice. Methods: A scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) extension and the methodology of the Joanna Briggs Institute. Searches were planned in PubMed/MEDLINE, Scopus, CINAHL, PsycINFO and Web of Science (2016–March 2026) using combined terms for grief, partner and LGBTIQ+ populations. Primary qualitative, quantitative and mixed-methods studies, as well as selected grey literature that explicitly addressed grief following the death of a partner in LGBTIQ+ individuals were considered. Results: The search identified 1032 records; after removing duplicates (n = 356), 676 titles/abstracts were screened, and 94 full texts were assessed. Eighteen studies were included, mainly qualitative, and conducted in high-income countries. Key themes included invisibility and lack of recognition of the relationship, managing the disclosure of sexual orientation and gender identity, social isolation and the role of chosen families, and intersectional vulnerabilities in subgroups such as older adults, bisexual people and trans people. Conclusions: The available evidence reflects specific bereavement experiences among LGBTIQ+ individuals that are not adequately captured in traditional models of bereavement care. Significant gaps remain, particularly in Spanish-speaking contexts and in the design and evaluation of nurse-led interventions. This scoping review provides a conceptual basis for future research and for the development of culturally safe clinical practices in supporting LGBTIQ+ individuals through bereavement. Full article

Spanish–English bilingual learners in U.S. dual language and bilingual education programs develop Spanish orthographic competence while receiving literacy instruction across two writing systems that differ in phonological transparency, orthographic depth, and grammatical marking. This study examined experts’ perceptions of the clarity, instructional coherence, pedagogical relevance, applicability, and refinement priorities of a pedagogical framework for Spanish orthographic development in contexts where Spanish is used as a language of instruction and literacy. The framework conceptualizes Spanish orthographic decision-making as the coordinated activation of phonological mapping, orthographic–grammatical reasoning, and visual–lexical retrieval within biliteracy development. Using a qualitative evaluative design, the study analyzed open-ended questionnaire and interview data from 44 experts in bilingual education and Spanish literacy-related fields. Findings show broad convergence regarding the framework’s clarity, instructional coherence, and relevance for bilingual contexts. Participants emphasized pre-dictation preparation, explicit metalinguistic analysis, visual-memory activation and retrieval routines, and cross-linguistic comparison between Spanish and English. They also identified refinement priorities, including classroom-ready examples, clearer articulation of error and autocorrection, and stronger integration with reading, writing, and oracy practices. This study positions Spanish orthographic instruction as a cognitively guided biliteracy practice and identifies design principles for strengthening orthographic, metalinguistic, and cross-linguistic instruction in bilingual programs. Full article

Welding aluminum (Al) to titanium (Ti) is particularly challenging because of the large differences in their melting points and the tendency to form cavities and brittle intermetallic compounds. Such issues can be mitigated in friction stir welding (FSW) by understanding the underlying mechanisms of microstructural evolution and tensile fracture behavior. In the present study, FSW was carried out on commercially pure Al and commercially pure Ti. X-ray micro-computed tomography results show that the distribution of Ti fragments depends on their morphology, with fine particles (volume 10³–10⁴ µm³) being distributed homogeneously, while large flakes (10⁷–10⁹ µm³) are concentrated near the joint interface. A three-dimensional analysis of Ti fragment distribution was performed to clarify material flow and particle dispersion within the weld nugget. EDS (Energy-Dispersive Spectroscopy) and EPMA (Electron Probe Microanalysis) composition mapping confirmed the formation of AlTi and Al₃Ti intermetallic phases, with Al₃Ti as the dominant phase (consistent with its lower Gibbs free energy of formation). Because Al is the primary element in the matrix and undergoes the highest degree of deformation, its microstructural evolution in Al was examined using Electron Backscatter Diffraction (EBSD). Grain refinement in Al was attributed to continuous dynamic recrystallization (CDRX). Mechanical mixing and intermetallic formation increased the hardness of the weld, while the tensile response corresponded to a joint efficiency of approximately 77%, alone with an 11% improvement in elongation over base Al. The study further establishes a correlation among Ti particle distribution, local microstructural evolution, and the tensile response of the joint. Fractographic analysis indicates a bimodal fracture mechanism, and failure occurred away from the joint interface, indicating a strong joint. To interpret this behavior, a spring-based model was proposed to relate the fracture location and tensile deformation to the spatial variation in microstructure across the welded zones. This approach provides a conceptual framework that is extendable to other dissimilar material systems with spatially varying microstructures. Full article

Education for Sustainability (EfS) has emerged as a key framework through which higher education engages ecological, social, and civic challenges. Although EfS is well represented in policy and conceptual scholarship, few empirical studies have examined how sustainability is enacted in everyday teaching practice. This exploratory qualitative collective case study investigates the pedagogical experiences of four faculty members at a U.S. land-grant university. Data were collected through semi-structured interviews, supported by syllabi, observations, and student responses, and analyzed using cross-case thematic analysis. Four interconnected themes were identified: latent complexity, personal commitment, inclusive scholarship, and adaptability to student motivations and context. Taken together, the findings offer an initial mapping of how EfS is interpreted and enacted in faculty teaching, while also underscoring the context-bound nature of these cases. The study contributes an exploratory, practice-based account of sustainability teaching and provides a foundation for future comparative research across institutions, disciplines, and regions. Full article

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related death worldwide, and first-line systemic treatment has shifted toward immune checkpoint inhibitor (ICI)-based combinations. Response is heterogeneous, and mechanistic interpretation has lagged behind clinical practice, leaving open the question of why some tumors respond while others do not. This review uses the cancer immunity cycle as an HCC-specific scaffold to map where anti-tumor immunity fails—across priming, trafficking, suppressive myeloid or stromal, and metabolic-hypoxic barriers—and interpret combination strategies and resistance through the dominant barrier each tumor presents. ICI monotherapy rescues only specific failure points within the cycle. Combination regimens may be more effective when they are matched to one or more dominant barriers, whereas response may fail when the selected partner addresses only a secondary barrier while the dominant ecological constraint remains intact. Resistance can be similarly organized into tumor cell autonomous, microenvironmental, treatment-induced, and etiology-specific layers, with disease etiology shaping both baseline immune ecology and therapy-context vulnerability. A mechanism-based, biomarker-guided, and etiology-aware framework may help move the field from broad empiricism toward precision immunotherapy, but it should be viewed as a conceptual and translational organizing model that requires prospective testing in biomarker-stratified studies. Full article

Cyber–Physical Systems (CPSs) are increasingly vulnerable to Distributed Denial-of-Service (DDoS) attacks, which can disrupt critical operations and compromise system safety. Although deep learning (DL) techniques are widely adopted for cyberattack detection, conventional DL-based classifiers often struggle to handle the uncertainty and ambiguity inherent in network traffic data. To address this limitation, this paper proposes an AI-driven hybrid framework, termed LSTM–Fuzzy–CPS, for adaptive DDoS detection in CPS environments. Unlike prior LSTM–Fuzzy approaches that are primarily restricted to SDN settings, the proposed framework is adapted for CPS environments and introduces continuous risk scoring, reduced false positives for safety-critical operation, and proportional mitigation mechanisms. The framework consists of a detection module and a conceptual mitigation module. The detection module, named LSTM–Fuzzy–Detector, integrates an LSTM network with a Mamdani-type fuzzy inference system that maps LSTM outputs into a continuous risk score using triangular membership functions (Low, Medium, High) and centroid defuzzification. The mitigation module is designed as a rule-based conceptual framework that translates risk levels into adaptive response actions; however, its experimental implementation is left for future work. The proposed detector is evaluated on the CICIoT2023 dataset and achieves an accuracy of 99.83% with a false-positive rate of 0.12%, demonstrating strong robustness against complex and evolving attack patterns. These results indicate that the proposed framework provides an effective, interpretable, and scalable solution for intelligent threat detection in CPS environments. Full article

Background/Objectives: Effective clinical leadership is a critical driver of healthcare quality, patient safety, and organisational performance. However, evidence on the leadership competencies of healthcare professionals in formal management roles remains fragmented. It is dispersed across professional groups, healthcare contexts, and conceptual frameworks, limiting opportunities for synthesis and cumulative knowledge development. This systematic review examined three questions: how clinical managers perceive their leadership competency; what challenges they encounter in exercising leadership roles; and what development mechanisms the literature identifies. Methods: A systematic review was conducted following PRISMA 2020 guidelines and registered in PROSPERO (CRD420261305279). Four databases were searched: Ovid MEDLINE, CINAHL, EMCARE, and Web of Science from January 2010 to February 2026. Two reviewers independently screened studies; methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT). Reported competencies were mapped to the five domains of the Clinical Leadership Competency Framework (CLCF) using narrative integrative synthesis. Results: Forty-nine studies were included across quantitative, qualitative, and mixed-methods designs from 24 countries. Competencies in the Working with Others and Demonstrating Personal Qualities domains were reported as strengths across the largest number of included studies. Competencies in Managing Services, Improving Services, and Setting Direction were reported as areas of weakness or developmental need across multiple studies. Leadership challenges included inadequate preparation, role ambiguity, limited authority, and organisational constraints. Development needs spanned formal training, strategic competency building, mentoring, and sustained organisational support. Conclusions: Clinical leadership competency is unevenly distributed across CLCF domains. This pattern reflects not only individual developmental gaps but also the organisational and contextual conditions that shape how leadership is enacted in practice. The findings support a contextual-relational model of clinical leadership. Both individual capability and enabling organisational conditions must be addressed to strengthen leadership effectiveness across healthcare systems. Full article

Background: Large language models and other generative artificial intelligence (AI) tools are increasingly being embedded in digital healthcare services, including mobile health applications, telerehabilitation, remote monitoring, and hybrid care pathways. In this review, digital healthcare refers to technology-mediated healthcare services in which digital platforms, mobile applications, wearables, remote communication, and AI-enabled interfaces support health assessment, self-management, rehabilitation, clinical decision support, or service delivery. When AI-generated exercise guidance moves from general education to individualized recommendations about dose, progression, contraindications, or rehabilitation, it may become directly actionable and safety-relevant. Objectives: This review aimed to clarify when AI-generated exercise guidance in digital healthcare may warrant safety-relevant governance attention and to outline implementation considerations for explainability, human oversight, and service-level governance. It addresses a gap in the literature: general AI-governance and exercise-prescription discussions rarely specify how point-of-use explanations, review thresholds, and escalation safeguards can be organized for directly actionable AI exercise guidance. Methods: We conducted a governance-oriented narrative review of peer-reviewed literature and representative regulatory or guidance documents. This review was not designed as a systematic review, scoping review, or exhaustive evidence map; transparent source mapping was used to support conceptual synthesis. Searches and source mapping focused on generative AI, large language models, explainable AI, clinical decision support, digital health, mobile health, exercise prescription, rehabilitation, trust, automation bias, and human oversight. Sources were included when they informed the safety, explainability, governance, or real-world implementation of patient-facing AI-generated exercise guidance. Extracted material was grouped by evidentiary role and synthesized through framework synthesis and governance mapping to distinguish literature-supported observations, author interpretation, and proposed implementation tools. Results: The included sources were first organized into five thematic groups: digital exercise delivery and exercise-prescription evidence; explainability, trust, and automation bias literature; professional responsibility, ethics, and patient disclosure literature; regulatory and policy documents; and digital literacy, patient/clinician attitudes, and equity literature. The synthesis then proceeded from safety relevance to explanation needs, human oversight and escalation needs, and selected regulatory and policy signals before translating these strands into conceptual and implementation-oriented outputs rather than empirically validated instruments. AI-generated exercise guidance was most safety-relevant in scenarios involving individualized dose, progression, contraindication-sensitive action, or rehabilitation strategy. Across the included sources, generic transparency alone was not sufficient to support reviewable use; relevant explanation elements included evidence sources, risk warnings, reasoning paths, and reasonable alternatives. Oversight considerations varied with embodied risk, clinical ambiguity, user vulnerability, and likelihood of direct enactment. Implementation considerations linked interface design, clinical review, escalation, auditability, and post-deployment monitoring. Conclusions: AI-generated exercise guidance in digital healthcare may warrant governance attention as a patient-safety and accountability issue when it influences actionable exercise decisions. The proposed framework offers a conceptual basis for designing more reviewable and accountable mobile and remote exercise-support services. Future work can validate these outputs in patient-facing services, clinician review workflows, usability studies, implementation pilots, and safety evaluations. Full article

Higher construction costs (CCs) linked to carbon reduction methods have hindered the adoption of low-carbon approaches in the built environment. The simultaneous minimisation of upfront embodied carbon (EC) and CCs has not received much attention in building design optimisation (BDO) research; most studies prioritise operational energy, operational carbon, and operational cost reduction. This paper develops an integrated conceptual framework for low-carbon, cost-effective BDO, particularly targeting upfront EC and CCs, to fill this research gap and meet industry demands. A systematic literature review was conducted following PRISMA guidelines, synthesising 41 peer-reviewed articles published between 2015 and 2026. Thematic and content analyses were employed to extract and categorise key methodological components, including optimisation problem characterisation, objective-driven design variable selection, constraint modelling, algorithm selection, and evaluation and validation approaches. Subsequently, the developed conceptual framework was validated through semi-structured expert interviews with participants comprising BDO researchers and building designers in the construction field. A cross-mapping of optimisation objectives, optimised parameters, and design variables was developed to clarify their interrelationships, alongside structured criteria for optimisation algorithm selection. Based on these insights, a conceptual framework named “ICCO-BD (Integrated Upfront Carbon and Construction Cost Optimisation for Building Design) framework” is proposed and validated, integrating problem formulation, parametric modelling, multi-objective optimisation, and systematic Pareto-based evaluation into a coherent end-to-end workflow, enabling improved time efficiency through reduced redesign iterations, enhanced solution quality via better pareto front exploration, and more robust decision-making through clearer trade-off interpretation. While expert feedback indicated strong conceptual relevance and practical applicability, the framework remains conceptual in nature and requires further empirical verification through real-world case studies and optimisation applications before broader industry implementation. Full article