Search Results (3,766)

Background/Objectives: Primary healthcare in Hungary faces acute physician shortages; in the Heves district, eight general practices are permanently vacant, leaving 11,191 residents without continuous care. In September 2025, the Hungarian Charity Service of the Order of Malta launched the Heves Haziorvosi Pilot (HHP), combining on-site nurse triage with remote telemedicine consultations. This study qualitatively evaluated its implementation and early operation. Methods: Three focus group discussions were held in two rounds, before and after implementation, with all 19 professionals delivering the programme (10 practice nurses, 4 general practitioners, 5 degreed professionals). Verbatim Hungarian transcripts were analysed in NVivo 15 (Lumivero, Denver, CO, USA) using the thematic analysis framework of Braun and Clarke with hybrid deductive-inductive coding by three independent researchers until full consensus. Results: Daily physician availability rose from approximately 2 to 8 h, and one remote physician concurrently supported four practices. Participants reported reduced professional isolation among nurses, longer and more focused consultations, and perceived, though unquantified, decreases in non-urgent ambulance use. Implementation was constrained by duplicate paper and digital documentation, patients’ limited familiarity with triage, and unresolved software interoperability and IT ergonomic problems. Conclusions: The hybrid model was perceived as operationally feasible; national scaling would require nursing curriculum reform, administrative simplification, interoperable medical software, and community health literacy programmes. Full article

The digital divide in developing economies persists as a multidimensional challenge encompassing infrastructure access, digital skills, usage patterns, and social inequality. This paper presents a structured literature review of 63 peer-reviewed articles (2018–2025) examining how intelligent connectivity, the convergence of fifth-generation (5G) mobile networks, artificial intelligence (AI), and the Internet of Things (IoT), can contribute to bridging this divide. The findings reveal that intelligent connectivity offers transformative potential across agriculture, healthcare, education, and financial services. However, its impact is contingent upon enabling governance, institutional capacity, and digital skills. Three contributions emerge: (1) a conceptual framework specifying directional pathways from enabling conditions to intelligent connectivity deployment and inclusive outcomes; (2) a comparative regional analysis (Sub-Saharan Africa, Southeast Asia, and Latin America) identifying context-specific barriers and opportunities; and (3) a socio-technical model positioning intelligent connectivity as an integrated system rather than a purely technological solution. A key limitation is acknowledged: only 16% of the reviewed corpus addresses developing economies, necessitating triangulation with institutional reports from the International Telecommunication Union (ITU), Organization for Economic Co-operation and Development (OECD), and Global System for Mobile Communications Association (GSMA). The paper concludes with open research challenges and policy recommendations for inclusive digital transformation. Full article

The rapid integration of solar photovoltaic (PV) generation, distributed energy resources, and advanced communication infrastructures is transforming conventional power systems into highly interconnected cyber–physical smart grids. Although this transition improves sustainability and operational flexibility, it also increases grid-management complexity and introduces cyber–physical vulnerabilities, including false data injection attacks, communication failures, equipment degradation, and renewable-induced operational instabilities. This paper presents the Digital-Twin-Enabled Resilience Framework (DTERF), a conceptual reference architecture for anomaly detection in high-PV cyber–physical smart grids. DTERF integrates heterogeneous cyber–physical data acquisition, Digital Twin-based contextual representation, machine-learning analytics, explainable decision support, adaptive operational response, continuous learning, and self-healing capabilities within a unified resilience cycle. The framework is grounded in a structured review and comparative assessment of contemporary machine-learning approaches and recent integrated smart-grid research. Its architecture is conceptually evaluated through requirements-to-architecture traceability, examining functional coverage and internal consistency across the complete operational cycle. The analysis shows that DTERF provides explicit architectural mechanisms addressing the principal requirements identified in the literature, including contextual anomaly analysis, interpretability, cybersecurity robustness, resilience support, and operational integration. Rather than proposing a new anomaly detection algorithm or claiming empirical performance superiority, DTERF provides a technology-agnostic architectural foundation for coordinating complementary capabilities required for resilient anomaly management. Future work should empirically validate the framework using Digital Twin simulation environments, representative high-PV distribution systems, cyber–physical anomaly scenarios, and real or utility-derived operational data. Full article

Universities play a pivotal role in advancing the United Nations Sustainable Development Goals (SDGs) through education, research, governance, operations, partnerships, and community engagement. However, limited evidence exists regarding the extent to which sustainability has been institutionally embedded within Jordanian higher education. This study presents an evidence-based comparative assessment of SDG institutionalization across ten Jordanian public universities using publicly available and verifiable institutional documents. Institutionalization was evaluated across eight dimensions: governance and strategy, education and curriculum, research and innovation, campus operations, partnerships, community engagement, inclusion and equity, and monitoring and reporting. A four-level institutionalization rubric ranging from “No eligible publicly verifiable institutional evidence” (0) to “Institutionalization with monitoring and reporting” (3) was applied to 80 university–dimension observations. The universities achieved a mean Institutional SDG Integration Index (ISDGI) of 66.3%, indicating that sustainability has progressed beyond isolated initiatives toward formal institutionalization across the sector. Monitoring and reporting demonstrated the highest level of institutionalization (80.0%), followed by campus operations (70.0%), while partnerships (60.0%) and community engagement (56.7%) remained comparatively less institutionalized. Overall, 71 of the 80 university–dimension observations (88.8%) reached at least the level of formal institutionalization. The findings indicate that sustainability has become embedded in many institutional structures within Jordanian public universities, although stronger institutionalization and systematic monitoring of external engagement, together with evidence-based evaluation, are needed to translate formal institutionalization into measurable societal impact. The proposed framework provides a transparent and replicable methodology for assessing SDG institutionalization in higher education institutions. Full article

The transition toward decentralized renewable energy systems creates monitoring problems that current digital infrastructures do not solve: sustainability claims are produced by the same actors they evaluate, environmental evidence is reported periodically rather than observed continuously, and the communities most affected by deployment cannot inspect the data used to represent their territories. Existing integrated platforms combine subsets of the blockchain, Internet of Things (IoT) sensing and life cycle assessment (LCA) at the data layer, but they do not organize that integration through an explicit governance structure. This paper contributes a cybernetic governance framework in which the Viable System Model (VSM) supplies the organizing structure of a blockchain–IoT–LCA monitoring architecture, so that sensing, distributed trust, strategic intelligence and participatory governance are recursively coupled rather than sequentially chained. The framework was developed and evaluated under the Design Science Research paradigm, and instantiated in the IMPACT Energy.CO platform across two technology routes, wind and solar, in La Guajira, Cesar, Atlántico and Magdalena, Colombia. Evaluation against six pre-declared criteria reports 45 executed test cases with a 100% pass rate, 90% unit and 87% integration code coverage, load tests up to 5000 concurrent users with zero errors and sub-second mean response, an operating hash-chained provenance layer issuing verifiable LCA certificates, 14 participatory validation workshops, 199 users trained and 166 technicians certified. We use traceability in a deliberately narrow sense throughout: the property whereby a committed record can be linked to the ingested data series, model version and computation that produced it, and its integrity and ordering checked by a party that does not trust the producer. It is provenance and integrity traceability from the point of ingestion onward, and it is not metrological traceability: the architecture cannot verify that an original sensor measurement corresponds to the physical quantity it purports to represent. We accordingly make explicit what the architecture does not guarantee: a ledger protects records after commitment but cannot certify measurement at the point of capture, and we present a threat model, a set of implemented controls and the residual risk that remains. This study contributes an architecture, a reproducible development and evaluation method, and a calibrated account of what verifiable environmental monitoring can and cannot deliver in contested Global-South territories. Full article

With the advancement of healthy city development and community renewal practices, the influence of perceived community environmental quality on residents’ psychological well-being has attracted increasing attention. Previous research has shown that the built environment is closely related to residents’ well-being; however, most studies have focused on objective spatial elements or facility provision and have paid insufficient attention to the mechanisms through which subjective environmental perceptions operate in the process by which the environment affects psychological well-being. From the perspective of Homo Urbanicus theory, this study takes 36 communities in Yangpu District, Shanghai, as cases and uses data from 882 valid questionnaires. Structural equation modeling is employed to examine the relationships among perceived community environmental quality, perceived environmental restorativeness, and residents’ psychological well-being, and to further explore age-related patterns among young-old, middle-old, and oldest-old groups. The results show that perceived community environmental quality has a significant positive effect on residents’ psychological well-being and produces a significant indirect effect through perceived environmental restorativeness. Exploratory age-stratified analyses further suggested different pathway patterns among the young-old, middle-old, and oldest-old groups. On this basis, the study further proposes a four-quadrant model of spatial contact opportunities and a five-category accessibility classification and accordingly develops community environment optimization strategies for older adults of different ages. The findings provide a theoretical basis and practical reference for healthy-community development and age-friendly community renewal. Full article

This study aims to identify the key destination-related factors associated with tourists’ revisit behavior in peri-urban rural areas within Chinese metropolitan regions in the context of short-video marketing, and to reveal the structural relationships and hierarchical characteristics among these factors. First, the LDA topic model was employed to conduct text mining on authentic tourist-generated comments posted on Douyin, through which the core factors related to revisit behavior were identified and conceptually standardized based on tourists’ expressions. Building on this process, 153 experts in relevant fields were invited to evaluate the direction and strength of the relationships among these factors. An integrated DEMATEL–ISM–MICMAC approach was then applied to analyze their causal attributes, hierarchical structure, and systemic roles. The results indicate that the identified factors do not operate independently but instead form a multilayered structure with clear hierarchical characteristics. Among them, rural visual imagery, escape-oriented experience, rural lifestyle experience, and rural industry integration occupy deeper structural levels and exert relatively strong structural influences on factors located at intermediate and surface levels. The findings further suggest that the sustained attractiveness of rural tourism destinations in metropolitan regions cannot rely solely on short-video exposure or isolated “internet-famous” attractions; rather, it requires coordinated alignment among digital communication content, rural industries, lifestyle experiences, and tourism supply. By integrating tourist-generated content, natural language processing, and expert-based structural assessment, this study extends research on short-video tourism marketing from a systems perspective and provides practical insights for peri-urban rural destinations in Chinese metropolitan regions seeking to optimize the structural configuration of tourism resources, products and services, and marketing communication. Full article

This article presents a practice-oriented methodological proposal for designing inclusive multimodal learning content with Generative AI (GAI) in migrant adult literacy. It does not report an experimental intervention or a statistical evaluation of learning outcomes. Its contribution lies in formalizing a context-sensitive design pathway for early preA1–A2 literacy and language-learning provision in Italian CPIA settings, where learner profiles are highly heterogeneous, attendance may be discontinuous, and written language is both a learning goal and a barrier to participation. Unlike generic AI-supported instructional design frameworks, the proposed approach starts from recurrent communicative needs in adult migrant education and translates them into short, modular and reusable learning artifacts that coordinate textual, visual, audio-oral and interactive layers. The framework distinguishes multimodal design, understood as the pedagogical coordination of different semiotic modes, from the mere use of multiple media. It also integrates accessibility as a set of concrete design criteria, including linguistic readability, visual clarity, audio quality, layout, font size, contrast, cognitive load and usability in print or mobile formats. The article outlines a sequence of design operations: mapping learner profiles, selecting situated communicative scenarios, generating and revising textual material, developing visual and audio scaffolds, structuring guided interaction, and applying pedagogical, cultural and ethical review. An illustrative micro-unit on asking for information at a municipal office shows how this pathway can support dialog, visual glossary, audio practice, role-play and formative assessment. The proposal is intended for CPIA educators, adult literacy professionals, instructional designers and researchers in multimedia learning and educational technology. Its educational implication is that GAI can support inclusive material design only when its outputs are treated as provisional resources to be selected, adapted and validated through human pedagogical judgment. Full article

Claims of AI sovereignty are increasingly invoked but operational control remains uneven. Claims to control are made through national models, sovereign clouds, data localization mandates, and procurement rules; however, whether such claims translate into demonstrable control over how AI systems are run, inspected, and contested remains poorly understood. This paper introduces credible sovereignty, the gap between declared and demonstrable control in deployment, as a conceptual lens for analyzing AI governance to examine how this gap is opened and closed across infrastructure, data, and model supply chains. Using a PRISMA-guided social-science corpus and machine learning-based BERTopic modeling, validated through topic diversity and topic separation diagnostics and triangulated through close reading, the analysis identifies four governance logics through which sovereignty is contested: data infrastructure and legitimacy frameworks; techno-bloc diplomacy and infrastructure politics; European regulatory sovereignty; and community-driven sovereignty in the Global South. Across these logics, sovereignty is enacted less through national capabilities than through proxy mechanisms—certification regimes, procurement clauses, cloud governance, and deployment architectures—each carrying trade-offs between autonomy, dependence, and accountability. Rereading the corpus through an Antecedents–Decisions–Outcomes lens yields a testable research agenda: antecedents that push actors toward sovereignty seeking; design and governance choices that translate ambition into implementation; and outcomes—resilience, inclusion, accountability—against which sovereign AI programs should be assessed. This paper reframes sovereignty as a layered operational capability rather than a discursive claim and links computational synthesis to a normative construct that applies across jurisdictions and scales. Full article

Terahertz (THz) metasurfaces have drawn considerable research interest, owing to their compelling potential in sensing, imaging, and wireless communication. However, most existing designs are constrained to a single predefined function, severely hindering their practical applicability in dynamic or multifunctional scenarios. Herein, we present a reconfigurable THz metasurface that enables on-demand functional transformation by harnessing the phase transition characteristics of vanadium dioxide (VO₂). The designed unit cell adopts a six-layer stacked configuration, sequentially comprising a VO₂ square ring, a first polyimide (PI) dielectric spacer, an elliptical gold patch, an intermediate VO₂ thin film, a second PI dielectric spacer, and a gold ground plane. When VO₂ is in its metallic phase, the metasurface operates as a metal–insulator–metal (MIM) absorber, achieving over 90% absorption in the frequency range of 1.01–1.91 THz. In the insulating state, it acts as a polarization converter, enabling efficient linear-to-circular polarization conversion (PC) with an axial ratio (AR) below 3 dB from 1.82 to 2.21 THz under linearly polarized (LP) incidence. Moreover, the metasurface exhibits robust performance under varying incident angles and different polarization conditions. Collectively, this design offers a flexible and reconfigurable platform for advanced THz devices, laying a solid foundation for THz communication, intelligent sensing, and imaging systems. Full article

Intensive yak fattening in cold alpine regions requires essential long-term high-concentration feeding, which disrupts rumen microbial homeostasis and causes inefficient digestion of nutrients. Lactobacillus may have probiotic potential for ruminants, yet its regulation of rumen function is poorly understood under high-energy diets. During a 120-day feeding experiment, 120 male Pamir yaks were allocated to four dietary treatments, with LEG and LLG serving as the primary comparison for evaluating 0.02% Lactobacillus supplementation to explore the regulatory effects of Lactobacillus supplementation on the rumen microbiota and host metabolism of yaks during a fattening process based on a concentrate feed diet via phenotypic data (body wight and nutrient digestibility) and multi-omics analyses (rumen microbial sequencing and rumen epithelial transcriptome) in a concentrate-based rearing yak model. The results showed that Lactobacillus intervention reduced OTU (Operational Taxonomic Unit) richness during the early fattening period and subsequently promoted microbial recovery through colonization resistance, which significantly enhanced microbial diversity (p < 0.05), and restructured the microbial community structure toward efficient energy utilization under high-concentrate feeding by reducing Prevotellaceae and Ruminococcaceae abundance, increasing the Bacillota/Bacteroidota ratio (p < 0.05). Concurrently, Lactobacillus enhanced the apparent digestibility of dry matter, crude protein, and fibrous components (p < 0.05). According to the transcriptomic analysis, there was an activation of signaling pathways related to IL-18 and TNF, and up-regulation of immune-and metabolism-related genes, in addition to strengthening the rumen mucosal barrier function. Multi-omics integration supported that dietary supplementation of Lactobacillus can optimize rumen fermentation, enhance nutrient digestion, and strengthen immune defense in Pamir yaks fed high-concentrate in cold seasons. These modifications demonstrate the positive effects of the Lactobacillus supplementation strategy on yak rumen health without interfering with the high-energy intensive rearing pattern. The present research presents a scientific basis for the use of targeted probiotic strategies to improve the rumen health and efficiency of alpine yak production systems. Full article

Commercial automotive technologies are advancing rapidly in areas such as electrification, connectivity, digital transformation, and autonomous systems. As military organizations increasingly incorporate commercial technologies, it is important to understand how commercial trends align with future battlefield requirements. This paper presents a framework for forecasting military ground vehicle requirements through 2040 by integrating commercial automotive technology trends with observations from contemporary warfare. This analysis identified automotive technology trajectories through a synthesis of published bibliometric studies covering major automotive research domains from 2016 to 2026. Military operational requirements were derived from battlefield narratives published by the Institute for the Study of War (ISW) and open-source vehicle loss data reported by Oryxspioenkop during the Russia–Ukraine War. The automotive and military analyses were then aligned to identify areas of convergence between commercial technology development and future battlefield requirements. The results indicate that future battlefields will be characterized by persistent surveillance, precision fires, contested logistics, contested electromagnetic environments, high attrition, and rapid battlefield adaptation. Future military vehicles will increasingly leverage commercial technologies to improve autonomous operations, predictive sustainment, fuel efficiency, resilient communications, and signature management while incorporating military-specific capabilities where required. The resulting framework provides a structured methodology for linking commercial automotive innovation with military vehicle modernization, acquisition planning, and future capability development. Full article

Destinations increasingly shift from promotional branding toward holistic and inclusive identity strategies, where cultural heritage operates as both an anchor of local distinctiveness and a catalyst for creative place-making. This study presents a mixed-methods Triple Bottom Line (TBL) assessment of four Greek cultural heritage institutions, a regional theatre, a municipal gallery, a university-affiliated foundation, and a regional film festival, to examine how governance structures, resource availability, and organisational capacity shape environmental, social, and economic sustainability performance. Through document analysis and semi-structured interviews, the research identifies eleven recurring thematic variables across the TBL dimensions and systematically compares their implementation across distinct governance models. Findings show that social sustainability, particularly accessibility, education, and community engagement, constitutes a structurally universal baseline across all institutions, regardless of governance type or resource level. In contrast, environmental and economic performance diverge sharply according to governance model, building ownership, and access to capital. As cultural institutions function as experiential landmarks within cultural and creative tourism, their inclusive practices enhance community wellbeing while co-producing destination identity as socially responsible and culturally authentic. The study concludes with targeted policy recommendations on reporting standards, capacity building, and structural constraints, offering transferable insights for creative place-making and sustainable destination branding in Greece and other Mediterranean contexts. Full article

The application of Machine Learning (ML) to bridge Structural Health Monitoring (SHM) is constrained by a lack of diverse and labelled datasets. Obtaining high-quality training data from operational infrastructure is inherently difficult because critical assets are typically repaired immediately upon the detection of defects, preventing the collection of data describing diverse failure modes. To address this scarcity and enable the prototyping of robust algorithms, this study presents a framework for generating synthetic modal frequencies using a calibrated Finite Element (FE) model of the S101 bridge. Aleatory uncertainties and environmental variability are incorporated through the stochastic variation of material properties and thermal loads derived from a 20-year climate record. Analysis of the generated dataset revealed that simulated thermal loads induced frequency shifts that often exceeded those caused by minor structural damage, confirming the necessity of training on environmentally representative data. The primary contribution of this work is an open-access, FAIR-compliant (Findable, Accessible, Interoperable, Reusable) synthetic dataset, intended to serve as a standardised benchmark for the SHM research community under conditions of combined structural and environmental uncertainty. To demonstrate the utility of the generated data, the performance of a supervised multi-layer perceptron and an unsupervised k-means clustering algorithm are evaluated, with the supervised approach achieving a maximum classification accuracy of 1.00. However, the framework also reveals a fundamental modelling limitation: the linear FE approach failed to replicate the physical response under pier settlement, producing frequency shifts an order of magnitude below those observed experimentally. Full article

Tool-level attacks on Large Language Model (LLM) agents—poisoned tool descriptions, prompt injection, and capability misrepresentation—are universally effective, yet no existing defense provides comprehensive protection. We propose Architectural Intent Collapse (AIC), a formal framework capturing the systematic loss of communicative intent when text from heterogeneous sources is flattened into a single context window. Grounded as a novel instantiation of the Confused Deputy Problem, AIC reveals that the missing boundary is not permission but intent: the architecture cannot distinguish descriptive statements from prescriptive commands. We formalize AIC via an architectural collapse operator, introduce Intent Separation Degree (ISD) as a measurable metric, and develop a mechanism-based taxonomy of five intent-disguise attack types, including two previously undescribed (Conditional Latency and Inference Inducement). Experiments across 25 framework–model combinations (employing GPT-4o, Claude-4-Sonnet, Gemini-2.5-Pro, DeepSeek-V3, and Qwen3-32B as LLM backends) confirm that ISD degrades with description verbosity, strongly predicts defense effectiveness ($r=-0.97$), and is uniformly low across all current frameworks. Three root-cause defense principles are derived; one retains substantial protection against adaptive attackers. This research is useful for agent framework designers, security practitioners, and researchers seeking a principled understanding of why tool-level attacks succeed and how architectural defenses can address their root cause. Full article