Search Results (59)

Authentic Digital Interaction (ADI) refers to citizens’ direct, secure, and independent engagement with e-government services without reliance on intermediaries. This systematic literature review applies ADI as an organizing lens to synthesize recent empirical evidence on determinants shaping citizen interaction with e-government. Following PRISMA, 178 peer-reviewed studies published between January 2020, and October 2025 were identified across five databases, and 43 met the inclusion criteria. Descriptive mapping and ADI-guided narrative synthesis were used to consolidate related determinants and interpret their associations and contextual conditions. The review identifies three dominant patterns: perceived usefulness and performance expectancy are most frequently associated with intention, use, and continuance; trust and confidence shape whether perceived benefits translate into engagement; and policy and governance condition service consistency and the effects of usability and accessibility. Theoretically, the review shows that ADI provides a useful lens for interpreting e-government research beyond adoption and satisfaction by emphasizing direct, trustworthy, inclusive, and independent citizen interaction. Practically, the findings suggest that public agencies should prioritize accessible design, transparent processes, visible safeguards, and supportive governance arrangements. However, no formal risk-of-bias assessment was conducted. In addition, the evidence base remains limited by the sparse examination of participation, value co-creation, autonomy, and empowerment, and the review protocol, although prepared in advance, was not registered. Full article

This study examines how AI-enhanced motion capture (AI-MoCap) mediates the preservation, transmission, and re-creation of Chinese shadow puppetry as performative intangible cultural heritage. Through a state-of-the-art review and comparative analysis of three representative application models—technology-driven, culturally integrated, and entertainment-oriented—the paper explores how AI-MoCap supports the digitization of performative techniques while reshaping modes of cultural presentation and interaction. Cross-case comparison highlights recurring tensions between technical standardization and cultural authenticity while also indicating possibilities for symbolic reconstruction, contextual continuity, and ethically grounded design. Based on this comparison, the paper develops a dual-channel inheritance framework—“perception–symbol” and “design–performance”—and treats cultural resolution and digital ethics as analytical and normative principles for resisting algorithmic homogenization. Rather than functioning only as a digitization tool, AI-MoCap can be understood as a mediating mechanism whose cultural value depends on how it remains embedded in community-based performative logics, symbolic systems, and ethical boundaries. The resulting framework offers transferable guidance for future research, curation, training, and policy discussion in the digital safeguarding of performance-based heritage. Full article

This interdisciplinary study integrates criminological theory with geospatial methods to analyse large, multi-format datasets using geostatistical techniques. The aim is to predict where Child Criminal Exploitation (CCE) is likely to cluster, based on the spatial convergence of contextual risk factors. Drawing on insights from General Strain Theory (GST) and prior research on CCE, this study integrated seven open-source datasets capturing educational attainment, age demographics, violent crime, deprivation, and paramilitary-related violence. These variables were operationalised to construct a proxy measure for strain. Spatial analysis was conducted using ArcGIS Pro, including the Data Interoperability extension, to enable efficient integration and interrogation of multi-format geospatial data. Geospatial analysis demonstrated that contextual risk factors for CCE are spatially clustered. Using four search parameters, a small subset of wards with elevated risk were identified. This resulted in a reduction in ward locations by 85–99%, land area under investigation from 14.45% to 0.84%, and affected population from 17.91% to 1.41%, enabling more targeted and efficient resource allocation. As understanding of the contextual factors contributing to CCE improves, this methodological approach offers scalable and data-driven means of identifying high-risk areas. By integrating geospatial analysis with criminological theory, the model supports more effective safeguarding strategies and prioritisation of limited public resources. This study is limited by the absence of multi-agency datasets, which were beyond its scope. Future research aims to incorporate cross-sector data to validate and refine the model through ground-truthing, enhancing its predictive accuracy and practical applicability. Full article

Background: The IQVIA Disease Analyzer (DA) database is a major outpatient electronic health record dataset in Germany. Over recent years, it has been increasingly used to study neurological diseases, comorbidities, treatment patterns, and long-term sequelae. We narratively summarized neurology-related studies using the German IQVIA Disease Analyzer (DA) database published since 2020 and to highlight methodological considerations relevant for interpreting DA-based neurological research. Methods: We conducted a narrative review of DA-based studies published between January 2020 and December 2025. PubMed was searched using DA-related keywords and major neurological disease terms. Eligible articles included peer-reviewed cohort, case–control, or descriptive studies using DA outpatient data. Results: The review identified studies covering epilepsy, cerebrovascular outcomes, Parkinson’s disease, dementia, multiple sclerosis, migraine, and sensory disorders. Most used retrospective cohort or nested case–control designs with regression or propensity score methods. Follow-up durations ranged from 3 to 10 years. Results consistently reflected routine care outpatient diagnostic and prescribing patterns. Discussion: Strengths of DA studies include large patient populations, long follow-up, and detailed prescription information. Limitations include reliance on outpatient ICD-10 coding, lack of detailed neurological phenotyping, and potential residual confounding and bias. Conclusions: DA-based analyses generate clinically relevant routine care evidence on neurological conditions in the German outpatient setting. Proper methodological safeguards and complementary data sources are required to contextualize findings for clinical and epidemiological use. Full article

The rapid expansion of the tourism industry underscores the necessity for sustainable hotel recommendation systems that guide user choices while safeguarding the long-term viability of the tourism ecosystem. However, existing methods often struggle to reconcile individual user preferences with sustainable consumption objectives, frequently encountering the “information cocoon” effect and lacking interpretability in their decision-making processes. To address these issues, this study proposes a multi-objective, context-aware hotel recommendation framework that integrates text mining, sequential behavior modeling, and reinforcement learning. The framework begins by employing unsupervised learning to extract multidimensional hotel features from online reviews, with an explicit emphasis on comprehensive sustainability metrics. It subsequently applies a dynamic state representation approach that merges long-term and short-term interests with real-time contextual information to accurately reflect evolving consumer needs. Furthermore, a dynamic feature weighting module is incorporated to enhance interpretability and enable context-adaptive evaluation of both commercial and sustainable attributes. The recommendation process is structured as a Markov Decision Process, leveraging a composite reward function comprising diversity penalties and sustainability incentives. Empirical analysis using real-world data validates the framework, demonstrating its contribution to sustainable tourism and achieving recommendation accuracy that surpasses existing benchmark models. Full article

Early and precise detection of plant diseases is essential for safeguarding crop yield and ensuring sustainable agricultural practices. In this study, we propose the Modified RefineNet with Attention based Fusion (MoRefNet-AF), a Modified RefineNet architecture enhanced with attention-based fusion for multi-class classification of corn (maize) and Pepper leaf diseases. Unlike the original RefineNet, which was segmentation-oriented and computationally heavy, MoRefNet-AF is redesigned for lightweight and discriminative classification. The modifications include replacing standard convolutions with depthwise separable convolutions for efficiency, adopting the Mish activation function for smoother gradient flow, redesigning the multi-resolution fusion module with concatenation and shared convolution for richer cross-scale integration, and incorporating Squeeze-and-Excitation (SE) blocks for adaptive channel recalibration. Additionally, Chained Residual Pooling (CRP) with atrous convolutions enhances contextual representation, while global average pooling with dense layers improves classification readiness. When evaluated on a curated six-class dataset combining PlantVillage and Mendeley leaf disease repositories, MoRefNet-AF achieved 99.88% accuracy, 99.74% precision, 99.73% recall, 99.95% F1-score, and 99.73% specificity. These results outperform strong baselines including ResNet152V2, DenseNet201, EfficientNet-B0, and ConvNeXt-Tiny, while maintaining only 0.3 M parameters. With its compact design and TensorFlow Lite (v2.13) compatibility, MoRefNet-AF offers a robust, lightweight, and real-time deployable solution for precision agriculture and smart plant disease monitoring. Full article

The rapid integration of generative artificial intelligence into scientific publishing is reshaping how academic text can be produced, revised, and scaled. While transparent and limited use of AI for language support may be acceptable, a new structural vulnerability may be emerging in medical publishing: the large-scale production of short, plausible, and weakly individualized correspondence across multiple specialties. In this viewpoint, we describe and conceptualize a pattern that may be termed synthetic authorship, defined not as undisclosed AI use alone, but as a reproducible mode of scholarly output structurally facilitated by automation. We focus particularly on letters to the editor, a format that combines brevity, rapid editorial handling, and formal indexation, and may therefore be especially exposed to this phenomenon. Based on recurring patterns observed in PubMed-indexed literature, including unusually high publication velocity, abrupt thematic dispersion, and stylistic uniformity across unrelated domains, we argue that such outputs may challenge the authenticity, epistemic value, and editorial function of scientific correspondence. We do not present empirical proof of misconduct, but rather outline a conceptual framework for understanding this emerging risk and propose proportionate editorial safeguards, including cross-domain pattern detection and contextual assessment of authorship coherence. As AI lowers the threshold for generating domain-plausible commentary at scale, scientific publishing must adapt its integrity frameworks accordingly. In this context, vigilance toward synthetic authorship may become an essential component of editorial responsibility and post-publication quality control. Full article

HM Inspectorate of Probation is committed to building and utilising the evidence base for high-quality youth justice services, and to promoting excellence and having a positive impact upon those inspected and the wider sector. Research evidence and inspection findings are used to inform understanding of what helps and what hinders services and to consider system-wide change. In this article, the latest inspection and research findings in relation to the high-profile areas of serious youth violence and criminal exploitation are highlighted. The article encompasses insights from core and thematic inspections, including those from recent joint targeted area inspections (JTAIs) undertaken with other inspectorates. Alongside the JTAIs which examined multi-agency responses to serious youth violence, research was commissioned to hear directly from children and families about their experiences. Other research commissioned and published by the Inspectorate has emphasised the importance of implementing relational, child-centred and trauma-informed approaches and to optimising collaborative/partnership working across agencies and sectors. Reports have also drawn attention to the value of paying attention to the socio-ecological framework, systemic resilience, adultification biases, and both contextual and transitional safeguarding. Full article

Archeological glass has attracted significant attention in recent years. Its archaeometric study has proven to provide remarkable insights into technological development and relationships among ancient cultures. Thus, ancient glass remains have been recovered from oblivion, and their preservation has become a priority. An extraordinarily well-contextualized collection of ancient glass beads, comprising over 1200 pieces, has been recovered from the archeological site of Pintia (Padilla de Duero, Valladolid, Spain). A large fraction of this collection appears to be well preserved. However, recent detailed studies on its most relevant piece, a Phoenician glass pendant, evidenced the presence of carbonatation processes. Accordingly, an extensive analysis of the preservation state of this collection was required to safeguard it for future generations. Thus, 64 representative samples from this collection, including diverse chronologies, morphologies, and colors, were analyzed by micro-FTIR spectroscopy at the IRIS beamline of the BESSY-II synchrotron (Berlin, Germany), yielding ATR and reflectance spectra. This work, the first micro-FTIR spectroscopy study of a large set of pre-Roman glass beads, provided evidence about the preservation of the glass structure of these pieces, as well as about the presence of crystalline weathering products. Full article

Cybersecurity effectiveness in digitally intensive university environments depends not only on technological safeguards but also on how individuals enact protective behaviors within decentralized systems. While prior research has largely emphasized compliance intention, limited empirical attention has examined how behavioral mechanisms translate into measurable confidentiality, integrity, and availability (CIA) outcomes in smart universities. This study develops and tests an integrated structural model grounded in the Theory of Planned Behavior and Social Cognitive Theory to examine how contextual exposure, cognitive resources, and motivational processes jointly influence security outcomes. Using structural equation modeling (SEM), data from 540 respondents across multiple higher education institutions were analyzed. Behavioral intention (β = 0.489) emerges as the strongest predictor of CIA, followed by self-efficacy (β = 0.190). Cybersecurity knowledge influences CIA indirectly through attitudes and intention rather than through a dominant direct path. Technological exposure (β = 0.250) and social norms (β = 0.540) primarily strengthen knowledge formation. The model demonstrates strong empirical fit (CFI = 0.997; RMSEA = 0.057; SRMR = 0.015). These findings show that CIA protection in smart universities emerges through structured cognitive–motivational pathways in which awareness is transformed into capability and intention, rather than through technological exposure alone. Full article

Introduction: The COVID-19 pandemic exposed structural weaknesses in European public health systems while simultaneously accelerating institutional and digital reforms at the European Union (EU) level. This study examines how the EU has evolved from reactive crisis management toward a governance paradigm conceptualized as transformative resilience, understood as the institutional capacity to anticipate, adapt, and structurally reconfigure health governance in response to systemic shocks. Methods: This study employs a structured qualitative policy analysis based on a purposive corpus of key EU legislative and strategic documents (2020–2025), complemented by a contextual review of selected EU-level indicators. The analysis focuses on reforms associated with the European Health Union, including the establishment of the Health Emergency Preparedness and Response Authority (HERA), the development of the European Health Data Space (EHDS), and the adoption of the Artificial Intelligence Act. Results: The findings indicate progressive consolidation of supranational coordination mechanisms, deeper integration of digital infrastructure into health governance, and strategic incorporation of health security into the EU’s broader security architecture. Rather than assessing policy effectiveness, the analysis documents a structural and regulatory shift toward anticipatory and embedded preparedness. Persistent challenges remain, including uneven implementation capacity across member states, disparities in digital maturity, and tensions between innovation and data protection. Conclusions: The EU’s post-pandemic trajectory reflects a distinctive governance model in which health security, digital sovereignty, and democratic safeguards are framed as mutually reinforcing dimensions of resilience within an increasingly complex risk environment. Full article

Ensuring equitable access to healthcare services safeguards individual wellbeing and enhances society’s overall happiness. This study investigates the complex relationships between spatial hospital accessibility, spatial inequality, and urban wellbeing, focusing on the physical dimension of access measured by travel time. Using geospatial and economic data from 13,776 hospitals, this study reveals that inequality in hospital accessibility, as measured by the Gini coefficient, significantly and negatively impacts urban happiness. Additionally, the results reveal a nonlinear, inverted U-shaped relationship between hospital accessibility and city-level happiness, indicating an optimal threshold beyond which marginal benefits decline. Additionally, the results indicate a key mediating mechanism: unequal access drives population out-migration and reduces the permanent resident population. This outcome, in turn, partially transmits adverse effects to city-level wellbeing. These findings demonstrate substantial spatial and contextual heterogeneity, underscoring the need for policymakers to tailor urban health policies that prioritize enhancing accessibility and ensure equitable distribution to foster sustainable demographic stability and overall urban wellbeing. Full article

Effective disaster management is critical for safeguarding lives, infrastructure and economies in an era of escalating natural hazards like floods and landslides. Despite advanced early-warning systems and coordination frameworks, a persistent “last-mile” challenge undermines response effectiveness: transforming fragmented and unstructured multimodal data into timely and accountable field actions. This paper introduces ResQConnect, a human-centered, AI-powered multimodal multi-agent platform that bridges this gap by directly linking incident intake to coordinated disaster response operations in hazard-prone regions. ResQConnect integrates three key components. It uses an agentic Retrieval-Augmented Generation (RAG) workflow in which specialized language-model agents extract metadata, refine queries, check contextual adequacy and generate actionable task plans using a curated, hazard-specific knowledge base. The contribution lies in structuring the RAG for correctness, safety and procedural grounding in high-risk settings. The platform introduces an Adaptive Event-Triggered (AET) multi-commodity routing algorithm that decides when to re-optimize routes, balancing responsiveness, computational cost and route stability under dynamic disaster conditions. Finally, ResQConnect deploys a compressed, domain-specific language model on mobile devices to provide policy-aligned guidance when cloud connectivity is limited or unavailable. Across realistic flood and landslide scenarios, ResQConnect improved overall task-quality scores from 61.4 to 82.9 (+21.5 points) over a standard RAG baseline, reduced solver calls by up to 85% compared to continuous re-optimization while remaining within 7–12% of optimal response time, and delivered fully offline mobile guidance with sub-500 ms response latency and 54 tokens/s throughput on commodity smartphones. Overall, ResQConnect demonstrates a practical and resilient approach to AI-augmented disaster response. From a sustainability perspective, the proposed system contributes to Sustainable Development Goal (SDG) 11 by improving the speed and coordination of disaster response. It also supports SDG 13 by strengthening adaptation and readiness for climate-driven hazards. ResQConnect is validated using real-world flood and landslide disaster datasets, ensuring realistic incidents, constraints and operational conditions. Full article

Biomarker-based prevention is rapidly expanding, driven by advances in molecular diagnostics, genetic profiling, and commercial direct-to-consumer (DTC) testing. General practitioners (GPs) increasingly encounter biomarker results of uncertain relevance, often introduced outside the guideline frameworks. This creates new challenges in interpretation, communication, and equitable resource use in primary care. This narrative review synthesizes evidence from population-based studies, guideline frameworks, consensus statements, and communication research to evaluate the predictive value, limitations, and real-world implications of biomarkers in asymptomatic adults. Attention is given to polygenic risk scores, DTC genetic tests, neurodegenerative and cardiovascular biomarkers, and emerging multi-omics and aging markers. Several biomarkers, including high-sensitivity cardiac troponins, N-terminal pro–B-type natriuretic peptide, lipoprotein(a), coronary artery calcium scoring, and plasma p-tau species, showed robust predictive validity. However, many widely marketed biomarkers lack evidence of clinical utility, offer limited actionable benefits, or perform poorly in primary care populations. Unintended consequences, such as overdiagnosis, false positives, psychological distress, diagnostic cascades, and widening inequities, are well documented. Patients often misinterpret unvalidated biomarker results, whereas DTC testing amplifies demand without providing adequate counseling or follow-up. Only a minority of biomarkers currently meet the thresholds of analytical validity, clinical validity, and clinical utility required for preventive use in general practices. GPs play a critical role in contextualizing biomarker results, guiding shared decision-making, and mitigating potential harm. The responsible integration of biomarkers into preventive medicine requires clear communication, strong ethical safeguards, robust evidence, and system-level support for equitable, patient-centered care. Full article

Elderly welfare institutions in Taiwan have experienced multiple severe fire incidents, with smoke inhalation accounting for the majority of fatalities. Hot smoke can rapidly propagate through interconnected ceiling spaces, complicating evacuation for residents with limited mobility who depend heavily on caregiving staff and external responders. Field inspections conducted in this study indicate that 82% of residents require assisted evacuation, underscoring the critical role of early detection, staff-mediated response, and effective smoke control. Drawing on disaster management theory, this study examines key determinants of fire safety performance in elderly welfare institutions, where caregiving staff are primarily trained in medical care rather than fire safety. A total of 64 licensed institutions in Tainan City were investigated through on-site inspections, structured checklist-based surveys, and statistical analyses of fire protection systems. In addition, a comparative review of building and fire safety regulations in Taiwan, the United States, Japan, and China was conducted to contextualize the findings. Using the defense-in-depth framework, this study proposes a three-layer fire safety strategy comprising (1) prevention of fire occurrence, (2) rapid fire detection and early suppression, and (3) containment of fire and smoke spread. From a sustainability perspective, this study conceptualizes fire safety in elderly welfare institutions as a problem of risk governance, illustrating how defense-in-depth can be operationalized as a governance-oriented framework for managing fire and smoke risks, safeguarding vulnerable older adults, and sustaining the resilience and continuity of long-term care systems in an aging society. Full article