Search Results (11,606)

This article compares selected fire safety regulatory systems in Japan, China, the United States, and the EU/UK, interpreted through the lens of responsive architecture and the implementation of digital technologies—building information modelling (BIM), digital twins (DTs), artificial intelligence (AI), and the Internet of Things (IoT). The study adopts a qualitative approach based on a structured review of legal acts, technical standards, public-sector reports, and the scientific and professional literature, organised using a common analytical framework. First, the analysis identifies shared foundations across regimes: the primacy of life safety, mandatory detection and alarm functions, fire compartmentation, requirements for protected means of exit, and the increasing importance of documenting the operational status of protection measures. Then, it contrasts key differences, including the permissibility of performance-based design (PBD), the degree to which digital documentation is formally recognised, organisational enforcement models, and cybersecurity approaches for integrated fire alarm/voice alarm/building management/IoT ecosystems. Japan and selected Chinese cities combine stringent requirements with openness to dynamic solutions and urban-scale data platforms. The USA relies on a decentralised code-based ecosystem with a strong role for professional and industry bodies, while the EU/UK continues to strengthen harmonised standards and digital building registers, reinforced by lessons after the Grenfell Tower fire. Against this background, Poland is discussed as broadly aligned in goals and baseline technical requirements yet lagging behind in implementing PBD pathways, digital registers, formal BIM/DT integration, and minimum cybersecurity requirements. The proposed directions for change aim to create a more predictable regulatory and technical framework for the development of responsive architecture and dynamic fire safety systems in Poland. The study contributes to the sustainability literature by framing regulatory readiness for digital fire safety as a lifecycle resilience strategy, directly relevant to safe, resource-efficient, and inclusive built environments. Full article

Against the backdrop of a technological revolution centered on green and low-carbon development, the deep integration of digitalization and greening has become a core engine for high-quality progress. Moving beyond linear perspectives of environmental governance, this study constructs tripartite evolutionary game models to dissect the strategic interactions among government, enterprises, and consumers. Focusing on the institutional context of e-commerce, we examine how platform-enabled transparency mechanisms (e.g., blockchain traceability and carbon labeling) shape these interactions through key parameters: greenwashing detection (θ), premium loss coefficient (η), and information screening cost (C_D). The analysis reveals that the long-term trajectory is fundamentally determined by the intrinsic economic viability of corporate transformation. Government intervention acts as an equilibrium selector, influencing the speed of convergence, while product value (consumer utility and premium) and platform transparency determine the sustainability of the equilibrium. Critically, the tripartite model shows that the optimal outcome—full enterprise transformation and consumer adoption—can be achieved without sustained government intervention when product fundamentals are sufficiently attractive. This demonstrates the potential for market self-regulation to sustain digital–green synergy. The study makes three contributions: it captures the full tripartite feedback loop, reveals the saturation effect of policy intensity, and embeds platform transparency mechanisms into an evolutionary framework. The findings reframe the government’s role as a temporary enabler and position e-commerce platforms as key governance intermediaries, offering a theoretical basis for adaptive governance strategies in digital commerce. Full article

Urban neighborhood parks are essential infrastructure for sustainable cities, supporting physical and mental health, social cohesion, and climate adaptation. Equity-oriented park planning, however, requires accurate identification of residents who can access parks within network-constrained travel time thresholds. Many accessibility studies estimate served populations using coarse administrative zones and areal-weighting assumptions, which can bias results in heterogeneous, vertically developed districts. This study develops a building-based population allocation framework (implemented via a building centroid overlay) that integrates Statistics Korea’s census output areas (2023 Q4 release) with the Ministry of Land, Infrastructure and Transport (MOLIT)’s GIS Integrated Building Information database (2023 Q4 release) and applies it to Yongsan-gu (Yongsan District), Seoul. Park entrances were verified and digitized using street-view imagery available on multiple web map platforms, and walkable service areas (5 and 10 min) were delineated via network analysis. Potential service coverage and unserved population were then estimated under three spatial configurations—administrative dong (neighborhood-level administrative unit in Seoul; hereafter administrative unit), census output area, and building-based allocation—and compared. Under the 10 min scenario, the unserved share reached 24.6% at the administrative unit level but decreased to 5.9% and 4.3% when using census output areas and building-based allocation, respectively. The building-based approach additionally revealed micro-scale clusters of unserved residents near localized pedestrian constraints and boundary-crossing areas that are obscured by zone-based methods. These findings demonstrate the sensitivity of access-based potential service coverage diagnostics to spatial unit choice and population disaggregation and suggest that building-based population allocation can improve the targeting of park pro-vision policies and promote spatial equity in dense, vertically developed cities. Full article

Background/Objectives: The objective of this scoping review was to map and critically describe emerging speech-in-noise assessment tools developed over the last decade for the evaluation of hearing loss beyond conventional audiological measures. Methods: This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A comprehensive literature search was performed in the PubMed/MEDLINE, Scopus, and Embase databases. A comprehensive review of studies describing novel or emerging SIN-based assessment tools was conducted, with a particular emphasis on those including adult participants with normal hearing and hearing loss. Results: Nine studies met the inclusion criteria and were included in the review. The identified tools cover a range of methodological innovations, including advanced digits-in-noise paradigms, antiphasic and binaural presentation modes, optimized adaptive procedures, and digital or automated testing platforms. Several studies also incorporated artificial intelligence-based approaches, such as machine learning, text-to-speech, and automatic speech recognition, to enhance test development, administration, and hearing loss classification. Across all studies, SIN measures demonstrated the ability to reliably differentiate between normal hearing listeners and individuals with hearing loss and to provide complementary information beyond pure-tone audiometry. Conclusions: Emerging speech-in-noise tools show considerable potential to improve the functional assessment of hearing loss and to support more sensitive, accessible, and scalable approaches for hearing evaluation. Further research is required to assess their clinical integration and long-term impact on hearing screening and diagnostic pathways. Full article

With the rapid growth of digital image transmission, ensuring data security has become increasingly important. However, existing chaos-based image encryption algorithms often suffer from insufficient chaotic randomness and weak integration between chaotic dynamics and encryption mechanisms. To address these issues, a novel image encryption scheme based on a two-dimensional hyperbolic–exponential Sine–Logistic map (2D-HESLM) is proposed. A Sierpiński carpet-inspired scrambling strategy and a cascaded diffusion mechanism are designed to enhance permutation and diffusion performance based on the 2D-HESLM. The experimental results show that the information entropy value is 7.9980, while NPCR and UACI are approximately averaged 99.6147% and 33.4672%, respectively, with correlation coefficients close to zero. These results demonstrate the effectiveness and security of the proposed scheme. Full article

This study examines institutional processes in digital justice through a mixed conceptual approach that integrates bibliometric analysis and technology-adoption modeling, incorporating artificial intelligence (AI) as a projected component rather than an implemented system. A corpus of approximately 200 Scopus-indexed documents (2003–2024) was analyzed, identifying five dominant thematic clusters: advanced technologies, institutional justice, digital government, judicial information management, and digital criminal justice. The results reveal persistent gaps in the literature, particularly in rural and underserved communities, where connectivity barriers and the limited application of adoption models hinder inclusive digital transformation. As an institutional contribution, the study presents the conceptual design of the digital solution “Travel Permits—Accessible Justice”, developed under a Service-Oriented Architecture (SOA) and projected for future integration with AI-supported components to automate judicial authorizations through biometric validation, electronic signatures, and digital delivery. To evaluate its potential acceptance, the Technology Acceptance Model (TAM) is analytically adapted and extended to the community-based judicial context, framing institutional learning processes as a prospective form of learning analytics focused on user interaction, perceived usefulness, perceived ease of use, and behavioral intention. Taken together, the integration of bibliometric evidence with an extended TAM, along with the projected incorporation of AI-supported institutional learning processes, offers a coherent foundation for future studies on inclusive digital innovation in justice environments. Full article

For the corrosion behavior of three extruded Mg alloys (WE43, Mg10Gd, ZX10), the corrosion morphology and the resulting local stress distribution are correlated with the residual strength using µCT, Digital Image Correlation and tensile tests. Samples are corroded in HBSS at 37 °C for various exposure times to increase the extent of corrosion. They are then examined by using the gravimetric method to determine the corrosion rate. Corroded tensile samples are subjected to µCT analysis before and after tensile testing. The crack formation originating from pitting corrosion is discussed on the basis of the stress distribution around local corrosion—its extent is clearly influenced on the morphology. µCT analyses reveals that fractures occur in different ways, either at the smallest cross section, at isolated deep pitting sites, or in other critical areas with critical pitting quantity or size. Mg10Gd has a slightly higher strength compared to WE43 and ZX10. ZX10 maintains superior residual strength over time. Pitting corrosion is mainly observed in Mg10Gd and WE43, with different degrees of residual strength. This study allows for a better understanding and prediction of critical areas of non-uniform corroded Mg alloys and provides information on the bearable stress concentration. Full article

In an era of rapid digital transformation, brand activism has emerged as a prominent strategy through which organizations seek to signal social and environmental commitment while engaging increasingly sceptical and digitally empowered consumers. Within this context, perceived authenticity has become a critical evaluative mechanism shaping how digital brand activism is interpreted and whether it contributes to sustainable consumption and trust-based market outcomes. This study examines how perceived authenticity in digital brand activism is associated with consumer trust, attitudes toward socially engaged brands, and behavioral intentions that support sustainable consumption. Grounded in attribution theory and the authentic brand activism framework, the study adopts a quantitative, cross-sectional design based on an online survey of 240 consumers. The findings indicate that perceived authenticity is strongly associated with higher levels of consumer trust and more favorable attitudes toward digitally activist brands, reinforcing authenticity as a key trust-building mechanism in digital environments. Trust and attitudes are, in turn, positively associated with behavioral intentions such as purchasing, recommending, and willingness to pay a premium for sustainable products. However, behavioral intentions are weaker than trust and attitudinal evaluations, providing evidence of a persistent attitude–behavior gap that limits the translation of positive digital evaluations into concrete sustainable consumption outcomes. Exploratory results further suggest that the association between perceived authenticity of brand and behavioral intentions operates primarily through trust and attitudes rather than through a strong direct relationship. By clarifying these indirect pathways, the study advances attribution-based explanations of digital brand activism and contributes to research on smart innovation and digital sustainability by highlighting the role of authenticity in trust-based market outcomes. It also underscores the importance of authentic, data-informed digital strategies for fostering consumer trust, aligning brand activism with ESG principles, and supporting sustainable growth in digitally empowered markets. Full article

Background: Emergency triage systems using machine learning traditionally rely on structured tabular data (vital signs), creating a “contextual blind spot” that ignores diagnostic information embedded in unstructured clinical narratives. Hybrid AI models that fuse tabular and text data may improve predictive discrimination, but the magnitude and conditions under which fusion adds value remain unclear. Methods: Five databases (PubMed, Scopus, Web of Science, IEEE Xplore, ACM Digital Library) were searched from 1 January 2015 to 15 December 2025. Eligible studies employed Hybrid AI models integrating structured and unstructured emergency department data with quantitative baseline comparisons. Twenty-five studies (N ≈ 4.8 million encounters) met inclusion criteria. We extracted marginal performance gains (ΔAUC), calibration metrics, and demographic reporting. Synthesis followed SWiM principles with subgroup meta-regression testing our novel “Complexity Gradient” hypothesis. Results: Hybrid models demonstrated superior discrimination compared to tabular baselines, with effect magnitude dependent on clinical task complexity. Low-complexity tasks (tachycardia prediction) showed minimal gains (median ΔAUC + 0.036, IQR: 0.02–0.05), while high-complexity tasks (hypoxia, sepsis) demonstrated substantial improvement (median ΔAUC + 0.111, IQR: 0.09–0.13). Meta-regression confirmed complexity significantly moderated effect size (R² = 0.42, p = 0.003). Only 12% (3/25) of studies reported calibration metrics (Brier scores: 0.089–0.142). Zero studies stratified performance by race/ethnicity; 88% (22/25) failed to report training data demographics. Discussion: The complexity gradient framework explains when multimodal fusion adds predictive value: tasks where diagnostic signal resides in narrative features (temporality, negation) rather than physiological measurements. However, systematic absence of calibration reporting and fairness auditing prevents clinical deployment. Seventy-two percent of studies had high risk of bias in the analysis domain due to retrospective designs without temporal validation. Conclusions: Hybrid triage models show promise for complex diagnostic tasks but require mandatory calibration reporting and demographic performance stratification before clinical implementation. We propose minimum reporting standards including Brier scores, race-stratified metrics, and temporal validation protocols. Full article

The long-term sustainability of Australian agriculture is fundamentally constrained by the capacity, condition, availability, and governance of soil resources. Australian soils are among the oldest and most weathered globally, highly heterogeneous, and often slow or effectively irreversible to recover once degraded. Traditional approaches centred on soil health, while valuable at paddock scale, are insufficient to address national-scale challenges related to spatial variability, data continuity, economic valuation, and policy integration. This paper examines soil security as a policy-relevant framework for supporting more sustainable Australian agriculture. Building on the dimensions of soil security (capacity, condition, capital, connectivity, and codification), we synthesise recent Australian case studies to show how soil security extends beyond soil health to integrate biophysical properties, digital soil infrastructure, socio-economic value, and governance mechanisms. Drawing on recent Australian case studies, this review identifies advances in digital soil mapping, national soil assessments, economic valuation of soil capital, stakeholder connectivity, and emerging policy frameworks, while also identifying persistent gaps in regulation, data standardisation, and institutional coordination. The paper argues that soil security can help operationalise 3-N agriculture—Net-Zero, Nature-Positive, and Nutrient-Balanced systems—by translating sustainability goals into spatially explicit, place-based decisions grounded in soil realities. By explicitly accounting for soil capacity limits, condition trajectories, capital value, information flows, and codified rules, soil security can support more realistic climate mitigation strategies, targeted nature-positive interventions, and durable nutrient security outcomes. We conclude that embedding soil security more explicitly within Australian agricultural research, policy, and governance would strengthen efforts to deliver productive, resilient, and socially legitimate food and fibre systems. Without soil security, sustainability frameworks may remain difficult to operationalise consistently; with soil security, they can be translated more effectively into measurable, place-based, and durable decisions. Full article

Background: Current endometriosis classification systems have important limitations in accurately describing total disease burden and predicting clinical outcomes. Existing staging frameworks often fail to integrate adenomyosis and do not adequately distinguish between genital and extragenital disease involvement. The aim of this article was to introduce the AGCES (American & Global College of Endometriosis Specialists) classification system, a novel framework designed to provide a more comprehensive and clinically meaningful approach to staging endometriosis. Methods: The AGCES classification system was developed through an expert consensus process involving scientific members of the American & Global College of Endometriosis Specialists (AGCES), informed by extensive surgical experience on thousands of endometriosis surgeries, synthesis of published evidence on disease pathophysiology and anatomical distribution, and systematic analysis of the limitations of existing classification systems (rASRM, ENZIAN, AAGL, EFI). Results: The framework integrates adenomyosis as a component of endometriosis staging and separates genital and extragenital disease into independent staging categories. Disease burden is reported using three parallel components representing adenomyosis (A), genital endometriosis (G), and extragenital endometriosis (E). A standardized operative reporting template and digital implementation through web-based applications were also developed to support clinical use. Conclusions: The AGCES classification system introduces a novel approach to endometriosis staging by integrating adenomyosis and separating genital and extragenital disease components. This framework provides a more complete assessment of disease burden and has the potential to improve clinical documentation, surgical planning, and research standardization in endometriosis care. Full article

This study proposes an interdisciplinary approach that integrates bioclimatology, agronomy, environmental education, and information and communication technologies (ICT) to analyze their potential to support sustainable land management in the context of climate change. The research focuses on the application of bioclimatic indices, the continentality index (Ic), the ombrothermic index (Io), and the thermicity index (It/Itc), combined with the use of a virtual herbarium as a didactic resource for interpreting ecological indicators associated with vegetation. The study was conducted using a pretest–posttest design aimed at assessing students’ self-reported understanding of ecological concepts, bioclimatology, geobotany, and the use of digital tools for learning plant species. The results show a significant improvement in students’ perceived understanding following the educational intervention, with the mean questionnaire score increasing from 21.99 (SD = 5.03) in the pretest to 31.33 (SD = 5.06) in the posttest (t(69) = 37.13, p < 0.001). The normalized gain (g = 0.42) indicates a moderate improvement in students’ self-reported comprehension of bioclimatic and ecological concepts. These findings highlight the potential of ICT to strengthen environmental education and to foster the development of competencies related to sustainable agricultural and forest land management. Full article

The current literature on electrical system maintenance highlights three technology domains—Building Information Modeling (BIM), Digital Twin (DT), and extended reality (XR)—that have independently demonstrated strong potential for improving lifecycle information management, predictive analytics, and operational support. However, their convergence remains largely underexplored, particularly in electrical system maintenance. This paper provides a structured review of BIM–DT–XR convergence in electrical system lifecycle management, examining their roles across lifecycle phases and their integration through literature synthesis and cross-domain implementation evidence. BIM is analyzed as a basis for modeling and integrating facility management with electrical asset lifecycles; DT as a framework for dynamic system representation and applications in electrical and power systems; and XR as a means of visualizing and interacting with BIM-DT environments. Cross-domain implementation evidence from an industrial electrical facility and a tertiary smart-building pilot shows that BIM–DT–XR integration is technically feasible at pilot scale. However, the analysis identifies five structural integration gaps: semantic misalignment between building-oriented IFC and grid-oriented CIM ontologies; fragmented standard adoption; inconsistent data governance and naming practices; validation approaches focused on syntactic rather than dynamic model fidelity; and the separation of XR visualization from predictive DT capabilities. The implementation evidence further indicates that real-world deployment remains constrained by data quality limitations, integration complexity, cost factors, and interoperability with legacy systems. The review concludes that, despite the maturity of individual technologies, their effective application depends on advances in semantic alignment, lifecycle data governance, validation of dynamic models, and scalable integration frameworks, enabling the transition toward integrated, interoperable, and lifecycle-aware infrastructures for electrical system maintenance. Full article

By 2024, global photovoltaic (PV) capacity exceeded 2000 GW, corresponding with a decline in levelized costs of approximately 90% since 2010. Artificial intelligence (AI) and machine learning (ML) are enabling novel approaches to solar energy system design and implementation. This survey offers a detailed evaluation of AI/ML methodologies utilized across the solar energy value chain, with a focus on solar irradiance forecasting, maximum power point tracking (MPPT), fault identification, and the expeditious discovery of system materials. The distinction between AI as the broader paradigm and ML as its data-driven subset is drawn and maintained throughout. The primary results cite forecasting improvements via deep learning architectures (LSTM, CNN, Transformer) of 10–40% over traditional methods, while hybrid numerical weather prediction and deep learning models achieve mean absolute error reductions of 15–25%. Reinforcement learning-based MPPT achieves tracking efficiencies in excess of 99% under partial shading, CNN-based fault classification reaches accuracies above 95%, and ML-based screening of materials accelerates perovskite optimization by a factor of 5–10×. Promising paradigms such as explainable AI, federated learning, digital twins, and physics-informed neural networks are evaluated alongside technical, economic, and regulatory constraints. This survey provides a consolidated reference and practical roadmap for the advancement of AI-driven solar energy technologies. Full article