Search Results (7,842)

Early architectural decisions shape building energy demand and life cycle carbon, yet digital support remains fragmented across modelling, simulation and performance workflows. This systematic review examines how digital twin (DT) frameworks, tools and workflows are applied at the architectural design stage to support net-zero building performance. It investigates whether design-stage digital twins function as decision support systems or remain BIM-plus-simulation workflows carrying a twin label. Following PRISMA 2020, Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink and Taylor & Francis Online were searched, supplemented by citation searching. Searches were completed on 28 March 2026 and restricted to English-language, peer-reviewed publications meeting eligibility criteria. Thirty-three studies formed the analysed corpus, comprising 10 Tier 1 core digital twin studies, 15 Tier 2 DT-oriented studies and 8 Tier 3 DT-enabling studies, while 19 review and contextual sources supported framing. Studies were coded by DT conceptualisation, framework type, enabling technology, life cycle stage, net-zero indicator and validation approach. Findings were synthesised descriptively and thematically, and evidence maturity was assessed using a six-domain appraisal covering reporting quality, digital twin completeness, design-stage relevance, validation quality, reproducibility and architect usability. Only 11 studies, representing 33% of the corpus, were anchored in concept or schematic design. BIM, building-performance simulation and parametric or generative modelling were dominant, while IoT and AI or machine learning supported prediction, surrogate modelling and control. Energy was addressed in 26 studies and thermal comfort in 10, whereas embodied carbon, daylight, renewable generation and indoor air quality received limited attention. Eight studies were classified in the ‘Measured/large empirical’ validation class. Methodological heterogeneity and limited empirical validation precluded meta-analysis. Design-stage digital twins remain emerging rather than mature decision support systems. The review was retrospectively registered on the Open Science Framework (DOI: 10.17605/OSF.IO/N5Z8H) and received no external funding. Full article

Background: Digital innovations have transformed health professions education, with podcasting emerging as a flexible, learner-centered educational modality. Podcasts support asynchronous, mobile, and self-directed learning, enabling access to educational content beyond traditional classroom environments. However, evidence regarding their effectiveness, integration strategies, and impact on educational and practice outcomes remains fragmented. Aim/Objective: This review aimed to map the existing literature on the use of podcasting in nursing and midwifery education and continuing professional development (CPD), identify how podcasts are used, summarize reported benefits and limitations, and highlight gaps for future research. Design: Scoping review. Methods: A scoping review was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Eligibility criteria were developed using the Population–Concept–Context (PCC) framework to define the review scope. A comprehensive literature search was performed across PubMed/MEDLINE, Scopus, Embase, CINAHL, Google Scholar, ProQuest, and OpenGrey to identify studies published in English between January 2005 and December 2024. Two reviewers independently screened titles, abstracts, and full-text articles against the predefined eligibility criteria. Data were extracted using a standardized charting form, and the included studies were synthesized using descriptive statistics and thematic analysis to map the characteristics and educational applications. They reported outcomes of podcasting in nursing and midwifery education. Results: Twenty-four studies were included. Podcasting was associated with enhanced learning flexibility, learner engagement, and knowledge retention in both academic and CPD contexts. It supported asynchronous and self-directed learning while reinforcing key concepts. Challenges included variable content quality, limited integration of assessment, and scarce evidence linking podcast use to clinical outcomes. Conclusions: Podcasting is a promising adjunct to nursing and midwifery education and CPD. Formal integration into curricula and professional development frameworks is recommended. Further research should focus on longitudinal outcomes, low- and middle-income settings, and impacts on clinical practice and interprofessional learning. Full article

The tympanic membrane (TM) is a thin, semi-transparent structure whose morphology and optical appearance provide important diagnostic cues. In the early stages of middle-ear pathology, subtle shape and compliance alterations may precede overt clinical signs, making them valuable early indicators of disease. Such structural changes are difficult to assess reliably using conventional (micro-)otoscopy, which lacks quantitative depth information and is operator-dependent. Data-driven monocular image analysis could enable quantitative assessment of TM geometry and compliance, but the limited availability of annotated three-dimensional datasets constrains the development of these methods. At the same time, realistic simulations of TM appearance remain challenging due to its complex reflectance and transmission behavior. The present study focuses on physiologically healthy tympanic membranes, which provide the baseline anatomical and optical model required before subtle pathological changes can be investigated. This work introduces a parametric physically grounded rendering model of the structures visible during otoscopy: the tympanic membrane, ear canal, and malleus–incus complex. Implemented in the open-source software Blender™ using procedural geometry nodes and physically motivated shaders, the framework generates anatomically plausible three-dimensional geometries via statistical parameter sampling and controlled mesh deformation. Optical appearance is simulated using a computationally efficient layered shading model based on literature-derived tissue reflectance, transmission, and scattering properties. A camera–projector setup models both conventional white-light otoscopy and structured-light imaging, enabling the generation of paired intensity images and corresponding depth maps. The proposed framework establishes a physically grounded representation of the human ear and enables a controllable, extensible modeling pipeline for virtual training, biomechanical finite element analysis, and synthetic data generation for supervised learning. Full article

The regulation of artificial intelligence (AI) and copyright in the European Union has proceeded almost entirely within the domain of economic rights, systematically overlooking the moral rights of authors whose works are ingested into AI training datasets and reproduced in AI-generated outputs. This article examines why this omission constitutes not merely a regulatory gap but a normative failure and proposes a framework for addressing it. Through doctrinal analysis of the DSM Directive’s text and data mining exceptions, the AI Act’s general-purpose AI obligations, and the AI Code of Practice, the article identifies a four-dimensional regulatory gap—substantive, jurisdictional, structural, and institutional—and situates it within the broader architecture of EU fundamental rights law. Drawing on the personhood theory of authorship, international human rights instruments, and the first European judicial engagement with AI-related moral and personality rights claims in GEMA v OpenAI (Regional Court of Munich I, November 2025), the article develops a rebuttable presumption model as the organising principle for author-centred AI regulation. Two complementary regulatory pathways are proposed: EU harmonisation of moral rights with AI-specific provisions, and the integration of moral rights obligations into existing AI compliance frameworks. The article concludes that in the algorithmic age, human creativity must be valued—not merely accommodated—by the legal systems that claim to protect it. Full article

This paper examines whether the adoption of Supervisory Technology (SupTech) is associated with a reduction in greenwashing in the European banking sector over the period 2014–2025. Using a progressive empirical framework combining the Synthetic Control Method (SCM), a split-sample SCM, an OLS regression of the SCM-estimated treatment effect, and a Causal Random Forest (CRL) via T-Learner applied to a panel of European banks, we provide evidence consistent with a meaningful reduction in greenwashing associated with SupTech adoption, which is robust across multiple identification and validation strategies. The split-sample SCM and OLS analyses reveal that this effect is amplified by higher capital adequacy, genuine ESG engagement, and stricter regulatory environments, while larger banks exhibit a systematically attenuated response. Contrary to the complementarity hypothesis, RegTech does not reinforce SupTech’s disciplining effect; instead, the evidence points to a substitution mechanism whereby banks with developed internal compliance infrastructure derive limited marginal benefit from external supervisory technology. The Causal Random Forest analysis provides evidence of a statistically significant and stable average treatment effect and indicates that bank digital maturity and FinTech adoption are the most consistent drivers of SupTech’s effectiveness. Policy simulations show that improving digital maturity, rather than RegTech endowment, yields the largest additional greenwashing-reduction gains. These findings suggest that SupTech acts as a credibilization mechanism whose effectiveness depends on the stringency of the external regulatory architecture and the digital absorptive capacity of supervised institutions. External validity to less harmonized regulatory environments remains an open empirical question. Full article

Background/Objective: The H-HT represents a critical vulnerability for patient–family caregiver dyads. This study developed and content-validated a middle-range nursing theory, the Adaptarte Model, designed to guide dyadic adaptation during the H-HT within the Latin American healthcare context. Methods: A sequential exploratory multimethod design was executed in two phases. Phase 1 integrated three evidence streams: clinical practice insights, a JBI-guided scoping review, and two focus groups with transitional care professionals. Qualitative content analysis and iterative consensus refined the model’s core concepts, assumptions, and propositions. Phase 2 evaluated the model’s content, structure, functionality, and projection using an international panel of eleven Latin American experts meeting strict eligibility criteria. Data were analyzed using Lawshe’s Content Validity Ratio (CVR) modified by Tristán (cutoff = 0.58) and the overall Content Validity Index (CVI). Reporting followed PRISMA-ScR and GRAMMS guidelines. Results: Expert consensus confirmed the essential model components. Item-level CVR values ranged from 0.90 to 0.99, yielding an overall CVI of 0.96, while external functionality and conceptual projection achieved an average rating of 0.88. Conclusions: The Adaptarte Model demonstrates high content validity and structural clarity, establishing a rigorous theoretical foundation for subsequent empirical research. Rather than being ready for immediate clinical implementation, it provides a structured blueprint for prospective protocol development. Systematic empirical testing and longitudinal studies are now imperative to evaluate its clinical utility and drive future healthcare transformations. The scoping review protocol was prospectively registered on the Open Science Framework (OSF) URL (accessed on 23 September 2024). Full article

Background: PADS combines two neural networks predicting ICU mortality and discharge within 48 h, placing critically ill patients into one of four clinically meaningful states. Developed on MIMIC-IV alone, it left open whether it generalizes to other ICUs, whether its models transfer across hospitals, and whether its predictions can be explained at the bedside. Methods: We evaluated PADS on four ICU databases from different hospitals and countries (MIMIC-IV, AmsterdamUMCdb, eICU-CRD, and HiRID), using the same routinely collected variables. Mortality is scored on the final 48-h window (terminal-window, not early-warning, discrimination). For each external database, we compared the MIMIC model used as-is, retrained from scratch, and retrained from the MIMIC weights, and added an explainability layer. Results: For mortality, reusing and retraining the MIMIC model gave the highest discrimination on every database (AUROC 0.955–0.986; terminal-window (near-outcome) discrimination) and stabilized training; used as-is, it ranged from chance (Amsterdam) to good (eICU, HiRID). For discharge, training fresh on local data matched or beat reusing MIMIC on every external database, consistent with discharge timing depending on local organization rather than physiology. The explainability layer produced clinically coherent, cross-checked explanations. Conclusions: Transportability was task-dependent: mortality transferred between hospitals, discharge did not. PADS demonstrated promising external transportability across heterogeneous ICU databases, particularly after local adaptation. Reusing and adapting the MIMIC-IV mortality model across hospitals improves accuracy. This approach also stabilizes training, providing a basis for potential federated deployment, whereas discharge is better trained locally. The mortality results reported here are terminal-window discrimination and do not support use of the framework as an early-warning model. A transparent explainability layer provides an interpretable representation of model predictions, addressing a key barrier to clinical adoption. Full article

Quantum communication is emerging as a foundation for next-generation networks, offering unprecedented capabilities in security, entanglement-based connectivity, and distributed computation. However, the classical Open Systems Interconnection (OSI) model, designed for deterministic, error-tolerant systems, is incompatible with quantum phenomena such as decoherence, probabilistic entanglement, and the no-cloning theorem. This paper surveys and redefines the OSI model for quantum networking in the context of 7G systems. We propose a Quantum-Converged OSI stack by extending the classical seven-layer model with two additional layers: (i) Layer 0, the Quantum Substrate, responsible for entanglement management, coherence preservation, and teleportation; and (ii) Layer 8, the Cognitive Intent Plane, which enables AI- and QML-driven orchestration. The survey synthesizes over 150 research works published between 2018 and 2025, classifying them by OSI layer, enabling technologies (e.g., Quantum Key Distribution, Quantum Error Correction, and Post-Quantum Cryptography), and application domains such as satellite quantum links, quantum IoT, and federated edge systems. We further provide a taxonomy of cross-layer enablers and discuss simulation tools, including NetSquid, QuNetSim, and QuISP. Finally, an evaluation framework with quantum-native metrics, such as entropy throughput, coherence latency, and entanglement fidelity, is introduced, along with open challenges for programmable stacks, digital twins, and AI-defined quantum agents. The specific and novel contribution of this work is a Quantum-Converged OSI stack that extends the classical seven-layer model with two additional layers: Layer 0, the Quantum Substrate, responsible for entanglement management, coherence preservation, and teleportation; and Layer 8, the Cognitive Intent Plane, which enables AI- and QML-driven orchestration. Unlike prior technology-centric surveys, the proposed framework classifies over 150 research works by OSI layer, maps enabling technologies (QKD, QEC, PQC) and application domains (satellite quantum links, quantum IoT, federated edge systems) to their functional layers, and introduces a quantum-native evaluation framework based on entropy throughput, coherence latency, and entanglement fidelity. This layer-resolved synthesis, together with the formal definition of cross-layer quantum-native metrics, constitutes the principal novelty distinguishing this survey from existing quantum-networking reviews. Full article

In recent years, the concept of “connectivity” between financial and sustainability reporting has emerged as a central construct in both regulatory frameworks and academic discourse, being recognised as a qualifying attribute of high quality corporate reporting by major standard setters, including the IIRC, the EFRAG and the IFRS/ISSB. Despite its growing normative prominence, the concept remains theoretically fragmented, empirically underexplored and methodologically contested—with interpretations varying significantly across regulatory frameworks, theoretical traditions and empirical research designs. A preliminary critical narrative review was conducted, combining structured searches of major academic databases (WOS, Scopus, Google Scholar)—which identified a total corpus of 115 publications—with a purposive examination of key regulatory documents produced by the IIRC, the EFRAG and the IFRS/ISSB. This review was structured around three research questions addressing, respectively, the conceptualisation of connectivity across theoretical and regulatory frameworks, its principal typologies and theoretical foundations, and the approaches developed for its empirical measurement. The analysis reveals that connectivity is an intrinsically multidimensional concept—aptly described as a “portemanteau” term—whose meanings, typologies and measurement approaches remain fragmented and only partially convergent. Five families of typologies are identified (direct/indirect; specific/general; technical–accounting/informational–managerial; investor-focused/stakeholder-oriented; textual/intertextual/relational), operating at different analytical levels and not mutually exclusive. Regulatory frameworks conceptualise connectivity as an attribute of holistic, coherent corporate reporting but diverge significantly in their underlying conception of materiality—single financial materiality in the IFRS/ISSB framework versus double materiality in the ESRS—with direct implications for the scope and direction of connectivity. Empirical measurement remains a genuine “work in progress”: disclosure indices, textual measures and hybrid indicators each capture different facets of the construct and do not yet converge towards a shared and widely validated framework. The available evidence—while broadly consistent in reporting low to medium-low levels of connectivity, with qualitative linkages predominating over quantitative ones—should be interpreted as associative rather than causal, given unresolved endogeneity concerns. This paper provides the first structured critical mapping of all principal dimensions of connectivity in corporate reporting, at a stage when the literature is still in formation. It identifies the distinction between formal and substantive connectivity as a central open question and outlines a specific future research agenda addressing construct validity, causal identification, longitudinal analysis and the role of assurance. Full article

This exploratory qualitative action research study examined the implementation and iterative refinement of mindfulness–flow–awareness–coaching (MFAC), a sensory-based mindfulness coaching approach, and explored participants’ reported experiences. Six Korean university students completed pre-MFAC and post-MFAC interviews and the six-session MFAC framework. One participant’s initial Session 4 was discontinued after an unexpected recollection resulted in emotional overwhelm, and an adjusted Session 4 was conducted one week later, resulting in 37 session deliveries. The study drew on interview and session transcripts, participant records, action records, and observational and reflexive notes. The first author conducted a chronological implementation analysis and reflexive thematic analysis, which identified four practice-based principles: prioritising sensory noticing over interpretation, establishing psychological safety before task completion, balancing structural consistency with responsive adaptation, and framing action-oriented activities as revisable attempts. Three themes characterised participants’ reported experiences: greater openness to sensory and emotional experience, contextualised self-understanding through meaning-making, and movement towards feasible action. Participants also reported discomfort, interruptions, ambiguous experiences, and incomplete actions. These findings offer a contextually grounded account of how we refined MFAC and how participants interpreted their engagement. This study positions MFAC as a practice-informed approach rather than as evidence of therapeutic efficacy or causal psychological change. Future research employing diverse methodologies should examine its applicability and transferability to broader populations and settings. Full article

Accurate precipitation data are fundamental for reliable hydrological modeling, water resources management, and climate resilience planning. Although extensive hydrometeorological datasets exist in Brazil, including those maintained by the Brazilian National Water and Sanitation Agency (ANA) and the National Institute of Meteorology (INMET), raw data acquisition and preprocessing often pose significant bottlenecks for researchers. To address this gap, this paper introduces a computational framework designed to streamline the management, visualization, and evaluation of Brazilian rainfall datasets. The platform provides station-level daily precipitation series for 602 INMET automatic stations, covering the period from May 2000 to April 2025 and comprising 113,102 station-months, of which 72,527 (64.1%) satisfy a strict completeness criterion under which a calendar month is retained only if every one of its days is observed. The platform includes an interactive map for station visualization and selection, a dataset explorer with date filtering, time series visualization, and data download capabilities, and a dedicated analytical interface for hydrological and statistical analyses. The analytical modules include monthly precipitation climatology, probability distribution fitting for annual maximum daily precipitation, Standardized Precipitation Index at the one-month scale (SPI-1), and Intensity–Duration–Frequency (IDF) curves. The main limitations affecting the interpretation of these outputs are stated explicitly: record-length limitations are explicitly communicated to users through station-specific warnings and informational notices, since no station in the network reaches the preferred 30-year span for robust SPI estimation, and the IDF module is scoped as a screening and pre-design instrument because its disaggregation coefficients were calibrated for a single Brazilian state. By bridging the gap between raw data availability and actionable insights, this open-source tool optimizes hydro-meteorological workflows and supports environmental assessments. Full article

Artificial intelligence (AI) models are increasingly deployed in edge-deployed cyber-physical security systems for tasks encompassing monitoring, threat classification, and automated decision-making. While these models offer robust performance, their deployment through open or semi-open Machine Learning as a Service (MLaaS) interfaces exposes them to severe security threats, prominently model extraction attacks. In such attacks, an adversary systematically queries a target API to replicate the victim model’s behavior. This study proposes a novel hybrid defense framework combining Adaptive Misinformation (AM) and Trigger-Based Watermarking (WM) to protect AI models against black-box extraction. Utilizing a LeNet architecture, the victim model was trained on the MNIST dataset, while a simulated attack utilized 50,000 EMNIST samples to train a clone model. The framework employs Maximum Softmax Probability (MSP) for out-of-distribution (OOD) detection to identify suspicious queries and strategically inject misleading responses, alongside a fine-tuned embedded watermark for ownership verification. Experimental evaluations using 10-fold cross-validation reveal that the baseline extraction attack yielded a clone model accuracy of 96.32%. Upon implementing the AM + WM framework, clone model accuracy degraded significantly to 53.67%, while the victim model maintained an accuracy of 98.97%. Furthermore, the protected model achieved a 100% Trigger Match Rate (TMR), ensuring reliable intellectual property verification. The proposed framework provides a prototype validation for lightweight edge architectures to balance security, model utility, and ownership protection in cyber-physical deployments. Full article

Na₄Fe₃(PO₄)₂P₂O₇ (NFPP), an iron-based mixed phosphate–pyrophosphate cathode material, has emerged as one of the most commercially promising candidates for large-scale sodium-ion battery (SIB) energy storage applications. Its exceptional characteristics—an ultralow volume change of less than 4% during Na⁺ de/intercalation, a three-dimensional open framework enabling rapid ionic diffusion, and the use of earth-abundant, low-cost iron as the redox center—collectively deliver a unique combination of structural stability, rate capability, and economic viability. However, the fundamental challenge of phase-purity control, arising from the three-phase thermodynamic competition among NFPP, electrochemically inert maricite-NaFePO₄, and Na₂FeP₂O₇ during synthesis, critically limits its electrochemical performance. This review provides a systematic overview of NFPP research progress from 2012 to 2026, covering crystal structure and sodium storage mechanisms, synthesis methodologies, and—most critically—Phase Adjustment and modification strategies including non-stoichiometric regulation, defect engineering, elemental doping, anionic substitution, and heterostructure design. Mechanistic insights into how each strategy addresses the phase-purity challenge and enhances electrochemical kinetics are critically examined. Industrialization progress, full-cell performance evaluation, cost analysis, and future research directions toward practical deployment are also discussed. Full article

Edge-enabled Internet of Things (IoT) systems are rapidly becoming the operational substrate of mission-critical infrastructure spanning industrial automation, smart healthcare, vehicular ecosystems, and cyber–physical environments. The distributed, resource-constrained, and physically exposed nature of these systems makes them persistent targets for a diverse and evolving spectrum of cyber attacks. Critically, cyber attacks on edge-enabled IoT systems do not merely threaten data confidentiality; they simultaneously erode two interdependent operational objectives: sustainability, the ability of the system to maintain continuous, energy-efficient operation within its resource envelope and resilience, the ability to absorb adversarial disruptions, recover operational continuity, and adapt to prevent recurrence. The structural conflict between defending sustainability and maintaining resilience under active cyberattack conditions constitutes a research gap that prior surveys have not systematically addressed. This survey introduces a cyber attack-driven Sustainability–Resilience (S-R) framework that positions cyber threats as the primary stressor forcing a bilateral trade-off between operational efficiency and continuity in edge-enabled IoT systems. A five-layer, attack-centric taxonomy is developed spanning: network-layer attacks (DDoS, MitM, routing manipulation, jamming); device and firmware attacks (malware injection, firmware compromise, sensor spoofing); data and AI/ML attacks (adversarial inputs, data poisoning, model inversion); federated and Byzantine attacks (gradient poisoning, backdoor injection, free-riding); and advanced persistent threats (APT-class intrusions, ransomware, LLM prompt injection, zero-day exploitation). For each attack class, the survey systematically analyses the impact on sustainability and resilience objectives, the resulting S-R conflict, and the state-of-the-art defensive strategies. The framework is formalised as a maximin optimisation over the joint S-R objective surface, incorporating the adaptive, goal-directed nature of the adversary through a game-theoretic formulation. Cross-domain analysis spanning Industrial IoT, smart healthcare, Internet of Vehicles, UAV-assisted IoT, smart grids, and tactical edge networks establishes domain-specific S-R operating constraints under representative attack scenarios. The survey concludes with a structured characterisation of open research challenges and forward-looking directions, providing a prioritised research agenda for advancing simultaneously sustainable and adversarially resilient edge-enabled IoT ecosystems. Full article

Purpose: This study examines how large multinational corporations translate sustained macroenvironmental volatility into deliberate strategic reconfiguration processes, and how adaptive mechanisms differ across business models and sectors under shared environmental pressures. Design/Methodology: The study employs a qualitative comparative multiple-case design with abductive logic and a longitudinal perspective covering 2019–2024. Apple, Amazon, and McDonald’s were selected through theoretical sampling for their sector heterogeneity, their shared global regulatory and operational exposure, and their contrasting adaptive architectures—vertical integration, platform diversification, and franchising, respectively—rather than a uniform majority-international-revenue criterion. A corpus of 78 primary and secondary documents was analysed through open coding, axial coding, thematic aggregation, cross-case comparison, and pattern matching. Findings: All three firms converge around digitalisation, regulatory compliance, and sustainability as environmental legitimacy requirements rather than differentiating strategic choices. Divergence emerges in execution mechanisms: Apple deploys anticipatory vertical integration; Amazon converts operational complexity into structural barriers; McDonald’s exploits franchise flexibility for local adaptation while preserving brand coherence. The analysis yields an original Adaptive Reconfiguration Cycle (ARC Framework) comprising five iterative stages, evidenced for each firm across the full cycle rather than only its dominant stage. Theoretical Contribution: The study develops an integrated macroenvironment–capability reconfiguration framework that bridges PESTEL analysis, dynamic capabilities theory, and contingency theory, addressing an under-explored integration gap in each tradition. Three theory-building propositions, generated inductively from the three comparative cases and not presented as empirically established relationships, are advanced together with their moderating conditions. Practical Implications: Firms must institutionalise environmental sensing as a permanent strategic function, treat compliance capabilities as competitive assets, and build adaptive capacity as a standing organisational competency. Originality/Value: This study is among the few comparative analyses to integrate PESTEL trigger structures with dynamic capabilities reconfiguration logic across heterogeneous sectors using longitudinal evidence. The ARC Framework is offered as an analytically transferable, theory-generating model with explicit boundary conditions and testable propositions, rather than as a broadly generalisable one. Full article