Search Results (146)

The proliferation of digital assets has catalyzed a profound decoupling between intangible property and traditional inheritance jurisprudence. Under the existing legal framework in Taiwan, practitioners must rely on the testamentary forms prescribed in Article 1189 of the Civil Code, which are fundamentally ill equipped to handle cryptographic assets. Specifically, Notarized Wills (Article 1191) necessitate full disclosure to a notary, creating a “Privacy–Security Paradox” where revealing private keys exposes assets to misappropriation. Conversely, while Sealed Wills (Article 1192) offer confidentiality, they are plagued by risks of physical degradation and technical non-executability. This study proposes zkWill, an EVM-compatible decentralized testamentary framework designed to bridge these structural gaps. By leveraging Zero-Knowledge Proofs (ZKPs), zkWill achieves a state of “blind compliance,” verifying that a sealed will meets the statutory requirements of the Civil Code without disclosing its underlying content. The system integrates the Permit2 protocol for secure asset migration and combines AES-256 encryption with IPFS to immunize testaments against centralized storage failures. Unlike conventional services that demand custodial trust, zkWill employs decentralized oracles to trigger automated execution, ensuring legacy distribution without compromising wallet private keys. Empirical data from the Arbitrum Sepolia testnet confirms that the framework maintains constant verification efficiency and a judicially resilient audit trail, providing a paradigm that harmonizes legal pragmatism with cryptographic security for digital inheritance. Full article

Digital transformation is reshaping construction asset compliance, but fragmented information and weak evidence trails still constrain effective management. This systematic grey literature review (2014–2025) identifies technologies supporting asset management and compliance assurance and compares adoption maturity across the United Kingdom (UK), the United States (US), Singapore, and the Gulf Cooperation Council (GCC). Using multi-channel search strategies and the AACODS appraisal (Authority, Accuracy, Coverage, Objectivity, Date, Significance), 131 records were identified; 92 full texts reviewed; 82 eligible; and 43 sources retained. Coding identified a recurring five-technology “core digital stack”: Building Information Modelling (BIM), Digital Twins (DT), Internet of Things (IoT), Artificial Intelligence/Machine Learning (AI/ML), and Blockchain (BC). Within the retained corpus, BIM and AI/ML were the most frequently referenced technologies, whereas BC was referenced more selectively and discussed mainly for tamper-evident traceability. DT and IoT were typically discussed alongside BIM, while IoT also frequently co-occurred with AI/ML in analytics-led compliance workflows. A (Region × Technology) maturity matrix suggests higher, policy-led maturity where mandates and audit-ready information align with national frameworks (UK, Singapore), and more uneven, project-led adoption in decentralised contexts (US, GCC). Overall, the findings emphasise that effective compliance relies on integrated, evidence-focused digital stacks supported by standardised information governance rather than isolated tools. Full article

Mechanized timber harvesting on steep slopes causes soil disturbance; however, comprehensive post-harvest assessment remains challenging because terrain complexity and safety constraints render traditional field-based methods labor-intensive, spatially limited, and difficult to implement systematically. In this study, we developed and evaluated a convolutional neural network-based semantic segmentation model for detecting soil disturbances using high-resolution unmanned aerial vehicle (UAV) imagery in a steep-slope harvested area (2.50 ha, mean slope of 53.4%) in Republic of Korea. A U-Net semantic segmentation model was trained on manually annotated orthomosaic tiles incorporating RGB and digital elevation model (DEM) inputs. Ensemble predictions at an optimized threshold of 0.65 achieved Intersection over Union (IoU) of 0.55 and F1-score of 0.71. Although moderate, these values reflect the inherently challenging conditions of steep-slope forest terrain compared to similar studies conducted under gentler terrain. DEM-derived depth estimation enabled severity classification of the detected disturbances, with light disturbances predominating. Field validation using 38 pinboard measurements demonstrated reliable spatial detection (ρ = 0.567, RMSE = 6.45 cm). This approach provides an effective alternative to traditional monitoring practices in mountainous forests, where systematic trail planning is impractical, and may support evidence-based assessment of harvesting impacts for sustainable forest management. Full article

The development of Smart Tourism often overlooks the “Wilderness Last Mile”, leading to the spatial exclusion of people with disabilities in mountain areas. This problem exists because standard tourist maps and urban-centric accessibility models rely on averaged terrain data, failing to identify critical micro-scale barriers (e.g., short, sudden steep ascents) that pose severe safety and traction risks for off-road wheelchair users. To address this gap, this article presents a novel GIS methodology for planning accessible off-road tourism for electric Specialized Off-Road Wheelchairs. The proposed four-stage analytical model includes (1) graph-based trail network topologization to enable precise routing; (2) traction safety verification utilizing high-resolution (1 × 1 m) Digital Elevation Model (DEM) micro-segmentation to detect hidden slope barriers; (3) multi-criteria evaluation combining a user-calibrated Difficulty Index (EDI) and a Tourism Quality Index (TQI); and (4) a hub optimization algorithm that prioritizes locations maximizing the diversity of accessible routes. The method was empirically tested in a case study of the Bieszczady Mountains (Poland), calibrating the model with the technical limits (25% max slope) of a prototype wheelchair. The experimental results clearly validate the model’s superiority over traditional approaches: the micro-segmentation successfully identified hidden terrain traps, disqualifying 55% of the standard trail network that would have otherwise been deemed safe by average-slope assessments. Furthermore, the model identified a contiguous safe network of 153 km and pinpointed the optimal rental hub location, ensuring the highest inclusivity and route variety. Ultimately, this approach transforms raw spatial data into safe, ready-made tourism products, providing a precise tool with which to implement Universal Design in natural environments. Full article

Intangible environmental externalities in informal economies are hard to detect, attribute, and regulate because transaction records and evidentiary trails are fragmented. This conceptual paper reframes pollution control from improving model performance to designing institutions for verifiability and examines how generative AI (GAI) can both strengthen and undermine that verifiability. Integrating transaction-structure theory, institutional economics, and digital-governance research, we derive four propositions: (P1) standardized, interoperable evidence and hybrid auditing allow GAI to lower verification costs; (P2) opaque, multi-tier transactions and concentrated data control enable plausible falsification; (P3) detection reduces pollution only when linked to remediation through enforcement capacity; and (P4) incentives must reward verified, not merely claimed, circularity to deter greenwashing. We illustrate feasibility and boundary conditions through three precedents: Amazon’s unit-level identifiers and sustainability labeling, India’s CPCB extended producer responsibility portal for plastic packaging, and Brazil’s nationwide e-invoicing infrastructure (NF-e/SPED). The framework offers actionable design principles, testable hypotheses, and measurable indicators (evidence linkage, audit-log completeness, time-to-remediation) for future empirical work. The framework is intended to support analytic generalization for policy and practice across contexts. Full article

Background/Objectives: Cognitive impairment and sleep disturbances are highly prevalent in individuals with multiple sclerosis (MS), particularly during middle age, and negatively affect functional independence and quality of life. Although physical exercise has demonstrated cognitive and sleep-related benefits in MS, tolerance to high-intensity training is often limited. Blood flow restriction (BFR) training, which combines low-load resistance exercise with partial vascular occlusion, has emerged as a feasible alternative. This study aimed to evaluate the effects of a 12-week BFR training program on performance in specific cognitive domains and sleep quality in middle-aged adults with MS. Methods: A randomized controlled trial was conducted in 65 adults with relapsing–remitting multiple sclerosis (RRMS) aged 40–65 years and an Expanded Disability Status Scale score below 7. Participants were randomly assigned to a BFR training group or a usual-care control group. The intervention consisted of supervised low-load resistance training with BFR performed twice weekly for 12 weeks. Outcomes assessed before and after the intervention included processing speed (Symbol Digit Modalities Test), executive function (Trail Making Test A and B), verbal fluency (Isaacs Set Test), and self-reported sleep quality (Pittsburgh Sleep Quality Index). Results: Compared with controls, participants in the BFR group showed significant improvements in specific cognitive domains, including processing speed, executive function, and verbal fluency. Significant reductions were also observed in self-reported global sleep disturbance and daytime dysfunction. No adverse events were reported. Conclusions: A 12-week BFR training program improved performance in key cognitive domains and self-reported sleep quality in middle-aged adults with MS, supporting its feasibility and potential clinical relevance as an exercise-based intervention. Full article

The Jeju Olle Trail has evolved from a grassroots initiative into a contested space where post-pandemic growth intersects with environmental limits and fragmented governance. Moving beyond environment-centric models, this study examines the trail as a transcultural walking tourism system. The authors triangulated 900 user-generated content (UGC) narratives from major travel platforms (Korean, Chinese, and English) with semi-structured interviews from three key institutional informants (NTO, RTO, and NPO). The analysis explores how sustainable experiences are negotiated in practice. Findings suggest that Self-Determination Theory (SDT) constructs like autonomy are not universal constants but are culturally mediated through Western “digital detox,” Korean “collective healing,” and Chinese chūxīn (original heart) narratives. Institutional and narrative data indicate that these experiences appear linked to managing governance tensions between national mandates and localized stewardship. The study concludes that experiential sustainability involves navigating trade-offs regarding narratively signaled environmental impacts and community capacity. By framing walking tourism as a governance-dependent practice, this research demonstrates how culturally embedded mechanisms shape destination viability. Full article

Background/Objectives: Accurate hydration assessment is critical for optimizing performance and preventing heat-related complications in ultra-endurance athletes. This study evaluated the agreement and reliability between urine reagent strips and refractometry for field-based hydration assessment via urine-specific gravity (USG) in ultra-trail runners. Methods: Thirty-four ultra-trail runners (22 males, 12 females; mean age 43.71 ± 11.50 years) participated during The Coastal Challenge, a 241-km multi-stage ultra-trail competition. Urine samples were collected before and after the first two stages (Stage 1: 41 km, 1071 m elevation; Stage 2: 40 km, 1828 m elevation). USG was measured using semi-quantitative urine reagent strips (Combur10Test M) and a handheld digital refractometer (Palm Abbe™). Agreement was assessed via paired t-tests, Pearson and Spearman correlations, intraclass correlation coefficients, and Bland-Altman plots across four measurement time points. Results: Strong agreement existed between methods with correlation coefficients of 0.92–0.99 (p < 0.01) within the hydration range typical of well-prepared ultra-endurance athletes (USG 1.010–1.020). No significant differences were found between devices at any time point (all p > 0.05). Bland-Altman analyses revealed minimal mean bias (range: −0.002 to +0.001 g/mL) and narrow limits of agreement, with fewer than 5% of values falling outside limits. Both methods detected significant increases in USG from pre- to post-stage (p < 0.01), indicating exercise-induced hypohydration. Conclusions: Semi-quantitative urine reagent strips and handheld refractometers demonstrate strong agreement for hydration assessment in ultra-trail runners under field conditions when not severely hypohydrated, supporting their interchangeable use for practical monitoring. Full article

This paper proposes a process-aware, tag-based digital identity framework that enhances interoperability while enabling identity unlinkability and selective disclosure across multi-party workflows involving sensitive data. We realize this framework within the self-sovereign identity (SSI) paradigm, employing zk-SNARK–based zero-knowledge proofs to enable verifiable identity authentication without plaintext disclosure. The framework introduces a protocol-tagging mechanism to support multiple proof systems within a unified architecture, thereby enhancing SSI scalability and interoperability. Its core innovation lies in combining identity unlinkability and process-driven data disclosure: derived sub-identities mitigate identity-linkage attacks, while layered encryption enables selective, stepwise decryption of sensitive information (e.g., delivery addresses), ensuring participants access only the minimal information necessary for their tasks. In addition, zero-knowledge proof-based verification guarantees that the validation of derived sub-identities can be performed without sharing any plaintext attributes or identifying factors. We applied the framework to logistics, where sub-identities anonymize participants and layered encryption allows for delivery addresses to be decrypted progressively along the logistics chain, with only the final courier authorized to access complete information. During the parcel receipt process, users can complete verification using derived sub-identities and zero-knowledge proofs alone, without disclosing any real personal information or attributes that could be linked back to their identity. Trusted Execution Environments (TEEs) ensure the authenticity of decryption requests, while blockchain provides immutable audit trails. A demonstration system was implemented, formally verified using Scyther, and performance-tested across multiple platforms, including resource-constrained environments, showing high efficiency and strong practical potential. The core paradigms of identity unlinkability and process-driven data disclosure are generalizable and applicable to multi-party scenarios involving sensitive data flows. Full article

Road infrastructure underpins Nigeria’s economic competitiveness, yet Public–Private Partnership (PPP) performance is constrained not by inadequate legislation but by persistent weaknesses in enforcement and governance. Transparency deficits across procurement, design management, certification, and toll-revenue reporting have produced chronic delays, cost overruns, and declining public trust. This study offers the first empirical investigation of blockchain–Building Information Modelling (BIM) integration as a transparency-enhancing mechanism within Nigeria’s PPP road sector, focusing on Lagos State. Using a qualitative design, ten semi-structured interviews with stakeholders across the PPP lifecycle were thematically analysed to diagnose systemic governance weaknesses and assess the contextual feasibility of digital innovations. Findings reveal entrenched opacity rooted in weak enforcement, discretionary decision-making, and informal communication practices—including biased bidder evaluations, undocumented design alterations, manipulated certifications, and toll-revenue inconsistencies. While respondents recognised BIM’s potential to centralise project information and blockchain’s capacity for immutable records and smart-contract automation, they consistently emphasised that technological benefits cannot be realised absent credible institutional foundations. The study advances an original theoretical contribution: the Codify–Condition–Capacitate framework, which explains the institutional preconditions under which digital governance tools can improve transparency. This framework argues that effectiveness depends on: codifying digital standards and legal recognition; conditioning enforcement mechanisms to reduce discretionary authority; and capacitating institutions through targeted training and phased pilots. The research generates significant practical implications for policymakers in Nigeria and comparable developing contexts seeking institution-aligned digital transformation. Methodological rigour was ensured through purposive sampling, thematic saturation assessment, and documented analytical trails. Full article

In this paper, we propose a data-driven decision-support approach for conceptual trail planning and management in protected natural areas, where environmental awareness (particularly climatic comfort and noise levels) is critical to ensuring a sustainable and enjoyable visitor mobility. Our case study is the Natural Park of La Mata and Torrevieja in Spain. The paper begins by identifying climate refuges in this park (areas offering shelter from heat and other adverse conditions based on meteorological data). We extend this with a novel comfort indicator that incorporates ambient noise levels, using acoustic data from sensors. A key challenge is the integration of heterogeneous data sources (climatic data and noise data from the park’s digital twin infrastructure). To demonstrate the potential of this approach for trail planning, we implement an A* pathfinding algorithm to explore comfort-oriented routing alternatives, guided by our combined climate-noise comfort index. The algorithm is applied to trail management in the Natural Park of La Mata and Torrevieja, enabling the identification of indicative high-comfort routes that can inform future trail design and management decisions, while accounting for ecological constraints and visitor well-being. Results show that the proposed comfort-aware routing improves average environmental comfort by 66.3% with only an additional 344 m of walking distance. Finally, this work constitutes a first step toward a data space use case, showcasing interoperable, AI-ready environmental data usage and aligning with the European Green Deal. Full article

In recent years, the convergence of advanced technologies has enabled real-time data access and sharing across diverse devices and networks, significantly amplifying cybersecurity risks. For organizations with digital infrastructures, network security is crucial for mitigating potential cyber-attacks. They establish security policies to protect systems and data, but employees may intentionally or unintentionally bypass these policies, rendering the network vulnerable to internal and external threats. Detecting these policy violations is challenging, requiring frequent manual system checks for compliance. This paper addresses key challenges in safeguarding digital assets against evolving threats, including rogue access points, man-in-the-middle attacks, denial-of-service (DoS) incidents, unpatched vulnerabilities, and AI-driven automated exploits. We propose a Blockchain-Enhanced Network Scanning and Monitoring (BENSAM) Framework, a multi-layered system that integrates advanced network scanning with a structured database for asset management, policy-driven vulnerability detection, and remediation planning. Key enhancements include device profiling, user activity monitoring, network forensics, intrusion detection capabilities, and multi-format report generation. By incorporating blockchain technology, and leveraging immutable ledgers and smart contracts, the framework ensures tamper-proof audit trails, decentralized verification of policy compliance, and automated real-time responses to violations such as alerts; actual device isolation is performed by external controllers like SDN or NAC systems. The research provides a detailed literature review on blockchain applications in domains like IoT, healthcare, and vehicular networks. A working prototype of the proposed BENSAM framework was developed that demonstrates end-to-end network scanning, device profiling, traffic monitoring, policy enforcement, and blockchain-based immutable logging. This implementation is publicly released and is available on GitHub. It analyzes common network vulnerabilities (e.g., open ports, remote access, and disabled firewalls), attacks (including spoofing, flooding, and DDoS), and outlines policy enforcement methods. Moreover, the framework anticipates emerging challenges from AI-driven attacks such as adversarial evasion, data poisoning, and transformer-based threats, positioning the system for the future integration of adaptive mechanisms to counter these advanced intrusions. This blockchain-enhanced approach streamlines security analysis, extends the framework for AI threat detection with improved accuracy, and reduces administrative overhead by integrating multiple security tools into a cohesive, trustworthy, reliable solution. Full article

Objective: Gliomas disrupt functional brain networks and impair neurological functions. While left-hemispheric tumors are well-studied because of their impact on language domains, the influence of right-sided gliomas on higher cognitive functions remains less understood. This study aimed to assess pre- and postoperative neurocognitive performance and to link cognitive outcomes with structural findings derived from function-based tractography in patients with right-hemispheric gliomas. Methods: Patients with gliomas were enrolled in this prospective observational study. A structured neurocognitive test battery was administered preoperatively, postoperatively, and at 3-month follow-up. Preoperative cortical mapping using navigated transcranial magnetic stimulation (nTMS) and function-based fiber tracking, based on diffusion tensor imaging (DTI), was performed. Results: Eighteen patients aged 52.7 ± 18.3 years were included. Preoperatively, 88.8% of patients showed impairments in at least one cognitive test, most frequently in the Nine-Hole Peg Test (66.7%), Bells Test task completion time (61.1%), Trail Making Test A and B (TMT-A: 50.0%; TMT-B: 44.4%), and digit symbol substitution test (27.8%). At follow-up, task performance improved on most cognitive tests. Function-based tractography showed that involvement of the superior longitudinal fasciculi I–III (44.4% of cases) was associated with impairments in attention, executive function, visuospatial processing, and processing speed. The involvement of the inferior frontooccipital fasciculus (55.5% of cases) was related to deficits in processing speed, attention, executive function, and episodic memory. Conclusions: Neurocognitive deficits are common in patients with right-hemispheric gliomas even before surgery. Maximal safe resection and sparing of these tracts is associated with cognitive recovery at follow-up. Function-based tractography emphasizes the structural involvement of key association fibers related to these cognitive deficits. Full article

In this work, we present a principled framework for the deployment of Large Language Models (LLMs) in enterprise big data management across digital governance, marketing, and accounting domains. Unlike conventional predictive applications, our approach integrates LLMs as auditable, sector-adaptive components that robustly and directly enhance data curation, lineage, and regulatory compliance. The study contributes (i) a systematic evaluation of seven LLM-enabled functions—including schema mapping, entity resolution, and document extraction—that directly improve data quality and operational governance; (ii) a distributed architecture that deploys Apache Spark orchestration with Markov Chain Monte Carlo sampling to achieve quantifiable uncertainty and reproducible audit trails; and (iii) a cross-sector analysis demonstrating robust semantic accuracy, compliance management, and explainable outputs suited to diverse assurance requirements. Empirical evaluations reveal that the proposed architecture persistently attains elevated mapping precision, resilient multimodal feature extraction, and consistent human supervision. These characteristics collectively reinforce the integrity, accountability, and transparency of information ecosystems, particularly within compliance-driven organizational settings. Full article

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore less about raw performance or scaling, and more about an architectural limitation: knowledge is treated as an after-the-fact annotation on computation, rather than as an organizing substrate that shapes computation. This paper introduces Mindful Machines, a computational paradigm that operationalizes coherence as an architectural property rather than an emergent afterthought. A Mindful Machine is specified by a Digital Genome (encoding purposes, constraints, and knowledge structures) and orchestrated by an Autopoietic and Meta-Cognitive Operating System (AMOS) that runs a continuous Discover–Reflect–Apply–Share (D-R-A-S) loop. Instead of a static model embedded in a one-shot ML pipeline or deep learning neural network, the architecture separates (1) a structural knowledge layer (Digital Genome and knowledge graphs), (2) an autopoietic control plane (health checks, rollback, and self-repair), and (3) meta-cognitive governance (critique-then-commit gates, audit trails, and policy enforcement). We validate this approach on the classic Credit Default Prediction problem by comparing a traditional, static Logistic Regression pipeline (monolithic training, fixed features, external scripting for deployment) with a distributed Mindful Machine implementation whose components can reconfigure logic, update rules, and migrate workloads at runtime. The Mindful Machine not only matches the predictive task, but also achieves autopoiesis (self-healing services and live schema evolution), explainability (causal, event-driven audit trails), and dynamic adaptation (real-time logic and threshold switching driven by knowledge constraints), thereby reducing the coherence debt that characterizes contemporary ML- and LLM-centric AI architectures. The case study demonstrates “a hybrid, runtime-switchable combination of machine learning and rule-based simulation, orchestrated by AMOS under knowledge and policy constraints”. Full article