Search Results (3,791)

Blockchain technology (BCT) is often discussed as digital support for timber traceability in the context of the EU Deforestation-Free Products Regulation (EUDR), which requires operators and traders to demonstrate legality, deforestation-free production, geolocation at the forest parcel level, and verifiable supply chain documentation from 30 December 2026, with an extended timeline for micro, small, and medium-sized enterprises (SMEs) until 30 June 2027. This study assesses where BCT can realistically add value in timber supply chains under operational forestry conditions and identifies the necessary technical, organizational, and legal prerequisites. A combination of a targeted literature review and empirical input from experts in the forestry and timber industry, including guided expert web-conferencing interviews (n = 41), an online survey (n = 69 completed responses), and a transdisciplinary workshop (n = 18) was utilized to obtain a comprehensive overview of industry perspectives. Qualitative data from interviews and workshop sessions were analyzed using structured qualitative content analysis, while survey data were evaluated using descriptive statistics. The expected benefits are associated with the introduction of tamper-proof timber harvesting practices and cross-organizational verification mechanisms. To address the discrepancy between biological uncertainty and digital rigidity, the study proposes a dynamic allocation model adapted from the energy sector that distinguishes between fixed and variable wood capacities to automate logistical planning via smart contracts. However, respondents emphasize that practical obstacles, such as limited digital maturity in forestry, fragmented data infrastructures across the supply chain, and unresolved issues of data sovereignty hinder the implementation of BCT. BCT alone is unable to resolve the problem of weak physical-digital identity continuity, a phenomenon widely known as the oracle problem; however, coupling the ledger with physical or biological anchors (e.g., photo-optical, automated inkjet marking identification) can re-establish this physical–digital continuity and thereby resolve the oracle problem. The results demonstrate that blockchain acts most plausibly as a supporting component within hybrid traceability architectures that prioritize event-based authentication, off-chain data processing where appropriate, and integration with existing certification systems. The study highlights the necessity of defining distinct organizational roles and responsibilities while integrating user-centric digital solutions tailored specifically to small and medium-sized enterprises (SMEs). Full article

Mobile health (mHealth) apps increasingly capture the physiological, biomechanical, and psychological variables involved in sport training, but the evidence remains fragmented across single-domain reviews, leaving practitioners without a consolidated basis for selecting and deploying these tools across the training process. This scoping review aimed to identify and characterize research on mHealth apps in sport training, focusing on their performance testing, training load and recovery monitoring, technical and skill development, injury screening and prevention, and athlete self-management. It also synthesized evidence regarding their applications, intended purposes, technical characteristics, and the evidence supporting their effectiveness. Five databases (PubMed, Scopus, Web of Science, SPORTDiscus, Embase) were searched from inception to July 2026 for journal articles reporting original empirical data on app research on the sport training process in athletes. Studies involving only the promotion of physical activity or lacking human-subject testing, including commercially available apps without supporting research on their effectiveness, were excluded. Findings were synthesized narratively, and methodological quality was appraised with the Mixed Methods Appraisal Tool. Of 9476 records identified, 111 studies met the inclusion criteria and were inductively classified into ten application categories: sport skill training (n = 26), performance measurement (n = 20), vertical jump measurement (n = 18), self-reported monitoring (n = 12), physiological measurement (n = 12), musculoskeletal screening (n = 10), psychological intervention (n = 5), nutrition (n = 3), anthropometric and maturation screening (n = 3), and tactical and match analysis (n = 2). Most apps relied on built-in smartphone sensors or no sensing at all and used manual or deterministic computation; processing location went unreported in 74.8% of studies, which reflects a reporting gap rather than an architectural profile of the field, and reported that AI or machine learning labels did not track with actual method disclosure. Validation and reliability designs dominated the evidence base (52%), while randomized or controlled effectiveness trials were rare (10%). Apps generally showed good relative validity but limited absolute accuracy against criterion instruments, and wherever apps were deployed longitudinally, adherence rather than accuracy determined their real-world value. mHealth apps now support nearly every stage of sport training and can substitute for laboratory instruments in select, validated use cases, including video-based sprint and jump timing and chest-strap-paired heart-rate variability monitoring. However, the field remains organized around demonstrating measurement accuracy rather than showing that app-guided decisions improve athlete outcomes. A successful pathway for mHealth app development should progress from technical validity, through measurement reliability and responsiveness, to decision rules, and then to practitioner adoption by coaches and athletes, ultimately yielding better athlete outcomes. Full article

The evolution of modern enterprise architecture has been strongly influenced by distributed web services, especially protocol-based standards such as Simple Object Access Protocol (SOAP) and resource-oriented architectural styles such as Representational State Transfer (REST). Cloud-native ecosystems, microservice architectures, and public API management commonly favor the lightweight, JSON-compatible, and horizontally scalable characteristics of RESTful services, whereas legacy configurations and highly regulated environments continue to use SOAP because of its formal contracts and compatibility with WS-* specifications for message-level security, reliable messaging, and transaction coordination. This paper presents a structured literature review that evaluates the architectural trade-offs, performance patterns, security boundaries, reliability considerations, and enterprise use cases of REST and SOAP. The review follows PRISMA-informed reporting practices and software-engineering review guidance, but it is not presented as an exhaustive systematic review because the original search strategy required REST and SOAP terms to appear together. IEEE Xplore, ACM Digital Library, ScienceDirect, and Scopus were searched for studies published between 2021 and 2026, resulting in 32 selected studies. The selected literature contains different evidence roles, including direct REST-SOAP empirical comparisons, REST-only and SOAP-only empirical studies, implementation studies, analytical papers, surveys, reviews, and contextual technical sources. The synthesis therefore separates direct empirical evidence from contextual and secondary evidence. The findings indicate that RESTful APIs generally show lower latency, smaller payloads, simpler parsing, and better horizontal scalability in the reported benchmark and web-facing settings, while SOAP remains relevant where formal service contracts, message-level protection, reliable messaging patterns, and transaction coordination are required. The paper identifies gaps in production-representative stress testing, empirical security comparison, reference-level traceability, and independent validation of hybrid REST-SOAP decision models. The resulting decision framework is presented as a provisional evidence-informed decision aid, not as an empirically validated tool. Full article

Phosphogypsum (PG) governance is a persistent challenge in industrial solid waste management, particularly in regions where large-scale stockpiling, uneven resource-utilization capacity, and policy implementation pressures coexist. Existing studies have paid considerable attention to regulatory instruments and recycling technologies, but less is known about how governments, waste-generating enterprises, and waste-utilizing enterprises adjust their strategies under bounded rationality. This study develops a tripartite evolutionary game model incorporating prospect theory. The technology–organization–environment (TOE) perspective is used as a parameter-identification lens to capture three external conditions: policy incentive intensity, enterprise governance input, and resource-utilization technology maturity. Based on case-informed parameter calibration from Guizhou and related policy-industrial evidence, numerical simulations are conducted to examine evolutionary paths, equilibrium conditions, and parameter sensitivity. The results show that PG governance may evolve from systemic inaction to policy-driven transformation and then to market-oriented sustainability. Technological maturity plays a threshold role, while excessive loss aversion can destabilize cooperative evolution. The interaction analysis further indicates that policy incentives are effective only when they are aligned with enterprise treatment behavior and viable market-entry conditions. These findings suggest that PG governance should move beyond short-term administrative intervention toward staged policy support, technical standardization, and market cultivation. Full article

Using data from Chinese listed non-financial firms from 2015 to 2023, this study examines how environmental, social, and governance (ESG) rating divergence affects banks’ loan limit decisions and the underlying decision-making mechanisms within the sustainable finance system. Drawing on cognitive psychology, we conceptualize bank lending as an organizational decision-making process in which conflicting ESG signals create information ambiguity and influence banks’ risk assessments. Greater ESG rating divergence is associated with significantly lower total annual bank loan limits. Mechanism analyses provide evidence consistent with heightened bank concerns about firm default risk, whereas we find no supporting evidence for the information-quality channel captured by discretionary accruals. Further analysis reveals an asymmetric response to conflicting ESG information: banks appear to place greater weight on relatively unfavorable ESG ratings, while favorable ratings do not exert a comparable moderating effect. The negative association between ESG rating divergence and bank loan limits is more evident among non-state-owned enterprises, firms with weaker repayment capacity, firms with lower financial information disclosure quality, and firms without third-party ESG assurance. These findings extend the literature on ESG rating divergence and sustainable finance by showing how conflicting ESG information is associated with contractual credit allocation through banks’ risk assessment and asymmetric information-processing behavior. They also highlight the importance of improving ESG information governance and banks’ capacity to evaluate inconsistent sustainability signals. Full article

Although fire service accessibility has been extensively studied for urban public facilities such as hospitals and parks, it remains largely unexplored for high-risk industrial facilities such as fireworks manufacturers. In this study, fire risk and transportation network conditions are jointly considered, and a dynamic risk-weighted fire service accessibility evaluation framework is developed. An empirical study was conducted using 35 fire stations and 410 fireworks manufacturers in Liuyang City, China. Fire truck travel times were simulated via online map application programming interfaces (APIs) at 10–30 min intervals over a continuous three-day period, generating 193 evaluation scenarios and 158,260 data samples. The results indicate that the total average travel time of different types of fire trucks to demand points with varying risk levels was found to range from 570.33 to 1362.18 s, and risk-weighted fire service accessibility ranges from 16.84 to 57.26, reflecting a generally poor level of fire service accessibility within the current transport network. To resolve these constraints, it is recommended to optimize the spatial configuration of micro fire stations, promote the collaborative planning of shared enterprise-based fire units, and cross-integrate accessibility metrics into regional industrial land-use planning. These spatial measures are expected to significantly enhance the spatial resilience of transportation systems serving peripheral high-risk industrial clusters. Full article

Decades-old enterprise-resource-planning (ERP) systems lock operational data inside unstructured, human-readable reports, forcing slow, costly, error-prone manual re-keying. Because multimodal large language model (MLLM) capability is uneven, deploying MLLMs for extraction means trusting outputs without a labeled reference. We test this with a within-document controlled experiment on 400 controlled-substance stock-ledger documents (2951 records, 11 fields, predominantly Thai) from a Thai pharmaceutical factory, comparing trained human double-entry against four open-weight MLLMs (2 × 2 design: vendor × architecture) via OpenRouter. Human double-entry left 14 discrepancies against the adjudicated gold standard, none common to both operators. The strongest model, Qwen3-VL-32B-Instruct (Dense), reached 93.95% cell accuracy; among these four models, field accuracy varied more across vendors, whereas structural completeness differed consistently between dense models (0 missing records) and Mixture-of-Experts models (up to 51 of 2951 dropped). Deterministic accounting invariants flagged 0.61% of its records, leaving the unflagged majority 94.1% accurate across all 11 fields; adding calendar rules flagged 4.61% and raised residual date accuracy from 92.1% to 95.9%. We report both operating points and recommend the extended level where date fidelity is regulatory-critical. The pipeline is 13.5–29.4× faster in wall-clock terms and 97.5–99.5% cheaper. Gold-free, rule-based verification thus locates where MLLM reliability holds, giving human–AI collaboration quantified, disclosed residual risk rather than an implied guarantee. Even at the more conservative operating point, unflagged records average 94.5% accuracy across all 11 fields but only 43.7% on the free-text Remarks field, which the triage cannot check; the results support risk reduction and the localization of review effort, not unrestricted regulatory reliability across all fields. Full article

City information systems are steadily relying on various stakeholders, heterogeneous resources, and interrelated digital services. Nevertheless, many initiatives still face difficulties in aligning actors, business processes, data, and technological resources within a unified information systems architecture. Accordingly, this study presents and evaluates a federated enterprise architecture-based model for value co-creation in city information systems. The studied model investigates how strategy and capture, relationship management, and community context contribute to federated enterprise architecture, and how the architecture structuring supports service value co-creation, continuous improvement, and global impact. To validate the research model, data were gathered using a structured questionnaire that was administered to actors involved in information system initiatives. The studied hypotheses were tested using partial least squares structural equation modeling. The main findings demonstrate that community context has a positive influence on enterprise architecture structuring, which, in turn, supports service value co-creation through the alignment of stakeholders, processes, data, and digital services. Service value co-creation then contributes to continuous improvement and ecosystem global impact, acting as a mediating mechanism between information system to city service findings. Thus, the research contributes to the information systems literature by highlighting how federated architecture contributes positively to digital service in a multi-actor ecosystem and supports value co-creation in the city information system. Full article

Production data transparency in smart manufacturing simultaneously enhances and impairs employee performance across organizational contexts. Existing research has not resolved this theoretical contradiction. Drawing on socio-technical systems theory, cognitive appraisal theory, and conservation of resources theory, this study develops a dual-pathway model. Data transparency influences adaptive performance through a bright empowerment pathway and a dark surveillance pathway mediated by EPM-induced strain. Procedural justice of data governance and digital self-efficacy operate as a perceived-institutional boundary condition and an individual-capability boundary condition, respectively. Latent moderated structural equations were applied to survey data from 412 employees in Chinese smart manufacturing enterprises. Results support both pathways and reveal a theoretically consequential asymmetry between these boundary conditions. Johnson–Neyman analysis indicates that institutional justice attenuates the strain pathway to non-significance within the observed distribution of responses. Conversely, digital self-efficacy requires near-ceiling levels to achieve the same pattern. This asymmetry indicates that continuous moderation produces sharply different practical outcomes across the observed data range. Institutional and individual remedies therefore address the visibility paradox on different practical scales. Governance adequacy represents a more attainable managerial lever than individual capability development. These findings advance socio-technical systems theory by detailing the asymmetric buffering capacities of different organizational resources. Full article

To address the issue where information asymmetry in third-party logistics (3PL)-led low-carbon supply chain coordination undermines coordination efficiency, thereby threatening the sustainability and resilience of supply chain cooperation, this paper develops a Stackelberg dynamic game model with the 3PL as the leader. This model is constructed within the context where consumers’ low-carbon preferences influence product demand, and a government carbon cap policy is implemented. By comparing decentralized and centralized equilibria, we verify that centralized collaboration achieves dual gains: higher carbon reduction levels and greater overall supply chain profits, which strengthens sustainability and resilience. To address efficiency losses from three types of information asymmetry, we propose a two-stage dynamic coordination mechanism adapted to evolving cooperation transparency. At the initial stage with opaque information, a bargaining-power-weighted profit-sharing contract is adopted, where negotiation weights are quantified by enterprise scale, resource control and industry influence. After data transparency improves, the system switches to a Nash bargaining framework supported by blockchain carbon data sharing to realize stable long-term collaboration. Numerical cases and sensitivity analysis demonstrate that manufacturer cost information asymmetry is the primary constraint on coordination efficiency. The proposed dynamic coordination scheme effectively mitigates systemic complexity, balancing economic benefits and carbon reduction targets. This study provides practical pathways for supply chain participants to navigate complex low-carbon environments and advance sustainable, resilient supply chain operation. Full article

The study addresses the design and development of a unified corporate information system for multimodal transport and logistics companies engaged in maritime and railway transportation. Analysis of the existing literature revealed the absence of a coherent methodological framework for the design of corporate information systems tailored to the specific operational characteristics of multimodal transport and logistics enterprises. To bridge this gap, a design methodology for corporate information system databases is proposed, intended for subsequent deployment in companies operating multimodal supply chains. The development of the unified corporate information system was guided by the principle of “total costs,” which requires that the decision-maker—when selecting transport modes, methods of transportation, carriers, routing, and auxiliary intermediaries (insurer, stevedore, bank, and customs broker)—address the problem as an integrated whole rather than optimizing individual components in isolation. The study encompasses information modeling of the business processes of multimodal transport and logistics companies, construction of an optimal model of the transport process for maritime and railway transportation using integrated computer automated manufacturing definition (IDEF) and structured analysis and design technique (SADT) modeling, and the design of a multilevel unified database structure for the coordination of different transport modes. A decision-making and support system has been developed for managing the operational activities of a multimodal transport and logistics company engaged in maritime and railway transportation. The proposed unified corporate information system enables the replacement of task resolution by local optimization criteria applied separately to each transport mode—such as freight cost and delivery time—with a single global optimization criterion for the multimodal supply chain. Full article

Data centers are large and rapidly growing electricity consumers, and cooling systems account for a substantial share of their energy demand. A key contribution of this study is a climate-sensitive, hourly techno-economic comparison of three data-center cooling configurations under consistent operating assumptions: a conventional water-cooled centrifugal chiller baseline, a magnetic bearing chiller (MBC) system, and an MBC system integrated with aquifer thermal energy storage (ATES). The comparison is performed for Phoenix, Arizona, and Fairbanks, Alaska, which represent substantially different cooling climates in the U.S. Hourly simulations use identical information technology (IT) load profiles, identical aggregate installed chiller capacity represented by two 4058 kW chiller units, common water-side economizer controls, and site-specific weather and electricity tariffs. Results show that the MBC system reduces annual cooling-system electricity consumption from 1169.4 to 957.4 MWh in Phoenix (18.1%) and from 361.6 to 319.4 MWh in Fairbanks (11.7%). Peak cooling-system electrical demand decreases by 119.4 kW in Phoenix and 71.6 kW in Fairbanks. Relative to the centrifugal baseline, the MBC case gives a 5.8-year simple payback in Phoenix but is not economically attractive in Fairbanks under the assumed tariff. The MBC-only case gives the lowest annual cooling electricity use in both climates. The MBC + ATES case is treated only as a screening-level, discharge-assisted cold-storage scenario rather than a full techno-economic assessment of seasonal ATES, and no site-specific hydrogeological feasibility assessment is performed. Under the assumed O&M cost structure, MBC + ATES gives a higher discounted value of savings than MBC-only, but this economic result is not caused by additional cooling-electricity savings relative to MBC-only. The MBC + ATES case also has a longer payback period because of its higher capital cost. These results show that the value of advanced cooling configurations depends on climate, free-cooling availability, electricity pricing, storage assumptions, and economic assumptions within the modeling framework considered in this study. Full article

Remote-access virtual private networks (VPNs) are a key component of enterprise security infrastructures. In practice, the effectiveness of a remote-access VPN is determined not only by cryptographic mechanisms but also by its ability to remain available and recover quickly under realistic operating conditions, which directly affects the operational security guarantees provided by the underlying cryptographic protocols. Enterprise deployments are characterized by heterogeneous client platforms, wireless access networks, and frequent endpoint and network disruptions. In this paper, we execute an exploratory case study of the operation of remote-access VPNs in enterprise environments through an empirical evaluation of WireGuard, OpenVPN, and IPsec. Using a controlled but realistic testbed with a cloud-hosted gateway and heterogeneous client platforms, we evaluate baseline performance as well as behavior under endpoint CPU stress, network impairments, MTU variation, and mobility-related disruptions, reflecting constrained and dynamically changing deployment conditions. The results suggest that VPN operational characteristics are influenced by both protocol design and execution environment. Within the evaluated deployment scenarios, kernel-based implementations generally exhibited higher resilience under endpoint resource contention and faster recovery after disruptions, while layered and virtualized environments exhibited increased variability and sensitivity to network imperfections. These findings underline that resilience in remote-access VPNs should be interpreted as a system-level property emerging from the interaction of implementation architecture, endpoint characteristics, and deployment conditions. Full article

Fruit and vegetable supply chains generate heterogeneous data across production, storage, logistics, and sales, creating challenges for trusted data sharing, privacy protection, and real-time traceability across distributed supply-chain information systems. Conventional single-chain blockchains suffer from limited scalability, data redundancy, and low retrieval efficiency, making them inadequate for high-frequency full-process information management. This study proposes a multi-chain blockchain framework for trusted full-process information management of fruit and vegetable supply chains. The framework integrates traceability, enterprise, notary, and regulatory chains to support hierarchical data management and privacy isolation. A reputation-based notary node election mechanism and a threshold-signature scheme based on Shamir secret sharing are designed to enhance cross-chain security and distributed regulatory consensus. To improve retrieval efficiency, a Cuckoo-Augmented Merkle Tree (CMerkle) and a skip-list-based block index are developed. Simulation results show that all malicious nodes were restricted by the 19th round, signature aggregation required 70.16 ms in a 500-node setting, and CMerkle achieved retrieval speedups of 14.7 and 153 times at data scales of 500 and 10,000 records, respectively. The framework supports trusted data governance, real-time traceability, privacy-preserving sharing, and regulatory decision support in blockchain-enabled supply-chain information systems. Full article

The increasing complexity of information technology (IT) systems requires reference frameworks that connect governance, service management, and quality assurance. This scoping review maps 109 core sources published between 1996 and June 2026 to characterize evidence on IT reference frameworks across organizational and sectoral contexts; records from 2026 are treated as partial-year data and are not interpreted as a complete annual trend. The review was structured using the Population–Concept–Context (PCC) framework and reported in alignment with PRISMA-ScR guidance. Peer-reviewed studies and selected grey literature sources were charted to identify research trends, framework families, implementation patterns, integration interfaces, evaluation practices, and evidence gaps. Dominant families included ISO/TQM-oriented quality systems, COBIT and ITIL/IT service management (ITSM), enterprise architecture, sectoral quality assurance, cybersecurity, and emerging AI/data-governance approaches. The findings indicate that integration is concentrated around recurring interfaces among strategic governance, service operation, quality and assurance controls, enterprise architecture, and evidence feedback. However, the mapped evidence remains methodologically uneven: conceptual frameworks, case studies, and reviews dominate, while longitudinal validation and comparable implementation metrics are limited. Emerging AI-related work is treated as an early research direction rather than as mature evidence of effectiveness. Full article