Search Results (62,417)

This paper develops a procedure for assessing both supply chain processes and strategic criteria in the context of platform-based supply chains, addressing the problem that organizations often invest in digital platforms without a clear understanding of how process effectiveness, process dysfunction, and strategic platform priorities jointly influence implementation success. The main research objective is to evaluate how effective and dysfunctional supply chain processes, together with prioritized strategic platform criteria, shape performance, productivity, and resilience outcomes in platform-based supply chain integration. The paper further discusses how identified dysfunctional processes and prioritized strategic criteria relate to operational vulnerabilities and resilience-building measures. The research adopts a multi-criteria decision-making (MCDM) approach to address the challenges of digital transformation and platform integration. An exploratory study was conducted applying the analytic hierarchy process (AHP) to evaluate functional and dysfunctional processes, complemented by the best worst method (BWM) to prioritize critical strategic criteria. The combined assessment highlights effective and dysfunctional processes while also identifying the most influential factors driving platform-based adoption and their potential implications for operational vulnerability and resilience. The results demonstrate how platform integration contributes to performance improvement, process alignment, and productivity gains across supply chain operations. The study contributes to both theory and practice by integrating MCDM techniques to support structured decision-making, enhancing responsiveness, resilience, and alignment with platform-oriented strategies. The primary contribution lies in providing a dual-level framework that enables supply chain managers to diagnose weaknesses, leverage strengths, and strategically guide the transition toward platform-based supply chain operations, with a measurable impact on organizational performance and productivity development. Full article

Mobility-as-a-Service (MaaS) has emerged as a sustainable solution that integrates multiple transport services through digital platforms. Across different cities, MaaS development exhibits variation in terms of economic support, infrastructure capacity, service integration level, and long-term sustainability orientation. The complexity of multistakeholder interactions and functional components in MaaS ecosystems calls for a more comprehensive performance evaluation framework. To address this, this study proposes a holistic four-dimensional indicator system covering economic, infrastructure, integration and sustainability aspects. To address the hybrid uncertainties arising from the heterogeneous information aggregated by the proposed framework, encompassing both quantitative statistics and qualitative expert judgements, a novel rough–fuzzy best–worst method (BWM) and rough–fuzzy data envelopment analysis (DEA) approach is developed. The empirical application to six representative core cities in China reveals that high performance in “Integration” and “Economic” dimensions plays a pivotal role in determining overall MaaS performance, and coordinated enhancement across dimensions is also important. Comparative and sensitivity analyses validate the framework’s robustness, offering policymakers a reliable tool for benchmarking MaaS maturity. Full article

Impersonation attacks causing online fraud are a growing challenge for digital services, demanding the integration of biometric and behavioral factors into traditional authentication methods. Behavioral impersonation detection during online sessions is particularly critical for online banking, and the existing solutions focus mostly on mouse and keyboard dynamics. We study behavioral patterns extracted from standard web-server logs and claim that our methods are applicable in a banking scenario. Using a Siamese neural network, we classify pairs of web sessions from the same user with 90% accuracy. Experiments conducted on real-world intranet weblogs, serving as a proxy for banking data, highlight challenges in filtering and aggregating data. To address variability in website technologies and browsing behaviors, we introduce an automated procedure for grouping requested pages based on a low-rank approximation of the user browsing matrix. This approach consistently improves classification accuracy while reducing reliance on costly, error-prone manual log analysis, offering a scalable, viable approach for fraud detection in online services. Full article

Background/Objectives: To thoroughly compare acoustic rhinometry (AR) with computed tomography (CT) cross-sectional areas that are approximately perpendicular to the direction of the nasal airflow (CT-CSA). Methods: We retrospectively examined subjects scheduled for functional nasal surgery, along with preoperative CT and AR. CT-CSAs were assessed in several nasal planes in the first 5 cm of the nasal airway. Area sizes and distances of the CT-CSAs from the columella served to create a CT curve analogous to the AR curve. AR curves were digitized. We examined the correlation and agreement (using the Bland–Altman method) between CT curves and digitized AR curves, as well as between selected CT-CSAs and the first two-encountered AR minimal cross-sectional areas (AR-MCA₁ and AR-MCA₂). Correlation was investigated by univariate analysis of variance and Pearson’s correlation. Agreement was examined by the Bland–Altman method. Results: In 33 subjects, the correlation of digitized AR with CT was moderate (r = 0.76; p < 0.001). AR, in general, underestimated the actual nasal area by 15%. AR-MCA₁ and AR-MCA₂ were closest to the CT-CSA of the nasal valve and the incisive canal, respectively. We noted a mainly moderate correlation between the CT-CSA of the nasal valve and AR-MCA₁ (all r > 0.59; all p < 0.001) in contrast to the weaker correlations between the CT-CSA of the incisive canal and AR-MCA₂. Conclusions: AR may underestimate the actual nasal area by 15%. AR-MCA₁ and AR-MCA₂ were closest to the CT-CSA of the nasal valve and the incisive canal, respectively. Full article

This investigation provides a critical analysis of the digital forensics field within the Kingdom of Saudi Arabia (KSA), specifically focusing on its educational systems, and the effectiveness of prosecutorial efforts. Utilizing a mixed-methodology framework and extensive literature reviews, this study reveals pronounced deficiencies in digital forensics against increased cybercrime activities. Furthermore, it highlights a general lack of preparedness among digital forensics professionals in KSA and notes significant variations in forensic applications across different judicial and educational contexts. The research recommends creating a uniform national educational framework for digital forensics, improving professional training programs, and strategically enhancing forensic technologies. Through a thorough analysis of demographic trends, educational programs, and adherence to procedural standards, this study proposes targeted strategies to fortify the digital forensic infrastructure of KSA, aligning with the strategic imperatives of Vision 2030. Full article

Patients with severe structural tooth wear present significant restorative challenges, including compromised oral function and the loss of essential anatomical landmarks such as marginal ridges, incisal edges, cusps, occlusal planes, and vertical dimension of occlusion (VDO). Successful management requires meticulous diagnosis, comprehensive treatment planning, and careful selection of restorative materials with appropriate biomechanical properties. Digital technologies have become integral to this process, particularly for enhancing diagnostic accuracy, material selection, and tooth preparation design within a fully digital workflow. This clinical case report illustrates a complete digital approach, beginning with an initial intraoral scan merged with a digital wax-up STL file featuring varying translucency dimensions to guide tooth preparation. This workflow enabled precise planning of tooth reduction, accurate assessment of available interocclusal space, and determination of material thickness requirements prior to irreversible procedures. Additionally, the integration of digital visualization improved patient communication, treatment predictability, and interdisciplinary collaboration. Overall, this case highlights the value of CAD/CAM technology in supporting complex oral rehabilitation for patients with advanced tooth wear, demonstrating its capacity to enhance efficiency, precision, and outcome quality in full-mouth zirconia ceramic restorations. Full article

Rural areas are currently facing a deepening “social-ecological divide,” where the fragmentation of natural, economic, and cultural data—often trapped in “data silos”—hinders effective systemic governance. To bridge this gap, in this study, the Rural Landscape Information Model (RLIM), an integrative framework designed to reconfigure rural connections through data fusion, process coordination, and performance feedback, is proposed. We validate the framework’s effectiveness through a comparative analysis of two distinct rural archetypes in China: the innovation-driven Yu Village and the heritage-conservation-oriented Hani Terraces. Our results reveal that digital technologies drive distinct empowerment pathways moderated by regional contexts: (1) In the data domain, heterogeneous resources were successfully integrated into the framework in both cases (achieving a Monitoring Coverage > 80%), yet served divergent strategic ends—comprehensive territorial management in Yu Village versus precision heritage monitoring in the Hani Terraces. (2) In the process domain, digital platforms restructured social interactions differently. Yu Village achieved high individual participation (Participation Rate ≈ 0.85) via mobile governance apps, whereas the Hani Terraces relied on cooperative-mediated engagement to bridge the digital divide for elderly farmers. (3) In the performance domain, the interventions yielded contrasting but positive economic-ecological outcomes. Yu Village realized a 25% growth in tourism revenue through “industrial transformation” (Ecology+), while the Hani Terraces achieved a 12% value enhancement by stabilizing traditional agricultural ecosystems (Culture+). This study contributes a verifiable theoretical model and a set of operational tools, demonstrating that digital technologies are not merely instrumental add-ons but catalysts for fostering resilient, collaborative, and context-specific rural socio-ecological systems, ultimately offering scalable governance strategies for sustainable rural revitalization in the digital era. Full article

Background: Arthroscopic anterior cruciate ligament (ACL) reconstruction is widely recognised as the primary treatment for ACL injuries. However, with the increasing incidence of sports-related injuries and growing demand for rehabilitation services, conventional rehabilitation models—largely reliant on therapists’ experience and subjective assessment—are increasingly insufficient to meet the clinical need for precise and individualised rehabilitation programmes. This study aimed to evaluate the effectiveness of a rehabilitation protocol incorporating an artificial intelligence (AI)-based assessment and correction system on functional recovery following ACL reconstruction. Methods: Using convenience sampling, 80 patients undergoing ACL reconstruction between June to December 2024 were recruited for this randomised controlled trial. Participants were randomly assigned to either a control group (n = 40), which received conventional functional exercise training, or a trial group (n = 40), which received rehabilitation intervention guided by an AI-based assessment and correction system. Knee function scores (Lysholm score, IKDC score), Berg Balance Scale (BBS) scores, joint range of motion (ROM), and rehabilitation exercise compliance scores were collected and analysed 1, 2, 3, and 4 months postoperatively. Results: Compared with the control group, the trial group demonstrated significantly greater improvements in Lysholm score, IKDC score, BBS score, and active knee joint ROM (p < 0.05) at postoperative assessment points. Additionally, rehabilitation exercise adherence was significantly higher in the trial group compared to the control group (p < 0.05). Conclusions: Rehabilitation protocols integrating AI-based assessment and correction systems effectively enhance knee function recovery, joint mobility and balance ability following ACL reconstruction. Moreover, these protocols significantly improve rehabilitation exercise adherence, demonstrating superior efficacy compared to conventional rehabilitation approaches. This digital rehabilitation model represents an efficient and promising intervention for postoperative ACL rehabilitation. Full article

This paper presents a numerical study on the deployment of Overfire Air (OFA) technology in coal-fired thermal power plants in Kazakhstan to reduce harmful emissions. The simulation utilized a digital model of the combustion chamber of the BKZ-75 boiler at Shakhtinsk thermal power plant, which utilizes high-ash Karaganda coal containing 35.10% ash. During the development of two-stage combustion technology, different methods of supplying extra air via OFA injectors were examined. Various positions within the combustion chamber were evaluated for their placement: at heights of h = 0.165 m; 0.75 m; 1.375 m; 2.25 m; 2.5 m; 8 m; 9.4 m; 10 m; 11 m; and 12 m. The baseline combustion mode (OFA = 0%) and several additional air injector settings were analyzed, including OFA levels of 5%, 10%, 15%, 18%, 20%, 25%, and 30% of the total air volume. Numerical simulations generated temperature distributions along with carbon monoxide (CO) and nitrogen (NO) concentration fields, both inside and outside the combustion chamber outlet. Research indicates that the most effective reduction in pollutant emissions happens when OFA injectors are positioned at 9.4 m and supply supplementary air at an OFA rate of 18%. Under these settings, the carbon monoxide concentration at the combustion chamber outlet decreases by approximately 36%, while nitrogen oxide levels drop by 25%, compared to the baseline condition (OFA = 0%). These insights can be utilized to upgrade boiler units, promoting cleaner fuel combustion in coal-fired thermal power plants. Full article

In the context of China’s dual-carbon agenda and the Digital China initiative, elucidating the role of digital literacy in shaping consumption-based household carbon emissions (HCE) is essential for advancing low-carbon urban living and supporting a broader green transition. Existing research has rarely examined, at the individual level, how digital capability shapes household consumption decisions and the structure of carbon emissions. Accordingly, this study draws on matched household-individual microdata from the China Family Panel Studies (CFPS). We employ a two-way fixed effects model, kernel density analysis, and qualitative comparative analysis. We test the nonlinear effect of digital literacy on household consumption-related carbon emissions and examine its heterogeneity. We also examined the mediating role of perceived environmental pressure, social trust and income level. The research results show that: (1) The net impact of digital literacy on carbon emissions related to household consumption shows an inverted U-shaped curve, rising first and then falling. When digital literacy is low, it mainly increases emissions by expanding consumption channels, reducing transaction costs and improving convenience. Once digital literacy exceeds a certain threshold, the mechanism will gradually turn to optimize the consumption structure, so as to support the low-carbon transformation of individuals. (2) The impact of digital literacy on HCE is structurally different in different types of consumption. In terms of transportation and communication expenditure, the emission reduction effect is the most significant, and with the improvement in digital literacy, this effect will become more and more obvious. For housing-related consumption, the turning point appeared the earliest. With the improvement in digital literacy, its effect will enter the emission reduction stage faster. (3) Digital literacy can reduce carbon emissions related to household consumption by enhancing residents’ perception of environmental pressure and strengthening social trust. However, it may also increase emissions by increasing residents’ incomes, because it will expand the scale of consumption, which will lead to an increase in carbon emissions related to household consumption. (4) The heterogeneity analysis shows that as digital literacy improves, carbon emissions increase more strongly among rural residents, people with low human capital, low-income households, and women. However, the turning-point threshold for emission reduction is relatively lower for women and rural residents. (5) Low-carbon transitions in household consumption are shaped by dynamic interactions among multiple factors, and multiple pathways can coexist. Digital literacy can work with environmental responsibility to endogenously promote low-carbon consumption behavior. It can also, under well-developed infrastructure, empower households and amplify the emission-reduction effects of technology. Full article

This paper presents a theoretical and empirical analysis of how banks apply artificial intelligence (AI) in digital and mobile banking to implement and communicate ESG (Environmental, Social, and Governance) strategies, with particular emphasis on environmental dimensions of sustainable finance. The study adopts a mixed methodological approach combining desk research, encompassing a synthesis of academic studies, industry reports, and European regulatory frameworks on AI and ESG, and case study analysis of selected banks implementing AI-based sustainability solutions. The findings reveal that AI supports ESG strategy implementation primarily through green investment recommendations, carbon footprint analytics, automated sustainability reporting, and ethical communication with clients. AI-driven tools enhance the operational efficiency, transparency, and customer engagement of financial institutions while simultaneously fostering low-carbon financial behaviors. However, the study also highlights ethical and governance challenges related to algorithmic transparency, data bias, and responsible AI oversight. The paper contributes to the growing body of literature on AI-driven digital transformation and sustainable finance by identifying research gaps and outlining future directions for exploring the role of AI in accelerating the transition of the banking sector. Full article

Coarse-Grained Reconfigurable Architectures (CGRAs) execute compute-intensive kernels on a reconfigurable processing mesh. Transparent CGRAs extend this model by generating configurations at runtime and storing them in a dedicated cache, removing compiler dependence and enabling adaptive behavior. Although prior work has explored mapping strategies and mesh scaling, the feasibility of the configuration cache remains unaddressed, as it is commonly treated as a generic storage block. This paper presents a feasibility study of configuration cache organizations and a design-space exploration of Transparent CGRAs, introducing a parameterized cache geometry model that relates cache parameters to the processing mesh and configuration structure. The model enables realistic estimates of area, latency, and energy at the digital system level and is applied to three Transparent CGRAs from the literature and five additional designs covering a wide range of spatial and temporal organizations. The results show that mesh scaling must be balanced with cache feasibility: wide I/O paths and large configurations lead to impractical caches, whereas well-proportioned meshes achieve competitive performance with modest overheads. Under the proposed exploration, selected expanded meshes outperform a two-issue out-of-order processor by up to 1.4× while increasing area by only 14.8% and energy by 2%. These findings demonstrate that Transparent CGRAs are viable, but their scalability depends on a realistic configuration cache design. The proposed parameterized cache model provides a structured and reproducible basis for analyzing transparency overheads and guiding future CGRA designs. Full article

This study presents a comprehensive digital workflow for the archaeological investigation and heritage enhancement of the Coëby megalithic necropolis (Brittany, France). Dating to the Middle Neolithic, between the 4th and 3rd millennia BC, this chronology is established through stratigraphy, material culture, and radiocarbon dating. Focusing on cairns TRED 8 and TRED 9, which are two excavation units, we combined field archaeology, photogrammetry, and topographic data with open-source 3D geometric modeling to reconstruct the monuments’ original volumes and test construction hypotheses. The methodology leveraged the free software Blender (version 3.0.1) and its Bagapie extension for the procedural simulation of lithic block distribution within the tumular masses, ensuring both metric accuracy and realistic texturing. Beyond static reconstruction, the research explores innovative dynamic and narrative visualization techniques. We employed the FILM model for smooth video interpolation of the construction sequences and utilized the Wan 2.1 AI model to generate immersive video scenes of Neolithic life based on archaeologically informed prompts. The entire process, from data acquisition to final visualization, was conducted using free and open-source tools, guaranteeing full methodological reproducibility and alignment with open science principles. Our results include detailed 3D reconstructions that elucidate the complex architectural sequences of the cairns, as well as dynamic visualizations that enhance the understanding of their construction logic. This study demonstrates the analytical potential of open-source 3D modelling and AI-based visualisation for megalithic archaeology. Full article

Health equity and financial inclusion (FI) are at the core of the Sustainable Development Goals, yet their intersection remains critically under-studied. This bibliometric study maps this emergent and fragmented field by analyzing 24,140 publications from Scopus, PubMed, Web of Science, and Lens.org over five decades. Employing co-citation and co-word analysis via VOSviewer, chart research trends, governance frameworks, and policy linkages were systematically presented. The analysis reveals that less than 0.3% of the identified literature explicitly bridges financial inclusion with health outcomes, and direct investigations into health equity are virtually absent. Despite recent growth, fundamental gaps persist, including a lack of empirical studies on digital financial tools in low- and middle-income countries (LMIC) health contexts and insufficient focus on disadvantaged populations. As the first comprehensive empirical mapping of this nexus, this study underscores the urgency for scholarly and policy action to strategically leverage financial instruments for equitable healthcare access. The findings provide a foundational map and a structured agenda to consolidate this nascent field. Full article

Introduction: Rare diseases disperse expertise across institutions and borders, making structured second-opinion systems a pragmatic way to concentrate subspecialty knowledge and reduce diagnostic delays. This scoping review mapped the design, governance, adoption, and impacts of such services across implementation scales. Objectives: To describe how second-opinion services for rare diseases are organized and governed, to characterize technological and workflow models, to summarize benefits and barriers, and to identify priority evidence gaps for implementation. Methods: Using a population–concept–context approach, we included peer-reviewed studies describing implemented second-opinion systems for rare diseases and excluded isolated case reports, purely conceptual proposals, and work outside this focus. Searches in August 2025 covered PubMed/MEDLINE, Scopus, Web of Science Core Collection, Cochrane Library, IEEE Xplore, ACM Digital Library, and LILACS without date limits and were restricted to English, Portuguese, or Spanish. Two reviewers screened independently, and the data were charted with a standardized, piloted form. No formal critical appraisal was undertaken, and the synthesis was descriptive. Results: Initiatives were clustered by scale (European networks, national programs, regional systems, international collaborations) and favored hybrid models over asynchronous and synchronous ones. Across settings, services shared reproducible workflows and provided faster access to expertise, quicker decision-making, and more frequent clarification of care plans. These improvements were enabled by transparent governance and dedicated support but were constrained by platform complexity, the effort required to assemble panels, uneven incentives, interoperability gaps, and medico-legal uncertainty. Conclusions: Systematized second-opinion services for rare diseases are feasible and clinically relevant. Progress hinges on usability, aligned incentives, and pragmatic interoperability, advancing from registries toward bidirectional electronic health record connections, alongside prospective evaluations of outcomes, equity, experience, effectiveness, and costs. Full article