Search Results (1,193)

The swift advancement of the digital economy presents new pathways toward achieving common prosperity in China. Based on microdata derived from the China Family Panel Studies (2010–2022), this study employs the “Broadband China” pilot policy as a quasi-natural experiment to explore how digital economy development influences multidimensional relative poverty. We develop a multidimensional relative poverty index encompassing economic, health, education, and living condition aspects utilizing the Alkire–Foster dual cutoff method and employ a staggered Difference-in-Differences design for empirical analysis. Results show that the policy leads to an average decrease of 1.8 percentage points in the probability of multidimensional relative poverty across households. The effect is more pronounced in central and western regions, rural households, and those with a high proportion of non-labor force, particularly in the dimensions of economic, health, and living conditions dimensions. Mechanism analysis via interaction term regression indicates that increased population mobility and improved informal employment are key channels. These findings suggest that enhancing digital infrastructure and tailoring mobility and employment policies to fit regional and urban–rural contexts can effectively alleviate multidimensional relative poverty. This study contributes empirical evidence connecting the advancement of the digital economy to poverty alleviation and aligns with the United Nations Sustainable Development Goal 1 (No Poverty). Full article

Artificial intelligence (AI), as a strategic technology leading the current technological revolution and industrial transformation, functions as a pivotal catalyst for enhancing high-quality supply chain development and as the primary engine driving supply chains towards environmentally sustainable, low-carbon models. This study seeks to clarify how AI bolsters supply chain resilience through enhanced information transparency and dynamic capabilities, while examining the moderating influence of digital government in this context. Based on this, this study selected A-share listed companies from 2012 to 2023 as research samples. An entropy-based approach was utilized to develop a supply chain resilience indicator system. A two-way fixed-effects model was employed to analyze the mechanism by which business AI impacts supply chain resilience. Studies demonstrate that company artificial intelligence can markedly improve supply chain resilience. In this process, information transparency, innovative capacity, and absorptive capacity partially mediate the effect, while digital governance exerts a positive moderating influence. Heterogeneity studies indicate that artificial intelligence has a significantly greater favorable effect on supply chain resilience for high-tech corporations, manufacturing firms, growth-stage companies, mature-stage businesses, and chain master enterprises. The research findings not only reveal the impact and underlying mechanisms of enterprise artificial intelligence on supply chain resilience, offering a new perspective for systematically understanding the relationship between enterprise AI and supply chain resilience, but also provide key pathways and empirical evidence for leveraging digital technologies to build sustainable supply chains. Full article

Background/Objectives: As healthcare increasingly utilizes digital delivery systems, equitable access and engagement are critical, particularly for caregivers of older adults in rural regions. This study examines how education levels and geographic rurality influence health information-seeking in Mississippi, a state with persistent structural inequities, through the theoretical lenses of Digital Divide Theory and Theory of Planned Behavior. Methods: A statewide survey was conducted among caregivers in Mississippi (N = 452) who support adults aged 50+. The survey assessed rurality level, educational attainment, attitudes toward various health information sources, perceived digital accessibility, and reported challenges in obtaining necessary health guidance. Results: Findings challenged conventional assumptions regarding rural digital engagement. Rural caregivers reported higher trust in both internet and interpersonal health information sources. Rurality did not significantly predict internet use or reported difficulty finding information. However, a significant interaction between education and rurality revealed an “Outcome Divide”: while higher education correlated with more positive attitudes toward online health information in urban areas, this association weakened and reversed in highly rural contexts. Conclusions: These results underscore the need for strategies beyond merely improving access to bridge digital health equity gaps. Policy and interventions must address contextual barriers, such as digital health literacy and relevance, limiting the effectiveness of digital tools, even when internet access is available. Promoting digital health literacy, integrating trusted local interpersonal networks, and adapting educational initiatives to rural realities are essential for advancing equitable and effective digital health engagement. Full article

Coastal marshes provide essential ecosystem services, yet they are vulnerable to anthropogenic stressors and climate change, particularly sea level rise (SLR). Restoration approaches like marsh terracing have emerged as nature-based strategies to enhance resilience and reduce habitat loss. This study applies the Sea Level Affecting Marshes Model (SLAMM) to assess the potential of marsh terraces to mitigate future losses, while also examining the model’s limitations, including its assumptions and capacity to reflect complex marsh processes. A geospatial approach was used to generate 3D representations of terraces through morphostatic modeling within digital elevation models (DEMs). Under a no-restoration scenario, SLAMM projections show that all marshes analyzed are at risk of total loss by 2100. In contrast, scenarios including terracing demonstrate a delay in net marsh loss, extending the persistence of key marsh habitats by approximately a decade. Although marsh degradation remains likely under high SLR conditions, the results underscore the utility of marsh terraces in prolonging habitat stability. Additionally, the study demonstrates the feasibility of integrating restoration features like terraces into DEMs and wetland models. Despite SLAMM’s simplified erosion and accretion assumptions, the model yields important insights into restoration effectiveness and long-term marsh dynamics, informing more adaptive, forward-looking coastal management strategies. Full article

Purpose: The construction industry is now experiencing a thorough transformation through digital technologies, especially with building information modeling (BIM). Despite significant BIM advantages, most construction projects suffer from low BIM performance due to the fragmented BIM use mode. To facilitate lifecycle-integrated BIM implementation, this study demonstrates that introducing BIM lifecycle service providers (BLSPs) is feasible and offers significant improvements in terms of BIM benefits. Hence, this study proposes a customized framework to select BLSPs. Approach: This study utilized both qualitative and quantitative methods. It first adopted semi-structured interviews as part of the qualitative method to deduce the initial criteria for BLSPs’ selection. 30 interviews were conducted iteratively with managers proficient and experienced in selecting BLSPs, through which 25 initial criteria were identified. Then, as the basis of the applied quantitative method, a questionnaire survey was used to evaluate these criteria by determining the critical ones, identifying the latent factor groupings, and assigning criteria weights. Subsequently, an assessment framework was established. Finally, the study was in favor of eight construction projects, highlighting the practicality and validity of the framework. Findings: The results depicted that project BIM service capability is a primary factor for BLSPs’ selection. Within this factor, several specialized criteria need to be considered, such as “boundary spanning competence of the BIM manager” and “BIM service plans with lifecycle cognition.” Meanwhile, “past innovative BIM service practices” and “BIM research and development (R&D)” that originate in corporate innovation capacity were emphasized when selecting BLSPs. Furthermore, for holistic assessment and recognizing the peculiarities of digital BIM service, the study found that criteria like “Privacy and security” and “Backup system” are required, which demonstrate BIM service reliability. Originality/value: This study expands on the conventional partner selection frameworks in the construction sector and thus defines and validates a tailored one for BLSPs’ selection. Moreover, drawing such a reference solution from the framework, the study enables the selection of appropriate BLSPs for clients. Full article

The global movement towards a cashless society has prompted the payment of tax obligations through digital platforms and sources. In this international race to ensure that transaction payments are not hindered by the lack of physical cash, Nigeria is also making progress. Therefore, the focus of this study is to assess the implications of digital payment systems in enhancing the effectiveness of tax revenue collection in Nigeria. The analysis spans from the first quarter of 2009 to the fourth quarter of 2023, utilizing the Autoregressive Distributed Lag and Error Correction Model. The research uses the most active digital payment systems that have been in operation during the study period. These electronic payment types include digital cheques (CHQs), Automated Teller Machines (ATMs), Point-of-Sales (POSs), Mobile payment (MPY), and Web-based payment (WPY). These are the predictor variables, while the tax revenue collection (TXC) during this period is the dependent variable. The control variables include information and telecommunication technology penetration rate (ICTPR), inflation, and gross domestic product. The outcomes of this study reveal that, over the long term, a percentage change in CHQs, ATMs, MPY, and ICTPR is linked to a decline of 8.1%, 12.5%, 6.7%, and 22.4% in TXC, respectively. In contrast, WPY indicates a 7.2% positive increase in TXC while inflation exerts a positive increase of 46.7%. The Error Correction Model (ECM) suggests that the deviations from the long-term equilibrium in earlier years are being corrected at a rate of 3.9% in the current year. In the short term, it is noted that digital payment systems do not influence TXC. On the other hand, GDP maintains a significant negative influence on TXC, in both the long- and short-term. Given these results, the study recommends the establishment of a robust information and communication technology (ICT) infrastructure to enhance effective tax collection, even from rural areas and the informal sector. It is also important for the government to develop strategies that will bring the informal sector into the tax net. Full article

Addressing the inefficiency, subjective errors, and limited adaptability of existing methods for surveying complex ancient structures, this study presents an intelligent hierarchical algorithm for generating line drawings guided by structured architectural features. Leveraging point cloud data, our approach integrates prior semantic and structural knowledge of ancient buildings to establish a multi-granularity feature extraction framework encompassing local geometric features (normal vectors, curvature, Simplified Point Feature Histograms-SPFH), component-level semantic features (utilizing enhanced PointNet++ segmentation and geometric graph matching for specialized elements), and structural relationships (adjacency analysis, hierarchical support inference). This framework autonomously achieves intelligent layer assignment, line type/width selection based on component semantics, vectorization optimization via orthogonal and hierarchical topological constraints, and the intelligent generation of sectional views and symbolic annotations. We implemented an algorithmic toolchain using the AutoCAD Python API (pyautocad version 0.5.0) within the AutoCAD 2023 environment. Validation on point cloud datasets from two representative ancient structures—Guanchang No. 11 (Luoyuan County, Fujian) and Li Tianda’s Residence (Langxi County, Anhui)—demonstrates the method’s effectiveness in accurately identifying key components (e.g., columns, beams, Dougong brackets), generating engineering-standard line drawings with significantly enhanced efficiency over traditional approaches, and robustly handling complex architectural geometries. This research delivers an efficient, reliable, and intelligent solution for digital preservation, restoration design, and information archiving of ancient architectural heritage. Full article

Rapid digitalization has resulted in an increase in the number of older adults playing electronic sports (eSports). Therefore, it must be investigated whether eSports have a positive effect on cognitive function in older adults. We explored the traits of facial color modification in Japanese older individuals while playing eSports by employing the facial color analysis technique proposed in this study. With the aging population, eSports have garnered interest as a means of extending healthy life expectancy. The quantitative detection of emotions obtained from eSports can function as an indicator for evaluating the degree to which individuals enjoy the games and can aid in the assessment of eSports to extend healthy life expectancy. Thus, in this study, we aimed to develop an indicator for quantitatively assessing the emotions experienced while playing eSports. The investigation revealed that information on color saturation in the cheek region exhibited a distinct relationship with the emotions generated while playing eSports. The identified characteristics can also be utilized in techniques to estimate the emotions generated during eSports activities. This can contribute to the evaluation of eSports in extending the healthy life expectancy of older adults. Furthermore, it can aid in the development of technologies that support remote communication. Full article

Over the past decade, educators have utilized flipped learning to augment students’ learning outside of the classroom. The COVID-19 pandemic disruptions in regular classroom teaching and learning activities intensified the use of the approach. This entry examines teacher educators’ and pre-service teachers’ perspectives of flipped learning, highlighting the opportunities and challenges during and after the pandemic. This entry also examines how flipped learning impacted educators’ work and pre-service teachers’ learning, which necessitated its continuous development and use in teacher education. A critical analysis of the literature and illustrations from other perspectives highlights the implication of adopting flipped learning and how educators, pre-service teachers, and universities can support the integration of the approach in the curriculum. While the approach provides substantial benefits to both educators and pre-service teachers, integrating digital technologies to adopt the approach presents challenges to practitioners, which need to be addressed with more support for professional development training. This entry contributes to the existing valuable information for policymaking for technological integration in the transformation of teacher education. Full article

The exponential growth of user-generated video content necessitates efficient summarization systems for improved accessibility, retrieval, and analysis. This study presents and benchmarks a multimodal video summarization framework that classifies segments as informative or non-informative using audio, visual, and fused features. Sixty hours of annotated video across ten diverse categories were analyzed. Audio features were extracted with pyAudioAnalysis, while visual features (colour histograms, optical flow, object detection, facial recognition) were derived using OpenCV. Six supervised classifiers—Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, and XGBoost—were evaluated, with hyperparameters optimized via grid search. Temporal coherence was enhanced using median filtering. Random Forest achieved the best performance, with 74% AUC on fused features and a 3% F1-score gain after post-processing. Spectral flux, grayscale histograms, and optical flow emerged as key discriminative features. The best model was deployed as a practical web service using TensorFlow and Flask, integrating informative segment detection with subtitle generation via beam search to ensure coherence and coverage. System-level evaluation demonstrated low latency and efficient resource utilization under load. Overall, the results confirm the strength of multimodal fusion and ensemble learning for video summarization and highlight their potential for real-world applications in surveillance, digital archiving, and online education. Full article

In the digital era where images are easily accessible, concerns about image authenticity and integrity are increasing. To address this, we propose a deep learning-based fragile watermarking method for secure image authentication and content recovery. The method utilizes bottleneck features extracted by the convolutional encoder to carry both authentication and recovery information and employs deconvolution at the decoder to reconstruct image content. Additionally, the Arnold Transform is applied to scramble feature information, effectively enhancing resistance to collage attacks. At the detection stage, block voting and morphological closing operations improve tamper localization accuracy and robustness. Experiments tested various tampering ratios, with performance evaluated by PSNR, SSIM, precision, recall, and F1-score. Experiments under varying tampering ratios demonstrate that the proposed method maintains high visual quality and achieves reliable tamper detection and recovery, even at 75% tampering. Evaluation metrics including PSNR, SSIM, precision, recall, and F1-score confirm the effectiveness and practical applicability of the method. Full article

Digital twin (DT) technology, integrated with building information modeling (BIM), enables real-time feedback and predictive analytics in construction. This study presents a BIM-enabled DT framework to optimize in situ production and yard-stock management of precast concrete (PC) components with a focus on minimizing CO₂ emissions. Using Oracle Crystal Ball, scenario-based simulations revealed up to an 8.9% reduction in environmental impact. Distinct from prior research that largely emphasized cost or off-site strategies, this study uniquely addresses on-site sustainability by embedding carbon metrics into the decision-making process. The framework was validated through a large-scale logistics warehouse project that showcased its practical utility. This research contributes a replicable method for enhancing sustainability in precast construction through digital technologies. Full article

There is a growing role of AI and intelligent systems in construction efficiency, safety, and decision-making. Reviewing existing applications helps guide future innovation, informs industry practices, and supports sustainable development. This paper discusses how advanced technologies in the construction industry such as Industry 4.0, IoT, and 3D printing are transforming the construction industry. The main objective is to explore how these tools are being used, what benefits these applications offer, what challenges companies face when adopting them, and what steps can make the transition easier. To do this, a structured review of 115 recent studies published between 2015 and 2025 was carried out by utilizing authentic databases such as Scopus, Web of Science, and ScienceDirect. The research shows that these technologies can make construction faster, safer, and more sustainable, but adoption is still held back by high upfront costs, a lack of digital skills, and unclear regulations. These issues are especially tough for smaller companies and those in developing regions. The paper offers practical recommendations for both researchers and practitioners to help bridge the gap between potential and practice and support a smarter, more efficient future for construction. Full article

This article presents an overview of selected research focusing on digital real-time simulation (DRTS) in the context of digital twin (DT) realization with the primary aim of enabling the intelligent fault diagnosis (FD) and condition-based monitoring (CBM) of electrical machines. The concept of standalone DTs in conventional multiphysics digital offline simulations (DoSs) is widely utilized during the conceptualization and development phases of electrical machine manufacturing and processing, particularly for virtual testing under both standard and extreme operating conditions, as well as for aging assessments and lifecycle analysis. Recent advancements in data communication and information technologies, including virtual reality, cloud computing, parallel processing, machine learning, big data, and the Internet of Things (IoT), have facilitated the creation of real-time DTs based on physics-based (PHYB), circuit-oriented lumped-parameter (COLP), and data-driven approaches, as well as physics-informed machine learning (PIML), which is a combination of these models. These models are distinguished by their ability to enable real-time bidirectional data exchange with physical electrical machines. This article proposes a predictive-level framework with a particular emphasis on real-time multiphysics modeling to enhance the efficiency of the FD and CBM of electrical machines, which play a crucial role in various industrial applications. Full article

The rapid developments in technology have brought about significant changes regarding accounting information extraction as a tool for optimized administrative and strategic decisions. The implementation of electronic bookkeeping as a dynamic application, combined with the continuous renewal of the International Financial Reporting Standards (IFRS), has modified accounting functions. The impacts of technology, the imposition of innovative reforms in the public administration system, the effects of COVID-19 and the continuous need for accounting reforms, shaped in Greece an economic and accounting system of particular research interest. The research approaches accounting management, the impacts of digitalization and the main advantages and obstacles of the ever evolving technological transition. The aim of this paper is to create a tool that utilizes the existing levels of technological training and correlate it with digitization’s weaknesses and opportunities discerning an optimized approach of modern technologies in accounting administration. Results highlight the positive response to the updates of digitization demonstrated in accounting, with the simultaneous resistance to change due to increasing workloads. In the aforementioned economic environment, focused monitoring of both methods and rate of utilizing an evolving technology, combined with the human factor could enable a smoother transition of accounting digitalization and optimized administrative decisions. Full article