Search Results (15,017)

Farmers’ land transfer practices optimize the allocation of agricultural resources by transferring them to more efficient operators. This enhances agricultural productivity and advances rural revitalization. However, due to the lack of financial institution outlets in rural areas, the availability of financial services in rural areas is limited, which in turn hinders the transfer of rural land. This study examines the impact of digital financial inclusion, characterized by the deep integration of internet technology and financial services, on farmers’ land transfer behavior in China. The study uses data from the China Family Panel Studies (2012–2022) and provincial digital financial inclusion data. The results show that digital financial inclusion significantly promotes rural land transfer-out. The mechanisms reveal two pathways: (1) digital financial inclusion expands non-agricultural entrepreneurship by easing credit constraints and reducing reliance on land livelihoods; (2) it increases participation in commercial insurance, mitigating risks of land abandonment. Heterogeneity analysis reveals stronger effects in eastern China and among educated households. Theoretically, the study identifies the dual role of financial technology in reshaping rural land markets through credit access and risk management. Practically, it reveals how DFI influences land transfer behavior, providing a basis for the government to formulate policies that combine the two, ultimately enhancing the production capacity, operational efficiency, and market competitiveness of smallholder farmers. The findings offer global insights for developing countries that are leveraging digital finance to activate rural land markets and achieve digital financial inclusion. Full article

(1) Background: In today’s digital society, challenges like cyberbullying, harmful social media use, and unhealthy digital habits demand innovative and inclusive educational responses. This study investigates the potential of service-learning (SL) and electronic service-learning (e-SL) as experiential approaches to enhance digital well-being among youth. By actively engaging students, educators, and community stakeholders in co-designed projects, SL/e-SL promotes critical awareness, digital citizenship, and prosocial values while addressing digital risks. (2) Methods: This review offers a literature-based analysis of existing programs and good practices that apply experiential education to encourage responsible digital engagement. It explores SL and e-SL experiences across various educational settings. (3) Results: The findings show that SL and e-SL can be effective educational tools, creating meaningful opportunities for youth to participate in tackling digital issues and building inclusive spaces where students, faculty, and communities collaborate to foster digital literacy and well-being. The analysis also led to the development of quality standards for SL and e-SL practices that promote digital well-being. (4) Conclusions: This study highlights key implications for teaching, underscoring the value of integrative pedagogies that connect experiential learning to digital challenges, promoting a more inclusive and responsible digital culture. Full article

Digital ink Chinese characters (DICCs) written by international students often contain various errors and irregularities, making the recognition of these characters a highly challenging pattern recognition problem. This paper designs a graph convolutional network with agent attention (GCNAA) for recognizing DICCs written by international students. Each sampling point is treated as a vertex in a graph, with connections between adjacent sampling points within the same stroke serving as edges to create a Chinese character graph structure. The GCNAA is used to process the data of the Chinese character graph structure, implemented by stacking Block modules. In each Block module, the graph agent attention module not only models the global context between graph nodes but also reduces computational complexity, shortens training time, and accelerates inference speed. The graph convolution block module models the local adjacency structure of the graph by aggregating local geometric information from neighboring nodes, while graph pooling is employed to learn multi-resolution features. Finally, the Softmax function is used to generate prediction results. Experiments conducted on public datasets such as CASIA-OLWHDB1.0-1.2, SCUT-COUCH2009 GB1&GB2, and HIT-OR3C-ONLINE demonstrate that the GCNAA performs well even on large-category datasets, showing strong generalization ability and robustness. The recognition accuracy for DICCs written by international students reaches 98.7%. Accurate and efficient handwritten Chinese character recognition technology can provide a solid technical foundation for computer-assisted Chinese character writing for international students, thereby promoting the development of international Chinese character education. Full article

Global ambitions for net-zero emissions and resource circularity are propelling industry from linear “make-use-dispose”models toward closed-loop value creation. Remanufacturing, which aims to restore end-of-life products to a “like-new” condition, plays a central role in this transition. However, its stochastic inputs and complex, multi-stage processes pose significant challenges to traditional production planning methods. This study delivers an integrated overview of remanufacturing scheduling by combining a systematic bibliometric review of 190 publications (2005–2025) with a critical synthesis of modelling approaches and enabling technologies. The bibliometric results reveal five thematic clusters and a 14% annual growth rate, highlighting a shift from deterministic, shop-floor-focused models to uncertainty-aware, sustainability-oriented frameworks. The scheduling problems are formalised to capture features arising from variable core quality, multi-phase precedence, and carbon reduction goals, in both centralised and cloud-based systems. Advances in human–robot disassembly, vision-based inspection, hybrid repair, and digital testing demonstrate feedback-rich environments that increasingly integrate planning and execution. A comparative analysis shows that, while mixed-integer programming and metaheuristics perform well in small static settings, dynamic and large-scale contexts benefit from reinforcement learning and hybrid decomposition models. Finally, future directions for dynamic, collaborative, carbon-conscious, and digital-twin-driven scheduling are outlined and investigated. Full article

This study explores the effectiveness of 3D Digital Image Correlation (DIC) for measuring displacement and strain of a propeller undergoing angular motion. Traditional methods, such as strain gauges, face limitations including physical interference, technical difficulties in sensor connections, and restricted measurement points, leading to inaccuracies in capturing true conditions. To overcome these challenges, this research utilizes non-contact 3D DIC technology, enabling measurement of surface displacements and deformations without interfering with the tested component. Experiments were conducted using the model aircraft propellers mounted on a custom-built test stand for partial angular motion. The 1 Mpx high-speed cameras captured strain and displacement data across the propeller blades during motion. The DIC strain measurements were then compared to strain gauge data to evaluate their accuracy and reliability. The results demonstrate that 3D DIC enables precise displacement measurements, while strain measurements are subject to certain limitations. Displacement measurements were achieved with a noise level of ±10 μm, while strain measurement noise ranged from 26 to 174 µm/m depending on direction. Strain gauge measurements were also performed for verification of the DIC measurements and calibration of the filtering procedure. Two types of non-metallic materials were used in the study: Nylon LGF60 PA6 for the propeller and 3D-printed PC ABS for the cantilever beam used in strain measurement validation. This study underscores the potential of DIC for monitoring rotating components, with a particular focus on measuring strains that are often overlooked in publications addressing similar topics. Additionally, it focuses on comparing DIC strain measurements with strain gauge data on rotating components, addressing a critical gap in existing literature, as strain measurement in rotating structures remains underexplored in current research. Full article

Miao embroidery holds significant cultural, economic, and aesthetic value. However, its transmission faces numerous challenges: a high learning threshold, a lack of interest among younger generations, and low production efficiency. These factors have created obstacles to its sustainable development. In the age of artificial intelligence (AI), generative AI is expected to improve the efficiency of pattern innovation and the adaptability of the embroidery industry. Therefore, this study proposes a Miao embroidery pattern generation and application method based on Stable Diffusion and low-rank adaptation (LoRA) fine-tuning. The process includes image preprocessing, data labeling, model training, pattern generation, and embroidery production. Combining objective indicators with subjective expert review, supplemented by feedback from local artisans, we systematically evaluated five representative Miao embroidery styles, focusing on generation quality and their social and business impact. The results demonstrate that the proposed model outperforms the original diffusion model in terms of pattern quality and style consistency, with optimal results obtained under a LoRA scale of 0.8–1.2 and diffusion steps of 20–40. Generated patterns were parameterized and successfully implemented in digital embroidery. This method uses AI technology to lower the skill threshold for embroidery training. Combined with digital embroidery machines, it reduces production costs, significantly improving productivity and increasing the income of embroiderers. This promotes broader participation in embroidery practice and supports the sustainable inheritance of Miao embroidery. It also provides a replicable technical path for the intelligent generation and sustainable design of intangible cultural heritage (ICH). Full article

In light of Industry 5.0’s emphasis on human-centric approaches, future engineering education should prioritise the development of soft skills to complement students’ digital and technological competencies. This paper investigates the in-demand skills for Industry 5.0 and explores pedagogical approaches to foster these skills in engineering students. The identified skill categories include digital and technological proficiency, analytical and critical thinking, creativity and innovation, and communication and interpersonal skills. However, to date, no universal teaching model exists that holistically imparts all these skills. To address this gap, the paper introduces a new universal pedagogical model: the EPIC framework, which integrates Experiential, Paired, Inquiry-based, and Collective learning principles and provides examples of how this framework can be incorporated into engineering curricula to simultaneously cultivate all Industry 5.0 skills. Successfully implemented in engineering courses spanning different academic levels (Year 1 to Year 4), EPIC offers a promising framework for equipping students with the skills essential for thriving in the Industry 5.0 era. Full article

As Higher Education (HE) institutions expand their digital resources, students may be struggling to engage effectively with these tools. Academic help-seeking (AHS) is a useful framework for exploring help-seeking behaviour and could be applied to HE contexts, yet its application has been reported as inconsistent. Given the possible utility of this concept to support the growth in technology, a review of how academic help-seeking theory is applied in digital contexts can help to consolidate current understanding and guide future research. This scoping review examines the intersection of AHS and digital technology use in HE through analysis of peer-reviewed literature (2019–2024). Several gaps emerge: bias towards human-centred support, limited investigation of help-avoidance behaviours, insufficient attention to early help-seeking stages, and few studies examining spontaneous help-seeking in authentic digital learning environments. These findings indicate the need for expanded theoretical frameworks that better reflect modern learning behaviours and environments, alongside more diverse research approaches to understand how students integrate both human and non-human help sources in contemporary HE contexts. Full article

Industry 4.0 (I4.0) is reshaping manufacturing by integrating advanced digital technologies and is increasingly seen as an enabler of the circular economy (CE). However, most research treats digitalisation and circularity separately, with limited empirical insight regarding their combined implementation. This study investigates I4.0 adoption to support sustainability and CE across industries, focusing on how enterprise size influences adoption patterns. Based on survey data from 69 enterprises, the research examines which technologies are applied, at what stages of the product life cycle, and what barriers and drivers influence uptake. Findings reveal a modest but growing adoption led by the Internet of Things (IoT), big data, and integrated systems. While larger firms implement more advanced tools (e.g., robotics and simulation), smaller enterprises favour accessible solutions (e.g., IoT and cloud computing). A positive link is observed between digital adoption and CE practices, though barriers remain significant. Five main categories of perceived obstacles are identified: political/institutional, financial, social/market-related, technological/infrastructural, and legal/regulatory. Attitudinal resistance, particularly in micro and small enterprises, emerges as an additional challenge. Based on these insights, and to support the twin transition, the paper proposes targeted policies, including expanded funding, streamlined procedures, enhanced training, and tools for circular performance monitoring. Full article

Background: The present study aims to evaluate the effectiveness of ESYSTA^® (Emperra GmbH E-Health Technologies, Germany), a CE-certified digital health application made to support insulin-treated diabetes patients to improve their disease management through better self-empowerment. Methods: To evaluate the effectiveness of ESYSTA^®, data from patients who used ESYSTA^® for at least 12 months and participated in an originally prospective one-arm study were evaluated. This study was conducted in cooperation with the German health insurance company AOK Nordost (2012–2015). From a real-world data pool of insured AOK Nordost patients, a control group was matched to mimic a controlled trial that allows the use of ESYSTA^® to be compared with standard care in the context of a disease management program (DMP). Results: The study results show significant and clinically relevant reductions in HbA1c values of at least 0.4% in ESYSTA^® users after 6 months. After 12 months, users achieved, on average, an HbA1c reduction of approximately 0.7%. These reductions are more pronounced compared to the matched control group. Conclusions: The present study shows the effectiveness of the digital health application ESYSTA^®. Using a matched control group further increased the internal and external validity of the study results. Full article

Floods are among the most frequent and damaging hazards worldwide, with impacts intensified by climate change and rapid urban growth. This review analyzes how satellite-based Earth Observation (EO) technologies are evolving to meet operational needs in flood detection and water depth estimation, with a focus on the Copernicus Emergency Management Service (CEMS) as a mature and widely adopted European framework. We compare the capabilities of conventional EO datasets—optical and Synthetic Aperture Radar (SAR)—with 3D geospatial datasets such as high-resolution Digital Elevation Models (DEMs) and Light Detection and Ranging (LiDAR). While 2D EO imagery is essential for rapid surface water mapping, 3D datasets add volumetric context, enabling improved flood depth estimation and urban impact assessment. LiDAR, in particular, can capture microtopography between high-rise structures, but its operational use is constrained by cost, data availability, and update frequency. We also review how artificial intelligence (AI), including machine learning and deep learning, is enhancing automation, generalization, and near-real-time processing in flood mapping. Persistent gaps remain in model transferability, uncertainty quantification, and the integration of scarce high-resolution topographic data. We conclude by outlining a roadmap towards hybrid frameworks that combine EO observations, 3D datasets, and physics-informed AI, bridging the gap between current technological capabilities and the demands of real-world emergency management. Full article

Building-integrated photovoltaics (BIPV) is expected to play a relevant role in decarbonising our cities, both in new buildings and retrofit projects, making them more sustainable, resilient and pleasant. However, BIPV remains a niche market. To understand the reasons and help boost its development, this paper provides insights into BIPV through a holistic and systematic analysis that considers BIPV’s dual nature as both photovoltaic and building product. The methodology is based on the analyses of several BIPV technological innovation systems (TISs) developed in six countries, as well as extensive comparative assessments and investigations to identify key global features of BIPV. Social aspects, market status and forecast, perspectives from the photovoltaic and building sectors, and related regulations and standardisation are key aspects analysed to develop recommendations for policymakers. Outcome examples are low to moderate acceptance of BIPV among building owners, who give cost reasons for choosing building-added photovoltaics (BAPV) over BIPV, as well as a need for information, official guidance, skilled personnel, improved cross-sector collaboration, availability of BIPV products, proper digital tools and specific regulation to improve BIPV’s legitimacy in the construction sector. Essential is developing policies that encourage the adoption of BIPV, including standardisation, promotion and financing. Full article

The construction industry is a major contributor to environmental degradation, primarily due to the substantial volumes of construction waste (CW) generated on-site. As sustainability becomes a global imperative aligned with the UN 2030 Agenda, identifying and mitigating the root causes of CW is essential. This study adopts a cross-disciplinary approach to explore the drivers of CW and support more effective, sustainable waste reduction strategies. A systematic literature review was conducted to extract 25 key CW source factors from academic publications. These were analyzed using Social Network Analysis (SNA) to reveal their structural relationships and relative influence. The results indicate that the lack of structured on-site waste management planning, accumulation of residual materials, and insufficient worker training are among the most influential CW drivers. Comparative analysis with industry data highlights theoretical–practical gaps and the need for improved alignment between research insights and site implementation. This paper recommends the adoption of tiered waste management protocols as part of contractual documentation, integrating Building Information Modeling (BIM)-based residual material traceability systems, and increasing attention to workforce training programs focused on material handling efficiency. Future research should extend SNA frameworks to sector-specific waste patterns (e.g., pavement or demolition projects) and explore the intersection between digital technologies and circular economy practices. The study contributes to enhancing waste governance, promoting resource efficiency, and advancing circularity in the built environment by offering data-driven prioritization of CW sources and actionable mitigation strategies. Full article

In the area of structured cabling systems, optimization, i.e., reducing design errors, mini-mizing the need for rework, and increasing overall design productivity, is a critical factor in both design and maintenance. Traditional CAD methods exhibit 12% cable length miscalculations, which our script methodology mitigates. This paper presents a novel approach to the use of scripts in low voltage cabling systems, with a particular focus on the automatic routing of cables based on modeled cable paths. The proposed approach enables the automated construction and calculation of individual cable routes, as well as the comprehensive storage of associated parameter data. The methodology is discussed at conceptual level, with ideas presented at code and user levels. The effectiveness of this methodology is demonstrated through a case study conducted in the context of a real-world project. Full article