Search Results (96)

As digital technologies and food environments evolve, ensuring children’s food safety has become a pressing public health priority. This study examines how the policy discourse on children’s dietary safety in Korea has shifted over time by applying Latent Dirichlet Allocation (LDA) topic modeling to news articles from 2010 to 2024. Using a large-scale news database (BigKinds), the analysis identifies seven key themes that have emerged across five phases of the national Comprehensive Plans for Safety Management of Children’s Dietary Life. These include experiential education, data-driven policy approaches, safety-focused meal management, healthy dietary environments, nutritional support for children’s growth, customized safety education, and private-sector initiatives. A significant increase in digital keywords—such as “big data” and “artificial intelligence”—highlights a growing emphasis on data-oriented policy tools. By capturing the evolving language and priorities in food safety policy, this study provides new insights into the digital transformation of public health governance and offers practical implications for adaptive and technology-informed policy design. Full article

It is well established that effective management and restoration of freshwater ecosystems is often limited by the availability of reusable data. Although numerous public, private, and nonprofit organizations collect data from freshwater ecosystems, much of what is collected remains inaccessible or unusable by Rights holders and end users (including researchers, practitioners, community members, and decision-makers). In Canada, the federal government plans to improve freshwater data sharing practices through the newly formed Canada Water Agency, which is currently drafting a National Freshwater Data Strategy. Our study aimed to support these efforts by synthesizing insights from the technology sector, where data management and sharing practices are more mature. We interviewed 12 experts from the technology sector, asking them for advice on how to improve data sharing practices in the freshwater science sector. Using a Reflexive Thematic Analysis of participants’ responses to semi-structured interview questions, we identified nine broad recommendations. Recommendations centred on motivating open data sharing, promoting data reuse through data licences, training and skill building, and developing standards and digital solutions that enable data discovery, accessibility, interoperability, and reuse. These recommendations can support the numerous initiatives that are working to improve access to high-quality freshwater data and help address the pressing crisis of global freshwater ecosystem degradation. Full article

This study explores the integration of digital media tools—specifically TikTok, online press news analysis, and digital portfolios—into pre-service chemistry teacher education to enhance student engagement, foster conceptual understanding, and highlight the relevance of chemistry in society. The educational intervention involved 138 pre-service teachers who analysed digital news articles to reflect on the societal and environmental implications of chemistry, promoting media literacy and awareness of socioscientific issues. Additionally, they created short-form TikTok videos, using social media to communicate scientific concepts creatively and interactively. All participants compiled their work into digital portfolios, which served as both a reflective and integrative tool. A post-course Likert-scale questionnaire (N = 77) revealed high overall satisfaction with the methodology, with 94.8% valuing the news analysis activity and 59.7% finding TikTok particularly engaging. Despite some limitations regarding access to technical infrastructure, the findings indicate that incorporating Information and Communication Technology (ICT) in this manner supports motivation, meaningful learning, and the development of key teaching competencies. This case study contributes practical insights into ICT use in science education. Full article

This academic article seeks to clarify the state of the literature on a very pertinent topic that is based on how digital innovation, considering emerging technologies and how they could be used in business management and marketing, could increase sustainable development. The sustainable economy, which should maintain long-term development through efficient resource management, has as allies emerging technologies such as artificial intelligence, blockchain, and the Internet of Things that can help reduce waste, reduce the carbon footprint, and automate tasks. Additionally, they could present themselves as a solution to improve aspects of digital communication between companies and their consumers in remote training, distribution chain, e-commerce, and process optimization in different sectors of activity. These advances will, on the one hand, allow the possibility of conducting a greater amount of professional training, increasing the number of qualified professionals and, on the other hand, facilitate trade exchanges, promoting the economy. Based on a systematic bibliometric review of the literature using the PRISMA framework, this study investigates how digital tools catalyze transformative changes in different sectors of activity. The results indicate that, overall, the academic articles analyzed in this literature review present studies focused on digitalization and sustainability (approximately 50%). In second place are topics related to digitalization and other topics such as: smart cities; Sustainable Development Goals; academia; the digital economy; government policies; academic education; and sustainable communication (29%). Finally, in third place, there are academic articles closely linked to digitalization and the environment, more specifically to sustainable practices and the management of natural resources (21%). The article concludes that digital development, when used wisely, serves as a crucial lever to address the world’s most pressing sustainability imperatives. Future research should emphasize interdisciplinary collaboration and adaptive governance to ensure that these digital changes produce lasting impacts for people and the planet. Full article

The coronavirus pandemic has spread globally, affecting over 700 million people and resulting in over 7 million deaths. In response, global pharmaceutical companies and disease control centers have urgently sought effective treatments and vaccines. However, the rise of counterfeit drugs has become a significant concern amid this urgency. To standardize the legal provision and usage of genetic resources, the United Nations Development Program (UNDP) introduced the Nagoya Protocol. Despite advancements in drug research, the production process remains tedious, complex and vulnerable to fraud. FairChain addresses this pressing challenge by creating a transparent ecosystem that builds trust among all stakeholders throughout the Drug Development Life Cycle (DDLC) by using decentralized, immutable, and transparent blockchain technology. This makes FairChain the first digital health tool to implement the principles of the UNDP’s Nagoya Protocol among all stakeholders throughout all DDLC stages, starting with sample collection, to discovery and development, to preclinical research, to clinical development, to regulator review, and ending with post-market monitoring. Therefore, FairChain allows pharmaceutical companies to document the entire drug production process, landowners to monitor bio-samples from their land, doctors to share clinical research, and regulatory agencies such as the Food and Drug Authority to oversee samples and authorize production. FairChain should enhance transparency, foster trust and efficiency, and ensure a fair and traceable DDLC. To date, no blockchain-based framework has addressed the integration of traceability, auditability, and Nagoya Protocol compliance within a unified system architecture. This paper introduces FairChain, a system that formalizes these requirements in a modular, policy-aligned, and verifiable digital trust infrastructure. Full article

This qualitative study investigates how critical thinking is cultivated in postgraduate distance learning, focusing on two time points, 2016 and 2025, in the context of the Greek higher education system. It draws on semi-structured interviews with 30 participants (15 tutors and 15 students or graduates) from two online postgraduate programmes: Education Sciences and Education and Technologies in Distance Teaching and Learning Systems. Thematic analysis was used to explore participants’ perceptions of critical thinking development. The two-phase comparison captures how understandings and practices have evolved, particularly in light of the emergence of generative AI between 2016 and 2025. In Phase B, this research specifically examines AI’s potential role in supporting critical thinking and the pedagogical adaptations required by tutors. Nine key themes were identified. One of the most pressing concerns raised was that educators are perceived as largely ineffective in fostering critical thinking through online teaching. This study contributes empirical insight and practical recommendations to improve critical thinking cultivation in digital learning environments, especially in the age of AI. Full article

Adhesive bonding has emerged as a transformative joining method across multiple industries, offering lightweight, durable, and versatile alternatives to traditional fastening techniques. This review provides a comprehensive exploration of adhesive bonding, from fundamental adhesion mechanisms, mechanical and molecular, to application-specific criteria and the characteristics of common adhesive types. Emphasis is placed on challenges affecting bond quality and longevity, including defects such as kissing bonds, porosity, voids, poor cure, and substrate failures. Critical aspects of surface preparation, bond line thickness, and adhesive ageing under environmental stressors are analysed. Furthermore, this paper highlights the pressing need for sustainable solutions, including the disassembly and recyclability of bonded joints, particularly within the automotive and aerospace sectors. A key insight from this review is the lack of a unified framework to assess defect interaction, stochastic variability, and failure prediction, which is mainly due complexity of multi-defect interactions, the compositional expense of digital simulations, or the difficulty in obtaining sufficient statistical data needed for the stochastic models. This study underscores the necessity for multi-method detection approaches, advanced modelling techniques (i.e., debond-on-demand and bio-based formulations), and future research into defect correlation and sustainable adhesive technologies to improve reliability and support a circular materials economy. Full article

Construction and demolition waste (CDW) management remains a pressing challenge in the construction industry, contributing significantly to environmental degradation and resource depletion. Accurate waste measurement is essential for improving resource recovery and circular economy adoption. However, existing research lacks standardised estimation methods, the integration of digital technologies, and comprehensive lifecycle analysis approaches, limiting the effectiveness of waste prediction and management strategies. This study addresses the gap by conducting a scientometric analysis using CiteSpace and SciMAT, examining research trends, thematic clusters, and knowledge evolution in CDW quantification and management from 2014 to 2024. It establishes a conceptual framework for integrating digital systems and sustainable practices in CDW, focusing on waste generation rate, carbon emission, and phase-based waste management analysis. Network cluster analysis reveals the integral role of estimation tools and modelling techniques in refining waste generation quantification for building constructions. It also examines the interplay of digital tools, their influence on environmental cost reduction, and factors affecting waste production and environmental protection across project phases. This conjugate approach highlights the importance of the successful implementation of waste quantification and the imperative of machine learning for further investigation. This review offers an evidence-based framework to identify key stakeholders, guide future research, and implement sustainable waste management policies. Full article

The growth in the adoption of circular economy principles in the construction industry has given rise to material passports as a critical implementation tool. Given the existing problems of high resource use and high waste generation in the construction industry, there is a pressing need to adopt novel strategies and tools to mitigate the adverse impacts of the built environment. However, research on the application of material passports in the context of construction waste management remains limited. The aim of this paper is to identify the contextual uses, stakeholders, requirements, and challenges in the application of material passports for managing waste generated from building construction and demolition processes through a systematic review approach. Comprehensive searches in Scopus and the Web of Science databases are used to identify relevant papers and reduce the risk of selection bias. Thirty-five (35) papers are identified and included in the review. The identified key contexts of use included buildings and cities as material banks, waste management and trading, and integrated digital technologies. Asset owners, waste management operators, construction and deconstruction teams, technology providers, and regulatory and sustainability teams are identified as key stakeholders. Data requirements related to material, components, building stock data, lifecycle, environmental impact data, and deconstruction and handling data are critical. Moreover, the key infrastructure requirements include modeling and analytical tools, collaborative information exchange systems, sensory tracking tools, and digital and physical storage hubs. However, challenges with data management, costs, process standardization, technology, stakeholder collaboration, market demand, and supply chain logistics still limit the implementation. Therefore, it is recommended that future research be directed towards certification and standardization protocols, automation, artificial intelligence tools, economic viability, market trading, and innovative end-use products. Full article

Background/Aim: Child and adolescent obesity is currently one of the most pressing public health challenges. Digital technology-based interventions are becoming increasingly important in supporting weight loss motivation and promoting healthy lifestyles. This review aims to assess the effectiveness of technology tools on the BMI (body mass index) and their impact on health attitudes in children and adolescents. Materials and Methods: The study was conducted according to PRISMA guidelines, analysing studies published between 2011 and 2024 on PubMed, Scopus, Web of Science and Google Scholar databases. Of the 1475 articles identified and analysed, 59 met the inclusion criteria. Studies were assessed based on the type of technology used, the type of intervention, family involvement, the level of personalisation and their impact on BMI and motivation. Results: The systematic review showed that digital technologies—in particular mobile apps, wearables and m-health platforms—can effectively support weight reduction and improved eating habits in children and adolescents. The most beneficial results were observed in interventions that were personalised and included caregiver support. In addition, digital technology was shown to have a positive impact on participants’ psychological well-being. Conclusions: Digital technology-based interventions can be an effective tool in the prevention and treatment of obesity in children and adolescents. However, their success depends on a comprehensive approach that includes psychological, social and cognitive developmental factors. Full article

Efficient solid–liquid separation is crucial in industries like mining, but traditional chamber filter presses depend heavily on manual monitoring, leading to inefficiencies, downtime, and resource wastage. This paper introduces a machine learning-powered digital twin framework to improve the operational flexibility and predictive control of a traditional chamber filter press. A key challenge addressed is the degradation of the filter medium due to repeated cycles and clogging, which reduces filtration efficiency. To solve this, a neural network-based predictive model was developed to forecast operational parameters, such as pressure and flow rates, under various conditions. This predictive capability allows for optimized filtration cycles, reduced downtime, and improved process efficiency. Additionally, the model predicts the filter medium’s lifespan, aiding in maintenance planning and resource sustainability. The digital twin framework enables seamless data exchange between filter press sensors and the predictive model, ensuring continuous updates to the training data and enhancing accuracy over time. Two neural network architectures, feedforward and recurrent, were evaluated. The recurrent neural network outperformed the feedforward model, demonstrating superior generalization. It achieved a relative $L^2$-norm error of $5\%$ for pressure and $9.3\%$ for flow rate prediction on partially known data. For completely unknown data, the relative errors were $18.4\%$ and $15.4\%$, respectively. Qualitative analysis showed strong alignment between predicted and measured data, with deviations within a confidence band of $8.2\%$ for pressure and $4.8\%$ for flow rate predictions. This work contributes an accurate predictive model, a new approach to predicting filter medium cycle impacts, and a real-time interface for model updates, ensuring adaptability to changing operational conditions. Full article

The world is currently experiencing an unprecedented period of disruption. Traditional theories of comparative advantage can no longer serve as the sole drivers for enhancing the international competitiveness of China’s manufacturing industry. In this new era, the future development of China’s manufacturing industry has become a pressing issue that demands immediate attention. With the rapid advancement of next-generation digital technologies and information and communication technologies, global digital trade has surged, emerging as a key engine of economic growth for countries worldwide. This trend undoubtedly presents new opportunities and platforms for strengthening the international competitiveness of China’s manufacturing industry. How China’s manufacturing industry can effectively leverage digital trade to secure a competitive advantage amid intensifying global competition has become a critical and urgent area of research. Using panel data from 31 provinces, autonomous regions, and municipalities in China spanning from 2012 to 2022, this study develops a comprehensive evaluation framework for digital trade and manufacturing competitiveness. It empirically investigates the impact and mechanisms through benchmark regression models, mediation effect models, and spatial econometric models. The findings reveal that digital trade has a significant positive impact on the international competitiveness of China’s manufacturing industry. The effect of digital trade on competitiveness is most pronounced in the eastern region and least evident in the western region. Additionally, foreign direct investment and technological research and development capabilities are found to indirectly enhance the international competitiveness of the manufacturing industry. Furthermore, digital trade exhibits spatial spillover effects, wherein improvements in manufacturing competitiveness within one province positively influence neighboring provinces. This study offers valuable theoretical and policy implications for evaluating the impact of digital trade on the international competitiveness of manufacturing and strategies for enhancing it. Full article

The unprecedented rise of deepfake technologies, leveraging sophisticated AI like Generative Adversarial Networks (GANs) and diffusion-based models, presents both opportunities and challenges in terms of digital media authenticity. In response, this study introduces a novel deep neural network ensemble that utilizes a tree-based hierarchical architecture integrating a vision transformer, ResNet, EfficientNet, and DenseNet to address the pressing need for effective deepfake detection. Our model exhibits a high degree of adaptability across varied datasets and demonstrates state-of-the-art performance, achieving up to 97.25% accuracy and a weighted F1 score of 97.28%. By combining the strengths of various convolutional networks and the vision transformer, our approach underscores a scalable solution for mitigating the risks associated with manipulated media. Full article

Fault diagnosis is essential in industrial production. With the advancement of IoT technology, real-time data acquisition and storage have become feasible, enabling deep learning-based fault diagnosis methods to achieve remarkable results. However, existing approaches often overlook the temporal characteristics of fault occurrences and struggle with data imbalance between normal and faulty conditions, impacting diagnostic performance. To address these challenges, this paper proposes an integrated fault diagnosis method that incorporates data balancing, feature extraction, and temporal information analysis. The approach consists of two key components: (1) dataset construction using digital twin technology and (2) an integrated fault diagnosis model (CNN-BLSTM-attention). Digital twin technology generates virtual data under various operating conditions, mitigating the small-sample issue. The proposed model leverages a sliding window mechanism to capture both feature and temporal information, enhancing fault pattern recognition. Experimental results demonstrate that, compared to traditional methods, this approach effectively reduces noise interference and achieves a high diagnostic accuracy of 96.46%, validating its robustness in complex industrial settings. This research provides valuable theoretical and practical insights for improving fault diagnosis in industrial equipment such as screw presses. Full article