Search Results (1,755)

Canada’s heritage sites, while fewer in number compared to Europe, face significant challenges in accessibility due to strict preservation laws. Many were built before social inclusion became a priority, making them difficult to navigate for individuals with disabilities. For example, cobbled streets with uneven surfaces or significant level differences create obstacles for wheelchair users and others with mobility challenges. This qualitative action research aimed to develop guidelines for shared street designs and innovative solutions that balance heritage preservation with inclusive accessibility. The study involved go along interviews with 21 participants with disabilities in Quebec City’s Petit-Champlain and Place-Royale areas, as well as the Old-Seminary. Insights from these interviews informed a co-design methodology involving three sessions with participants with disabilities, family caregivers, and experts in accessibility and heritage. The co-design sessions generated practical solutions such as lift platforms, accessible signage, and guided handrails, addressing both outdoor and indoor accessibility barriers. For indoor spaces, solutions included integrating acoustic panels and foldable seating, while outdoor recommendations emphasized autonomous access solutions like non-slip coatings and accessible elevators for winter conditions. It was concluded that achieving a balance between preservation and accessibility requires collaboration among architects, designers, heritage experts, and individuals with disabilities. This research highlights the importance of co-design as a method to ensure heritage sites remain culturally significant and universally accessible. Full article

In the era of artificial intelligence (AI), economic efficiency has an obvious role to play, but “non-economic benefits” have gradually become the focus of corporate attention; thus, environmental, social, and governance (ESG) has become a mainstream investment strategy. This paper empirically examines the impact of corporate application of AI technology on corporate ESG performance using a sample of 4858 listed companies in China from 2007 to 2022. The study finds that: (1) corporate application of AI technology can significantly enhance corporate ESG performance, and this conclusion still holds after a series of endogeneity treatments and robustness tests; (2) mechanism analysis shows that the degree of corporate digitalization has a positive moderating effect in the process of AI technology affecting corporate ESG performance. The channel analysis shows that the application of AI technology can enhance environmental (E) performance by strengthening corporate green technology innovation, social (S) performance by improving corporate philanthropic responsibility, and overall ESG performance with the above two sub-items as the main aspects. However, AI technology also weakens the effectiveness of corporate internal control, which leads to a decline in corporate governance (G) performance; (3) Heterogeneity analysis shows that AI technology promotes ESG more significantly in more competitive industries and tech-nology-intensive firms, and more significantly in the eastern and central regions than in the western and northeastern regions, and that large- and medium-sized firms are similarly superior to small-sized firms, while medium-sized firms have more room for upward mobility than large-sized firms, which embody a higher promotion effect than large enterprises. This paper provides theoretical evidence that enterprises apply AI technology to improve ESG performance and empirical support around investing in ESG practices and promoting ESG development. Full article

The fourth agricultural revolution (or Agriculture 4.0) promises to lead the way to an agricultural sector that is smarter, more efficient, and more environmentally and socially responsible. Digital and data generating tools are seen as critical enablers for this transformation and are expected to make farming more planned, predictive, productive, and efficient. To make this vision a reality, agricultural producers will first adopt and use the technologies, but this is easier said than done. Barriers such as limited digital infrastructure, low (digital) literacy, low incomes, and socio-cultural norms are major factors causing sub-optimal access to and use of digital technologies among smallholder farmers. Beyond these use challenges of access and usage, limited evidence exists to support the notion that extant digital technologies add enough value to provide substantial benefits for targeted farmers. In this paper, we unravel insights from a six-year digital agriculture innovation project which was implemented to develop and deploy multi-modal digital tools for the control of a major banana disease. By reaching over 272,200 smallholder farmers in Rwanda through a smartphone app, unstructured supplementary service data, a chatbot, and other ancillary channels, we assessed various assumptions regarding intrinsic motivation, incentives, and skills retention among the target digital tool users. These insights suggest that embedding digital innovation requires intentional user-engagement, proper incentivization of next-users, and targeted communication to foster adoption. We present ten (10) salient, but non-exhaustive, lessons to showcase the realities of developing and delivering digital tools to farmers over an extended period, spanning from ideation, development, and testing to scaling stages. The lessons are relevant for a broad audience, including stakeholders across the digital innovation space who can utilize our experiential notes to guide the development and deployment of similar digital innovations for improved outcomes in smallholder farming systems. Full article

As cities evolve into smarter and more connected environments, there is a growing need for innovative solutions to improve urban mobility. This study examines the potential of integrating blockchain technology into passenger transportation systems within smart cities, with a particular emphasis on a blockchain-enabled Mobility-as-a-Service (MaaS) solution. In contrast to traditional technologies, blockchain’s decentralized structure improves data security and guarantees transaction transparency, thus reducing the risk of fraud and errors. The proposed MaaS framework enables seamless collaboration between key transportation stakeholders, promoting more efficient utilization of services like buses, trains, bike-sharing, and ride-hailing. By improving integrated payment and ticketing systems, the solution aims to create a smoother user experience while advancing the urban goals of efficiency, environmental sustainability, and secure data handling. This research evaluates the feasibility of a Hyperledger Fabric-based solution, demonstrating its performance under various load conditions and proposing scalability adjustments based on pilot results. The conclusions indicate that blockchain-enabled MaaS systems have the potential to transform urban mobility. Further exploration into pilot projects and the expansion to freight transportation are needed for an integrated approach to city-wide transport solutions. Full article

The aim and novelty of this study lie in analyzing the role of the European Social Fund (ESF) in supporting the implementation of the Sustainable Development Goals (SDGs) within European Union (EU) countries over the 2015–2023 period. EU Member States were grouped into two clusters: Cluster A (15 lower-income states) and Cluster B (12 higher-income states). The study used ESF payments as the explanatory variable and 17 SDG indicators as dependent variables. The methodology combined exploratory factor analysis (EFA) and robust regression to analyze the relationship between the ESF and the SDGs. The ESF has a significant impact on reducing poverty (SDG1), fostering economic growth (SDG8), and enhancing industry and innovation (SDG9), particularly in less-developed regions where its interventions address structural challenges through vocational training, job creation, and infrastructure development. However, its contributions to sustainability goals (SDGs 7, 12, and 13) are limited or even negative in some cases, as economic priorities often overshadow environmental objectives, especially in more-developed regions where climate and energy transitions rely on other funding sources. The ESF excels in fostering partnerships (SDG17) in less-developed regions by mobilizing resources and promoting collaboration, but its role is less impactful in developed regions where institutional frameworks are already well-established. Full article

Intensive Care Unit-Acquired Weakness (ICU-AW) is a common and severe complication in critically ill patients, characterized by profound and often prolonged muscle weakness. The complexity of its diagnosis and management requires a multidimensional approach that integrates clinical, electrophysiological, and imaging tools. This review focuses on the challenges in diagnosing ICU-AW, emphasizing the limitations of traditional methods such as manual muscle testing and electrophysiological studies, and highlights the emerging role of neuromuscular ultrasound (NMUS) as a promising, non-invasive diagnostic aid. Despite its utility, no gold standard exists for NMUS, making it an evolving area of research. The pathophysiological basis of ICU-AW involves multiple mechanisms, including critical illness polyneuropathy (CIP), critical illness myopathy (CIM), and muscle atrophy due to disuse. Understanding these underlying mechanisms is crucial for advancing diagnostic strategies and informing therapeutic interventions. Recent insights into the molecular and cellular pathways involved, such as the role of oxidative stress, mitochondrial dysfunction, and the ubiquitin-proteasome system, have opened new avenues for targeted therapies. Management of ICU-AW remains challenging as no specific treatment has been proven fully effective. Current strategies focus on early mobilization, minimizing sedation, and optimizing nutritional support. Emerging therapies targeting molecular pathways involved in muscle degradation are under investigation, highlighting the potential to translate pathophysiological understanding into therapeutic innovations. This review underscores the need for ongoing research to establish standardized diagnostic protocols and develop targeted treatments for ICU-AW. Full article

Background: Artificial Intelligence (AI) technologies are now essential as the agenda of nutrition research expands its scope to look at the intricate connection between food and health in both an individual and a community context. AI also helps in tracing and offering solutions in dietary assessment, personalized and clinical nutrition, as well as disease prediction and management, such as cardiovascular diseases, diabetes, cancer, and obesity. This review aims to investigate and assess the different applications and roles of AI in nutrition and research and understand its potential future impact. Methods: We used PubMed, Scopus, Web of Science, Google Scholar, and EBSCO databases for our search. Results: Our findings indicate that AI is reshaping the field of nutrition in ways that were previously unimaginable. By enhancing how we assess diets, customize nutrition plans, and manage complex health conditions, AI has become an essential tool. Technologies like machine learning models, wearable devices, and chatbot applications are revolutionizing the accuracy of dietary tracking, making it easier than ever to provide tailored solutions for individuals and communities. These innovations are proving invaluable in combating diet-related illnesses and encouraging healthier eating habits. One breakthrough has been in dietary assessment, where AI has significantly reduced errors that are common in traditional methods. Tools that use visual recognition, deep learning, and mobile applications have made it possible to analyze the nutrient content of meals with incredible precision. Conclusions: Moving forward, collaboration between tech developers, healthcare professionals, policymakers, and researchers will be essential. By focusing on high-quality data, addressing ethical challenges, and keeping user needs at the forefront, AI can truly revolutionize nutrition science. The potential is enormous. AI is set to make healthcare not only more effective and personalized but also more equitable and accessible for everyone. Full article

Background: The digital phenotyping tool has great potential for the deep characterization of neurological and quality-of-life assessments in brain tumor patients. Phone communication activities (details on call and text use) can provide insight into the patients’ sociability. Methods: We prospectively collected digital-phenotyping data from six brain tumor patients. The data were collected using the Beiwe application installed on their personal smartphones. We constructed several daily sociability features from phone communication logs, including the number of incoming and outgoing text messages and calls, the length of messages and duration of calls, message reciprocity, the number of communication partners, and number of missed calls. We compared variability in these sociability features against those obtained from a control group, matched for age and sex, selected among patients with a herniated disc. Results: In brain tumor patients, phone-based communication appears to deteriorate with time, as evident in the trend for total outgoing minutes, total outgoing calls, and call out-degree. Conclusions: These measures indicate a possible decrease in sociability over time in brain tumor patients that may correlate with survival. This exploratory analysis suggests that a quantifiable digital sociability phenotype exists and is comparable for patients with different survival outcomes. Overall, assessing neurocognitive function using digital phenotyping appears promising. Full article

A near-real-time change detection network can consistently identify unauthorized construction activities over a wide area, empowering authorities to enforce regulations efficiently. Furthermore, it can promptly assess building damage, enabling expedited rescue efforts. The extensive adoption of deep learning in change detection has prompted a predominant emphasis on enhancing detection performance, primarily through the expansion of the depth and width of networks, overlooking considerations regarding inference time and computational cost. To accurately represent the spatio-temporal semantic correlations between pre-change and post-change images, we create an innovative transformer attention mechanism named Sliding-Window Dissimilarity Cross-Attention (SWDCA), which detects spatio-temporal semantic discrepancies by explicitly modeling the dissimilarity of bi-temporal tokens, departing from the mono-temporal similarity attention typically used in conventional transformers. In order to fulfill the near-real-time requirement, SWDCA employs a sliding-window scheme to limit the range of the cross-attention mechanism within a predetermined window/dilated window size. This approach not only excludes distant and irrelevant information but also reduces computational cost. Furthermore, we develop a lightweight Siamese backbone for extracting building and environmental features. Subsequently, we integrate an SWDCA module into this backbone, forming an efficient change detection network. Quantitative evaluations and visual analyses of thorough experiments verify that our method achieves top-tier accuracy on two building change detection datasets of remote sensing imagery, while also achieving a real-time inference speed of 33.2 FPS on a mobile GPU. Full article

Graphene-based piezoelectric nanogenerators (PENGs) have emerged as a promising technology for sustainable energy harvesting, offering significant potential in powering next-generation electronic devices. This review explores the integration of graphene, a highly conductive and mechanically robust two-dimensional (2D) material, with PENG to enhance their energy conversion efficiency. Graphene’s unique properties, including its exceptional electron mobility, high mechanical strength, and flexibility, allow for the development of nanogenerators with superior performance compared to conventional PENGs. When combined with piezoelectric materials, polymers, graphene serves as both an active layer and a charge transport medium, boosting the piezoelectric response and output power. The graphene-based PENGs can harvest mechanical energy from various sources, including vibrations, human motion, and ambient environmental forces, making them ideal for applications in wearable electronics, and low-power devices. This paper provides an overview of the fabrication techniques, material properties, and energy conversion mechanisms of graphene-based PENGs, and integration into real-world applications. The findings demonstrate that the incorporation of graphene enhances the performance of PENG, paving the way for future innovations in energy-harvesting technologies. Full article

This study introduces a novel single-degree-of-freedom polycentric knee mechanism specifically designed for transfemoral prostheses to address dual challenges of stability during the stance phase and biomimetic motion during the swing phase. Leveraging analytical kinematic synthesis, the proposed mechanism integrates separate kinematic designs for each of the gait phases into a combined structure that prevents singularity issues during full knee flexion, which is a significant limitation in conventional active designs. The stance phase mechanism emphasizes stability through precise control of the instantaneous center of rotation (ICR) and weight-bearing support, while the swing phase mechanism adopts a biomimetic motion trajectory. In order to validate the proposed methodology, kinematic synthesis, numerical simulations, and visual analyses were conducted. Incorporating insights from polycentric prostheses and orthotic applications, the proposed mechanism achieves a seamless transition between two different configurations by keeping its overall mobility. Additionally, its possible compatibility with motorized actuation offers a foundation for active prosthesis systems, paving the way for adapting the advantages of polycentric prosthesis to active devices. This innovative approach offers a scientifically grounded pathway for improving transfemoral prosthetic systems, advancing both their biomechanical utility and user comfort. Full article

This paper explores contemporary approaches to preserving and promoting cultural heritage by integrating game elements and advanced technologies, such as Virtual Reality (VR) and Augmented Reality (AR). In an era increasingly shaped by digital innovation, preserving cultural heritage demands new strategies to sustain engagement with historical narratives and artifacts. Emerging technologies like VR and AR offer immersive, interactive experiences that appeal to modern audiences, especially younger generations accustomed to digital environments (Bekele and Champion). Gamification—the use of game design principles in non-game contexts—has gained significant traction in education and cultural heritage, providing new methods for increasing user engagement and retention (Werbach and Hunter). By incorporating gamified features, heritage can be made more accessible, fostering emotional connections and deeper understanding (Huotari and Hamari; Zichermann and Cunningham). This aligns with the shift toward interactive digital storytelling as a tool to transform static heritage presentations into dynamic, participatory experiences (Champion and Rahaman). Central to this research is the conceptualization and development of a mobile application leveraging VR and AR to enhance user engagement and education around cultural heritage. Drawing on the principles of self-determination theory (Deci and Ryan) and empirical findings on gamified learning (Landers and Landers), the application combines educational content with interactive elements, creating an immersive learning environment. By addressing both content accessibility and interactive immersion, this application bridges the gap between traditional heritage preservation and the expectations of a digitally native audience. The recent literature underscores the potential of VR and AR in cultural preservation, emphasizing their ability to transcend physical boundaries, simulate historical environments, and promote active participation (Milgram and Kishino, Addison; Azuma). As virtual environments evolve, platforms like the metaverse expand possibilities for experiencing cultural heritage in spaces free of geographical limitations (Cipresso et al.; Radianti et al.). Such advancements have already demonstrated significant educational and experiential benefits (Wu et al.; Akçayır and Akçayır). This study employs both quantitative and qualitative methods to examine the target group’s attitudes toward gamified technologies for cultural heritage preservation. The initial results indicate substantial interest and willingness among users to engage with applications employing VR and AR. This aligns with findings in the literature that suggest immersive experiences can enhance learning outcomes and foster long-term engagement (Merchant et al.; Speicher et al.). The project has garnered significant recognition, receiving the Rector’s Award for the best scientific paper in the technical field at the University of Zagreb and earning bronze medals at the ARCA Innovation Fair and the INOVA Fair. These accolades underscore the project’s innovative approach and its potential for real-world application. By presenting a robust framework for integrating gamification and immersive technologies into cultural heritage preservation, this paper contributes to the growing discourse on utilizing advanced digital tools to ensure the sustainability and relevance of cultural heritage for future generations. Full article

Video location prediction for handwritten digits presents unique challenges in computer vision due to the complex spatiotemporal dependencies and the need to maintain digit legibility across predicted frames, while existing deep learning-based video prediction models have shown promise, they often struggle with preserving local details and typically achieve clear predictions for only a limited number of frames. In this paper, we present a novel video location prediction model based on Convolutional Gated Recurrent Units (ConvGRU) that specifically addresses these challenges in the context of handwritten digit sequences. Our approach introduces three key innovations. Firstly, we introduce a specialized decoupling model using modified Generative Adversarial Networks (GANs) that effectively separates background and foreground information, significantly improving prediction accuracy. Secondly, we introduce an enhanced ConvGRU architecture that replaces traditional linear operations with convolutional operations in the gating mechanism, substantially reducing spatiotemporal information loss. Finally, we introduce an optimized parameter-tuning strategy that ensures continuous feature transmission while maintaining computational efficiency. Extensive experiments on both the MNIST dataset and custom mobile datasets demonstrate the effectiveness of our approach. Our model achieves a structural similarity index of 0.913 between predicted and actual sequences, surpassing current state-of-the-art methods by 1.2%. Furthermore, we demonstrate superior performance in long-term prediction stability, with consistent accuracy maintained across extended sequences. Notably, our model reduces training time by 9.5% compared to existing approaches while maintaining higher prediction accuracy. These results establish new benchmarks for handwritten digit video prediction and provide practical solutions for real-world applications in digital education, document processing, and real-time handwriting recognition systems. Full article

This study reviews advancements in high-frequency converters for renewable energy systems and electric vehicles, emphasizing their role in enhancing energy efficiency and sustainability. Using the PRISMA 2020 methodology, 73 high-quality studies from 2014 to 2024 were synthesized to evaluate innovative designs, advanced materials, control strategies, and future opportunities. Key findings reveal significant progress in converter topologies, such as dual active bridge and LLC resonant designs, which enhance efficiency and scalability through soft-switching. Wide-bandgap semiconductors, including silicon carbide and gallium nitride, have driven improvements in power density, thermal management, and compactness. Advanced control strategies, including adaptive and AI-driven methods, enhance stability and efficiency in microgrids and vehicle-to-grid systems. Applications in photovoltaic and wind energy systems demonstrate the converters’ impact on improving energy conversion and system reliability. Future opportunities focus on hybrid and multifunctional designs that integrate renewable energy, storage, and electric mobility with intelligent control technologies like digital twins and AI. These innovations highlight the transformative potential of high-frequency converters in addressing global energy challenges driving sustainable energy and transportation solutions. This review offers critical insights into current advancements and pathways for further research and development in this field. Full article

This paper highlights the complexity and urgency of addressing plastic pollution, drawing attention to the environmental challenges posed by improperly discarded plastics. Petroleum-based plastic polymers, with their remarkable range of physical properties, have revolutionized industries worldwide. Their versatility—from flexible to rigid and hydrophilic to hydrophobic—has fueled an ever-growing demand. However, their versatility has also contributed to a massive global waste problem as plastics pervade virtually every ecosystem, from the depths of oceans to the most remote terrestrial landscapes. Plastic pollution manifests not just as visible waste—such as fishing nets, bottles, and garbage bags—but also as microplastics, infiltrating food chains and freshwater sources. This crisis is exacerbated by the unsustainable linear model of plastic production and consumption, which prioritizes convenience over long-term environmental health. The mismanagement of plastic waste not only pollutes ecosystems but also releases greenhouse gases like carbon dioxide during degradation and incineration, thereby complicating efforts to achieve global climate and sustainability goals. Given that mechanical recycling only addresses a fraction of macroplastics, innovative approaches are needed to improve this process. Methods like pyrolysis and hydrogenolysis offer promising solutions by enabling the chemical transformation and depolymerization of plastics into reusable materials or valuable chemical feedstocks. These advanced recycling methods can support a circular economy by reducing waste and creating high-value products. In this article, the focus on pyrolysis and hydrogenolysis underscores the need to move beyond traditional recycling. These methods exemplify the potential for science and technology to mitigate plastic pollution while aligning with sustainability objectives. Recent advances in the pyrolysis and hydrogenolysis of polyolefins focus on their potential for advanced recycling, breaking down plastics at a molecular level to create feedstocks for new products or fuels. Pyrolysis produces pyrolysis oil and syngas, with applications in renewable energy and chemicals. However, some challenges of this process include scalability, feedstock variety, and standardization, as well as environmental concerns about emissions. Companies like Shell and ExxonMobil are investing heavily to overcome these barriers and improve recycling efficiencies. By leveraging these transformative strategies, we can reimagine the lifecycle of plastics and address one of the most pressing environmental challenges of our time. This review updates the knowledge of the fields of pyrolysis and hydrogenolysis of plastics derived from polyolefins based on the most recent works available in the literature, highlighting the techniques used, the types of products obtained, and the highest yields. Full article