Search Results (1,283)

Introduction: Although Brazil includes industrialized regions, such as the Southeast, it also has underdeveloped areas with poor sanitation, such as the North and Northeast, resembling regions in Africa and Asia where HEV is endemic. In Brazil, HEV is suspected to occur mainly as a zoonosis. Given the wide variation in HEV prevalence across the five regions, a scoping review was conducted to systematically evaluate its prevalence and circulating genotypes. Aim: To investigate the epidemiological characteristics of HEV in Brazil, including modes of transmission, by reviewing genotyping studies in humans and swine. Methods: This scoping review followed the methodological framework of the Joanna Briggs Institute (JBI) and the PRISMA-ScR checklist. Gray literature was retrieved from Google Scholar, the Brazilian Digital Library of Theses and Dissertations, and the Thesis and Dissertation Catalog of the Coordination for the Improvement of Higher Education Personnel. Searches were performed in June and July 2025 in MEDLINE and LILACS. The evidence on HEV epidemiology in Brazil was mapped using the Population, Concept, and Context strategy. Results: Among 57 studies on HEV prevalence in Brazil, 45 (78.9%) involved humans and 12 (21.1%) involved swine. IgG prevalence ranged from 0.5% in the North to 59.4% in the South. IgM prevalence was lowest in the Northeast (0.1%) and highest in the North (16.3%). In swine, HEV was detected in all regions, with variation in sample types, husbandry practices, and prevalence. Genotyping revealed exclusively HEV-3 in all regions where analysis was performed. Conclusions: HEV infection is present throughout Brazil, with higher prevalence in the South and Southeast. The circulating genotype is HEV-3, and transmission is likely linked to swine breeding and consumption. Full article

This study explored the Erasmus+ project ’H2OMap: Innovative Learning by Hydraulic Heritage Mapping’, integrating environmental awareness and cultural heritage into secondary education through interdisciplinary, ICT, and STEM-based approaches. Focused on water-related heritage in the Mediterranean, the study pursued three aims: integrate ICT-supported participatory mapping bridging history/geography subjects with digital innovation; identify learning benefits and implementation conditions; and generate transferable outputs and datasets for classroom reuse. Intellectual outputs include a methodological guide, an e-learning course, and an educational multiplatform comprising a mobile mapping app for in situ geocataloguing, an online database, and a geoportal with interactive StoryMaps. Evidence came from classroom testing across age groups, teacher feedback from the e-learning course, student mobilities in Spain, Italy, and Portugal, and platform usage records. More than 390 students and teachers participated, documenting over 100 hydraulic heritage elements. Additionally, dissemination through nine multiplier events and conferences reached over 550 external attendees. Findings show increased student engagement and ICT/GIS skills, clearer cross-curricular integration, and a replicable open workflow supported by structured coordination that strengthens school–university partnerships. Learner experience emphasised hands-on, place-based exploration and collaborative documentation of water heritage. Recommendations include using open geospatial standards, providing teacher training, and maintaining geoportals for classroom reuse. Full article

Tool structure design methodologies predominantly rely on trial-and-error approaches or single-objective optimization but fail to achieve coordinated enhancement of multiple performance metrics while lacking thorough investigation into complex cutting coupling mechanisms. This study proposes a multi-objective optimization framework integrating joint simulation approaches. First, a finite element model for orthogonal turning was developed, incorporating the hyperbolic tangent (TANH) constitutive model and variable coefficient friction model. The cutting performance of four micro-groove configurations is comparatively analyzed. Subsequently, parametric modeling coupled with simulation–data interaction enables multi-objective optimization targeting minimized cutting force, reduced cutting temperature, and decreased wear rate. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) explores Pareto-optimized solutions for arc micro-groove geometric parameters. Finally, optimized tools manufactured via powder metallurgy undergo experimental validation. The results demonstrate that the optimized tool achieves significant improvements: a 19.3% reduction in cutting force, a 14.2% decrease in cutting temperature, and tool life extended by 33.3% compared to baseline tools. Enhanced chip control is evidenced by an 11.4% reduction in chip curl radius, accompanied by diminished oxidation/adhesive wear and superior surface finish. This multi-objective optimization methodology effectively overcomes the constraints of conventional single-parameter optimization, substantially improving comprehensive tool performance while establishing a reference paradigm for cutting tool design under complex operational conditions. Full article

As supply chains rapidly digitize, platform-driven models have become central to global commerce, requiring sophisticated mathematical modeling for optimization. This systematic review comprehensively analyzes research across six critical technological domains in platform supply chains (PSCs): blockchain integration, Internet of Things applications, Industry 4.0 systems, cloud computing, live streaming commerce, and generative artificial intelligence. Our analysis finds that operational coordination and strategic decision-making under information asymmetry represent primary research focuses, with pricing strategies receiving predominant attention. Methodologically, game theory, particularly Stackelberg models, emerges as the dominant optimization framework across all domains. However, significant gaps remain in dynamic modeling capabilities, empirical validation of theoretical frameworks, and cross-technology integration. This review provides foundational insights into mathematical optimization techniques and highlights the critical need for incorporating stochastic approaches and real-world data to advance PSC management in the digital era. Full article

Agricultural markets are increasingly exposed to global risks as climate change intensifies and macro-financial volatility becomes more prevalent. This study examines the dynamic interconnection between major agricultural commodities—soybeans, corn, wheat, rough rice, and sugar—and key uncertainty indicators, including climate policy uncertainty, global economic policy uncertainty, geopolitical risk, financial market volatility, oil price volatility, and the U.S. Dollar Index. Using a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with monthly data, we assess both internal spillovers within the commodity system and external spillovers from macro-level uncertainties. On average, the external shock from the VIX to corn reaches 12.4%, and the spillover from RGEPU to wheat exceeds 10%, while internal links like corn to wheat remain below 8%. The results show that external uncertainty consistently dominates the connectedness structure, particularly during periods of geopolitical or financial stress, while internal interactions remain relatively subdued. Unexpectedly, recent global disruptions such as the COVID-19 pandemic and the Russia–Ukraine conflict do not exhibit strong or persistent effects on the connectedness patterns, likely due to model smoothing, stockpiling policies, and supply chain adaptations. These findings highlight the importance of strengthening international macro-financial and climate policy coordination to mitigate the propagation of external shocks. By distinguishing between internal and external connectedness under climate stress, this study contributes new insights into how systemic risks affect agri-food systems and offers a methodological framework for future risk monitoring. Full article

Emerging power electronic devices like soft open points (SOPs) and electric springs (ESs) play a vital role in enhancing active distribution network (ADN) efficiency. SOPs enable flexible active/reactive power control, while ESs improve demand-side management and voltage regulation. This paper proposes a two-stage stochastic programming model to optimize ADN’s operation by coordinating these fast-response devices with legacy mechanical equipment. The first stage determines hourly setpoints for conventional devices, while the second stage adjusts SOPs and ESs for intra-hour control. To handle ES nonlinearities, a hybrid data–knowledge approach combines knowledge-based linear constraints with a data-driven multi-layer perceptron, later linearized for computational efficiency. The resulting mixed-integer second-order cone program is solved using commercial solvers. Simulation results show the proposed strategy effectively reduces power loss by 42.5%, avoids voltage unsafety with 22 time slots, and enhances 4.3% PV harvesting. The coordinated use of SOP and ESs significantly improves system efficiency, while the proposed solution methodology ensures both accuracy and over 60% computation time reduction. Full article

Banking has evolved from ancient times of using grain banks and temple lending to modern banking practices. The transformation of the banking sector has ensured that banks play the crucial role of facilitating faster and efficient service delivery. This paper traced the evolution of banking and examined associated disruptions, opportunities, and challenges. With the specific objective of influencing policy-oriented discussions on the future of banking, this study adopted a literature review methodology of integrating various sources, such as scholarly journals, policy reports, and institutional publications. Public interest theory and disruptive innovation theory underpinned this study. Findings revealed that banking has evolved from Banking 1.0 to Banking 5.0 due to disruptive factors which have been pivotal to the significant structural sector changes: Banking 1.0 (pre-1960s); Banking 2.0 (1960s to 1980s); Banking 3.0 (1980s–2000s); Banking 4.0 (2000s–2020s); and Banking 5.0 (2020s to the future). Despite the existence of opportunities in the transformation, challenges include regulations, skills shortages, legacy systems, and cybersecurity that must be addressed. This calls for a coordinated response from stakeholders, with banking’s future requiring collaborations as cashless economies, digital economies, and digital currencies take centre stage. Full article

Metropolitan areas have become the primary spatial form for China’s new-era urbanization. However, these boundaries have traditionally been delineated based on administrative factors, resulting in a notable discrepancy with the actual functional connections. To tackle this challenge, this study aims to devise and implement an innovative ‘PET’ tri-dimensional coupling model, leveraging the principles of integrated urban subsystems to scientifically delineate functional metropolitan boundaries. The proposed method integrates Population flow (P), Economic density (E), and Transportation accessibility (T) on a fine-grained 1 km raster grid. To enhance accuracy, the crucial population flow component is simulated using a gravity model calibrated with real-world Baidu Migration data. Applying this model to 35 potential metropolitan areas, our findings reveal two key points. First, a comparative analysis with five officially approved plans reveals a significant spatial alignment in core functional zones, which corroborates the model’s accuracy. effectiveness. Secondly, these delineations clearly quantify the notable difference between the ‘functional space’ influenced by socioeconomic factors and the ‘administrative space’ delineated by jurisdictional boundaries. In summary, this research presents a replicable methodology for delineating functional metropolitan areas. It offers vital technical support and policy guidance for optimizing regional planning, enhancing inter-city coordination, and promoting China’s national strategy for regional development. Full article

Background/Objectives: Multiple sclerosis (MS) often leads to gait impairments, even in early stages, and can affect autonomy and quality of life. Traditional assessment methods, while widely used, have been criticized because they lack sensitivity to subtle gait changes. This scoping review aims to map the landscape of advanced gait analysis technologies—both wearable and non-wearable—and evaluate their application in detecting, characterizing, and monitoring possible gait dysfunction in individuals with MS. Methods: A systematic search was conducted across PubMed and Scopus databases for peer-reviewed studies published in the last decade. Inclusion criteria focused on original human research using technological tools for gait assessment in individuals with MS. Data from 113 eligible studies were extracted and categorized based on gait parameters, technologies used, study design, and clinical relevance. Results: Findings highlight a growing integration of advanced technologies such as inertial measurement units, 3D motion capture, pressure insoles, and smartphone-based tools. Studies primarily focused on spatiotemporal parameters, joint kinematics, gait variability, and coordination, with many reporting strong correlations to MS subtype, disability level, fatigue, fall risk, and cognitive load. Real-world and dual-task assessments emerged as key methodologies for detecting subtle motor and cognitive-motor impairments. Digital gait biomarkers, such as stride regularity, asymmetry, and dynamic stability demonstrated high potential for early detection and monitoring. Conclusions: Advanced gait analysis technologies can provide a multidimensional, sensitive, and ecologically valid approach to evaluating and detecting motor function in MS. Their clinical integration supports personalized rehabilitation, early diagnosis, and long-term disease monitoring. Future research should focus on standardizing metrics, validating digital biomarkers, and leveraging AI-driven analytics for real-time, patient-centered care. Full article

The construction of buildings using shipping containers (SCs) is a way to extend their useful life. They are constructed by modifying the structure, thermal, and acoustic conditioning by improving the envelope and creating openings for lighting and ventilation purposes. This study explores the architectural adaptation of SCs to sustainable residential housing, focusing on structural, thermal, and acoustic performance. The project centers on a case study in Madrid, Spain, transforming four containers into a semi-detached, multilevel dwelling. The design emphasizes modular coordination, spatial flexibility, and structural reinforcement. The retrofit process includes the integration of thermal insulation systems in the ventilated façades and sandwich roof panels to counteract steel’s high thermal conductivity, enhancing energy efficiency. The acoustic performance of the container-based dwelling was assessed through in situ measurements of façade airborne sound insulation and floor impact noisedemonstrating compliance with building code requirements by means of laminated glazing, sealed joints, and floating floors. This represents a novel contribution, given the scarcity of experimental acoustic data for residential buildings made from shipping containers. Results confirm that despite the structure’s low surface mass, appropriate design strategies can achieve the required sound insulation levels, supporting the viability of this lightweight modular construction system. Structural calculations verify the building’s load-bearing capacity post-modification. Overall, the findings support container architecture as a viable and eco-efficient alternative to conventional construction, while highlighting critical design considerations such as thermal performance, sound attenuation, and load redistribution. The results offer valuable data for designers working with container-based systems and contribute to a strategic methodology for the sustainable refurbishment of modular housing. Full article

Agroforestry landscape has undergone changes, namely land abandonment, which when combined with negative attitudes towards fire, is associated with the eradication of agroforestry leftovers and acts towards the proliferation of fires, threatening sustainability concerns. Agroforestry leftovers recovery presents high potential to act on this problem; however, the logistical costs associated with the recovery chain make it unfeasible. The lack of coordination/transparency between stakeholders is one of the main explanations for these costs. This study develops a digital tool to enhance the residual biomass supply chain for energy recovery and fire risk mitigation. In addition to this concept, this work also proposes conceptual models and a prototype, two essential contributions to software development. Methodologically, this study consulted 10 experts to validate a concept previously presented in the literature, supplemented with UML modeling and prototyping with Figma^®. The main results point to the creation of a disruptive concept that will allow access to information/transparency about agroforestry services, with the goal that this will improve the functioning of the RBSC, resulting in a reduction in fire risk and, consequently, improvements in sustainability concerns associated with this hazard. Full article

During the last decade, artificial intelligence (AI) has enabled key technological innovations within the modern dementia and frailty healthcare and prevention landscape. This has boosted the impact of technology in the clinical setting, enabling earlier diagnosis with improved specificity and sensitivity, leading to accurate and time-efficient support that has driven the development of preventative interventions minimizing the risk and rate of progression. Background/Objectives: The rapid ageing of the European population places a substantial strain on the current healthcare system and imposes several challenges. COMFORTage is the joint effort of medical experts (i.e., neurologists, psychiatrists, neuropsychologists, nurses, and memory clinics), social scientists and humanists, technical experts (i.e., data scientists, AI experts, and robotic experts), digital innovation hubs (DIHs), and living labs (LLs) to establish a pan-European framework for community-based, integrated, and people-centric prevention, monitoring, and progression-managing solutions for dementia and frailty. Its main goal is to introduce an integrated and digitally enabled framework that will facilitate the provision of personalized and integrated care prevention and intervention strategies on dementia and frailty, by piloting novel technologies and producing quantified evidence on the impact to individuals’ wellbeing and quality of life. Methods: A robust and comprehensive design approach adopted through this framework provides the guidelines, tools, and methodologies necessary to empower stakeholders by enhancing their health and digital literacy. The integration of the initial information from 13 pilots across 8 European countries demonstrates the scalability and adaptability of this approach across diverse healthcare systems. Through a systematic analysis, it aims to streamline healthcare processes, reduce health inequalities in modern communities, and foster healthy and active ageing by leveraging evidence-based insights and real-world implementations across multiple regions. Results: Emerging technologies are integrated with societal and clinical innovations, as well as with advanced and evidence-based care models, toward the introduction of a comprehensive global coordination framework that: (a) improves individuals’ adherence to risk mitigation and prevention strategies; (b) delivers targeted and personalized recommendations; (c) supports societal, lifestyle, and behavioral changes; (d) empowers individuals toward their health and digital literacy; and (e) fosters inclusiveness and promotes equality of access to health and care services. Conclusions: The proposed framework is designed to enable earlier diagnosis and improved prognosis coupled with personalized prevention interventions. It capitalizes on the integration of technical, clinical, and social innovations and is deployed in 13 real-world pilots to empirically assess its potential impact, ensuring robust validation across diverse healthcare settings. Full article

This study presents and evaluates three sampling strategies to characterize electricity demand in regions of Colombia with limited metering infrastructure. These areas lack Advanced Metering Infrastructure (AMI), relying instead on traditional monthly consumption records. The objective of the research is to obtain user samples that are representative of the original population and logistically efficient, in order to support energy planning and decision-making. The analysis draws on five years of historical data from 2020 to 2024. It includes monthly energy consumption, geographic coordinates, customer classification, and population type, covering over 500,000 users across four subregions of operation determined by the region grid operator: North, South, Center, and East. The proposed methodologies are based on Shannon entropy, consumption-based probabilistic sampling, and Kullback–Leibler divergence minimization. Each method is assessed for its ability to capture demand variability, ensure representativeness, and optimize field deployment. Representativeness is evaluated by comparing the differences in class proportions between the sample and the original population, complemented by the Pearson correlation coefficient between their distributions. Results indicate that entropy-based sampling excels in logistical simplicity and preserves categorical diversity, while KL divergence offers the best statistical fit to population characteristics. The findings demonstrate how combining information theory and statistical optimization enables flexible, scalable sampling solutions for demand characterization in under-instrumented electricity grids. Full article

The Chengdu–Chongqing Economic Mega Region (CCEMR), as a strategic economic hub in Western China, is increasingly facing challenges in balancing urban growth, agricultural stability, and ecological conservation within its territorial spatial planning framework. This study addresses the critical need to integrate multidimensional resilience assessment into China’s territorial spatial planning system. A framework for functional resilience assessment was developed through integrated GIS spatial analysis, with three resilience dimensions explicitly aligned to China’s “Three Zones and Three Lines” (referring to urban, agricultural, and ecological space and spatial control lines) territorial planning system: urban resilience was evaluated using KL-TOPSIS ranking, where weights were derived from combined Delphi expert consultation and AHP; agricultural resilience was quantified through the entropy method for weight determination and GIS raster calculation; and ecological resilience was assessed via a Risk–Recovery–Potential (RRP) model integrating Ecosystem Risk, Recovery Capacity (ERC), and Service Value (ESV) metrics, implemented through GIS spatial analysis and raster operations. Significant spatial disparities emerge, with only 1.29% of CCEMR exhibiting high resilience (concentrated in integrated urban–ecological zones like Chengdu). Rural and mountainous areas demonstrate moderate-to-low resilience due to resource constraints, creating misalignments between resilience patterns and current territorial spatial zoning schemes. These findings provide scientific evidence for optimizing the delineation of the Three Major Spatial Patterns: urbanized areas, major agricultural production zones, and ecological functional zones. In this research, a transformative methodology is established for translating resilience diagnostics directly into territorial spatial planning protocols. By bridging functional resilience assessment with statutory zoning systems, this methodology enables the following: (1) data-driven resilience construction for the Three Major Spatial Patterns (urbanized areas, major agricultural production zones, and ecological functional zones); (2) strategic infrastructure prioritization; and (3) enhanced cross-jurisdictional coordination mechanisms. The framework positions spatial planning as a proactive tool for adaptive territorial governance without requiring plan revision. Full article

The integration of electric vehicles (EVs) into smart grids (SGs) is reshaping both energy systems and mobility infrastructures. This review presents a comprehensive and cross-disciplinary synthesis of current technologies, methodologies, and challenges associated with EV–SG interaction. Unlike prior reviews that address these aspects in isolation, this work uniquely connects three critical pillars: (i) the evolution of energy storage technologies, including lithium-ion, second-life, and hybrid systems; (ii) optimisation and predictive control techniques using artificial intelligence (AI) for real-time energy management and vehicle-to-grid (V2G) coordination; and (iii) cybersecurity risks and post-quantum solutions required to safeguard increasingly decentralised and data-intensive grid environments. The novelty of this review lies in its integrated perspective, highlighting how emerging innovations, such as federated AI models, blockchain-secured V2G transactions, digital twin simulations, and quantum-safe cryptography, are converging to overcome existing limitations in scalability, resilience, and interoperability. Furthermore, we identify underexplored research gaps, such as standardisation of bidirectional communication protocols, regulatory inertia in V2G market participation, and the lack of unified privacy-preserving data architectures. By mapping current advancements and outlining a strategic research roadmap, this article provides a forward-looking foundation for the development of secure, flexible, and grid-responsive EV ecosystems. The findings support policymakers, engineers, and researchers in advancing the technical and regulatory landscape necessary to scale EV–SG integration within sustainable smart cities. Full article