Search Results (812)

This study aims to systematically reveal, from the perspective of organizational scale, the differences between large and small architectural design organizations in China in terms of characteristics of production activities, technological capabilities and innovation levels, resource integration capabilities, and client groups, and to quantify the priority order of clients’ attention to architectural design products, thereby providing a reference for industry structure optimization and strategic decision making. This research combines case analysis and comparative study to construct a four-dimensional comparative framework. The results show that large design organizations, leveraging their advantages in technological research and development as well as resource integration, focus on large-scale complex projects, technology-driven projects, cultural landmark projects, and multi-stakeholder collaborative projects, primarily serving government agencies and large enterprises. In contrast, small design organizations excel in flexibility, concentrating on small-scale simple projects, specialized niche projects, localized projects, and short-cycle, low-budget projects, serving individual owners and small businesses. Furthermore, this study adopts the Analytic Hierarchy Process (AHP) to establish an evaluation model. Twenty experts from architectural design organizations, construction organizations, and research institutions were invited to score the survey questionnaires, and quantitative weight analysis was performed. The research findings provide support for the optimization of the industry. Full article

Digital twin technology is reshaping modern agriculture. Digital twins are the virtual replicas of real-world farming systems, which are continuously updated with real-time data, and are revolutionizing the monitoring, simulation, and optimization of agricultural processes. The literature on agricultural digital twins is multidisciplinary, growing rapidly, and often fragmented across disciplines, which lacks well-curated documentation. A bibliometric analysis includes thematic content analysis and science mapping, which provides research trends, gaps, thematic landscape, and key contributors in this continuously evolving and emerging field. Therefore, in this study, we conducted a bibliometric review that included collecting bibliometric data via keyword search strategies on popular scientific databases. The data was further screened, processed, analyzed, and visualized using bibliometric tools to map research trends, landscapes, collaborations, and themes. Key findings show that publications have grown exponentially since 2018, with an annual growth rate of 27.2%. The major contributing countries were China, the USA, the Netherlands, Germany, and India. We observed a collaboration network with distinct geographic clusters, with strong intra-European ties and more localized efforts in China and the USA. The analysis identified seven major research theme clusters revolving around precision farming, Internet of Things integration, artificial intelligence, cyber–physical systems, controlled-environment agriculture, sustainability, and food system applications. We observed that core technologies, such as sensors, artificial intelligence, and data analytics, have been extensively explored, while identifying gaps in research areas. The emerging interests include climate resilience, renewable-energy integration, and supply-chain optimization. The observed transition from task-specific tools to integrated, system-level approaches underline the growing need for adaptive, data-driven decision support. By outlining research trends and identifying strategic research gaps, this review offers insights into leveraging digital twins to improve productivity, sustainability, and resilience in global agriculture. Full article

Efficient human detection in video streams is essential for various IoT applications, including surveillance, smart cities, intelligent transportation systems (ITSs), and industrial automation. However, resource-constrained IoT devices often face limitations in handling deep learning-based object detection. This study proposes a collaborative edge computing framework utilizing multiple Raspberry Pi-based IoT devices to improve YOLOv11-based human detection performance. By distributing video frames across multiple edge devices, the proposed system effectively balances the computational load, resulting in an increase in the FPS (Frames Per Second) for processed video outputs. The experimental results confirm that as more edge devices collaborate, overall video processing efficiency improves, demonstrating the feasibility of distributed object detection for scalable and cost-effective IoT-based video analytics. In particular, the proposed approach holds significant potential for ITS applications such as pedestrian monitoring at intersections, real-time incident detection, and enhancing traffic safety by enabling responsive and decentralized analysis at the edge. Full article

Soil–groundwater pollution is a complex environmental phenomenon formed by the coupling of multiple processes. Due to the concealment of pollution, the persistence of harm, and the complexity of the system, soil–groundwater pollution has become a major environmental issue of increasing concern. The simulation and prediction of different types of models, different pollutants, and different scales in soil and groundwater have always been the research hotspots for pollution prevention and control. Starting from the mathematical mechanism of pollutant transport in soil and groundwater, this study reviews the method models represented by empirical models, analytical models, statistical models, numerical models, and machine learning, and expounds the characteristics and applications of the various representative models. Our Web of Science analysis (2015–2025) identifies 3425 relevant studies on soil–groundwater pollution models. Statistical models dominated (n = 1155), followed by numerical models (n = 878) and machine learning (n = 703). Soil pollution studies (n = 1919) outnumber groundwater research (n = 1506), with statistical models being most prevalent for soil and equally common as numerical models for groundwater. Then this study summarizes the research status of soil–groundwater pollution simulation and prediction at the level of multi-scale numerical simulation and the pollutant transport mechanism. It also discusses the development trend of artificial intelligence innovation applications such as machine learning in soil–groundwater pollution, looks forward to the challenges and measures to cope with them, and proposes to systematically respond to core challenges in soil and groundwater pollution simulation and remediation through new technology development, multi-scale and multi-interface coupling, intelligent optimization algorithms, and pollution control collaborative optimization methods for pollution management, so as to provide references for the future simulation, prediction, and remediation of soil–groundwater pollution. Full article

Background/Objectives: This study aimed to evaluate estimated pulse wave velocity (ePWV) in different glaucoma types. Methods: This was observational, cross-sectional, non-interventional study conducted on 127 primary open-angle glaucoma (POAG) patients, 59 primary angle-closure glaucoma (PACG) patients, 34 pseudoexfoliative glaucoma (PEX) patients, and 55 normotensive glaucoma (NTG) patients (total of 275 glaucoma patients). The control group (CG, 92 patients) consisted of patients with cataract. ePWV was calculated by the formula that was recommended by the Reference Values for Arterial Stiffness Collaboration from data on age and mean arterial blood pressure. The obtained results were processed by applying methods of descriptive (arithmetical mean, standard deviation) and analytical statistics, and comparisons of tested variables were performed using ANOVA. A p value less than 0.05 was considered statistically significant. Results: Statistically significant differences were found between patients with POAG and the CG (p value 0.042), and between those with NTG and the CG (p value 0.001). There was a statistically significant difference in ePWV values when comparing all tested patients with glaucoma and the control group (p = 0.001). Conclusions: Estimated pulse wave velocity may be a helpful tool in future risk assessment models for glaucoma. Full article

To address the collaborative demand for low-frequency vibration control and energy recovery, this paper proposes a dual-functional structure integrating low-frequency vibration isolation and broadband energy harvesting. The structure consists of two core components: one is a quasi-zero stiffness (QZS) vibration isolation module composed of a linkage-horizontal spring (negative stiffness) and a vertical spring; the other is an energy-harvesting component with an array of parameter-differentiated piezoelectric cantilever beams. Aiming at the conflict between the structural dynamic stiffness approaching zero and broadening the effective working range, this paper establishes a dual-objective optimization function based on the Pareto principle on the basis of static analysis and uses the grid search method combined with actual working conditions to determine the optimal parameter combination. By establishing a multi-degree-of-freedom electromechanical coupling model, the harmonic balance method is used to derive analytical solutions, which are then verified by numerical simulations. The influence laws of external excitations and system parameters on vibration isolation and energy-harvesting performance are quantitatively analyzed. The results show that the optimized structure has an initial vibration isolation frequency below 2 Hz, with a vibration isolation rate exceeding 60% in the 3 to 5 Hz ultra-low frequency range and a minimum transmissibility of the order of 10⁻² (vibration isolation rate > 98%). The parameter-differentiated piezoelectric array effectively broadens the energy-harvesting frequency band, which coincides with the vibration isolation range. Synergistic optimization of both performances can be achieved by adjusting system damping, parameters of piezoelectric vibrators, and load resistance. This study provides a theoretical reference for the integrated design of low-frequency vibration control and energy recovery, and its engineering implementation requires further experimental verification. Full article

The creation of the LitUCA lithotheque represents a significant methodological advance in geoarchaeological research in the southwest of Spain. This article presents a systematic framework for the conservation, documentation, and digital integration of lithic collections, with particular emphasis on data traceability, reproducibility, and interoperability. The methodology adopted is inspired by international standards, adapted to the regional context, and incorporates rigorous protocols for sampling, analytical documentation, and a relational database system. The collection comprises over 5000 items, all of which are catalogued, photographed, and characterised both petrographically and morphometrically, with metadata being progressively aligned with FAIR principles, aiming for full compliance in the future. Preliminary analysis demonstrates the collection’s capacity to facilitate comparative studies of procurement, mobility, and lithic technological organisation. Furthermore, the digital infrastructure developed promotes remote access and fosters both academic and societal collaboration. Despite ongoing challenges regarding sample representativeness and interoperability, LitUCA stands as a scalable and versatile model for the management of lithotheques. This study highlights the importance of integrated lithotheques for scientific progress, heritage management, and interdisciplinary education. Full article

The development of E-commerce and digitalization drives the rapid change in logistics management practices and poses challenges to traditional talent training modes in logistics field. Nowadays, companies expect university graduates equipped with more practical logistics skills to connect tighter with the industry. This motivates universities to establish more practically relevant curriculums to enhance students’ career competitiveness. Under such background, industry–university collaboration courses are increasingly adopted in higher education institutes in logistics discipline. Due to the difference between this type of course and the traditionally taught courses, the learning outcome of it can be difficult to guarantee. Therefore, it is necessary to identify the influential factors of the learning outcomes of industry–university collaboration courses and establish the actionable strategies to enhance course quality. However, the current literature in logistics management education has little focus on this topic, resulting in gaps on clarifying the influential factors of learning outcomes of industry–university collaboration courses in this discipline. Applying a mixed method, this study conducted a case study for an industry–university collaboration course of a logistics discipline in a Chinese university. The interval-valued Pythagorean fuzzy (IVPF) numbers and the Weighted Aggregated Sum Product Assessment (WASPAS) methods were used. The results showed that there are 15 factors which can influence the outcomes of industry–university collaboration courses in logistics discipline. Among them, the most important factor is the working environment, followed by the students’ own ability. Also, the results indicated that students’ optimistic attitudes towards the course, whether students take the course seriously, and course evaluations can be influential factors for good learning outcomes. The sensitivity analysis was then conducted, showing that the results were robust. This study can contribute to the existing literature by providing a theoretical framework to understand and assess the quality of industry–university collaboration courses in logistics and relevant subjects, as well as offering new analytical tools for management educational studies. Moreover, this study can provide practical implications for educators to develop and maintain good industry–university collaboration courses and trainings. Specifically, a practical life-cycle view was suggested to put pertinent efforts in all periods before/during/after the course to achieve high course outcomes. Full article

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future. Full article

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling continuous monitoring and real-time data collection through interconnected medical devices such as wearable sensors and smart health monitors. These devices generate sensitive physiological data, including cardiac signals, glucose levels, and vital signs, that are integrated into electronic health records (EHRs). Machine Learning (ML) and Deep Learning (DL) techniques have shown significant potential for predictive diagnostics and decision support based on such data. However, traditional centralized ML approaches raise significant privacy concerns due to the transmission and aggregation of sensitive health information. Additionally, compliance with data protection regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR), restricts centralized data sharing and analytics. To address these challenges, this study introduces FED-EHR, a privacy-preserving Federated Learning (FL) framework that enables collaborative model training on distributed EHR datasets without transferring raw data from its source. The framework is implemented using Logistic Regression (LR) and Multi-Layer Perceptron (MLP) models and was evaluated using two publicly available clinical datasets: the UCI Breast Cancer Wisconsin (Diagnostic) dataset and the Pima Indians Diabetes dataset. The experimental results demonstrate that FED-EHR achieves a classification performance comparable to centralized learning, with ROC-AUC scores of 0.83 for the Diabetes dataset and 0.98 for the Breast Cancer dataset using MLP while preserving data privacy by ensuring data locality. These findings highlight the practical feasibility and effectiveness of applying the proposed FL approach in real-world IoMT scenarios, offering a secure, scalable, and regulation-compliant solution for intelligent healthcare analytics. Full article

In the era of data elements, it is extremely necessary and practically important to analyze network characteristics and evolutionary trends in academic research collaboration in the field of data assets research, which can provide valuable insights for promoting deep cooperation of scholars and enhancing their collaborative efficiency. However, existing studies on data assets research rarely delve into key differentiating characteristics and core thematic priorities between Chinese and international samples of collaboration networks. Based on bibliometric methods and social network analysis, a systematic comparative analysis between Chinese collaboration networks and international collaboration networks is conducted via CiteSpace software by using core literature from the China National Knowledge Infrastructure (CNKI) and Web of Science Core Collection, developed by Clarivate Analytics (WoS). Through observation, we find that the number of publications in this field has reached a preliminary scale with distinct differences in research focus and collaborative features between cooperation networks in China (CNCs) and international cooperation networks (ICNs). In recent years, Chinese samples have primarily focused upon research themes related to data value realization, such as data rights confirmation, data assets accounting, and data trusts. The overall connectivity of CNCs seems relatively weak, and a stable core author group has not formed, while collaborations in CNCs are predominantly localized and short-term. In contrast, international samples in recent years have mainly addressed the contextual application of data assets, exhibiting a collaboration network characterized by multi-center, interdisciplinary, and cross-institutional synergy, while core authors in ICNs are closely interconnected and their connectivity and structure are generally stronger than those of CNCs. Full article

Urban sustainability poses a deeply interdisciplinary challenge, spanning technical fields like data science and environmental science, design-oriented disciplines like architecture and spatial planning, and domains such as economics, policy, and social studies. While numerous advanced tools are used in these domains, ranging from geospatial systems to AI and network analysis-, they often remain fragmented, domain-specific, and difficult to integrate. This paper introduces a semantic framework that aims not to replace existing analytical methods, but to interlink their outputs and datasets within a unified, queryable knowledge graph. Leveraging semantic web technologies, the framework enables the integration of heterogeneous urban data, including spatial, network, and regulatory information, permitting advanced querying and pattern discovery across formats. Applying the methodology to two urban contexts—Thessaloniki (Greece) as a full implementation and Marine Parade GRC (Singapore) as a secondary test—we demonstrate its flexibility and potential to support more informed decision-making in diverse planning environments. The methodology reveals both opportunities and constraints shaped by accessibility, connectivity, and legal zoning, offering a reusable approach for urban interventions in other contexts. More broadly, the work illustrates how semantic technologies can foster interoperability among tools and disciplines, creating the conditions for truly data-driven, collaborative urban planning. Full article

As global energy systems shift to low-carbon models, microgrid systems play an increasingly vital role in decentralized energy management. This study proposes a collaborative scheduling strategy, incorporating both power and carbon contribution for multi-microgrid systems. Through the utilization of a cooperative Stackelberg game and a Nash bargaining model, a bi-level game framework is established between grid operators and microgrid alliances, enabling efficient resource sharing and equitable benefit distribution. To accurately assess each microgrid’s impacts, a VCG (Vickrey–Clarke–Groves)-based mechanism is introduced to quantify its marginal contribution to both power supply and carbon mitigation. The contribution factors are then embedded into the bargaining process, guiding incentive-compatible allocation. Furthermore, to improve computational efficiency and enable distributed problem-solving, an enhanced analytical target cascading (ATC) algorithm is applied. Experimental results reveal that this approach improves both economic and environmental performance, effectively reducing carbon emissions and dependence on the main grid. Full article

Emerging technologies present significant challenges for society as a whole. Among these, Artificial Intelligence (AI) stands out for its transformative potential, with the capacity to fundamentally reshape human thought, behavior, and lifestyle. This article seeks to explore the business-oriented perspective on how AI should be approached in Higher Education (HE) in order to serve the commercial objectives of companies. The motivation for this inquiry stems from recurrent criticisms directed at HE institutions, particularly their perceived inertia in adopting innovations, resistance to change, and delayed responsiveness to evolving labor market demands. In this context, the study examines what businesses deem essential for universities to provide in the context of AI familiarity and examines how companies envision future collaboration between the business sector and Higher Education institutions in using AI for business applications. Adopting a qualitative research methodology, this study conducted interviews with 16 middle-management representatives from international corporations operating across diverse industries. The data were analyzed using Gioia’s methodology, which facilitated a structured identification of first-order concepts, second-order themes, and aggregate dimensions. This analytical framework enabled a nuanced understanding of business expectations regarding the role of HE institutions in preparing graduates capable of meeting economic and commercial imperatives under the pressure of AI diffusion. Full article

The growing consumer awareness of functional foods has increased interest in fermented plant-based products with enhanced nutritional and health-promoting properties. This comprehensive narrative literature review examines the potential of diverse raw materials for tempeh production beyond traditional soybeans, analysing their nutritional composition, bioactive compounds, and functional properties. A structured literature search was conducted on peer-reviewed publications up to July 2025, focusing on tempeh fermentation technology, chemical composition, and bioactive compounds from various substrates using recognised analytical methods according to Association of Official Analytical Collaboration (AOAC) standards. The analysis of over 25 different substrates revealed significant opportunities for enhancing tempeh’s nutritional profile through alternative raw materials including legumes, cereals, algae, seeds, and agricultural by-products. Several substrates demonstrated superior nutritional characteristics compared with traditional soybean tempeh, notably tarwi (Lupinus mutabilis) with exceptional protein content ((32–53% dry matter (DM)) and mung bean (Vigna radiata) exhibiting remarkably high polyphenol concentrations (137.53 mg gallic acid equivalents (GAE)/g DM). Fermentation with Rhizopus oligosporus consistently achieved substantial reductions in anti-nutritional factors (64–67% decrease in trypsin inhibitors, up to 65% reduction in phytates) while maintaining consistent antioxidant activities (39–70% 2,2-diphenyl-1-picrylhydrazyl (DPPH) inhibition) across most variants. The diversity of bioactive compounds across different substrates demonstrates potential for developing targeted functional foods with specific health-promoting properties, supporting sustainable food system development through protein source diversification. Full article