Search Results (4,015)

The optimisation design of agricultural machinery is shifting from offline, experience-driven engineering towards adaptive, data-driven, and closed-loop intelligent optimisation. Conventional approaches based on computer-aided engineering (CAE), empirical testing, mathematical modelling, and static multi-objective optimisation have provided an important engineering foundation, but they remain limited under unstructured field conditions involving soil heterogeneity, crop variability, climatic disturbance, and nonlinear machinery–environment interactions. This review systematically examines the evolution of intelligent optimisation design for agricultural machinery from conventional simulation-based methods to artificial intelligence (AI)- and digital twin (DT)-enabled paradigms. First, mathematical modelling, response surface methodology, discrete element method (DEM), computational fluid dynamics (CFD), multi-body dynamics (MBD), heuristic algorithms, and early AI-assisted surrogate optimisation are reviewed to clarify their contributions and limitations. Second, frontier enabling technologies are analysed, including agriculture-specific large models, generative AI, lightweight edge intelligence, deep reinforcement learning (DRL), embodied AI, federated learning (FL), and privacy-preserving computing. Third, system-level applications integrating DT and AI are discussed, with emphasis on full-lifecycle machinery optimisation, device–edge–cloud collaborative control, multi-agent fleet coordination, predictive maintenance, and Agriculture 5.0-oriented intelligent equipment systems. Key deployment bottlenecks are further identified, including sim-to-real inconsistency, virtual–physical mismatch in DTs, edge-side trade-offs among accuracy, latency, energy consumption, and cost, insufficient validation standards, and economic adoption barriers. Finally, a 2025–2030 roadmap is proposed, highlighting large-model–DT closed loops, control biomimetics, green low-carbon optimisation, and trustworthy human–machine symbiosis for sustainable Agriculture 5.0. Full article

Essential amino acids and unsaturated fatty acids are key nutritional indicators. The human body preferentially absorbs these compounds, which have beneficial effects on health. In aquatic ecosystems, plankton communities serve as the primary food source for aquatic organisms, playing a crucial role in shaping their nutritional composition. In this study, we collected populations of Macrobrachium nipponense and corresponding water samples from ten distinct geographical locations across China. These sites included five freshwater resources and five saline–alkaline water resources. This study measured the ionic composition and plankton community structure of water samples, and analyzed the nutritional components of M. nipponense, aiming to identify indicator taxa linked to the nutritional value in this species. The results show significant differences in both nutritional components and plankton community structures between freshwater and saline–alkaline environments. This suggests a correlation between specific plankton taxa and the nutritional value of M. nipponense. Using relative sequence abundance data from metabarcoding, linear discriminant analysis effect size (LEfSe) analysis identified six plankton indicator taxa at the genus level. Their abundance differed significantly between the two habitat types. The saline–alkaline region had three associated taxa: Cyclotella, Brachionus, and Chaetoceros. In contrast, Arctodiaptomus, Cryptomonas, and Limnoithona were identified as indicator taxa for freshwater regions. Redundancy analysis (RDA) and Pearson correlation analysis revealed that, with the exception of the SY site, the abundance of Chaetoceros and Brachionus in saline–alkaline waters tracked with levels of K⁺, Ca²⁺, and HCO₃⁻. Meanwhile, at the SZ site, plankton community richness rose with CO₃²⁻. Furthermore, the potential correlations between plankton indicator taxa and the formation of specific nutritional components in M. nipponense were explored. These findings highlight the complex interactions among ionic composition, plankton indicator taxa, and nutritional value in M. nipponense. Ultimately, this study can support the development of artificial techniques to regulate the nutritional components of this commercially important species. Full article

Digital transformation in higher education has increasingly shifted from a technology-centered agenda toward a multidimensional institutional process involving governance, quality assurance, process redesign, and data-driven decision-making. This study proposes and operationalizes an analytical framework for examining digital transformation in universities through an interactive Human–Machine Interface developed in Python. The framework is structured around three complementary methodological cores: ontology-based modeling, statistical reliability analysis, and multiobjective optimization. The ontology module organizes the semantic structure of digital transformation dimensions, revealing their relational hierarchy and structural relevance. The statistical module evaluates internal consistency and distributional behavior through Cronbach’s alpha, corrected item–total correlation, and density-based inspection. The optimization module formulates intervention selection as a constrained multiobjective problem, allowing the identification of efficient portfolios under cost, readiness gain, equity, and feasibility criteria. The analytical environment also incorporates interactive dashboards, VOSviewer-style relational exploration, and exportable high-resolution figures. Results show that digital transformation readiness is heterogeneous across groups, that governance-oriented dimensions occupy a central semantic role, and that institutional intervention planning benefits from Pareto-efficient decision support rather than single-criterion ranking. The study contributes a coherent bridge between conceptual models of digital transformation and an operational analytical environment capable of supporting institutional diagnosis, evidence-based prioritization, and strategic planning in regulated higher education settings. Full article

This perspective paper develops a system-level characterization of the transportation metaverse as a persistent, policy-aware digital environment integrating digital twins, real-time data, advanced analytics, and human–machine interaction into a unified operational framework. The study presents a cross-modal review of metaverse applications in road, rail, maritime, and aviation systems, identifying common opportunities, limitations, and research challenges. It further proposes a structured metaverse-based framework for smart roads as a reference case. The framework demonstrates how persistent virtualization, parallel future scenarios, embedded governance constraints, and human-in-the-loop decision support can improve uncertainty-aware planning, management, and operations. The paper positions the metaverse not as a deployable technology, but as an emerging paradigm for transportation governance. The study provides an architectural vision and research agenda for developing more resilient, transparent, and adaptive transportation systems. Potential applications include smart road management, multimodal traffic coordination, real-time operational control, infrastructure resilience planning, and decision support for policymakers under uncertain conditions. Full article

With the rapid development of flexible electronics technology, flexible wearable sensors based on Lanthanum Strontium Manganese Oxide (La_1-xSr_xMnO₃) have garnered extensive attention in recent years due to their excellent multi-functional integration, environmental stability and biocompatibility. This review systematically analyzes the preparation methods, process optimization strategies, multi-performance integration technologies, and the expansion of the application field of La_1-xSr_xMnO₃-based flexible sensors. Firstly, the basic characteristics and sensing mechanism of the La_1-xSr_xMnO₃ material were presented, including its temperature sensitivity, strain response characteristics, and magnetoresistance effect. Secondly, the fabrication process of flexible sensors was elaborately discussed, with a focus on analyzing crucial technologies, such as laser induction and transfer printing technology. Subsequently, the strategies for regulating the electrical, thermal, and mechanical properties of materials through element doping, along with the multimodal sensing integration and signal decoupling methods, were expounded. Furthermore, the actual performance of this type of sensor in fields such as health monitoring, human–computer interaction, and extreme environment applications was summarized. Finally, the challenges and future development directions of La_1-xSr_xMnO₃-based flexible sensors are outlined, providing theoretical references for the design and optimization of next-generation flexible electronic devices. Full article

Perceived street vitality directly reflects residents’ assessments of the attractiveness of the street environment; it is not only an important focus of urban vitality research but also closely related to human-centred sustainable urban development. However, limited data availability and the complexity of urban environments have constrained fine-grained spatial analysis at the city scale. To address this issue, this study quantified perceived street vitality by collecting street-view imagery, extracting streetscape features, and integrating these data with questionnaire survey results. After comparing multiple models, a geographically weighted machine learning model was employed to identify key visual predictors, model-estimated marginal associations, interaction patterns, and spatial heterogeneity related to perceived street vitality. The results show that areas with high perceived street vitality are mainly located along street segments with abundant greenery and open spaces, whereas low-value areas are concentrated in densely built and enclosed environments. Among the various streetscape elements, buildings, vegetation, and sky are the key visual elements most strongly associated with perceived street vitality. A model incorporating these elements accounted for 67.2% of the variance in perceived street vitality. Notably, the strength of these associations varied significantly across different areas. This study provides empirical evidence and evidence-based support for sustainable urban renewal, the optimisation of street-space layouts in high-density urban areas, and the improvement in street environmental quality. Full article

This systematic review comprehensively explores the integration of Extended Reality (XR) technologies, comprising Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), into New Zealand’s STEM education framework. In alignment with PRISMA 2020 guidelines, we systematically analyzed 127 peer-reviewed studies from the Web of Science (n = 48), Scopus (n = 57), and Dimensions (n = 22) and incorporated 15 grey literature sources, resulting in 142 studies included in the review. Our meta-analysis found substantial improvements in student conceptual understanding from XR-enhanced STEM modules. Specifically, we observed an average increase of 23.4% when compared to traditional instructional methods (95 percent Confidence Interval: 18.7 to 28.1 percent, p < 0.001). These gains were especially prominent in interactive learning environments where immersive XR applications supported deeper engagement and the visualization of abstract STEM concepts. The qualitative synthesis highlighted several key barriers that limit effective XR integration. These include technological infrastructure gaps reported in 68 percent of reviewed studies, a critical need for educator training cited by 82 percent of studies, and curriculum alignment issues present in 57 percent of cases. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT) 2018, and the qualitative component employed a deductive thematic coding approach with inter-coder reliability verification. Successful institutional implementations were also identified. At Auckland University of Technology, XR-supported courses produced a 67 percent increase in student engagement, while Wellington High School achieved a 41 percent reduction in STEM achievement gaps through targeted XR interventions. Based on the evidence, we propose a four-phase implementation framework that addresses the technological, pedagogical, and policy requirements for sustainable XR adoption. These findings highlight the role of immersive technologies in supporting human-centered digital transformation and future skills development in the transition to Industry 5.0. The review contributes evidence-based insights that support the transition from technology-driven approaches associated with Industry 4.0 to the human-centered, socially oriented priorities of Industry 5.0. It also identifies critical research gaps, particularly in long-term learning outcomes and the integration of Mātauranga Māori within XR-enabled STEM environments. Full article

Rapid urbanization has intensified the need for vibrant, walkable, and socially sustainable urban environments, particularly within mixed-use and commercial districts. The way buildings and streets are spatially configured in these districts plays a critical role in shaping pedestrian movement, spatial accessibility, commercial vitality, and social interaction within these environments. This paper investigates the role of spatial configuration in shaping the resilience and sustainability of urban commercial districts through a comprehensive review of recent space syntax applications. The review synthesizes methodological approaches for examining spatial structures, urban morphology, spatial accessibility, and urban activity patterns, including segment-based spatial analysis, visibility graph analysis, agent-based modeling, and predictive spatial simulation. This study consolidates recent methodological developments in spatial analytics and identifies key analytical trends that clarify how spatial configuration contributes to urban vitality and sustainability in commercial districts. Particular attention is given to the methodological evolution of space syntax research and its increasing integration with complementary datasets and analytical frameworks for evaluating urban vitality. Across the reviewed studies, highly integrated and spatially accessible street networks were consistently associated with higher pedestrian flow, greater commercial density, stronger land-use clustering, and improved walkability, particularly within compact, mixed-use urban districts. Movement-based metrics such as integration and Normalized Angular Choice (NACH) repeatedly emerged as dominant predictors of pedestrian movement, land-use intensity, and commercial concentration. Despite significant methodological advances in spatial analysis, a persistent gap remains in linking configurational metrics with lived human experience and broader social sustainability outcomes. Overall, the findings demonstrate that spatial configuration is a fundamental driver of walkability, commercial vitality, and socio-spatial interaction, reinforcing the growing role of space syntax as a framework for evidence-based and sustainable urban design. Full article

This work involves the building occupants of a smart building during a period of hybrid working, with the purpose of co-designing data-rich workplaces that support wellbeing. Through the design of a custom card-kit based on PROWELL Model of Workplace Wellbeing Assessment, this work provides insights from an online 90 min co-design workshop with six building occupants utilizing the card-kit to speculate on the design of data-driven physical interventions that support workplace wellbeing. Transcript data from the video-recorded workshop were thematically analyzed, producing findings namely framing novel socio-technical dimensions for biophilic and biomimetic designs in the built environment. Contributing to discourses on Human-Building Interaction (HBI) research, findings were synthesized into a design agenda and considerations for supporting wellbeing in the hybrid workplace that utilizes physical feedback and passive sensing. Composed under the premise of co-creating smart environments together with their occupants, the proposed agenda highlights areas of critical research interest for HBI, Biophilic Design and Soft Robotics in the built environment. Full article

With the growing demand for assistive robots in aging societies, task-oriented grasping in household environments has become increasingly important. Compared with structured industrial settings, household scenarios are characterized by diverse objects, unstructured layouts, and strong variability in task semantics. However, traditional methods focus on geometric stability and fail to capture task-relevant semantic constraints on manipulation regions, while existing approaches suffer from unstable reasoning and lack effective mechanisms for incorporating human intervention into the reasoning process. To address these challenges, we propose Hi-RAGrasp, a task-oriented grasping framework that integrates progressive multi-stage reasoning, Human-in-the-Loop (HITL) interaction, and Retrieval-Augmented Generation (RAG). A coarse-to-fine pipeline progressively refines predictions from object-level localization to part-level grounding, enabling robust mapping from human instructions to fine-grained task-relevant regions. Meanwhile, a HITL correction mechanism and a structured human experience database are introduced and combined with RAG to form a unified paradigm that aligns with prior experience when available and falls back to reasoning otherwise, enabling experience reuse and future experience accumulation without retraining. In addition, a Geometric Heuristic Segmentation (GHS) method is proposed to improve task-relevant region localization for textureless objects. Experiments show that our method achieves a segmentation success rate of 77.73% on the evaluation dataset and a grasp success rate of 75% in real-world scenarios, significantly outperforming existing methods and demonstrating strong effectiveness and practicality in open environments. Full article

Inflammatory bowel disease (IBD) is a chronic inflammatory condition resulting from complex interactions between the immune system, genetic predisposition, and the gut microbiota. In this context, Escherichia coli (E. coli) plays a dual role in the human gut, ranging from harmless commensal strains to pathobionts capable of promoting intestinal inflammation. A growing body of evidence suggests that specific E. coli pathotypes, such as adherent-invasive E. coli (AIEC) and diffusely adherent E. coli (DAEC), contribute to the development and progression of IBD. This narrative review critically examines the microbiological, immunological, and clinical evidence supporting the role of E. coli in IBD, with particular emphasis on mechanisms of mucosal colonization, host–microbe interactions, and persistence within the inflamed intestinal environment. Furthermore, the lack of a standardized operational definition and the limited reproducibility of the AIEC phenotype are addressed, as well as uncertainty about the role played by E. coli as a primary initiator of the disease or as an opportunistic amplifier of intestinal inflammation, and the varying strength of evidence supporting associations with Crohn’s disease versus ulcerative colitis. Diagnostic implications, antimicrobial resistance, and therapeutic aspects are addressed as downstream and context-dependent consequences of E. coli–host interactions, with relevance for disease management and therapeutic response in patients with established IBD. By integrating data from experimental models, clinical studies, and translational research, the review identifies areas of consensus, ongoing controversy, and major knowledge gaps in IBD pathophysiology and clinical practice. Full article

This paper presents a Digital Twin (DT)-based framework for the control, monitoring, and intelligent optimization of an Assembly/Disassembly/Repair Mechatronic Production Line (A/D/R MPL), developed as a laboratory platform aligned with Industry/Education 4.0/5.0 paradigms. The A/D/R MPL is assisted by two complementary cyber–physical robotic systems: an Assembly/Disassembly/Replacement Cyber–Physical Robotic System (A/D/R CPRS), and a Mobile Cyber–Physical Robotic System (MCPRS), enabling both fixed and mobile intelligent operations. The CPRS is equipped with an industrial robotic manipulator (IRM) responsible for A/D/R tasks, while the A/D Mechatronic Line (A/D ML) consists of seven interconnected workstations (WS1–WS7) dedicated to storage, transport, quality control, and final product handling. MCPRS includes a wheeled mobile robot (WMR), carrying a robotic manipulator (RM) and Mobile Visual Servoing System (MVSS). Each workstation is connected to a local slave programmable logic controller (PLC), which communicates via PROFIBUS with a master PLC located at the CPRS level. Additional communication infrastructures include LAN PROFINET and LAN Ethernet for local integration, and WAN Ethernet connectivity enabled through open platform Communication-Unified Architecture (OPC-UA), ensuring interoperability, scalability, and remote accessibility. Also, MODBUS TCP as serial industrial communication is used between the master PLC and the MCPRS. Virtual environment supports task planning through Augmented Reality (AR) and real-time monitoring through Virtual Reality (VR). The system behaviour is modelled with synchronized hybrid Petri Nets (SHPNs) which describe the discrete and hybrid dynamics of A/D/R processes. Artificial intelligence (AI) techniques are integrated into the DT framework for optimal task scheduling and adaptive decision-making. As a laboratory-scale implementation, the proposed system provides a comprehensive platform for experimentation, validation, and education. It supports Education 4.0/5.0 objectives by facilitating hands-on learning, human–machine interaction, and the integration of emerging technologies such as AI, Digital Twins, AR/VR, and cyber–physical systems. At the same time, it embodies Industry 4.0/5.0 principles, including interoperability, decentralization, sustainability, robustness, and human-centric design. Full article

This paper addresses the important issue of the proper management and protection of subterranean monuments. It concerns the analysis and decoding of the microclimate that is created in heritage structures, which are structures located beneath the soil or carved into rock. The aim of this study is to understand the hygrothermal processes occurring in the mass of underground structural elements, such as evaporation, condensation, water content, and heat fluxes, based on the principles of building physics. The methodology used is the following: a systematic literature review on the topic, an overview of the factors affecting the microclimate, the assessment methodology, and the simulation tools used to decode and evaluate microclimate in subterranean heritage structures; a discussion of the current gaps; and finally, a proposal for future directions for research. A review of the literature reveals that researchers worldwide have employed similar methodologies to approach this complex issue. Recordings and analyses of the microclimate inside underground monuments lead to decision-making and the formulation of actions for optimal preservation. Due to the large number of parameters involved in microclimate analysis, computer software for numerical simulation has been used in many cases. Following the review of the relevant literature in the field of study, a critical discussion concludes by proposing directions for future research on this important topic. Basic results of this research identify current gaps, problems, and limitations. These include technical and practical issues or gaps concerning lack of data for material properties and weather conditions. Another significant limitation arises from the complexity of physical interactions, as well as from the human factor, which involves the proper use of the simulation program and the correct interpretation of the calculation results. This study demonstrates that the microclimate of subterranean heritage structures is the result of complex interactions between climate, geology, architectural design, material properties, and human use. Across different geographical and cultural contexts, subterranean monuments exhibit distinct microclimatic behaviors. The comparative analysis of case studies highlights that while subterranean environments generally benefit from thermal stability, they remain highly vulnerable to moisture dynamics, ventilation changes, and external climatic coupling. Hence, there is a necessity for context-specific approaches rather than generalized conservation solutions. Decoding subterranean microclimates requires a multidisciplinary framework that combines environmental monitoring, material indicators, architectural analysis, and numerical modeling. Full article

Industry 4.0 (I4.0) is transforming industrial systems through interconnected, data-driven technologies, raising questions about how these developments affect Occupational Health and Safety (OHS). This study investigates research trends, thematic structures, and knowledge gaps at the intersection of I4.0 and OHS using a multilevel bibliometric framework applied to Scopus records published from 2011 to 2025. The analysis moves from a broad overview of the I4.0 landscape to more focused examinations of specific I4.0–OHS publications, prevention-oriented studies, and emerging-risk research. The results show that OHS has limited visibility in the general I4.0 literature and is more prominent mainly in targeted subsets, where digital sensing, artificial intelligence, machine learning, and immersive technologies drive prevention-focused research. Conversely, emerging risks such as cognitive load, psychosocial stressors, and human–autonomy interaction appear in smaller, more dispersed clusters. Overall, the findings suggest that the relationship between I4.0 and OHS is unevenly developed, with established prevention mechanisms and early-stage conceptualization of new risks. Strengthening this field will require integrating human factors with digital indicators, better characterizing emerging risks, and ensuring that digital transformation supports SDG 8 by fostering safe and healthy working environments. Full article

Rapid urbanization, spatial inequality, and environmental pressures highlight the need for planning approaches that strengthen urban sustainability and quality of urban life (QoUL). While these issues are often assessed at metropolitan scales, the key processes shaping daily urban experience occur at the neighborhood level. This study examines how neighborhood planning contributes to QoUL and sustainability through a comparative analysis of three neighborhoods with different urban development patterns in Konya, Türkiye. A neighborhood-scale spatial multi-criteria QoUL assessment framework was developed, consisting of four dimensions—Core Services, Physical Structure, Spatial Quality, and Social Dynamics—covering eleven sub-criteria and 55 measurable criteria. The framework uses objective data from planning documents, institutional sources, and field observations to ensure a robust and comparable neighborhood scale QoUL and sustainability performance evaluation. Findings reveal significant variations in livability between neighborhoods. Accessibility to services, human-scale design, and opportunities for social interaction emerge as more influential than the quantity of services alone. Neighborhoods with permeable street networks, mixed land use, and accessible public spaces demonstrate higher QoUL performance. The study highlights the critical role of neighborhood-scale planning in enhancing livability and supporting the transition toward more sustainable urban environments, while offering practical insights for planning and policy development. Full article