Search Results (7,790)

Considering consumer health, consistency in processes, and developing trust among the public, food manufacturing facilities are expected to adhere to strict regulatory policies. Along with these expectations, machinery capabilities, especially considering reliability, maintainability, and hygienic designs, would play a significant role in delivering quality products and developing efficient processes. This paper focuses on a belt-style depositor machine, whose primary purpose is to deposit product pieces onto product passing below it. First, the key issues with the current machine are pinpointed. Next, alternative designs are provided aimed at testing, evaluating, and building belt-driven depositing machines. The original design experienced persistent belt tracking issues, frequent maintenance interruptions, and sanitation concerns due to its complex, heavy components. The project applied the Define, Measure, Analyze, Design, and Verify (DMADV) framework to test alternative belt configurations and implement improvements that significantly reduced maintenance time, improved tracking reliability, and enhanced hygienic design. Lab and real-world tests compared three prototypes, namely the V-Rib, Crowned Roller, and Pin Drive. The prototypes were compared against defined performance targets. The final system, built around a self-tracking V-Rib belt with modular components and reduced tool disassembly, demonstrated a 75% reduction in belt change time, and improved product consistency and compliance with sanitation standards. This redesign offers a replicable model for upgrading depositor systems across production lines. Full article

The complexity of city centre functions and urban intensity often result in lower safety indicators and a decreased sense of security among users. The author attempts to explain the relationship between the structurally, spatially, and functionally distinctive city centre and the level of residential development intensity. This relationship is analysed from the perspective of security criterion as one of the basic components of the quality of urban life and the construction of sustainable residential spaces. The research method used in the paper was a case study. Based on CEN/TS 14383-2:2022 and the source literature, security aspects were defined and divided into three frameworks of safety and security strategy. The aforementioned aspects formed the basis for the qualitative assessment of the area. The assessment covered areas of city centre development divided according to the concentration of residential function. The results of the conducted research justified the accuracy of the adopted research method, especially taking into account the structure of the division related to the context, the urban structure of the area and its management. The subject area of the research is consistent with current sustainability urban policy approaches related to building urban cohesion and sustainable intensity of city centre spaces. Full article

Recently, the progress of foundation models such as CLIP and SAM has shown the great potential of zero-shot anomaly detection tasks. However, existing methods usually rely on general descriptions such as “abnormal”, and the semantic coverage is insufficient, making it difficult to express fine-grained anomaly semantics. In addition, CLIP primarily performs global-level alignment, and it is difficult to accurately locate minor defects, while the segmentation quality of SAM is highly dependent on prompt constraints. In order to solve these problems, we proposed DCS, a unified framework that integrates Grounding DINO, CLIP and SAM through three key innovations. First of all, we introduced FinePrompt for adaptive learning, which significantly enhanced the modeling ability of exception semantics by building a fine-grained exception description library and adopting learnable text embeddings. Secondly, we have designed an Adaptive Dual-path Cross-modal Interaction (ADCI) module to achieve more effective cross-modal information exchange through dual-path fusion. Finally, we proposed a Box-Point Prompt Combiner (BPPC), which combines box prior information provided by DINO with the point prompt generated by CLIP, so as to guide SAM to generate finer and more complete segmentation results. A large number of experiments have proved the effectiveness of our method. On the MVTec-AD and VisA datasets, DCS has achieved the most state-of-the-art zero-shot anomaly detection results. Full article

Background/Objectives: Aged care residents are highly vulnerable to vaccine-preventable diseases. Despite recommendations and funding under Australian programs, vaccination rates among residents for COVID-19, influenza, pneumococcal and shingles remain sub-optimal. The aim of this work was to assess if tailored quality improvement interventions would improve vaccination coverage in aged care residents. Methods: This was a quality improvement initiative evaluated using a quasi-experimental pre–post design. Building on previously identified barriers and enablers, a package of interventions and resources was developed to support consent processes, vaccination planning, and tracking. Pre- and post-intervention vaccination coverage was assessed using resident lists from participating aged care facilities and data extracted from the Australian Immunisation Register (AIR) at two time points, 14 months apart. A process evaluation survey was distributed to RACF staff. Results: Of the 6964 residents listed, 5153 (74%) remained registered in AIR when data was extracted post-intervention. Shingles showed the greatest improvement in absolute difference (+23.4%), followed by pneumococcal (+14.2%) and influenza (+10.9%), despite a high baseline of 68.5%. COVID-19 coverage declined by 7.4% when applying a 6-month reporting interval. Twenty-five staff completed the process evaluation survey; 45% of respondents identified discrepancies between AIR data and internal records, indicating underreporting by external providers. Interventions including the consent template and vaccination tracker were reported as useful and were used to support local vaccination. Conclusions: This quality improvement initiative improved coverage for three of the four recommended and funded vaccines for RACF residents and demonstrated the value of tailored interventions informed by consumer and provider feedback. The approach potentially offers a scalable model for improving vaccination rates in aged care across Australia. Full article

Arable land quality is of the essence for the sustenance of grain production and food security. The continuous monitoring of the physical and chemical properties of arable land is instrumental in facilitating a comprehensive understanding of the evolution patterns of soil quality. This, in turn, provides fundamental evidence that is crucial for the optimization of cultivation practices, the establishment of appropriate plough layers, and the enhancement of soil quality. The near-surface sensing methodologies facilitate the acquisition of soil data at reduced scales, thus signifying a pivotal research trajectory for the procurement of soil-related information. The present study undertakes an examination of the current state of research on acquiring key parameters of farmland soil and provides an overview of the fundamental ground-level techniques employed for the assessment of farmland soil parameters. These techniques encompass single-parameter fixed-point detection, encompassing Soil Moisture Content (SMC), Soil Electrical Conductivity (EC), and nutrient analysis, multi-parameter fusion detection, and dynamic parameter monitoring. The study systematically reviews field sensing methods for major soil physicochemical parameters (such as SMC, Soil Penetration Resistance (SPR), EC, and nutrients) while analyzing the current application of Artificial Intelligence (AI) in soil parameter detection. The present paper proposes a developmental trajectory that shifts from “single-parameter static” to “multi-parameter dynamic” monitoring. This trajectory is proposed as a building upon the analysis of existing research. This evolution emphasizes intelligent algorithm-driven data enhancement to improve detection accuracy, forming a closed-loop progression of “dynamic detection—precise modeling—decision support”. This framework provides a reference for the advancement of soil sensing monitoring technologies and the scaling of precision agriculture applications. Full article

Indoor air quality (IAQ) in healthcare facilities is increasingly recognized as a key determinant of occupant health, comfort, and operational performance. Owing to heterogeneous space functions, varying occupancy patterns, and dynamic operational conditions, IAQ parameters may exhibit marked spatial and temporal variability within the same facility. University-affiliated healthcare buildings, where clinical services coexist with academic and administrative activities, represent particularly complex indoor environments that remain relatively underexplored in the current IAQ literature. This study examines the spatiotemporal variability of indoor carbon dioxide (CO₂) and fine particulate matter (PM_2.5) concentrations across four representative functional zones within a university-affiliated healthcare facility, including a patient waiting room, an academic office, an administrative office, and a restorative dental clinic. Continuous, long-term monitoring was conducted over a multi-month period to capture both spatial differences and diurnal dynamics under real operational conditions. Daily mean CO₂ concentrations varied across functional zones, ranging from approximately 540 to 620 ppm, with higher levels generally observed in spaces with sustained occupancy and limited ventilation. Daily mean PM_2.5 concentrations ranged from approximately 13 to 18 µg/m³, with greater variability detected in zones associated with intermittent activities and procedural sources. Unlike many IAQ studies focusing on single departments or short-term campaigns, this multi-zone, long-term assessment within a shared building infrastructure enables direct comparison of functional spaces and identification of time-specific exposure patterns. Overall, the findings highlight that IAQ conditions within healthcare facilities are shaped by both space function and temporal factors, even under shared ventilation infrastructure. The results emphasize the value of zone-specific and time-resolved IAQ assessment approaches and provide evidence-based insights to support targeted ventilation strategies, activity-aware operational controls, and improved indoor environmental management in healthcare settings. Full article

It is a hot topic to enhance the stability, security, and sustainability of industrial chains, against the backdrop of adjustments and rising uncertainty in global value chains. Using Chinese A-share listed firms from 2012 to 2022 as the research sample, this study treats the establishment of Cross-Border E-Commerce Comprehensive Pilot Zones (CBECCPZs) as a quasi-natural experiment and employs a difference-in-differences approach to empirically examine the impact of digital trade (DT) on industrial chain resilience (ICR) and its underlying mechanisms. The findings demonstrate that DT exerts a significantly positive effect on ICR, providing strong support for the long-term sustainability of the economic system. This conclusion remains robust after a series of robustness checks, including the incorporation of high-dimensional fixed effects, exclusion of confounding policy effects, adjustments to the sample, dimension-specific tests, consideration of lagged effects, and propensity score matching. Mechanism analysis reveals that DT strengthens ICR primarily by promoting firms’ digital transformation and improving human capital levels. The heterogeneity results suggest that the contribution of digital trade to resilience differs markedly across structural dimensions: the effect is more significant among firms located in eastern regions, state-owned enterprises, firms operating in regions with higher levels of digitalization, manufacturing firms, firms in more competitive industries, and firms with stronger internal control systems. From the perspective of ICR, this study elucidates the intrinsic mechanisms through which DT fosters high-quality development and sustainable economic growth. The findings provide robust empirical evidence for understanding the strategic role of DT in enhancing the security, stability, and sustainable operation of industrial chains and in building a modern industrial system that is autonomous, controllable, secure, and efficient. Moreover, the study offers important policy implications for governments seeking to advance DT institutional innovation and promote coordinated regional development, as well as for firms aiming to leverage DT to enhance long-term competitiveness and achieve sustainable development goals. Full article

Low-voltage mini-grids play a crucial role in expanding electricity access for rural and remote communities. However, they continue to face technical and operational barriers that hinder their performance and reliability. This study reviewed the evolution of research on technical challenges in low-voltage mini-grids from 2005 to 2025. Using the PRISMA approach, data were extracted from the Scopus database, yielding 155 publications for bibliometric analysis. Bibliometrix in R Studio was used to examine publication trends, geographical contributions, and thematic evolution, while qualitative synthesis identified key engineering and operational constraints. The findings revealed a steady increase in research outputs since 2020, driven by global policy commitments, including Sustainable Development Goal 7 and the Paris Agreement. Persistent technical barriers include voltage and frequency instability, inadequate power quality monitoring, inefficient integration of energy storage, poor control coordination, and limited system design optimisation. African nations contribute less to global research despite being most affected by energy poverty, highlighting capacity and funding gaps. The study highlights the need for integrated solutions combining smart control, hybrid storage, and grid-interconnection technologies to enhance resilience and reliability. For policymakers and practitioners, the findings advocate for investment in research, capacity building, and locally tailored technical standards designed for resource-constrained contexts. This review provides a comprehensive evidence base to guide future research and policy directions aimed at achieving sustainable, technically robust, and financially viable mini-grid systems for universal energy access. Full article

The selection of building materials increasingly prioritizes aesthetic and comfort-related experiences, yet the perceptual pathways linking physical properties to emotional judgments remain underexplored, particularly among Chinese users. This study aimed to clarify how different sensory modalities contribute to the perceptual pathways linking physical properties of wood to emotional judgments under multisensory conditions. Sixty young Chinese adults evaluated wood samples under visual, tactile, auditory, and multisensory conditions. Multivariate modeling approaches were applied to identify perceptual structures, mediating pathways to aesthetic judgments, and associations between subjective impressions and physical parameters. A three-factor perceptual structure was identified, comprising surface qualities, internal qualities, and emotional judgment. Path analyses showed that perceived cleanliness acted as the primary mediator from low-level perceptions to emotional responses, whereas naturalness played a limited role. Multisensory integration was vision-dominant (relative sensory weights from Bayesian weighted regression > 0.50), with touch providing secondary contributions (weights > 0.30) and audition exerting minimal influence. Lightness strongly predicted surface qualities, while density predicted internal qualities, with both achieving conditional and marginal R² values above 0.50. In contrast, higher-order impressions showed strong between-group but weak individual-level explanatory power (marginal R² < 0.30), indicating that physical parameters capture group-level tendencies but offer limited precision for individual emotional responses. These results inform culturally sensitive, multisensory design strategies for wood in biophilic and human-oriented environments and highlight the need to incorporate non-physical factors for precise personalization. Full article

With the advancements in instrumentation, image processing algorithms, and computational capabilities, single-particle cryo-electron microscopy (cryo-EM) has achieved atomic resolution in determining the 3D structures of viruses. The virus structures play a crucial role in studying their biological function and advancing the development of antiviral vaccines and treatments. Despite the effectiveness of artificial intelligence (AI) in general image processing, its development for identifying and extracting virus particles from cryo-EM micrographs has been hindered by the lack of manually labeled high-quality datasets. To fill the gap, we introduce CryoVirusDB, a labeled dataset containing the coordinates of expert-picked virus particles in cryo-EM micrographs. CryoVirusDB comprises 9941 micrographs from nine datasets representing seven distinct non-enveloped viruses exhibiting icosahedral or pseudo-icosahedral symmetry, along with coordinates of 339,398 labeled virus particles. It can be used to train and test AI and machine learning (e.g., deep learning) methods to accurately identify virus particles in cryo-EM micrographs for building atomic 3D structural models for viruses. Full article

The global shift toward decarbonized power systems is driving unprecedented penetration of variable renewable energy sources, especially wind and solar PV. Legacy grid architectures, built around centralized, dispatchable synchronous generation, are ill-suited to manage the bidirectional power flows, reduced inertia, and new stability constraints introduced by inverter-based resources. Existing research offers deep but fragmented insights into individual elements of this transition, such as advanced power electronics, microgrids, or market design, but rarely integrates them into a coherent architectural vision for resilient, high-renewable grids. This review closes that gap by synthesizing technical, architectural, and institutional perspectives into a unified framework for resilient grid design toward 2030 and beyond. First, it traces the evolution from traditional hierarchical grids to smart, prosumer-centric, and modular multi-layer architectures, highlighting the implications for reliability and resilience. Second, it critically examines the core technical challenges of high VRES penetration, including stability, power quality, protection, and operational planning in converter-dominated systems. Third, it reviews the enabling roles of advanced power electronics, hierarchical control and wide-area monitoring, microgrids, and hybrid AC/DC networks. Case studies from Germany, China, and Egypt are used to distil context-dependent pathways and common design principles. Building on these insights, the paper proposes a scalable multi-layer framework spanning physical, data, control, and regulatory/market layers. The framework is intended to guide researchers, planners, and policymakers in designing resilient, converter-dominated grids that are not only technically robust but also economically viable and socially sustainable. Full article

This study presents a hybrid digital twin system designed for real-time indoor air quality (IAQ) monitoring and filtration optimization within a residential environment. Using a network of low-cost sensors, physics-based simulations, and machine learning models, the system dynamically replicates the indoor environment to enable continuous assessment and optimization of key pollutants, including particulate matter, volatile organic compounds, and carbon dioxide. The system architecture integrates mass balance and decay models, computational fluid dynamics simulations, regression models, and neural network algorithms, all evaluated under both filtering and non-filtering conditions. A graphical user interface allows users to interact with the system, test air purifier placements, and visualize air quality dynamics in real time. The results demonstrate that, within this system, simpler models, such as linear regression, outperform more complex architectures under data-limited conditions, achieving test-set coefficients of determination ranging from 0.97 to 0.99 across multiple IAQ parameters. At the same time, the hybrid modelling approach enhances interpretability and robustness. Overall, this digital twin system contributes to smart building management by offering a scalable, interpretable, and cost-effective solution for proactive IAQ control and personalized decision-making. Full article

The regulatory framework governing EU electricity markets is highly complex, fragmented across multiple normative acts and sensitive to citation accuracy and contextual completeness. While Large Language Models (LLMs) offer promising capabilities for regulatory question answering (QA), their tendency to hallucinate legal references and omit critical conditions makes them unreliable for compliance-sensitive domains. This paper presents the design of a domain-specific Retrieval-Augmented Generation (RAG) system for EU electricity market regulations, explicitly engineered to deliver source-grounded, traceable and low-hallucination answers. The answering component is based on Google’s gemini-2.5-flash model. The Open AI’s gpt-4o-mini model is responsible for both relevant document selection before building the RAG prompt and playing the judge LLM role for Retrieval Augmented Generation Assessment (RAGAS) evaluation. We build a legal corpus comprising multiple core EU regulatory acts related to REMIT and market operation and propose a regulatory QA architecture that integrates: (i) three chunking strategies (article-based, structure-aware, sliding window), (ii) two embedding models and (iii) a novel LLM-based document selection agent that restricts retrieval to the most relevant normative acts before vector search, improving contextual focus and retrieval precision. Using a fixed benchmark of regulatory questions and a reproducible evaluation protocol, we quantitatively assess system performance with RAGAS metrics and classical information-retrieval measures. While all configurations achieve strong faithfulness (up to 0.96), answer relevancy varies substantially with embedding and chunking choices. The findings confirm that retrieval engineering, particularly embedding selection, chunking strategy and pre-retrieval document filtering, has a high impact for building reliable regulatory AI systems. The sliding window strategy combined with bge-small-en-v1.5 delivered the strongest rank-sensitive retrieval performance, achieving the highest Precision@10 and NDCG@10. In contrast, article-level chunking with the same model yielded a modest improvement in Recall@10, indicating a clear trade-off between recall and precision-oriented ranking quality in legal corpora. Full article

Farmland quantity continues to decline, land abandonment is a serious concern, and local quality degradation remains unresolved. This situation, in which large-scale farmland abandonment continues, is likely to induce a series of food security and ecological protection problems. However, strengthening the protection and development of abandoned farmland (AF) is very difficult. In response to this issue, this paper provides a comprehensive review and synthesis of domestic and international research on AF. The results show that the prior research has largely focused on information acquisition and the analysis of driving factors, while relatively limited attention has been given to pathways for the reuse and management of AF, with few relevant studies and practical examples available. In addition, no clear theoretical framework has been developed to evaluate and manage the multiple elements of and the overall process leading to AF. Building on an examination of the feasibility of applying resilience theory to the management of AF, this paper defines the conceptual scope and core meaning of AF resilience management and constructs a resilience management implementation path based on the steps of objective determination, problem profiling, evaluation feedback, and scheme formulation. This framework helps reveal the structure–process–function evolutionary characteristics of AF across different development stages and provides analytical support for the design of differentiated and adaptable management strategies. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article