Search Results (244)

Currently, deep mining has become the development trend of underground mines, and the harsh working environment underground seriously affects the efficiency of personnel and equipment operations. The operational efficiency of the human–machine system composed of personnel and equipment is not only affected by the status of personnel and equipment, but also closely related to the interaction between human–machine–environment. How to ensure the efficient operation of human–machine systems has become the key to improving the quality and efficiency of mines. Therefore, in order to analyze the interaction relationship between human–machine–environment in the process of human–machine system operation and explore the variation law of human–machine system efficiency. This paper constructs a deep mining human–machine system efficiency system dynamics model under the multi-factor coupling effect of deep well mining, guided by system dynamics theory, and obtains the variation laws of system efficiency under single-factor changes and multi-factor coupling effects. The research results solve the problem of difficulty in quantitatively describing the logical and quantitative relationships between various elements in the study of human–machine system efficiency, providing new ideas for the study of underground work efficiency. Through mathematical modeling, the temperature threshold for the efficient operation of the human–machine system is determined, and the quantitative relationships among temperature, humidity, and wind speed are elaborated, providing a reference for ensuring the efficient operation of the human–machine system in deep mining. Full article

As complex system engineering involving multiple stakeholders, multi-objective collaboration, and multi-spatiotemporal scales, the components, logical structure, and functional mechanisms of production equipment operation management and control (PEOMC) can be generalized through functional modelling to support dynamic analysis and intelligent decision-making of PEOMC in the industrial internet environment. To address the diversity of scenarios and objectives of PEOMC, a hierarchical construction method for the functional model of PEOMC based on IDEF0 is proposed. By analysing relevant international standards, such as ISO 55010, ISO/IEC 62264, and OSA-CBM, the generic functional modules for the first and second layers of the functional model are identified and defined. On the basis of semi-supervised machine learning, topic clustering is used to extract the components, functional mechanisms, and logical relationships of production equipment operation management and control from approximately 200 standard texts and to construct a reference resource pool for the third-layer functional module. On this basis, an interface matching and recursive traversal algorithm for functional modules is designed, and a composition and orchestration strategy of functional modules for specific scenarios is provided to support the flexible construction of diversified and personalized PEOMC scenarios. The proposed construction and application method was validated through an engineering case study in an aero-engine transmission unit manufacturing workshop: the average process capability index of the enterprise’s production equipment steadily increased from 1.28 to approximately 1.60, the mean time to repair (MTTR) of production equipment failures significantly decreased from 8 h to 3 h, and the average overall equipment effectiveness (OEE) increased from 56.43% to a stable 68.57%, demonstrating its effectiveness and practicality. Full article

To address the challenges associated with identifying causal relationships among event mentions in the event causality identification (ECI) task, ECI has emerged as a pivotal area of research for comprehending event structures. Recent studies have leveraged Transformer-based models, augmented by auxiliary components, to develop effective contextual representations for causality prediction. A critical step in ECI models involves transforming intricate event context representations into causal label representations, thereby facilitating the logical score calculations necessary for both training and inference. However, existing models frequently depend on simplistic feedforward networks for this transformation process, which often struggle to bridge the semantic gap between complex event contexts and target causal labels, particularly in linguistically nuanced scenarios. To address these limitations, we propose Contrastive Learning for Event Causality Identification (CLECI), an innovative ECI framework that enhances representation learning through the integration of contrastive learning techniques, a generator-discriminator mechanism with causal label embeddings. In contrast to traditional direct transformation methods, CLECI generates latent causal label embeddings that filter out irrelevant information while aligning with potential label representations. By incorporating contrastive learning principles, CLECI further augments the discriminative capability of event representations by constructing positive and negative pairs of events. Experimental evaluations conducted on the EventStoryLine (ESL), Causal-TimeBank (CTB), and MECI datasets demonstrate that CLECI achieves competitive performance, with F1-score improvements of 4.3%, 7.9%, and 2.5%, respectively, compared with the strongest baseline methods, while maintaining strong robustness in complex and noisy multilingual event contexts. Full article

This paper presents a mathematical modeling and advanced control strategy for the beer fermentation process, which is characterized by nonlinear biochemical kinetics and time-dependent dynamics. A biokinetic model was developed to describe the relationship between yeast growth, sugar consumption, and ethanol formation. The system was represented as a cascade of several continuous stirred-tank reactors (CSTRs), and experimental data confirmed a fermentation cycle of approximately 10 days. During this period, biomass concentration reached 6.8 g/L and ethanol levels exceeded 42 mmol/L. Substrate concentration (S) declined from 120 to 5 g/L, demonstrating effective conversion. The model was linearized around an operating point and reformulated into a 12-state-space system with input variables: temperature (set at 20–22 °C) and pH (maintained within 4.2–4.5). These inputs were controlled using fuzzy logic control (FLC) and model predictive control (MPC). Simulation results indicated that the FLC reduced temperature deviation to ±0.3 °C and minimized pH fluctuation below ±0.05. The MPC strategy improved substrate consumption efficiency by 8.5% and decreased fermentation time by 12 h under optimized input profiles. The combined FLC–MPC scheme demonstrated superior robustness, smooth trajectory tracking, and adaptability to biological variability compared to traditional methods. The developed framework supports intelligent brewery automation and provides a scalable foundation for further integration of digital fermentation technologies. Full article

In evolutionary computation, distinct clusters that address different subproblems evolve independently of each other, which makes it difficult to exchange genetic information between them. However, a vaguely defined task within one system may be expressed more clearly within another. Effective interaction methods enable subsystems to collaborate more effectively in solving global tasks. By analysing how ambiguous intentions regarding electricity consumption influence actual behaviour in real-world scenarios, we discovered that transaction and negotiation patterns within electricity markets can effectively support this process. By introducing time and third parties, the study presents a semiautomatic, interpretable reasoning community logic system that enables machines to express transaction negotiation patterns. Through formalised operations, it facilitates the conversion of intentions, uncovering hidden relationships within global structures through this liberated form of expression. This paper examines its impact on computational and search paradigms through case studies, enabling collaborative approaches and granularity control via dynamic anchor points, and explores automated peer-to-peer transactions and electricity monetisation within highly abstracted power trading processes. Full article

(1) Background: The purpose of this paper is to explicate the logic of community engagement in American policing. In the United States, the police are organized for crime control and social order through law enforcement. In fact, the terms police and law enforcement are often used interchangeably. This linguistic trap reifies the law-enforcer identity and disposition, while producing a logic of professional practice that prioritizes enforcement over more effective crime prevention activities. We ask, “Are there better ways to organize the police to make communities safer?” If so, what could the police do and why? To answer these questions, we first explore the structure of American policing and the logic it creates. We then examine latent community dynamics and their impact on public safety. (2) Methods: Using survey data from a statewide probability sample of households, the authors examine the impact of these dynamic processes on crime, informal social control, and support for those returning to the community from prison. (3) Findings: The findings demonstrate, in measurable ways, the essential function of community-engagement in creating safe, strong neighborhoods. (4) Conclusions: The study’s findings suggest a new framework for policing that prioritizes community engagement for relationship building and problem-oriented policing over more aggressive law enforcement campaigns. Full article

By analyzing the devotional practices at Qinglong Temple in Chaozhou, this article illuminates a symbolic circuit in Chinese folk religion wherein sincerity is materialized through reverent offerings to secure divine reciprocity. It further explores the ethical logic, symbolic mechanisms, and processes of social construction underlying the pattern. More broadly, the vibrant ritual life at Qinglong Temple demonstrates that far from being a relic of the past, such economies of sincere exchange are a vital and adaptive mechanism through which folk traditions negotiate their place and thrive within the complexities of modern China. The study reveals that Chinese folk religion operates as a dynamic system of practices embedded in everyday rituals, emotional ethics, and social relationships. Its legitimacy arises not from abstract doctrine but from ritual performance, moral expression, and affective interaction. The article elucidates how monetary offerings, when grounded in sincerity, are reinterpreted as symbolic gifts and subsequently transformed into symbolic capital through practices such as temple donations and vow fulfillment. While resisting full assimilation into market rationality, folk religion simultaneously engages official structures to construct a hybrid religious economy that reinforces communal ethics and sustains transcendent relationships through public ritual and collective devotion. Full article

This study examines how digital transformation (DX) unfolds in Small and Medium-sized Enterprises (SMEs) through an analytical integration of dynamic capabilities (DCs) and service-dominant logic (SDL). While DX research is abundant, existing studies tend to discuss internal organizational capabilities (DCs) and external value co-creation (SDL) in isolation, offering limited insight into how resource-constrained SMEs execute transformation in practice. Employing a multiple case study approach based on Japanese SMEs, this paper uses the micro-foundations of DC (sensing, seizing, and transforming) as an analytical lens to examine how the resource integration processes emphasized in SDL are operationalized through phased organizational decision-making. The findings illustrate that while DC provides the organizational process logic for change, SDL offers the perspective through which SMEs overcome internal resource scarcity by engaging in external collaboration. By bridging internal capability-based and external co-creation perspectives, this study contributes to a more granular and contextually grounded understanding of transformation processes under resource constraints. From a practical perspective, the findings highlight the importance of fostering dialogue and building external relationships as conditions for activating dynamic capabilities and mitigating organizational rigidity, offering practically relevant implications for SME managers and policymakers. 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

Background/Objectives: Synthetic artificial intelligence (AI) is increasingly used in cardiovascular medicine to generate realistic clinical data from limited samples while preserving patient privacy. Despite its promise, concerns remain regarding the clinical reliability of synthetic datasets, which hampers their integration into routine practice. This article introduces the TIMA method (Team-Implementation Multidisciplinary Approach), designed to involve clinicians directly in every phase of synthetic data development. The objective of this work is to describe the TIMA framework and to illustrate how structured clinician–data scientist collaboration can enhance the clinical robustness and plausibility of synthetic AI outputs. Methods: The TIMA approach structures the synthetic data generation workflow around continuous interaction between clinicians and data scientists. Cardiologists define clinical constraints, verify inter-variable relationships, and assess the coherence and plausibility of generated records. The framework is illustrated through multiple cardiology use cases, including atrial fibrillation risk prediction and surgical mortality estimation in infective endocarditis, to demonstrate its adaptability across different clinical contexts. Each phase includes iterative validation steps aimed at ensuring alignment with established clinical knowledge rather than reporting quantitative performance outcomes. Results: Application of the TIMA framework supported the development of synthetic datasets that adhered more closely to clinical logic and domain-specific constraints. Clinician–data scientist collaboration enabled early detection of implausible variable interactions, improved interpretability of synthetic data patterns, and enhanced internal consistency across different cardiology-oriented scenarios. Conclusions: TIMA represents a scalable and replicable methodological model for integrating synthetic AI into cardiology by embedding clinical expertise throughout the data generation process. Its structured, multidisciplinary workflow supports the production of synthetic data that is not only statistically coherent but also clinically meaningful, thereby strengthening trust and reliability in AI-assisted cardiovascular research. Full article

Efficient coordination within the supply chain of prefabricated construction remains a significant challenge due to the high level of interdependence among supply chain participants, the complexity of information flows, and the sensitivity of construction processes to communication delays. This study proposes an integrated methodological framework that combines fuzzy logic and social network analysis (SNA) to evaluate the structural stability and interaction dynamics of supply chain participants. First, a synthetic indicator—link stability—is introduced to quantify the robustness of relationships between supply chain actors. Link stability is defined as a function of five determinants: collaboration level, trust level, communication quality, adoption of digital tools, and effectiveness of dispute resolution. Fuzzy logic is applied to calculate this indicator for each pair of participants, reducing subjectivity in expert assessments. Second, the link stability matrix is used to compute a wide set of centrality measures, including degree, betweenness, closeness, eigenvector, PageRank, information, harmonic, and second-order centralities. These metrics reveal the structural influence of each actor within the network and allow for the identification of core, semi-peripheral, and peripheral roles. A heatmap demonstrates a highly centralized network structure dominated by managerial and design roles. The results contribute to improving supply chain resilience, enhancing communication pathways, and supporting decision-making in prefabricated construction projects. Full article

Rural spatial commodification serves as a vital pathway toward comprehensive rural revitalization. Its development is closely intertwined with land use transition, with each process exerting reciprocal influence on the other. Research on the coupling between these two systems has emerged as a cutting-edge interdisciplinary field bridging rural geography and land system science. Based on a systematic review of research advances in rural spatial commodification and land use transition, this paper summarizes the existing gaps in the literature and attempts to construct a coupling framework integrating rural spatial commodification and land use transition. The findings indicate that, although the academic community has amassed a substantial body of research on rural spatial commodification, land use transition, and their coupled relationship with rural transformation, several gaps persist. These encompass the absence of systematic indicator frameworks and quantitative validation methods for rural spatial commodification, insufficient exploration into the coupling mechanisms between rural spatial commodification and land use transition, and a notable scarcity of empirical studies examining land use optimization driven by rural spatial commodification. Future research on the coupling between rural spatial commodification and land use transition should follow the logical framework of “elucidating theoretical connotations, characterizing coupling relationships, analyzing coupling mechanisms, simulating coupling processes, and regulating coupling states”. It is essential to strengthen the interdisciplinary integration of rural geography and land system science, thereby providing scientific guidance for the allocation of resources in rural areas and the implementation of rural revitalization practices. Full article

The Social Internet of Things (SIoT) enables collaborative service provisioning among interconnected devices by leveraging socially inspired trust relationships. This paper proposes a socially driven SIoT protocol for trust-aware service selection, enabling dynamic friendship formation and ranking among distributed service-providing devices based on observed execution behavior. The protocol integrates detection accuracy, round-trip time (RTT), processing time, and device characteristics within a graph-based friendship model and employs PageRank-based scoring to guide service selection. Industrial computer vision workloads are used as a representative testbed to evaluate the proposed SIoT trust-evaluation framework under realistic execution and network constraints. In homogeneous environments with comparable service-provider capabilities, friendship scores consistently favor higher-accuracy detection pipelines, with F1-scores in the range of approximately 0.25–0.28, while latency and processing-time variations remain limited. In heterogeneous environments comprising resource-diverse devices, trust differentiation reflects the combined influence of algorithm accuracy and execution feasibility, resulting in clear service-provider ranking under high-resolution and high-frame-rate workloads. Experimental results further show that reducing available network bandwidth from 100 Mbps to 10 Mbps increases round-trip communication latency by approximately one order of magnitude, while detection accuracy remains largely invariant. The evaluation is conducted on a physical SIoT testbed with three interconnected devices, forming an 11-node, 22-edge logical trust graph, and on synthetic trust graphs with up to 50 service-providing nodes. Across all settings, service-selection decisions remain stable, and PageRank-based friendship scoring is completed in approximately 20 ms, incurring negligible overhead relative to inference and communication latency. Full article

Pedestrian safety in school zones is shaped by traffic conditions and street design characteristics, whose combined effects involve uncertainty and gradual transitions rather than sharp thresholds. This study presents an integrated assessment framework based on the analytic hierarchy process (AHP) and fuzzy logic to evaluate pedestrian traffic stress level (PTSL) at the street-segment scale in school environments. AHP is used to derive input-variable weights from expert judgments, while a Mamdani-type fuzzy inference system models the relationships between traffic and geometric variables and pedestrian stress. The model incorporates vehicle density, pedestrian density, lane width, sidewalk width, buffer zone, and estimated traffic flow speed as input variables, represented using triangular membership functions. Genetic Algorithm (GA) optimization is applied to calibrate membership-function parameters, improving numerical consistency without altering the linguistic structure of the model. A comprehensive rule base is implemented in MATLAB (R2024b) to generate a continuous traffic stress score ranging from 0 to 10. The framework is applied to street segments surrounding major schools in the study area, enabling comparison of spatial variations in pedestrian stress. The results demonstrate how combinations of traffic intensity and street geometry influence stress levels, supporting data-driven pedestrian safety interventions for sustainable school environments and low-stress urban mobility. Full article

This study aims to (1) examine the impact of AI-driven learning tools (AI-LTs) on educational sustainability (EDS) and (2) investigate the mediating role of students’ engagement (SE) and the moderating effect of digital resilience (DR) in this relationship. Based on sociotechnical systems theory (STS), self-determination theory (SDT), and resilience theory, and (3) developing a multidimensional framework to explore how technological, psychological, and contextual factors interact to shape sustainable learning outcomes. Data were gathered from 387 university students in Saudi universities using a standardized questionnaire and subsequently analyzed utilizing SPSS version 28 and PROCESS Macro Version 4.0. The study performed multiple regression and moderated mediation to evaluate the proposed relationships. The results confirmed that AI-LTs significantly enhance educational sustainability. Based on the findings, AI-LTs significantly improve the long-term viability of education, particularly when it is tailored to individual students, encourages active participation, and is logical from a pedagogical perspective. Student engagement was found to influence the relationship, suggesting that when AI tools are utilized effectively, they foster a sustained commitment to education and improved learning outcomes. Furthermore, digital resilience has a significant influence on the connection between AI-LT–EDS, indicating that students who exhibited improved adaptability to digital challenges reaped considerable benefits. The research enhances the existing literature by integrating three complementary frameworks—STS, SDT, and resilience theory—to provide a comprehensive understanding of AI’s role in sustainable education. Practically, the study underscored the importance of AI integration strategies that improve digital resilience, student engagement, and structural imbalance. The results demonstrated that AI usage necessitates significant institutional support and improved technology to establish educational environments that are adaptable, resilient, and easily accessible to students. Full article