Search Results (5,479)

Intrinsically self-sensing cementitious composites offer a promising basis for continuous structural health monitoring. Their electrical response, however, is strongly affected by reversible moisture change and freeze–thaw exposure. This study presents a computational proof-of-concept for the AI-driven design of a federated digital-twin architecture for damage identification and adaptive sensing in sustainable self-sensing cementitious infrastructure. The framework is developed and evaluated entirely in software on a physics-based synthetic testbed. At its present maturity, it is therefore a digital-twin precursor rather than an operational digital twin: it has no calibrated physical counterpart and no live, two-way data coupling, and no experimental validation is claimed. A transparent, physics-based signal generator produces fractional-change-in-resistance signals for twelve virtual CNT/biochar-functionalized LC3 and geopolymer specimens. Each passes through four progressive damage stages interleaved with wet–dry and freeze–thaw conditioning. The framework integrates a CNN-LSTM damage classifier, unsupervised domain adaptation, federated learning, reinforcement-learning-based active sensing, and quantum-inspired aggregation optimization. On three unseen virtual specimens (654 evaluation windows), the CNN-LSTM achieved 70.3% four-stage accuracy (95% Wilson confidence interval 66.7–73.7%) and a macro-F1 score of 0.650, with per-specimen accuracy ranging from 63.8% to 77.1%. It reached 85.2% (95% CI 82.3–87.7%) for the damaged-versus-undamaged decision and reduced environment-induced false alarms by 74.7% (95% CI 61.7–83.4%) relative to a calibrated threshold detector. Federated averaging was less accurate and less stable than centralized training; the 5.2 percentage-point gain from quantum-inspired aggregation lies within the resolution of the evaluation set and is not established as a real improvement. The active-sensing controller reduced measurement cost by 98.9% but detected only four of 27 damage-progression events. All sensing data are synthetic, and every interval reported here is recomputed from the evaluation counts already reported rather than obtained from additional experiments. The results therefore establish algorithmic feasibility only and identify the components requiring refinement before experimental validation. Full article

Distributed generation (DG) is reshaping distribution networks through bidirectional power flows, operational variability, and dependence on coordinated regulation, pricing, and control. This paper examines how regulatory architecture, grid-code requirements, tariff design, and Distributed Energy Resource Management Systems (DERMS) influence DG integration, emphasizing Honduras. A structured mixed-source review is applied, combining Scopus-based bibliometric analysis of 2438 records (2000–2026) with targeted synthesis of technical, regulatory, tariff-related, and institutional sources. The bibliometric results show sustained growth and a thematic shift from conventional voltage-control studies toward active distribution networks, DER coordination, storage, demand response, tariff reform, and digital energy management. The analytical synthesis shows that effective DG integration requires more than interconnection compliance: it depends on grid-support functions, cost-reflective and equitable tariffs, and operational tools capable of managing voltage deviations, reverse power flow, congestion, protection coordination, and limited visibility. DERMS is an enabling layer for voltage control, active and reactive power management, congestion mitigation, adaptive protection, and predictive operation. For Honduras, current regulatory progress should be complemented by phased modernization focused on observability, smart metering, data infrastructure, local flexibility, and progressive DERMS deployment. The study provides an integrated framework for aligning regulatory, economic, and operational dimensions of DG integration in emerging distribution systems. Full article

Climate change has intensified heat stress in poultry production systems, raising concerns about productivity, animal welfare, and system sustainability. This study aimed to analyze the evolution, structure, and emerging trends of scientific research on climate change and heat stress in industrial poultry production using a bibliometric approach. Records were retrieved from the Scopus and Web of Science Core Collection databases. After deduplication and eligibility screening, 342 documents published between 1974 and 2025 were retained. Bibliometric analyses were performed using Bibliometrix/Biblioshiny in RStudio, including publication dynamics, international collaboration, author co-citation, conceptual structure, thematic evolution, trend topics, and keyword co-occurrence. Scientific production increased markedly in recent years, with an annual growth rate of 8.36% and international co-authorship in 22.51% of the publications. China and the United States were the leading contributors. At the same time, the international research structure showed interconnected collaboration networks and complementary intellectual communities focused on physiological and neuroimmune responses, cellular and oxidative mechanisms, and nutritional and management-based mitigation. Heat stress was the main conceptual hub, connecting thermoregulation, oxidative stress, immunity, welfare, productive performance, and product quality. Heat tolerance, thermotolerance, chronic heat stress, and genetic adaptation showed recent or developing bibliometric visibility, although their structural and temporal patterns differed across analyses. However, heat waves, long-term resilience, validation under commercial conditions, climate-vulnerable regions, environmental engineering, and digital monitoring exhibited lower bibliometric representation and network centrality within the analyzed corpus. Future research should combine nutritional, physiological, genetic, environmental, and precision-monitoring approaches to support economically viable and climate-resilient poultry production. Full article

Engine noise is a predominant source of noise in the cabin of internal combustion engine vehicles and new energy hybrid vehicles. The conventional multi-channel active noise control (ANC) system, based on the adaptive notch filtered-X least mean square algorithm, is commonly employed to mitigate such multi-tonal noise. However, the computational efficiency and convergence performance of this system may be significantly hindered by the large estimated secondary path length and the frequency-dependent convergence behavior. To overcome these limitations, this paper proposes a computationally efficient and fast-converging multi-channel ANC system by incorporating a local secondary path (LSP) equalization method. The proposed method enhances the convergence speed by equalizing the magnitude responses of estimated secondary paths and reduces the computational complexity through an improved LSP modeling approach. Accordingly, a set of low-order equalized LSP models with normalized amplitude-frequency responses is generated and employed for reference filtering. A computational complexity analysis comparing the conventional system, a recent cost-effective system, and the proposed system is presented. Numerical simulations are conducted to evaluate the convergence speed and noise attenuation performance of these three systems. Additionally, real vehicle experiments are performed using a digital signal processing controller. The results demonstrate that the proposed multi-channel ANC system achieves a superior noise reduction effect. Under accelerated conditions, the average attenuation of the second-order noise component at the four error microphones is measured at 4.4 dB(A), 6.2 dB(A), 13.4 dB(A), and 10.0 dB(A). These findings confirm the practical effectiveness of the proposed multi-channel ANC system. Full article

Building Information Modelling (BIM) and Life Cycle Assessment (LCA) are increasingly integrated to support environmental assessment in the built environment. However, applications to existing and heritage buildings remain fragmented, and the contribution of Heritage Building Information Modelling (HBIM) to Life Cycle Assessment is still poorly defined. This study presents a systematic review of 41 peer-reviewed publications retrieved from Scopus and reported according to the PRISMA 2020 guidelines, combining bibliometric, thematic and workflow-oriented analyses. Studies were selected according to predefined eligibility criteria based on relevance to HBIM-LCA integration. Three main BIM–LCA workflow typologies are identified: manual/export-based, plug-in-based and IFC-enabled interoperability approaches. While these workflows have progressively improved automation and data exchange, their application to existing and heritage buildings remains largely scenario-driven and focused on comparing refurbishment, retrofit, adaptive reuse and reconstruction alternatives. The review shows that HBIM distinctive contribution does not lie in environmental calculation methods, but in its ability to structure knowledge related to existing assets, including conservation state, intervention history, information provenance, uncertainty and life cycle transformations. However, current HBIM information structures remain only partially aligned with LCA requirements, particularly regarding service life, end-of-life scenarios and knowledge management. Semantic technologies emerge as a promising pathway towards knowledge-level interoperability. Limitations are related to the heterogeneity of the reviewed studies and the predominance of qualitative evidence. Digital-Green Heritage Workflows integrating HBIM, LCA and semantic technologies enable more transparent, life cycle-oriented and sustainable heritage conservation. Full article

Artificial intelligence (AI)-based digital twins (DTs) are emerging as a groundbreaking paradigm in rehabilitation and physiotherapy, enabling the creation of dynamic virtual representations of patients for continuous monitoring, prognostic assessment and personalised therapeutic interventions. This article presents a global, interdisciplinary review of AI-based DT technologies in rehabilitation settings utilising the Internet of Medical Things (IoMT), with particular emphasis on the integration of wearable and implantable sensor systems in next-generation wireless healthcare applications. The article analyses how multimodal wearable sensors, implantable devices and smart wireless communication networks can support the acquisition of real-time biomechanical and physiological data for adaptive rehabilitation. By combining perspectives from biomedical engineering, physiotherapy, computational intelligence and wireless healthcare systems, this article highlights the emerging opportunities and challenges associated with the creation of scalable digital twin ecosystems for precision rehabilitation. The proposed vision contributes to the development of smart, connected and personalized rehabilitation infrastructures, in line with future paradigms of healthcare and wireless communication. Full article

Rapid urbanization has led to habitat fragmentation in peri-urban areas, continuously eroding the ecological foundation of sustainable food systems in urban–rural transition zones and posing a real threat to regional food security. Against the backdrop of urbanization disturbances, traditional nature-based solutions have limitations in addressing socioecological nonlinear responses, whereas digital tools offer new governance pathways for enhancing food system resilience. To elucidate the intrinsic mechanisms through which digital technology empowers the resilience of peri-urban food systems, this study, which is grounded in ecological wisdom theory, constructs a system dynamics model that integrates “digital technology-ecological perception-ecological wisdom capital” in a three-dimensional linkage. This model simulates the dynamic process through which sustainable food systems in urban–rural transition zones resist the risks of habitat fragmentation and achieve synergistic steady-state evolution. According to the simulation results, a synthesized steady-state transition in sustainable food systems can be regarded as a self-organizing phase transition process. During resource metabolism, system elements show strong nonlinear symbiotic and mutually beneficial features. Further, there is a significant time-lag effect on improving food system resilience through digital technology empowerment and policy coordination. Also, the effects of governance are not immediately visible. Further, as an important instrumental empowerment carrier, urban–rural spatial and information barriers can be broken through means like digital ecological monitoring. Moderate investment in this regard can promote the acceleration of the system’s self-organizing phase transition. Also, this can enhance resilience against disturbance from habitat fragmentation while ensuring food production and supply. Finally, the ecological carrying capacity of core food production spaces does not increase monotonically. This means that the system possesses an adaptive cyclical fluctuation mechanism, with a periodic oscillatory evolution of carrying capacity. This study breaks through static analytical paradigms, fills the quantitative research gap on the resilience evolution of peri-urban food systems driven by the integration of digital technology and ecological wisdom, and can provide scientific evidence and decision-making support for food–ecological collaborative governance in China’s urban–rural transition zones. Full article

This article presents a systematic review of recent literature on classroom pedagogy adopted in early and primary education to address linguistic diversity in early childhood and primary education. The study aims to answer the research question: ‘What does the literature say about current pedagogical practices and trends in addressing multilingualism in early and primary education?’ The review synthesizes evidence from a period of five years (2019–2024), utilising keywords such as “multilingualism,” “classroom pedagogy,” and “early and primary education.” From an initial screening of 806 studies, a total of 46 studies focusing on pedagogical practices in early childhood and primary education were included in this review. Findings highlight a shift towards multilingual pedagogies that recognise and build on learners’ linguistic and cultural strengths, particularly through translanguaging, multilingual and multimodal scaffolding, culturally responsive teaching, and practices that promote student agency, identity, and well-being. Emerging trends emphasise flexible use of learners’ full linguistic repertoires, digital and multimodal learning, and more formative and inclusive approaches to assessment, while highlighting the need for sustained teacher professional development and systemic changes to curriculum, assessment, and language policy to move beyond persistent monolingual norms. These findings have practical and policy implications, highlighting the need to strengthen teacher professional learning and develop curriculum, assessment, and language policies that embed multilingualism as a resource for learning. This synthesis contributes to a deeper understanding of how educational systems can adapt to and embrace linguistic diversity, ultimately improving educational outcomes for early and primary school-aged learners. Full article

Production data transparency in smart manufacturing simultaneously enhances and impairs employee performance across organizational contexts. Existing research has not resolved this theoretical contradiction. Drawing on socio-technical systems theory, cognitive appraisal theory, and conservation of resources theory, this study develops a dual-pathway model. Data transparency influences adaptive performance through a bright empowerment pathway and a dark surveillance pathway mediated by EPM-induced strain. Procedural justice of data governance and digital self-efficacy operate as a perceived-institutional boundary condition and an individual-capability boundary condition, respectively. Latent moderated structural equations were applied to survey data from 412 employees in Chinese smart manufacturing enterprises. Results support both pathways and reveal a theoretically consequential asymmetry between these boundary conditions. Johnson–Neyman analysis indicates that institutional justice attenuates the strain pathway to non-significance within the observed distribution of responses. Conversely, digital self-efficacy requires near-ceiling levels to achieve the same pattern. This asymmetry indicates that continuous moderation produces sharply different practical outcomes across the observed data range. Institutional and individual remedies therefore address the visibility paradox on different practical scales. Governance adequacy represents a more attainable managerial lever than individual capability development. These findings advance socio-technical systems theory by detailing the asymmetric buffering capacities of different organizational resources. Full article

Environmental conditions have become more unstable; therefore, innovative and eco-friendly methods of food production are urgently required. Most existing hydroponic systems lack the capacity for real-time responses and decision-making based on integrated data, similar to contemporary farms. This document outlines the creation of an advanced hydroponic farming system that utilizes Internet of Things (IoT) sensors and a digital twin (DT) simulator to address these challenges. A completely monitored and continuously assessed hydroponic farming simulator operating on a Raspberry Pi, employing various sensors, data management and processing, and automated environmental regulation. The development of this intelligent hydroponic farming system employs a dual-model machine learning pipeline: one that identifies plant diseases through image analysis, and another that assesses plant nutrient levels based on sensor data. The data from the two models are combined using a cloud-based DT, enabling remote access to the DT and offering closed-loop control for irrigation, nutrient dosing, and management of all environmental factors related to crop growth in a hydroponic setting. This research showcases the capability to develop scalable, data-focused precision agriculture solutions that can adapt to the demands of today’s agricultural environment by combining all elements of IoT sensing, machine learning, and DT simulations into one functional hyperphysical system. Full article

Manufacturing sectors and ecosystems can improve their innovation performance through digital platforms, connected intelligence, and organisational settings that enable collaboration among experts and ecosystem members. The convergence of skills and capabilities distributed across humans, organisations, communities, and AI agents creates intelligent environments that can support ecosystemic and transformative innovation. To examine this hypothesis, we follow a three-stage methodology. First, we develop a modelling framework based on a vector autoregressive model, in which a weighted matrix representing directed binary couplings among human, collective, and machine intelligence drives the transition of a manufacturing ecosystem from a baseline innovation state to a more advanced one. Second, we present the SmartGreenEcos experiment, which develops an intelligent environment adapted to a specific manufacturing ecosystem. The experiment demonstrates the feasibility of the model’s abstract architecture by implementing digital platforms, e-services, and AI agents that facilitate inter-company collaboration, experimentation, and innovation. Third, we use simulations and analyse the eigenvalues and eigenvectors of the weighted matrix to examine the internal dynamics of intelligent environments and identify key thresholds and drivers of change. The results of this three-stage methodology provide insights into the design of intelligent environments and the interaction parameters through which connected intelligence can improve innovation performance. Full article

Architectural heritage interventions often preserve visual form while leaving acoustic memory and everyday sound practices outside the design brief. This article positions soundscape as an architectural material for heritage media architecture and tests it through Sanggar Kungfu Kapasan, a historic Chinese-diaspora kampung in Surabaya, Indonesia. The research combined contextual and soundmark mapping, Pepper’s Ghost prototyping, architectural translation of a semi-transparent virtual layer, and comparative evaluation using 2D visualization and immersive VR. After deduplication, 37 participants evaluated the 2D material, 31 evaluated VR, and 24 completed both modes for within-subject analysis. VR significantly improved perceived physical quality (Δ = +0.46, p = 0.0007, d_z = 0.80), communication quality (Δ = +0.42, p = 0.0218, d_z = 0.50), and overall evaluation (Δ = +0.20, p = 0.0285, d_z = 0.48). Sound-specific VR ratings were positive for place-meaning support (M = 5.87) and scene formation (M = 5.65), and the soundscape design index correlated with communication quality (r = 0.47, p = 0.007). The findings show that soundscape strengthens narrative legibility and architectural fit when deliberately coordinated with visual layering, scene sequencing, and cultural context. The study contributes a soundscape-informed, feng shui-readable workflow for pre-installation evaluation of multisensory heritage interventions. Because the respondent pool was predominantly composed of architecture students, the results are interpreted as a preliminary design-user evaluation rather than community validation. Full article

In this paper, we investigate a simulation-based optimization framework that implements discrete-event simulation with evolutionary search methods to optimize stochastic manufacturing systems efficiently. The proposed methodology couples a Tecnomatix Plant Simulation model with a MATLAB R2025b-based optimization environment using a data exchange interface, allowing for the iterative assessment of complex manufacturing systems. The study examines an adaptive replication strategy designed to manage stochastic variability in simulation outcomes. In the proposed method, the required number of simulation runs are determined dynamically based on confidence interval estimation. The stopping criterion is specified using a 95% confidence interval, ensuring adequate statistical accuracy while decreasing excess computational effort. The framework allows multiple performance indicators, such as throughput, congestion levels, and machine failures, which are built into an objective function. The optimization is driven by a (1, λ)-evolution strategy with Gaussian mutation and adaptive step-size control, allowing robust search in noisy objective function. However, thanks to the framework presented, it is also possible to apply other optimization algorithms. A case study of a manufacturing system was built and modeled in Tecnomatix Plant Simulation to validate the proposed methodology. In comparison with the baseline production configuration in one of the simulation runs, the suggested framework reduced the objective function by 43.36%. Benchmark experiments demonstrated that the adaptive replication strategy achieved a solution quality comparable to fixed replication schemes while requiring fewer simulation evaluations on average, thereby reducing the computational effort without compromising statistical reliability. The benchmark comparison showed that the adaptive replication strategy improved the objective value by up to 17.20% compared with fixed-replication strategies while requiring substantially less computational time than the fixed-20 and fixed-30 strategies. The robustness analysis further demonstrates that the adaptive replication strategy produces consistent optimization results across independent runs despite the stochastic nature of both the simulation model and the optimization process. From an industrial perspective, the proposed framework provides a practical decision-support tool for the optimization of manufacturing systems under uncertainty, enabling more reliable parameter tuning with reduced computational effort and facilitating the implementation of digital twin technologies. Full article

Cross-phase collaborative management in complex product systems (CoPS) development is inherently challenged by heterogeneous organizational coupling and stochastic disturbances. Although digital twin (DT) and model-based systems engineering (MBSE) technologies provide foundations for physical–virtual synchronization and model traceability, existing approaches remain fragmented in three respects: insufficient requirements-traceable architectural integration, limited cross-phase semantic interoperability and runtime evolution, and weak operational links between semantic reasoning and adaptive decision models. To address these gaps, this paper proposes an MBSE-driven digital twin framework with semantic enhancement. First, a four-layer architecture is derived using the MagicGrid methodology, encompassing physical–virtual mapping, semantic reasoning, decision support, and service interaction. Second, a collaboration-oriented SysML profile is developed to standardize the representation of tasks, resources, materials, disturbances, and management constraints across engineering phases. Third, a knowledge-driven adaptive collaboration mechanism maps runtime disturbance inputs into semantic states, propagates their cross-phase impacts, supports process-topology reconfiguration, and generates decision-ready constraints for adaptive management. A case-based prototype for aero-engine turbofan blade development demonstrates the feasibility of the mapping–reasoning–decision chain and provides case-level evidence of improved cross-phase coordination under controlled disturbance scenarios. The results indicate a feasible engineering pathway from perceptive DT functions toward reasoning-enabled collaborative decision support. Full article

The digital transformation of financial services has increased the need for intelligent information systems capable of supporting credit risk assessment in dynamic and data-intensive environments. Traditional credit scoring approaches often face limitations when evaluating customers with limited financial histories, heterogeneous data sources, and rapidly evolving behavioral patterns. In response to these challenges, this study proposes an adaptive credit risk assessment framework that integrates machine learning, an Adaptive Champion–Challenger strategy, and MLOps practices within a unified information systems architecture. The proposed framework was evaluated using real operational data obtained from a Mexican FinTech company specializing in mobile phone financing. Three machine learning algorithms—Logistic Regression, XGBoost, and TabNet—were implemented and continuously evaluated through a rolling Champion–Challenger process supported by out-of-time validation and statistically validated model promotion criteria. Experimental results indicate that different algorithms became optimal during different evaluation periods, indicating that model effectiveness varied over time as customer behavior and portfolio characteristics evolved. While XGBoost served as the initial static baseline model, TabNet and Logistic Regression achieved superior performance during several evaluation periods, illustrating the potential benefits of adaptive model selection under changing data conditions. The proposed Adaptive Champion–Challenger Framework achieved a mean AUC of 0.817, compared with 0.798 obtained by the static baseline model. Statistical validation using the DeLong test for correlated ROC curves confirmed that the observed performance improvement was significant (p = 0.0021), providing evidence that the performance gains achieved by the adaptive strategy were unlikely to be attributable to random variation. From a Digital Transformation and Information Systems perspective, the findings suggest that maintaining predictive effectiveness in dynamic FinTech environments requires not only high-performing machine learning algorithms but also governance mechanisms that support continuous model evaluation, monitoring, traceability, and adaptive model selection. The results indicate that periodic model replacement based on statistically validated out-of-time performance can help maintain predictive effectiveness under changing data conditions while supporting model governance and operational reliability. Overall, the proposed framework provides a practical and scalable approach for implementing adaptive credit risk assessment systems that support continuous model governance, data-driven decision-making, and the management of machine learning models in alternative financing environments. Full article