Search Results (4,054)

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

Precise control of feeding rates is critically important in intensive shrimp farming for cost reduction, optimization of farming strategies, and protection of the aquatic environment. However, current assessment of residual feed in feeding trays relies predominantly on manual visual inspection, which is inefficient, highly subjective, and difficult to standardize. The residual feed particles typically exhibit characteristics such as small size, high density, irregular shapes, and mutual occlusion, posing significant challenges for automated visual detection. To address these issues, this study proposes a lightweight detection model named YOLO-Shrimp. To enhance the network’s capability in extracting features from small and dense targets, a novel attention mechanism termed EnSimAM is designed. Building upon the SimAM structure, EnSimAM incorporates local variance and edge response to achieve multi-scale feature perception. Furthermore, to improve localization accuracy for small objects, an enhanced weighted intersection over union loss function, EnWIoU, is introduced. Additionally, the lightweight RepGhost module is adopted as the backbone of the model, significantly reducing both the number of parameters and computational complexity while maintaining detection accuracy. Evaluated on a real-world aquaculture dataset containing 3461 images, YOLO-Shrimp achieves mAP@0.5 and mAP@0.5:0.95 scores of 70.01% and 28.01%, respectively, while reducing the parameter count by 19.7% and GFLOPs by 14.6% compared to the baseline model. Full article

This study presents the development and first manufacturing realization of the ECOTIRE concept, a modular and sustainable tire system featuring a removable tread mechanically interlocked with a reusable casing. The concept aims to reduce rubber waste and improve recyclability by eliminating adhesive bonding and enabling tread replacement. Building on previous experimental and numerical studies that validated the interlocking performance, this work focuses on producing a scaled prototype using a low-cost molding process, which can serve as the basis for accessible and sustainable manufacturing. VMQ silicone rubber was selected as an eco-friendly material due to its durability, thermal stability, and processing versatility. A custom two-part aluminum mold was designed to replicate the optimized interlocking geometry, enabling accurate casting, curing, and assembly. The resulting prototype achieved precise fit, dimensional uniformity, and easy disassembly, confirming the manufacturing feasibility of the ECOTIRE concept and demonstrating its potential to support circular economy strategies through reduced material waste and extended tire component lifetimes. Full article

Residential buildings on the Qinghai–Tibet Plateau face persistent thermal discomfort due to high-altitude climatic extremes. This study investigates how building morphology—including aspect ratio (AR), orientation, and area scaling—affects indoor thermal comfort. Field surveys in Xinghai County informed representative dwelling reconstructions, which were simulated using Ladybug 1.8.0 and Honeybee 1.8.0. Thermal performance was evaluated using PMV, SET, Winter solstice apparent form factor (WSAFF), and surface-to-volume ratio (S/V). Results indicate that compact, near-square forms enhance seasonal thermal stability, with higher WSAFF improving winter solar gains but raising summer overheating risk. South-facing orientations (0° to −30°) optimize summer comfort, while geometric scaling (0.4–2.0) stabilizes indoor temperatures and improves summer PMV and SET, though winter benefits are limited. Comparison of prototype layouts shows that elongated footprints increase vertical variation in comfort, highlighting upper-floor sensitivity to geometry. The study provides a climate-specific framework linking building form with indoor thermal performance. These insights offer practical guidance for sustainable settlement planning and adaptive building design in cold, high-altitude regions. Full article

Under the pressing imperative of achieving “dual carbon” goals and advancing urban low-carbon transitions, understanding how neighborhood spatial environments influence carbon emissions has become a critical challenge for enabling refined governance and precise planning in urban carbon reduction. Taking the central urban area of Xining as a case study, this research establishes a high-precision estimation framework by integrating Semantic Segmentation of Street View Images and Point of Interest data. This study employs a Geographically Weighted XGBoost model to capture the spatial non-stationarity of emission drivers, achieving a median R² of 0.819. The results indicate the following: (1) Socioeconomic functional attributes, specifically POI Density and POI Mixture, exert a more dominant influence on carbon emissions than purely visual features. (2) Lane Marking General shows a strong positive correlation by reflecting traffic pressure, Sidewalks exhibit a clear negative correlation by promoting active travel, and Building features display a distinct asymmetric impact, where the driving effect of high density is notably less pronounced than the negative association observed in low-density areas. (3) The development of low-carbon neighborhoods should prioritize optimizing functional mixing and enhancing pedestrian systems to construct resilient and low-carbon urban spaces. This study reveals the non-linear relationship between street visual features and neighborhood carbon emissions, providing an empirical basis and strategic references for neighborhood planning and design oriented toward low-carbon goals, with valuable guidance for practices in urban planning, design, and management. Full article

Traditional robust model predictive control (RMPC) strategies often face challenges of excessive conservatism and limited dynamic performance, which can hinder their effectiveness in trajectory tracking applications. To overcome these issues, this paper proposes a novel RMPC strategy based on crisscross optimization (CSO). The core innovation lies in a composite control framework that jointly designs a nominal controller and an additional optimized term. The nominal controller, derived from a min-max optimization problem, guarantees the closed-loop stability of the system. Building upon this stable foundation, the CSO algorithm is innovatively employed to search for a more effective control input within the feasible region, thereby actively enhancing the transient performance. The proposed method is validated through two trajectory tracking simulation cases on an angular positioning system in comparison with conventional RMPC. Results demonstrate that the new strategy not only maintains system stability but also significantly reduces the dynamic response time and improves overall control performance, confirming its superiority in mitigating conservatism while achieving better tracking responsiveness. Full article

Digital twin (DT) technology is recognized for its transformative potential to enhance efficiency in the construction process. However, the full potential of DT in construction practices remains largely unrealised. Moreover, few studies explore how DT success factors affect efficiency improvement (EI), energy optimization (EO), and cost control (CC) in the context of economic strength (ES). The study applied a hybrid research method to examine the impact of key DT success factors on EI, EO, and CC under the moderation of ES. After a critical literature review, five key DT success factors were identified. Then, 490 valid questionnaires were analyzed with the Partial Least Squares Structural Equation Model (PLS-SEM) to assess how success factors affect DT effectiveness. This is complemented using extreme gradient boosting (XGBoost) to assess prediction accuracy and understand which factors most influenced EI, EO, and CC. Research shows that ES exerts a significant positive influence on the relationships between most success factors and performance outcomes. High levels of ES enhance the contribution of success factors to performance in EI, EO, and CC. Resource management (RM) has a strong influence on EI and EO, but a weaker influence on CC; process optimization (PO) has the strongest influence on EO, a moderate influence on CC, and the weakest influence on EI; real-time monitoring (R-Tm) primarily affects EI; sustainable design (SD) has a comprehensive and significant regulatory effect on EI, EO, and CC; and predictive maintenance (PM) has a strong influence on both EI and CC. In practice, it offers practical guidance for implementing DT and supports policy and resource planning for building stakeholders. Full article

Improving the thermal performance of building envelopes is an effective approach for reducing energy consumption and carbon emissions in cold and heating-dominated regions. This study presents an experimentally calibrated thermal–economic optimization of external wall insulation systems for residential buildings in Northwest China, using Xi’an as a representative cold–dry continental climate. A guarded hot-box apparatus was employed to measure the steady-state thermal transmittance (U-value) of multilayer wall assemblies incorporating expanded polystyrene (EPS), extruded polystyrene (XPS), and rock wool at different insulation thicknesses. The measured U-values were integrated into a dynamic building energy simulation model (DeST-h), and the simulated energy demand was subsequently evaluated through life-cycle cost (LCC) analysis to identify cost-optimal insulation configurations. The results indicate a nonlinear reduction in heating energy demand with increasing insulation thickness, with diminishing marginal returns beyond approximately 50 mm. Among the investigated materials, XPS exhibits the most favorable thermal–economic performance. For the climatic and economic conditions of Xi’an, a 50 mm XPS insulation layer minimizes total life-cycle cost while reducing annual building energy consumption by approximately 23–24% compared with the uninsulated reference case. This experimentally calibrated framework provides practical and policy-relevant guidance for insulation design and retrofit strategies in cold and dry regions. Full article

The purpose of this article is to develop optimal control strategies for discrete-time multi-agent systems (DT-MASs) with unknown disturbances, with the goal of enhancing their consensus performance and disturbance rejection capabilities. Complex flight conditions, such as the scenario of multi-unmanned aerial vehicle (multi-UAV) maintaining consensus under strong wind gusts, pose significant challenges for MAS control. To address these challenges, this article develops an optimal controller for UAV-based MASs with unknown disturbances to reach consensus. First, a novel improved nonlinear extended state observer (INESO) is designed to estimate disturbances in real time, accompanied by a corresponding disturbance compensation scheme. Subsequently, the consensus error systems and cost functions are established based on the disturbance-free DT-MASs. Building on this, a policy iterative algorithm based on a momentum-accelerated Actor–Critic network is proposed for the disturbance-free DT-MASs to synthesize an optimal consensus controller, whose integration with the disturbance compensation scheme yields an optimal disturbance rejection controller for the disturbance-affected DT-MASs to achieve consensus control. Comparative quantitative analysis demonstrates significant performance improvements over a standard gradient Actor–Critic network: the proposed approach reduces convergence time by 12.8%, improves steady-state position accuracy by 22.7%, enhances orientation accuracy by 42.1%, and reduces overshoot by 22.7%. Finally, numerical simulations confirm the efficacy and superiority of the method. Full article

Hardware-in-the-loop (HIL) simulation is an essential tool for rapid and cost-effective development and validation of power-electronic systems. The primary objective of this work is to validate and fine-tune a PLECS-based HIL model of a single dual active bridge (DAB) DC-DC converter, thereby laying the foundation for building more complex models (e.g., multiple converters connected in series or parallel). To this end, the converter is experimentally characterized, and the HIL model is validated across a wide range of operating conditions by varying the PWM phase-shift angle, voltage gain, switching frequency, and leakage inductance. Power transfer and efficiency are analyzed to quantify the influence of these parameters on converter performance. These experimental trends provide insight into the optimal modulation range and the dominant loss mechanisms of the DAB under single phase shift (SPS) control. A detailed comparison between HIL simulations and hardware measurements, based on transferred power and efficiency, shows close agreement across all the tested operating points. These results confirm the accuracy and robustness of the proposed HIL model, demonstrate the suitability of the PLECS platform for DAB development and control validation, and support its use as a scalable basis for more complex multi-converter studies, reducing design time and prototyping risk. Full article

Developing low-carbon building materials is essential for achieving sustainability in the construction sector. This study proposes a carbon-negative concrete (CNC) system that combines low-carbon binders derived from industrial by-products with carbonated recycled aggregates capable of CO₂ absorption. To enhance particle packing and mechanical performance, the Modified Andreasen–Andersen (MAA) model was adopted for mix design optimization and experimentally validated. The optimized CNC mixture containing 22% coarse aggregate achieved the minimum residual sum of squares between the graded particle distribution and the theoretical MAA curve, as well as the highest strength performance. Compared with a 14% coarse aggregate mixture, the 22% mix exhibited 13.5% and 19.8% increases in compressive strength at 7 and 28 days, confirming the applicability of the MAA model for CNC proportioning. Carbon emission assessment, limited to raw material production, demonstrated significant environmental benefits. CNC incorporating both low-carbon binders and carbonated recycled aggregates reduced total emissions and CO₂ intensity by 87.1% and 86.2%, respectively, compared with ordinary concrete of the same strength grade. Economic evaluation further showed that CNC reduced material cost by 48.1% relative to ordinary concrete. It should be emphasized that the reported CO₂ reduction and negative emission effects are limited to the defined raw material production boundary and do not represent a fully net-negative life cycle. Overall, these results confirm the technical, environmental, and economic feasibility of CNC as a sustainable alternative to traditional concrete. Full article

This article examines an integrated approach to data acquisition and transmission within an intelligent thermal conditioning system for engines and vehicles that operates using thermal energy storage and the digital twin concept. The system is characterized by its use of multiple primary energy sources to power internal subsystems and maintain optimal engine and vehicle temperature conditions. Building on a formalized conceptual model of the intelligent thermal conditioning system, the study identifies key technological features required for implementing complex operational processes, as well as the stages necessary for applying the proposed approach during the design and modernization phases throughout the system’s life cycle. A core block diagram of the system’s digital twin is presented, developed using mathematical models that describe support and monitoring processes under real operating conditions. Additionally, an architectural framework for organizing data collection and transmission is proposed, highlighting the integration of digital twin technologies into the thermal conditioning workflow. The article also introduces methods for adaptive data formation, transfer, and processing, supported by a specialized onboard software-diagnostic complex that enables structured information management. The practical implementation of the proposed solutions has the potential to enhance the energy efficiency of thermal conditioning processes and improve the reliability of vehicles employing thermal energy storage technologies. Full article

Waterborne polyurethane (WPU) and waterborne poly(urethane-urea) (WPUU) dispersions allow safer and more sustainable manufacturing of rechargeable batteries via water-based processing, while offering tunable adhesion and segmented-domain mechanics. Beyond conventional roles as binders and coatings, WPU/WPUU chemistries also support separator/interlayer and polymer-electrolyte designs for lithium-ion and lithium metal systems, where interfacial integrity, stress accommodation, and ion transport must be balanced. Here, we review WPU/WPUU fundamentals (building blocks, dispersion stabilization, morphology, and film formation) and review prior studies through a battery-centric structure–processing–property lens. We point out key performance-limiting trade-offs—adhesion versus electrolyte uptake and ionic conductivity versus storage modulus—and relate them to practical formulation variables, including soft-/hard-segment selection, ionic center/counterion design, molecular weight/topology control, and crosslinking strategies. Applications are reviewed for (i) electrode binders (graphite/Si; cathodes such as LFP and NMC), (ii) separator coatings and functional interlayers, and (iii) gel/solid polymer electrolytes and hybrid composites, with a focus on practical design guidelines for navigating these trade-offs. Future advancements in WPU/WPUU chemistries will depend on developing stable, low-impedance interlayers, enhancing electrochemical behavior, and establishing application-specific design guidelines to optimize performance in lithium metal batteries (LMB). Full article

In this study, a procedure to build Bayesian optimal designs using utility functions and exploiting existing data is proposed. The procedure is illustrated through a case study in the field of reliability, by applying a hierarchical Bayesian model and performing Markov Chain Monte Carlo simulations. Two innovative contributions are introduced: (i) the definition of specific utility functions that involve several key issues and (ii) the use of observational data. The use of observational data makes it possible to build the optimal design without additional costs for the company, while the definition of the utility functions accounts for the specific characteristics of the reliability study. Features like model residuals, i.e., discrepancies between observed and predicted response values, and the costs of the electronic component are addressed. Costs are also weighted considering the environmental impact. Satisfactory results are obtained and subsequently validated through an in-depth sensitivity analysis. Full article

Topology optimization is a powerful and efficient design tool, but the structures obtained by element-based topology optimization methods are often limited by fuzzy or jagged boundaries. The smooth-edged material distribution for optimizing topology algorithm (SEMDOT) can effectively deal with this problem and promote the practical application of topology optimization structures. This review outlines the theoretical evolution of SEMDOT, including both penalty-based and non-penalty-based formulations, while also providing access to open access codes. SEMDOT’s applications cover diverse areas, including self-supporting structures, energy-efficient manufacturing, bone tissue scaffolds, heat transfer systems, and building parts, demonstrating the versatility of SEMDOT. While SEMDOT addresses boundary issues in topology optimization structures, further theoretical refinement is needed to develop it into a comprehensive platform. This work consolidates the advances in SEMDOT, highlights its interdisciplinary impact, and identifies future research and implementation directions. Full article