Search Results (2,102)

Building energy consumption accounts for 30% of global energy use, making building management pivotal to achieving global sustainability. Occupants have profound impacts on the building environment. Incorporating occupant-related factors into the environmental control process is essential for optimizing the efficiency of building management systems (BMSs), which thus gives rise to the concept of occupant-centric control (OCC). Conventional methods rely on simplified models and fixed schedules that fail to satisfy environmental control and occupant requirements, while constructing credible models places strict requirements on the dataset. In this paper, we propose a Model-Aware Predictive Control (MAPC) framework that can construct credible models with limited data and provide room-level control strategies to optimize the trade-off between occupant comfort and energy consumption. The technological innovations of this research are twofold. On the one hand, we design a model construction and fine-tuning method that combines data-driven subspace projection approach with physical priors that can construct credible thermal dynamic models with limited data. On the other hand, to balance the potential conflicts between enhancing occupant comfort and saving energy, we present a hierarchical decision-making mechanism that enables adaptive multi-objective room-level control considering dynamic occupant comfort requirements and energy usage. The experimental results obtained on an EnergyPlus-based simulation dataset and a publicly available dataset demonstrate that MAPC can provide room-level control strategies based on dynamic occupant requirements and user preferences and achieve superior trade-offs between occupant comfort and energy consumption. The ablation experiments also demonstrated the superiority of MAPC in constructing reliable models on limited datasets. MAPC provides pivotal support for the advancement of the intelligent buildings and sustainable indoor environment. Full article

Indoor air quality has a significant impact on occupant health, comfort, and productivity in residential and commercial indoor environments. This paper proposes an IoT-edge enabled deep–fuzzy hybrid framework for real-time IAQ prediction and adaptive control. The proposed system integrates IoT-based environmental sensing, Temporal Fusion Transformer-based multivariate forecasting, knowledge distillation, edge-deployed Bi-LSTM inference, and Mamdani fuzzy logic control within a unified IAQ management architecture. A composite Comfort Risk Index is introduced to combine environmental parameters and occupant discomfort feedback into a single adaptive control indicator. Experimental evaluation under varying indoor conditions demonstrated strong forecasting performance, with prediction accuracies reaching 96.3% for CO₂ and 95.7% for PM_2.5 prediction, while reducing inference latency from 575 ms to 295 ms. Comparative analysis against baseline threshold-based control strategies further indicated improved comfort stability, smoother actuator behavior, and reduced estimated actuator operating intensity during deployment. The proposed framework also demonstrated resilient operation under simulated sensor-failure conditions while maintaining low computational overhead suitable for resource-constrained IoT-edge environments. Overall, the results indicate that combining lightweight deep learning models with interpretable fuzzy control can provide an effective, scalable, and energy-aware solution for intelligent real-time IAQ optimization in smart indoor environments. Full article

This paper provides a systematic synthesis of recent developments in physics-informed neural networks (PINNs) applied to aerospace engineering, with an emphasis on their role in physically consistent surrogate modeling, forward simulation, and inverse parameter estimation. Using a PRISMA-based methodology, the study surveys peer-reviewed works published between 2017 and 2025 across aviation- and space-related domains, including aerodynamics, structural mechanics, aeroelasticity, propulsion, control, structural health monitoring, satellite-orbit prediction, space-debris collision avoidance, and spacecraft radiation-impact modeling. The analysis shows that embedding governing equations, boundary conditions, and observational data into composite loss functions enables PINNs to improve predictive consistency, reduce dependence on dense simulation or experimental datasets, and support parameter identification under sparse or noisy measurements. Attention is given to architectural variants such as XPINNs, cPINNs, gPINNs, operator-learning approaches, and hybrid PINN-CFD/FEM formulations, as well as to training strategies based on adaptive sampling, domain decomposition, transfer learning, and dynamic loss weighting. Reported benefits include reduced approximation error, improved convergence in selected high-gradient or multiphysics problems, and enhanced interpretability compared with purely data-driven models. At the same time, the review identifies persistent open challenges, including scalability to large aerospace domains, sensitivity to loss-weighting and collocation strategies, limited robustness under noise and uncertainty, high computational cost, and the lack of standardized aerospace benchmarks. Overall, the review highlights PINNs as a promising but still developing framework for fast, interpretable, and physically consistent modeling of aircraft and spacecraft systems. Full article

The rapid development of generative artificial intelligence (GenAI) is transforming both the health sector and health profession education, although AI-based systems have existed in these sectors for decades. GenAI-integrated virtual agents and simulations now play novel and critical roles in simulation-based education and are potential solutions to enhance the adaptability of health profession education. This systematic review was conducted using the PRISMA guidelines and explores how GenAI-integrated virtual agents and simulations are being applied in health profession education, with a particular focus on their educational impact, technical features and functionalities, and current limitations. This review aims to synthesize the pedagogical value and technological design of GenAI-integrated simulations and to inform health professionals and educators about the effective use, impact, and challenges of GenAI in health education simulations. A total of 16 papers were reviewed. Results show that GenAI-integrated virtual agents and simulations have potential to enhance clinical communication, diagnostic accuracy, multilingual interactions, and learner confidence for health profession education. Related theoretical, technological, and educational implications of generative AI-integrated virtual agents and simulations are discussed to inform future design and application. Limitations include insufficient educational effectiveness, response accuracy issues, and unresolved ethical and privacy concerns. Future studies should focus on long-term efficacy, ethical considerations, and optimizing AI–human collaboration in various health profession education contexts. Full article

The vertical landing of quadrotor unmanned aerial vehicles (UAVs) involves highly transient impact dynamics that generate significant vibrations on the UAV body, particularly under uncertain touchdown conditions such as uneven terrain, asymmetric ground contact, and high-impact landing. In this study, a robust semi-active vibration control framework is proposed for a quadrotor UAV equipped with a four-point soft landing gear system. The UAV is modeled as a three-degree-of-freedom rigid body including heave, pitch, and roll motions, while each landing gear leg is represented by an equivalent spring-damper mechanism with adaptively controllable damping characteristics. To evaluate the effectiveness of the proposed framework, PID (Proportional–Integral–Derivative), GA-PID (Genetic Algorithm-Based Proportional–Integral–Derivative), Fuzzy–PID (Fuzzy Logic-Based Proportional–Integral–Derivative), and ANFIS-PID (Adaptive Neuro-Fuzzy Inference System-Based Proportional–Integral–Derivative) controllers are comparatively investigated under five different landing scenarios. The nonlinear touchdown dynamics are implemented in the MATLAB/Simulink environment using a state-space-based simulation model. The results demonstrate that intelligent adaptive control methods significantly improve landing stability and vibration attenuation compared to the conventional PID controller. Among all methods, the ANFIS-PID controller achieved the best overall performance. Under the most severe landing condition, the peak vertical displacement was reduced from 0.114 m to 0.025 m, while the maximum pitch and roll angles decreased from approximately 11° to nearly 2°. Additionally, the settling time was reduced from nearly 10 s to below 3 s. Full article

In the context of global climate change and rapid urbanization, urban outdoor thermal environment issues in summer have become increasingly severe. Shading has been widely recognized as an effective strategy for improving outdoor thermal comfort, yet existing evaluation methods still suffer from limitations in adaptability and accuracy. Taking Chongqing, a typical hot-humid city in China, as a case study, this paper proposes an evaluation method that accounts for human thermal adaptation, introducing three complementary indicators, namely Universal Thermal Climate Index Load (UTCIL), cumulative UTCIL (cUTCIL), and Heat Stress Duration (HSD). Focusing on four shading-related urban canyon morphological factors—orientation, aspect ratio (H/W), building asymmetry, and leaf area index (LAI) of street trees—a series of simulation scenarios was designed to quantitatively explore their impacts on summer outdoor thermal comfort. The applicability and reliability of the ENVI-met model for block-scale outdoor thermal environment simulation were validated by comparing field-measured microclimate data with simulation results. The findings demonstrate that all four morphological factors substantially influence the outdoor thermal environment. Canyon orientation considerably affects thermal comfort, with a 30° clockwise deviation from the north–south yielding optimal conditions, whereas the east–west (90°) orientation produces the poorest thermal environment, with a maximum UTCI of approximately 48.9 °C. For aspect ratio, thermal comfort improves continuously as H/W increases, with the benefit stabilizing beyond H/W = 3.5. Building asymmetry also plays a notable role: raising building height on one side can effectively reduce outdoor thermal stress, and canyons with taller west-side buildings show better thermal performance under the same asymmetry ratio. Furthermore, street tree shading and aspect ratio exhibit a synergistic cooling effect, where high LAI (e.g., 4.77) reduces UTCI_max by approximately 1.8 °C at H/W = 1, but this benefit diminishes as H/W increases. The optimal outdoor thermal environment is achieved through the combination of a high aspect ratio and high LAI. These findings provide a quantitative basis and design references for optimizing outdoor thermal comfort in Chongqing. In addition, the quantitative evaluation proposed method can offer a methodological reference for other hot-humid regions. Full article

The prevention and control of forest fires are of vital importance for ecological security. The efficiency and environmental friendliness of fire-extinguishing agents remain the core focus of current research. This paper reviews the research progress and fire extinguishing mechanisms of three types of forest-fire-extinguishing agents, namely, foam extinguishing agents, gel extinguishing agents, and fire-resistant barrier materials. These three types of extinguishing agents work together to extinguish fires through three principles: isolating combustibles, reducing the oxygen concentration, and lowering the temperature. This paper systematically summarizes the performance evaluation methods, covering the cooling rate, fire extinguishing time, and re-ignition rate, and combines numerical simulation and field experiments to build a multi-scale verification system. The environmental assessment focuses on biodegradability, the ecological toxicity to soil and water systems, and the impact on plant germination and biodiversity. It clearly indicates that degradability, low toxicity, and low residue are key development directions. The current research still needs to further deepen in aspects such as long-term stability, adaptability to complex terrains, and ecological risk assessment during the life cycle. In the future, priority should be given to promoting green, multi-functional, and precise application technologies to provide solid support for scientific forest fire prevention and ecological protection. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

The widespread dissemination and societal impact of fake news underscore the critical need for effective detection. Existing methods remain limited, as they often fail to learn joint representations from multi-modal data and rely heavily on complete social interaction signals. Such information is frequently unavailable in practice, especially during the early propagation stages. To address early fake news detection in social media, this paper proposes a hierarchical cross-modal fusion framework with structured LLM-simulated social interaction (HCF-LSIM). The framework employs a progressive cross-modal attention mechanism to systematically align semantic representations across multiple levels, integrating textual, thematic, and visual features. Additionally, HCF-LSIM designs an LLM-powered social interaction simulator that generates structured triplets from adapted user profiles, effectively compensating for missing real-time interaction data. Experiments on public benchmarks demonstrate strong performance, with accuracies of 93.5% on Weibo and 87.2% on X (formerly Twitter), ranking first on Weibo and second on Twitter. Full article

Bayesian estimation of nonlinear mixed-effects models typically relies on Markov-Chain Monte Carlo (MCMC) methods due to the intractability of the posterior distribution. While widely used for longitudinal data with missing observations, the performance of MCMC algorithms is often taken for granted, despite their critical impact on inference quality. This paper investigates MCMC-based estimation for Bayesian nonlinear mixed-effects models with missing data, focusing on convergence behavior and computational efficiency. We propose a hybrid sampling framework that combines Gibbs sampling with Metropolis–Hastings (MH) and adaptive MH algorithms to improve mixing and stability. Convergence diagnostics, the effective sample size, and computational performance are systematically evaluated. Simulation studies assess the effects of the iteration length, burn-in proportion, and sample size, and the methodology is illustrated using orthodontic growth data and the Treatment of Lead-Exposed Children (TLC) trial. Full article

Tool wear significantly impacts the productivity and efficiency of sheet metal stamping operations, particularly in high-volume progressive die applications. This study presents an iterative calibration framework for Archard’s wear model, tailored to industrial stamping processes. The proposed methodology integrates finite element simulations with experimentally measured wear data obtained from production components, enabling data-driven calibration of the wear coefficient $K_{sim}$. The framework achieves high predictive accuracy, with deviations of 1.4–3.7% between simulated and optically measured wear depths and localization, after more than 15 million strokes. Rapid convergence is obtained within two to three calibration cycles, significantly reducing computational effort while maintaining physical fidelity. The simulation setup incorporates detailed modelling of contact pressure, sliding velocity, and stress distribution, validated using optical surface measurement systems and coordinate-based metrology. Beyond the specific industrial case, the framework demonstrates transferability to other sheet metal forming processes, such as bending, blanking, and coining, by leveraging physically based parameter adaptation across comparable pressure–velocity regimes. The approach enables predictive wear modeling in data-scarce environments and supports early-stage tool design workflows. Overall, the proposed methodology bridges the gap between empirical calibration and generalized simulation, contributing both methodological rigour and practical applicability to manufacturing science. Full article

With the increasing integration of grid-friendly concentrated solar power (CSP) plants into high-proportion new energy power systems, the system is confronted with challenges such as insufficient regulation capability and power balance difficulties. To address these issues, this paper proposes a multi-stage optimal regulation strategy for CSP–wind power systems based on adaptive step-size model predictive control (ASMPC), from the perspectives of tapping transmission line current-carrying capacity and coordinating system regulation resources. This strategy first establishes an electro–thermal–mechanical coupling dynamic line rating (DLR) model to characterize line safety margins, then constructs an optimization decision-making model aiming at minimizing the total multi-stage coordinated scheduling cost and adopts ASMPC to dynamically adjust the control step size, effectively improving scheduling accuracy and real-time correction capability. Simulation results based on the modified IEEE 39-bus system show that the proposed method reduces the total system cost by 26.8% (nearly 30%), increases the CSP unit output ratio by 27.9%, and decreases the average grid load rate by 12.6 percentage points. The proposed strategy can effectively mitigate the impact of source-load uncertain fluctuations and significantly improve the economic operation level of the CSP–wind power combined system. Full article

Effective traffic management relies on the availability of high-quality traffic data to support real-time decision-making for optimizing traffic flow, enhancing safety, and reducing environmental impacts. This study aims to address the lack of integrated and operational approaches for traffic data quality management by proposing a scalable and adaptable framework for the systematic assessment and enhancement of traffic data. The framework consists of four interconnected layers, including data ingestion, data quality assessment, data imputation and correction, and a real-time alerting mechanism. Its applicability is demonstrated through a real-world case study on traffic signal control plan selection, using sensitivity and simulation-based analyses in SUMO. The results indicate that degraded data quality, particularly due to missing or invalid records, can significantly affect system behavior, leading to suboptimal decisions and reduced traffic performance. These findings highlight the importance of continuous and systematic data quality monitoring as a critical component for reliable and efficient traffic management systems. Full article

The blockchain-based Automated Market Maker (AMM) mechanism establishes a multilateral trading market for multi-source homogeneous data assets. Its advantage lies in realizing algorithmic dynamic pricing and automated circulation through decentralized liquidity pools, effectively avoiding the single-point failure issues and pricing inefficiencies associated with traditional centralized platforms, while significantly improving the trading efficiency and value conversion potential of data assets. However, in high-frequency, large-scale, multilateral data trading scenarios, these AMM liquidity pools face intensified Impermanent Loss (IL) that cannot be easily addressed by conventional risk mitigation approaches, necessitating domain-specific tailored solutions. To address this issue, our study proposes a blockchain on-chain liquidity pool-based data trading market model. Through mathematical modeling and simulation experiments, we quantify how trader behavioral characteristics, including price sensitivity differentials, heterogeneous trading frequencies, and trading size variations, impact the value of AMM liquidity pool. On this basis, we propose a Behavior-Aware Real-time Trading-driven Impermanent Loss optimization method (BART-IL), which uses multi-factor scoring to dynamically sequence trades, generating low-impermanent-loss execution paths to mitigate risks for Liquidity Providers (LPs). Experimental results demonstrate that BART-IL reduces IL for LPs, capping maximum loss at 25.6% in large-scale trading scenarios and achieving over 40% loss reduction in high-frequency-dominant markets. Accordingly, the method substantially lowers the overall risk of data trading. This research addresses the adaptability bottleneck of AMM mechanisms for non-standard assets. By integrating innovations in mechanism design and algorithm optimization, we construct a low-cost blockchain-based decentralized data trading framework with enhanced fairness, offering important implications for ensuring the robustness and attractiveness of data trading platforms. Full article

Robust quadruped locomotion in real-world environments remains challenging because external disturbances, sensor noise, and model uncertainties are coupled with intermittent foot–ground contact. Reinforcement learning has shown strong capability in generating agile locomotion, but many existing methods handle unobserved disturbances through implicit latent representations or domain randomization. This paper presents a disturbance-aware locomotion framework that integrates state and disturbance estimation with learning-based control. The core component is a deep generalized-momentum-based Kalman filter, which combines generalized momentum disturbance modeling with adaptive covariance inference to estimate the base velocity and external disturbance force. These physically meaningful estimates are incorporated into the policy observation space, reducing the gap between privileged simulation states and deployable onboard observations. The framework was evaluated in a simulation and on a quadruped robot platform under disturbance and outdoor locomotion scenarios. Compared with the baseline and ablated variants, the proposed method reduced estimation and tracking errors, limited impact-induced torque peaks, and improved locomotion success rates under the evaluated conditions. The results suggest that explicit disturbance estimation can complement a latent adaptation for quadruped locomotion under impact-rich conditions. Full article