Search Results (30,907)

Infrastructure drainage projects play a critical role in urban development but are increasingly exposed to environmental, operational, and climate-related risks that challenge their long-term sustainability. Despite this, decision-makers continue to lack risk-informed, structured methods to assess sustainability performance in an uncertain environment. In order to facilitate evidence-based decision-making and sustainable risk management, this study suggests a risk-informed sustainability index for infrastructure drainage projects. The study first points out a weakness in the methods currently used for sustainability assessments, specifically the lack of risk-sensitive, standardized frameworks designed for drainage infrastructure systems. Altogether, 28 sustainability indicators are identified, with 22 indicators retained after the application of fuzzy set theory criteria. The sustainability index is developed by normalizing, weighting, and combining these indicators using a multi-criteria decision analysis (MCDA) method. To show the usefulness and practicality of the suggested approach in assessing sustainability performance and pinpointing risk-critical improvement areas, it is used for a long-term infrastructure drainage project. In order to improve infrastructure resilience, the findings emphasize the significance of early integration of sustainability and risk considerations, stakeholder engagement, and ongoing performance monitoring. The suggested approach offers a flexible and transferable framework for risk-informed decision-making, assisting engineers, project managers, and policymakers in enhancing the resilience and sustainability of infrastructure drainage systems. Full article

Background: Autologous fat grafting is widely used in reconstructive, aesthetic and regenerative surgery and often requires repeated applications. Cryopreservation of lipoaspirate enables autologous fat banking and off-the-shelf availability; however, its clinical implementation is limited by freezing-induced tissue injury, regulatory requirements and uncertainties regarding the optimal preservation protocol. Glycerol is a biocompatible cryoprotective agent with promising preliminary data. Nevertheless, the optimal concentration for lipoaspirate cryopreservation remains unknown. The aim of this study was to determine the optimal glycerol concentration for preservation of adipose tissue processed according to the Cell-Enriched Lipotransfer (CELT) protocol in clinically relevant volumes under GMP-compatible conditions. Methods: Lipoaspirates from 10 patients were processed by centrifugation according to the CELT protocol and allocated into experimental groups: fresh unfrozen control, frozen samples without cryoprotectant, frozen samples with PBS, and frozen samples supplemented with glycerol in concentrations ranging from 10% to 60%. Samples were cryopreserved using a controlled freezing rate at a temperature of −80 °C for 24 h. Large-volume cryopreservation was additionally performed with the best concentration of glycerol. Post-thaw tissue quality was assessed by resazurin assay of whole tissue, stromal vascular fraction (SVF) cell live/dead counting, and resazurin assay after short-term cell culture. Results: Glycerol supplementation improved post-thaw tissue viability compared with cryopreservation without cryoprotectant or with PBS alone. An optimal concentration range between 10% and 30% glycerol was identified, with highest preservation of metabolic activity and surviving cell yield observed at 20%. Higher glycerol concentrations resulted in a marked decline in tissue quality. Cryopreservation in large volume was feasible and did not impair post-thaw viability compared with small-volume samples. Conclusions: Glycerol-based cryopreservation allows effective and GMP-compatible preservation of human lipoaspirate. An optimal glycerol concentration range was identified, enabling large-volume fat banking without compromising tissue quality. This protocol provides a clinically applicable strategy for autologous fat storage and may facilitate repeated reconstructive and regenerative treatments. Full article

To address the challenges of coordinated optimization in building integrated energy systems (IES) under the dual-carbon targets—characterized by strong multi-energy coupling, significant uncertainty in renewable generation, and stringent safety constraints—a novel safe deep reinforcement learning algorithm, Safe-DDPG, is proposed. Traditional deep reinforcement learning methods often suffer from high constraint-violation risk and limited policy reliability due to coupled objectives in building IES optimization. To overcome these limitations, a dual-channel critic architecture is designed to independently evaluate and decouple economic and safety objectives. In addition, a dynamic safety–penalty mechanism based on logarithmic barrier functions is introduced, together with an adaptive exploration strategy, enabling dynamic balancing between economic cost and constraint satisfaction according to system states during training. Experimental results demonstrate that, compared with mainstream algorithms, Safe-DDPG achieves substantial improvements across multiple key performance indicators: safety violations are reduced by up to 96.7%, average daily operating costs decrease by 18.5%, and cumulative rewards increase by more than 30%. Ablation studies further confirm the effectiveness and necessity of each core component. Two DRL methods from reference papers are reproduced, and their performance is compared with the proposed method in the existing experimental results, showing that the proposed method has significant advantages in reward value and economic cost. This work provides a safe, reliable, and efficient reinforcement-learning-based approach for optimization and scheduling of building energy systems under complex operational constraints. Full article

To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrated solution combining a neighborhood feature aggregation-based graph attention network (NFA-GAT) and multi-agent deep deterministic policy gradient (MADDPG). First, the D-RTPD problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), which effectively captures the stochastic game characteristics of multi-regional agents and the partial observability of grid states. Second, the NFA-GAT is designed to enhance agents’ perception of grid operating states: by introducing a spatial discount factor, it realizes rational aggregation of multi-order neighborhood information while modeling the attenuation of electrical quantity influence with topological distance. Third, a prior-guided mechanism is integrated into the MADDPG framework to eliminate constraint-violating actions by setting their actor logits to negative infinity, improving training efficiency and strategy reliability. Simulation validations on the IEEE 118-bus test system (75.2% RES installed capacity ratio) show that the proposed method achieves efficient training convergence. Compared with the multi-layer perceptron (MLP) structure, it attains higher cumulative reward values and scenario win rates. When compared with traditional model-driven (ADMM) and data-driven (Q-MIX) methods, the proposed method balances solution efficiency, operational safety (98.7% maximum line load rate, zero power flow violation rate), and economic performance ($12,845 daily dispatch cost), providing a reliable technical support for D-RTPD under high-proportion RES integration. Full article

In current IGBT reliability assessment methods, there is a lack of modeling for overstress failures and insufficient consideration of epistemic uncertainty. To address this, this paper proposes a novel reliability assessment method based on belief reliability theory and uncertainty theory. By establishing an IGBT reliability domain model and an external-stress model, a margin-evaluation framework integrating multi-operating-condition characteristics is constructed. Furthermore, a first-order information-based belief reliability calculation algorithm is developed. This method, for the first time, incorporates overstress failures into a quantitative assessment framework and overcomes the inaccuracy of traditional methods under small-sample testing scenarios, providing a technical basis for IGBT device selection and operational reliability assurance in power electronic systems. Full article

Recent space missions demand higher pointing accuracy, smoother attitude transitions and lower energy consumption than those typically achievable with conventional control approaches. This motivates the exploration of intelligent and nonlinear control methods. The FuzzyBuzz experiment investigates the application of fuzzy logic for spacecraft attitude control using NASA’s Astrobee robotic system aboard the International Space Station. Unlike traditional control methods, fuzzy logic introduces a rule-based approach capable of handling uncertainties and nonlinearities inherent in space environments, making it particularly suited for autonomous operations in microgravity. The objective of FuzzyBuzz is to evaluate the effectiveness of fuzzy controllers compared to traditional linear ones, such as Proportional–Integral–Derivative (PID) and $H_{\infty }$ controllers. In addition, a comparison with a nonlinear controller based on a Model Predictive Control (MPC) strategy is considered. The controllers will be tested through predefined attitude maneuvers, evaluating precision, energy efficiency, and real-time adaptability. This work presents the design of the FuzzyBuzz experiment, including the software architecture, simulation environment, experiment protocol, and the development of a fuzzy logic-based attitude control system for Astrobee robots. The proposed fuzzy controller and a PID controller are optimized using a Multi-Objective Particle Swarm Optimization (MOPSO) method, providing a range of operational points with different trade-offs between two metrics, related to convergence time and energy consumption. Results show that the PID controller is better suited for scenarios demanding low convergence times, whereas the fuzzy controller provides smoother responses, reduced steady-state error, and maintains convergence under significant parametric uncertainties. Results from $H_{\infty }$ and MPC controllers will be reported once the in-orbit experiment is performed. Full article

We propose a Cognitive Load Theory (CLT)-informed attention mechanism for transformer-based text classification. The proposed attention mechanism computes a per-token cognitive-load signal—derived from attention entropy, margin-based classification uncertainty, and optional inverse document frequency—and maps this signal to a learnable attention “budget” that scales outgoing attention mass during decoding. Unlike architectural efficiency techniques such as Multi-Query or Grouped-Query Attention, the CLT mechanism requires no structural modifications and introduces only modest per-step computational overhead while preserving full compatibility with standard transformer architectures. Experiments across four datasets (IMDB, AG News, SST-2, and DBpedia) show that CLT-informed attention achieves accuracy comparable to or exceeding a fixed-budget baseline while delivering consistently lower test loss, faster convergence to the best validation checkpoint, reduced attention entropy, and strong alignment between cognitive load and attention mass. Among all variants, an entropy-only load signal yields the most stable and consistent performance across datasets. These results demonstrate that lightweight, cognitively motivated constraints can structure transformer attention while maintaining or improving downstream classification performance. Full article

The problem of fault-tolerant attitude tracking control for the civil aircraft with model uncertainties and actuator faults is studied. A robust multiple inversion-based incremental nonlinear dynamic inversion (RMI-INDI) fault-tolerant control method is proposed for the problem. Firstly, considering that the higher-order term is neglected in the INDI method, an RMI method is proposed to deal with the higher-order term and model uncertainties of the INDI control. Secondly, to achieve the optimal control parameters for the INDI controller, a reinforcement learning (RL) method is suggested, where a Deep Deterministic Policy Gradient (DDPG) algorithm with a smooth reward function is designed. Finally, performances of the proposed RL-RMI-INDI fault-tolerant controller are demonstrated by using two scenario simulations. Compared with the SMC control, RMI-NDI control and INDI control without RL, tracking errors and overshoots are greatly reduced by the proposed RL-RMI-INDI controller for attitude tracking missions, even under model uncertainties and actuator faults. Full article

As typical underactuated systems, overhead cranes are widely utilized in heavy-load transportation. However, their strong nonlinear coupling and underactuated characteristics complicate precise positioning and payload swing suppression. Furthermore, model uncertainties and external disturbances in practical environments increase control complexity and degrade system performance. To address these issues, this paper develops a trajectory tracking control scheme based on time delay estimation (TDE). Specifically, some transformations are made for the dynamic model and the TDE mechanism is used to estimate unknown nonlinear dynamics and external disturbances. Then, a sliding mode trajectory tracking controller, along with the TDE mechanism, is proposed for the trajectory tracking control and uncertainties estimation of the overhead crane system. Rigorous mathematical analysis is provided to demonstrate the asymptotic stability of the closed-loop system. Finally, simulation results verify the effectiveness of the proposed method in comparison with the existing control methods. Full article

As nuclear energy expands, nuclear emergency response systems increasingly exhibit strong human–machine–environment (H–M–E) coupling, long-duration operations, and multi-department coordination, in which minor disturbances can be amplified by feedback loops into cascading failures and loss of situational control. To address the inability of conventional static and linear methods to represent dynamic risk evolution and chaotic uncertainty, this study proposes an integrated “risk network–chaotic evolution–synchronization control” framework. Based on 12-year-old on-site comprehensive drill reports from a Chinese nuclear power base, we construct a directed H–M–E risk network in a semi-quantitative, qualitative–quantitative manner and identify critical nodes using a composite betweenness–PageRank risk metric. We further abstract the system into a three-dimensional nonlinear coupled dynamical model; phase portraits, Lyapunov exponents, and bifurcation analysis confirm threshold effects, period-doubling routes, and chaotic attractors, revealing nonlinear amplification under strong coupling. Finally, an adaptive chaotic synchronization controller driven by network coupling strength is designed. Simulations show all strategies suppress chaos and achieve synchronization, while the machine-dominated strategy offers the best speed–energy trade-off for emergency resource allocation. Full article

The development of next-generation terahertz (THz) transmitters and receivers based on 3D stacked packaging technology relies heavily on the integration of high-performance waveguide directional couplers. This paper presents an accurate and efficient method based on the mode-matching method (MMM) for the rapid analysis of a branch waveguide coupler fabricated through a silicon-based 3D stacking process. In contrast to the traditional method using the finite-element method (FEM) in HFSS, which is cumbersome and time-consuming, the proposed method offers orders-of-magnitude speed improvement. It is especially well-suited for large-scale uncertainty error analysis and statistical evaluation of THz waveguide couplers and related components. This theoretical MMM is validated through an experiment by characterizing a deep reactive ion etching (DRIE) fabricated and 3D stacked 220 GHz waveguide coupler. Full article

Exogenous shocks can affect equity markets by changing volatility and cross-market co-movement. This study examines how two U.S.-centred events, treated as different shock types, influence time-varying conditional correlations between the U.S. stock market and other G7 markets. The uncertainty shock is proxied by the U.S. presidential election of 5 November 2024, while the action shock is proxied by President Trump’s 2 April 2025 announcement of reciprocal tariffs. Using daily log returns for the S&P 500 and leading indices for Canada, France, Germany, Italy, Japan and the United Kingdom, we cover January 2010 to July 2025 and assess event effects using correlation paths for June 2024–June 2025 and symmetric ±30-day windows. We employ a DCC-GARCH model to jointly estimate conditional variances and dynamic correlations for six USA-G7 pairs. The results indicate persistent correlation dynamics, with Canada/USA the highest and Japan/USA the lowest. Election-related uncertainty is associated with declines in correlation for European pairs, suggesting temporary decoupling, while Canada and Japan show only small changes. By contrast, the tariff action shock significantly increases conditional correlations across all country/USA pairs, implying stronger market synchronisation, with the largest increases in North America and parts of Europe, and the smallest adjustment in Japan. Full article

Utility-scale photovoltaic (PV) plants rely on supervisory control and data acquisition (SCADA) streams for performance verification, yet high-frequency measurements are routinely affected by sensor dropouts, intermittency, and operating-state transitions that bias regression-based capacity estimates. This study evaluates a reproducible SCADA processing workflow for capacity-style reporting and a complementary soiling–clean temperature prediction model using data from a documented October 2022 test window (5 s SCADA aggregated to 1 min). The following three filtering approaches are compared: (i) naïve thresholds (Baseline A), (ii) deterministic stability screening using ramp-rate and rolling-variability constraints (Baseline B), and (iii) an optional residual-based outlier trimming step (Method C). Capacity is estimated via a multivariate regression evaluated on a fixed-size reporting-condition subset (RC197) with day-coverage constraints. All methods achieved high fit quality on RC197 ($R^2\approx 0.99$), with Baseline B improving error and uncertainty over Baseline A (RMSE 2.05 vs. 2.18 MW; $U_{95}$ 0.97% vs. 1.03%) while preserving day coverage; Method C yielded the lowest in-sample RMSE (1.89 MW) but reduced day coverage. For temperature prediction, a baseline-plus-residual learning formulation substantially improved leave-one-day-out performance, reducing MAE/RMSE from 2.99/3.76 °C to 1.43/1.80 °C and increasing $R^2$ from 0.60 to 0.91. The results highlight trade-offs between fit tightness and representativeness in capacity-style filtering and demonstrate residual learning is an effective approach for SCADA-based thermal characterization. Full article

Master–slave bilateral teleoperation systems face several practical challenges, including model uncertainties, time-varying communication delays, and environment-induced force disturbances. To address these issues, this paper proposes an adaptive super-twisting sliding-mode control scheme to achieve high-precision position tracking and real-time force-feedback synchronization. First, joint-space dynamic models are established for both the master and the slave manipulators, and a passive impedance model is adopted to characterize the interaction dynamics at the operator–master and environment–slave interfaces. Second, to attenuate measurement noise in the environment interaction force, a first-order low-pass filter is used to preprocess the raw force measurements, and a radial basis function neural network (RBFNN) is employed to approximate the environment torque online. Furthermore, a super-twisting sliding-mode controller is developed and combined with an adaptive law to compensate online for system uncertainties, including dynamic parameter variations and environment-induced force disturbances. The stability of the resulting closed-loop system is rigorously analyzed using Lyapunov stability theory. Finally, the effectiveness of the proposed method is validated through numerical simulations, virtual experiments conducted in the MuJoCo physics engine, and real-world hardware experiments. The results show that the proposed strategy achieves accurate position synchronization and force tracking while maintaining stable haptic interaction in the presence of bounded time-varying delays, parameter uncertainties, and external disturbances. Full article

Early detection of battery degradation is essential for ensuring the safety and reliability of electric vehicle (EV) systems under real-world operating variability. This paper proposes a physics-guided multi-sensor learning framework, termed SensorFusion-Former (SFF), for early warning of short-term EV battery performance degradation. The proposed approach integrates a physics-based baseline model for operational normalization, a multi-sensor fusion attention mechanism to model cross-modality interactions, and a lightweight transformer architecture for efficient temporal representation learning. Weak supervision is derived from physics-consistent residual analysis with temporal smoothing, enabling scalable training without dense manual annotations. To support reliable deployment, evidential uncertainty modeling and conformal calibration are incorporated to obtain statistically controlled decision thresholds. Experiments conducted on a real driving cycle dataset from IEEE DataPort demonstrate that SFF consistently outperforms classical machine learning methods, deep neural networks, and standard transformer models in terms of early-warning lead time, false alarm rate, and inference efficiency while maintaining competitive discriminative performance. Cross-scenario evaluations under diverse thermal conditions further confirm the robustness and generalization capability of the proposed framework. Full article