A novel distributed control framework is developed for the optimal energy management (OEM) of microgrids, integrating a predefined-time control method and an event-triggered (ET) mechanism. Unlike conventional finite-time or fixed-time stability approaches, the upper bound of the convergence time is directly predefined by users, independent of initial conditions and system parameters. Stability and predefined-time attainment are guaranteed via Lyapunov analysis. To further mitigate communication load, a dynamic ET mechanism is introduced, where a time-varying variable adapts the triggering threshold, significantly reducing transmission events. Furthermore, the power ramp rate constraints are incorporated, and a quantitative relationship between the predefined-time and the maximum power ramp rate is established, providing a practical guideline for selecting a physically feasible convergence time. The proposed approach is verified to be effective and robust through simulation results.
Based on the Kirchhoff plate theory incorporating Gurtin-Murdoch surface elasticity, this paper investigates the bending failure behavior of a bimaterial welded microplate stiffened by a rigid line. A mixed boundary value problem is given and solved by the Fourier transform technique. Dual integral equations are derived and then converted to a weakly singular integral equation with a logarithmic kernel. The exact elastic fields in the entire welded stiffened microplate, as well as the asymptotic singular fields near the tips of the rigid line, are analytically obtained. The results demonstrate that the bending moment and the effective shear force exhibit the r-3/2 and r-5/2 singularity, respectively, near the line tips, where r denotes the distance from the tips. The transverse deflection, bending moment, and effective shear force are all dependent on the material properties of both the surface phase and the bulk phase. The influence of surface elasticity on the mechanical behavior of the welded microplate becomes more sensitive for the thickness close to the intrinsic length scale and negligible for thickness much larger than the intrinsic length scale. The proposed methodology provides a theoretical foundation for the design of interfacial strength in flexible ultrathin electronic devices.
Nuclear power plants (NPPs) are safety–critical systems where reliable fault diagnosis and accident identification are essential for operational safety. However, in practical applications, training and deployment environments exhibit domain shift caused by both observational and evolutionary discrepancies, leading to distribution mismatch and weakening the generalization ability of conventional machine learning and domain adaptation methods. To address this challenge, this study introduces a Large Language Model-based Sequential Data framework (LLM-SD) for NPP classification tasks. The method reformulates multivariate sensor data into patch-based sequences and leverages a pre-trained large language model as a universal model of the framework. By freezing the backbone and training only lightweight embedding and classification layers, the introduced framework achieves cross-domain classification capabilities. Extensive experiments are conducted on a pressurized water reactor (PWR) simulation dataset generated using PCTRAN and the MIT Graphite Exponential Pile (MGEP) experimental dataset under multiple cross-domain scenarios. Results demonstrate that LLM-SD consistently outperforms baseline models including SVM, XGBoost, CNN, ResNet, and Transformer. The introduced framework maintains stable and robust performance across all scenarios, highlighting the effectiveness of LLM as a generalizable paradigm for classification tasks in NPPs under complex and uncertain operating conditions.
Molecular dynamics simulations are performed to investigate the impact of irradiation-induced point defects on the uniaxial tensile mechanical properties of single-crystal aluminum nitride (AlN). Cascade collision simulations reveal that the number of point defects increases linearly with primary knock-on atom (PKA) energy. Within the PKA energy range of 1–4 keV, the defect recombination rate exceeds 98%, demonstrating that AlN possesses excellent radiation resistance. Tensile simulations show that unirradiated AlN exhibits homogeneous fracture, with cracks propagating obliquely along the [21‾1‾0] orientation at approximately 60° to the tensile direction [12‾10]. At a PKA energy of 1 keV, corresponding to the formation of one Frenkel pair (FP), cracks initiate at the defect center and propagate along [21‾1‾0], accompanied by secondary cracks along [101‾0], yielding a fracture mode similar to unirradiated AlN. A critical transition occurs when the number of FPs reaches or exceeds three: the crack propagation path becomes more concentrated and localized, extending rapidly along the single [101‾0] direction until penetrating the entire workpiece. Irradiation-induced point defects reduce both the tensile strength and fracture strain. As the number of FPs increases from 0 to 7, the tensile strength decreases from 24.38 to 19.55 GPa, while the fracture strain drops from 0.236 to 0.134. In-depth analysis reveals that point defects trigger localized stress concentration, altering the atomic displacement vector field and modulating crack propagation paths, ultimately reducing the deformation capacity and accelerating brittle fracture. This study provides a theoretical guidance for evaluating mechanical properties and designing irradiation-resistant AlN under irradiated environments.
Background Intensive uranium mining discharges acidic effluent, posing significant risks to ecosystems and public health. Methods The adsorption of U(VI) by cow bone (CB) and cow bone biochars (CBCs, i.e., CBC400, CBC600, CBC800) prepared in a simple, efficient, and low-cost way at different pyrolysis temperatures was explored. Significant Findings With increasing pyrolysis temperature, the yield of CBC decreased, whereas the specific surface area, pore size, and aromaticity increased, and the number of oxygen-containing functional groups decreased. Among the types of prepared CBCs, CBC600 exhibited the highest U(VI) removal ratio (99.40%) and theoretical adsorption capacity (1125.99 mg/g) at pH=4.0 and T = 303 K. The adsorption data for CBC600 follows Langmuir and pseudo-second-order models, suggesting a monolayer process that is controlled by chemical adsorption. After five cycles, the desorption efficiency of CBC600 for U(VI) remained above 85%. Additionally, CBC600 demonstrated suitable stability against ion interference. Quantitative analysis revealed that the primary mechanisms for U(VI) adsorption by CBC600 were ion exchange (57%), π-π bonding interactions (20.11%), surface functional group complexation (11.76%), mineral precipitation (11.12%), and physical adsorption/electrostatic attraction (0.01%). Therefore, pyrolyzing cow bone into biochar for pollutant removal is a key strategy to achieve the goal of "turning waste into treasure".
