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Silicon is a promising anode material for lithium-ion and post lithium-ion batteries but suffers from a large volume change upon lithiation and delithiation. The resulting instabilities of bulk and interfacial structures severely hamper performance and obstruct practical use. Stability improvements have been achieved, although at the expense of rate capability. Herein, a protocol is developed which we describe as two-dimensional covalent encapsulation. Two-dimensional, covalently bound silicon-carbon hybrids serve as proof-of-concept of a new material design. Their high reversibility, capacity and rate capability furnish a remarkable level of integrated performances when referred to weight, volume and area. Different from existing strategies, the two-dimensional covalent binding creates a robust and efficient contact between the silicon and electrically conductive media, enabling stable and fast electron, as well as ion, transport from and to silicon. As evidenced by interfacial morphology and chemical composition, this design profoundly changes the interface between silicon and the electrolyte, securing the as-created contact to persist upon cycling. Combined with a simple, facile and scalable manufacturing process, this study opens a new avenue to stabilize silicon without sacrificing other device parameters. The results hold great promise for both further rational improvement and mass production of advanced energy storage materials. Stabilizing silicon without sacrificing other device parameters is essential for practical use in lithium and post lithium battery anodes. Here, the authors show the skin-like two-dimensional covalent encapsulation furnishing a remarkable level of integrated lithium storage performances of silicon.

Temperature’s influence on the physical and mechanical properties of rocks is a crucial concern for the rational design of deep rock engineering structures and the assurance of their long-term stability. To systematically comprehend the impact of the evolution of mineral composition and micro characteristics on the physical and mechanical behavior of thermally damaged granite, we observed the microscopic structural defects inside the rocks with a polarizing microscope and revealed the thermal damage mechanism of granite from a microscopic perspective by combining ultrasound detection and XRD phase characteristic analysis. The results show that the physical properties of the specimens changed significantly at three characteristic temperature points: 400 °C, 800 °C, and 1000 °C. Under high temperature conditions, the diffraction intensity of all minerals in granite, except for quartz, generally decreased, and stable minerals decomposed. Albite and potash feldspar decomposed to form anorthoclase, thereby reducing the structural stability of the rock material. In addition, the peak width of various minerals decreased to varying degrees with increasing temperature. The increase in mineral volume further damaged the internal structure of the rock material while promoting the transformation from grain boundary to intergranular cracks and from intragranular cracks to transgranular cracks, ultimately forming a interconnected crack network. Thermal damage significantly reduced the longitudinal wave velocity, uniaxial compressive strength, and elastic modulus of the specimens, while the stress–strain curve relationship indicated that the specimens underwent two opposite processes of transformation from brittleness to ductility and then from ductility to brittleness. The thermal damage threshold of granite in this study was 600 °C.

In order to study the fractal characteristics of the pomegranate biotite schist under the effect of blasting loads, a one-dimensional SHPB impact test was carried out to test the dynamic compressive strength, damage morphology, fracture energy dissipation density, and other parameters of the rocks under different strain rates; besides, sieve tests were conducted to count the mass fractal characteristics of the crushed masses under different strain rates to calculate the fractal dimension of the crushed rock D. Finally, the relationships between fractal dimension and dynamic compressive strength, crushing characteristics, and energy dissipation characteristics were analysed. The results show that under different impact loads, the strain rate effect of the rock is significant and the dynamic compressive strength increases with the increasing strain rate, and they show a multiplicative power relationship. The higher the strain rate of the rock, the deeper the fragmentation and the higher the fractal dimension, and the fractal dimension and rock crushing energy density are multiplied by a power relationship. By performing the comparative analysis of the pomegranate biotite schist, a reasonable strain rate range of 78.75 s-1~82.51 s-1 and a reasonable crushing energy consumption density range of 0.78 J·cm-3~0.92 J·cm-3 were determined. This research provides a great reference for the analysis of dynamic crushing mechanism, crushing block size distribution, and crushing energy consumption of the roadway surrounding rock.

Induction solution heat treatment can change the mechanical characteristics and corrosion resistance properties of 308 L manufactured via wire arc additive manufacturing (WAAM). Moreover, compare with traditional heat treatment methods, this method can reduce heat treatment time and achieve in-situ local heat treatment. In this paper, in-situ induction heat treatment at 1100 °C for 2, 4, and 6 min were applied on 308 L thin-walled parts produced by WAAM. The result show that ferrite and austenite phase proportions were changed after induction solution heat treatment. Heat treatment at 1100 °C effectively reduced the δ-Fe and σ-Fe content, resulting in a slight decrease in UTS and microhardness, while YS and EL have a certain degree of increase. σ-Fe exhibits a more pronounced strengthening effect than austenite, albeit at the potential expense of steel’s elasticity. At the same time, induction heat treatment alters the ferrite to austenite ratio, which also enhances the anti-corrosion properties of the stainless steel. However, the presence of σ-Fe will cause a worsening of the corrosion resistance of the steel. In addition, as the heat treatment progresses, the ferrite’s microstructure in the deposition direction undergoes a significant transformation, changing from continuous dendrites to a few equiaxed grains.

Accurate short-term load forecasting is of great significance to the safe and stable operation of power systems and the development of the power market. Most existing studies apply deep learning models to make predictions considering only one feature or temporal relationship in load time series. Therefore, to obtain an accurate and reliable prediction result, a hybrid prediction model combining a dual-stage attention mechanism (DA), crisscross grey wolf optimizer (CS-GWO) and bidirectional gated recurrent unit (BiGRU) is proposed in this paper. DA is introduced on the input side of the model to improve the sensitivity of the model to key features and information at key time points simultaneously. CS-GWO is formed by combining the horizontal and vertical crossover operators, to enhance the global search ability and the diversity of the population of GWO. Meanwhile, BiGRU is optimized by CS-GWO to accelerate the convergence of the model. Finally, a collected load dataset, four evaluation metrics and parametric and non-parametric testing manners are used to evaluate the proposed CS-GWO-DA-BiGRU short-term load prediction model. The experimental results show that the RMSE, MAE and SMAPE are reduced respectively by 3.86%, 1.37% and 0.30% of those of the second-best performing CSO-DA-BiGRU model, which demonstrates that the proposed model can better fit the load data and achieve better prediction results.

To realize the region determination and accurate location of cable partial discharge, this paper proposes a cable partial discharge double-end location technique based on frequency-domain reflectometry. The cable partial discharge double-end location technique based on frequency-domain reflectometry mainly includes the frequency band modulation technique and partial discharge location method. The frequency band modulation technique determines the effective frequency band range of the acquired cable transfer function through the frequency band range of the partial discharge signals measured at both ends, which ensures the reliability of the transfer function. The partial discharge location method constructs the cable partial discharge location function and the region determination function via spectral analysis of the cable transfer function obtained from the partial discharge signals, which realizes region determination and determines precise location of the cable partial discharge, respectively. Our simulation and experiment show that the cable partial discharge double-end location technique based on frequency-domain reflectometry can effectively determine the existence region of cable partial discharge and its accurate location (with a location error of less than 1%), showing good potential for practical application in engineering.

Fragmentation size is an important indicator for evaluating blasting effectiveness. To address the limitations of conventional blasting fragmentation size prediction methods in terms of prediction accuracy and applicability, this study proposes an NRBO-CNN-LSSVM model for predicting mean fragmentation size, which integrates Convolutional Neural Networks (CNN), Least Squares Support Vector Machines (LSSVM), and the Newton-Raphson Optimizer (NRBO). The study is based on a database containing 105 samples derived from both previous research and field collection. Additionally, several machine learning prediction models, including CNN-LSSVM, CNN, LSSVM, Support Vector Machine (SVM), and Support Vector Regression (SVR), are developed for comparative analysis. The results showed that the NRBO-CNN-LSSVM model achieved remarkable prediction accuracy on the training dataset, with a coefficient of determination ( R 2 ) as high as 0.9717 and a root mean square error ( RMSE ) as low as 0.0285. On the test set, the model maintained high prediction accuracy, with an R 2 value of 0.9105 and an RMSE of 0.0403. SHapley Additive exPlanations (SHAP) analysis revealed that the modulus of elasticity ( E ) was a key variable influencing the prediction of mean fragmentation size. Partial Dependence Plots (PDP) analysis further disclosed a significant positive correlation between the modulus of elasticity ( E ) and mean fragmentation size. In contrast, a distinct negative correlation was observed between the powder factor ( P f ) and mean fragmentation size. To enhance the convenience of the model in practical applications, we developed an interactive Graphical User Interface (GUI), allowing users to input relevant variables and obtain instant prediction results.

Long non-coding RNAs (lncRNAs) have been manifested to manipulate diverse biological processes, including tumor-induced immune tolerance. Thus, we aimed in this study to identify the expression pattern of lncRNA homeobox A cluster antisense RNA 2 (HOXA-AS2) in glioma and decipher its role in immune tolerance and glioma progression. We found aberrant upregulation of lncRNA HOXA-AS2, lysine demethylase 2A (KDM2A), and jagged 1 (JAG1) and a downregulation of microRNA-302a (miR-302a) in glioma specimens. Next, RNA immunoprecipitation, chromatin immunoprecipitation, and dual-luciferase reporter gene assay demonstrated that lncRNA HOXA-AS2 upregulated KDM2A expression by preventing miR-302a from binding to its 3′untranslated region. The functional experiments suggested that lncRNA HOXA-AS2 could promote regulatory T (T reg ) cell proliferation and immune tolerance, which might be achieved through inhibition of miR-302a and activation of KDM2A/JAG1 axis. These findings were validated in a tumor xenograft mouse model. To conclude, lncRNA HOXA-AS2 facilitates KDM2A/JAG1 expression to promote T reg cell proliferation and immune tolerance in glioma by binding to miR-302a. These findings may aid in the development of novel antitumor targets.

With the rapid advancement of intelligent technologies, permanent magnet synchronous motor (PMSM) servo systems have seen increasing applications in industrial fields, accompanied by continuously rising control performance demands. Moreover, the adjustment of controller parameters is pivotal for the performance optimization of servo systems. This paper presents an optimization method for PMSM servo systems based on the coupling technique of the neural network surrogate model and intelligent optimization algorithm. A hybrid model is constructed by the proposed method, integrating a mathematical model based on transfer functions with an artificial neural network surrogate model, which is employed to compensate for the discrepancies between the mathematical model and the actual measured values. The accuracy and superiority of the hybrid model are comprehensively validated through training and validation loss analysis, fitting plot construction, and ablation experiments. Subsequently, based on the hybrid model, the qualitative and quantitative comparative analysis of the Pareto fronts of five commonly used multi-objective intelligent optimization algorithms is conducted. The optimal algorithm is determined through experimental validation of the optimization results to obtain the optimal result. The optimal result demonstrates that, compared to the initial result before optimization, the overshoot is reduced by 89.7%, and the settling time is reduced by 80.1%. Additionally, several other non-dominated solutions are available for selection, and all optimized results are superior to the initial result. This study provides a novel idea and method for the performance optimization of PMSM servo systems, contributing to the field with a robust and effective approach to enhance system control performance.

Several challenges hinder accurate and physically consistent dynamic parameter estimation in power systems, particularly under scenarios involving limited measurements, strong system nonlinearity, and high variability introduced by renewable integration. Although data-driven methods such as Physics-Informed Neural Networks (PINNs) provide a promising direction, they often suffer from poor generalization and training instability when faced with complex dynamic regimes. To address these challenges, we propose a Residual Physics-Informed Neural Network (Res-PINN) framework, which integrates a residual neural architecture with the swing equation to enhance estimation robustness and precision. By replacing the traditional multilayer perceptron (MLP) in PINN with residual connections and injecting normalized time into each network layer, the proposed model improves temporal awareness and enables stable training of deep networks. A physics-constrained loss formulation is employed to estimate inertia and damping parameters without relying on large-scale labeled datasets. Extensive experiments on a 4-bus, 2-generator power system demonstrate that Res-PINN achieves high parameter estimation accuracy across various dynamic conditions and outperforms traditional PINN and Unscented Kalman Filter (UKF) methods. It also exhibits strong robustness to noise and low sensitivity to hyperparameter variations. These results show the potential of Res-PINN to bridge the gap between physics-guided learning and practical power system modeling and parameter identification.