Hechi University

Abstract Sodium-ion batteries (SIBs) represent a compelling alternative to lithium-ion battery technology for large-scale energy storage, primarily owing to the natural abundance and low cost of sodium. The cathode material is a critical determinant of the overall electrochemical performance, cost, and safety of SIBs. Among various candidates, layered transition metal oxides and sulfides are particularly promising due to their high theoretical capacities. However, they often suffer from issues like structural instability, sluggish kinetics, and complex phase transitions during sodiation/desodiation. A promising yet underexplored strategy to address these challenges is the use of hydrotalcite-like compounds, also known as layered double hydroxides (LDHs), as precursors. This review critically examines the stateof-the-art and future potential of employing LDH precursors for the synthesis of advanced layered metal oxide and sulfide cathodes for SIBs. We first introduce the unique structural advantages of the LDH precursor route, which enables atomic-level mixing of metal cations, leading to exceptional compositional homogeneity and the formation of porous nanostructures upon thermal treatment. Subsequently, we delve into the progress and prospects of LDH-derived layered metal oxides, discussing how this synthetic approach can potentially mitigate detrimental phase transitions and enhance structural stability compared to materials prepared via conventional methods. We then extend the discussion to LDH-derived layered metal sulfides, a nascent but promising class of materials, exploring how the precursor methodology can be leveraged to engineer unique nanostructures with enhanced conductivity and cycling stability. Finally, this review outlines the pressing challenges, including precise stoichiometric control and air sensitivity, and proposes future research directions, such as the application of advanced in-situ characterization and computational modeling, to accelerate the development of this promising class of cathode materials. This work aims to provide a comprehensive roadmap and inspire further research into the rational design of high-performance SIB cathodes via the versatile hydrotalcite precursor platform.

Subjective well-being is a key area of research in psychology. Based on survey data from 384 university students, this study employed automated machine learning methods to construct a predictive model of subjective well-being, in which the Support Vector Machine (SVM) model performed best, achieving an overall prediction accuracy of 81.81%. The results indicate that the overall subjective well-being of the current university student population is relatively high; depression and interpersonal sensitivity are the most significant influencing factors, followed by hostility and paranoid ideation, among others. Family emotion also has a significant impact on subjective well-being. Based on these findings, fostering positive psychological traits and optimizing family functioning are suggested as approaches to enhance university students’ subjective well-being, thereby promoting their academic achievement and mental health development.

The modeling and control of photovoltaic and other engineering systems highly depend on the accuracy of parameter identification. However, parameter extraction for photovoltaic equivalent models typically presents a high-dimensional, strongly nonlinear, and multimodal global optimization problem. Traditional analytical or gradient-based methods are sensitive to initial values and easily fall into local optima. To address this issue, this paper proposes a multi-strategy improvement teaching–learning-based optimization algorithm (SNTLBO). A social learning network structure with symmetric interaction topology is introduced into the classical TLBO framework to characterize the knowledge propagation relationships among individuals. Through this symmetric and balanced information exchange mechanism, learners can be guided not only by the teacher but also by multiple neighbors within the network, enabling more diverse and symmetric exploration of the search space and enhancing population diversity and global search capability. Furthermore, a teacher reputation mechanism is constructed, where historical performance is used to weight teacher influence, strengthening the guidance of high-quality solutions and accelerating convergence. Meanwhile, an adaptive teaching factor is designed to dynamically adjust the teaching intensity based on the distance between the teacher and students in the solution space, maintaining a dynamic balance (symmetry) between exploration and exploitation. To evaluate the performance of the proposed algorithm, SNTLBO is systematically compared with 11 advanced optimization algorithms on two benchmark test suites, CEC2017 (30D, 50D) and CEC2022 (10D, 20D). Non-parametric statistical tests are conducted to assess significance. The results demonstrate that SNTLBO shows competitive advantages in terms of convergence speed, solution accuracy, and stability. Finally, SNTLBO is applied to the parameter estimation of single-diode, double-diode, triple-diode, quadruple-diode, and photovoltaic module models. Experimental results show that the proposed algorithm achieves higher identification accuracy and robustness in terms of RMSE, IAE, and I–V/P–V curve fitting, verifying its effectiveness and practical value for complex global optimization and practical engineering applications.

Accurate inversion of the deep initial in situ stress field is a fundamental prerequisite for stability analysis of surrounding rock in underground engineering, roadway support design, and prevention and control of dynamic disasters. To address the problems of scarce in situ stress measurements in deep mining areas, the inability of conventional regression methods to capture the nonlinear characteristics of complex tectonic stress fields, and the tendency of traditional inversion algorithms to fall into local optima and overfitting, this paper proposes an intelligent inversion method based on support vector regression optimized by the artificial bee colony algorithm (ABC-SVR). The artificial bee colony algorithm is employed to adaptively optimize the core parameters of the SVR model, thereby enabling high-precision inversion of complex deep stress fields. Comparing the results with acoustic emission tests demonstrated that the ABC-SVR model significantly outperforms conventional SVR and backpropagation neural networks across various performance metrics. The inversion results show high consistency with the measured data, achieving a root mean square error (RMSE) of 1.25, a mean absolute percentage error (MAPE) of 4.16%, and a coefficient of determination (R2) of 0.908. This method can rapidly reconstruct high-precision initial in situ stress fields in deep unmined regions, providing highly reliable boundary conditions for numerical simulations and demonstrating significant engineering application potential.

To address the digitization needs of Cantonese embroidery, a human intangible cultural heritage, and resolve the limitations of existing simulation techniques—insufficient stitch diversity, unnatural pattern transitions, and inaccurate structure-color reproduction—this study proposes a diffusion-based method that generates high-quality Cantonese embroidery-style images with hundreds of labeled samples. In this method, lightweight LoRA fine-tuning endows the large model with ultrahigh-fidelity texture reproduction; SAM semantic segmentation imposes high-precision spatial semantic constraints on generation; ControlNet multi-condition guidance performs accurate structure‒color restoration. This synergistic combination achieves superior feature reconstruction and detail generation, a balance that existing models struggle to maintain under limited data. It outperforms existing approaches in key metrics (LPIPS: 0.244; FID: 95.57; PSNR: 16.38), with remarkable visual and user evaluation advantages. This work enables applications such as relic restoration, design reference, and intelligent manufacturing simulation, providing a critical path for the digital preservation of intangible cultural heritage and for innovative design.

Let $ R $ be a ring. An element $ a \in R $ is called a quasi-idempotent if there exists a central unit $ k $ in $ R $ such that $ a^2 = ka $. The quasi-idempotent graph of $ R $, denoted by $ G_{Qid}(R) $, is the simple undirected graph with vertex set $ R $ itself, where two distinct vertices $ a $ and $ b $ are adjacent if and only if $ a+b $ is a quasi-idempotent. This paper presents a systematic study of the graph $ G_{Qid}(R) $. We examine its basic structural properties, including connectivity and girth. We introduce a new invariant of the ring, termed the quasi-idempotent sum number, and establish the precise relationship between this invariant and the graph diameter. Furthermore, a complete classification is obtained for all finite commutative rings $ R $ according to the genus of $ G_{Qid}(R) $, thereby characterizing the rings for which this graph has genus $ 0 $, $ 1 $, or $ 2 $.