β-Ga2O3 has many excellent properties, but poor electrical conductivity limits its application in optoelectronic devices. At present, some progress has been made in the study of Ti doping on the photoelectric properties of β-Ga2O3. However, few studies on the effects of Ti doping and VO-Hi co-existence on photoelectric properties of β-Ga2O3. Defects (VO) and unintentional impurities (Hi) will inevitably exist during the preparation of β-Ga2O3. Given these problems, the effects of Ti doping and VO-Hi co-existence on photoelectric properties of β-Ga2O3 were studied using first-principles GGA+U method. The electronic structure, dielectric function, absorption spectrum, effective mass, mobility, and conductivity of the system were calculated and analyzed. The results indicate that with the increase of Ti doping concentration, the bandgap of the Ti doped β-Ga2O3 system gradually narrows. Ti doping causes a red shift in the peak of the absorption spectrum and effectively improves the conductivity of the system. The O vacancy reduces the conductivity of the system, while the interstitial H increases the conductivity of the system. Thus, Ti-doped β-Ga2O3 material is helpful for preparing new optoelectronic devices.

The main aim of this study was to utilize remote sensing data to establish regression models through machine learning to predict locust density in the upcoming year. First, a dataset for monitoring grassland locust density was constructed based on meteorological data and multi-source remote sensing data in the study area. Subsequently, an SVR (support vector regression) model, BP neural network regression model, random forest regression model, BP neural network regression model with the PCA (principal component analysis), and deep belief network regression model were built on the dataset. The experimental results show that the random forest regression model had the best prediction performance among the five models. Specifically, the model achieved a coefficient of determination (R2) of 0.9685 and a root mean square error (RMSE) of 1.0144 on the test set, which were the optimal values achieved among all the models tested. Finally, the locust density in the study area for 2023 was predicted and, by comparing the predicted results with actual measured data, it was found that the prediction accuracy was high. This is of great significance for local grassland ecological management, disaster warning, scientific decision-making support, scientific research progress, and sustainable agricultural development.

The development of the digital economy constitutes a key component of China's endeavors to advance towards "Digital China." The sports industry functions as a new catalyst for high-quality economic growth. This study systematically evaluated the integration between these two sectors. First, we conducted two levels of grey relational analysis to assess their integration between 2016 and 2021. Second, we conducted a VAR analysis to determine whether their integration between 2009 and 2021 represents a causal relationship. At the macro level, the grey relational analysis reveals that the sports industry (grade = 0.770) ranked second among China's eight key economic sectors in terms of digital economy integration. At the meso level, a wide variation (ranging from 0.606 to 0.789) existed in the grade of integration between the digital economy and the sub-sectors of the sports industry. According to the VAR model, the digital economy does not Granger cause (p = 0.344) the growth of the sports industry. This study yielded two added values to the existing literature: First, there exists a sectoral imbalance in the digitization process; second, the explosive growth of the sports industry was not primarily caused by the digital economy. Accordingly, the "sports + digital" complex is still in the first wave of technological integration. We propose three policy recommendations, namely, sectoral synergistic development, overtaking via esports IP, and new economy and new regulation. Collectively, these findings provide updated insights for the digital transformation towards "building a leading sports nation" and "Digital China."

As is widely recognized, the Long Period Stacking Ordered (LPSO) phase is an important strengthening phase in the rare earth magnesium alloys. The LPSO phase not only enhances the strength of the alloy, but also improves its plasticity, thereby effectively addressing the strength-ductility trade-off phenomenon. Therefore, the preparation of metallic materials containing LPSO phase provides a new idea for designing a new generation of high-strength and high-toughness alloys. In present study, A novel nano-sized LPSO phase is observed and characterized for the first time in GH4169 nickel-based alloy. The results show that the stacking sequence of the LPSO phase is ABCBCBA, which indicates that this LPSO phase has a 6H structure. Meanwhile, the orientation relationships between LPSO phase and the Ni matrix are (11–1)γ//(0006)LPSO and [011]γ//[2-1-10]LPSO.

The warm mix recycling technology of asphalt pavement can improve the utilization rate of solid waste in highway construction industry on a large scale, and reduce the emission of harmful gases ( NOX, CO2, benzopyrene, etc.) in the process of road construction. It is one of the effective ways to realize carbon neutrality in highway construction industry. The main purpose of this study is to systematically analyze the fatigue characteristics of warm mix recycled asphalt (WMRA) from the perspective of quantitative research, and to provide scientific basis for the popularization and application of this technology. Therefore, this study mainly evaluated the anti-fatigue characteristics of WMRA under different conditions based on the linear amplitude sweeping test ( LAS test). Based on the calculation and analysis of the commonly used fatigue judgment indexes ( stress-strain curve, phase angle-strain curve, S×N curve ( S is the ratio of the instantaneous modulus to the initial modulus of the material, which is often replaced by the virtual modulus in the LAS test, and N is the number of cyclic loadings.), virtual strain storage energy-strain curve), the peak strain of virtual strain storage energy is recommended as the fatigue judgment standard from the perspective of reliability and accuracy, and the peak strain of damage factor is proposed as one of the new fatigue judgment standards. In this paper, the nucleation of microcracks and the coalescence of microcracks are distinguished in the process of fatigue damage evolution, and the peak strain of virtual strain energy storage and the peak strain of damage factor are used as the judging indexes respectively. Finally, based on the theory of continuum damage mechanics, the loss modulus of WMRA is selected as the representative value of material properties to calculate the virtual modulus and damage factor, and the fatigue damage evolution model of WMRA based on the viscoelastic continuum damage model ( VECD model) was constructed. Based on this model, the anti-fatigue characteristics of WMRA with the reclaimed asphalt pavement ( RAP) content were analyzed.

Mechanical water detection is recognized as the most reliable and safe production technology for coal mines, mainly for the detection of water hazards in pre-mining operations. Intelligent water detectors are currently the main research direction in mechanical water detection, and the automatic installation of drilling bars is the key to achieving intelligent water detection. Improving the connection accuracy in the process of installing drilling bars is an important research topic for the improvement of control links. To improve the connection accuracy of the drilling bars at the time of supplying material, we used the modified Denavit–Hartenberg method to analyze the motion gestures of the supplied material device and the Lagrange equation to establish a dynamic analysis model. We aimed at better control precision by improving the sliding mode control algorithm and at increasing the convergence rate of tracking errors with a sliding controller based on an exponential approximation law and using saturated functions instead of the symbol functions in the reaching law to weaken the vibration in the control process. We then used particle swarm optimization (PSO) to find the optimum combination parameters of the sliding mode controllers and test the performance of the sliding mode controllers before and after PSO with MATLAB/Simulink. The results showed that the optimized controller has a strong resistance to parameter fluctuations, and the system responds quickly, achieves a good performance, and improves the convergence rate of tracking errors.

The variability and intermittency inherent in renewable energy sources poses significant challenges to balancing power supply and demand, often leading to wind and solar energy curtailment. To address these challenges, this paper focuses on enhancing Time of Use (TOU) electricity pricing strategies. We propose a novel method based on equivalent load, which leverages typical power grid load and incorporates a responsibility weight for renewable energy consumption. The responsibility weight acts as an equivalent coefficient that accurately reflects renewable energy output, which facilitates the division of time periods and the development of a demand response model. Subsequently, we formulate an optimized TOU electricity pricing model to increase the utilization rate of renewable energy and reduce the peak–valley load difference of the power grid. To solve the TOU pricing optimization model, we employ the Social Network Search (SNS) algorithm, a metaheuristic algorithm simulating users’ social network interactions to gain popularity. By incorporating the users’ mood when expressing opinions, this algorithm efficiently identifies optimal pricing solutions. Our results demonstrate that the equivalent load-based method not only encourages renewable energy consumption but also reduces power generation costs, stabilizes the power grid load, and benefits power generators, suppliers, and consumers without increasing end users’ electricity charges.

AbstractApplying evidence theory to structural reliability analysis under epistemic uncertainty, it is necessary to consider the correlation of evidence variables. Among them, solving the joint basic probability assignment (BPA) of the evidence variables is a crucial link. In this study, a solution method of joint BPA based on neural network copula function is proposed. This method is to automatically construct copula function through neural network, which avoids the process of selecting the optimal copula function. Firstly, the neural network copula function is constructed based on the sample set of evidence variables. Then, the expression for solving the joint BPA using the neural network copula function is derived through vectors. Furthermore, the expression is used to map the marginal BPA of evidence variables to joint BPA, thus realizing the solution of joint BPA. Finally, the effectiveness of this method is verified by three examples. The results show that the neural network copula function describes the data distribution better than the optimal copula function selected by the traditional method. In addition, there is actually an error in solving the reliability intervals using the traditional optimal copula function method, whereas the results of this paper's neural network copula function method are more accurate and better for decision making.