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The first topic covered in this paper is the development of cultural confidence among college students within the context of the Internet, including its manifestations, characteristics, and value implications. Secondly, elite guidance is used to carry out adaptive parameter optimization of the BSO algorithm, and then horizontal and dynamic cultural communication strategies are proposed. Finally, an examination is conducted on college students' current state of cultural self-confidence. The results show that the average knowledge of cultural confidence of different majors is 0.0477, 0.3097, 0.4492 and 0.1935, respectively, and 882 college students indicate that they are very familiar with the knowledge of social core values. It follows that to foster college students’ cultural confidence, society and families must work with colleges and universities to encourage students to identify with and have confidence in Chinese culture.

Weigh-in-motion (WIM) systems are used to measure the weight of moving vehicles. Aiming at the problem of low accuracy of the WIM system, this paper proposes a WIM model based on the beetle swarm optimization (BSO) algorithm and the error back propagation (BP) neural network. Firstly, the structure and principle of the WIM system used in this paper are analyzed. Secondly, the WIM signal is denoised and reconstructed by wavelet transform. Then, a BP neural network model optimized by BSO algorithm is established to process the WIM signal. Finally, the predictive ability of BP neural network models optimized by different algorithms are compared and conclusions are drawn. The experimental results show that the BSO-BP WIM model has fast convergence speed, high accuracy, the relative error of the maximum gross weight is 1.41%, and the relative error of the maximum axle weight is 6.69%.

Operation optimization for large-scale offshore wind farms can cause the fatigue loads of single wind turbines to exceed their limits. This study aims to improve the economic profit of offshore wind farms by conducting multi-objective optimization via decoupled group operations of turbines. To do this, a large-scale wind farm is firstly divided into several decoupled subsets through the parallel depth-first search (PDFS) and hyperlink-induced topic search (HITS) algorithms based on the wake-based direction graph. Next, three optimization objectives are considered, including total output power, total fatigue load, and fatigue load dispatch on a single wind turbine (WT) in a wind farm. And then, the combined Monte Carlo and beetle swarm optimization (CMC-BSO) algorithms are applied to solve the multi-objective non-convex optimization problem based on the decentralized communication network topology. Finally, the simulation results demonstrate that the proposed method balances the total power output, fatigue load, and single fatigue loads with fast convergence.

Due to the inappropriate or untimely distribution of post-disaster goods, many regions did not receive timely and efficient relief for infected people in the coronavirus disease outbreak that began in 2019. This study develops a model for the emergency relief routing problem (ERRP) to distribute post-disaster relief more reasonably. Unlike general route optimizations, patients’ suffering is taken into account in the model, allowing patients in more urgent situations to receive relief operations first. A new metaheuristic algorithm, the hybrid brain storm optimization (HBSO) algorithm, is proposed to deal with the model. The hybrid algorithm adds the ideas of the simulated annealing (SA) algorithm and large neighborhood search (LNS) algorithm into the BSO algorithm, improving its ability to escape from the local optimum trap and speeding up the convergence. In simulation experiments, the BSO algorithm, BSO+LNS algorithm (combining the BSO with the LNS), and HBSO algorithm (combining the BSO with the LNS and SA) are compared. The results of simulation experiments show the following: (1) The HBSO algorithm outperforms its rivals, obtaining a smaller total cost and providing a more stable ability to discover the best solution for the ERRP; (2) the ERRP model can greatly reduce the level of patient suffering and can prioritize patients in more urgent situations.

Soil salinization is one of the most frequent environmental concerns that contribute to the degradation of agricultural land, particularly in arid and semi-arid regions. The correct methods must be developed by farm owners and decision-makers in order to reduce soil erosion and increase crop output. For this, accurate spatial forecasting and soil salinity modeling in agricultural areas are needed. The accurate consideration of environmental elements under the scale effects, which have received less attention in prior research, is essential for digital soil mapping. The goal of this research is to create a special technique for predicting soil salinity. Preprocessing is done on the sentinel image input first. The next step is to determine the spectral channels, salinity index, and vegetation index. The development of transformation-based features also takes advantage of enhanced PCA. The suggested hybrid classifier uses \"Deep Belief Network (DBN) and Bidirectional Long Short Term Memory (Bi-LSTM)\" to predict salinity while accounting for these variables. The final forecast result is determined by the increased score level fusion. To improve the precision and accuracy of the prediction, Self Upgraded BSO (SU-BSO) calibrates the weights of the Bi-LSTM and DBN. The MSE values of the suggested technique are lower than those of other conventional methods like CNN, DBN, SVM, BI-LSTM, MLP-FFA, and MLSR metrics, achieving lower values of 0.13, 0.07, 0.03, 0.05, 0.09, and 0.094%, respectively. Finally, numerous measurements are employed to demonstrate the value of the selected approach.

With the continuous research in seismology, the influence of the heterogeneity caused by microstructure interactions of the medium on seismic wave propagation has been paid more attention. Wang et al. (2020) proposed to describe the complex microstructures of the medium in the framework of the generalized continuum mechanics theory, and derived the asymmetric elastic wave equations containing the characteristic length scale parameter of the medium. In addition, scale effects of seismic wave propagation will appear when considering microstructures of the medium. In this work, in order to better analyze the scale effects and extract the new components of wave fields owing to the microstructure interactions, we propose an optimized finite-difference (FD) method based on the improved bat swarm optimization (BSO) algorithm. Then the optimized FD method is used to perform numerical modeling for the asymmetric elastic wave equations considering microstructures of the medium. Numerical dispersion analysis indicate that the optimized FD method has high accuracy. According to the numerical modeling results obtained by the optimized FD method, scale effects of seismic wave propagation can be clearly observed when considering microstructures of the medium.

Aiming at problems of low optimization accuracy and slow convergence speed in the gait optimization algorithm of lower limb exoskeleton robot, a novel gait multi-objectives optimization strategy based on beetle swarm optimization (BSO)-elite opposition-based learning (EOL) levy flight foraging (LFF) algorithm was proposed. In order to avoid the algorithm from falling into the local optimum, the EOL strategy with global search capability, the LFF strategy with local search capability and the dynamic mutation strategy with high population diversity were introduced to improve optimization performance. The optimization was performed by establishing a multi-objectives optimization function with the robot’s gait zero moment point (ZMP) stability margin and driving energy consumption. The joint comparative tests were carried out in SolidWorks, ADAMS and MATLAB software. The simulation results showed that compared with the particle swarm optimization algorithm and the BSO algorithm, the ZMP stability margin obtained by the BSO-EOLLFF algorithm was increased, and the average driving energy consumption was reduced by 25.82% and 17.26%, respectively. The human-machine experiments were conducted to verify the effectiveness and superiority. The robot could realize stable and smooth walking with less energy consumption. This research will provide support for the application of exoskeleton robot.

With the proposal of carbon neutrality, how to improve the proportion of clean energy in energy consumption and reduce carbon dioxide emissions has become the important challenge for the traditional energy industry. Based on the idea of multi-energy complementarity, a typical integrated energy system consisting of electric system and gas system is constructed based on the application of power to gas (P2G) technology and gas turbine in this paper. Furthermore, a multi-objective optimization model with economic improvement, carbon emission reduction and peak-load shifting as objectives is proposed, and solved by BSO algorithm. Finally, a typical power-gas coupling system is selected as an example to verify the effectiveness of the model. The results showed that the proposed multi-objective optimization model based on BSO algorithm can better play the complementary characteristics of the electric and gas system, and significantly improve the comprehensive benefits of system operation.

This paper puts forward an optimal quick-response variable structure control with a single-phase sine-wave inverter application, which keeps harmonic distortion as low as possible under various conditions of loading. Our proposed solution gives an improvement in architecture in which a quick-response variable structure control (QRVSC) is combined with a brain storm optimization (BSO) algorithm. Notwithstanding the intrinsic resilience of a typical VSC with respect to changes in plant parameters and loading disruptions, the system state convergence towards zero normally proceeds at an infinitely long-time asymptotically, and chattering behavior frequently takes place. The QRVSC for ensuring speedy limited-time convergence with the system state to the balancing point is devised, whilst the BSO will be employed to appropriately regulate the parametric gains in the QRVSC for the elimination of chattering phenomena. From the mix of both a QRVSC together with a BSO, a low total harmonic distortion (THD) as well as a high dynamic response across different types of loading is generated by a closed-loop inverter. The proposed solution is implemented on a practicable single-phase sine-wave inverter under the control of a TI DSP (Texas Instruments Digital Signal Processor). It has experimentally shown the simulation findings as well as the mathematical theoretical analysis, displaying that both quick transient reaction as well as stable performance could be obtained. The proposed solution successfully inhibits voltage harmonics in compliance with IEEE 519-2014’s stringent standard of limiting THD values to less than 5%.

Spectrum management policies are responsible for poor utilisation of the radio spectrum. By carrying out dynamic spectrum management (DSM), cognitive radio (CR) can increase the radio spectrum in wireless systems efficiently. CR technology accounts for the improvement in the spectrum utilisation significantly. One issue of DSM in CR is the assignment of frequency channels among its users. Herein, a general model and four utility functions for optimal channel assignment in open spectrum systems such as CR networks have been defined. First, a new utility function with a better fairness than the other functions is proposed. Then, two new different channel assignment methods, based on the artificial bee colony (ABC) and bee swarm optimisation (BSO) algorithms, are proposed, whereas other certain evolutionary algorithms and colour sensitive graph colouring (CSGC) are used to compare the performances. In order to decrease the search space, based on the channel availability and interference constraints a mapping process between the channel assignment matrix and the position of the bees has been proposed. Our simulation results, compared to the optimal solutions, show that our algorithms drastically improve network performance by reducing interference.