The sorting residue of copper clad laminate stacked on site contains polybrominated diphenyl ethers (PBDEs) and novel brominated flame retardants (NBFRs). If not properly treated, they will be discarded into the surroundings and cause secondary pollution. The PBDEs and several NBFRs were detected in the sorting residue of copper clad laminate (SRCCL) of the storage yard. The ∑9PBDEs and ∑5NBFRs concentrations ranged from 2.71 to 122.83mg/kg. Different storage yards displayed three composition patterns of PBDEs, indicating that their sources were different, with domestic and imported ones. All results indicate that untreated SRCCL dumping sites are an important source of PBDEs and their emissions.

The emergence of ride-hailing services has revolutionized the transportation industry for passengers, prompting taxi services to evolve from the conventional method of street-hailing to a combined “online-offline” operational approach. In this new model, taxis combine on-street pickups with platform-based orders. When market supply and demand are imbalanced, leading to excess orders, taxis prioritize street-hailing for faster customer acquisition. Meanwhile, ride-hailing platforms address surging passenger demand by offering subsidies to attract more vehicles to participate in online dispatching. This study focuses on the strategic choices of ride-hailing platforms and taxis during order overflow scenarios. An evolutionary game model is constructed to simulate taxi street hailing behavior under such conditions. Simulations are conducted to generate interpolation-based probability curves, including the probability of taxis accepting offline orders and the probability of regional orders being served. These findings offer recommendations for ride-hailing platforms on designing subsidy strategies in response to changes in regional order density. Additionally, the study examines how factors such as order distance, passenger-seeking costs, and platform commission rates influence taxis’ order acceptance strategies.

Aiming at the problem that military equipment resources are easily affected by high-frequency random disturbances such as emergency order insertion, abnormal processing quality, equipment operation failure, etc. in the process of processing task execution in the cloud manufacturing environment, which causes the quality of service (QoS) of product processing to fail to meet the personalized needs of customers, a dynamic selection method of equipment resources in the cloud manufacturing environment is proposed. According to the running characteristics of cloud manufacturing services, a dynamic evolution model of service quality towards the process of processing task execution under cloud manufacturing environment is constructed. Taking the state vector and control vector in the dynamic evolution model as node variables, combined with Bayesian network, a decision model for dynamic selection of military equipment resources under random disturbance is established. By solving the model, the corresponding scheme of the optimal QoS value is obtained, and the dynamic selection of military equipment resources is realized. The experimental results show that this method can effectively and dynamically select military equipment resources, reduce the price and time cost of military equipment manufacturing, and improve the reliability of product processing, platform satisfaction and comprehensive QoS score.

This study examines the relationship between career aspiration and career adaptability, and focuses on the mediating effect of coping efficacy. A total of 377 students of higher vocational education were measured using the career aspiration scale, the coping efficacy scale and the career adaptability scale, and the study showed these results. (1) There were significant differences in career aspiration in terms of whether they are student leaders , coping efficacy in terms of whether they were student leaders, and career adaptability in terms of whether they were student leaders, whether they are the only child of their parents，whether they have received career counseling, and whether they participated in parttime jobs, internships or social practices. And there were also significant differences in career aspiration and career adaptability in terms of the interaction of gender and whether they have received career counseling. (2) Career aspiration and its three dimensions were positively correlated with coping efficacy and coping efficacy was also positively with career adaptability and its four dimensions. (3)career aspiration was a significant direct positive predictor of career adaptability. When the mediating variable coping efficacy was included, career aspiration could still significantly predict career adaptability, coping efficacy mediated the relationship between career aspiration and career adaptability. Career education can enhance career adaptability of students in higher vocational education by improving their coping efficacy.

Ammonia synthesis is vital for fertilizer production, but the traditional Haber-Bosch process is energyintensive and environmentally burdensome due to its high-temperature and high-pressure operations. Plasma-catalytic ammonia synthesis offers a sustainable alternative, generating large datasets under various experimental conditions. To optimize energy efficiency, we established a database with 305 data points and 7 experimental parameters, each linked to its corresponding energy efficiency. We employed an Extreme Gradient Boosting (XGBoost) regression tree model, achieving an average R² value of 0.9434 for predictions. Bayesian Optimization (BO), using Gaussian Process Regression as a surrogate model, systematically explored the experimental parameter space. It utilized XGBoost predictions to identify parameter combinations that maximized energy efficiency. After 50 iterations, the optimal parameters were identified: 6.4 g catalyst mass, 50 mm grounding electrode length, nickel metal catalyst, Al₂O₃ catalyst support, 5 W power, 160 ml·min⁻¹ flow rate, and a 1:2 feed ratio. Under these conditions, the energy efficiency of plasma-catalytic ammonia synthesis improved to 1.49 g·kW·h⁻¹, a 22.1% increase from the highest value of 1.22 g·kW·h⁻¹ in the dataset.

With the gradual improvement of the resilience and vitality of the tourism market, promoting the high quality development of the tourism industry with the new development concept has become an important fundamental issue for the sustainable growth of the regional green economy. The article measures and analyzes the level of high-quality development of Guizhou’s tourism industry from 2012 to 2021 on the basis of constructing an evaluation index system for high-quality development of tourism, using methods such as entropy value method and gray correlation analysis. The results found that: the average value of the development index of Guizhou’s tourism high-quality development subsystem is ranked in the order of GD, ED, ID, SD, OD and CD, the level of green development and effective development of Guizhou’s tourism industry is higher, while the level of coordinated development of the tourism industry and the level of openness are insufficient; HQD, ID, GD, OD, SD and ED show a fluctuating upward trend, while CD is in a fluctuating downward state, and the tourism high-quality development system of Guizhou has gone through a fluctuating upward trend. Guizhou tourism high-quality development system has experienced three stages of evolution, namely, “stable rise, rapid rise and fluctuating rise”, and the level of Guizhou tourism high-quality development and the development level of its various sub-systems have been affected by the New Crown Epidemic to varying degrees, with a greater impact on the level of open development of the tourism industry. GDP, per capita park green space area and tourism high-quality development index correlation is larger, while the total amount of SO2 emission and tourism high-quality development index correlation ranked at the bottom, tourism industry R & D funding is the most important factor affecting the level of high-quality development of Guizhou’s tourism industry, and the total amount of SO2 emission has the smallest impact on it. On this basis, countermeasures for the high-quality development of Guizhou’s tourism industry are proposed.

In this paper, the vibration characteristics of the helical cylindrical gear split-torque transmission system with diaphragm coupling misalignment are studied. Firstly, the 14-DOF nonlinear simulation model of the helical cylindrical gear split-torque transmission system are established. To improve the model accuracy, time-varying mesh stiffness, random backlash, mesh error and bending deformation of shaft are considered respectively. Secondly, according to the nonlinear simulation model, the differential equations are established, and the differential equations are sovled with the time-varying stiffness of diaphragm coupling misalignment. Finally, the relationship between the phase of bolt group in diaphragm coupling and the asymmetric property of the split-torque transmission system is determined by numerical methods. The results show that the asymmetric property of split-torque transmission system could be effectively improved by changing the phase of bolt group in diaphragm coupling. The method is proven effectiveness by a modification work involved in this paper, and have reference significance for solving engineering problems.

Due to the complexity of genome structure and technical conditions, wheat genome structure variation has not yet been comprehensively and accurately detected and evaluated for genetic effects. The aim of this study is to construct a method based on deep learning algorithm to accurately detect genomic structure variation in wheat. The method converts genomic data into image form by genomic structure variation image generation algorithm. A gene structure variation prediction model is constructed based on deep learning, and efficient and accurate structure variation prediction is realized by automatically extracting and analyzing the variation features in the image. The experimental results show that this method has better detection performance than other structural variation detection methods based on third-generation sequencing data, especially in the structural variation detection of the “Sequencing and Assembly of Spring Wheat Genome in China” project, and the accuracy, precision, and recall rate of this method are all over 90%. This study provides a novel deep learning framework for efficiently detecting structural variants in the wheat genome, and provides powerful technical support for genetic improvement and breeding research of wheat.

This paper analyzes and evaluates high school examination questions based on machine learning. The study first introduces Bloom’s classification method and constructs a categorized dataset of high school exam questions according to three steps of data collection, data annotation and data analysis. Then an automatic assessment model (WoBERT-CNN) based on WoBERT and Text-CNN is designed. The semantic similarity of word vector mapping is used to label the cases for determination, the improved WoBERT encoder is used to represent the text in word vectors, Text-CNN is used as a text classifier to extract the textual semantic features, and the features are integrated and screened, so as to realize the automatic classification of the cases in Bloom’s taxonomy. Finally, based on the deep representation framework, the text information of the test questions is deeply mined and utilized to establish the relationship between the text of the test questions and the actual difficulty, and to realize the difficulty prediction of the test questions.The classification accuracy of the WoBERT-CNN model reaches more than 92%.The prediction error range of the H-MIDP model on the score rate of the test questions is between 1.3% and 3.2%, which is not too far from the real value. In conclusion, the automatic assessment model and difficulty prediction model designed in this paper can be applied in the analysis and evaluation of high school test questions, helping the high school test paper proposition and talent cultivation strategy.

Aiming at the limitations of the sample weight updating mechanism of the traditional Adaboost algorithm, the article proposes three improved algorithms based on the joint weight updating mechanism to solve the problems of sample distribution imbalance, etc. The MW_UA algorithm is centered on the updating of the proportion of the sample weight, the OW_UA algorithm realizes the updating of the weight of the sample set based on the classification effect of the initial samples, the MAR_UA algorithm employs sample The MAR_UA algorithm uses the sample Margin to quantify the degree of difficulty of sample classification and then obtain the corresponding sample weights. The performance test experiments and prediction simulation experiments of the improved algorithm are based on the MWSP and Caltech datasets. The experimental results show that the average accuracy and F1 score of MAR_UA algorithm in the two datasets are over 90%, which is the best performance among all the improved algorithms. The algorithm also shows optimal prediction error convergence performance in both datasets, and the training error can be converged to the minimum within 40 times of training. When the algorithm is applied to the simulation experiment of pedestrian recognition, it has the best recognition effect in the sunny environment, with a detection rate of 94.1%. In addition, the error between its predicted and real values of offshore wind speed is no more than 0.2 m/s, and the ERMS and EMA are reduced by 63.52% and 55.5%, respectively, compared with the traditional Adaboost. This study optimizes the weight updating mechanism of the joint Adaboost algorithm using various methods, which can provide new ideas for the optimization research of the weight updating mechanism.