The research in this article aims to consider low-carbon factors, through reasonable vehicle allocation and optimization of distribution routes, to achieve high satisfaction and low total cost, and... Show More

The research in this article aims to consider low-carbon factors, through reasonable vehicle allocation and optimization of distribution routes, to achieve high satisfaction and low total cost, and to provide an optimized solution for fresh food distribution companies. In this article, cargo damage cost, energy cost, and carbon emission cost are added to the total cost, and customer satisfaction constraints based on time and quality are added, respectively, to construct a multi-vehicle cold chain VRP model under the low-carbon perspective. In order to obtain a good initial path method, a good chromosome is generated and added to the initial chromosome population according to the constraints of the vehicle type and time window, and the local elite retention strategy is combined to speed up the population convergence. Finally, taking the data of A Fresh Food Company as an example, the MATLAB software is used to realize the programming, which verifies the validity and superiority of the multi-vehicle cold chain VRP model under the low-carbon perspective.

In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF)... Show More

In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF) algorithms have encountered computational challenges such as similarity errors and user rating habits. This research addresses these issues by emphasizing user profiling techniques. This article proposes an innovative user profile updating technique that explores the key components of user profile (basic information, behavior, and domain knowledge). An enhanced kernel fuzzy mean clustering algorithm constructs a dynamic user portrait based on domain knowledge mapping. This dynamic portrait is combined with e-commerce personalized recommendation to improve the accuracy of inferring user interests, thus facilitating accurate recommendation on the platform. The method proposed in this article greatly improves the overall performance and provides strong support for developing smarter and more personalized e-commerce product recommendation systems.

How to correctly understand the existence of local government debt, study its risk classification and impact, give full play to the “dual nature” of debt with a full-caliber indicator system, and... Show More

How to correctly understand the existence of local government debt, study its risk classification and impact, give full play to the “dual nature” of debt with a full-caliber indicator system, and avoid debt risks to the greatest extent. That is the research direction of this article. In order to improve the accuracy and efficiency of risk assessment and effectively reduce the debt risk of government platform companies, a risk assessment method based on optimized back-propagation (BP) neural network is proposed. First, the method uses quantum genetic algorithm (quantum genetic algorithm, QGA) to adjust and determine the initial weight and threshold of BP neural network and realize the optimization of BP neural network model parameter setting. Then, the QGA-BP debt risk assessment of government platforms is verified that it performs well in the debt risk prediction of government platform companies, and its prediction accuracy and prediction speed are improved.

Based on the fuzzy method, this paper establishes a ranking model of the psychological quality of college teachers and an interception model of assessment indicators. On this basis, a quantitative... Show More

Based on the fuzzy method, this paper establishes a ranking model of the psychological quality of college teachers and an interception model of assessment indicators. On this basis, a quantitative evaluation method of college teachers' psychological quality is proposed by using the principles of fuzzy psychological evaluation and fuzzy recognition. According to empirical study, this evaluation approach is capable of providing a theoretical foundation for the next teacher training as well as a thorough assessment of the psychological qualities of teachers. The research concludes by pointing out that the model and evaluation approach can also be used to introduce and train university teachers, and it makes some sound recommendations for their development. An empirical study on the quantitative evaluation method of college teachers' psychological quality based on fuzzy psychological evaluation and fuzzy recognition principle is beneficial to better build the foundation of college teachers' psychological quality under the concept of harmonious education.

This study investigates the method of analyzing emotional tendencies in music courses and its application in lesson plan evaluation. Using a weighted method to analyze emotional tendencies in music... Show More

This study investigates the method of analyzing emotional tendencies in music courses and its application in lesson plan evaluation. Using a weighted method to analyze emotional tendencies in music curriculum, the study compares the results with existing literature, demonstrating the superior accuracy of the proposed method. To evaluate lesson plan quality, a combination of self-assessment, mutual evaluation, group evaluation, and the middle school music lesson plan evaluation form is recommended for comprehensive assessment. The study's method for comment polarity achieves an accuracy rate of 69.19%, significantly outperforming other methods. Additionally, improvements in lexical feature extraction reduce computation complexity and interference factors in sentiment polarity analysis. In conclusion, this study offers valuable insights for enhancing teaching effectiveness, lesson plan quality, and understanding course feedback.

This article takes college students' learning adaptability as the research object, adopts B/S structure to develop a learning adaptive platform, designs a learner data model, a learning style model... Show More

This article takes college students' learning adaptability as the research object, adopts B/S structure to develop a learning adaptive platform, designs a learner data model, a learning style model, a learning resource presentation module, and an ability level test module; tests the platform through simulated data; and analyzes college students' learning style, knowledge level and learner collaboration level. The results show that college students' learning adaptation has the characteristics of flexibility, individuality, initiative, and reflection. A self-adaptive learning platform can understand its learning state and effect through learning evaluation, adjust its learning strategies and methods in time, and help college students better understand and master knowledge. The research results provide theoretical data support for the exploration of college students' learning adaptability under the background of wisdom education.

Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental... Show More

Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental temperature's influence on thermoelectric piles' measurement accuracy and proposes a environment temperature compensation based on GA-BP (Genetic Algorithm-Back Propagation) neural network. The GA algorithm makes up for the slow iterative speed and easy to fall into local optimization of BP algorithm. The experimental simulation results show that environment temperature compensation based on GA-BP can accurately correct the measurement error caused by environmental temperature and other factors.

This paper introduces a technology, a data-driven optimization model of manufacturing service in intelligent manufacturing process using deep learning algorithm and resource agent (DDR), and a... Show More

This paper introduces a technology, a data-driven optimization model of manufacturing service in intelligent manufacturing process using deep learning algorithm and resource agent (DDR), and a data-driven resource agent that represents available manufacturing resources. Asset agent is an intelligent module of entity production unit, which has powerful functions of data processing and service management. This paper includes the method of designing expert-based processes, the current process realization model, and the key performance indicators (KPI) used to evaluate the optimization work. The model aims to maximize efficiency, reduce the cost of manufacturing resources, improve the production and maintenance efficiency of network resources, and improve the manufacturing service level. Finally, the efficiency and technical feasibility of the model are evaluated through a typical example of industrial product production process.

The comprehensive evaluation and selection of suppliers under the environment of supply chain management has become a key factor affecting the success of supply chain. How to select suppliers and... Show More

The comprehensive evaluation and selection of suppliers under the environment of supply chain management has become a key factor affecting the success of supply chain. How to select suppliers and the strategic partnership between suppliers under the environment of supply chain management has become an important challenge. To solve this problem, this paper takes the supplier evaluation and selection of Guangzhou Automobile Toyota Company as the research object, constructs the index system of supplier comprehensive evaluation and selection, uses the RBF neural network algorithm to establish the supplier evaluation and selection model, and makes an experimental study. The results show that radial basis function neural network is a local approximation network, which has a unique and definite solution to the problem, and there is no local minimum problem in BP network. It is a method that enables enterprises and suppliers to have a clear understanding and seek further promotion together. The research provides theoretical data support for enterprise managers to make decisions.