Abstract Credit rationing problem in agriculture can seriously restrict the process of agricultural modernization. The paper starts from the definition and types of credit rationing, systematizes the classic literature and the latest research progress on credit rationing, summarizes and expands the types of credit rationing, introduces the concept of discriminatory rationing, and reclassifies the concept of volume rationing. In addition, the paper summarizes the research results on credit rationing in the agricultural sector from two perspectives: ″internal″ and ″external″, and believes that future research should be further studied from the perspectives of theoretical depth and differentiation of national conditions.
Abstract In this project, a near infrared spectrometer is used to obtain the spectral images of typical defects of composite insulators of high-voltage transmission lines, and the HOG features and LBP features in the spectral images are extracted based on image processing techniques. The two are added to the support vector machine model (SVM) in a fusion way, and the construction of a typical defect recognition model for composite insulators is completed by iterative training, and the model in this paper is verified and analyzed based on simulation experiments. The LBP-HOG-SVM model in this paper has significant recognition effect compared with the original Faster R-CNN model, in which the recall and precision of this model are 9.76% and 9.2% higher than the original model for glass-type insulators, and 11.49% and 13.06% higher than the original model for composite insulators, which confirms that the LBP-HOG-SVM model has significant recognition effect in the identification of typical defects of composite insulators of ultra-high-voltage transmission line, and that the model has significant recognition effect compared with the original model. Line composite insulators in the field of typical defect identification technology.
Abstract Conjugated linoleic acid has a large medicinal value, and the biosynthesis of conjugated linoleic acid has advantages that traditional chemical synthesis methods do not have. In this paper, taking Lactobacillus acidophilus mutant strain B-5 as an example, a kinetic model of conjugated linoleic acid synthesis catalysed by conjugated linoleic acid isomerase was constructed, and genetic algorithm was used to solve the parameters of the model and experimentally verified the fitting effect of the model to the real value. The pathway for the isomerase-catalysed synthesis of conjugated linoleic acid was optimised experimentally, and it was found that factors such as linoleic acid concentration, pH and reaction temperature had the greatest influence on the yield of conjugated linoleic acid. Experimental and orthogonal tests showed that the theoretical yield of conjugated linoleic acid was maximum when the substrate concentration was 2%, pH was 6 and the reaction temperature was 36°C.
Abstract Urban-rural integration in peri-urban areas of metropolitan cities faces many challenges, involving resource allocation, coordination of social structure, and improvement of infrastructure in the process of urbanization. Traditional urban-rural planning models can no longer cope with the rapidly changing needs, and new ideas and methods are urgently needed. The introduction of AI models provides data-driven support for urban-rural integration, which enables more accurate planning guidance and path realization. In this study, an intelligent planning framework is proposed to comprehensively assess the key elements of urban-rural integration planning by analyzing the regional characteristics and development trends of metropolitan peri-urban areas and combining the predictive capability of AI models. First, a multidimensional evaluation system for urban-rural integration is established through data collection and analysis, involving various aspects such as economic, spatial, and social integration dimensions. Second, the path realization model of urban-rural integration is constructed by combining hybrid machine learning algorithms, and the planning gets the implementation strategies in different development stages. The results show that the planning scheme based on the AI model can improve the efficiency of resource allocation, optimize the spatial layout of urban and rural areas, and effectively promote the sharing and flow of social resources. Finally, through simulation and field verification, it is concluded that intelligent planning not only improves the science of decision-making, but also provides a new path for the sustainable development of metropolitan peri-urban areas.
Abstract In today’s education field, information technology is increasingly becoming an important driving force for changes in the teaching mode. In order to explore the influencing factors and development paths of IT-assisted early childhood teachers’ teaching competence, this paper, based on the contextualized expected value theory, selected early childhood teachers to conduct a questionnaire survey to explore the role of IT-assisted and expected value beliefs (i.e., self-efficacy and perceived usefulness) in the development of IT-supported teaching competence. Based on this, the path of IT-assisted teachers’ teaching competence enhancement is proposed, and two groups of early childhood teachers are selected to conduct experiments to validate the effect of the path of IT-assisted teachers’ teaching competence enhancement. The results showed that IT-assisted positively affected teachers’ perceived usefulness and self-efficacy through positively affecting teachers’ self-learning and informationalized teaching (p < 0.05). After IT-assisted teaching, the teaching ability of teachers in the observation group was significantly improved, with 29.0% to 58.2% improvement in teaching awareness and attitude, teaching content innovativeness, combination of foundation and skills, and theory-to-practice. In this paper, the path of improving teachers’ teaching ability with the assistance of information technology is practicable.
Abstract At present, with the growing demand for innovative cultural and tourism products, how to explore a path of high-quality development of cultural and tourism industry under the background of smart tourism has become a research hotspot. This paper uses entropy power method and regression analysis method, takes province A as a case study, measures and analyzes the level of high-quality development of cultural and tourism industry in province A, and explores the influencing factors affecting the high-quality development of cultural and tourism industry, and finally puts forward the high-quality development path of cultural and tourism industry in province A. The study concludes that the high-quality development path of cultural and tourism industry in Province A can be carried out in three aspects: deepening the new layout of cultural and tourism integration and development, expanding the new mode of cultural and tourism integration and development, and building the new carrier of cultural and tourism integration and development.
Abstract In order to improve the accuracy of estimating the feeder line loss rate in distribution networks and make it more effective for line maintenance management, a feeder line loss estimation method based on the fuzzy C-means clustering long short-term memory network Transformer model is proposed. Firstly, based on the two dimensions of data parameter availability and line loss correlation, a three-dimensional evaluation index for the feeder line loss rate of the distribution system was constructed. Fuzzy clustering technology was used to effectively classify the feeders, identify the benchmark feeders of each category, and preprocess the original data. Secondly, a line loss prediction model with a dual layer structure is introduced, in which the first layer adopts a gate mechanism of long short-term memory network, aiming to capture the dependency characteristics in the data sequence related to feeder line loss in the distribution network. The second layer integrates the multi head self attention mechanism of the Transformer model, and obtains prediction data by combining it with the characteristic data of distribution network feeder line loss, which can ensure the efficiency and accuracy of short-term distribution network feeder line loss prediction. Finally, to verify the effectiveness and practicality of the proposed method, an application analysis was conducted using the distribution network feeder of a power supply enterprise in a city in Guangdong Province as an actual case.
Abstract In order to effectively promote the development of industry-teaching integration and innovative education model, it is necessary to actively build higher vocational industrial colleges. In this paper, we take the “Colorful Craftsmen” Automobile Repair Industrial College as an example, establish the evaluation index system of industrial college construction, determine the index weights jointly with the improved IF-AHP and CRITIC method, and analyze the evaluation results of the actual cases by using the grey correlation degree method. The influencing factors of talent cultivation development in industrial colleges are examined, and a multiple linear regression model is used to explore, after which a talent cultivation model is proposed for the development of industrial colleges. The results show that the evaluation score for the construction of this automobile repair industrial college is 3.68, which is a good level, and the evaluation of its management system has the best performance (4.07), which is an excellent level. In the integration of industry and education, education concept, curriculum system, practical teaching quality, enterprise participation, guarantee mechanism, and cooperation effect are all significantly correlated with the development of talent cultivation at the 1% level, with the effect of industry-university-research cooperation, enterprise participation, and practical teaching quality being the most prominent, with the coefficients of 0.365, 0.352, and 0.344, respectively. Building a talent cultivation model covering the top-level design of teaching, curriculum teaching, and practical teaching three links of talent cultivation model to promote the cultivation of high-quality talents in industrial colleges.
Abstract In the era of booming Internet, the network affects every aspect of people unconsciously, while illegal attacks in network traffic bring security risks, and traditional traffic detection faces serious challenges. In this study, the stacked self-coding neural network is combined with the twin neural network model to construct the SAE-SCNN model, and the traffic features are extracted by using the convolutional layer and pooling layer in the model. The traffic features are classified according to the calculation results of the distance function, and then piggyback on the method to build a big data traffic analysis model. The performance evaluation test results show that in the public datasets CICIDS2017 and UNSW-NB15, the detection performance of this paper for traffic shows significant improvement compared to other models. For normal data traffic in the simulation test, the model detection accuracy can reach up to 99.99%, and it also has high accuracy and generalization for abnormal traffic detection.
Abstract The yangqin was mainly used as an accompaniment instrument in the early days, and after years of development, it has become one of the most important members of Chinese folk musical instruments, and plays an important role in ensembles, repertoire and orchestra. In this paper, a model for analyzing the spectral data of yangqin performance is constructed. The feature selection based on recursive feature elimination and random forest is used to characterize the spectral features of the yangqin more accurately. Following the nearest neighbor principle and the center of mass principle, the codebook and iterative training parameters are set based on the LGB algorithm to realize the quantization of spectral data. This paper takes the yangqin concerto work “Smoke Gesture” by young composer Liu Chang as the research object, and analyzes the spectral data and artistic expression in its performance. In percussive playing, from the bass to the treble area, the peaks decrease from about 20 to about 10, and the distribution of peaks is more concentrated. The energy in the scraping method was concentrated at 394~2442 Hz, while the pitch frequency in the anti-bamboo method was generally higher than the reference frequency value, and the energy was mainly concentrated at 440~740 Hz. The average ratings of all dimensions of the total dimension of the evaluation in terms of artistic expression were all greater than 70, and the performance skills were better.
Abstract Salvia miltiorrhiza is a plant of the genus Sage in the family Labiatae, and the dried roots and rhizomes are important medicinal herbs in traditional Chinese medicine, and triterpenoids are important bioactive substances in the fruits with pharmacological effects. The study utilized transcriptome and triterpenoid metabolite analyses to obtain 21 candidate structural genes that may be related to triterpenoid biosynthesis and 8 transcription factors involved in triterpenoid biosynthesis. During the growth and development of Salvia miltiorrhiza, the content of triterpenes and the expression of structural genes farnesyl pyrophosphate synthase (ZjFPS) and squalene synthase (ZjSQS) were highly correlated, and based on the correlation analysis of the functional genes with the precursors of the salvinor pathway of salvia glycoside synthesis, salvia glycoside element and de-benzoyl salvinorhizaide, 21 CYP450 candidate genes, 13 2DD candidate genes, and 14 UGT candidate genes were identified. We identified 21 CYP450 candidate genes, 13 2ODD candidate genes and 14 UGT candidate genes, and successfully cloned 5 highly expressed UGTs and carried out crude enzyme extraction.
Abstract In recent years, the incidence of Parkinson’s disease has increased linearly, and evidence-based treatment is the basic method of Parkinson’s disease Chinese medicine diagnosis and treatment. Based on Bayesian theory, this paper proposes an algorithm for the classification of TCM symptoms. Seizing the defects of uneven identification of signs and complex relations between signs, the algorithm is optimized by using the method of theme modeling, and finally an improved version of the TCM sign classification algorithm is formed. Comparing the ROC curves with DenseNet121 and DAMNet models, the improved TCM evidence classification algorithm achieves an accuracy of 96.73% and a precision of 97.45% with a minimum parameter of 7.8M. It proves that the improved TCM evidence classification algorithm is more efficient and precise in the diagnosis of Parkinson’s disease, and provides basic data and directional guidance for the future clinical application of the TCM evidence classification algorithm in the diagnosis and treatment of Parkinson’s disease.
Abstract Chinese dress cheongsam is a treasure of Chinese culture, with the reality of online shopping and virtual cultural experience requirements, the virtual display and simulation technology of cheongsam and hanbok is also getting more and more attention. In this paper, we use GCN to deeply learn the deformation characteristics of cheongsam and hanbok, and successively realize the three-dimensional reconstruction of cheongsam and hanbok through pose estimation, feature line fitting and surface refinement. The spatio-temporal feature progressive fusion, multi-scale feature extraction and reconstruction modules are designed, and the fabric animation simulation method based on geometric images is proposed to enhance the display effect of cheongsam and hanfu. The reconstruction results of this paper’s method realize a more obvious improvement compared with all the reference models. The animation simulation error of cheongsam fabric is about 14% of PCA algorithm, and the time consumption is only about 2% of PBD algorithm, which verifies the feasibility of this paper’s work.
Abstract The rapid development of the times has caused information technology to penetrate into students’ lives at an unprecedented speed, which not only changes the way of students’ interpersonal communication and entertainment, but also profoundly affects their spiritual world, and puts forward new challenges for college students’ psychological education. This paper integrates social cognitive theory and behavioral theory to summarize the principles of cognitive behavior. Using knowledge mapping technology, knowledge is strung together using a mesh structure, and the relationship between knowledge points is represented by visualization technology. Applying knowledge mapping to the field of education, the construction of knowledge mapping for college students’ psychoeducation courses is completed sequentially through the steps of identifying resources and tools, sorting out the hierarchical structure of knowledge, creating knowledge points at all levels, establishing the relationship between knowledge points, and associating knowledge points with learning resources. Introducing MinsCourse teaching system, combined with knowledge mapping, obtaining students’ cognitive behavior data and using K-Means algorithm, dividing students’ cognitive behavior. The clustering results show that the samples are clustered into five categories, in which the category with the largest sample size accounts for 27.36% and the smallest category accounts for 10.82%, and the difference between each category is more significant. SPSS software was used to carry out paired-sample t-test on the data of students’ diagnostic and summary knowledge test scores, and the average scores of the pre- and post-tests were 60.92 and 83.93, respectively, and the mean value of the students’ scores after learning was higher than that before they had not learned, so it can be seen that the teaching model of college students’ psycho-educational courses designed by the knowledge mapping in this paper can effectively achieve the purpose of promoting learning through evaluation and improve the level of students’ mastery of knowledge.
Abstract Aiming at the problem of low sharing efficiency of teaching resources and limited sharing scope of traditional teaching platforms, this paper first designs a digital English teaching resource sharing platform through cloud computing technology. After that, the cloud platform resource structure, educational application services and other modules are refined, and the authentication framework is constructed to realize the safe sharing of data. The results show that the remote classroom teaching method of English teaching resource sharing based on cloud computing demonstrates significant advantages. In addition, the platform not only plays a stable role in resource sharing efficiency, but also has high efficiency, which is highly practical and effective. The English teaching resource sharing and remote classroom platform proposed in this paper is applied to English classroom teaching in colleges and universities, which can stimulate students’ learning initiative, improve students’ learning efficiency, and comprehensively enhance students’ interest in learning English.
Abstract Motor loss will reduce the working efficiency and affect the service life of the motor. Therefore, reducing motor losses and improving the working efficiency of motors have been the focus and hotspot of research in the motor industry. In this paper, for the special structure of LFSPM (Linear Flux Switching Permanent Magnet Motor) motor, based on the multi-physical field coupling analysis, the calculation of permanent magnet eddy current loss is studied and discussed. Then the effects of the rotational frequency on the loss at different operating speeds are compared by simulation. The difference between the two in terms of motor losses is analyzed. The experimental results show that the permanent magnet eddy current loss under the rated load and secondary motion speed reaches stability after 25ms; the total iron consumption of the motor basically remains unchanged when the overload multiplier is one to three times. In terms of current loss, after stabilization, the secondary loss is 2.5kW at constant differential frequency, and the secondary current loss is 2.2kW under vector control, which indicates that constant differential frequency also has an advantage in terms of secondary loss, and it is suitable for occasions with high requirements on normal force.
Abstract This paper mainly takes the visual elements of infomercials as the perspective and the theory related to advertising effect as the basis, and uses the regression neural network model to study the influence of elements’ color, shape, brightness, etc. on the effect of infomercials and their functioning mechanism. A collaborative attention model combining the visual features of advertisement images and text features is constructed to improve the accuracy of users’ visual attention prediction. Element color, shape, brightness, etc., element position, size, number, etc., style selection and design all predicted social presence significantly (P=0.001), and the overall social facilitation effect and social presence predicted the advertising effect significantly, with the standardized coefficients of 0.617, 0.847, and 0.835, respectively, with a P- value equal to 0.001. Advertisement likability, advertisement aesthetics, and advertisement brand likability were negatively related to the average visual attention intensity. degree are negatively correlated with the average visual attention intensity, with correlation coefficients corresponding to -0.68, -0.86 and -0.84, respectively, which suggests that the more aesthetically pleasing the visual effect, the faster the user’s attention is perceived.
Abstract Creative thinking is both an important part of scientific literacy and one of the most important indicators of the development level of students’ scientific literacy. In art and design teaching, the way of cultivating students’ creative thinking ability is particularly important. This paper initially constructs a set of evaluation index system covering 12 indicators, and invites 40 scientific career experts to conduct three rounds of research using the Delphi method, and optimizes the indicators of this evaluation system based on the results of the research and analysis. In order to assess students’ creative thinking ability more objectively, this paper introduces BP neural network, draws on its training process and genetic algorithm optimization to carry out computer algorithm simulation, which is applied in the comprehensive assessment of students’ ability. Combined with the results of the computer algorithm evaluation, this paper suggests that in the future art design teaching process, teachers should boldly innovate, change the original teaching mode, and focus more on the student’s subjectivity. At the same time, they should also promote the formation of students’ creative consciousness in various aspects, encourage students to put into practice, and help the growth and maturity of their creative thinking ability.
Abstract The popularization of various types of information technology and equipment will inevitably affect the current marketing situation, the proportion of data technology in product marketing is increasingly aggravated. This paper points out the benefits of big data technology added to the marketing link, and outlines the main implementation methods of the current smart marketing. Establish the sales marketing forecasting model based on ARIMA, use the data time series chart as well as the DF test to carry out a smooth test on the time series of product sales data, draw the seasonal difference after the time series chart, and obtain the difference order. The AIC criterion was applied to determine the model parameters, and the sales forecast results based on the ARIMA model were compared with the actual sales volume. In order to optimize the sales forecasting results of ARIMA model, a combined forecasting model of ARIMA model and BP model is established. Analyze the construction premise of the combined forecasting model and carry out the combined model prediction. Comparing the relative error percentages of the prediction results of ARIMA-BP model, ARIMA model, and BP model, which are 2.948%, 9.045%, and 7.233%, respectively, the combination model reflects a better effect, and the constructed ARIMA-BP combination prediction model is able to optimize the prediction accuracy of the ARIMA model, which is more convenient to carry out the analysis of the product wisdom marketing.
Abstract In order to realize the personalized recommendation system for college students’ Civic and Political Education, this paper improves the Pearson’s similarity calculation method of traditional recommendation algorithm by adding the popular resource penalty factor and the time decay penalty factor on the basis of resource collaborative filtering hybrid recommendation algorithm, and obtains the resource similarity model of hybrid recommendation algorithm. On this basis, the hybrid recommendation algorithm is used to recommend the learning resources of Civic and Political Education for college students on the online learning platform, and the accuracy and adaptive effect of the hybrid recommendation algorithm are analyzed. The results show that the cumulative hit rate of students increases with the intensity of the recommendation list, and the accuracy rate of active learners is always the highest (97.43%), followed by potential learners (83.77%) and inactive learners (63.16%). The greater the number of videos watched by the three types of college students, the greater the F1 value (88.26%, 77.26% and 43.71%), and the better the model performs. The average difficulty of the educational video resources recommended by the hybrid recommendation algorithm is in line with the students’ own weak ability, and the recommended difficulty is mostly higher than or equal to the difficulty of the actual learning videos. Its recommendation difficulty classification for three types of learners is between 0.1163-0.1399, 0.1163-0.1399 and -0.0173-0.0191, and it is clear that the collaborative filtering hybrid recommendation algorithm model of educational videos recommended by the algorithm proposed in this paper has good adaptability.
