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This integrative review aims to evaluate the experiences of health and social care practitioners with regard to how they exercise professional curiosity in child protection practice. Professional curiosity gained significant currency following the Munro Review of Child Protection (2010) in England, as a means of seeking clarity on what is happening within a family. However, a recurrent finding from child safeguarding practice reviews is that practitioners continue to struggle to exercise curiosity. This is evident within both the United Kingdom and international literature, although descriptors for the concept may differ. This study attempted to identify facilitators and barriers to applying professional curiosity to provide a greater understanding of this theoretical concept. Title and abstract review of 1428 articles identified from databases and 11 from other sources resulted in 52 papers for full‐text review. The quality of each article was appraised using the Critical Appraisal Skills Programme tool for qualitative studies, the Mixed Methods Appraisal Tool (MMAT) for quantitative/mixed method studies and the Joanna Briggs framework for theoretical/opinion papers. Key findings were recorded in the Summary Table of Literature Reviewed. Data extracts were thematically analysed. Twenty‐four papers predominantly from the UK, but also from Australia, Italy, Sweden and USA formed the data set. Overarching themes that emerged from the thematic analysis included: noticing dissonance, emitting curiosity, constructing meaning, facilitators, individual professional challenges, organisational and macro‐level influences and conceptual development. This review demonstrated that professional curiosity is multifaceted and involves a whole system approach, from empowered, knowledgeable and competent frontline practitioners to creative, innovative and empathic organisations, that value staff contributions and place the child's best interests at the forefront of service development. Recommendations are made for practice and further research. [ABSTRACT FROM AUTHOR]

The online individual behavior analysis is an important means for mining user interests. The user retweeting behavior prediction is typical problem for online individual behavior analysis. In order to make online learning behavior prediction method more suitable for the application of large-scale datasets, the improved condensed K nearest neighbor (ICKNN) method is proposed in this paper. Inspired by the idea of compressing samples in the condensed nearest neighbor (CNN) algorithm, this proposed method has adopted the Hadoop platform to parallelize the traditional CNN algorithm. For the traditional CNN method, as the value of K increases, the compression ratio decreases and so as the efficiency. The proposed ICKNN method can parallelize the traditional CNN method under the Hadoop framework to enhance efficiency. The proposed ICKNN method in this paper is validated by actual Twitter retweeting dataset. It can be seen that the proposed method in this paper has a higher compression rate than the traditional CNN algorithm. In terms of accuracy, the classification accuracy of the proposed ICKNN method has decreased compared with the traditional KNN method. However, the time consumed by the ICKNN method has significantly reduced compared with the traditional KNN method and CNN method, which can greatly improve the efficiency. [ABSTRACT FROM AUTHOR]

With the rapid development of computer science, there are more and more kinds of discrete dynamic systems. Computer integrated system CIMS, network communication database administrator system, and human behavior analysis system are all discrete dynamic systems. At present, many researchers have studied by adding human behavior data to discrete dynamic systems. This paper aims to study the behavior data of English learners by using the discrete dynamic modeling technology of complex systems and the discrete dynamic system modeling method of Petri nets. By adding the behavior data of learners to the discrete dynamic system of fuzzy Petri nets, the system is diagnosed and optimized. The experimental results show that the complex discrete dynamic system in this paper has achieved good experimental results according to the performance indicators selected in theory. Based on the combination of the above technologies and systems, the fuzzy Petri net discrete dynamic system studied in this paper improves the processing speed of English learners' behavior data. [ABSTRACT FROM AUTHOR]

There are some problems in modern English education, such as difficulties in classroom teaching quality evaluation, lack of objective evaluation basis in teaching process management, and quality monitoring. The development of artificial intelligence technology provides a new idea for classroom teaching evaluation, but the existing classroom evaluation scheme based on artificial intelligence technology has a series of problems such as high system cost, low evaluation accuracy, and incomplete evaluation. In view of the above problems, this paper proposes a solution of English classroom concentration evaluation system based on deep learning. The program studies the evaluation methods of students' class concentration, class activity, and enrichment degree of teaching links, and constructs an information evaluation system of students' learning process and class teaching quality. Based on the edge computing system architecture, a hardware platform with cloud platform AI+ embedded visual edge computing devices managed by an FPGA deep learning accelerated server was built. The design, debugging, and testing of classroom evaluation and student behavior statistics-related functions were completed. This scheme uses edge computing hardware architecture to solve the problem of high system cost. Deep learning technology is used to solve the problem of low accuracy of classroom evaluation. It mainly evaluates the classroom objectively by extracting indicators such as the students' attention in the classroom, and solves the problems of the students' inattentiveness in the classroom. After the test, the classroom evaluation system designed by the paper runs stably and all functions run normally. The test results show that the system can basically meet the requirements of classroom teaching evaluation application. [ABSTRACT FROM AUTHOR]

With the extensive application of virtual technology and simulation algorithm, motion behavior recognition is widely used in various fields. The original neural network algorithm cannot solve the problem of data redundancy in behavior recognition, and the global search ability is weak. Based on the above reasons, this paper proposes an algorithm based on genetic algorithm and neural network to build a prediction model of behavior recognition. Firstly, genetic algorithm is used to cluster the redundant data, so that the data are in fragment order, and then it is used to reduce the data redundancy of different behaviors and weaken the influence of dimension on behavior recognition. Then, the genetic algorithm clusters the data to form subgenetic particles with different dimensions and carries out coevolution and optimal location sharing for subgenetic particles with different dimensions. Through simulation test, the algorithm constructed in this paper is better than genetic algorithm and neural network algorithm in terms of calculation accuracy and convergence speed. Finally, the prediction model is constructed by setting the initial value and threshold to predict the behavior recognition, and the results show that the accuracy of the model constructed in this paper is improved in the analysis of behavior recognition. [ABSTRACT FROM AUTHOR]

This paper divides the research modes of consumer purchase behavior characteristics into three categories: experience-driven mode, theory-driven mode, and data-driven mode. An analysis algorithm based on customer consumption behavior is proposed, and the idea of combining customer consumption behavior factors such as satisfaction and loyalty is proposed. Through comparison, it is pointed out that the data-driven model is most suitable for analyzing the characteristics of online consumers' purchasing behavior. Using the decision support of knowledge base, different service schemes for customers with different evaluation degrees are realized. In order to improve the accuracy of sample classification and maximize the output function, genetic algorithm is used to optimize the samples. A deep neural network structure algorithm is proposed to classify customer transaction data samples. In this algorithm, the sheep nodes are not fixed, but the number of hidden layers and unit nodes of the neural network are dynamically determined according to the sample training. The research excavates various kinds of valuable information such as consumer preferences and consumption structure from the huge consumption data of consumers. It is not only helpful for enterprises to analyze consumers' consumption behavior and organize production but also helpful for enterprises to realize the concept of personalization. [ABSTRACT FROM AUTHOR]

Both dynamic and interactive, cross-cultural education emphasizes respect, interaction, and understanding between various cultural groups. Its fundamental principle is to acknowledge and value cultural diversity while opposing attempts to artificially unite cultures in order to recognise the equality and common heritage of all cultures. The goal of intercultural education, from an individual's perspective, is to support those who are educated in understanding, accepting, and respecting other foreign cultures in order to better adapt to and thrive in a multicultural society. To develop students' intercultural communication skills is the goal of intercultural education. Based on the associations between intercultural education and speculative reading, this essay analyzes the crucial role that intercultural English teaching and speculative reading instruction play in colleges and universities before going over the situation and approaches that English speculative reading instruction is currently facing. This paper primarily discusses the data on students from the student work management system, the relationship between grades and actual behavior, and the rules that are mined to provide a scientific basis for decision-making for the relevant units in colleges and universities. According to experimental analysis, the data mining algorithm in this paper performs better than the other two in terms of predicting students' English achievement, with an average accuracy of 0.86 compared to 0.71, 0.075, and 0.73 for the other three algorithms. The decision tree created using this methodology can quantitatively reflect the impact of different teaching factors on learning quality and offers a clear and reliable foundation for formulating targeted improvement strategies. [ABSTRACT FROM AUTHOR]

The investment decision-making behavior of investors refers to the capital purchase behavior of investors in order to achieve a certain purpose or goal. As an individual decision-maker, investors will be affected by various factors when making investment decisions, and the influencing factors may be caused by the outside or generated by themselves. The focus of this paper is to analyze the impact of investors' past returns on investors' decision-making based on multisource heterogeneous data. Multisource heterogeneous data can show the results of investors' behavior evaluation at multiscale and multilevel. Using conceptual deduction and empirical research methods, based on the analysis of investors' behavior, the financial risk prediction results are proposed. Financial risk prediction mainly analyzes the impact of investment returns on investors' decision-making from two aspects: individual returns and overall returns. The measurement indicators of investment decisions use investors' transaction frequency and asset size. [ABSTRACT FROM AUTHOR]

Human pose estimation (HPE) is a fundamental problem in computer vision, and it is also the basis of applied research in many fields, which can be used for virtual fitting, fashion analysis, behavior analysis, human-computer interaction, and auxiliary pedestrian detection. The purpose of HPE is to use image processing and machine learning methods to find out the positions and types of joints of people in pictures. There are two main difficulties in HPE. First, the complex human images make the model need to learn a highly nonlinear mapping relationship, and the learning of this mapping relationship is extremely difficult. Second, the highly nonlinear mapping relationship needs to be learned by using a model with high complexity, and a model with high complexity requires a lot of computational overhead. In this context, this paper studies the 3D HPE based on the transformer. We introduce the research status of HPE at home and abroad and provide a theoretical basis for designing the transformer 3D HPE model in this paper. We introduce the technical principle and optimization scheme of CNN and transformer and propose a 3D HPE model based on transformer. We used two datasets, COCO and the MPII datasets, and performed a number of experiments to find the best parameters for model development and then assess the model's performance. The experimental findings suggest that the strategy described in this study outperforms all other methods on both datasets. The average precision (AP) of our model reaches up to 79% on COCO dataset but a PCKh-0.5 score of 81.5% on the MPII dataset. [ABSTRACT FROM AUTHOR]

In order to develop a more efficient and accurate marketing strategy for consumers' purchase behavior, this paper establishes a user value model by modeling and learning the user historical data of e-commerce enterprises. The improved K-means algorithm is used to cluster the purchase behavior of users, and the customer value matrix is constructed from two dimensions of consumption frequency and average consumption amount. Finally, e-commerce users are classified into four categories by marking points. The test results show that the improved K-means algorithm is stable and efficient, and the analysis of user clustering characteristics is helpful to develop more accurate marketing strategies. [ABSTRACT FROM AUTHOR]

With the continuous development of online technology, online education has become a trend. To improve the quality of online education, a comprehensive and effective analysis of educational behaviour is necessary. In this paper, we proposed a network model based on the ResNet50 network fused with a bilinear hybrid attention mechanism and proposed an adaptive pooling weight algorithm based on the average pooling algorithm for the problems of image feature extraction caused by traditional pooling algorithm such as mutilation and blurring. At the same time, the hyperparameters of the convolutional neural network model are adaptively adjusted based on the particle swarm algorithm to improve the model recognition accuracy further. Through the experimental validation on NTU-RGB + D and NTU-RGB + D120 data set, the recognition accuracy of this paper is 88.8% for cross-subject (CS), 94.7% for cross-view (CV), 82.8% for cross-subject (CSub) 83.2%, and 84.3% for cross-setup (CSet), respectively. The experimental results show that the algorithm in this paper is an effective method for educational behaviur recognition. [ABSTRACT FROM AUTHOR]

The space shafting is the core component of the momentum exchange attitude control actuator for spacecraft.The dynamic behavior of space shafting has an important impact on the performance of the actuators. Based on the dynamic theory of rolling bearing, this paper presents a dynamic analysis model of space shafting for the interaction between bearing balls and oil-containing nonmetallic cage under combined loads. Also, the accuracy of the analysis model was verified through a high-speed camera system to conduct a cage speed test. In addition, the dynamic behavior of balls and cage under combined loads and the interaction between them is also analysed. The results show that the axial displacements of balls fluctuate periodically under combined loads, and the rotation speeds of balls and cage are easily affected by the load, presenting as the oscillation of speed. Also, the force between balls and cage increases as the load increases. The dynamic behavior of balls and cage could be effectively improved by avoiding excessive torque loads and limiting the axial preload to 40 N. The wear failure caused by unstable operation of bearings cannot be ignored. This model is more practical in completing simulation analysis of different operating conditions and structural parameters of the shafting system. It provides a theoretical reference for the structural design and performance analysis of space shafting. [ABSTRACT FROM AUTHOR]

The current English teaching mode focuses on the traditional offline teaching and online teaching. In order to solve the problems that some students are inefficient and cannot teach students according to their aptitude in the teaching process, this paper uses the big data analysis strategy based on a neural network algorithm. This paper studies the discrete dynamic modeling method of learner behavior analysis in English teaching. Firstly, it summarizes the current situation of English teaching and the research status of the hybrid application of discrete dynamic modeling technology. Secondly, combined with English teaching content and teaching objectives, through the analysis of various data of students' learning behavior, this paper evaluates students' English teaching quality from five aspects that affect the students' English teaching quality and puts forward a personalized English teaching quality evaluation model based on discrete dynamic modeling technology and learners' behavior analysis. Finally, through the practical teaching application in a university, the feasibility of the discrete dynamic English teaching model is verified. The results show that compared with the current innovative English teaching methods based on a dynamic iterative decision algorithm, the personalized discrete dynamic English teaching model based on learner behavior analysis significantly improves the quality of English teaching and students' academic performance. [ABSTRACT FROM AUTHOR]

In order to meet the needs of the current personalized education, improve the shortcomings of the current digital learning system in personalized learning, and introduce the service concept into education, the development and research of the personalized learning system based on the analysis of user behavior patterns has become the development of digital learning. This paper studies a language learning method, that includes obtaining the user's language learning interaction data to determine the user's language level: wherein, the user's language level data includes the user's initial language level and the current language level; initial learning model: according to the current language level, an adaptive algorithm is used to update the initial learning model, and the user performs linguistics further according to the updated learning model. In addition, in order to realize the clustering analysis of Chinese language online learning users' learning behavior, since the Fuzzy C-means (FCM) clustering results are easily affected by the selection of their initial cluster centers, a Harmony Search (HS)-FCM-based Chinese language learning user learning behavior clustering analysis is proposed. The participation dimension, focus dimension, regularity dimension, interaction dimension, and academic performance are selected as the analysis indicators of learning behavior. The learner level is divided into 5 levels, namely excellent, good, medium, qualified, and poor. Compared with HSFCM, and decision tree, it is found that the algorithm Improved HS (HIS)-FCM in this paper has higher clustering accuracy, faster convergence speed, and lower fitness, which provides new opportunities for learner level division and optimization of course learning. [ABSTRACT FROM AUTHOR]

With the continuous development of internet economy and e-commerce, the scale of data produced by users on e-commerce platform is increasing explosively. Mining the behavior of individual users and group users from massive user behavior data and analyzing the value and law behind the data are of great significance to the development of e-commerce. Taking the user behavior log data of an e-commerce website as the data source, this paper, firstly, processes and analyzes the original dataset through the data filtering and storage module, and it uses the combination of Kafka and Flume to store the user behavior log with reasonable structure and complete fields in HDFS. Secondly, a hierarchical system of data warehouse is constructed in Hive, and each layer of log data is effectively mined and multidimensionally analyzed with the help of log mining technology. Finally, based on the big data framework and Bi tools, a data warehouse system is designed and implemented, which could store and analyze massive data and visually display the results. The system uses dimensional modeling to build a data warehouse hierarchical system to mine and analyze user behavior data through log mining algorithm deeply. The K-means clustering algorithm and RFM model are used to divide the user behavior characteristics in detail, and AARRR funnel model is used to analyze the logs in a modular way. Through the effective mining and multidimensional visual analysis of user behavior data, the behavior analysis of group users and individual users, as well as the analysis of commodity sales flow and sales linkage are realized, which provides support for internal decision-making and precision marketing. [ABSTRACT FROM AUTHOR]

Knowledge sharing among teachers is one of the important ways to improve their teaching and research ability. From the perspectives of teachers' knowledge sharing willingness, knowledge sharing ability, knowledge sharing environment, knowledge sharing technology support, knowledge sharing effect, and so forth, this paper constructs a teacher knowledge sharing behavior evaluation model, develops a knowledge sharing behavior evaluation index system, and proposes a comprehensive teacher's knowledge sharing behavior evaluation method based on the improved entropy-TOPSIS method. This is a comprehensive evaluation method combining subjective and objective weights, which avoids the subjectivity of traditional expert evaluation methods and other multilevel and multi-index weight determination methods and makes the evaluation results more objective, accurate, and more realistic. Finally, by taking the example of evaluating knowledge sharing behavior of teachers in a university of Chongqing, China, this paper verifies the feasibility and practicability of the proposed comprehensive teachers' knowledge sharing behavior evaluation system and method. [ABSTRACT FROM AUTHOR]

With the advent of the big data era, the combination of information technology and education has become an important way for the development of the industry. The large-scale realization of teaching tasks under the background of information data requires the prediction and analysis of learners' characteristics, behavior, and development trend. Based on the above situation, this paper uses discrete dynamic modeling technology in big data environment to study the learners' behavior in physical education teaching. By quantifying the learning process data, the feature points of each learner are extracted to realize the personalized construction of dynamic learning data. Due to the rapid development of network technology, we mainly analyze the online education platform and explore the influencing factors of learners' behavior characteristics from many aspects. Finally, it carries out dynamic modeling and prediction for physical education learners from the aspect of achievement change, uses the grey model to build the achievement change system, and combines the dynamic modeling technology to reflect the development trend of achievement. The results show that the main factor affecting learners' behavior change in physical education is video learning. Most students are passive and lack initiative in the learning process. Discrete dynamic modeling technology can improve the accuracy of predicting student achievement changes and provide effective data for the research content. [ABSTRACT FROM AUTHOR]

Human motion gesture recognition is the most challenging research direction in the field of computer vision, and it is widely used in human-computer interaction, intelligent monitoring, virtual reality, human behaviour analysis, and other fields. This paper proposes a new type of deep convolutional generation confrontation network to recognize human motion pose. This method uses a deep convolutional stacked hourglass network to accurately extract the location of key joint points on the image. The generation and identification part of the network is designed to encode the first hierarchy (parent) and the second hierarchy (child) and show the spatial relationship of human body parts. The generator and the discriminator are designed as two parts in the network, and they are connected together in order to encode the possible relationship of appearance and, at the same time, the possibility of the existence of human body parts and the relationship between each part of the body and its parental part coding. In the image, the key nodes of the human body model and the general body posture can be identified more accurately. The method has been tested on different data sets. In most cases, the results obtained by the proposed method are better than those of other comparison methods. [ABSTRACT FROM AUTHOR]

In power Internet of Things environment, the existing border-based protection system and the "one-time authentication, one-time authorization, and long-term effective" approach are difficult to deal with the threat of attacks from internal and external devices and users with legal authority. In order to solve the problem of authorized access of power equipment and users, combined with behavior risk assessment, a continuous trust evaluation scheme of power equipment and users is presented in this paper. The scheme is evaluated by the combination of direct trust, indirect trust, and comprehensive trust and adds the penalty reward factor and time attenuation function to improve the reliability of the results. In addition, this paper will quantify the risk of the behavior of power equipment and users and regard it as a factor affecting the degree of trust, so as to achieve continuous trust evaluation of equipment and users. [ABSTRACT FROM AUTHOR]

Human behavior analysis has been a leading technology in computer vision in recent years. The station operation room is responsible for the dispatch of trains when they enter and leave the station. By analyzing the behaviors of the operators in the operation room, we can judge whether the operators have violations. However, there is no scheme to analyze the operator's behavior in the operation room, so we propose an operator behavior analysis system in the station operation room to detect operator's violations. This paper proposes an improved target tracking algorithm based on Deep-sort. The proposed algorithm can improve the target tracking performance through the actual test compared with the traditional Deep-sort algorithm. In addition, we put forward the detection scheme for common violations in the operation room: off-position, sleeping, and playing mobile phone. Finally, we verify that the proposed algorithm can detect the behaviors of operators in the station operation room in real time. [ABSTRACT FROM AUTHOR]

PVC (polyvinyl chloride) is a tough polymer used in applications, including plumbing and construction materials. As natural fibre-reinforced composites have more advantages over conventional synthetic composites, this paper focuses on the fatigue analysis of PVC composite which is reinforced with coir fibre. The influences of three input parameters, namely, the size of the coir fibre, coir fibre content, and the chemicals that are used in the treatment of coir fibre on the fatigue life of the composite are examined. In the response surface model (RSM), Box-Behnken designs (BBD) are employed for the preparation/analysis/optimization of the samples. ANSYS software is used to perform the fatigue analysis of different samples containing various combinations of the parameters. To determine the effects of various input parameters on the fatigue behaviour of composites, ANOVA is employed to determine their optimal levels. Regression equations are established to determine the fatigue limit. When treated with triethoxy(ethyl)silane, coir with a concentration of 6 wt.% and a particle size of 75 μm exhibits a maximum fatigue limit of 2.819 MPa. [ABSTRACT FROM AUTHOR]

In recent years, online and offline teaching activities have been combined by the Small Private Online Course (SPOC) teaching activities, which can achieve a better teaching result. Therefore, colleges around the world have widely carried out SPOC-based blending teaching. Particularly in this year's epidemic, the online education platform has accumulated lots of education data. In this paper, we collected the student behavior log data during the blending teaching process of the "College Information Technology Fundamentals" course of three colleges to conduct student learning behavior analysis and learning outcome prediction. Firstly, data collection and preprocessing are carried out; cluster analysis is performed by using k-means algorithms. Four typical learning behavior patterns have been obtained from previous research, and these patterns were analyzed in terms of teaching videos, quizzes, and platform visits. Secondly, a multiclass classification framework, which combines a feature selection method based on genetic algorithm (GA) with the error correcting output code (ECOC) method, is designed for training the classification model to achieve the prediction of grade levels of students. The experimental results show that the multiclass classification method proposed in this paper can effectively predict the grade of performance, with an average accuracy rate of over 75%. The research results help to implement personalized teaching for students with different grades and learning patterns. [ABSTRACT FROM AUTHOR]

Abnormal behavior detection of social security funds is a method to analyze large-scale data and find abnormal behavior. Although many methods based on spectral clustering have achieved many good results in the practical application of clustering, the research on the spectral clustering algorithm is still in the early stage of development. Many existing algorithms are very sensitive to clustering parameters, especially scale parameters, and need to manually input the number of clustering. Therefore, a density-sensitive similarity measure is introduced in this paper, which is obtained by introducing new parameters to transform the Gaussian function. Under this metric, the distance between data points belonging to different classes will be effectively amplified, while the distance between data points belonging to the same class will be reduced, and finally, the distribution of data will be effectively clustered. At the same time, the idea of Eigen gap is introduced into the spectral clustering algorithm, and the verified gap sequence is constructed on the basis of Laplace matrix, so as to solve the problem of the number of initial clustering. The strong global search ability of artificial bee colony algorithm is used to make up for the shortcoming of spectral clustering algorithm that is easy to fall into local optimal. The experimental results show that the adaptive spectral clustering algorithm can better identify the initial clustering center, perform more effective clustering, and detect abnormal behavior more accurately. [ABSTRACT FROM AUTHOR]

In this new era that is full of social changes, ongoing economic transformation, an abundance of information resources, and a fast pace of life, the pressure that people feel to compete with one another is also increasing day by day. Because of the vast differences in people's states of consciousness and worldviews, interpersonal relationships have become increasingly difficult to navigate. Students in higher education institutions will eventually emerge as the dominant demographic in society. Their mental health has a significant bearing on all aspects of life, including learning and future growth. An objective condition that must be met in order to guarantee that the next generation of talent will have a high level of overall quality is the improvement of the mental health of college students (CSMH) in the new era. One component of public health is the emotional well-being of students in higher education. The state of the public's health is consistently ranked among the most urgent problems facing modern society. However, there is not much hope for the Chinese CSMH. In order to effectively manage their mental health, a variety of educational institutions, including colleges and universities, have proposed a large number of management strategies for CSMH. The vast majority of these strategies are not targeted, and they do not offer a variety of management strategies that are based on the many different psychological states. It is necessary to first be able to accurately predict the mental health status of each individual college student in order to achieve the goal of improving the mental health management of students attending colleges and universities. This study proposes using a multi-view K-means algorithm, abbreviated as MvK-means, to analyze the CSMH's data on mental health. This is possible because the data can be obtained from multiple perspectives. This paper presents a multi-view strategy as well as a weight strategy in light of the fact that each point of view contributes in its own unique way. Different weight values should be assigned to each view's data, which will ultimately result in an improved evaluation effect of the model. The findings of the experiments indicate that the model that was proposed has a beneficial impact on the analysis of the data pertaining to the mental health of college students. [ABSTRACT FROM AUTHOR]

This paper proposes an improved k-means clustering algorithm to analyze the mental health education of college students. It offers an improved k-means clustering algorithm with optimized centroid selection to address the problems of randomly selected class cluster centroids that lead to inconsistent algorithm results and easily fall into local optimal solutions of the traditional k-means clustering algorithm. The algorithm determines the neighborhood parameter based on the Euclidean distance between the data object and its nearest neighbor in the data set. It counts the object density based on the neighborhood parameter Eps. In the initial class cluster centroid selection phase, the algorithm randomly selects the first-class cluster centroid, and subsequent class cluster centroids are chosen based on the data object density information and the distance information between the data object and the existing class cluster centroids. The proposed improved k-means clustering algorithm and clustering validity metrics are tested using several simulated and real datasets. In this paper, the characteristics and application areas of the improved k-means clustering algorithm are sorted out, the self-determination theory related to the enhanced k-means clustering algorithm is investigated, and the behavior of the improved k-means clustering algorithm in the enhanced k-means clustering algorithm system and the octagonal behavior analysis method is also sorted out through the improved k-means clustering algorithm mental health management cases. The path of intervention in mental health education is designed through the improved k-means clustering algorithm. The intervention points are explained, including motivation discovery, mechanism setting, and component matching of the enhanced k-means clustering algorithm. [ABSTRACT FROM AUTHOR]

Ideological and political education (IPE) is aimed at achieving people's free and all-around growth through the use of appropriate methods, and the use of educational methods is integral to the execution of education. Under the influence of big data, it is imperative to strengthen the research on the accuracy of ideology education in colleges and universities (IPECU), which necessitates that ideology educators adopt big data thinking, investigate novel pedagogical approaches, and consistently develop new IPECU conditions. In this paper, a collaborative filtering- (CF-) based algorithm for IPE resource recommendations is presented. Users are given recommendations for educational resources based on their browsing history, browsing patterns, and preferences. The accurate recommendation system can determine users' needs by examining how they use the website in order to suggest more useful information to them. In comparison to the conventional algorithm, the accuracy of the ideological and political education precision recommendation model in this study is 16.75% greater. Teachers can use big data technology to gather students' data information that is dispersed throughout cyberspace, understand students' states in real time, and deliver accurate instructional materials in accordance with students' various states and needs by utilizing the intelligent ideology mode. [ABSTRACT FROM AUTHOR]

With the deepening of globalization and the improvement of the fineness of production division and cooperation, various forms of suppliers coexist, and the supply chain network becomes complex and fragmented. Traditional supply chain management mode and purchasing mechanism cannot respond to the changes of the times in time, and enterprises and suppliers cannot connect. As a new type of financial service, supply chain finance plays an important role in solving the financing difficulties and expensive problems of SMEs. However, in recent years, traditional supply chain finance has encountered many problems, and it has gradually become a trend to empower traditional supply chain finance with blockchain technology. This paper proposes a blockchain supply chain financing risk assessment and behavior prediction algorithm. By analyzing the development status of supply chain business, it puts forward corresponding preventive measures to effectively improve the efficiency of supply chain financing. Firstly, the paper puts forward the double-chain management system and constructs the double-chain management architecture and operation flow; Thirdly, the weighting method of enterprise financing risk evaluation index based on variance homogeneity test is put forward to explain; Finally, five enterprises are tested on the data set, and the performance of TPR, FPR, Precision, Recall, F-Measure, and Accuracy is compared. Different enterprises have the problem of supply chain financing risk. [ABSTRACT FROM AUTHOR]

From the perspective of the environment-behavior science, this paper takes the space design of a digital village in Youzhaqiao Village on the outskirts of Guangshui City, for example, to record and track the behavior of residents and tourists in the area via mobile phone motion track recording software. Based on the collection, selection, and collation of the trajectory data, the valid data has been counted and analyzed; finally, the field investigation and questionnaires have been integrated to propose optimization strategy for the use and design of the space of Youzhaqiao Village from aspects such as human perception, cognition, and behavior. [ABSTRACT FROM AUTHOR]

Currently, the global consumer market is moving toward digitalization, and cross-border e-commerce enterprises are no exception and have to make adjustments in their operational strategies. Digitalization brings complexity, dynamism, and fragmentation in sales channels, media environment, and consumer behavior, requiring cross-border e-commerce enterprises to react more quickly and timely, and the need for intelligent operations is becoming more and more urgent. In this paper, through the problem of predicting the e-commerce user behavior process, the model usually needs to focus on both long-term preferences and short-term preferences of e-commerce users in the current behavior sequence; otherwise, the behavior prediction will be much less effective. Specifically, through a comprehensive analysis of typical cases, the strategies and problems in the intelligent operation process of China's cross-border e-commerce enterprises at the present stage are discussed, and corresponding suggestions are put forward, so as to promote the rapid development of China's cross-border e-commerce enterprises. [ABSTRACT FROM AUTHOR]

In recent years, people's living standards have gradually improved, and informatization has brought convenience, but it has also led to many criminal cases. Because the criminals' criminal methods are diverse and constantly renovated, this feature is very prominent, and the illegal activities on the Internet are becoming more and more intense. Therefore, it is necessary to strengthen the research on the trend and identification of anti-investigation behavior in crime. The purpose of this paper is to study how to use machine learning fusion algorithms in the trend and identification of anti-investigation behavior in crime. This paper proposes a machine learning fusion algorithm and the basic conceptual knowledge of anti-investigation behavior in crime. The experimental results of this paper show that with the increasing number of criminal incidents, criminals' means of committing crimes have also been improved. The anti-investigation capabilities of criminals have also become more sophisticated, which makes the work of law enforcement officers more difficult. The anti-investigation behavior of criminals in crime has many characteristics, among which the concealment reaches 54%-68%, and the virtuality reaches 68%-79%. It can be seen that the characteristics of criminals' anti-investigation behavior provide a wider space for criminals to commit crimes. The advantage of the machine fusion algorithm is that the classification rules generated are easy to understand and have high accuracy, so it is very suitable for the classification and identification of anti-investigation behaviors in crimes. Therefore, it is urgent to use machine fusion algorithms to identify them. [ABSTRACT FROM AUTHOR]

The core of consumer purchase behavior analysis lies in building prediction models. This paper combines consumer behavior prediction with deep learning models, proposes rDNN models and KmDNN models, uses AUC and F value as evaluation indicators, implements algorithms using Python as experimental tools, derives prediction results, and conducts comparative analysis. Focusing on deep neural network models in deep learning, the performance of deep neural network models for consumer purchase behavior analysis is explored from three aspects: the underlying theory of deep neural network models, the construction and implementation of the models, and the improvement of the models, and empirical analysis is conducted through experimental results. [ABSTRACT FROM AUTHOR]

The rapid aggregation of modern urban population and the rapid growth of car travel lead to traffic congestion, environmental pollution, and other problems. In view of the limited land resources in our country, it is impractical to meet residents' travel demand by blindly increasing traffic supply. Therefore, addressing the urban road congestion problem for sustainable development of modern cities, the paper makes research on residents' travel behavior characteristics and travel preference under the condition of multimodal transportation to formulate reasonable traffic demand management strategy for the guide on public traffic demand, bus priority strategy, and congestion management. The operation characteristic of each transportation mode is analyzed by comparing its related traffic and economic characteristics. Multimode traffic choice behavior is discussed by establishing multiple logistic regression models to analyze the main influencing factors to travelers' social and economic attributes, travel characteristics, and preference based on travel survey data of urban residents. The paper proposes the development of an urban public transportation system and travelling mode shift from cars to public transportation as reasonable travel structure for congestion management and sustainable development of modern cities. [ABSTRACT FROM AUTHOR]

In this paper, the seismic behavior of fiber-reinforced polymer (FRP) strengthened and unstrengthened steel fiber-reinforced high-strength concrete frame joints under low cyclic loading was tested. Then, the nonlinear finite element program was used to simulate the seismic behavior of FRP strengthened and unstrengthened steel fiber-reinforced high-strength concrete frame joints under low cyclic repeated load. The influence of FRP bond direction on the seismic behavior of steel fiber-reinforced high-strength concrete frame joints was studied. Through the comparison of the test values and numerical simulation values of the hysteretic curve, skeleton curve, energy dissipation capacity, displacement ductility, bearing capacity degradation, stiffness degradation, and other performance indexes of frame joints, the rule was obtained. The results showed that the 45° bonding direction of carbon fiber cloth is better than the 0° bonding direction, and the digital simulation results are in good agreement with the test results. Therefore, the constitutive model, element, end constraint, and loading method used in the finite element numerical simulation of this paper were reasonable, which can provide reference for the similar research in the future. [ABSTRACT FROM AUTHOR]

Mapping knowledge domain (MKD) is an important application in bibliometrics, which is a method of visually presenting and explaining newly developed interdisciplinary scientific fields using data mining, information analysis, scientific measurement, and graphic rendering. This study combines applied mathematics, visual analysis technology, information science, and scientometrics to systematically analyze the development status, research distribution, and future trend of the heterogeneous traffic flow by using the MKD software tools VOSviewer and CiteSpace. Based on the MKD and Bibliometrics approaches, 4709 articles have been studied, which were published by Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) from 2004 to 2021 in the field of heterogeneous traffic flows. Firstly, this paper presents the annual numbers of articles, origin countries, main research organizations, and groups as well as the source journals on heterogeneous traffic flow studies. Then, cocitation analysis is used to divide heterogeneous traffic flow into three main research directions, which include "heterogeneous traffic flow model," "traffic flow capacity analysis," and "traffic flow stability analysis." The keyword cooccurrence analysis is applied to identify five dominant clusters: "modeling and optimization methods," "traffic flow characteristics analysis," "driving behavior analysis," "simulation experiment," and "policies and barriers." Finally, burst keywords were studied according to the publication date to present more clearly the change of research focus and direction over time. [ABSTRACT FROM AUTHOR]

This article studies the model of the impact of real negative news on stock prices and provides evidence using China's A-share listed companies as an example. It first defines the negative news of the network. Then, it constructs the negative news to the stock price influence model, from the perspective of the theory of behavioral finance, the relationship between stock market and investor sentiment is systematically analyzed, and the theoretical support for the research of this paper is provided according to the definition of network negative news concept using the event study, abnormal returns test, regression analysis of the sample selected out of the study, observation of negative news before and after the abnormal returns, and excess returns changes. Studies have shown that negative news before 1 day and after 4 days makes stock price volatility larger, resulting in excess returns. The network negative news on the listed company's stock price fluctuations can cause a certain degree of impact; in the short term, the fluctuation of the stock price of listed companies is more active. This article finds that when online media disclose negative information on listed companies from one day before to four days later, stock prices will fluctuate greatly, generate excess returns, and continue to make stock fluctuations more active in the short term. In addition, when studying the influencing factors of the stock price fluctuations of listed companies, we found that stock price fluctuations are affected by the company's performance. The better the company's performance is, the smaller the stock price fluctuations will be when it is affected by negative news. Finally, we find that the higher the shareholding ratio of institutional investors in listed companies, the more stable the stock price. [ABSTRACT FROM AUTHOR]

In the process of sports, athletes often have aggressive behaviors because of their emotional fluctuations. This violent sports behavior has caused many serious bad effects. In order to reduce and solve this kind of public emergencies, this paper aims to create a swarm intelligence model for predicting people's sports attack behavior, takes the swarm intelligence algorithm as the core technology optimization model, and uses the Internet of Things and other technologies to recognize emotions on physiological signals, predict, and intervene sports attack behavior. The results show the following: (1) After the 50-fold cross-validation method, the results of emotion recognition are good, and the accuracy is high. Compared with other physiological electrical signals, EDA has the worst classification performance. (2) The recognition accuracy of the two methods using multimodal fusion is improved greatly, and the result after comparison is obviously better than that of single mode. (3) Anxiety, anger, surprise, and sadness are the most detected emotions in the model, and the recognition accuracy is higher than 80%. Sports intervention should be carried out in time to calm athletes' emotions. After the experiment, our model runs successfully and performs well, which can be optimized and tested in the next step. [ABSTRACT FROM AUTHOR]

With the advent of Industry 4.0, economic development has become a rapid information age. The content of macroeconomic forecast is very extensive, and the existence of big data technology can provide the government with multilevel, diversified, and complete information and comprehensively process, integrate, summarize, and classify these pieces of information. This paper forecasts the CPI value in the next 12 months according to the CPI in China in the recent 20 years. Compared with the traditional forecasting methods, the forecasting results have higher accuracy and timeliness. At the same time, the trend of growth rate of industrial value-added is analyzed, and the experiments on MAE and RMSE show that the method proposed in this paper has obvious advantages. It also analyzes the disadvantages of traditional psychological decision-making behavior analysis, introduces the development status and advantages of big data-driven psychological decision-making behavior analysis, and opens up new research ideas for psychological decision-making analysis. [ABSTRACT FROM AUTHOR]

This paper presents a numerical study on the high-temperature mechanical properties of a long-span double-deck suspension bridge. The main focus of this paper is the behavior analysis of Wuhan Yangtze River Bridge. A three-dimensional thermal model of the bridge was established by the Fire Dynamics software (FDS) to obtain the 3D temperature field distribution, and the thermal analysis result was then applied to the three-dimensional finite element model of the suspension bridge. The shortest failure time of the main cable and sling was determined to obtain the rescue time of a bridge fire. According to the calculation results of the suspension bridge under a tanker fire initiated at the upper deck of the bridge, the middle lane in the upper deck of the suspension bridge was determined to be a safe lane. Thus, the tanker should be guided to go in this lane of the bridge. The numerical analysis of the experimental results shows that when the fuel tanker is located on the upper and lower floors of the bridge, the bridge structure is affected by the fire. When the oil tanker burns in the outermost lane of the upper bridge, it will have a great impact on the main cables and slings of the bridge. When the fuel tanker burns in the lower nonmotorized lane of the bridge, it will have a great impact on the upper stiffening beam steel plates and truss rods. [ABSTRACT FROM AUTHOR]

Passenger travel flows of urban rail transit during holidays usually show distinct characteristics different from normal days. To ensure efficient operation management, it is essential to accurately predict the distribution of holiday passenger flow. Based on Automatic Fare Collection (AFC) data, this paper explores the passengers' destination choice differences between normal days and holidays, as well as one-way tickets and public transportation cards, which provides support for variable selection in modeling. Then, a forecasting model of holiday travel distribution is proposed, in which the destination choice model is established for representing local and nonlocal passengers. Meanwhile, explanatory variables such as land matching degree, scenic spot dummy, and level of service variables are introduced to deal with the particularity of holiday passengers' travel behavior. The parameters calibrated by the improved weighted exogenous sampling maximum likelihood (WESML) method are applied to predict passenger flow distribution in different holiday cases with annual changes in the metro network, using the data collected from Guangzhou Metro, China. The results show that the proposed model is valid and performs better than the other comparable models in terms of forecasting accuracy. The proposed model has the capability to provide a more universal and accurate passenger flow distribution prediction method for urban rail transit in different holiday scenarios with network changes. [ABSTRACT FROM AUTHOR]

Social group behavior analysis has always been the key research direction of sociologists and psychologists. With the rapid development of the Internet of Things and the proposal of deep learning theory, convolutional neural networks are also used in social group research. In the process of social development, group incidents continue to increase, and there are more and more studies on social group behavior analysis. Although the research content and research methods are also richer, the research that combines the Internet of Things, convolutional neural network, and group behavior is more. This article will specifically propose a social group behavior analysis model that combines multitask learning and convolutional neural networks. This paper deeply learns the research of convolutional neural network and group behavior-related theories, makes full use of the advantages of convolutional neural network algorithm and multitask learning mode, and builds a social group behavior analysis model based on multitask learning and convolutional neural network. The experimental results on different data sets are analyzed. The results show that the accuracy rate of the experimental algorithm of convolutional neural network is as high as 95.10%, and it is better than other algorithms in time complexity, which is very suitable for social group behavior analysis. [ABSTRACT FROM AUTHOR]

In public places, some behavior that violates public order and endangers public safety is defined as abnormal behavior. Moreover, it is a necessary auxiliary means to maintain public order and safety by detecting abnormal behavior in a large number of surveillance videos. However, due to the small proportion of abnormal behavior in video data, the extreme imbalance of data seriously restricts the effectiveness of detection. So, weakly supervised learning has become the most suitable and effective detection method. However, existing weakly supervised methods rarely take the locality and slightness of abnormal behavior into account and ignore the details of extracted features. Based on this, an attention-directed abnormal behavior detection model is proposed. In the two common prediction and reconstruction abnormal behavior detection methods based on weak supervision, suitable attention mechanisms are introduced, respectively, and two corresponding attention-directed networks are proposed. In addition, aiming at the problem of inaccurate thresholds for abnormal behavior division, the loss function of the model is improved and a new abnormal behavior evaluation method is proposed. Experiments were carried out on three classical datasets (the USCD Ped1, USCD Ped2, and CUHK Avenue dataset) for abnormal behavior detection. The best results for the area under the curve (AUC) indicator reached 82.7%, 94.5%, and 87.3%, respectively, which are better than many existing literature results. [ABSTRACT FROM AUTHOR]

To improve learners' performance in online learning, a teacher needs to understand the difficulty of knowledge points learners of different cognitive encounter levels in the learning process. This paper proposes a difficulty-based knowledge point clustering algorithm based on collaborative analysis of multi-interactive behaviors. Firstly, combining the group-directed learning path network, forgetting factors and the degree of student-system interaction, we propose a measurement model to calculate the similarity of the difficulty between knowledge points on student-system interactive behavior. Secondly, to solve the data sparsity problem of interaction, we propose an improved similarity model to calculate the similarity of the difficulty between knowledge points on student-teacher and student-student interactive behavior. Finally, the knowledge point difficulty similarity matrix is obtained by integrating the difficulty similarity of knowledge points obtained from student-system interactive behavior, student-teacher interactive behavior, and student-student interactive behavior. The spectral clustering algorithm is used to achieve knowledge point difficulty classification based on the obtained similarity matrix. The experiments on real datasets show that the proposed method has better knowledge point difficulty classification results than the existing methods. [ABSTRACT FROM AUTHOR]

The moving particle semi-implicit (MPS) method as a Lagrangian method is attracting increasing attention in severe accident analysis. In this paper, we developed an MPS code for the corium behavior analysis with several additional models added: an improved heat transfer model to improve the calculation between different materials, an enthalpy-based viscosity model to realize a smooth transition of viscosity at the solid-liquid interface, and a surface tension model for better simulation of surface shape. Validation of the developed simulation approach is carried out on a classical water column collapse example. The development of the heat transfer model is validated by the example of a one-dimensional semi-infinite plate. A comprehensive example of the melting of "Wood's alloy" is carried out to verify the capacity of MPS method in the simulation of melting and expansion. The simulation results are in good agreement with the experimental results, which indicates that MPS method promises well in the field of severe accidents. [ABSTRACT FROM AUTHOR]

Background. In the acute phase of stroke, it is well known that the incidence and severity of unilateral spatial neglect (USN) are more significant in the right hemisphere injuries. Still, the detection of USN in left hemisphere injuries has been increasing in recent years. This trend is because behavioral assessments have prevented the exclusion of patients who are difficult to assess for USN or apathy using conventional paper-and-pencil tests (e.g., aphasia). Right USN and post-stroke apathy share many common lesions. Therefore, clinical symptoms may overlap, but little validation considers this. Case Study. A man (62 years old) determined to have the right USN and apathy was treated for six weeks in 3 terms. In the first term (weeks 1 to 2), the patient was treated for the right USN by conventional therapy. In the second term (3–4 weeks), treatment for right USN and apathy by goal-directed therapy based on affinity behavior was implemented. In the third term (5–6 weeks), goal-directed therapy based on affinity behavior was discontinued, and treatment was returned to conventional therapy only. In the second term (goal-directed therapy based on affinity behavior), the improvement in patients' apathy (clinical assessment for spontaneity) was more significant than the effect size in the third term (conventional therapy). There were no significant differences in USN (catherine bergego scale) and intrinsic motivation (pittsburgh rehabilitation participation scale). However, the effect size in the second term tended to be larger than in the third term (conventional therapy). Clinical Rehabilitation Impact. This report aims to demonstrate the limitations of current treatment for cases determined to have both right USN and apathy. Second, to assess the extent to which this new intervention can complement the limitations of current treatment. [ABSTRACT FROM AUTHOR]

Herdsmen's access to credit funds plays an important supporting role in promoting the modern economic development of pastoral areas and broadening the channels for herders to increase their income. This paper studies the credit behavior of herdsmen from the perspective of emotional and psychological state identification to analyze the current credit behavior of herdsmen. Firstly, the internal mechanism of herdsmen's credit and emotional and psychological identification research content are analyzed. Secondly, a scheme of emotional and psychological state identification is proposed, and a corresponding survey of herdsmen is carried out according to the scheme. Finally, the Probit and Heckman models are introduced to analyze the credit behavior of herdsmen. The results show that herdsmen with high emotional and psychological scores strongly demand credit. They are likely to have access to credit and have a high level of credit. Herdsmen with low scores of emotional and psychological characteristics have low or no apparent demand for credit, and their credit availability is also low. The herdsmen with high emotional and psychological characteristics have a 71% higher probability of generating credit demand than herdsmen with low openness. Their likelihood of obtaining credit is also 33.47% higher than herdsmen with low emotional and psychological characteristics. They also have easy access to higher loan amounts. The results provide a reference for related research and analysis of herdsman's credit behavior and economic development. [ABSTRACT FROM AUTHOR]

In recent years, sand production has been frequently observed in offshore weakly consolidated sandstone reservoirs. Permeability changes due to sand migration seriously affect the confidence in well test interpretation, production forecasts, and oilfield development plan schedules. The purpose of this paper is to propose a comprehensive model of coupled sand migration, stress sensitivity, and high viscosity oil and to study the effect of sand production induced permeability zoning on transient pressure behavior by combining discrete boundary and discrete wellbore with the boundary element method. In this two-zone composite model, the reservoir can be divided into the inner zone with the improved permeability due to sand migration and the outer zone with initial reservoir permeability. The multifactor effects of stress-sensitive, highly viscous oil, sand migration, and horizontal well are included in this model. Thus, the seepage equation presents a highly nonlinear and difficult to obtain an accurate analytical solution. In this paper, the boundary element method (BEM) is introduced to separate the boundary and wellbore, and the semianalytical solution of the hybrid model is obtained. The comparative analysis of measured pressure curve fitting from a horizontal well, located in the eastern of the South China Sea, proves that this comprehensive model can be used for pressure transient analysis of the weakly consolidated sandstone reservoir. The flow regime analysis indicates that a two-zone composite system may develop seven flow regimes: the wellbore storage stage, early-time radial stage, first transition stage, inner linear stage, inner pseudoradial flow, transition flow from the inner area to the outer area, and outer pseudoradial flow. Sensitivity analysis indicates that the smaller the sand production radius, the shorter the duration of the transition flow from the inner to the outer zone, which suggests the well is mainly affected by the outer boundary in the later period. The larger the permeability ratio, the higher the pressure curves may move up. [ABSTRACT FROM AUTHOR]

In the intelligent transportation system, the license information can be automatically recognized by the computer and the vehicle can be tracked. Red light running, illegal change of lanes, vehicle retrograde, and other illegal driving events are reasonably recorded. This is undoubtedly an effective help for the traffic police to relieve the huge work pressure. However, in China, a considerable number of vehicle tracking methods have certain limitations in resisting complex external environmental influences. The external environmental factors include but not limited to variable factors such as camera movement, jitter, and severe rain and snow. These factors cannot be controlled well, so the tracking accuracy is greatly reduced. In regard to this, this paper proposes an optimization method for moving vehicle tracking based on SPF. First, according to the size of the overlapping area of the motion area between the two images, the researcher can construct and simplify the vertex adjacency matrix that reflects the characteristics of the undirected bipartite graph. Then according to the corresponding relationship between the vertex adjacency matrix and the regional behavior and vehicle behavior, the researcher completes the regional behavior analysis and vehicle behavior analysis. On this basis, a particle filter vehicle tracking algorithm based on segmentation compensation is introduced, and the vector sum of the tracked segmentation area is used as the final position of the target vehicle. In this way, as many scattered particles fall on the target area as possible, which will greatly improve the efficiency of particle utilization, enhance tracking accuracy, and avoid the problem of tracking failure caused by too fast vehicle movement. Through experimental simulation, it can be seen that the method proposed in this paper can greatly enhance the vehicle tracking ability when tracking vehicles in "complex environments." [ABSTRACT FROM AUTHOR]

This paper proposes a method to improve the effect of English classroom teaching based on the real-time monitoring of college students' status. Education informatization reinsert teaching evaluation and teaching management methods, use the new generation of information technology to track the entire process of monitoring and teaching, and carry out academic analysis and learning diagnosis, to pay attention to knowledge teaching to more emphasis on the cultivation of ability quality. Behavior analysis can identify students' classroom behavior in real time to track changes in the state of students. We herein count the classroom status of students and formulate quantitative guidelines and analyze the sequence diagram of each classmate's class behavior state and statistics from the time and duration of various classroom behaviors in classmates. Each student's listening quality is scored, and the visual methods such as sequential maps and distributed cake maps are used in class behavior to enable students to understand their own quality of the lesson. By analyzing the laws of student class behavior status distribution, we provide a data-driven mechanism for teacher classroom teaching quality evaluation and teaching method improvement. [ABSTRACT FROM AUTHOR]

Solve the problem of agricultural product big data mining based on e-commerce platform, meet the needs of e-commerce development to agricultural products, meet the diversified needs of e-commerce platforms, and improve people's living standards and convenience. According to 1000 online questionnaires, 866 people believe that e-commerce can bring them convenience, and 134 people believe that the convenience is insufficient. Even agricultural products, as a traditional primary industry, have begun to be "involved" in the sales mode of e-commerce platforms. In the face of the increasingly huge online consumer demand market, the agricultural product economy has redisplayed a strong market vitality. Of course, the huge market base also makes the e-commerce model of agricultural products pay attention to big data mining and analysis. This paper focuses on how to carry out big data mining and analysis of agricultural products more efficiently from the technical level. Therefore, the agricultural product user data mining technology of e-commerce platform based on Hadoop is proposed. Through the intervention of association rule analysis and algorithm, the improvement of relevant algorithms and agricultural products user behavior analysis system under e-commerce platform based on Hadoop is proposed. The results show that the system can realize the analysis of commodity association degree under various agricultural products user behavior modes and can better help the e-commerce platform of agricultural products realize precision marketing. [ABSTRACT FROM AUTHOR]

College students are a special group of society, having received university education and being at the forefront of new ideas and technologies in society. The basic psychological characteristics common to modern college students and the unique role psychological characteristics of ordinary students are the two major group factors. The basic psychological factors include cognition, personality, and social adaptive behavior. Hierarchical analysis (AHP) is a scientific method for assessing and identifying problems, correlating quantitative treatment of problems with qualitative analysis, and is well suited for semistructured or unstructured decision-making problems. This paper preliminarily studies the structure of College Students' psychological quality under the background of localization and the construction method of College Students' psychological quality evaluation model based on analytic hierarchy process. It is proposed to introduce the hierarchical analysis method into the behavioral assessment model of college students' psychological disorders as the entry point. A model was built to help college ideological and political educators determine whether college students have psychological disorders and disorders provide important insights. The results showed that the split-half reliability of this constructed method was 0.782, and the retest reliability was 0. The standard deviation of the coefficients among all the test subjects was 0.842, indicating that there is a positive correlation between the coefficients. The standard deviation of the coefficients between all test subjects was 0.842, indicating good confidence. Therefore, the model can objectively assess the psychological state of college students and realize the quantitative safety psychological assessment of college students, which is useful for reference in the implementation of teaching decisions. [ABSTRACT FROM AUTHOR]