scholarly journals Student Behavior Analysis and Research Model Based on Clustering Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Guozhang Li ◽  
Rayner Alfred ◽  
Xue Wang
Keyword(s):  
Behavior Analysis ◽  
Management System ◽  
Student Behavior ◽  
Academic Research ◽  
Computer Software ◽  
Current Data ◽  
Algorithm Analysis ◽  
Research Model ◽  
Model Based ◽  
Kmeans Algorithm

Now, entering the 21st century, with the continuous improvement of my country’s higher education level, the enrollment rate of all colleges and universities across the country is increasing year by year. Faced with the information management of a large number of students, the workload and work pressure of consultants at various universities have doubled. The rapid and effective development of modern computer software and hardware has also initiated and effectively developed the informatization process of universities. The student management system is the core and foundation of the entire school education management system. This study mainly introduces the application of student behavior analysis and research models based on clustering technology. This paper uses the application research of student behavior analysis and research model based on clustering technology, uses clustering technology to analyze student behavior, and reasonably analyzes the feasibility of KMEANS algorithm and campus data mining. The cluster analysis algorithm is used to divide students into different groups according to the characteristics of the students, and then, data analysis and data association rules’ mining are performed on each group of students. At the same time, the decision tree algorithm is used to predict the future of students based on the historical data of the students and the current data of the students. The development status of the school helps the school to understand the situation of the students in real time, make predictions and warnings for possible situations, provide personalized applications for teachers and students, and provide decision-making support for the management. It can be seen from the experimental analysis that the application of student behavior analysis and research models based on clustering technology has increased the efficiency of student education by 17%. The limitations of student behavior analysis and research on clustering technology provide good applications for the KMEANS algorithm. Analysis, discussion, and summary of the methods and approaches are obtained to enrich the academic research results.

An Electrochemical Model Based Optimal Charging Algorithm for Lithium-Ion Batteries

2015 ◽  
Author(s):  
Sourav Pramanik ◽  
Sohel Anwar
Keyword(s):  
Management System ◽  
Optimization Problems ◽  
Optimal Algorithm ◽  
Algorithm Design ◽  
Lithium Ion ◽  
Cell Voltage ◽  
Realistic Model ◽  
Constant Current ◽  
Battery Management System ◽  
Model Based

In recent years, Lithium-Ion battery has gathered lot of importance in many forms of energy storage applications due to its overwhelming benefits. Any battery pack alone cannot achieve its optimal performance unless there is a robust and efficient energy management system, commonly known as battery management system or BMS. The Lithium-Ion charger is a voltage-limiting device that is similar to the lead acid system. The difference lies in a higher cell voltage; tighter voltage tolerance and the absence of trickle or float charge at full charge. In this work, we propose the design of a novel optimal strategy for charging the battery that better suits the battery performance. A performance index is defined that aims at minimizing the effort of regeneration along with a minimum deviation from the rated maximum thresholds for cell temperature and charging current. A more realistic model based on battery electrochemistry is used for the optimal algorithm design as opposed to equivalent circuit models. To solve the optimization problem, Pontryagin’s principle is used which is very effective for constrained optimization problems with both state and input constraints. Simulation results show that the proposed optimal charging algorithm is capable of shortening the charging time of a Lithium Ion cell while maintaining the temperature constraint when compared with the standard constant current charging.

Integrated management system model for organization based on quality management and lean production technologies

2021 ◽  
pp. 20-24
Author(s):  
V.V. Silaeva ◽  
◽  
V.P. Semenov ◽  
Keyword(s):  
Quality Management ◽  
Management System ◽  
Integrated Management ◽  
Lean Production ◽  
System Model ◽  
Integrated Management System ◽  
Value Stream ◽  
Model Based ◽  
Production Technologies

The article describes managing the processes of an organization as managing a holistic entity through the characteristics of the value stream. At the same time, the value stream is an activity aimed at creating the value for customer, which is implemented through a system of interconnected processes/operations. The article demonstrates the possibilities of integrating the modern models, standards, methods and tools for quality management and lean production into the processes of an organization in order to achieve the aims. Integrated management system model based on quality management and lean production technologies is presented.

scholarly journals Machine Learning for Building Immune Genetic Model in Hepatocellular Carcinoma Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jun Liu ◽  
Zheng Chen ◽  
Wenli Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Risk Model ◽  
Cox Model ◽  
Cancer Genome ◽  
Algorithm Analysis ◽  
Immune Genes ◽  
Normal Tissues ◽  
Model Based ◽  
Tumor Stages

Background. Hepatocellular carcinoma (HCC) is the leading liver cancer with special immune microenvironment, which played vital roles in tumor relapse and poor drug responses. In this study, we aimed to explore the prognostic immune signatures in HCC and tried to construct an immune-risk model for patient evaluation. Methods. RNA sequencing profiles of HCC patients were collected from the cancer genome Atlas (TCGA), international cancer genome consortium (ICGC), and gene expression omnibus (GEO) databases (GSE14520). Differentially expressed immune genes, derived from ImmPort database and MSigDB signaling pathway lists, between tumor and normal tissues were analyzed with Limma package in R environment. Univariate Cox regression was performed to find survival-related immune genes in TCGA dataset, and in further random forest algorithm analysis, significantly changed immune genes were used to generate a multivariate Cox model to calculate the corresponding immune-risk score. The model was examined in the other two datasets with recipient operation curve (ROC) and survival analysis. Risk effects of immune-risk score and clinical characteristics of patients were individually evaluated, and significant factors were then used to generate a nomogram. Results. There were 52 downregulated and 259 upregulated immune genes between tumor and relatively normal tissues, and the final immune-risk model (based on SPP1, BRD8, NDRG1, KITLG, HSPA4, TRAF3, ITGAV and MAP4K2) can better differentiate patients into high and low immune-risk subpopulations, in which high score patients showed worse outcomes after resection ( p < 0.05 ). The differentially enriched pathways between the two groups were mainly about cell proliferation and cytokine production, and calculated immune-risk score was also highly correlated with immune infiltration levels. The nomogram, constructed with immune-risk score and tumor stages, showed high accuracy and clinical benefits in prediction of 1-, 3- and 5-year overall survival, which is useful in clinical practice. Conclusion. The immune-risk model, based on expression of SPP1, BRD8, NDRG1, KITLG, HSPA4, TRAF3, ITGAV, and MAP4K2, can better differentiate patients into high and low immune-risk groups. Combined nomogram, using immune-risk score and tumor stages, could make accurate prediction of 1-, 3- and 5-year survival in HCC patients.

scholarly journals Knowledge Re-production and Transfer: Translating Practice into Theory

2019 ◽  
Author(s):  
Ahmed Alaoui
Keyword(s):  
Professional Practice ◽  
Academic Research ◽  
Research Model ◽  
Leading Role ◽  
Academic Researchers ◽  
The Future ◽  
Practice Based Research ◽  
The University ◽  
Cross Fertilization ◽  
Positive Impacts

This paper argues that cross-fertilization among translation academic researchers, practitioners and trainers is needed for all the actors involved in the translation enterprise. It calls for a practice-based research model to materialize the mechanisms needed for the interaction and collaboration of the three stakeholders, which would have positive impacts on the translation landscape. Given that this cross-fertilization can only be beneficial if it is structured and sustained, then it has to be formalized and institutionalized. A plan will be proposed as to how this can be materialized. It is a thesis of this paper that professional practice needs academic research (theories) to shape it, and theory can only have functional dimensions through professional practice; therefore, there is a pressing need to bridge the gap between “knowing” and “doing” in translation. To the extent that this position is valid the university is invited to play a leading role in materializing this objective, with a view to shaping the future of the translation profession and preserving translation education in Arab universities.

scholarly journals Safety Risk Assessment of Tourism Management System Based on PSO-BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenru Guo
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Management System ◽  
Evaluation Model ◽  
Risk Index ◽  
Security Risk ◽  
Bp Network ◽  
Model Based ◽  
Tourism Management ◽  
Better Than

With the development of science and technology, system management is gradually applied to tourism management. How to correctly assess the security risks of the tourism management system has become an important means to maintain passenger information. The security risk index of the travel management system is input into the PSO-BP network as a sample, and the corresponding risk value of the index is used as the network output. The results show that the error results, accuracy (96.53%), training time (216 s), number of iterations (275 times), and convergence speed are all better than traditional BP network. The relative error of PSO-BP network (0.32%) is better than that of BP network, with 300 iterations, and the error is close to 10–5. The average evaluation accuracy of S based on PSO-BP network is 99.72%, and the average time consumed is 2.512 s. It is superior to the evaluation model based on fuzzy set and entropy weight theory and the evaluation model based on gray correlation analysis and radial basis function neural network. In conclusion, the security risk assessment of the tourism management system based on PSO-BP network can effectively assess the security risk of the tourism management system.

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