Analysis of The Characteristics of emotional Experience in MOOC Learning

With the rapid development of massive open online courses (MOOCs), researchers have begun to pay attention to the experience of teachers and students in the MOOC classroom. Select the middle school mathematics curriculum standard and textbook research course in the MOOC platform of Chinese universities and collect 66 selfreported data of instantaneous experience and long-term experience of 10 learners on the course learning within two weeks. Qualitative and quantitative analyses were carried out in 8 sub-dimensions, including device usage preferences and problems, and platform tool application. The purpose of the research is to investigate the learning experience of MOOC learning platform more comprehensively and deeply. The research results show that mobile learning has become the main way of MOOC learning. The appearance, education and economy of the tools displayed on the platform directly affect the learning experience of learners. Demonstration tools are highly dependent, but the frequency of application, types and functions of tools are limited, and there is a lack of awareness and application of tools that promote advanced learning, deep learning, and reflective learning; the overall emotional experience of learners is in a positive emotional state and shows a distinct group characteristic. The learning experience of MOOC is directly related to the appearance, education and economy of the display tools in the platform; Learners have diverse experience of platform communication and cooperation tools, and are highly dependent on learning content display tools in the platform; Learners' emotional experience is both positive and negative, but it is dominated by positive emotions and shows distinct group characteristics

Dynamic Analysis of Corporate ESG Reports: A Model of Evolutionary Trends

Environmental, social, and governance (ESG) reports are globally recognized as a keystone in sustainable enterprise development. This study aims to map the changing landscape of ESG topics within firms in the global market. A dynamic framework is developed to analyze ESG strategic management for individual classes, across multiple classes, and in alignment with a specific sustainability index. The output of these analytical processes forms the foundation of an ESG strategic model. Utilizing a rich collection of 21st-century ESG reports from technology companies, our experiment elucidates the changes in ESG perspectives by incorporating analytical keywords into the proposed framework. This work thus provides an empirical method that reveals the concurrent evolution of ESG topics over recent years.Comment: 22 pages, 13 figure

Mathematics Learning Performance: Its Correlation with Chemistry Learning Performance

Mathematics is a rigorous, logical, and instrumental subject. Many concepts in chemistry learning is inseparable from mathematics. This study aims to determine the relationship between student learning performance in learning mathematics and chemistry. This research adopted literature analysis, statistical analysis, and interview (from subjective and objective aspects). The data of this study used the mathematics and chemistry scores of 452 students from 6 third-level grades at a secondary school in the city of Guilin. SPSS22.0 software was used to analyze comprehensive data in exploring the correlation between math scores and chemistry scores from various perspectives. Then, several students were interviewed to verify the results of the data analysis from emotional perspectives. The results of the research objectively indicate that there is a strong positive linear relationship between students’ mathematics learning achievement and subjectively, students agree that good mathematics performance can improve chemistry scores. Accordingly, mathematics learning performance is related to chemistry learning performance. Teachers can strengthen chemistry knowledge through mathematical knowledge. So, it is recommended that teachers adopt and use appropriate teaching strategies, and strengthen the application of mathematical knowledge in chemistry learning

Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression Values

Several artificial neural networks (ANNs) have recently been developed as theCox proportional hazard model for predicting cancer prognosis based on tumortranscriptome. However, they have not demonstrated significantly betterperformance than the traditional Cox regression with regularization. Trainingan ANN with high prediction power is challenging in the presence of a limitednumber of data samples and a high-dimensional feature space. Recentadvancements in image classification have shown that contrastive learning canfacilitate further learning tasks by learning good feature representation froma limited number of data samples. In this paper, we applied supervisedcontrastive learning to tumor gene expression and clinical data to learnfeature representations in a low-dimensional space. We then used these learnedfeatures to train the Cox model for predicting cancer prognosis. Using datafrom The Cancer Genome Atlas (TCGA), we demonstrated that our contrastivelearning-based Cox model (CLCox) significantly outperformed existing methods inpredicting the prognosis of 18 types of cancer under consideration. We alsodeveloped contrastive learning-based classifiers to classify tumors intodifferent risk groups and showed that contrastive learning can significantlyimprove classification accuracy

Health State Estimation of On-Board Lithium-Ion Batteries Based on GMM-BID Model

As a single feature parameter cannot comprehensively evaluate the health status of a battery, a multi-source information fusion method based on the Gaussian mixture model and Bayesian inference distance is proposed for the health assessment of vehicle batteries. The missing and abnormal data from real-life vehicle operations are preprocessed to extract the sensitive characteristic parameters which determine the battery performance. The normal state Gaussian mixture model is established using the fault-free state data, whereas the Bayesian inference distance is constructed as an index to quantitatively evaluate the battery performance state. In order to solve the problem that abnormal data may exist in the measured data and introduce errors into evaluation results, the determination rules of abnormal data are formulated. The verification of real-life vehicle operation data reveals that the proposed method can accurately evaluate the onboard battery state and reduce safety hazards of electric vehicles during the normal operation process