Heart disease is a major threat to human health, with a variety of contributing factors, and is not easily cured. This paper will present a dataset from a cardiovascular study of residents of Framingham, Massachusetts. First, the validity of the three models, logistic regression, random forest, and decision tree, is estimated by comparing information such as accuracy, precision, recall, and F1 values. The optimal model, i.e., the logistic regression model, was selected by plotting ROC curves and using AUC as a reference criterion for assessing the predictive effectiveness of the models. Then the raw data and data were preprocessed, including dealing with missing values. Finally, a logistic regression model was developed to analyze the influencing factors of heart disease. The purpose of this study was to use the results of the logistic model to help doctors and patients in heart disease treatment. The results show that the model has a good predictive effect.

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This research looks into the association between fishing and microplastic contamination across countries, with a particular focus on how the growth of microplastic levels can impact fishery production. Through the use of a mixed-effect model we were able to look through data from 100 nations during the period 1990-2021, where fishery yield data was extracted from The World Bank and microplastic concentration data was obtained from the National Centers for Environmental Information; population data served as a control variable in our analysis. We found that there is indeed a significant negative correlation between levels of microplastics and fishing yield: an increase by 1 piece of microplastic per cubic meter leads to decrease in fishing yield by anything between 65 and 100 metric tonnes (95% confidence interval). This relationship held true for about 93% of all coastal countries studied. To accommodate for differences amongst nations, we introduced random intercepts and slopes in our mixed-effect model which helped capture variations specific to each country while still identifying an overarching pattern. The research we are doing is on the connection between fish catches and microplastic pollution which takes place in the different countries of the world, where we focus more on how high microplastic levels influence fishery production. Having made use of a mixed-effect model, we have been able to look at data that represents 100 nations within the period of time between 1990 and 2021; The World Bank provided us with fishery yield data while microplastic concentration data came from the National Centers for Environmental Information. In addition to these variables, population data was used as a control variable. The summary of our analysis points towards a significant inverse relationship noted between microplastic levels and fishing yield: an increase by one piece of plastic results in a decline by somewhere between 65 to 100 metric tonnes (95% confidence interval). This generalization held true for about 93% coastal countries considered under this study. To capture specific variations among nations but also identify an overall trend line while dealing with inter-country variability, random intercepts and slope components were included as part of our mixed-effect model methodology.

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The evolution of white LEDs, both organic and inorganic, has marked a transformative era in lighting technology known as solid-state lighting. This revolution is driven by electroluminescence, where electron-hole pairs recombine to emit light. While OLEDs have achieved high internal quantum efficiency, limited out-coupling efficiency remains a challenge. Improving light out-coupling efficiency is crucial for enhancing LED performance. This paper employs the Transfer Matrix Method to analyze the optical properties of OLEDs and aims to enhance light coupling, efficiency, and overall impact of LED and OLED technologies.

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With information entropy gradually taking the lead in modern information theory development, it begins to hold greater influence over multiple research areas as well as technology innovation. This paper aims to clarify people’s confusion with the development of entropy theory and provide a brief overview of the origin of entropy theory, including the original Shannon’s proposal, variants such as relative entropy and conditional entropy, and entropy concepts proposed by other scientists, such as Rényi Tsallis entropy. The paper also includes the current application of entropy, studies hotspots, and predicts future entropy development trends. This research paper is able to add more coherence and consistency to information entropy’s development, helping more people to better understand the concept of entropy and its derivation. At the same time, with hotspots of entropy fields of study, this paper hopes to attract more people to devote themselves to studying entropy-related fields, and boost technological development.

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