The authors looked at the ability of different machine learning algorithms to predict the level of financial corruption in different countries.
Read More...Predicting and explaining illicit financial flows in developing countries: A machine learning approach
The authors looked at the ability of different machine learning algorithms to predict the level of financial corruption in different countries.
Read More...Risk-adjusted return measures for selecting optimal mutual fund investment portfolios
The authors looked at different combinations of risk-adjusted return measures to determine which combination would provide an optimal return for investors. They found that different combinations performed better dependent on investment timeframe.
Read More...Relating socioeconomic position (SEP) and vaccination with Covid-19 rates in select populations
This article describes the relationship between socioeconomic factors and the extent of how the COVID-19 Pandemic affected communities. Factors such as infection rate, vaccination rate, and economic status were all evaluated within the context of this article.
Read More...Survival of Escherichia coli K-12 in various types of drinking water
For public health, drinking water should be free of bacterial contamination. The objective of this research is to identify the fate of bacteria if drinking water becomes contaminated and inform consumers on which water type enables the least bacteria to survive. We hypothesized that bottled mineral water would provide the most sufficient conditions for E. coli to survive. We found that if water becomes contaminated, the conditions offered by the three water types at room temperature allow E. coli to survive up to three days. At 72 hours, the bottled spring water had the highest average colony forming units (CFUs), with tap and mineral water CFU values statistically lower than spring water but not significantly different from each other. The findings of this research highlight the need of implementing accessible quality drinking water for the underserved population and for the regulation of water sources.
Read More...The Effect of Poverty on Mosquito-borne Illness Across the United States
Mosquito-borne diseases are a major issue across the world, and the objective for this project was to determine the characteristics that make some communities more susceptible to these diseases than others. The authors identified and studied characteristics that make communities susceptible to mosquito-borne diseases, including water in square miles, average temperature, population, population density, and poverty rates per county. They found that the population of a county is the best indicator of the prevalence of mosquito-borne diseases.
Read More...Immunogenicity of Minhai 13-valent pneumococcal polysaccharide conjugate vaccine in experimental mice
The authors looked at the immunogenicity of a newly developed pneumococcal conjugate vaccine compared to a previously developed one. They found the newly developed vaccine did elicit an immune response.
Read More...Analyzing carbon dividends’ impact on financial security via ML & metaheuristic search
Impact of carbon tax and dividend on financial security
Read More...Genetic algorithm based features selection for predicting the unemployment rate of India
The authors looked at using genetic algorithms to look at the Indian labor market and what features might best explain any variation seen. They found that features such as economic growth and household consumption, among others, best explained variation.
Read More...A comparison of the water quality between Chinatown and Bayside: two demographically different regions
The authors looked at differences in water quality between Chinatown and Bayside. They wanted to look at the racial and economic demographics of each region and how that correlated to access to clean drinking water. Ultimately they did not find any significant differences in water quality, but identified important future directions for this work.
Read More...Implementing machine learning algorithms on criminal databases to develop a criminal activity index
The authors look at using publicly available data and machine learning to see if they can develop a criminal activity index for counties within the state of California.
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