Investigating machine learning methods for tuberculosis risk factors prediction:a comparative analysis and evaluation

Abstract Tuberculosis (TB) is a killer disease, and its root can be traced to Mycobacterium tuberculosis. As the world population increases, the burden of tuberculosis is growing along. Low-and-middle-income nations are not exempted from the tuberculosis crisis. Due to a shortage of medical supplies, tuberculosis bacteria have become a huge public health concern. This study reviewed recent literature from 2015 to 2020 to critically examine what earlier researchers have done about TB burden and treatment. The data used were based on the hospital’s medical department’s record and used a machine-learning algorithm to predict and determine the risk factors associated with the disease. Furthermore, it developed five predictive models to offer the medical managers a valid alternative to the manual estimation of TB patients’ status as cured or not cured. The overall classification showed that all the classification methods performed well for classifying the TB treatment outcome (ranging between 67.5% and 73.4%). Our findings showed that MLP (testing) is the best model to predict TB patients’ treatment outcomes. Age and length of stay were identified as significant risk factors for TB patients in this study. This study explains the study’s limitation, contributions, managerial implications, and suggest future work

Concomitant with Nigerian road traffic accidents:an application of a generalized linear model

Abstract This study aims to apply a generalized linear model for investigating the relationship between road traffic accidents and the resulting fatalities in Nigeria. The main objectives are to determine the most suitable model fits, compare the models used, and examine the relationship between the total cases and log deaths by modelling the number of road traffic accidents in Nigeria. The study adopts Poisson regression and negative binomial regression model for data analysis to achieve the set goals. The data used for this research are secondary data collected from annual reports on road traffic accidents of the Federal Road Safety Commission of Nigeria between 1960 and 2017. The study establishes that the number of traffic accidents on roads in Nigeria is continually increasing, and efforts by the government and relevant agencies have been mostly unsuccessful in addressing this danger. Moreover, the highly dangerous conditions on Nigerian roads result in a daily loss of innocent lives that otherwise would have significantly contributed to economic growth

Invited Article: Electric solar wind sail: Toward test missions

The electric solar wind sail (E-sail) is a space propulsion concept that uses the natural solar wind dynamic pressure for producing spacecraft thrust. In its baseline form, the E-sail consists of a number of long, thin, conducting, and centrifugally stretched tethers, which are kept in a high positive potential by an onboard electron gun. The concept gains its efficiency from the fact that the effective sail area, i.e., the potential structure of the tethers, can be millions of times larger than the physical area of the thin tethers wires, which offsets the fact that the dynamic pressure of the solar wind is very weak. Indeed, according to the most recent published estimates, an E-sail of 1 N thrust and 100 kg mass could be built in the rather near future, providing a revolutionary level of propulsive performance (specific acceleration) for travel in the solar system. Here we give a review of the ongoing technical development work of the E-sail, covering tether construction, overall mechanical design alternatives, guidance and navigation strategies, and dynamical and orbital simulations