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Population Forecasting by Population Growth Models based on MATLAB Simulation

Li et al. | Aug 31, 2020

Population Forecasting by Population Growth Models based on MATLAB Simulation

In this work, the authors investigate the accuracy with which two different population growth models can predict population growth over time. They apply the Malthusian law or Logistic law to US population from 1951 until 2019. To assess how closely the growth model fits actual population data, a least-squared curve fit was applied and revealed that the Logistic law of population growth resulted in smaller sum of squared residuals. These findings are important for ensuring optimal population growth models are implemented to data as population forecasting affects a country's economic and social structure.

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Effects of Wi-Fi EMF on Drosophila melanogaster

Anand et al. | Jan 29, 2020

Effects of Wi-Fi EMF on <em>Drosophila melanogaster</em>

While increased access to Wi-Fi has been a great advancement, we have a limited understanding if there are any health effects on animals. In this study, Anand and Anand exposed fruit flies (Drosophila melanogaster) to different concentrations of Wi-Fi electromagnetic fields, and observed effects on their reproduction and survivability.

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Bacteria and Antibiotic Resistance in School Bathrooms

Ciarlet et al. | Aug 24, 2020

Bacteria and Antibiotic Resistance in School Bathrooms

Since school bathrooms are widely suspected to be unsanitary, we wanted to compare the total amount of bacteria with the amount of bacteria that had ampicillin or streptomycin resistance across different school bathrooms in the Boston area. We hypothesized that because people interact with the faucet, outdoor handle, and indoor handle of the bathroom, based on whether or not they have washed their hands, there would be differences in the quantity of the bacteria presented on these surfaces. Therefore, we predicted certain surfaces of the bathroom would be less sanitary than others.

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Prediction of molecular energy using Coulomb matrix and Graph Neural Network

Hazra et al. | Feb 01, 2022

Prediction of molecular energy using Coulomb matrix and Graph Neural Network

With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.

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Are alkaline spices the future of antibiotics?

Jani et al. | Jan 23, 2022

Are alkaline spices the future of antibiotics?

The authors experimented with several commonly available alkaline spices (turmeric, cayenne pepper, and cinnamon) to study their antimicrobial properties, hypothesizing that alkaline spices would have antimicrobial activity. Results showed a zone of inhibition of bacterial growth, with the largest zone of inhibition being around turmeric, followed by cayenne pepper, and the smallest around cinnamon. These results are impactful, as common alkaline spices generally do show antibacterial properties and both bacteriostatic and bactericidal effects correlated with degree of alkalinity.

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