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Estimation of Reproduction Number of Influenza in Greece using SIR Model

Skarpeti et al. | Nov 18, 2020

Estimation of Reproduction Number of Influenza in Greece using SIR Model

In this study, we developed an algorithm to estimate the contact rate and the average infectious period of influenza using a Susceptible, Infected, and Recovered (SIR) epidemic model. The parameters in this model were estimated using data on infected Greek individuals collected from the National Public Health Organization. Our model labeled influenza as an epidemic with a basic reproduction value greater than one.

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A land use regression model to predict emissions from oil and gas production using machine learning

Cao et al. | Mar 24, 2023

A land use regression model to predict emissions from oil and gas production using machine learning

Emissions from oil and natural gas (O&G) wells such as nitrogen dioxide (NO2), volatile organic compounds (VOCs), and ozone (O3) can severely impact the health of communities located near wells. In this study, we used O&G activity and wind-carried emissions to quantify the extent to which O&G wells affect the air quality of nearby communities, revealing that NO2, NOx, and NO are correlated to O&G activity. We then developed a novel land use regression (LUR) model using machine learning based on O&G prevalence to predict emissions.

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Mapping the Electromagnetic Field in Front of a Microwave Oven

Xiang et al. | Sep 21, 2019

Mapping the Electromagnetic Field in Front of a Microwave Oven

There is limited evidence that extended exposure to an electromagnetic field (EMF) has negative health effects on humans. The authors measured the power density and strength of EMF at different distances and directions in front of a microwave oven, and they discuss the safety of different distances.

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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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Using NLP to ascertain changes in the fast-fashion industry based on UN sustainable development goals

Chadha et al. | Sep 11, 2023

Using NLP to ascertain changes in the fast-fashion industry based on UN sustainable development goals
Image credit: Prudence Earl

Here, the authors sought to evaluate the efforts of fast fashion clothing companies towards sustainability, specifically in regards to the United Nations Sustainable Development Goals. The authors used natural language processing to investigate the sustainability reports of fast fashion companies focusing on terms established by the UN. They found that the most consistently addressed areas were related to sustainable consumption/production, with a focus on health and well-being emerging during the recent pandemic.

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The Effect of Cooking Method on the Amount of Fat in an Egg

Srinivasan et al. | Dec 01, 2014

The Effect of Cooking Method on the Amount of Fat in an Egg

Fat can be chemically altered during cooking through a process called lipid oxidation, which can have a negative impact on health. In this study, the authors measured the extracted fat in raw, fried and hard-boiled eggs and found that cooking eggs to a higher temperature resulted in a lower amount of extracted fat, indicating a greater amount of oxidized fat.

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Quantitative definition of chemical synthetic pathway complexity of organic compounds

Baranwal et al. | Jun 19, 2023

Quantitative definition of chemical synthetic pathway complexity of organic compounds

Irrespective of the final application of a molecule, synthetic accessibility is the rate-determining step in discovering and developing novel entities. However, synthetic complexity is challenging to quantify as a single metric, since it is a composite of several measurable metrics, some of which include cost, safety, and availability. Moreover, defining a single synthetic accessibility metric for both natural products and non-natural products poses yet another challenge given the structural distinctions between these two classes of compounds. Here, we propose a model for synthetic accessibility of all chemical compounds, inspired by the Central Limit Theorem, and devise a novel synthetic accessibility metric assessing the overall feasibility of making chemical compounds that has been fitted to a Gaussian distribution.

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