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Comparative analysis of CO2 emissions of electric ride-hailing vehicles over conventional gasoline personal vehicles

Raman et al. | Jan 12, 2024

Comparative analysis of CO<sub>2</sub> emissions of electric ride-hailing vehicles over conventional gasoline personal vehicles
Image credit: Paul Hanaoka

While some believe that ride-hailing services offer reduced CO2 emissions compared to individual driving, studies have found that driving without passengers on ride-hailing trips or "deadheading" prevents this. Here, with a mathematical model, the authors investigated if the use of electric vehicles as ride-hailing vehicles could offer reduced CO2 emissions. They found that the improved vehicle efficiency and cleaner generation could in fact lower emissions compared to the use of personal gas vehicles.

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Effect of pH on the antibacterial properties of turmeric

Ganga et al. | Aug 31, 2023

Effect of pH on the antibacterial properties of turmeric

Some spices have antimicrobial or antibacterial properties that people have already tested. Turmeric has a wide variety of uses and has even been implemented in alternative medicine as a treatment for cancer, inflammation, osteoarthritis, and other diseases. We tested the antimicrobial effects of turmeric under two different pHs to characterize this effect in vitro. Decreasing the pH of a solution of turmeric may increase antibacterial properties.

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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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Using economic indicators to create an empirical model of inflation

Kasera et al. | Dec 01, 2022

Using economic indicators to create an empirical model of inflation

Here, seeking to understand the correlation of 50 of the most important economic indicators with inflation, the authors used a rolling linear regression to identify indicators with the most significant correlation with the Month over Month Consumer Price Index Seasonally Adjusted (CPI). Ultimately the concluded that the average gasoline price, U.S. import price index, and 5-year market expected inflation had the most significant correlation with the CPI.

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Propagation of representation bias in machine learning

Dass-Vattam et al. | Jun 10, 2021

Propagation of representation bias in machine learning

Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.

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