Browse Articles

Utilization of neural network to analyze Free Word Association to predict accurately age, gender, first language, and current country.

This study explores how to predict and minimize distortion in 3D printed parts, particularly when using affordable PLA filament. The researchers developed a model using a gradient boosting regressor trained on 3D printing data, aiming to predict the necessary CAD dimensions to counteract print distortion.

Additive manufacturing (AM) is transforming the production of complex metal parts, but challenges like internal cracking can arise, particularly in critical sectors such as aerospace and automotive. Traditional methods to assess cracking susceptibility are costly and time-consuming, prompting the use of machine learning (ML) for more efficient predictions. This study developed a multi-model ML pipeline that predicts solidification cracking susceptibility (SCS) more accurately by considering secondary alloy properties alongside composition, with Random Forest models showing the best performance, highlighting a promising direction for future research into SCS quantification.

In this article the authors look at the lung microbiome in patients with cystic fibrosis to determine what the major bacterial species present are.

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, NO_x, 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.

The authors found that SARIMAX-GARCH is more accurate than SARIMAX for load forecasting with respect to energy consumption.

Here, seeking to address the growing threat of multidrug-resistant bacteria (MDR). the authors used in silico virtual screening to target MDR Pseudomonas aeruginosa. They considered a key protein in its biosynthesis and virtually screened 20,000 candidates and 30 derivatives of brequinar. In the end, they identified a possible candidate with the highest degree of potential to inhibit the pathogen's lipid A synthesis.

Did the COVID-19 pandemic and travel restrictions improve air quality? The authors investigate this question in New York City using existing pollution data and forecasting trends.

In this study, the authors survey students and adults to better understand their basic financial knowledge and money saving skills to measure the extent of knowledge in each group and make comparisons between.

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.