The authors look at malnutrition in children and how socio-economic factors impact this.
Read More...Socio-economic factor impact on malnutrition in South Indian government school children
The authors look at malnutrition in children and how socio-economic factors impact this.
Read More...Examining the Accuracy of DNA Parentage Tests Using Computer Simulations and Known Pedigrees
How accurate are DNA parentage tests? In this study, the authors hypothesized that current parentage tests are reliable if the analysis involves only one or a few families of yellow perch fish Perca flavescens. Their results suggest that DNA parentage tests are reliable as long as the right methods are used, since these tests involve only one family in most cases, and that the results from parentage analyses of large populations can only be used as a reference.
Read More...Longer Exposure to 2% India Ink Increases Average Number of Vacuoles in Tetrahymena pyriformis
Phagocytes feed by forming food vacuoles. In this article the authors investigate the extent that exposure of non-nutritional food, such as India Ink, to Tetrahymena pyriformis affects the number of vacuole formation. These studies provide insight to how organisms budget their energy and metabolic processes during an energy shortage.
Read More...Does Music Directly Affect a Person’s Heart Rate?
People react to music by moving and dancing. This study examined if different types of music were correlated with higher heart rates and if this was at all affected by music preferences.
Read More...Evaluating the performance of Q-learning-based AI in auctions
Advertising platforms like Google Ads use AI to drive the algorithms used to maximize advertisers benefits. This study shows that AI does not adjust it strategy based on auction type and highlights the limitations of AI running without explicit guidance.
Read More...Rover engineered to evaluate impacts of microclimatic parameters on pediatric asthma in Dallas schools
Pediatric asthma remains a significant health issue for Dallas students. This study examined the relationship between microclimatic parameters, vegetation, and pediatric asthma vulnerability (PAV) in urban schools.
Read More...Depression detection in social media text: leveraging machine learning for effective screening
Depression affects millions globally, yet identifying symptoms remains challenging. This study explored detecting depression-related patterns in social media texts using natural language processing and machine learning algorithms, including decision trees and random forests. Our findings suggest that analyzing online text activity can serve as a viable method for screening mental disorders, potentially improving diagnosis accuracy by incorporating both physical and psychological indicators.
Read More...Effects of social support on adolescent identity development
Adolescence is a critical period for self-identity formation, heavily influenced by feedback from social networks. This research examined the interplay between social support from parents and peers and self-concept development in adolescents using data from the National Longitudinal Study of Adolescent to Adult Health. While individual support from parents and peers did not directly impact self-concept, their combined interaction significantly influenced it, highlighting the importance of various social supports in fostering healthy self-concept development and overall adolescent well-being.
Read More...Exploring the effects of diverse historical stock price data on the accuracy of stock price prediction models
Algorithmic trading has been increasingly used by Americans. In this work, we tested whether including the opening, closing, and highest prices in three supervised learning models affected their performance. Indeed, we found that including all three prices decreased the error of the prediction significantly.
Read More...Transfer Learning with Convolutional Neural Network-Based Models for Skin Cancer Classification
Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.
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