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Examining the Accuracy of DNA Parentage Tests Using Computer Simulations and Known Pedigrees

Wang et al. | Jul 13, 2020

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.

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Impact of carbon number and atom number on cc-pVTZ Hartree-Fock Energy and program runtime of alkanes

Pan et al. | Mar 06, 2024

Impact of carbon number and atom number on cc-pVTZ Hartree-Fock Energy and program runtime of alkanes
Image credit: The authors

It's time-consuming to complete the calculations that are used to study nuclear reactions and energy. To uncover which computational chemistry tools are useful for this challenge, Pan, Vaiyakarnam, Li, and McMahan investigated whether the Python-based Simulations of Chemistry Framework’s Hartree-Fock (PySCF) method is an efficient and accurate way to assess alkane molecules.

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The influence of purpose-of-use on information overload in online social networking

Agarkar et al. | Nov 01, 2022

The influence of purpose-of-use on information overload in online social networking

Here, seeking to understand the effects of social media in relation to social media fatigue and/or overload in recent years, the authors used various linear models to assess the results of a survey of 27 respondents. Their results showed that increased duration of use of social media did not necessarily lead to fatigue, suggesting that quality may be more important than quantity. They also considered the purpose of an individual's social media usage as well as their engagement behavior during the COVID-19 pandemic.

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Comparison of three large language models as middle school math tutoring assistants

Ramanathan et al. | May 02, 2024

Comparison of three large language models as middle school math tutoring assistants
Image credit: Thirdman

Middle school math forms the basis for advanced mathematical courses leading up to the university level. Large language models (LLMs) have the potential to power next-generation educational technologies, acting as digital tutors to students. The main objective of this study was to determine whether LLMs like ChatGPT, Bard, and Llama 2 can serve as reliable middle school math tutoring assistants on three tutoring tasks: hint generation, comprehensive solution, and exercise creation.

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Functional Network Connectivity: Possible Biomarker for Autism Spectrum Disorders (ASD)

Wang et al. | Feb 23, 2015

Functional Network Connectivity: Possible Biomarker for Autism Spectrum Disorders (ASD)

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and is difficult to diagnose in young children. Here magnetoencephalography was used to compare the brain activity in patients with ASD to patients in a control group. The results show that patients with ASD have a high level of activity in different areas of the brain than those in the control group.

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An efficient approach to automated geometry diagram parsing

Date et al. | Oct 02, 2022

An efficient approach to automated geometry diagram parsing

Here, beginning from an initial interest in the possibility to use a computer to automatically solve a geometry diagram parser, the authors developed their own Fast Geometry Diagram Parser (FastGDP) that uses clustering and corner information. They compared their own methods to a more widely available, method, GeoSolver, finding their own to be an order of magnitude faster in most cases that they considered.

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Transfer Learning for Small and Different Datasets: Fine-Tuning A Pre-Trained Model Affects Performance

Gupta et al. | Oct 18, 2020

Transfer Learning for Small and Different Datasets: Fine-Tuning A Pre-Trained Model Affects Performance

In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.

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