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A comparative study on the suitability of virtual labs for school chemistry experiments

Praveen et al. | Aug 22, 2022

A comparative study on the suitability of virtual labs for school chemistry experiments

Virtual labs have been gaining popularity over the last few years, especially during the worldwide lockdown due to the COVID-19 pandemic. In this study, the suitability of virtual labs for school chemistry experiments is addressed and their effectiveness is compared to traditional physical lab experiments by focusing on physical and human resources, convenience, cost, safety, and time involved as well as topic "matter".

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Comparing Measurements of Sun-Earth Distance: Shadow Method and Two Pinhole Method Variations

Rajakumar et al. | Feb 21, 2022

Comparing Measurements of Sun-Earth Distance: Shadow Method and Two Pinhole Method Variations

This study compares three methods regarding their accuracy in calculating the distance between the Earth and the Sun. The hypothesis presented was that the shadow method would have the greatest mean accuracy, followed by the tube pinhole method, and finally the plate pinhole method. The results validate the hypothesis; however, further investigation would be helpful in determining effective mitigation of each method’s limitations and the effectiveness of each method in determining the distance of other light-emitting objects distant from the Earth.

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Prediction of molecular energy using Coulomb matrix and Graph Neural Network

Hazra et al. | Feb 01, 2022

Prediction of molecular energy using Coulomb matrix and Graph Neural Network

With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.

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A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

Vangal et al. | Sep 28, 2020

A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

Each year, over 100,000 patients die from Sudden Unexpected Death in Epilepsy (SUDEP). A reliable seizure warning system can help patients stay safe. This work presents a comprehensive, comparative analysis of three different signal processing algorithms for automated seizure/ictal detection. The experimental results show that the proposed methods can be effective for accurate automated seizure detection and monitoring in clinical care.

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The impacts of different Al(NO3)3 concentrations on the mitotic index of Allium sativum

Jimenez Pol et al. | Jul 10, 2023

The impacts of different Al(NO<sub>3</sub>)<sub>3</sub> concentrations on the mitotic index of <i>Allium sativum</i>
Image credit: Kylie Paz

Recognizing the increasing threat of acid deposition inn soil through the reaction of NOx and SO2 pollutants with water in Spain, the authors investigates the effects of Al(NO3)3 concentrations on the health of Allium sativum. By tracking its mitotic index, they found a negative exponential correlation between Al(NO3)3 concentrations and the mitotic index of A. sativum.

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