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The comparative effect of remote instruction on students and teachers

Ng et al. | Jan 16, 2022

The comparative effect of remote instruction on students and teachers

In this study, high school students and teachers responded to a survey consisting of Likert-type scale, multiple-choice, and open-ended questions regarding various aspects of remote instruction. After analyzing the data collected, they found that remote learning impacted high school students academically and socially. Students took longer to complete assignments, and both students and teachers felt that students do not learn as much in remote learning compared to in-person instruction. However, most high school students demonstrated a comprehensive understanding of the topics, and an overall negative impact on students' grades was not detected.

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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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Effects of spices on rice spoilage

Govindaraj et al. | Aug 15, 2022

Effects of spices on rice spoilage

In this work, based on centuries of history where spices have been used and thought to have antimicrobial properties that prolong the shelf life of food, the authors investigated if several spices used in Indian cooking could delay the spoilage of cooked white rice. Based on changed in appearance and smell, as well as growth on agar plates, they found that cinnamon was the most effective in delaying spoilage, followed by cumin, pepper, garlic, and ginger. Their findings suggest the ability to use spices rather than chemical food preservatives to prolong the shelf life of foods.

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Investigating KNOX Gene Expression in Aquilegia Petal Spur Development

Hossain et al. | Feb 03, 2014

Investigating KNOX Gene Expression in Aquilegia Petal Spur Development

Plants, and all other multi-cellular organisms, develop through the coordinated action of many sets of genes. The authors here investigate the genes, in a class named KNOX, potentially responsible for organizing a certain part of Aquilegia (columbine) flowers called petal spurs. Through the technique Reverse Transcription-Polymerase Chain Reaction (RT-PCR), they find that certain KNOX genes are expressed non-uniformly in petal spurs, suggesting that they may be involved, perhaps in a cell-specific manner. This research will help guide future efforts toward understanding how many beautiful flowers develop their unique shapes.

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Measuring Exoplanetary Radii Using Transit Photometry

Tang et al. | May 07, 2019

Measuring Exoplanetary Radii Using Transit Photometry

Studying exoplanets, or planets that orbit a star other than the Sun, is critical to a greater understanding the formation of planets and how Earth's solar system differs from others. In this study the authors analyze the transit light curves of three hot Jupiter exoplanets to ultimately determine if and how these planets have changed since their discovery.

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A comparative analysis of machine learning approaches for prediction of breast cancer

Nag et al. | May 11, 2021

A comparative analysis of machine learning approaches for prediction of breast cancer

Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor.

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