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Significance of Tumor Growth Modeling in the Behavior of Homogeneous Cancer Cell Populations: Are Tumor Growth Models Applicable to Both Heterogeneous and Homogeneous Populations?

Reddy et al. | Jun 10, 2021

Significance of Tumor Growth Modeling in the Behavior of Homogeneous Cancer Cell Populations: Are Tumor Growth Models Applicable to Both Heterogeneous and Homogeneous Populations?

This study follows the process of single-cloning and the growth of a homogeneous cell population in a superficial environment over the course of six weeks with the end goal of showing which of five tumor growth models commonly used to predict heterogeneous cancer cell population growth (Exponential, Logistic, Gompertz, Linear, and Bertalanffy) would also best exemplify that of homogeneous cell populations.

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The Impact of Effective Density and Compressive Strength on the Structure of Crumpled Paper Balls

Chu et al. | Nov 19, 2020

The Impact of Effective Density and Compressive Strength on the Structure of Crumpled Paper Balls

Crumpling is the process whereby a sheet of paper undergoes deformation to yield a three-dimensional structure comprising a random network of ridges and facets with variable density. The authors hypothesized that the more times a paper sheet is crumpled, the greater its compressive strength. Their results show a relatively strong linear relationship between the number of times a paper sheet is crumpled and its compressive strength.

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The effect of activation function choice on the performance of convolutional neural networks

Wang et al. | Sep 15, 2023

The effect of activation function choice on the performance of convolutional neural networks
Image credit: Tara Winstead

With the advance of technology, artificial intelligence (AI) is now applied widely in society. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes.

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Evaluating machine learning algorithms to classify forest tree species through satellite imagery

Gupta et al. | Mar 18, 2023

Evaluating machine learning algorithms to classify forest tree species through satellite imagery
Image credit: Sergei A

Here, seeking to identify an optimal method to classify tree species through remote sensing, the authors used a few machine learning algorithms to classify forest tree species through multispectral satellite imagery. They found the Random Forest algorithm to most accurately classify tree species, with the potential to improve model training and inference based on the inclusion of other tree properties.

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Blockchain databases: Encrypted for efficient and secure NoSQL key-store

Mehrota et al. | Mar 18, 2023

Blockchain databases: Encrypted for efficient and secure NoSQL key-store
Image credit: Ayushi Mehrota & David Kim

Although commonly associated with cryptocurrency, blockchains offer security that other databases could benefit from. These student authors tested a blockchain database framework, and by tracking runtime of four independent variables, they prove this framework is feasible for application.

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Mask wearing and oxyhemoglobin saturation effects during exercise

Foss et al. | Jul 15, 2022

Mask wearing and oxyhemoglobin saturation effects during exercise

Wearing face masks has become a common occurrence in everyday life and during athletics due to the spread of diseases. This study tested if masks would affect blood percent saturation of hemoglobin (SpO2) during treadmill exercise. The data analysis showed that mask type, time, and the interaction of mask type and time were significant results, regardless of physical ability. These results may assist athletes in understanding the differences between training and competing with and without a mask.

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