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In this study, the authors tested the ability and accuracy of a neural net to identify patterns in complex number matrices.

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.

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.

In this article, the authors systematically study whether the type of a star is correlated with the number of planets it can support. Their study shows that medium-sized stars are likely to support more than one planet, just like the case in our solar system. They predict that, of the hundreds of planets beyond our solar system, 6% might be habitable. As humans work to travel further and further into space, some of those might truly be suited for human life.

In this work, the authors develop an algorithm that solves the problem of efficient space travel between planets. This is a problem that could soon be of relevance as mankind continues to expand its exploration of outer space, and potentially attempt to inhabit it.

The use of gamification in cybersecurity education, particularly through capture-the-flag competitions, involves scoring challenges based on their difficulty and the number of teams that solve them. The study investigated how changing the scoring formulas affects competition outcomes, predicting that different formulas would alter score distributions.

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.

Here, the authors sought to identify a method to optimize the lift generated by an airfoil based solely on its shape. By beginning with a Bernoullian model to predict an optimized wing shape, the authors then tested their model against other possible shapes by constructing them from Styrofoam and testing them in a small wind tunnel. Contrary to their hypothesis, they found their expected optimal airfoil shape did not result in the greatest lift generation. They attributed this to a variety of confounding variables and concluded that their results pointed to a correlation between airfoil shape and lift generation.

End Stage Renal Disease (ESRD) is a growing health concern in the United States. The authors of this study present a study of ESRD incidence over a 32-year period, providing an in-depth look at the contributions of age, race, gender, and underlying medical factors to this disease.

Global reliance on extractive energy sources has many downsides, among which are inconsistent supply and consequent price volatility that distress companies and consumers. It is unclear if renewable energy offers stable and affordable solutions to extractive energy sources. The cost of solar energy generation has decreased sharply in recent years, prompting a surge of installations with a range of financing options. Even so, most existing options require upfront payment, making installation inaccessible for towns with limited financial resources. The primary objective of our research is to examine the use of green bonds to finance solar energy systems, as they eliminate the need for upfront capital and enable repayment through revenue generated over time. We hypothesized that if we modeled the usage of green bonds to finance the installation of a solar energy system in New Jersey, then the revenue generated over the system’s lifetime would be enough to repay the bond. After modeling the financial performance of a proposed solar energy-producing carport in Madison, New Jersey, financed with green bonds, we found that revenue from solar energy systems successfully covered the annual green bond payments and enabled the installers to obtain over 50% of the income for themselves. Our research demonstrated green bonds as a promising option for New Jersey towns with limited financial resources seeking to install solar energy systems, thereby breaking down a financial barrier.