About 25% of the food grown never reaches consumers due to spoilage, and 11.5 billion pounds of produce from gardens are wasted every year. Current solutions involve farmers manually looking for and treating diseased crops. These methods of tending crops are neither time-efficient nor feasible. I used a convolutional neural network to identify signs of plant disease on leaves for garden owners and farmers.

While remarkable in its ability to mirror human cognition, machine learning and its associated algorithms often require extensive data to prove effective in completing tasks. However, data is not always plentiful, with unpredictable events occurring throughout our daily lives that require flexibility by artificial intelligence utilized in technology such as personal assistants and self-driving vehicles. Driven by the need for AI to complete tasks without extensive training, the researchers in this article use fluid intelligence assessments to develop an algorithm capable of generalization and abstraction. By forgoing prioritization on skill-based training, this article demonstrates the potential of focusing on a more generalized cognitive ability for artificial intelligence, proving more flexible and thus human-like in solving unique tasks than skill-focused algorithms.

Voice pitch affects perceived authoritativeness, competency, and leadership capacity. In this study, the authors suggest that examining certain measures of brain activity collected using an affordable EEG could predict advertising effectiveness, which may be invaluable in future neuromarketing research. Understanding voice pitch and other factors that cause implicit bias may allow significant advances in marketing, facilitating business success.

Life requires many things, including a hospitable temperature, elements, and energy. Here the authors utilize Newton's laws of physics and information relating a star's luminosity and temperature to determine the minimum and maximum masses and luminosities of planets and stars that would support life as we know it. This work can be used to determine the likelihood of a planet being able to support life based on attributes we can measure from here on Earth.

Here, the authors investigated engagement with #GMOFOODS, a hashtag on TikTok. They hypothesized that content focused on the negative effects of genetically modified organisms would receive more interaction driven by consumers. They found that the most common cateogry focused on the disadvantages of GMOs related to nutrition and health with the number of views determining if the video would be provided to users.

In this study the authors looked at developing a more efficient particle collision classification method with the goal of being able to more efficiently analyze particle trajectories from large-scale particle collisions without loss of accuracy.

Here the authors sought to better understand glioma, cancer that occurs in the glial cells of the brain with gene expression profile analysis. They considered the expression of complement system genes across the transcriptional and IDH-mutational subtypes of low-grade glioma and glioblastoma. Based on their results of their differential gene expression analysis, they found that outcomes vary across different glioma subtypes, with evidence suggesting that categorization of the transcriptional subtypes could help inform treatment by providing an expectation for treatment responses.

While resources on the safety of household cleaning products are plentiful, measures of efficacy of these cleaning chemicals against bacteria and viruses remain without standardization in the consumer market. The COVID pandemic has exasperated this knowledge gap, stoking the growth of misinformation and misuse surrounding household cleaning chemicals. Arriving at a time dire for sanitization standardization, the authors of this paper have created a quantifying framework for consumers by comparing a wide range of household cleaning products in their efficacy against bacteria generated by a safe and easily replicable yogurt model.

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.

The authors compare RNA expression profiles across three human cell types following infection with MERS-CoV