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Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.

This article describes the classification of medical text data using vector databases and text embedding. Various large language models were used to generate this medical data for the classification task.

Here the authors performed a comparative analysis to investigate the viability of using PLAY® instead of fetal bovine serum (FBS) as a growth medium to culture cells with an enzyme-linked immunosorbent assay.

In this study, the authors test whether providing a larger dataset of glass transition temperatures (T_g) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and T_g is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's T_g, providing valuable improvements to this useful platform.

The "Uncanny Valley" is a phenomenon in which humans feel discomfort in the presence of objects that are almost, but not quite, human-like. In this study, the authors tested whether this phenomenon could be overcome by sharing a stressful experience with a humanoid robot. They found that human subjects more readily accepted a robot partner that they had previously shared a stressful experience with, suggesting a potential method for increasing the effectiveness of beneficial human-robot interactions by reducing the Uncanny Valley effect.

The authors test whether soaking maize seeds in a solution of melatonin improves seed germination and drought tolerance.

Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.

Androgens are natural or synthetic steroid hormones that control secondary male sex characteristics. Androgens are excreted in cattle urine and feces, and can run off or seep into nearby waters, negatively impacting aquatic life and potentially polluting human water sources. Here, the authors investigated the effectiveness of soil as a natural barrier against androgen flow into vulnerable waterways. Their results, obtained by testing diffusion patterns of luminol, an androgen chemical analog, indicated that soil is a poor barrier to androgen diffusion.

Hemani et al. tackled the problem of rampant hospital waste by implementing staff training to help inform hospital workers about proper waste disposal. The authors observed a significant increase in proper waste disposal after the training, showing that simple strategies, such as in-person classroom training and posters, can have a profound effect on limiting improper waste handling.

Sound waves can be amazingly powerful, especially when they work together. Here the authors create an “acoustic lens” that focuses sound waves on a single location. This makes the sound waves very powerful, capable of causing damage at a precise point. In the future, acoustic lenses like this could potentially be used to treat cancer by killing small tumors without surgery.