Here the authors examined a population of Massachusetts marine isopods, seeking to classify them based on comparison of their morphology, movement, and seaweed preference compared to those of known species. In this process they found that they were most similar to Idotea balthica. The authors suggest that this knowledge combined with monitoring populations of marine biology such as these isopods in different physical and ecological areas can provide useful insight into the effects of climate change.

In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.

Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.

Here, seeking to develop an understanding of the properties that determine the viability of piezoelectric flexible materials for applications in electro-mechanical sensors, the authors investigated the effects of the inclusion BaTiO₃ nanoparticles in electrospun Polyvinyledene Fluoride. They found the voltage generated had a piecewise linear dependence on the applied force at a few temperatures.

With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.

In this study, the authors investigate whether the COVID-19 pandemic is affecting the mental health of teens. Using data from a study done in Islamabad, Pakistan, the authors find that many teens between the ages of 13 and 19 show signs of mental illness. This study reports important data regarding the mental health of youth and points toward an increased need to address this topic during the pandemic.

Alzheimer's disease is one of the leading causes of death in the United States and is characterized by neurodegeneration. Mishra et al. wanted to understand the role of two transport proteins, LRP1 and AQP4, in the neurodegeneration of Alzheimer's disease. They used a model organism for Alzheimer's disease, the nematode C. elegans, and genetic engineering to look at whether they would see a decrease in neurodegeneration if they increased the amount of these two transport proteins. They found that the best improvements were caused by increased expression of both transport proteins, with smaller improvements when just one of the proteins is overly expressed. Their work has important implications for how we understand neurodegeneration in Alzheimer's disease and what we can do to slow or prevent the progression of the disease.

Here, recognizing the effects of the COVID-19 pandemic on young peoples' mental health and wellbeing the authors used an online survey which included the short General Health Questionnaire (GHQ-12) to probe 102 young adults. Overall they found that young adults perceived the pandemic to be detrimental to many areas of their wellbeing, with females and those aged 18-19 and 22-23 reporting to be the most significantly impacted.

This study assesses the capacity for milkweed species, an important host plant for Monarch butterflies, to grow in desert environments with different water levels.

The authors created a board game to teach young children about healthy eating habits to see whether an interactive and family-oriented method would be effective at introducing and maintaining a love for fruits and veggies. Results showed that children developed a liking for fruits and vegetables, and none regressed. Half maintained their level of enjoyment for fruits and vegetables during the research period, while the other half had a positive increase. The results show that a simple interactive game can shape how young children relate to food and encourage them to maintain healthy habits.