Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.

Here the authors used morphological characters and DNA barcoding to identify arthropods found within a residential house. With this method they identified their species and compared them against pests lists provided by the US government. They found that none of their identified species were considered to be pests providing evidence against the misconception that arthropods found at home are harmful to humans. They suggest that these methods could be used at larger scales to better understand and aid in mapping ecosystems.

In our work we followed the formation of gas bubbles on the surface of the vessel walls in different carbonated liquids, over different time intervals, at different temperatures and in vessels made of different materials. Our results made it possible to identify patterns affecting the process of formation and disappearance of carbon dioxide bubbles.

The burning of fossil fuels, leading to an increased amount of carbon emissions, is the main cause of acid rain. Acid rain affects the process of photosynthesis, which makes the topic valuable to investigate. Our group utilizes plants to further investigate the relationship between pH value and photosynthesis. In this experiment, our group hypothesized that rain with a lower pH will decrease the rate of photosynthesis, causing less oxygen to be produced in the reaction.

Recent research suggests that the "American Dream" of income mobility may be becoming increasingly hard to obtain. Datta and Schmitz explore the role of government spending in socioeconomic opportunity by determining which state government spending components are associated with increased income mobility.

Solar and radio frequency harvesters serve as a viable alternative energy source to batteries in many cases where the battery cannot be easily replaced. Using specifically designed circuit models, the authors quantify the reliability of different harvested energy sources to identify the most practical and efficient forms of renewable energy.

The purpose of this study was to investigate the relationship between colorism and police killings of unarmed African American suspects. The authors collected data from the Washington Post database, which reports unarmed African American victims from 2015–2021, and found that the victims who were killed by police were darker on average than a control population of African Americans that had not encountered the police.

Human intelligence is correlated with variation in the protein neuroplastin-65, which is encoded by the NPTN gene. The authors examine the evolution of this gene across different animal species.

Dhar and colleagues established a Type II diabetes mellitus (T2DM) model in fruit flies, using this model to induce insulin resistance and characterize the effects Resveratrol and Pterostilbene on a number of growth and activity metrics. Resveratrol and Pterostilbene treatment notably overturned the weight gain and glucose levels. The results of this study suggest that Drosophila can be utilized as a model organism to study T2DM and novel pharmacological treatments.

Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.