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Jazz music is a unique American art form that has spread around the world. Iyer and Iyer study this spread through a computational sociology project examining how jazz popularity is correlated with postmaterialism (an ideology that values self-expression) and political activity.

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.

In this article the authors investigate whether there is a correlation between age of a person and their outlook on global issues such as technology, politics, and environment. They find a correlation between increased age and decreased optimism. However regardless of age, they find that respondents believe certain characteristics such as technology and willingness to change are essential for improvements.

The global mental health crisis has led to increased substance abuse among youth. Prescription drug abuse causes approximately 115 American deaths daily. Understanding intergenerational transmission of substance abuse is complex due to lengthy human studies and socioeconomic variables. Recent FDA guidelines mandate abuse liability testing for neuro-active drugs but overlook intergenerational transfer. Brown planaria, due to their nervous system development similarities with mammals, offer a novel model.

Coronary heart disease (CHD) is a global disease that causes fatal buildup of plaque in the arteries. Currently stents are placed in the artery for many patients with CHD to support proper blood flow. Here, the authors build a system to explore how the shape of the stent affects blood flow rate, a finding that can help optimize stents for patients.

COVID-19 has officially been downgraded from the status of a global health emergency, but have COVID-19 safety practices become a new way of life for students? The authors collected survey data on COVID-19-related knowledge and behaviors of high-school students in Punjab, Pakistan and Santa Clara County, California, USA, so see where high-schoolers stand on pandemic safety today.

Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.

Pandemics involve the high transmission of a disease that impacts global and local health and economic patterns. Epidemiological models help propose pandemic control strategies based on non-pharmaceutical interventions such as social distancing, curfews, and lockdowns, reducing the economic impact of these restrictions. In this research, we utilized an epidemiological Susceptible, Exposed, Infected, Recovered, Deceased (SEIRD) model – a compartmental model for virtually simulating a pandemic day by day.

Enzymes that metabolize carbohydrates and lipids play a key role in our health, including global health challenges like cardiovascular diseases and diabetes. To learn more about these important enzymes, Gandhi and Gandhi test whether various natural substances (ginger, Aloe vera, lemon, and mint leaves) affect the activity of α-amylase and lipase enzymes.

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.