In the modern world, communication and mobility are no longer obstacles. A natural consequence is that people from all over the world are mixing like never before and national identity can no longer be determined simply by a person's appearance or manner of speech. In this article, the authors study how a person's accent interferes with the perception of a their national identity and proposes ways to eliminate such biases.
The authors looked at using genetic algorithms to look at the Indian labor market and what features might best explain any variation seen. They found that features such as economic growth and household consumption, among others, best explained variation.
The COVID-19 pandemic demonstrated the depth and significance of healthcare inequality in the United States. Xiao, Xiao, and Gong examine healthcare disparities in the Richmond (Virginia) metropolitan area by analyzing whether people from disadvantaged populations must travel for longer to reach healthcare facilities.
In recent years, usage of interactive electronic devices such as computers, smartphones, and tablets has increased dramatically. Many studies have examined the potential adverse effects of excessive usage of such devices on children and adolescents, but the effects on adults are not well understood. In this study, the authors examined the relationship between adult usage of interactive electronic devices and a variety of clinical measures of psychological well-being. They found that according to some metrics, higher usage of interactive electronic devices is associated with several adverse psychological outcomes, suggesting a need for more careful consideration of such usage patterns in clinical settings.
Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.
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.
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, Harvell and Nicholson hypothesized that increased ocean acidity would decrease the photosynthetic ability of Chaetoceros gracilis, a diatom prolific in Monterey Bay, because of the usually corrosive effects of carbonic acid on both seashells and cells’ internal structures. They altered pH of algae environments and measured the photosynthetic ability of diatoms over four days by spectrophotometer. Overall, their findings indicate that C. gracilis may become more abundant in Monterey Bay as the pH of the ocean continues to drop, potentially contributing to harmful algal blooms.
Oil spills are one of the most devastating events for marine life. Finding ways to clean up oil spills without the need for harsh chemicals could help decrease the negative impact of such spills. Here the authors demonstrate that using a combination of several biodegradable substances can effectively adsorb oil in seawater in a laboratory setting. They suggest further exploring the potential of such a combination as a possible alternative to commonly-used non-biodegradable substances in oil spill management.