A bottleneck in deleting algal blooms is that current data section is manual and is reactionary to an existing algal bloom. These authors made a custom-designed Seek and Destroy Algal Mitigation System (SDAMS) that detects harmful algal blooms at earlier time points with astonishing accuracy, and can instantaneously suppress the pre-bloom algal population.

In this study, the authors conduct a survey to evaluate the impact of household socioeconomic status on effectiveness of distance learning for students.

To explore the parent-child relationship during college planning, authors surveyed high school juniors from two private schools (boarding school vs. non-boarding parochial school). After coding, survey answers indicate students at boarding schools were found to have greater fear of parental control and disappointment, while students at non-boarding parochial schoolexpressed a greater need for parental assistance.

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 explore how similar exoplanets are to Earth and whether they could be inhabited by humans and other living organisms.

Each year, over 100,000 patients die from Sudden Unexpected Death in Epilepsy (SUDEP). A reliable seizure warning system can help patients stay safe. This work presents a comprehensive, comparative analysis of three different signal processing algorithms for automated seizure/ictal detection. The experimental results show that the proposed methods can be effective for accurate automated seizure detection and monitoring in clinical care.

Many diabetics agree that the current glucometer methods are invasive, inefficient, and unsustainable for measuring blood glucose. These authors investigate the possibility of using a non-invasive glucometer patch that predicts blood glucose from patient sweat, with high accuracy.