Browse Articles

This manuscript explores the use of Spanish language translators in an outpatient clinic in New Jersey. The authors surveyed patients before and after in person, video, and telephone appointments to determine which modality was most acceptable to the patients. The authors found that the three modalities did not differ in patient satisfaction, but that patients were grateful for translations services and that patient trust may be expanded by the use of these services and by focuses of translator soft skills.

The authors looked at how the focus of someone's attention impacted what information was retained in their working memory.

This study evaluates the potential of natural language processing (NLP) models in an emotion-driven bibliotherapy framework to improve mental health challenges.

Here the authors investigated the relationship between offensive play-calling styles and scoring success within the NFL's red zone by analyzing play-by-play data and expected points metrics. Their findings suggest that a conservative approach to play design and execution is more strongly associated with maximizing efficiency and point-value gains than aggressive strategies.

The authors looked at the importance of different point breakdowns needed to win in a game of tennis.

To address the limitations in predicting the severity of Atrial Septal Defect (ASD), here the authors utilized a fluid-filled chamber model to quantify the relationship between defect size and right atrial fluid output. The findings confirmed that larger ASD diameters result in a linear increase in fluid output, validating a cost-effective model that can improve clinical prognosis and treatment planning for heart failure risks.

The authors looked at genes and pathways that are enriched in glioblastoma multiforme.

The authors looked at how storage temperature of potatoes can impact the sweetness of potatoes (due to the break down of the starch into simple sugars).

The authors looked at ways to provide better forecasting on website traffic. They found that deep learning models performed better than statistical models.

The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.