The authors survey adolescents about aspects of the COVID-19 pandemic to explore perspectives that may give rise to cognitive dissonance.
Read More...Investigating the impact of the COVID-19 pandemic on the cognitive dissonance of adolescents
The authors survey adolescents about aspects of the COVID-19 pandemic to explore perspectives that may give rise to cognitive dissonance.
Read More...Does technology help or hurt learning? Evidence from middle school and high school students
Here, recognizing the vastly different opinion held regarding device usage, the authors considered the effects of technology use on middle and high school students' learning effectiveness. Using an anonymous online survey they found partial support that device use at school increases learning effectiveness, but found strong support for a negative effect of technology use at home on learning effectiveness. Based on their findings they suggest that the efficacy of technology depends on environmental context along with other important factors that need consideration.
Read More...How Ya Doin'? with COVID-19
In this study, the authors survey students and adults about how the COVID-19 pandemic has impacted their sleep patterns, eating habits, mood, physical activity, and screen time.
Read More...Giving Teens a Voice: Sources of Stress for High School Students
The authors investigate the negative effects stress has on teen mental and physical health. Through a survey, they give Virginia teens a voice in revising the Health and Physical Education curriculum to include a standards of learning (SOL). Notably they identify factors contributing to stress levels including homework level, amount of free and sleep time, parental pressure and family encouragement.
Read More...Trust in the use of artificial intelligence technology for treatment planning
As AI becomes more integrated into healthcare, public trust in AI-developed treatment plans remains a concern, especially for emotionally charged health decisions. In a study of 81 community college students, AI-created treatment plans received lower trust ratings compared to physician-developed plans, supporting the hypothesis. The study found no significant differences in AI trust levels across demographic factors, suggesting overall skepticism toward AI-driven healthcare.
Read More...Analyzing relationships and distribution between age, sex, and eye disease at IGMCH eye OPD
This study analyzed patient demographics in the ophthalmology department at Indira Gandhi Medical College and Hospital (IGMCH) to assess relationships between age, sex, and eye conditions. While the overall sex distribution was equal, individual conditions varied, with cataracts and retinal disorders more common in males and conjunctival conditions slightly more prevalent in females, though none were statistically significant (p > 0.05) except for cataract patients aged 50–89 (p < 0.001). Understanding these trends can help medical facilities allocate resources more effectively for improved patient care.
Read More...Contribution of Indian Women to the National GDP
The authors assessed the degree of women participation in India's economy as a way to estimate woman's participation in India's economic growth.
Read More...Generation of a magnetic field on Mars
The authors propose and test a method that would allow for the generation of a magnetic field on Mars sufficient to support future colonization.
Read More...Anonymity Reduces Generosity in High School Students
The disinterested willingness a person has for helping others is known as altruism. But is this willingness to help others dependent on external factors that make you more or less inclined to be generous? We hypothesized that generosity in adolescents would depend on external factors and that these factors would change the amount of help given. To evaluate altruism and generosity, we conducted non-anonymous and anonymous variations of the dictator game and ultimatum game experiments and explored the role of anonymity, fairness, and reciprocity in high school students.
Read More...Optimizing AI-generated image detection using a Convolutional Neural Network model with Fast Fourier Transform
Recent advances in generative AI have made it increasingly hard to distinguish real images from AI-generated ones. Traditional detection models using CNNs or U-net architectures lack precision because they overlook key spatial and frequency domain details. This study introduced a hybrid model combining Convolutional Neural Networks (CNN) with Fast Fourier Transform (FFT) to better capture subtle edge and texture patterns.
Read More...