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Analyzing relationships and distribution between age, sex, and eye disease at IGMCH eye OPD

Sareen et al. | Feb 20, 2025

Analyzing relationships and distribution between age, sex, and eye disease at IGMCH eye OPD
Image credit: Amanda Dalbjörn

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.

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Low female employment rates in South Korea are linked to the gender-specific burden of childrearing

Lee et al. | Aug 07, 2024

Low female employment rates in South Korea are linked to the gender-specific burden of childrearing
Image credit: Karolina Kaboompics

Female employment rates in South Korea are far below those of other countries that are members of the Organization for Economic Co-operation and Development. We assessed job satisfaction, job retention, and the underlying factors that impact these variables for both genders and various ages through a survey. Among 291 adult participants (161 women, 130 men) aged 20 to 59, working in various fields, 95% of responders were college graduates. These results suggest that even highly educated women feel more pressure from an innate sense of responsibility and societal perception to care for children than men.

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Forecasting air quality index: A statistical machine learning and deep learning approach

Pasula et al. | Feb 17, 2025

Forecasting air quality index: A statistical machine learning and deep learning approach
Image credit: Amir Hosseini

Here the authors investigated air quality forecasting in India, comparing traditional time series models like SARIMA with deep learning models like LSTM. The research found that SARIMA models, which capture seasonal variations, outperform LSTM models in predicting Air Quality Index (AQI) levels across multiple Indian cities, supporting the hypothesis that simpler models can be more effective for this specific task.

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The effects of age on quality of mental health during the COVID-19 pandemic

Bui et al. | Jul 15, 2024

The effects of age on quality of mental health during the COVID-19 pandemic

The impact of age on mental health is a crucial yet understudied aspect of public health. While mental health is gaining recognition as a vital component of overall well-being, its correlation with age remains largely unexplored. In Canada, where the median age has risen significantly over the past half-century, understanding this relationship becomes increasingly pertinent. Researchers hypothesized that older adults would exhibit lower rates of mental health disorders and report better perceived mental health due to increased emotional stability and maturity.

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Impact of carbon number and atom number on cc-pVTZ Hartree-Fock Energy and program runtime of alkanes

Pan et al. | Mar 06, 2024

Impact of carbon number and atom number on cc-pVTZ Hartree-Fock Energy and program runtime of alkanes
Image credit: The authors

It's time-consuming to complete the calculations that are used to study nuclear reactions and energy. To uncover which computational chemistry tools are useful for this challenge, Pan, Vaiyakarnam, Li, and McMahan investigated whether the Python-based Simulations of Chemistry Framework’s Hartree-Fock (PySCF) method is an efficient and accurate way to assess alkane molecules.

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Monitoring drought using explainable statistical machine learning models

Cheung et al. | Oct 28, 2024

Monitoring drought using explainable statistical machine learning models

Droughts have a wide range of effects, from ecosystems failing and crops dying, to increased illness and decreased water quality. Drought prediction is important because it can help communities, businesses, and governments plan and prepare for these detrimental effects. This study predicts drought conditions by using predictable weather patterns in machine learning models.

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