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The authors found that SARIMAX-GARCH is more accurate than SARIMAX for load forecasting with respect to energy consumption.

The authors demonstrate computationally that two types of nanoplastics can bind to estrogen receptor beta and potentially disrupt endocrine systems.

Recent observations by the “Extremely Metal-Poor Representatives Explored by the Subaru Survey” (EMPRESS) collaboration found normal deuterium levels but unexpectedly low helium-4, challenging current cosmological theories. This study used simulations with the PRyMordial package to test whether dark matter particles interacting with neutrinos in the early universe could explain the discrepancy.

The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.

The authors compare the predicted versus actual geyser eruption times for the Old Faithful and Beehive Geysers at Yellowstone National Park.

This study investigates how bending radius, wavelength, and number of turns affect signal attenuation in single-mode optical fibers.

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

Advertising platforms like Google Ads use AI to drive the algorithms used to maximize advertisers benefits. This study shows that AI does not adjust it strategy based on auction type and highlights the limitations of AI running without explicit guidance.

The authors looked factors that could lead to earlier diagnosis of cardiovascular disease thereby improving patient outcomes. They found that advances in imaging and electrocardiography contribute to earlier detection of cardiovascular disease.

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.