Atmospheric water vapor is vital to weather and climate research, yet estimating it from global navigation satellite system (GNSS) signals depends on an intermediate temperature value that global models may not represent accurately at regional scales. In Geodesy and Geodynamics, researchers introduce three models calibrated for Iran using radiosonde observations and radio occultation profiles from multiple satellite missions. Models using measured surface temperature performed best with radiosonde data, while a surface-temperature-independent model proved more reliable for satellite profiles and cut errors in GNSS-derived precipitable water vapor by about half at test stations in Tehran and Tabriz.

The Natural Product Institute at the Gangneung Institute of the Korea Institute of Science and Technology (KIST, President Oh Sang-Rok) has long conducted research leveraging natural-product-based core technologies to improve public quality of life. Building on this research foundation, a team led by Dr. Koo Song Yi, Senior Researcher at the Center for Natural Product Systems Biology, has developed a 3D food printing-based manufacturing technology for customized medi-foods. To address the mealtime challenges faced by dysphagia patients, the team engineered a composite ink for 3D printing that allows precise control of physical properties tailored to each patient's individual swallowing ability.

What if cancer screening no longer had to look for one cancer at a time? Cancer screening has long followed a one-organ-at-a-time model: mammography for breast cancer, colonoscopy for colorectal cancer, low-dose CT for lung cancer, and cervical testing for cervical cancer. These programs save lives, but they also leave gaps. Many cancers still lack routine screening, while patients often face multiple appointments, tests, and follow-up procedures.

New tool automates carbon reporting for researchers, emphasizing ease of use and real-time grid forecasting.

A new study published in Big Earth Data develops explainable Random Forest models to predict stream total nitrogen (TN) and total phosphorus (TP) concentrations using upstream catchment characteristics derived from openly accessible environmental datasets. The results show that the models provide robust predictions for TN, while reduced and spatial covariates help mitigate overfitting and improve model interpretability, demonstrating the potential of explainable machine learning for regional nutrient monitoring.

Ancient single-celled red algae that thrive in scalding, acidic waters are emerging as far more than specialized survivors.

Research by Rutgers evolutionary biologist Debashish Bhattacharya and his collaborators show that the organisms, known as Cyanidiophyceae , form the foundation of entire microbial ecosystems, providing new insights into how complex life may have evolved and adapted to some of Earth’s harshest environments.