Predictive modeling of air quality in the Tehran megacity via deep learning techniques.

Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental safety. The present study aims to forecast the concentrations of various air pollutants, including CO, O, NO, SO, PM, and PM, from 2013 to 2023 in the Tehran megacity, Iran, via deep learning (DL) models and evaluate their effectiveness over conventional machine learning (ML) methods. Key driving variables, including temperature, relative humidity, dew point, wind speed, and air pressure, were considered.

Application of the Lasso regularisation technique in mitigating overfitting in air quality prediction models.

As a significant global concern, air pollution triggers enormous challenges in public health and ecological sustainability, necessitating the development of precise algorithms to forecast and mitigate its impacts, which has led to the development of many machine learning (ML)-based models for predicting air quality. Meanwhile, overfitting is a prevalent issue with ML algorithms that decreases their efficacy and generalizability. The present investigation, using an extensive collection of data from 16 sensors in Tehran, Iran, from 2013 to 2023, focuses on applying the Least Absolute Shrinkage and Selection Operator (Lasso) regularisation technique to enhance the forecasting precision of ambient air pollutants concentration models, including particulate matter (PM and PM), CO, NO, SO, and O while decreasing overfitting.

Pulmonary asymptomatic recurrent cavitary sarcoidosis: A case report.

Cavitary sarcoidosis is a rare form and represents non-caseating granulomatous diseases of the lungs exhibiting a narrow range of differential diagnoses. The peculiarity of this case lies in the difficulty of distinguishing atypical manifestations of pulmonary sarcoidosis, such as cystic lesions, from cavernous tuberculosis. Both possess similar clinical and radiological features.

A computationally efficient quasi-harmonic study of ice polymorphs using the FFLUX force field.

FFLUX is a multipolar machine-learned force field that uses Gaussian process regression models trained on data from quantum chemical topology calculations. It offers an efficient way of predicting both lattice and free energies of polymorphs, allowing their stability to be assessed at finite temperatures. Here the Ih, II and XV phases of ice are studied, building on previous work on formamide crystals and liquid water.