Integrating deep learning algorithms for forecasting evapotranspiration and assessing crop water stress in agricultural water management.

The increasing impacts of climate change on global agriculture necessitate the development of advanced predictive models for efficient water management in crop fields. This study aims to enhance the forecasting of evapotranspiration (ET), potential evapotranspiration (PET), and crop water stress index (CWSI) using state-of-the-art deep learning techniques. This research integrates high-resolution climatic data from the ACCESS-ESM model and incorporates four shared socioeconomic pathways (SSPs) to represent a wide range of future climate scenarios.

Assessing the impact of organic and inorganic micropollutants released from a wastewater treatment plant on humans and aquatic environment, Al-Hoceima city, Morocco.

Wastewater contains a variety of compounds qualified as pollutants. These undergo incomplete treatment in wastewater treatment plants. The objective of this study is to determine the potential impacts on humans and aquatic environment of 46 organic and inorganic micropollutants using the USE-tox® model.

Role of artificial intelligence in brain tumour imaging.

Artificial intelligence (AI) is a rapidly evolving field with many neuro-oncology applications. In this review, we discuss how AI can assist in brain tumour imaging, focusing on machine learning (ML) and deep learning (DL) techniques. We describe how AI can help in lesion detection, differential diagnosis, anatomic segmentation, molecular marker identification, prognostication, and pseudo-progression evaluation.

Evaluation of non-invasive diagnostic tools for diarrhea: a systematic review of point-of-care tests and biomarkers.

Background: Diarrhea is a prevalent condition affecting millions worldwide. However, current standard diagnostic methods have many drawbacks. This review examines various non-invasive point-of-care (POC) tests and biomarkers aiding rapid diagnosis of diarrhea from different causes.