A geospatial approach for assessing urban flood risk zones in Chennai, Tamil Nadu, India

Chennai, the capital city of Tamil Nadu in India, has experienced several instances of severe flooding over the past two decades, primarily attributed to persistent heavy rainfall. Accurate mapping of flood-prone regions in the basin is crucial for the comprehensive flood risk management. This study used the GIS-MCDA model, a multi-criteria decision analysis (MCDA) model that incorporated geographic information system (GIS) technology to support decision making processes. Remote sensing, GIS, and analytical hierarchy technique (AHP) were used to identify flood-prone zones and to determine the weights of various factors affecting flood risk, such as rainfall, distance to river, elevation, slope, land use/land cover, drainage density, soil type, and lithology. Four groups (zones) were identified by the flood susceptibility map including high, medium, low, and very low. These zones occupied 16.41%, 67.33%, 16.18%, and 0.08% of the area, respectively. Historical flood events in the study area coincided with the flood risk classification and flood vulnerability map. Regions situated close to rivers, characterized by low elevation, slope, and high runoff density were found to be more susceptible to flooding. The flood susceptibility map generated by the GIS-MCDA accurately described the flood-prone regions in the study area.

Enantioselective Mixed Matrix Membranes for Chiral Resolution

Most pharmaceuticals are stereoisomers that each enantiomer shows dramatically different biological activity. Therefore, the production of optically pure chemicals through sustainable and energy-efficient technology is one of the main objectives in the pharmaceutical industry. Membrane-based separation is a continuous process performed on a large scale that uses far less energy than the conventional thermal separation process. Enantioselective polymer membranes have been developed for chiral resolution of pharmaceuticals; however, it is difficult to generate sufficient enantiomeric excess (ee) with conventional polymers. This article describes a chiral resolution strategy using a composite structure of mixed matrix membrane that employs chiral fillers. We discuss several enantioselective fillers, including metal-organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, porous organic cages (POCs), and their potential use as chiral fillers in mixed matrix membranes. State-of-the-art enantioselective mixed matrix membranes (MMMs) and the future design consideration for highly efficient enantioselective MMMs are discussed.

MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey

Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer's disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.