Gate modulation of barrier height of unipolar vertically stacked monolayer ReS/MoS heterojunction.

This study investigates vertically stacked CVD grown ReS/MoS unipolar heterostructure device as Field Effect Transistor (FET) device wherein ReS on top acts as drain and MoS at bottom acts as source. The electrical measurements of ReS/MoS FET device were carried out and variation in Ids (drain current) Vs Vds (drain voltage) for different Vgs (gate voltage) revealing the n-type device characteristics. Furthermore, the threshold voltage was calculated at the gate bias voltage corresponding to maximum transconductance (g) value which is ~ 12 V.

Mixed-Dimensional van der Waals Heterostructure (2D ReS/0D MoS Quantum Dots)-Based Broad Spectral Range with Ultrahigh-Responsive Photodetector.

The remarkable properties of two-dimensional (2D) materials have led to significant advancements in photodetection and optoelectronics research. Currently, there are many successful methods that are employed to improve the responsivity of photodetectors, but the limited spectral range of the device remains a limitation. This work demonstrates the development of a mixed-dimensional (2D/0D) hybrid photodetector device fabricated using chemical vapor deposition (CVD)-grown monolayer ReS and solution-processed MoS quantum dots (QDs).

Correlation of mutated gene and signalling pathways in ASD.

Autism is a complicated spectrum of neurodevelopmental illnesses characterized by repetitive and constrained behaviors and interests, as well as social interaction and communication difficulties that are first shown in infancy. More than 18 million Indians, according to the National Health Portal of India, and 1 in 160 children worldwide, according to the WHO, are diagnosed with autism spectrum disorders. This review aims to discuss the complex genetic architecture that underlies autism and summarizes the role of proteins likely to play in the development of autism.

: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning.

Pulmonary fibrosis is a severe chronic lung disease that causes irreversible scarring in the tissues of the lungs, which results in the loss of lung capacity. The Forced Vital Capacity () of the patient is an interesting measure to investigate this disease to have the prognosis of the disease. This paper proposes a deep learning-based architecture to predict the progression of the disease from the patient's computed tomography (CT) scan and the patient's metadata.

SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network.

COVID-19 has emerged as one of the deadliest pandemics that has ever crept on humanity. Screening tests are currently the most reliable and accurate steps in detecting severe acute respiratory syndrome coronavirus in a patient, and the most used is RT-PCR testing. Various researchers and early studies implied that visual indicators (abnormalities) in a patient's Chest X-Ray (CXR) or computed tomography (CT) imaging were a valuable characteristic of a COVID-19 patient that can be leveraged to find out virus in a vast population.