MMIT-DDPM - Multilateral medical image translation with class and structure supervised diffusion-based model.

Unified translation of medical images from one-to-many distinct modalities is desirable in healthcare settings. A ubiquitous approach for bilateral medical scan translation is one-to-one mapping with GANs. However, its efficacy in encapsulating diversity in a pool of medical scans and performing one-to-many translation is questionable.

MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images.

Medical image segmentation has made a significant contribution towards delivering affordable healthcare by facilitating the automatic identification of anatomical structures and other regions of interest. Although convolution neural networks have become prominent in the field of medical image segmentation, they suffer from certain limitations. In this study, we present a reliable framework for producing performant outcomes for the segmentation of pathological structures of 2D medical images.

Techno-economic analysis of ethanol production from lignocellulosic biomass-a comparison of fermentation, thermo catalytic, and chemocatalytic technologies.

Bioethanol produced from 2nd generation biomass comprising of agricultural residues and forest wastes is a viable alternate fuel. Besides fermentation and biomass gasification to syngas and its further conversion to ethanol, a direct chemocatalytic conversion of lignocellulosic biomass into ethanol is being investigated as a viable route which avoids the emission of greenhouse gases. In this work, a detailed configuration of chemocatalytic route is simulated and optimized for minimizing the cost of ethanol production.

In Vitro Identification of the Hamiltonian Cycle Using a Circular Structure Assisted DNA Computer.

Adleman's illustration of molecular computing using DNA paved the way toward an entirely new direction of computing (Adleman, L. M. 1994, 266, 1021).