Inaugural Scholarly Explorations Issue

•March 2026

Published by the Khoinchha Editorial Team, Ballia. Open Access. E-ISSN: 2584-1599.

Editorial Overview & Introduction

We are proud to present the inaugural issue of Khoinchha Journal of Research (Vol. 628, No. 7995). This edition represents a milestone in publishing multidisciplinary research across various disciplines, celebrating regional languages such as Sanskrit, Hindi, Bangla, Bhojpuri, Telugu, and English.

All papers presented in this issue have undergone a rigorous double-blind peer-review process to maintain high scholarly standards and academic integrity.

Table of Contents (3 Papers)

Pages 421-430DOI: 10.2584/khoinchha.2026.34114

Evidence of Room-Temperature Superconductivity in Carbonaceous Sulfur Hydride

Superconductivity at room temperature has been a long-sought goal of condensed-matter physics. Here we report the discovery of superconductivity in a carbonaceous sulfur hydride system under high pressure. We observe a superconducting transition temperature of 287.7 Kelvin (approximately 15 degrees Celsius) at a pressure of 267 gigapascals, using a diamond-anvil cell. Superconductivity is established by electrical resistance measurements, magnetic susceptibility tests, and Raman spectroscopy. This work opens avenues for room-temperature quantum electronics and energy transmission.

Mercer, A., Jenkins, S.

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Pages 431-445DOI: 10.2584/khoinchha.2026.34115

Single-Cell Transcriptomic Atlas of Embryonic Lineage Differentiation in Homo sapiens

Deciphering the spatial and temporal cell lineage pathways in developing human embryos remains a central challenge in developmental biology. We utilize high-throughput single-cell RNA sequencing to profile embryo transcription dynamics across development stages. By tracking 42,000 cells from pre-implantation to early organogenesis, we reconstruct the cell differentiation trajectory, highlighting the transcription factors that govern embryogenesis. These findings provide a comprehensive structural map of early human lineage decisions, serving as a baseline for regenerative medicine.

Rostova, E., Miller, D.

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Pages 446-458DOI: 10.2584/khoinchha.2026.34116

Deep Generative Learning for Automated Protein Folding and Molecular Docking Predictions

Predicting the three-dimensional structures of proteins and their docking configurations with ligands represents a key computational bottle-neck in structural biology and drug discovery. We present FoldingNet, a deep neural network that directly predicts atomic coordinates of protein structures from amino acid sequences. By leveraging attention networks and evolutionary coupling inputs, FoldingNet achieves sub-angstrom accuracy across 450 test folds. Furthermore, we demonstrate real-time molecular docking simulation with high-affinity ligands, dramatically accelerating drug candidate search times.

Jenkins, S., Mercer, A.

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