Studied multidrug-resistant tuberculosis by modeling the genetic co-heritability of Rifampicin and Isoniazid resistance using k-mer analysis and machine learning. Built a genomic prediction model with 85.68% accuracy on 2,200 samples, identifying key resistance-associated k-mers in rpoB and katG, highlighting the potential of genomic data for rapid TB resistance detection.

Foot drop, a condition that impairs foot mobility due to weakened dorsiflexor muscles, increases the risk of falls and unstable gait. This study investigates the use of electromyography (EMG) signals from functional muscles to control assistive devices for individuals with foot drop, focusing on optimal sensor placement for accurate signal acquisition. Using Arduino-based hardware and MATLAB algorithms, EMG signals were recorded from targeted muscles during controlled ankle movements, with Butterworth filtering and smoothing applied to enhance signal clarity. A 3D virtual actuation model demonstrated real-time control of object movements based on EMG data, achieving a strong correlation (R = 0.968) between EMG amplitude and actuator precision. These results suggest that accurate EMG-based control is feasible for developing effective assistive devices, improving mobility in individuals with foot drop.