Designing a fixed-time observer-based adaptive non-singular sliding mode controller for flexible spacecraft.

This paper investigates the stabilization problem with a fixed-time approach for a flexible spacecraft subject to vibrations of flexible modes, unknown bounded disturbance, and inherent uncertainty. To estimate the modal variables of a flexible spacecraft which are often unmeasurable in practice, an observer with guaranteed fixed-time convergence is designed. Using the estimated modal variables, a fixed-time non-singular sliding mode controller is designed so that the desired attitude can be reached before a pre-specified time threshold regardless of the spacecraft's initial attitude.

Classification of Right/Left Hand Motor Imagery by Effective Connectivity Based on Transfer Entropy in Electroencephalogram Signal.

Introduction: The right and left-hand motor imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and lefthand MI tasks.

Methods: TE is calculated among EEG channels as the distinctive, effective connectivity features.

An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals.

One of the most effective treatments for drug-resistant Major depressive disorder (MDD) patients is repetitive transcranial magnetic stimulation (rTMS). To improve treatment efficacy and reduce health care costs, it is necessary to predict the treatment response. In this study, we intend to predict the rTMS treatment response in MDD patients from electroencephalogram (EEG) signals before starting the treatment using machine learning approaches.

The glycocalyx, a novel key in understanding of mechanism of diabetic nephropathy: a commentary.

Introduction: Diabetes is a chronic and progressive disease that usually causes disrupts the function of the body's organs and can eventually lead to cardiomyopathy, nephropathy, retinopathy, and neuropathy. Diabetic nephropathy (DN) is the most common cause of chronic kidney disease and causes chronic structural changes in different parts of the affected kidney. Glycocalyx layer is one of the most important components of the vascular base found in the endothelium throughout the body's arteries and it has been shown that glycocalyx is also damaged during diabetic nephropathy.