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. By incorporating the estimated modal variables in the control design, significant reduction in the steady-state error of the system response is achieved. The proposed control system is further enhanced with an adaptive law to increase robustness against unknown external disturbances and uncertainties. Stability analysis based on Lyapunov theory guarantees the convergence of observer estimation error and spacecraft attitude error to a pre-determined set before a fixed threshold. Simulation results validate the promising performance of the proposed control system, highlighting its effectiveness in achieving accurate and robust attitude control for flexible spacecraft.

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. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely relief-F, Fisher, Laplacian, and local learningbased clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) methods are used for classification. Results: Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via the Relief-F algorithm as feature selection and support vector machine (SVM) classification with 91.02% accuracy. Conclusion: The TE index and a hierarchical feature selection and classification can be useful for the discrimination of right- and left-hand MI tasks from multichannel EEG signals. Highlights: Effective connectivity features were extracted from electroencephalogram (EEG) to analyze relationships between regions.Four feature selection methods used to select most significant effective features.Support vector machine (SVM) used for discrimination of right and left hand motor imagery (MI) task. Plain Language Summary: In this study, we investigated brain activity using effective connectivity during MI task based on EEG signals. The motor imagery task can accomplish the same goal as motor execution, since they are both activated by the same brain area. Transfer entropy, coherence, and Granger casualty were employed to extract the features. Differential patterns of activity between the left vs. right MI task showed activity around the motor area rather than other areas. In order to reduce redundant information and select the most significant effective connectivity features, four feature subset selection algorithms are used: Relief-F, Fisher, Laplacian, and learning-based clustering feature selection (LLCFS). Then, support vector machine (SVM) and linear discriminant analysis (LDA) are used to classify left and right hand MI task. Comparison of three different connectivity methods showed that TE index had the highest classification accuracy, and could be useful for the discrimination of right and left hand MI tasks from multichannel EEG signals.

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. Effective brain connectivity of 19-channel EEG data of MDD patients was calculated by the direct directed transfer function (dDTF) method. Then, using three feature selection methods, the best features were selected and patients were classified as responders or non-responders to rTMS treatment by using the support vector machine (SVM). Results on the 34 MDD patients indicated that the Fp2 region in the delta and theta frequency bands has a significant difference between the two groups and can be used as a significant brain biomarker to assess the rTMS treatment response. Also, the highest accuracy (89.6%) using the SVM classifier for the best features of the dDTF method based on the area under the receiver operating characteristic curve (AUC-ROC) criteria was obtained by combining the delta and theta frequency bands. Consequently, the proposed method can accurately detect the rTMS treatment response in MDD patients before starting treatment on the EEG signal to avoid financial and time costs to patients and medical centers.

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. Our goal is to conduct this systematic review study is to find the cause-and-effect relationship between glycocalyx and diabetic nephropathy and also to clarify the role of the endothelial renal glycocalyx in understanding of mechanism of the course of diabetic nephropathy, and to provide an accurate background for further important studies. METHODS: All databases included MEDLINE (PubMed), Science Direct, Scopus, Ovid and Google Scholar were systematically searched for related published articles. In all databases, the following search strategy was implemented and these key words (in the title/abstract) were used: "diabetes" AND "glycocalyx" OR "diabetic nephropathy" AND "glycocalyx". RESULTS AND DISCUSSION: A total of 19 articles were retrieved from all databases using search strategy. After screening based on the title and abstract, number of 17 of them selected for full text assessment. Finally, after extracting the key points and making connections between the articles, we came up with new points to consider. It can be said that diabetes with the action of reactive oxygen species through oxidative stress, increases ICAM-1 and TNF-α and decreases heparanase enzyme, it affects the glomerular endothelium and eventually leads to albuminuria and destruction of the Glx layer. CONCLUSION: Diabetes causes super-structural changes in the kidney nephrons at the glomerular level. The glomerular filter barrier, which includes the epithelial cell called the podocyte, endothelial pore cells, and basal membrane of the glomerulus, plays a major role in stabilizing the selective glomerular function in healthy individuals. Diabetic nephropathy also causes changes in endothelial glycocalyx.