Review Paper on Smart Systems

Nowadays, technology has become an inseparable part of human lives. Technologies are smart enough to respond to human commands, provide protection to them and their accessories. Smart systems provide functionalities that of humans and are able to execute them much more efficiently than us. Smart systems search for multiple solutions for a particular problem and based on their intellectual capability and available knowledge base and are able to process solutions from solution set to give optimized output to the user

240th ENMC workshop: the involvement of skeletal muscle stem cells in the pathology of muscular dystrophies 25 -27 January 2019, Hoofddorp, the Netherlands

Satellite cells are dysfunctional in several neuromuscular disorders. Some muscles are more susceptible than others to disease and ageing. In vitro and in vivo model systems have shed light on many of the processes involved. in satellite cell function and dysfunction, but the drawbacks of each model system must be considered. Skeletal muscle pathology in mouse models of neuromuscular disease are affected by genetic background. A single cell approach will be useful to identify dysfunction in subsets of cell populations

Association of the PHACTR1/EDN1 genetic locus with spontaneous coronary artery dissection

Background: Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of acute coronary syndromes (ACS) afflicting predominantly younger to middle-aged women. Observational studies have reported a high prevalence of extracoronary vascular anomalies, especially fibromuscular dysplasia (FMD) and a low prevalence of coincidental cases of atherosclerosis. PHACTR1/EDN1 is a genetic risk locus for several vascular diseases, including FMD and coronary artery disease, with the putative causal noncoding variant at the rs9349379 locus acting as a potential enhancer for the endothelin-1 (EDN1) gene. Objectives: This study sought to test the association between the rs9349379 genotype and SCAD. Methods: Results from case control studies from France, United Kingdom, United States, and Australia were analyzed to test the association with SCAD risk, including age at first event, pregnancy-associated SCAD (P-SCAD), and recurrent SCAD. Results: The previously reported risk allele for FMD (rs9349379-A) was associated with a higher risk of SCAD in all studies. In a meta-analysis of 1,055 SCAD patients and 7,190 controls, the odds ratio (OR) was 1.67 (95% confidence interval [CI]: 1.50 to 1.86) per copy of rs9349379-A. In a subset of 491 SCAD patients, the OR estimate was found to be higher for the association with SCAD in patients without FMD (OR: 1.89; 95% CI: 1.53 to 2.33) than in SCAD cases with FMD (OR: 1.60; 95% CI: 1.28 to 1.99). There was no effect of genotype on age at first event, P-SCAD, or recurrence. Conclusions: The first genetic risk factor for SCAD was identified in the largest study conducted to date for this condition. This genetic link may contribute to the clinical overlap between SCAD and FMD

Background Invariant Faster Motion Modeling for Drone Action Recognition

Visual data collected from drones has opened a new direction for surveillance applications and has recently attracted considerable attention among computer vision researchers. Due to the availability and increasing use of the drone for both public and private sectors, it is a critical futuristic technology to solve multiple surveillance problems in remote areas. One of the fundamental challenges in recognizing crowd monitoring videos’ human action is the precise modeling of an individual’s motion feature. Most state-of-the-art methods heavily rely on optical flow for motion modeling and representation, and motion modeling through optical flow is a time-consuming process. This article underlines this issue and provides a novel architecture that eliminates the dependency on optical flow. The proposed architecture uses two sub-modules, FMFM (faster motion feature modeling) and AAR (accurate action recognition), to accurately classify the aerial surveillance action. Another critical issue in aerial surveillance is a deficiency of the dataset. Out of few datasets proposed recently, most of them have multiple humans performing different actions in the same scene, such as a crowd monitoring video, and hence not suitable for directly applying to the training of action recognition models. Given this, we have proposed a novel dataset captured from top view aerial surveillance that has a good variety in terms of actors, daytime, and environment. The proposed architecture has shown the capability to be applied in different terrain as it removes the background before using the action recognition model. The proposed architecture is validated through the experiment with varying investigation levels and achieves a remarkable performance of 0.90 validation accuracy in aerial action recognition

Azilsartan ameliorates aluminium chloride induced Alzheimer’s disease like pathology

Alzheimer’s disease (AD) is a neurodegenerative disease manifested with accumulation of neurotoxic proteins like beta-amyloid (Aβ) and hyperphosphorylated tau. Administration of angiotensin receptor blockers (ARBs) such as Telmisartan has demonstrated to generate significant memory improvement in AD. Azilsartan is an ARB with better bioavailability than Telmisartan. Hence, the present work evaluates the efficacy of Azilsartan against aluminium chloride (AlCl3) induced AD. In the work, albino rats were divided into five groups (n=6). Group I served as control and received saline (10 ml/kg). Group-II was treated with AlCl3 (100 mg/kg) for 42 days; Group-III and IV received Azilsartan (5 mg/kg) and Telmisartan (10 mg/kg) with AlCl3 daily for 42 days. Y-maze, elevated plus maze and radial arm maze were used to evaluate memory functions. This was followed by biochemical and histological studies, along-with determination of Aβ content and anti-oxidant status. AlCl3 was found to significantly (p <0.05) reduce cognition and increased concentration of Aβ in a hippocampus with elevated lipid peroxidation levels. It also significantly (p<0.05) decreased superoxide dismutase and increased malondialdehyde content. However, brain histology showed presence of neurofibrillary tangles, neuronal dead cells, and pyknotic cells compared to normal group. Still, Azilsartan and Telmisartan significantly (p<0.05) reversed cognitive dysfunction, improved antioxidant status and decreased Aβ production. Thus we conclude that Azilsartan protects AlCl3 induced AD-like pathology but, to a degree less than Telmisartan

Hybrid adaptive framework for coordinated control of distributed generators in cyber-physical energy systems

With the development of information and communications technology (ICT) and inundation of sensing devices, the control of smart grid is undergoing a paradigm shift from centralised/decentralised to a more distributed nature allowing each distributed generator to receive information from sensors at distant buses. In such systems, there is much interdependency between various power, control and communication parameters due to which the control of parameters from one domain gets affected by other. The central idea of this study is to develop a generic, hybrid and customised framework to jointly model the multi-disciplinary variables and their interactions present in the smart grid and to develop controllers in an adaptive manner to ensure better control of physical variables such as voltage irrespective of the changes in operating point brought about by changes in physical/cyber parameters. Hence, the different operating conditions of the power system have been modelled as multiple subsystems of a hybrid switching system and controller design is carried out by solving the optimisation formulations developed for delay-free and delay-existent cases using the theory of common Lyapunov function. The optimisation is carried out using the block coordinate descent methodology by converting the non-convex formulation into a series of convex problems to obtain a solution

Question Answer System: A State-of-Art Representation of Quantitative and Qualitative Analysis

Question Answer System (QAS) automatically answers the question asked in natural language. Due to the varying dimensions and approaches that are available, QAS has a very diverse solution space, and a proper bibliometric study is required to paint the entire domain space. This work presents a bibliometric and literature analysis of QAS. Scopus and Web of Science are two well-known research databases used for the study. A systematic analytical study comprising performance analysis and science mapping is performed. Recent research trends, seminal work, and influential authors are identified in performance analysis using statistical tools on research constituents. On the other hand, science mapping is performed using network analysis on a citation and co-citation network graph. Through this analysis, the domain&rsquo;s conceptual evolution and intellectual structure are shown. We have divided the literature into four important architecture types and have provided the literature analysis of Knowledge Base (KB)-based and GNN-based approaches for QAS

An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment

Human emotions affect psychological health to a great level. Positive emotions relate to health improvement; whereas negative emotions may aggravate psychological disorders such as anxiety, stress, and depression. Although there exist several computational methods to predict psychological disorders, most of them provide a black-box view of uncertainty. This research involves developing a novel predictive model for multi class psychological risk recognition with an accurate explainable interface. Standard questionnaires are utilized as data set and a new approach called a Q-Prioritization is employed to drop insignificant questions from the data set. Moreover, a novel balanced decision tree method based on repetitive oversampling is applied for the training and testing of the model. Predictive nature along with its contributing factors are interpreted with three techniques such as permuted feature importance, contrastive explanation, and counterfactual method, which together form a reasoning engine. The prediction outcome generated an impressive performance with an aggregated accuracy of 98.25%. The mean precision, recall, and F-score metric recorded were 0.98, 0.977, and 0.979, respectively. Also, it was noted that without applying Q-Prioritization, the accuracy significantly drops to 90.25%. The error rate observed with our model was only 0.026. The proposed multi-level psychological disorder predictive model can successfully serve as an assistive deployment for medical experts in the effective treatment of mental health