Spin dynamics and frequency dependence of magnetic damping study in soft ferromagnetic FeTaC film with a stripe domain structure

Perpendicular magnetic anisotropy (PMA) and low magnetic damping are the key factors for the free layer magnetization switching by spin transfer torque technique in magnetic tunnel junction devices. The magnetization precessional dynamics in soft ferromagnetic FeTaC thin film with a stripe domain structure was explored in broad band frequency range by employing micro-strip ferromagnetic resonance technique. The polar angular variation of resonance field and linewidth at different frequencies have been analyzed numerically using Landau-Lifshitz-Gilbert equation by taking into account the total free energy density of the film. The numerically estimated parameters Land\'{e} $g$-factor, PMA constant, and effective magnetization are found to be 2.1, 2$\times10^{5}$ erg/cm$^{3}$ and 7145 Oe, respectively. The frequency dependence of Gilbert damping parameter ($\alpha$) is evaluated by considering both intrinsic and extrinsic effects into the total linewidth analysis. The value of $\alpha$ is found to be 0.006 at 10 GHz and it increases with decreasing precessional frequency.Comment: 5 Pages, 6 Figures, Regular Submissio

Improving the Accuracy of Fuzzy Decision Tree by Direct Back Propagation with Adaptive Learning Rate and Momentum Factor for User Localization

AbstractMost prevailing availability of wireless networks has elevated an interest in developing a smart indoor environment by utilizing the hand held devices of the users. The user localization helps in automating the activities like automating switch on/off of the room lights, air conditioning etc., which makes the environment smart. Here, we consider locating the users as a pattern classification problem and use Fuzzy decision tree (FDT) as a knowledge discovery method to locate the users based on the wireless signal strength observed by their handheld devices. To increase the FDT accuracy and to achieve faster convergence, we came up with a novel strategy named Improved Neuro Fuzzy Decision Tree with an adaptive learning rate and momentum factor to optimize the parameters of FDT. The proposed approach can be used for any classification problem. From the results obtained, we observe that our proposed algorithm achieves better convergence and accuracy

Nutritional assessment and bioactive potential of Sargassum polycystum C. Agardh (Brown Seaweed)

492-498The phytochemical screening, nutritional composition and bioactive potential of Sargassum polycystum (Brown Seaweed) were investigated. The bioactive compounds of Sargassum polycystum showed significant activity against four human pathogens, namely, Bacillus subtilis, Escherichia coli, Staphylococcus aureus, and Klebsiella pneumoniae. The biochemical composition of Sargassum polycystum exhibited high nutritional potential of protein (14.2%), carbohydrate (25.0%), lipid (7.6%), fiber (21.3%), and ash (29.0%) than that in terrestrial plants and animal products. The Sargassum polycystum could be providing more opportunities for discovering new drugs which may be used as a source of healthy food for human regular diet

Melatonin in Alzheimer's disease and other neurodegenerative disorders

Increased oxidative stress and mitochondrial dysfunction have been identified as common pathophysiological phenomena associated with neurodegenerative disorders such as Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD). As the age-related decline in the production of melatonin may contribute to increased levels of oxidative stress in the elderly, the role of this neuroprotective agent is attracting increasing attention. Melatonin has multiple actions as a regulator of antioxidant and prooxidant enzymes, radical scavenger and antagonist of mitochondrial radical formation. The ability of melatonin and its kynuramine metabolites to interact directly with the electron transport chain by increasing the electron flow and reducing electron leakage are unique features by which melatonin is able to increase the survival of neurons under enhanced oxidative stress. Moreover, antifibrillogenic actions have been demonstrated in vitro, also in the presence of profibrillogenic apoE4 or apoE3, and in vivo, in a transgenic mouse model. Amyloid-β toxicity is antagonized by melatonin and one of its kynuramine metabolites. Cytoskeletal disorganization and protein hyperphosphorylation, as induced in several cell-line models, have been attenuated by melatonin, effects comprising stress kinase downregulation and extending to neurotrophin expression. Various experimental models of AD, PD and HD indicate the usefulness of melatonin in antagonizing disease progression and/or mitigating some of the symptoms. Melatonin secretion has been found to be altered in AD and PD. Attempts to compensate for age- and disease-dependent melatonin deficiency have shown that administration of this compound can improve sleep efficiency in AD and PD and, to some extent, cognitive function in AD patients. Exogenous melatonin has also been reported to alleviate behavioral symptoms such as sundowning. Taken together, these findings suggest that melatonin, its analogues and kynuric metabolites may have potential value in prevention and treatment of AD and other neurodegenerative disorders

Role of Interferon Gamma Release Assay in Active TB Diagnosis among HIV Infected Individuals

immunodeficiency virus (HIV) infected individuals. In this study, we assessed the sensitivity of Interferon gamma release assay (IGRA) in active tuberculosis patients who were positive for HIV infection and compared it with that of tuberculin skin test (TST). Methodology/Principal Findings: A total of 105 HIV-TB patients who were naı¨ve for anti tuberculosis and anti retroviral therapy were included for this study out of which 53 (50%) were culture positive. Of 105 tested, QuantiFERON-TB Gold intube (QFT-G) was positive in 65% (95% CI: 56% to 74%), negative in 18% (95% CI: 11% to 25%) and indeterminate in 17% (95% CI: 10% to 24%) of patients. The sensitivity of QFT-G remained similar in pulmonary TB and extra-pulmonary TB patients. The QFT-G positivity was not affected by low CD4 count, but it often gave indeterminate results especially in individuals with CD4 count ,200 cells/ml. All of the QFT-G indeterminate patients whose sputum culture were positive, showed #0.25 IU/ml of IFN-c response to phytohemagglutinin (PHA). TST was performed in all the 105 patients and yielded the sensitivity of 31% (95% CI: 40% to 22%). All the TST positives were QFT-G positives. The sensitivity of TST was decreased, when CD4 cell counts declined. Conclusions/Significance: Our study shows neither QFT-G alone or in combination with TST can be used to exclude the suspicion of active TB disease. However, unlike TST, QFT-G yielded fewer false negative results even in individuals with low CD4 count. The low PHA cut-off point for indeterminate results suggested in this study (#0.25 IU/ml) may improve the proportion of valid QFT-G results

Synergic Deep Learning For Smart Health Diagnosis Of Covid-19 For Connected Living And Smart Cities

COVID-19 pandemic has led to a significant loss of global deaths, economical status, and so on. To prevent and control COVID-19, a range of smart, complex, spatially heterogeneous, control solutions, and strategies have been conducted. Earlier classification of 2019 novel coronavirus disease (COVID-19) is needed to cure and control the disease. It results in a requirement of secondary diagnosis models, since no precise automated toolkits exist. The latest finding attained using radiological imaging techniques highlighted that the images hold noticeable details regarding the COVID-19 virus. The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional neural networks (DCNNs) and involves a mutual learning process from one another. Particularly, the representation of images learned by both DCNNs is provided as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images come under the identical class. Besides, the proposed SDL model involves a fuzzy bilateral filtering (FBF) model to pre-process the input image. The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods