Azimuthal Anisotropy in High Energy Nuclear Collision - An Approach based on Complex Network Analysis

Recently, a complex network based method of Visibility Graph has been applied to confirm the scale-freeness and presence of fractal properties in the process of multiplicity fluctuation. Analysis of data obtained from experiments on hadron-nucleus and nucleus-nucleus interactions results in values of Power-of-Scale-freeness-of-Visibility-Graph-(PSVG) parameter extracted from the visibility graphs. Here, the relativistic nucleus-nucleus interaction data have been analysed to detect azimuthal-anisotropy by extending the Visibility Graph method and extracting the average clustering coefficient, one of the important topological parameters, from the graph. Azimuthal-distributions corresponding to different pseudorapidity-regions around the central-pseudorapidity value are analysed utilising the parameter. Here we attempt to correlate the conventional physical significance of this coefficient with respect to complex-network systems, with some basic notions of particle production phenomenology, like clustering and correlation. Earlier methods for detecting anisotropy in azimuthal distribution, were mostly based on the analysis of statistical fluctuation. In this work, we have attempted to find deterministic information on the anisotropy in azimuthal distribution by means of precise determination of topological parameter from a complex network perspective

Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNN

The rapid spread of COVID-19 has necessitated efficient and accurate diagnostic methods. Computed Tomography (CT) scan images have emerged as a valuable tool for detecting the disease. In this article, we present a novel deep learning approach for automated COVID-19 CT scan classification where a modified Xception model is proposed which incorporates a newly designed channel attention mechanism and weighted global average pooling to enhance feature extraction thereby improving classification accuracy. The channel attention module selectively focuses on informative regions within each channel, enabling the model to learn discriminative features for COVID-19 detection. Experiments on a widely used COVID-19 CT scan dataset demonstrate a very good accuracy of 96.99% and show its superiority to other state-of-the-art techniques. This research can contribute to the ongoing efforts in using artificial intelligence to combat current and future pandemics and can offer promising and timely solutions for efficient medical image analysis tasks

AMELIORATION OF ANXIOLYTIC BEHAVIOR IN INTRACEREBROVENTRICULAR COLCHICINE INJECTED RATS BY NAPROXEN

Objective: Anxiety behavior in experimental model of Alzheimer's disease (AD) in rats by intracerebroventricular (ICV) injection of colchicine isimportant to characterize this animal model, but it has not been sufficiently investigated in this animal model. The different attributes of anxietybehavior in ICV colchicine injected rats (ICIR) was studied, and the effects of naproxen, a non-steroidal anti-inflammatory drug on the anxiety statusof these AD animals were assessed since in earlier studies naproxen protected cognitive impairments and neurodegeneration in ICIR.Methods: The anxiety status was assessed in an elevated open field with a novel object in two study durations (14-day and 21-day study). Aftermeasuring the anxiety behavior in two study durations, rats were sacrificed, and blood was collected for measuring the serum corticosterone (CORT)level.Results: Anxiolytic behavior along with lower CORT level was observed in ICIR in both the 14- and 21-day studies. After p.o. administration ofdifferent doses of naproxen (5, 10, 20 mg/kg body wt.) in ICIR, this anxiolytic behavior along with low serum CORT level showed gradual recovery andeventually both the parameters attained normal level at the dose of 20 mg/kg body weight in 21-day study.Conclusion: The present study showed an anxiolytic behavior in ICIR, and which may result from the colchicine induced neurodegeneration alongwith the impaired activity of the hypothalamo-pituitary-adrenal axis. Some parameters appeared to be sensitive for determination of anxiety statusin this model.Keywords: Colchicine, Anxiolytic, Naproxen, Corticosterone, Alzheimer's disease

Incorporating ancestors' influence in genetic algorithms

A new criterion of fitness evaluation for Genetic Algorithms is introduced where the fitness value of an individual is determined by considering its own fitness as well as those of its ancestors. Some guidelines for selecting the weighting coefficients for quantifying the importance to be given to the fitness of the individual and its ancestors are provided. This is done both heuristically and automatically under fixed and adaptive frameworks. The Schema Theorem corresponding to the proposed concept is derived. The effectiveness of this new methodology is demonstrated extensively on the problems of optimizing complex functions including a noisy one and selecting optimal neural network parameters