Some observations related to intermittency and multifractality in Si and C-nucleus collisions at

An attempt is made to study the existence of dynamical fluctuations of relativistic particles using the methods of modified multifractal moments, G_q, and scaled factorial moments, F_q, in terms of new scaled variable X(η) suggested by Bialas and Gazdzicki. For this purpose analyses of experimental and UrQMD data involving interactions of ²⁸Si and ¹²C nuclei at with nuclear emulsion are used. The variation of lnG_q and lnF_q with lnM in pseudorapidity (η) phase space reveals power law behaviour. The values of slopes, τ_q and _q determined from the analyses of G_q and F_q moments are discussed. The generalized fractal dimensions, D_q, determined from the above methods are found to decrease with the order of the moments, q, indicating multifractality in multiparticle production. It is also observed that the spectral function f(α_q) for heavier projectile is much broader than for lighter beam due to larger number of participating nucleons present in heavier projectile.     

Scope of plasma focus with argon as a soft X-ray source

The X-radiation emission from a low energy plasma focus with argon as a filling gas is investigated. Specifically, the attention is paid to determine the system efficiency for argon K-lines and Cu-K/sub /spl alpha// line emission at different filling pressures, and identify the radiation emission region. The highest argon line emission found at 1.5 mbar is about 30 mJ and the corresponding efficiency is 0.0015%. The same pressure is suitable for high Cu-K/sub /spl alpha// emission, which is about 70 mJ in 4/spl pi/ geometry and the system efficiency is 0.003%. The bulk of X-radiation is emitted from the region close to the anode tip, whereas some radiation emission takes place from the formed hot spots along the focus axis. These radiations are found suitable for backlighting in Al (1-1.56 keV) and Ti (2.9-4.96 keV) energy transmission bands.     

Unsteady MHD flow over exponentially stretching sheet with slip conditions

The present paper examines the hydromagnetic three-dimensional flow induced by a stretched surface. An incompressible material saturates the porous medium. Velocity and thermal slip boundary conditions are considered. Suitable transformations are used to obtain the nonlinear ordinary differential equations. Series solutions of the resulting systems are constructed. The effects of various pertinent parameters on the axial velocity and temperature distributions are analyzed graphically. The skin friction and the Nusselt number are computed numerically and graphically.     

Studies of the chemical and biological properties of the bone and acute myocardial imaging agent99mTc-PYP

Correlation between the in vivo distribution and the chemical formulation of^99mTc-PYP complex has been studied. We chose mice to evaluate in vivo biodistribution and gel chromatography column scanning technique for radiochemical analysis. The influence of the pH, Sn(II), pyrophosphate concentration and molar ratios of Sn: PYP on the labelling of pyp with^99mTc has been investigated in vitro and in vivo. The reaction between^99mTc and Sn-PYP was complete within a few minutes. The stability studies were evaluated against dilution. Induced myocardial infarction was evaluated in rats. The clinical evaluation showed excellent definition of sternum and ribs with little blood background activity with normal subjects. Discrete localization of abnormally high activity was shown in the site of recent infarction of the left ventricular myocardium.     

A hybrid wrapper–filter approach to detect the source(s) of out-of-control signals in multivariate manufacturing process

With modern data-acquisition equipment and on-line computers used during production, it is now common to monitor several correlated quality characteristics simultaneously in multivariate processes. Multivariate control charts (MCC) are important tools for monitoring multivariate processes. One difficulty encountered with multivariate control charts is the identification of the variable or group of variables that cause an out-of-control signal. Expert knowledge either in combination with wrapper-based supervised classifier or a pre-filter with wrapper are the standard approaches to detect the sources of out-of-control signal. However gathering expert knowledge in source identification is costly and may introduce human error. Individual univariate control charts (UCC) and decomposition of T²$T^2$ statistics are also used in many cases simultaneously to identify the sources, but these either ignore the correlations between the sources or may take more time with the increase of dimensions. The aim of this paper is to develop a source identification approach that does not need any expert-knowledge and can detect out-of-control signal in less computational complexity. We propose, a hybrid wrapper–filter based source identification approach that hybridizes a Mutual Information (MI) based Maximum Relevance (MR) filter ranking heuristic with an Artificial Neural Network (ANN) based wrapper. The Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA) has been combined with MR (MR-ANNIGMA) to utilize the knowledge about the intrinsic pattern of the quality characteristics computed by the filter for directing the wrapper search process. To compute optimal ANNIGMA score, we also propose a Global MR-ANNIGMA using non-functional relationship between variables which is independent of the derivative of the objective function and has a potential to overcome the local optimization problem of ANN training. The novelty of the proposed approaches is that they combine the advantages of both filter and wrapper approaches and do not require any expert knowledge about the sources of the out-of-control signals. Heuristic score based subset generation process also reduces the search space into polynomial growth which in turns reduces computational time. The proposed approaches were tested by exhaustive experiments using both simulated and real manufacturing data and compared to existing methods including independent filter, wrapper and Multivariate EWMA (MEWMA) methods. The results indicate that the proposed approaches can identify the sources of out-of-control signals more accurately than existing approaches.     

Bioinspired NC Coated BM-ZIF for Electrochemical Monitoring of Adrenaline from Blood and Pharmaceutical Samples

Herein, we have developed highly sensitive and selective non-enzymatic bioinspired polydopamine derived nitrogen rich carbon (NC) coated bimetallic zeolitic imidazolate framework (BM-ZIF) electrochemical sensor via simple hydrothermal approach for monitoring adrenaline (AD) from COVID-19 quarantined person blood and pharmaceutical sample. The designed NC-BM-ZIF electrode shows excellent sensitive and selective performance towards AD monitoring with detection limit (LOD) of 0.01 nM and 0.1931 μA/nM/cm² sensitivity over a wide linear range of 50–1625 nM. To the best of our knowledge, this is the first study of using of NC-BM-ZIF electrode for the electrochemical sensing of AD from quarantined person blood and pharmaceutical sample.     

Enhanced bactericidal and in vivo wound healing potential of biosynthesized zinc oxide nanoparticles from psyllium mucilage

The multifunctional zinc oxide nanoparticles are synthesized using a cost-effective, efficient, eco-friendly, simple, and clean synthesis approach. Herein, we reported the antibacterial and wound healing potential of zinc oxide nanoparticles (ZnO-NPs) prepared using psyllium gel (PG) as the reducing and stabilizing agent. The PG-mediated zinc oxide nanoparticles (PG-ZnO-NPs) were characterized using UV–Vis, photoluminescence (PL), FTIR, XRD, Raman, and SEM. UV–Vis spectral studies confirmed the surface plasmonic resonance (SPR) band at 364 nm. PL results demonstrated the fluorescent or emission nature of PG-ZnO-NPs. FTIR analysis confirmed characteristic peaks at 873.82 and 619.88 cm⁻¹ due to the tetrahedral coordination of zinc and the formation of the Zn-O bond. XRD and Raman confirm the formation of PG-ZnO-NPs, whereas SEM analysis revealed PG-ZnO-NPs are rod-shaped, having hexagonal prism-like bases, and EDX exhibited the elemental composition of PG-ZnO-NPs. The as-synthesized PG-ZnO-NPs possessed prominent microbicidal potential against gram-positive (Bacillus subtilis and Bacillus licheniformis) and gram-negative (Escherichia coli and Salmonella shigella) bacterial strains in terms of zone of inhibition (ZOI), minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC). In vivo biological investigations with mice show that the synthesized PG-ZnO-NPs possess outstanding biocompatibility and wound healing potential. PG-ZnO-NPs dressing significantly speeds up full-thickness wound repair by triggering a decrease in MMP-1 and MMP-2 and escalating the mRNA levels of collagen types (I & III) and fibronectin. Thus, our work validates that the inclusion of PG-ZnO-NPs in dressing shows excellent potential for acute wound management.     

Optimization of the numerical treatment of the Darcy–Forchheimer flow of Ree–Eyring fluid with chemical reaction by using artificial neural networks

In this study, Darcy Forchheimer flow paradigm, which is a useful paradigm in fields such as petroleum engineering where high flow velocity effects are common, has been analyzed with artificial intelligence approach. In this context, first of all, Darcy–Forchheimer flow of Ree–Eyring fluid along a permeable stretching surface with convective boundary conditions has been examined and heat and mass transfer mechanisms have been investigated by including the effect of chemical process, heat generation/absorption, and activation energy. Cattaneo–Christov heat flux model has been used to analyze heat transfer properties. Within the scope of optimizing Darcy–Forchheimer flow of Ree–Eyring fluid; three different artificial neural network models have been developed to predict Nusselt number, Sherwood number, and skin friction coefficient values. The developed artificial neural network model has been able to predict Nusselt number, Sherwood number, and skin friction coefficient values with high accuracy. The findings obtained as a result of the study showed that artificial neural networks are an ideal tool that can be used to model Darcy–Forchheimer Ree–Eyring fluid flow towards a permeable stretch layer with activation energy and a convective boundary condition.