New Density-based Thermal Conductivity Equation for Snow

More than two hundred thermal conductivity measurements for different snow densities and snow types were carried out in-situ at a field research station located in greater Himalayan range of India. These measurements were carried out using a commercially available portable thermal conductivity meter. Thermal conductivity measurements were carried out on the fresh snow, equi-temperature snow, and surface hoar and temperaturegradient snow. Average thermal conductivity of snow varied from 0.08 W/mK (Fresh snow of 120 kg/m3 density) to 0.32 W/m K (Equi-temperature snow of 420 kg/m3 density). Based on these measurements, a new density-based thermal conductivity equation is proposed. Using this proposed equation, modeled snowpack temperatures showed closer agreement with the observed data as compared to the predictions based on other well-known empirical and theoretical thermal conductivity equations for snow. This study highlights the advantages and limitations of empirical based thermal conductivity equations over the complex models based on snow microstructure.Defence Science Journal, 2009, 59(2), pp.126-130, DOI:http://dx.doi.org/10.14429/dsj.59.149

Formulation and development of colon-targeted mucopenetrating metronidazole nanoparticles

Purpose: To formulation and develop colon-targeted mucopenetrating metronidazole nanoparticles.Methods: Metronidazole-loaded chitosan nanoparticles with a pH-sensitive polymer, hydroxyl propyl methyl cellulose phthalate (HPMCP), were prepared by ionic gelation technique and then coated with Eudragit S100 by solvent evaporation method. The nanoparticles were optimized using one variable at a time (OVAT) approach. Further, the nanoparticles were evaluated by scanning electron microscopy (SEM) and zeta sizer, as well as for in-vitro release. Muco-adhesion was evaluated by modified bioadhesion detachment force measurement balance and muco-penetration of fluorescein isothiocyanate (FITC) labeled optimized nanoparticles was determined by microscopic techniqueResults: Morphological assessment results revealed smooth, spherical particles with homogeneous distribution and polydispersity index (PDI) of 0.213. The optimized formulation showed particle size of 202 ± 27 nm, zeta potential of 26.9 ± 2.4 mV as well as and entrapment efficiency of 79 ± 5.4 %. There was significant difference in drug release between coated (8.46 ± 2.49 %) and uncoated (28.96 ± 4.04%) nanoparticles at the 5th h in simulated gastric conditions. Muco-adhesion data revealed that uncoated nanoparticles (14.98 x 103 dyne/cm2) showed higher muco-adhesion detachment force compared to coated (12.34 x 103 dyne/cm2) nanoparticles. Muco-penetration results confirm the retention (for up to 12 h) of the developed formulation at the target site for enhanced therapeutic exposure of the entrapped drug.Conclusion: Eudragit S100 coating of chitosan-HPMCP nanoparticles promotes efficient drug targeting and thus provides a strategy for treating mucosal infections. .Keywords: Metronidazole, pH-sensitive nanoparticles, Hydroxylpropyl methylcellulose phthalate, Ionic gelation, Mucoadhesion, Mucopenetration, Intestinal infectio

A deep artificial neural network architecture for mesh free solutions of nonlinear boundary value problems

YesSeeking efficient solutions to nonlinear boundary value problems is a crucial challenge in the mathematical modelling of many physical phenomena. A well-known example of this is solving the Biharmonic equation relating to numerous problems in fluid and solid mechanics. One must note that, in general, it is challenging to solve such boundary value problems due to the higher-order partial derivatives in the differential operators. An artificial neural network is thought to be an intelligent system that learns by example. Therefore, a well-posed mathematical problem can be solved using such a system. This paper describes a mesh free method based on a suitably crafted deep neural network architecture to solve a class of well-posed nonlinear boundary value problems. We show how a suitable deep neural network architecture can be constructed and trained to satisfy the associated differential operators and the boundary conditions of the nonlinear problem. To show the accuracy of our method, we have tested the solutions arising from our method against known solutions of selected boundary value problems, e.g., comparison of the solution of Biharmonic equation arising from our convolutional neural network subject to the chosen boundary conditions with the corresponding analytical/numerical solutions. Furthermore, we demonstrate the accuracy, efficiency, and applicability of our method by solving the well known thin plate problem and the Navier-Stokes equation

Pangenomics in microbial and crop research: Progress, applications, and perspectives

Advances in sequencing technologies and bioinformatics tools have fueled a renewed interest in whole genome sequencing efforts in many organisms. The growing availability of multiple genome sequences has advanced our understanding of the within-species diversity, in the form of a pangenome. Pangenomics has opened new avenues for future research such as allowing dissection of complex molecular mechanisms and increased confidence in genome mapping. To comprehensively capture the genetic diversity for improving plant performance, the pangenome concept is further extended from species to genus level by the inclusion of wild species, constituting a super-pangenome. Characterization of pangenome has implications for both basic and applied research. The concept of pangenome has transformed the way biological questions are addressed. From understanding evolution and adaptation to elucidating host–pathogen interactions, finding novel genes or breeding targets to aid crop improvement to design effective vaccines for human prophylaxis, the increasing availability of the pangenome has revolutionized several aspects of biological research. The future availability of high-resolution pangenomes based on reference-level near-complete genome assemblies would greatly improve our ability to address complex biological problems

Genuine Correlations of Like-Sign Particles in Hadronic Z0 Decays

Correlations among hadrons with the same electric charge produced in Z0 decays are studied using the high statistics data collected from 1991 through 1995 with the OPAL detector at LEP. Normalized factorial cumulants up to fourth order are used to measure genuine particle correlations as a function of the size of phase space domains in rapidity, azimuthal angle and transverse momentum. Both all-charge and like-sign particle combinations show strong positive genuine correlations. One-dimensional cumulants initially increase rapidly with decreasing size of the phase space cells but saturate quickly. In contrast, cumulants in two- and three-dimensional domains continue to increase. The strong rise of the cumulants for all-charge multiplets is increasingly driven by that of like-sign multiplets. This points to the likely influence of Bose-Einstein correlations. Some of the recently proposed algorithms to simulate Bose-Einstein effects, implemented in the Monte Carlo model PYTHIA, are found to reproduce reasonably well the measured second- and higher-order correlations between particles with the same charge as well as those in all-charge particle multiplets.Comment: 26 pages, 6 figures, Submitted to Phys. Lett.