Temperature scaling in a dense vibro-fluidised granular material

The leading order "temperature" of a dense two dimensional granular material fluidised by external vibrations is determined. An asymptotic solution is obtained where the particles are considered to be elastic in the leading approximation. The velocity distribution is a Maxwell-Boltzmann distribution in the leading approximation. The density profile is determined by solving the momentum balance equation in the vertical direction, where the relation between the pressure and density is provided by the virial equation of state. The predictions of the present analysis show good agreement with simulation results at higher densities where theories for a dilute vibrated granular material, with the pressure-density relation provided by the ideal gas law, are in error. The theory also predicts the scaling relations of the total dissipation in the bed reported by McNamara and Luding (PRE v 58, p 813).Comment: ReVTeX (psfrag), 5 pages, 5 figures, Submitted to PR

Non-Gaussian velocity distributions in excited granular matter in the absence of clustering

The velocity distribution of spheres rolling on a slightly tilted rectangular two dimensional surface is obtained by high speed imaging. The particles are excited by periodic forcing of one of the side walls. Our data suggests that strongly non-Gaussian velocity distributions can occur in dilute granular materials even in the absence of significant density correlations or clustering. When the surface on which the particles roll is tilted further to introduce stronger gravitation, the collision frequency with the driving wall increases and the velocity component distributions approach Gaussian distributions of different widths.Comment: 4 pages, 5 figures. Additional information at http://physics.clarku.edu/~akudrolli/nls.htm

Transport coefficients for an inelastic gas around uniform shear flow: Linear stability analysis

The inelastic Boltzmann equation for a granular gas is applied to spatially inhomogeneous states close to the uniform shear flow. A normal solution is obtained via a Chapman-Enskog-like expansion around a local shear flow distribution. The heat and momentum fluxes are determined to first order in the deviations of the hydrodynamic field gradients from their values in the reference state. The corresponding transport coefficients are determined from a set of coupled linear integral equations which are approximately solved by using a kinetic model of the Boltzmann equation. The main new ingredient in this expansion is that the reference state $f^{(0)}$ (zeroth-order approximation) retains all the hydrodynamic orders in the shear rate. In addition, since the collisional cooling cannot be compensated locally for viscous heating, the distribution $f^{(0)}$ depends on time through its dependence on temperature. This means that in general, for a given degree of inelasticity, the complete nonlinear dependence of the transport coefficients on the shear rate requires the analysis of the {\em unsteady} hydrodynamic behavior. To simplify the analysis, the steady state conditions have been considered here in order to perform a linear stability analysis of the hydrodynamic equations with respect to the uniform shear flow state. Conditions for instabilities at long wavelengths are identified and discussed.Comment: 7 figures; previous stability analysis modifie

Unified description of long-time tails and long-range correlation functions for sheared granular liquids

Unified description on the long-time tail of velocity autocorrelation function and the long-range correlation for the equal-time spatial correlation functions is developed based on the generalized fluctuating hydrodynamics. The cross-over of the long-time tail from $t^{-3/2}$ to $t^{-5/2}$ is predicted independent of the density, and the equal-time spatial density correlation function and the equal-time spatial velocity correlation function respectively satisfy $r^{-11/3}$ and $r^{-5/3}$ for large $r$ limit.Comment: 10 pages. to be published in Euro. Phys. J.

Thermal decomposition studies of halogenated organic compounds

Thermal decomposition results for CCl{sub 4}, CHCl{sub 3}, CH{sub 2}Cl{sub 2}, CH{sub 3}Cl, C{sub 3}H{sub 3}Cl, CFCl{sub 3}, CF{sub 2}Cl{sub 2}, CF{sub 3}Cl, CF{sub 2}HCl, CF{sub 3}I, CH{sub 3}I, C{sub 2}H{sub 5}I, C{sub 6}H{sub 5}I, and CCl{sub 2}O are presented. The results were obtained by shock tube techniques coupled with optical spectroscopic detection of transient species formed from dissociation. The method is illustrated with the CH{sub 3}I (+ Kr) {yields} CH{sub 3} + I (+ Kr) reaction where decomposition was monitored using I-atomic resonance absorption spectrometry (ARAS). Modern unimolecular rate theoretical analysis has been carried out on the present cases, and the conclusions from these calculations are discussed. Lastly, the possible destruction of halo-organics by incineration is considered and some implications are discussed

EVALUATION OF IN VITRO CYTOTOXIC AND ANTICANCER ACTIVITY OF HEPATOPROTECTIVE POLYHERBAL FORMULATION IN CELL LINE MODEL

Introduction: The use of natural products as anticancer agents has a long history that began with folklore medicine and through the years has been incorporated into traditional and allopathic medicine. Several drugs currently used are derived from medicinal plants. Objective: The main objective of this study is to investigate the cytotoxic potential of hepatoprotective polyherbal formulation in normal and cancer cell lines. Methods: A 3-(4,5-dimethythiazol-2-yl)-2,5-diphenyltetrazolium bromide assay was utilized to screen the cytotoxic activity. Results: The results revealed that the formulation does not induce much mortality in normal liver and kidney cell lines, and LC50 value of liver cell lines was found 1716.355 μg/ml and kidney cell lines 2464.910 μg/ml. The in vitro anticancer activity was performed on liver, colon, and prostate cancer cell lines, and IC50 values are found 2.077, 3.850, and 11.989 μg/ml, respectively, which show excellent anticancer activity. Conclusion: Based on the results obtained, the hepatoprotective polyherbal formulation is safe for normal cells and cytotoxic for cancer cells. Further, identification and quantification of phytoconstituents responsible for the activity are in progress

Efficacy of new herbicide (bispyribac sodium 10% SC) against different weed flora, nutrient uptake in rice and their residual effects on succeeding crop of green gram under zero tillage

Field experiment was conducted in the wetland farms of Tamil Nadu Agricultural University, Coimbatore during rabi season (August to February) of 2011-12 to evaluate the herbicide (Bispyribac sodium 10% SC) on weed control and their nutrient management in direct seeded lowland rice. The experiment was laid out in a Randomized Block Design (RBD) with three replications. The results revealed that Early Post Emergence (EPOE) application of bispyribac sodium 10% SC 40 g ha-1 recorded higher weed control efficiency and lesser weed density, nutrient uptake at reproductive stage of the crop. Different weed management practices imposed on rice crop did not affect the germination of succeeding green gram

Lamin A/C Haploinsufficiency Modulates the Differentiation Potential of Mouse Embryonic Stem Cells

BACKGROUND: Lamins are structural proteins that are the major determinants of nuclear architecture and play important roles in various nuclear functions including gene regulation and cell differentiation. Mutations in the human lamin A gene cause a spectrum of genetic diseases that affect specific tissues. Most available mouse models for laminopathies recapitulate disease symptoms for muscle diseases and progerias. However, loss of human lamin A/C also has highly deleterious effects on fetal development. Hence it is important to understand the impact of lamin A/C expression levels on embryonic differentiation pathways. METHODOLOGY AND PRINCIPAL FINDINGS: We have investigated the differentiation potential of mouse embryonic stem cells containing reduced levels of lamin A/C by detailed lineage analysis of embryoid bodies derived from these cells by culture. We initially carried out a targeted disruption of one allele of the mouse lamin A/C gene (). Undifferentiated wild-type and embryonic stem cells showed similar expression of pluripotency markers and cell cycle profiles. Upon spontaneous differentiation into embryoid bodies, markers for visceral endoderm such as α-fetoprotein were highly upregulated in haploinsufficient cells. However, neuronal markers such as β-III tubulin and nestin were downregulated. Furthermore, we observed a reduction in the commitment of cells into the myogenic lineage, but no discernible effects on cardiac, adipocyte or osteocyte lineages. In the next series of experiments, we derived embryonic stem cell clones expressing lamin A/C short hairpin RNA and examined their differentiation potential. These cells expressed pluripotency markers and, upon differentiation, the expression of lineage-specific markers was altered as observed with embryonic stem cells. CONCLUSIONS: We have observed significant effects on embryonic stem cell differentiation to visceral endoderm, neuronal and myogenic lineages upon depletion of lamin A/C. Hence our results implicate lamin A/C level as an important determinant of lineage-specific differentiation during embryonic development

Enhancing Skin Cancer Diagnosis with Deep Learning-Based Classification

The diagnosis of skin cancer has been identified as a significant medical challenge in the 21st century due to its complexity, cost, and subjective interpretation. Early diagnosis is critical, especially in fatal cases like melanoma, as it affects the likelihood of successful treatment. Therefore, there is a need for automated methods in early diagnosis, especially with a diverse range of image samples with varying diagnoses. An automated system for dermatological disease recognition through image analysis has been proposed and compared to conventional medical personnel-based detection. This project proposes an automated technique for skin cancer classification using images from the International Skin Imaging Collaboration (ISIC) dataset, incorporating deep learning (DL) techniques that have demonstrated significant advancements in artificial intelligence (AI) research. An automated system that recognizes and classifies skin cancer through deep learning techniques could prove useful in the medical field, as it can accurately detect the presence of skin cancer at an early stage. The ISIC dataset, which includes a vast collection of images of various skin conditions, provides an excellent opportunity to develop and validate deep learning algorithms for skin cancer classification. The proposed technique could have a significant impact on the medical industry by reducing the workload of medical personnel while providing accurate and timely diagnoses.