Current-voltage characteristics in donor-acceptor systems: Implications of a spatially varying electric field

We have studied the transport properties of a molecular device composed of donor and acceptor moieties between two electrodes on either side. The device is considered to be one-dimensional with different on-site energies and the non-equilibrium properties are calculated using Landauer's formalism. The current-voltage characteristics is found to be asymmetric with a sharp Negative Differential Resistance at a critical bias on one side and very small current on the other side. The NDR arises primarily due to the bias driven electronic structure change from one kind of insulating phase to another through a highly delocalized conducting phase. Our model can be considered to be the simplest to explain the experimental current-voltage characteristics observed in many molecular devices.Comment: 5 pages, 4 figures (accepted for publication in Physical Review B

Human’s Developmental Thoughts in the Tamil Folk Songs

At first oral songs appeared in the archaic language. These oral songs are sung in different forms. Folk songs have no grammatical restrictions it is just sung. The song is very simple and it is easy to sing. Though the song is simple it has many meanings that are useful for life. This article shows up the emerging management ideas in the folk songs such as leadership characteristics, education, morals and human resources

U-Capkidnets++-: A Novel Hybrid Capsule Networks with Optimized Deep Feed Forward Networks for an Effective Classification of Kidney Tumours Using CT Kidney Images

Chronic Kidney Diseases (CKD) has become one among the world wide health crisis and needs the associated efforts to prevent the complete organ damage. A considerable research effort has been put forward onto the effective seperation and classification of kidney tumors from the kidney CT Images. Emerging machine learning along with deep learning algorithms have waved the novel paths of tumor detections. But these methods are proved to be laborious and its success rate is purely depends on the previous experiences. To achieve the better classification and segmentation of tumors, this paper proposes the hybrid ensemble of visual capsule networks in U-NET deep learning architecture and w deep feed-forward extreme learning machines. The proposed framework incorporates the data-preprocessing powerful data augmentation, saliency tumor segmentation (STS) followed by the classification phase. Furthermore, classification levels are constructed based upon the feed forward extreme learning machines (FFELM) to enhance the effectiveness of the suggested model .The extensive experimentation has been conducted to evaluate the efficacy of the recommended structure and matched with the other prevailing hybrid deep learning model. Experimentation demonstrates that the suggested model has showed the superior predominance over the other models and exhibited DICE co-efficient of kidney tumors as high as 0.96 and accuracy of 97.5 %respectively

A Unique Common Fixed Point Theorem for Four Maps under Contractive Conditions in Cone Metric Spaces

In this paper, we prove existence of coincidence points and a common fixed point theorem for four maps under contractive conditions in cone metric spaces for non continuous mappings and relaxation of completeness in the space. These results extend and improve several well known comparable results in the existing literature. AMS Subject Classification: 47H10, 54H25. Keywords: Cone metric space; Common fixed point; Coincidence point

Critical Analysis on Detection and Mitigation of Security Vulnerabilities in Virtualization Data Centers

There is an increasing demand for IT resources in growing business enterprises. Data center virtualization helps to meet this increasing demand by driving higher server utilization and utilizing un-used CPU cycles without causes much increase in new servers. Reduction in infrastructure complexities, Optimization of cost of IT system management, power and cooling are some of the additional benefits of virtualization. Virtualization also brings various security vulnerabilities. They are prone to attacks like hyperjacking, intrusion, data thefts, denial of service attacks on virtualized servers and web facing applications etc. This works identifies the security challenges in virtualization. A critical analysis on existing state of art works on detection and mitigation of various vulnerabilities is presented. The aim is to identify the open issues and propose prospective solutions in brief for these open issues

Second order hydrodynamics based on effective kinetic theory and electromagnetic signals from QGP

We study thermal particle production from relativistic heavy ion collisions in presence of viscosities by employing the recently developed second order dissipative hydrodynamic formulation estimated within a quasiparticle description of thermal QCD (Quantum Chromo-Dynamics) medium. The sensitivity of shear and bulk viscous pressures to the temperature dependence of relaxation time is analyzed within one dimensional boost invariant expansion of quark gluon plasma (QGP). The dissipative corrections to the phase-space distribution functions are obtained from the Chapman-Enskog like iterative solution of effective Boltzmann equation in the relaxation time approximation. Thermal dilepton and photon production rates for QGP are calculated by employing this viscous modified distribution function. Particle emission yields are quantified for the longitudinal expansion of QGP with different temperature dependent relaxation times. Our analysis employing this second order hydrodynamic model indicates that the particle spectra gets enhanced by both bulk and shear viscosities and is well behaved. Also, the particle yields are found to be sensitive to relaxation time.Comment: 12 pages, 9 figure

The Secured Attribute-Based Document Collection Hierarchical Encryption Scheme in Cloud Computing

This paper is an endeavor to give an upgraded data storage security model in Cloud Computing and making a put stock in condition in cloud computing. There are a considerable measure of convincing purposes behind organizations to convey cloud-based storage. For another business, start-up costs are fundamentally decreased in light of the fact that there is no compelling reason to contribute capital in advance for an inner to help the business. By a long shot, the main inquiry customers considering a move to cloud storage ask is regardless of whether their data will be secure. Stored data offsite doesn't change ata security necessities; they are the same as those confronting data put away on location. Security ought to be based on business prerequisites for particular applications and data sets, regardless of where the data is stored. We trust that data storage security in Cloud Computing, a zone brimming with challenges and of fundamental significance, is still in its earliest stages now, and numerous examination issues are yet to be distinguished. In this paper, we examined the issue of data security in cloud data storage, to guarantee the rightness of customers' data in cloud data storage. We proposed a Hierarchical Attribute-Based Secure Outsourcing for moldable Access in Cloud computing which likewise guarantees data storage security and survivability accordingly giving put stock in condition to the customers. To battle against unapproved data spillage, delicate data must be encoded before outsourcing in order to give end-to-end data confidentiality affirmation in the cloud and past. It upgrades the security in the proposed model successfully

MULTIVARIATE CALIBRATION TECHNIQUE FOR THE SPECTROPHOTOMETRIC QUANTIFICATION OF IVERMECTIN IN PHARMACEUTICAL FORMULATION

Objective: The present abstract makes the use of multivariate calibration technique for the quantification of ivermectin in pharmaceutical dosage form. Methods: Multivariate calibration technique is based on the use of linear regression equations, by correlating the relation between concentration and absorbance at seven different selected wavelengths. The λmax of ivermectin was found to be 245 nm. The results were treated statistically. This statistical approach gives optimum results by eliminating the fluctuations arising from the instrumental or experimental conditions. Results: The developed method was validated as per the ICH guidelines and was found to be simple, linear, accurate, precise, and reproducible. The method was found to be linear over a concentration range of 5–15 μg/mL with a correlation coefficient (r2) value of about 0.9998. The limit of detection and quantification were found to be 0.029 and 0.087 μg/mL, respectively. The percentage relative standard deviation for intraday and interday precision was found to be in the range of 0.473–1.373 and 0.301–1.617, respectively. The percentage recovery was found within the range of 97.60–101.80% w/w. Conclusion: The results evidence that a simple, linear, precise, accurate, sensitive, and reproducible multivariate calibration technique has been developed and validated for the quantification of ivermectin in bulk and pharmaceutical formulation