Entropy and Time

The emergence of a direction of time in statistical mechanics from an underlying time-reversal-invariant dynamics is explained by examining a simple model. The manner in which time-reversal symmetry is preserved and the role of initial conditions are emphasized. An extension of the model to finite temperatures is also discussed.Comment: 9 pages, 8eps figures. To appear in the theme issue of the American Journal of Physics on Statistical Physic

Novel concept of a single-mass adaptively controlled triaxial angular rate sensor

This paper presents a novel concept for an adaptively controlled triaxial angular rate (AR) sensor device that is able to detect rotation in three orthogonal axes, using a single vibrating mass. Pedestrian navigation is presented as an example demonstrating the suitability of the proposed device to the requirements of emerging applications. The adaptive controller performs various functions. It updates estimates of all stiffness error, damping and input rotation parameters in real time, removing the need for any offline calibration stages. The parameter estimates are used in feedforward control to cancel out their otherwise erroneous effects, including zero-rate output: The controller also drives the mass along a controlled oscillation trajectory, removing the need for additional drive control. Finally, the output of the device is simply an estimate of input rotation, removing the need for additional demodulation normally used for vibratory AR sensors. To enable all unknown parameter estimates to converge to their true values, the necessary. model trajectory is shown to be a three-dimensional Lissajous pattern. A modified trajectory algorithm is presented that aims to reduce errors due to discretization of the continuous time system. Simulation results are presented to verify the operation of the adaptive controller. A finite-element modal analysis of a preliminary structural design is presented. It shows a micro electro mechanical systems realizable design having modal shapes and frequencies suitable for implementing the presented adaptive controller

IN SILICO CHARACTERIZATION, MOLECULAR DOCKING, AND IN VITRO EVALUATION OF TRIAZOLE DERIVATIVES AS POTENTIAL ANTICANCER AGENTS

Objective: The objective of the study was to perform in silico molecular docking and in vitro anticancer studies of proposed 1,2,4-triazole derivatives for the determination of their anticancer activity. Methods: A series of 10 triazole compounds with different substituents were drawn in ACD Lab ChemSketch software. Molecular and biological properties were identified using Molinspiration software. The compounds that obeyed Lipinski rule of five are subjected for pharmacokinetic parameters prediction and docking analysis. SwissDock ADME software is used for the prediction of absorption, distribution, metabolism, and elimination. Then, the compounds are docked with target enzymes in Chimera software 1.14 version. The molecular docking studies revealed favorable molecular interactions and binding energies. The compounds that showed good docking results were synthesized through wet lab synthesis and further preceded for in vitro anticancer studies. Results: Three compounds are selected for wet lab synthesis due to their good docking results compared to other compounds. The synthesized compounds are subjected to different in vitro anticancer studies and found to be having potential anticancer activity. Conclusion: The pharmacokinetic and docking studies conclude that the triazole compounds have potential as anticancer agents. The in vitro anticancer studies revealed that the triazole derivatives are having high potency of anticancer activity against pancreatic cell lines

Mastering the Chargemaster: Minimizing Price-Gouging and Exposing the Structural Flaws in the Healthcare "Market"

In his seminal article, Bitter Pill: Why Medical Bills Are Killing Us,1 Steven Brill recounts stories of Americans of modest to comfortable means, whose lives were turned upside-down, not just by tragic illness; but, by the cost of the cure.

EVALUATION OF ANTI-ALZHEIMER ACTIVITY OF ETHANOLIC AND METHANOLIC EXTRACTS OF POLYGONUM GLABRUM AGAINST ALUMINUM CHLORIDE-INDUCED ALZHEIMER’S IN EXPERIMENTAL RATS

Objective: The current study aimed at the investigation of the effectiveness of ethanolic and methanolic extract of Polygonum glabrum in aluminum chloride-induced Alzheimer’s disease in experimental rats. Methods: The behavioral parameters evaluated by following methods such as Morris water maze test, radial arm maze test, and active avoidance test. Biochemical parameters were also estimated such as acetylcholine and acetylcholine esterase. Results: Polygonum glabrum extract was instituted to be neuroprotective against AlCl3-induced toxicity. Enhanced learning and memory were allied to the ingestion of extract in rats. Al overload, acetylcholinesterase enzyme hyperactivity is responsible for Alzheimer’s disease which is neutralized or reduced with treatment of extract, which might be due to the synergistic action of its active constituents. Ethanolic extract was shown slightly higher efficacy as compared to methanolic extract. Conclusion: Based on these current findings, it is suggested that lowering Aβ is an unproven strategy, and it may be time to refocus on other targets for the treatment of this disease, including pathological forms of tau

Finding the unfound: Recovery of missing URLs through Internet Archive

The study investigated the accessibility and permanency of citations containing URLs in the articles published in DESIDOC Journal of Library and Information Technology journal during 2006-2015. A total of 2133 URL citations were identified out of which 823 were found to be incorrect or missing. HTTP-404 was the most common error message associated with the missing URLs. The study also tried to recover the incorrect or URL citations using Internet Archive and recovered a total of 484 (58.81%) missing URL citations

A Machine Learning Technique for Abstraction of Modules in Legacy System and Assigning them on Multicore Machines Using and Controlling p-threads

Hardware and Software technology has undergone a sea-of-change in recent past. Hardware technology has moved from single-core to multi-core machine, thus capable of executing multi-task at the same time. But traditional software’s (Legacy system) are still in use today in business world. It is not easy to replace them with new software system as they carry loads of knowledge, business value with them. Also, to build new software system by taking the requirements afresh involves lot of resources in terms of skilled human resources, time and financial resources. At last the customer may not have confidence in this new software. Instead of building a new software, an attempt is made to develop a semi-automated methodology by learning about the program itself (machine learning about the program) to abstract the independent modules present in the same abstraction level (implementation level) and recode the legacy program (single threaded program) into multi-threaded parallel program. A case study program is considered and execution time is noted and analyzed for both the original program and reengineered program on a multi-core machine

Steganography and Steganalysis : Different Approaches

Steganography is the technique of hiding confidential information within any media. Steganography is often confused with cryptography because the two are similar in the way that they both are used to protect confidential information. The difference between the two is in the appearance in the processed output; the output of steganography operation is not apparently visible but in cryptography the output is scrambled so that it can draw attention. Steganlysis is process to detect of presence of steganography. In this article we have tried to elucidate the different approaches towards implementation of steganography using ‘multimedia’ file (text, static image, audio and video) and Network IP datagram as cover. Also some methods of steganalysis will be discussed

Supervised Hashing for Retrieval of Multimodal Biometric Data

Biometric systems commonly utilize multi-biometric approaches where a person is verified or identified based on multiple biometric traits. However, requiring systems that are deployed usually require verification or identification from a large number of enrolled candidates. These are possible only if there are efficient methods that retrieve relevant candidates in a multi-biometric system. To solve this problem, we analyze the use of hashing techniques that are available for obtaining retrieval. We specifically based on our analysis recommend the use of supervised hashing techniques over deep learned features as a possible common technique to solve this problem. Our investigation includes a comparison of some of the supervised and unsupervised methods viz. Principal Component Analysis (PCA), Locality Sensitive Hashing (LSH), Locality-sensitive binary codes from shift-invariant kernels (SKLSH), Iterative quantization: A procrustean approach to learning binary codes (ITQ), Binary Reconstructive Embedding (BRE) and Minimum loss hashing (MLH) that represent the prevalent classes of such systems and we present our analysis for the following biometric data: Face, Iris, and Fingerprint for a number of standard datasets. The main technical contributions through this work are as follows: (a) Proposing Siamese network based deep learned feature extraction method (b) Analysis of common feature extraction techniques for multiple biometrics as to a reduced feature space representation (c) Advocating the use of supervised hashing for obtaining a compact feature representation across different biometrics traits. (d) Analysis of the performance of deep representations against shallow representations in a practical reduced feature representation framework. Through experimentation with multiple biometrics traits, feature representations, and hashing techniques, we can conclude that current deep learned features when retrieved using supervised hashing can be a standard pipeline adopted for most unimodal and multimodal biometric identification tasks.</p

Trace Detection of Nerve Agent Simulant in the Fuel Vapour Environment using Metal Oxide Surface Acoustic Wave E Nose

Nerve agents are often used at the military warfront, where diesel is a very common interferant. In the present work, a group of surface acoustic wave (SAW) sensors, called E-Nose with dissimilar sensing layers is developed for the recognition of the mixture of diesel and dimethyl methylphosphonate (DMMP) vapors. The exposure of DMMP and diesel vapors is kept at ppb and ppm levels respectively. Varied response patterns of DMMP and diesel vapors were obtained by SAW E-nose. Principal component analysis (PCA) has been used to extract features from the response curves of SAW sensors. Artificial Neural Network pattern recognition has been implemented to identify the precise detection of DMMP vapors in the binary mixture of DMMP and diesel. The effect of pre-processing (using PCA) the raw data before feeding it to artificial neural network is also studied