Explosive chemistry: Simulating the chemistry of energetic materials at extreme conditions

In this article, we review recent atomistic computational techniques to study the electronic structure aspects and chemistry of energetic materials at high-pressure and/or high temperature. While several mechanisms have been proposed for the initial events of energetic materials at high-pressure, we explore the validity of a proposed shear-induced local metallization via molecular bond bending in the insensitive explosive TATB. We study the effect of high-stress (both uniform and uniaxial) on the electronic energy band-gap and the first chemical event of a prototypical energetic material, that of nitromethane. We also determine chemical reactions rate laws and decomposition mechanisms from a quantum-based molecular dynamics simulation of HMX, a widely used explosive material, at conditions of high density and temperature similar to that encounter under detonation. Finally, we review a new multi-scale computational tool recently developed to model the shock-induced chemistry of energetic materials at the atomistic level, and report its applicability to shocked solid nitromethane. This revised version was published online in June 2006 with corrections to the Cover Date.     

Phytochemical and biological investigation of <Emphasis Type="Italic">Stevia rebaudiana</Emphasis> Bertoni; 1-labdane-type diterpene

Five labdane diterpenoids, austroinulin, iso-austroinulin, sterebin E, sterebin E acetate, and sterebin A acetate, along with hydrocarbons, aliphatic alcohols, β-amyrin, β-sitosterol and stigmasterol were isolated from the chloroform soluble fraction of the methanol extract of Stevia rebaudiana leaves. Chlorogenic and caffeic acids were isolated from the EtOAc fraction. All the isolated compounds were identified using spectral tools. The chloroform and methanol extracts proved significant anti-inflammatory effect and caused marked inhibition of carrageenan-induced paw oedema in rats.     

A predictive bottom-up parser

Despite all advances in parsing, parser size, conflict resolution and error recovery are still of important consideration. In this research, we propose a predictive bottom-up parser. The parser is implemented in two versions. Both versions constitute an algorithm that simulates the run of a shift–reduce automaton, defined and constructed in a way that integrates its parsing actions with reduction prediction, conflict resolution and error recovery. However, the first implementation version performs explicit shift–reduce parsing actions based on implicit prediction of the reduction sequences. The second one performs parsing actions based on explicit prediction of the reduction sequences with implied shift–reduce actions. The proposed parser has been experimented against the ones based on similar approaches. 10–20% reduction of the parser size has been achieved, with a parsing behaviour proportional to a factor reflecting the grammar ambiguity.     

Analysis of the Earthʼs magnetic field variations on the basis of a wavelet-based approach

In the present paper we will discuss a new wavelet-based approach aimed at processing and analyzing different features of complex geomagnetic signals. This approach makes it possible to automatically extract different kinds of disturbances in the Earth?s magnetic field variations, which characterize solar activity and help to predict magnetic storms. In order to analyze geomagnetic signals wavelet packets are used in order to isolate local variations for quiet and perturbed periods and determine their intensity. Furthermore, a new automatic method of calculating the index of geomagnetic activity K is suggested on the basis of forming a quiet-day diurnal variation (Sq-curve). This method allows us to do calculations in the way that is closest to that developed by J. Bartels, who introduced the K-index in 1938. The results are compared with those obtained by INTERMAGNET and the original method of J. Bartels and the advantages of the suggested method are clearly demonstrated. For geomagnetic data collected in high-latitude regions of our planet it has become possible to reduce the error of estimating the K-index by 20% and unlike the technique used by INTERMAGNET here all the calculations can be done automatically. We will use geomagnetic signals that were kindly provided to us by the Institute of Cosmophysical Research and Radio Wave Propagation (Paratunka, Kamchatka region, Far East of Russia) for the period from January, 2002 till December, 2010.     

Comparison of an effect-model-law-based method versus traditional clinical practice guidelines for optimal treatment decision-making: application to statin treatment in the French population

Healthcare authorities make difficult decisions about how to spend limited budgets for interventions that guarantee the best cost-efficacy ratio. We propose a novel approach for treatment decision-making, OMES—in French: Objectif thérapeutique Modèle Effet Seuil (in English: Therapeutic Objective–Threshold–Effect Model; TOTEM). This approach takes into consideration results from clinical trials, adjusted for the patients'' characteristics in treatment decision-making. We compared OMES with the French clinical practice guidelines (CPGs) for the management of dyslipidemia with statin in a computer-generated realistic virtual population, representing the adult French population, in terms of the number of all-cause deaths avoided (number of avoided events: NAEs) under treatment and the individual absolute benefit. The total budget was fixed at the annual amount reimbursed by the French social security for statins. With the CPGs, the NAEs was 292 for an annual cost of 122.54 M€ compared with 443 with OMES. For a fixed NAEs, OMES reduced costs by 50% (60.53 M€ yr⁻¹). The results demonstrate that OMES is at least as good as, and even better than, the standard CPGs when applied to the same population. Hence the OMES approach is a practical, useful alternative which will help to overcome the limitations of treatment decision-making based uniquely on CPGs.     

Improving Deep Attractor Network by BGRU and GMM for Speech Separation

Deep Attractor Network (DANet) is the state-of-the-art technique in speech separation field, which uses Bidirectional Long Short-Term Memory (BLSTM), but the complexity of the DANet model is very high. In this paper, a simplified and powerful DANet model is proposed using Bidirectional Gated neural network (BGRU) instead of BLSTM. The Gaussian Mixture Model (GMM) other than the k-means was applied in DANet as a clustering algorithm to reduce the complexity and increase the learning speed and accuracy. The metrics used in this paper are Signal to Distortion Ratio (SDR), Signal to Interference Ratio (SIR), Signal to Artifact Ratio (SAR), and Perceptual Evaluation Speech Quality (PESQ) score. Two speaker mixture datasets from TIMIT corpus were prepared to evaluate the proposed model, and the system achieved 12.3 dB and 2.94 for SDR and PESQ scores respectively, which were better than the original DANet model. Other improvements were 20.7% and 17.9% in the number of parameters and time training respectively. The model was applied on mixed Arabic speech signals and the results were better than that in English.     

Effects of Speeds,Materials, and Tool Rake Angles on Metallic Particle Emission During Orthogonal Cutting

Dry high speed machining has been proposed as a viable and cost-effective process in metal cutting industries. However, it produces fine and ultra-fine metallic particles, also referred to as dust, which can be harmful to the machine-tool operator. The risk associated with exposure to metallic particles increases as the particle size decreases. For machining processes, little data exist on the size and distribution of dust generated during the shaping of materials. In order to reduce or eliminate the generation of these particles, it is necessary to understand how and under which conditions they are formed, as well as to be able to make predictions. In this study, the effects exerted by tool geometry, material, and machining parameters on dust emission were studied experimentally in order to understand the mechanisms of dust generation and to develop a predictive model. The particle sizes studied include the PM2.5 (particles with aerodynamique diameter below 2.5 μm) and a distribution of nanoparticles varying in size from 10 nm to 10 μm. Using dry machining and reducing the amount of dust generated should improve the air quality in machine shops in addition to helping protect the environment.     

Modeling of Particle Emission During Dry Orthogonal Cutting

Because of the risks associated with exposure to metallic particles, efforts are being put into controlling and reducing them during the metal working process. Recent studies by the authors involved in this project have presented the effects of cutting speeds, workpiece material, and tool geometry on particle emission during dry machining; the authors have also proposed a new parameter, named the dust unit (D _u), for use in evaluating the quantity of particle emissions relative to the quantity of chips produced during a machining operation. In this study, a model for predicting the particle emission (dust unit) during orthogonal turning is proposed. This model, which is based on the energy approach combined with the microfriction and the plastic deformation of the material, takes into account the tool geometry, the properties of the worked material, the cutting conditions, and the chip segmentation. The model is validated using experimental results obtained during the orthogonal turning of 6061-T6 aluminum alloy, AISI 1018, AISI 4140 steels, and grey cast iron. A good agreement was found with experimental results. This model can help in designing strategies for reducing particle emission during machining processes, at the source.     

Awareness of prior success or failure: A critical factor in task performance.

Exp I, with 36 undergraduates, determined the effects of 2 levels of history of success (high or low) and the presence or absence of an audience on task performance. Results show that after prior failure, alone Ss performed significantly better than audience Ss, although after prior success, the performance of alone and audience Ss did not differ. These results counter K. J. Good's (1973) results, since his experiment produced a social facilitation effect after prior success, not after prior failure. Performance differences were probably due to differences in audience awareness. In the present experiment, the audience was presumably unaware; whereas in Good's experiment, the audience was presumably aware of S's prior performance level. Exp II (60 Ss) tested the merits of this explanation. Ss with a history of success or failure performed either alone or in 1 of 2 audience manipulations. Ss were told that the audience was either aware or unaware of their prior performance level. Results support the hypothesis. It is argued than an audience can affect the type of standard that an S uses to evaluate performance and that the quality of an S's performance is a function not only of the criterion but also of S's expectation of meeting the criterion (16 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)