Semiclassical transition state theory/master equation kinetics of HO + CO: Performance evaluation

Previously, master equation (ME) simulations using semiclassical transition state theory (SCTST) and high-accuracy extrapolated ab initio thermochemistry (HEAT) predicted rate constants in excellent agreement with published experimental data over a wide range of pressure and temperatures ≳250 K, but the agreement was not as good at lower temperatures. Possible reasons for this reduced performance are investigated by (a) critically evaluating the published experimental data and by investigating; (b) three distinct ME treatments of angular momentum, including one that is exact at the zero- and infinite-pressure limits; (c) a hindered-rotor model for HOCO that implicitly includes the cis- and trans-conformers; (d) possible empirical adjustments of the thermochemistry; (e) possible empirical adjustments to an imaginary frequency controlling tunneling; (f) including or neglecting the prereaction complex PRC1; and (g) its possible bimolecular reactions. Improvements include better approximations to factors in SCTST and using the Hill and van Vleck treatment of angular momentum coupling. Evaluation of literature data does not reveal any specific shortcomings, but the stated uncertainties may be underestimated. All ME treatments give excellent fits to experimental data at T ≥ 250 K, but the discrepancy at T < 250 K persists. Note that each ME model requires individual empirical energy transfer parameters. Thermochemical adjustments were unable to match the experimental H/D kinetic isotope effects. Adjusting an imaginary frequency can achieve good fits, but the adjustments are unacceptably large. Whether PRC1 and its possible bimolecular reactions are included had little effect. We conclude that none of the adjustments is an improvement over the unadjusted theory. Note that only one set of experimental data exists in the regime of the discrepancy with theory, and data for DO + CO are scanty.     

Structural and luminescence characteristics of CuAlxIn1−xSe2 (0 < x ≤ 0.30) chalcopyrite solid solutions

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On the use of AES data analysis to determine local enrichments of conversion coating elements at intermetallic particles on rolled Al

In this paper, the behaviour of the industrial applied Ti/Zr conversion coating (CC) pretreatment on rolled automotive aluminium samples (AA5182) is analysed. Due to its nanometre lateral and depth resolution, Auger electron spectroscopy (AES) is used to analyse the CC distribution at surface cathodic intermetallic particles. As a result of its high surface sensitivity, the AES technique is very susceptible to differences in the top contamination layer thickness. It is demonstrated that AES point measurements performed on aluminium model samples coated with 1.5 and 3 nm of Ti (oxide) layer cannot differentiate the two-layer thicknesses if a difference in the top contamination thickness is not taken into account. A data analysis methodology is introduced, based on the ratio of normalized peak areas (enrichment ratios), to eliminate the effect of the contamination layer thickness. The experimental validation of the methodology is performed on the model samples, demonstrating errors of 2% on the enrichment ratios on similar samples with different contamination layer thicknesses, while the conventional spectra quantification results in errors of 49%. The methodology is also theoretically substantiated within certain constraints. By the use of the AES methodology, an enriched Ti and Zr deposition is confirmed at the cathodic intermetallic particles at the surface of the industrial no-rinse CC sprayed automotive aluminium sheet samples.     

THE MODIFIED TRIPLES ALGORITHM FOR FLUSH AIR DATA SENSING SYSTEM APPLIED TO THE VEHICLE WITH BLUNT FORE-BODIES1)

针对传统的用于钝头体飞行器的嵌入式大气数据传感系统的经典三点式算法对测压孔配置约束性严格的特点,对经典三点式算法进行了改进及验证。首先,对于经典三点式算法及改进算法进行了论述;其次,针对不同测压孔配置对算法精度的影响进行了系统的评估;最后,对于传统的经典三点式算法与改进三点式算法的优劣进行了比较。结论为：(1)改进的三点式算法对于测压孔的配置敏感性变差,即对测压孔约束很宽松,可用测压孔数增加,从而使得算法的适用性更强;(2)改进的三点式算法的精度与三点式算法相当,但是需要对测压孔进行系统选取及对比验证。     

Global existence and time decay estimate of solutions to the Keller–Segel system

We consider the chemotaxis‐Navier–Stokes system 1.1-1.4 (Keller–Segel system) in the whole space, which describes the motion of oxygen‐driven bacteria, eukaryotes, in a fluid. We proved the global existence and time decay estimate of solutions to the Cauchy problem 1.1-1.2 in with the small initial data. Moreover, when the fluid motion is described by the Stokes equations, we established the global weak solutions to 1.3-1.4 in with the potential function ? is small and the initial density n₀(x) has finite mass.     

A problem of determining a special spatial part of 3D memory kernel in an integro‐differential hyperbolic equation

The inverse problem of determining 2D spatial part of integral member kernel in integro‐differential wave equation is considered. It is supposed that the unknown function is a trigonometric polynomial with respect to the spatial variable y with coefficients continuous with respect to the variable x. Herein, the direct problem is represented by the initial‐boundary value problem for the half‐space x>0 with the zero initial Cauchy data and Neumann boundary condition as Dirac delta function concentrated on the boundary of the domain . Local existence and uniqueness theorem for the solution to the inverse problem is obtained.     

Code2vect: An efficient heterogenous data classifier and nonlinear regression technique

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.