Experimental tests of reaction rate theory: Mu+H2 and Mu+D2

Copyright @ 1987 American Institute of Physics.Bimolecular rate constants for the thermal chemical reactions of muonium (Mu) with hydrogen and deuterium—Mu+H2→MuH+H and Mu+D2→MuD+D—over the temperature range 473–843 K are reported. The Arrhenius parameters and 1σ uncertainties for the H2 reaction are log A (cm3 molecule-1 s-1)=-9.605±0.074 and Ea =13.29±0.22 kcal mol-1, while for D2 the values are -9.67±0.12 and 14.73±0.40, respectively. These results are significantly more precise than those reported earlier by Garner et al. For the Mu reaction with H2 our results are in excellent agreement with the 3D quantum mechanical calculations of Schatz on the Liu–Siegbahn–Truhlar–Horowitz potential surface, but the data for both reactions compare less favorably with variational transition-state theory, particularly at the lower temperatures.NSERC (Canada) and the Petroleum Research Foundation of the Americal Chemical Society

Reaction kinetics of muonium with the halogen gases (F2, Cl2, and Br2)

Copyright @ 1989 American Institute of PhysicsBimolecular rate constants for the thermal chemical reactions of muonium (Mu) with the halogen gases—Mu+X2→MuX+X—are reported over the temperature ranges from 500 down to 100, 160, and 200 K for X2=F2,Cl2, and Br2, respectively. The Arrhenius plots for both the chlorine and fluorine reactions show positive activation energies Ea over the whole temperature ranges studied, but which decrease to near zero at low temperature, indicative of the dominant role played by quantum tunneling of the ultralight muonium atom. In the case of Mu+F2, the bimolecular rate constant k(T) is essentially independent of temperature below 150 K, likely the first observation of Wigner threshold tunneling in gas phase (H atom) kinetics. A similar trend is seen in the Mu+Cl2 reaction. The Br2 data exhibit an apparent negative activation energy [Ea=(−0.095±0.020) kcal mol−1], constant over the temperature range of ∼200–400 K, but which decreases at higher temperatures, indicative of a highly attractive potential energy surface. This result is consistent with the energy dependence in the reactive cross section found some years ago in the atomic beam data of Hepburn et al. [J. Chem. Phys. 69, 4311 (1978)]. In comparing the present Mu data with the corresponding H atom kinetic data, it is found that Mu invariably reacts considerably faster than H at all temperatures, but particularly so at low temperatures in the cases of F2 and Cl2. The current transition state calculations of Steckler, Garrett, and Truhlar [Hyperfine Interact. 32, 779 (986)] for Mu+X2 account reasonably well for the rate constants for F2 and Cl2 near room temperature, but their calculated value for Mu+Br2 is much too high. Moreover, these calculations seemingly fail to account for the trend in the Mu+F2 and Mu+Cl2 data toward pronounced quantum tunneling at low temperatures. It is noted that the Mu kinetics provide a crucial test of the accuracy of transition state treatments of tunneling on these early barrier HX2 potential energy surfaces.NSERC (Canada), Donors of the Petroleum Research Fund, administered by the American Chemical Society, for their partial support of this research and the Canada Council

Size-resolved aerosol and cloud condensation nuclei (CCN) properties in the remote marine South China Sea - Part 1: Observations and source classification

Abstract. Ship-based measurements of aerosol and cloud condensation nuclei (CCN) properties are presented for 2 weeks of observations in remote marine regions of the South China Sea/East Sea during the southwestern monsoon (SWM) season. Smoke from extensive biomass burning throughout the Maritime Continent advected into this region during the SWM, where it was mixed with anthropogenic continental pollution and emissions from heavy shipping activities. Eight aerosol types were identified using a k-means cluster analysis with data from a size-resolved CCN characterization system. Interpretation of the clusters was supplemented by additional onboard aerosol and meteorological measurements, satellite, and model products for the region. A typical bimodal marine boundary layer background aerosol population was identified and observed mixing with accumulation mode aerosol from other sources, primarily smoke from fires in Borneo and Sumatra. Hygroscopicity was assessed using the κ parameter and was found to average 0.40 for samples dominated by aged accumulation mode smoke; 0.65 for accumulation mode marine aerosol; 0.60 in an anthropogenic aerosol plume; and 0.22 during a short period that was characterized by elevated levels of volatile organic compounds not associated with biomass burning impacts. As a special subset of the background marine aerosol, clean air masses substantially scrubbed of particles were observed following heavy precipitation or the passage of squall lines, with changes in observed aerosol properties occurring on the order of minutes. Average CN number concentrations, size distributions, and κ values are reported for each population type, along with CCN number concentrations for particles that activated at supersaturations between 0.14 and 0.85 %

Rough set theory applied to pattern recognition of partial discharge in noise affected cable data

This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data

Aerodynamic Load Measurements and Opening Characteristics of Automatic Leading Edge Slats on a 45 deg Sweptback Wing at Transonic Speeds

Measurements of the normal force and chord force were made on the slats of a sting-mounted wing-fuselage model through a Mach number range of 0.60 to 1.03 and at angles of attack from 0 to 20 deg at subsonic speeds and from 0 to 8 deg at Mach number 1.03. The 20-percent-chord tapered leading-edge slats extended from 25 to 95 percent of the semispan and consisted of five segments. The model wing had 45 deg sweep, an aspect ratio of 3.56, a taper ratio of 0.3, and NACA 64(06)AO07 airfoil sections. Slat forces and moments were determined for the slats in the almost-closed and open positions for spanwise extents of 35 to 95 percent and 46 to 95 percent of the semispan. The results of the investigation showed little change in the slat maximum force and moment coefficients with Mach number. The coefficients for the open and almost-closed slat positions had similar variations with angle of attack. The loads on the individual slat segments were found to increase toward the tip for moderate angles of attack and decrease toward the tip for high angles of attack. An analysis of the opening and closing characteristics of aerodynamically operated slats opening on a circular-arc path is included

A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications

Understanding breast cancer patients' preference for two types of exercise training during chemotherapy in an unblinded randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Patient preference for group assignment may affect outcomes in unblinded trials but few studies have attempted to understand such preferences. The purpose of the present study was to examine factors associated with breast cancer patients' preference for two types of exercise training during chemotherapy.</p> <p>Methods</p> <p>Breast cancer patients (N = 242) completed a battery of tests including a questionnaire that assessed patient preference and the theory of planned behavior (TPB) prior to being randomized to usual care, resistance exercise training (RET), or aerobic exercise training (AET).</p> <p>Results</p> <p>99 (40.9%) participants preferred RET, 88 (36.4%) preferred AET, and 55 (22.7%) reported no preference. Past exercisers (p = 0.023), smokers (p = 0.004), and aerobically fitter participants (p = 0.005) were more likely to prefer RET. As hypothesized, participants that preferred AET had more favorable TPB beliefs about AET whereas participants that preferred RET had more favorable TPB beliefs about RET. In multivariate modeling, patient preference for RET versus AET was explained (R²= .46; p < 0.001) by the difference in motivation for RET versus AET (β = .56; p < 0.001), smoking status (β = .13; p = 0.007), and aerobic fitness (β = .12; p = 0.018). Motivational difference between RET versus AET, in turn, was explained (R²= .48; p < 0.001) by differences in instrumental attitude (β = .27; p < 0.001), affective attitude (β = .25; p < 0.001), and perceived behavioral control (β = .24; p < 0.001).</p> <p>Conclusion</p> <p>Breast cancer patients' preference for RET versus AET during chemotherapy was predicted largely by a difference in motivation for each type of exercise which, in turn, was based on differences in their beliefs about the anticipated benefits, enjoyment, and difficulty of performing each type of exercise during chemotherapy. These findings may help explain patient preference effects in unblinded behavioral trials.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov Identifier NCT00115713.</p