Search

The interaction between ammonia (NH 3) and nitric oxide (NO) at high temperatures is studied in this work using a shock tube combined with laser absorption diagnostics. The system simultaneously measured the NH 3 and NO time-histories during the reaction processes of the shock-heated NH 3 /NO/CO/Ar mixtures (NH 3 :NO ≈ 0.9:1.0 and 1.4:1.0). The absorption cross-sections of NH 3 near 1122.10 cm–1 and NO at 1900.52 cm–1 (characterized in this study) were used for measuring NH 3 and NO time-histories with the temperature measured by two CO absorption lines. The measured NH 3 and NO time-histories at 1614–1968 K and 2.4–2.8 atm were compared with predictions of seven recent kinetics models. The predictions that based on different mechanisms are very different and the measured profiles are within the range of the predictions. The Glarborg, NUI Galway Syngas-NO x , and Mathieu mechanisms give the closest predictions to the measurements. Kinetics analyses indicate that the NH 3 and NO consumption rates are extremely sensitive to the rate constants and branching ratio of NH 2 + NO = N 2 + H 2 O and NH 2 + NO = NNH + OH, which are more reliably represented in the Glarborg and NUI Galway Syngas-NO x mechanisms. The performances of Glarborg mechanisms at lower initial temperatures can be apparently improved by revising the rate constants and branching ratio of NH 2 + NO = N 2 + H 2 O and NH 2 + NO = NNH + OH. These two reactions are also the primary pathways for NO reduction and NH 3 is mainly consumed via NH 3 + OH = NH 2 + H 2 O and NH 3 + H = NH 2 + H 2. Trace amounts of NO 2 and N 2 O impurities decompose to form O radical followed by the generation of OH radical via H-abstraction reactions, which significantly affects the predictions of NH 3 and NO according to kinetics analyses. [ABSTRACT FROM AUTHOR]

In wireless rechargeable sensors (WRSNs), the system is vulnerable to be attacked by malware. Because of the distributed network structure of WRSNs, the malware attack has great influence on the security system of WRSNs. With the variability in malware, the problem of decryption and coding errors will lead to the malware mutating. In this paper, there are two problems to be solved, including the malware mutation and the charging delay in WRSNs. The malware mutation state and the low-energy state are introduced. Firstly, three different equilibrium solutions of the mutation model are given. Then, the local stability is proven by the characteristic equation, and the system will be stabilized at different equilibrium solutions when the base reproductive number is different. With the condition of charging delay, the bifurcation phenomenon is investigated by using the Hopf bifurcation theory. Furthermore, to improve the security of WRSNs and decrease the control cost, the Pontryagin's Maximum principle is applied to obtain an optimal control scheme under mutation and charging delay. Finally, the numerical simulation is applied by Matlab to confirm this model. The simulation results show that the mutation malware can be controlled when the delay is less than the maximum threshold. [ABSTRACT FROM AUTHOR]

An optical sensor was developed for simultaneous detection of sulfur dioxide (SO2) and sulfur trioxide (SO3) at elevated temperatures using a quantum cascade laser (QCL) spectrometer. The SO2/SO3 concentrations and the SO2 conversions were measured during the heterogeneous catalytic reaction with S-101 intermediate-temperature vanadium catalysts at pressures of 3–20 kPa and temperatures of 550–1000 K. The SO2 spectra in the ν3 band and the SO3 spectra in the ν3 + ν4 band near 7.16 µm at elevated temperatures were well resolved in the strongly overlapping spectra by measuring the SO2 and SO3 high-temperature individual absorption spectra that were extended from room-temperature spectra in the HITRAN database. A SO2/SO3 separation unit was designed to extract and measure the remaining SO2 after conversion at room temperature, with the results comparing well with the optically measured concentrations in the high-temperature catalytic reactions. The system was then used to investigate the pressure and temperature dependencies of the heterogeneous conversions of SO2. SO3 was first detected at temperatures higher than 590 K with the SO2 conversion increasing to 92% at 797 K and then decreasing to less than 3% at 1014 K. The measured conversions show a logarithmic growth with increasing pressures at each temperature. The difference between the measured heterogeneous conversion and the theoretical conversion calculated using a thermodynamic model was also analyzed. [ABSTRACT FROM AUTHOR]

Transport sustainability is an important foundation for achieving sustainable development in an economy. Particularly, environmental policies cannot overlook the growing importance in the transport sector as the economy expands. In this context, it is necessary to investigate the relationship between transport energy consumption (TEN), transport economic growth (TGDP), and transport CO2 emissions (TCE) to formulate the reasonable and specific transport policies. This paper investigated the long-run elasticity and causality between TEN, TGDP, and TCE for the case of China using the panel data covering 30 provincial regions during the period 2004–2016. Firstly, the stationary of each variable series was examined using the panel unit root test. Secondly, the cointegrating relationship in bivariate models was investigated by employing the panel Pedroni cointegration test. Thirdly, based on the fully modified ordinary least squares (FMOLS), the long-run elasticity coefficients in bivariate models were estimated. Finally, the panel Granger causality test based on the vector error correction model was utilized to determine whether the causality existed between the variables. The empirical results indicated that: (1) each variable was found to be integrated of order one, and the cointegrating relationship existed in all bivariate modes. (2) The panel FMOLS tests revealed that the long-run elasticity of TCE to TEN was almost equal to the long-run elasticity of TEN to TCE; the long-run elasticity of TCE to TGDP was significantly higher than the long-run elasticity of TGDP to TCE; and the long-run elasticity of TEN to TGDP was significantly higher than the long-run elasticity of TGDP to TEN. (3) Importantly, the bidirectional causal relationships existed between the TEN, TGDP, and TCE both in the short-run and in the long-run. [ABSTRACT FROM AUTHOR]

CO containing plasmas are of growing interest for greenhouse gas remediation and dry gas reforming. In this paper, we show that the optical emission spectrum of CO( B-A) transition can be used for gas temperature determination in CO containing plasmas. The study was performed in a packed-bed reactor and compared with previously published results for a MW discharge. The CO emission is mainly produced by direct electron excitation from ground state CO. The rotational temperature is determined by a fitting algorithm of the CO(B-A) (0-1) spectrum and the accuracy of the deduced rotational temperature is shown to be better than 30 K. Meanwhile, we also compared the results with the widely used Boltzmann plots of the CO( B-A) (0-1). The rotational lines corresponding to Q(18-24) yield accurately the gas temperature for spectra with a resolution in excess of 0.05 nm FWHM. Rotational lines with N < 18 cannot be used due to the overlap of rotational lines from different branches unless a spectral resolution of at least 5 pm is used. [ABSTRACT FROM AUTHOR]

In this paper, a novel framework of sparse kernel learning for support vector data description (SVDD) based anomaly detection is presented. By introducing 0-1 control variables to original features in the input space, sparse feature selection for anomaly detection is modeled as a mixed integer programming problem. Due to the prohibitively high computational complexity, it is relaxed into a quadratically constrained linear programming (QCLP) problem. The QCLP problem can then be practically solved by using an iterative optimization method, in which multiple subsets of features are iteratively found as opposed to a single subset. However, when a nonlinear kernel such as Gaussian radial basis function kernel, associated with an infinite-dimensional reproducing kernel Hilbert space (RKHS) is used in the QCLP-based iterative optimization, it is impractical to find optimal subsets of features due to a large number of possible combinations of the original features. To tackle this issue, a feature map called the empirical kernel map, which maps data points in the input space into a finite space called the empirical kernel feature space (EKFS), is used in the proposed work. The QCLP-based iterative optimization problem is solved in the EKFS instead of in the input space or the RKHS. This is possible because the geometrical properties of the EKFS and the corresponding RKHS remain the same. Now, an explicit nonlinear exploitation of the data in a finite EKFS is achievable, which results in optimal feature ranking. Comprehensive experimental results on three hyperspectral images and several machine learning datasets show that our proposed method can provide improved performance over the current state-of-the-art techniques. [ABSTRACT FROM PUBLISHER]