Determination of the Enthalpies of Vaporization of Cyclic Organic Peroxides by Correlation of Changes in Gas Chromatographic Retention Times

Liquid and solid cyclic peroxides derived from aliphatic ketones are explosive materials so their enthalpies of vaporization and other thermodynamic or condensed-phase properties cannot be measured directly. In this work the enthalpies of vaporization of peroxides at 298.15 K were estimated simply from gas chromatographic retention times measured at different temperatures. The technique correlates changes in the retention times of compounds whose enthalpies of vaporization are known (called the reference series), with those of the compounds of interest. If t _R′ is the adjusted retention time (retention time of each compound minus the retention time of unretained diethyl ether, used as solvent) a plot of ln t _R′ against 1/T for each compound (reference compounds and cyclic peroxides) results in a straight line (r ² > 0.99 for all compounds). The enthalpy of transfer from solution to the vapor state (Δ_sol^g H _m) can be obtained by multiplying the slope by the gas constant (R). A second plot correlates the enthalpies of transfer from solution to the vapor state (Δ_sol^g H _m), as measured by gas–liquid chromatography (GLC), with enthalpies of vaporization of reference materials (Δ_vap H _m at 298.15 K) available in the literature. C₉–C₁₅ fatty acid methyl esters and hydrocarbons were used as reference compounds. The enthalpies of vaporization of the cyclic organic peroxides were calculated from the equation of the line obtained in this second correlation, the slope of which was Δ_vap H _m (at 298.15 K)/Δ^g _sol H _m. The experiments were performed under isothermal conditions with a DB-5 capillary column, flame-ionization detection (FID), and nitrogen as carrier gas. The column temperature was varied over a range of at least 30–70 K between 403 and 473 K, with chromatograms being acquired at 10 K intervals. Enthalpies of vaporization of cyclic organic peroxides are not available in the literature, and the values given in this paper, obtained by gas chromatography, are the first to be reported.     

Alkylidinphosphane und -arsane. I [P ≡ C?S]−[Li(dme)3]+ – Synthese und Struktur

Alkylidynephosphanes and -arsanes. I [P ≡ C? S]^?[Li(dme)₃]⁺ – Synthesis and Structure O,O′-Diethyl thiocarbonate and bis(tetrahydrofuran)-lithium bis(trimethylsilyl)phosphanide dissolved in 1,2-dimethoxyethane, react below 0°C to give ethoxy trimethylsilane and tris(1,2-dimethoxyethane-O,O′)lithium 2λ³-phosphaethynylsulfanide – [P≡C? S]^? [Li(dme)₃]⁺ – ( 1a ). Apart from bis(trimethylsilyl)sulfane or carbon oxide sulfide, dark red concentrated solutions of λ³-phosphaalkyne 1 are also obtained from reactions of carbon disulfide with bis(tetrahydrofuran)-lithium bis(trimethylsilyl)phosphanide or with the homologous lithoxy-methylidynephosphane ( 2 ) [1]. The ir spectrum shows two absorptions at 1762 and 747 cm^?1 characteristic for the P≡C and C? S stretching vibrations. The nmr parameters {δ(³¹P) ? 121.3; δ(¹³C) 190.8 ppm; ¹J_CP 18.2 Hz} resemble much more values of diorganylamino-2λ³-phosphaalkynes than those of bis(1,2-dimethoxyethane-O,O′)lithoxy-methylidyne-phosphane ( 2a ). As found by an X-ray structure analysis (P2₁/c; a = 1192.6(16); b = 1239.1(19); c = 1414.8(26) pm; β = 105.91(13)° at ?100 ± 3°C; Z = 4 formula units; wR = 0.064) of pale yellow crystals (mp. + 16°C) isolated from the reaction with O,O′-diethyl thiocarbonate, the solid is built up of separate [P≡C? S]^? and [Li(dme)₃]⁺ ions. Typical bond lengths and angles are: P≡C 155.5(11); C? S 162.0(11); Li? O 206.4(17) to 220.3(20) pm; P≡C? S 178.9(7)°.     

Ein Neues,tricyclisches schwefelreiches Phosphan

A New Tricyclic Sulfur-rich Phosphane From the products of the reaction of P₄S₃ with tert-Butyliodide the compound (t-Bu(S)P)₂P₄S₃ was isolated in low yield. The compound is stable and has a structure related to the hydrocarbon brexane. By reaction with triphenylphosphin the compounds (t-BuP)(t-Bu(S)P)P₄S₃ and (t-BuP)₂P₄S₃ were obtained. The ³¹P-nmr spectra of all compounds were solved and used to determine the structure of the molecules. A complete set of ³¹P-nmr data is given.     

A Hybrid Data-Differencing and Compression Algorithm for the Automotive Industry

We propose an innovative delta-differencing algorithm that combines software-updating methods with LZ77 data compression. This software-updating method relates to server-side software that creates binary delta files and to client-side software that performs software-update installations. The proposed algorithm creates binary-differencing streams already compressed from an initial phase. We present a software-updating method suitable for OTA software updates and the method’s basic strategies to achieve a better performance in terms of speed, compression ratio or a combination of both. A comparison with publicly available solutions is provided. Our test results show our method, Keops, can outperform an LZMA (Lempel–Ziv–Markov chain-algorithm) based binary differencing solution in terms of compression ratio in two cases by more than 3% while being two to five times faster in decompression. We also prove experimentally that the difference between Keops and other competing delta-creator software increases when larger history buffers are used. In one case, we achieve a three times better performance for a delta rate compared to other competing delta rates.     

Low-Complexity Alternatives to the Optimal Linear Coding Scheme for Transmitting ARMA Sources

In the era of the Internet of Things, there are many applications where numerous devices are deployed to acquire information and send it to analyse the data and make informed decisions. In these applications, the power consumption and price of the devices are often an issue. In this work, analog coding schemes are considered, so that an ADC is not needed, allowing the size and power consumption of the devices to be reduced. In addition, linear and DFT-based transmission schemes are proposed, so that the complexity of the operations involved is lowered, thus reducing the requirements in terms of processing capacity and the price of the hardware. The proposed schemes are proved to be asymptotically optimal among the linear ones for WSS, MA, AR and ARMA sources.     

Neural Networks for Financial Time Series Forecasting

Financial and economic time series forecasting has never been an easy task due to its sensibility to political, economic and social factors. For this reason, people who invest in financial markets and currency exchange are usually looking for robust models that can ensure them to maximize their profile and minimize their losses as much as possible. Fortunately, recently, various studies have speculated that a special type of Artificial Neural Networks (ANNs) called Recurrent Neural Networks (RNNs) could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast: (i) the closing price of eight stock market indexes; and (ii) the closing price of six currency exchange rates related to the USD, using the RNNs model and its variants: the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The results show that the GRU gives the overall best results, especially for the univariate out-of-sample forecasting for the currency exchange rates and multivariate out-of-sample forecasting for the stock market indexes.     

Intelligent Fault Diagnosis of Industrial Robot Based on Multiclass Mahalanobis-Taguchi System for Imbalanced Data

One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.     

Adversarial Data Hiding in Digital Images

In recent studies of generative adversarial networks (GAN), researchers have attempted to combine adversarial perturbation with data hiding in order to protect the privacy and authenticity of the host image simultaneously. However, most of the studied approaches can only achieve adversarial perturbation through a visible watermark; the quality of the host image is low, and the concealment of data hiding cannot be achieved. In this work, we propose a true data hiding method with adversarial effect for generating high-quality covers. Based on GAN, the data hiding area is selected precisely by limiting the modification strength in order to preserve the fidelity of the image. We devise a genetic algorithm that can explore decision boundaries in an artificially constrained search space to improve the attack effect as well as construct aggressive covert adversarial samples by detecting “sensitive pixels” in ordinary samples to place discontinuous perturbations. The results reveal that the stego-image has good visual quality and attack effect. To the best of our knowledge, this is the first attempt to use covert data hiding to generate adversarial samples based on GAN.     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.     

Deep Image Steganography Using Transformer and Recursive Permutation

Image steganography, which usually hides a small image (hidden image or secret image) in a large image (carrier) so that the crackers cannot feel the existence of the hidden image in the carrier, has become a hot topic in the community of image security. Recent deep-learning techniques have promoted image steganography to a new stage. To improve the performance of steganography, this paper proposes a novel scheme that uses the Transformer for feature extraction in steganography. In addition, an image encryption algorithm using recursive permutation is proposed to further enhance the security of secret images. We conduct extensive experiments to demonstrate the effectiveness of the proposed scheme. We reveal that the Transformer is superior to the compared state-of-the-art deep-learning models in feature extraction for steganography. In addition, the proposed image encryption algorithm has good attributes for image security, which further enhances the performance of the proposed scheme of steganography.