Optimization of cholesterol oxidase production by Brevibacterium sp. employing response surface methodology

An ultrasound-assisted emulsification as a pretreatment for cholesterol oxidase production by submerge fermentation using Brevibacterium sp. in a batch system was studied. Medium improvement for the production employing response surface methodology (RSM) was optimized in this paper. The concentration of Tween-80, cholesterol and the time of ultrasonic pretreatment medium were further optimized by RSM. Results from RSM showed that the initial concentration of cholesterol, Tween-80 and pretreatment time exerted a significant effect on cholesterol oxidase production, but these factors did not exert a significant effect on cell growth. The improved medium consisted of 4.076 g/L cholesterol, 0.2932‰ (v/v) Tween-80, 22.361 (min) treatment time, and cholesterol oxidase production reached 1.483 U/ml after 36 h culture, which was 83.57% greater than the control medium.Keywords: Ultrasonic, cholesterol oxidase, response surface methodolog

Palaeostructure, evolution and tight oil distribution of the Ordos Basin, China

International audienceWhether or not the tight oil in the Triassic Yanchang Formation of the Ordos Basin is controlled by structural factors is a controversial issue, the relationship between the structural factors of the strata and the distribution of tight oil is limited to the study of current structures. The traditional view is that structural factors have no obvious control over the formation and distribution of the oil reservoir. Taking the Chang 8 member of the Triassic Yanchang Formation in the Ordos Basin as an example, this paper studies respectively the burial of strata-hydrocarbon generation history of the individual well and the structural evolution history of strata in the basin by using software tools of the Genex burial-hydrocarbon generation history restoration and TemisFlow evolution of stratigraphic structures. It is considered that the hydrocarbon generation period of the source rock of the Triassic Yanchang Formation in the Ordos Basin is from early Middle Jurassic to end of Early Cretaceous. By reconstructing the evolution and structure of the Chang 8 member during the hydrocarbon accumulation period, combined with a comprehensive analysis on the distributional characteristics of the Chang 8 oil reservoir, we found the palaeoslopes and palaeohighs of the Chang 8 reservoir to represent areas in which tight oils were distributed. Palaeo-structural characteristics of the target layer exhibit control over the Chang 8 reservoir. The new theory underlying tight oil exploration, which is based on the recovery of the palaeogeomorphology of the target layer during the hydrocarbon generation period, incorporates the vital roles of key controlling factors over tight oil accumulation, so that the mind-set on tight oil exploration in the Ordos Basin has evolved

A Semiblind Two-Way Training Method for Discriminatory Channel Estimation in MIMO Systems

Discriminatory channel estimation (DCE) is a recently developed strategy to enlarge the performance difference between a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. Specifically, it makes use of properly designed training signals to degrade channel estimation at the UR which in turn limits the UR's eavesdropping capability during data transmission. In this paper, we propose a new two-way training scheme for DCE through exploiting a whitening-rotation (WR) based semiblind method. To characterize the performance of DCE, a closed-form expression of the normalized mean squared error (NMSE) of the channel estimation is derived for both the LR and the UR. Furthermore, the developed analytical results on NMSE are utilized to perform optimal power allocation between the training signal and artificial noise (AN). The advantages of our proposed DCE scheme are two folds: 1) compared to the existing DCE scheme based on the linear minimum mean square error (LMMSE) channel estimator, the proposed scheme adopts a semiblind approach and achieves better DCE performance; 2) the proposed scheme is robust against active eavesdropping with the pilot contamination attack, whereas the existing scheme fails under such an attack.Comment: accepted for publication in IEEE Transactions on Communication

Nonnegative Matrix Factorization Applied to Nonlinear Speech and Image Cryptosystems

Nonnegative matrix factorization (NMF) is widely used in signal separation and image compression. Motivated by its successful applications, we propose a new cryptosystem based on NMF, where the nonlinear mixing (NLM) model with a strong noise is introduced for encryption and NMF is used for decryption. The security of the cryptosystem relies on following two facts: 1) the constructed multivariable nonlinear function is not invertible; 2) the process of NMF is unilateral, if the inverse matrix of the constructed linear mixing matrix is not nonnegative. Comparing with Lin\u27s method (2006) that is a theoretical scheme using one-time padding in the cryptosystem, our cipher can be used repeatedly for the practical request, i.e., multitme padding is used in our cryptosystem. Also, there is no restriction on statistical characteristics of the ciphers and the plaintexts. Thus, more signals can be processed (successfully encrypted and decrypted), no matter they are correlative, sparse, or Gaussian. Furthermore, instead of the number of zero-crossing-based method that is often unstable in encryption and decryption, an improved method based on the kurtosis of the signals is introduced to solve permutation ambiguities in waveform reconstruction. Simulations are given to illustrate security and availability of our cryptosystem

Blind extraction using fractional lower-order statistics

In traditional method to blindly extract interesting source signals sequentially, the second-order or higher-order statistics of signals are often utilized. However, for impulsive sources, both of the second-order and higher-order statistics may degenerate. Therefore, it is necessary to exploit new method for the blind extraction of impulsive sources. Based on the best compression-reconstruction principle, a novel model is proposed in this work, together with the corresponding algorithm. The proposed method can be used for blind extraction of sources which are distributed from alpha stable process. Simulations are given to illustrate availability and robustness of our algorithm

Blind source separation by fully nonnegative constrained iterative volume maximization

Blind source separation (BSS) has been widely discussed in many real applications. Recently, under the assumption that both of the sources and the mixing matrix are nonnegative, Wang develop an amazing BSS method by using volume maximization. However, the algorithm that they have proposed can guarantee the nonnegativities of the sources only, but cannot obtain a nonnegative mixing matrix necessarily. In this letter, by introducing additional constraints, a method for fully nonnegative constrained iterative volume maximization (FNCIVM) is proposed. The result is with more interpretation, while the algorithm is based on solving a single linear programming problem. Numerical experiments with synthetic signals and real-world images are performed, which show the effectiveness of the proposed method

Estimating number of speakers via density-based clustering and classification decision

It is crucial to robustly estimate the number of speakers (NoS) from the recorded audio mixtures in a reverberant environment. Some popular time-frequency (TF) methods approach this NoS estimation problem by assuming that only one of the speech components is active at each TF slot. However, this condition is violated in many scenarios where the speeches are convolved with long length of room impulse response coefficients, which causes degenerated performance of NoS estimation. To tackle this problem, a density-based clustering strategy is proposed to estimate NoS based on a local dominance assumption of speeches. Our method consists of several steps from clustering to classification of speakers with the consideration of robustness. First, the leading eigenvectors are extracted from the local covariance matrices of mixture TF components and ranked by the combination of local density and minimum distance to other leading eigenvectors with higher density. Second, a gap-based method is employed to determine the cluster centers from the ranked leading eigenvectors at each frequency bin. Third, a criterion based on averaged volume of cluster centers is proposed to select reliable clustering results at some frequency bins for the classification decision of NoS. The experiment results demonstrate that the proposed algorithm is superior to the existing methods in various reverberation cases with noise-free condition or noise condition

Blind Source Separation by Nonnegative Matrix Factorization with Minimum-Volume Constraint

Recently, nonnegative matrix factorization (NMF) attracts more and more attentions for the promising of wide applications. A problem that still remains is that, however, the factors resulted from it may not necessarily be realistically interpretable. Some constraints are usually added to the standard NMF to generate such interpretive results. In this paper, a minimum-volume constrained NMF is proposed and an efficient multiplicative update algorithm is developed based on the natural gradient optimization. The proposed method can be applied to the blind source separation (BSS) problem, a hot topic with many potential applications, especially if the sources are mutually dependent. Simulation results of BSS for images show the superiority of the proposed method