Current status of genetic transformation technology developed in cucumber(Cucumis sativus L.)

Genetic transformation is an important technique for functional genomics study and genetic improvement of plants. Until now, Agrobacterium-mediated transformation methods using cotyledon as explants has been the major approach for cucumber, and its frequency has been up to 23%. For example, significantly enhancement of the transformation efficiency of this plant species was achieved from the cotyledon explants of the cultivar Poinsett 76 infected by Agrobacterium strains EHA105 with efficient positive selection system in lots of experiments. This review is to summarize some key factors influencing cucumber regeneration and genetic transformation, including target genes, selection systems and the ways of transgene introduction, and then to put forward some strategies for the increasing of cucumber transformation efficiency. In the future, it is high possible for cucumber to be potential bioreactor to produce vaccine and biomaterials for human beings.

In-pit coal mine personnel uniqueness detection technology based on personnel positioning and face recognition

Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance management such as multiple cards for one person, and swiping one's cards by others in China at present. Therefore, the research introduces a uniqueness detection system and method for in-pit coal-mine personnel integrated into the in-pit coal mine personnel positioning system, establishing a system mode based on face recognition + recognition of personnel positioning card + release by automatic detection. Aiming at the facts that the in-pit personnel are wearing helmets and faces are prone to be stained during the face recognition, the study proposes the ideas that pre-process face images using the 2D-wavelet-transformation-based Mallat algorithm and extracts three face features: miner light, eyes and mouths, using the generalized symmetry transformation-based algorithm. This research carried out test with 40 clean face images with no helmets and 40 lightly-stained face images, and then compared with results with the one using the face feature extraction method based on grey-scale transformation and edge detection. The results show that the method described in the paper can detect accurately face features in the above-mentioned two cases, and the accuracy to detect face features is 97.5% in the case of wearing helmets and lightly-stained faces.

DSC STUDY OF MARTENSITIC TRANSFORMATION KINETICS IN A Cu－Zn－Al－Mn－Ni SHAPE MEMORY ALLOY

The kinetics of the reversible martensitic transformation in a Cu-Zn-Al-Mn-Ni shape memory alloy has been studied by means of differential scanning calorimetry.The apparent activation energy has been calculated and the kinetic equations of positive and adverse martensitic transformation have been established with the variations of temperature and time.

Non-normal Hasemann Boundary Value Problem

We will discuss the non-normal Hasemann boundary value problem: we may find these results are coincided with those of normal Hasemann boundary value problem and non-normal Riemann boundary value problem.

An approach for parameter estimation of combined CPPM and LFM radar signal

In this paper, the problem of parameter estimation of the combined radar signal adopting chaotic pulse position modulation （CPPM） and linear frequency modulation （LFM）, which can be widely used in electronic countermeasures, is addressed. An approach is proposed to estimate the initial frequency and chirp rate of the combined signal by exploiting the second-order cyclostationarity of the intra-pulse signal. In addition, under the condition of the equal pulse width, the pulse repetition interval （PRI） of the combined signal is predicted using the low-order Volterra adaptive filter. Simulations demonstrate that the proposed cyclic autocorrelation Hough transform （CHT） algorithm is theoretically tolerant to additive white Gaussian noise. When the value of signal noise to ratio （SNR） is less than 4 dB, it can still estimate the intra-pulse parameters well. When SNR = 3 dB, a good prediction of the PRI sequence can be achieved by the Volterra adaptive filter algorithm, even only 100 training samples.