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Engineering and practicality are the main characteristics of electronic technology and application course. Engineering application ability and practical operation ability are the key contents of this kind of course assessment. The course assessment needs to closely follow the pace of curriculum reform, timely and dynamically reflect the teaching effect, and provide support for the formation of a closed loop in the teaching process. Firstly, this paper designs the overall idea of the examination of engineering practice courses. And taking electronic technology and application as an example, this paper adopts the combination of theory and practice, makes full use of the information conditions, emphasizes the breadth and depth of knowledge, pays attention to the examination of practical operation skills and application ability, increases the proportion of practical examination, and realizes the examination of the completion of teaching objectives; Secondly, it also gives the detailed design of the implementation of the theoretical content assessment scoring standard and the detailed design of the organization and implementation of the practical assessment based on non-standard answers. The practice results show that the integration of multiple assessment and evaluation, whole process assessment and evaluation, practical assessment and the implementation of online and offline, summative and procedural, standard and non-standard answer evaluation has improved the breadth and challenge of students’ learning content, and the teaching effect has been significantly improved. This paper has certain reference significance for the examination reform of engineering practice courses.

Currently, it is indicated that terahertz irradiation is capable of permeabilizing cell membrane by electroporation, which is able to facilitate transmembrane transport of molecules and ions for medicine and clinical applications. This paper numerically studies the changes in cell membrane permeability to ions owing to membrane hydrophilic pores by electroporation in response to terahertz picosecond pulse train. The capacitive current and ion channel currents of cell membrane are taken into account in the simulation of membrane conductance of the hydrophilic pores. The numerical simulation results reveal that the membrane permeability to ions by terahertz irradiation increases firstly at the locations around the two poles of a cell where the membrane is nearly perpendicular to the terahertz electric fields. And then the increase in the permeability gradually extends towards the parts of the cell where the cell membrane is nearly parallel to the terahertz electric fields. The results also show that the larger the amplitude of terahertz irradiation is, the earlier the membrane conductance of a whole cell starts to increase and the larger the conductance can be.

The review deals with lactic acid bacteria in characterizing the stress adaptation with cross-protection effects, mainly associated with Lactobacillus, Bifidobacterium and Lactococcus. It focuses on adaptation and cross-protection in Lactobacillus, Bifidobacterium and Lactococcus, including heat shocking, cold stress, acid stress, osmotic stress, starvation effect, etc. Web of Science, Google Scholar, Science Direct, and PubMed databases were used for the systematic search of literature up to the year 2020. The literature suggests that a lower survival rate during freeze-drying is linked to environmental stress. Protective pretreatment under various mild stresses can be applied to lactic acid bacteria which may enhance resistance in a strain-dependent manner. We investigate the mechanism of damage and adaptation under various stresses including heat, cold, acidic, osmotic, starvation, oxidative and bile stress. Adaptive mechanisms include synthesis of stress-induced proteins, adjusting the composition of cell membrane fatty acids, accumulating compatible substances, etc. Next, we reveal the cross-protective effect of specific stress on the other environmental stresses. Freeze-drying is discussed from three perspectives including the regulation of membrane, accumulation of compatible solutes and the production of chaperones and stress-responsive proteases. The resistance of lactic acid bacteria against technological stress can be enhanced via cross-protection, which improves industrial efficiency concerning the survival of probiotics. However, the adaptive responses and cross-protection are strain-dependent and should be optimized case by case.

Label free single photon microscopic imaging has natural advantages in noninvasive in vivo tissue imaging such as high resolution and rapid imaging speed. Although label free multi-photon microscopy can be used for imaging thick tissue samples, it requires high excitation light power and is phototoxic to the samples. Conventional label free single photon microscopy requires lower excitation light power, but it has limited imaging depth. Observing some highly scattering thick tissue samples with single photon microscopy is a great challenge. To solve the problem, we developed a label free deep penetration single photon microscopic imaging technique with an ultralong anti-diffracting (UAD) beam. The penetrating ability of the UAD beam was verified by passing through turbid media and performed with autofluorescence of chloroplasts in fresh Epipremnum aureum leaves. Benefiting from the anti-diffracting properties and the elongated focal depth of the UAD beam, single photon UAD microscopy has deeper penetration depth and better anti-scattering ability and is one of the ideal methods to observe the deep structure of biological samples.

This paper analyzes the differences between online and offline courses in teaching organization, course content and practice, as well as the differences between hybrid teaching and hybrid courses in concept and emphasis. This paper puts forward the basic principles and requirements of hybrid courses teaching design, and discusses the key points and application scenarios of hybrid courses teaching design from three aspects: teaching objectives, teaching elements and teaching activities. And the key issues that need to be figured out are explained.

Ring Learning With Errors (RLWE)-based key exchange is one of the most efficient and secure primitive for post-quantum cryptography. One common approach to achieve key exchange over RLWE is error reconciliation. Recently, an efficient attack against reconciliation-based RLWE key exchange protocols with reused keys was proposed. This attack can recover a long-term private key if a key pair is reused. We also know that in the real world, key reuse is commonly adopted in applications like the Transport Layer Security (TLS) protocol to improve performance. Directly motivated by this attack, we construct a new randomized RLWE-based key exchange protocol against this attack. Our lightweight approach incorporates an additional ephemeral public error term into key exchange materials, so that this attack no longer works. With the same attack, we practically show that the signal value of our protocol is indistinguishable from uniform random, therefore, this attack no longer works. We explain how the attack fails, present 200-bit classic and 80-bit quantum secure parameter choice, efficient implementations, comparisons and discussion. Benchmark shows our protocol is truly efficient and even faster than related vulnerable protocols.

A highly efficient, rapid, green and safe procedure for the preparation of free all-trans-astaxanthin from Haematococcus pluvialis algal extract, by a crude extracellular enzyme extract, was reported. The free all-trans-astaxanthin obtained by the biocatalysed method had fewer side products compared to the saponification procedure. Through single-factor experiments and a Box-Behnken design, it was possible to find the optimal biocatalytic conditions for the hydrolysis of 2 mg of H. pluvialis oil with 14.7 mg (protein content) of lyophilised crude extracellular enzyme extract obtained from Pseudomonas aeruginosa. The reaction was carried out in 30 min at pH 9.16 and 36 °C, in 5.5 mL total reaction volume, under nitrogen atmosphere and dark conditions. The hydrolysis ratio of the astaxanthin esters was 98.72%, and the production of free all-trans-astaxanthin was 82.83 ?g per mg of H. pluvialis oil. The method herein reported was simpler than other enzymatic methods previously described and allowed saving of time and costs.

A thermostable and alkaline-stable novel esterase (Est7) was identified through the whole genome sequencing of Stenotrophomonas maltophilia OUC_Est10. The open reading frame of this gene encoded 617 amino acid residues. After heterologous expression in Escherichia coli BL21 (DE3), the purified Est7 was separated as a single protein and presented a molecular mass of 70.6 kDa. Multiple sequence alignment indicated that Est7 had a typical catalytic triad (Ser-Asp-His) and the conserved sequence (GDSL) typical of the family II lipid hydrolase proteins. Est7 showed good stability in alkaline buffers, especially in Tris-HCl buffer at pH 9.0 (residual activity 93.8% after 96 h at 4 &deg, C) and in the medium temperature conditions (residual activity 70.2% after 96 h at 45 &deg, C and pH 8.0). The enzyme also retained higher stability toward several hydrophilic and hydrophobic organic solvents (e.g., after incubation in 100% acetonitrile or in n-hexane the enzyme retained about 97% and 84% of the activity in the absence of organic solvent, respectively). Furthermore, Est7 could catalyze the transesterification reaction of vinylacetate with 2-phenylethanol and cis-3-hexen-1-ol to their corresponding acetate esters in petroleum ether or tert-butyl methyl ether. These results indicate Est7 as a promising biocatalyst for applications of Est7 in non-aqueous media.

This study proposes a novel secured management method for renewable microgrids considering the policies required for diagnosing cyber-attacks happening in the communication networks, usually applied in the secondary control layer of microgrids (MGs). Due to the so long stochastic and bad information entering the systems in order to make malicious attacks, their location and time data links have the ability of straying of those acting in normal conditions that attempt to have an effect on the precise voltage regulation and current dividing via influencing sensors of current and voltage. The ability to extract high-level features due to the usage of fast fourier transform (FFT) and deep learning (DL) for attack detection in cyberspace has made them to be considered as a strong technique in the face of small mutations or new attacks. These self-educated and compaction abilities of DL architectures have been considered as basic techniques for hidden scheme detection from the training datum for this reason attacks have been distinguished from benign traffic. A novel method, deep learning and FFT, for cyber-security has been used in the following paper with the aim of enabling the attacks detection in DC smart MG. The deep model and traditional machine learning way are evaluated in terms of performance, and distributed attack detection has been compared to the centralized diagnosing procedure. The tests proved that the distributed attack detection system studied can be more advanced in comparison with centralized detection systems applying FFT in the role of the input index of the DL model. This suggested distributed method enables for scalable monitoring of a MG and has the ability of detecting the existence of cyber-attacks in the communications between distributed generation agents (DGAs) controlled via a control on the basis of consensus and isolating the communication link over that the attack has been injected. Any local attack detection needs restricted information about its neighbor’s dynamics. The most important factor of the proposed detection plan can be that has the ability of detecting cyber-attacks with great precision and distinguishing cyber-attack from load changes.in addition, this has been shown that the suggested model can be further useful in the detection of the attack.

Air pollution originating from anthropogenic emission, which is an important factor for environmental policy to regulate the sustainable development of enterprises and the environment. However, the missing or mislabeled discharge data make it impossible to apply this strategy in practice. In order to solve this challenge, we firstly discover that the energy consumption in a factory and the air pollutants are linearly related. Given this observation, we propose a support vector regression based Single-location recovery model to recover the air pollutant emission by using the energy consumption data in a factory. To further improve the precision of air pollutant emission estimation, we proposed a Gaussian process regression based multiple-location recovery model to estimate and recover the missing or mislabeled air pollutant emission from surrounding available air quality readings, collected by the government’s air quality monitoring station. Moreover, we optimally combine the two approaches to achieve the accurate air air pollutant emission estimation. To our best of knowledge, this is the first paper for monitoring the air pollutant emission taking both a factory’s energy consumption and government’s air quality readings into account. The research model in this article uses actual data(10,406,880 entries of data including weather, PM 2.5, date, etc.) from parts of Shandong Province, China. The dataset contains 33 factories (5 types) and we use the co-located air quality monitoring station as ground truth. The results show that, our proposed single-location recovery, multi-location recovery, and combined method could acquire the mean absolute error of 8.45, 9.69, and 7.25, respectively. The method has consistent accurate prediction behavior among 5 different factory types, shows a promising potential to be applied in broader locations and application areas, and outperforms the existing spatial interpolation based methods by 43.8%.

Compressed Sensing (CS) has been successfully applied to image compression in the past few years. However, there are still several challenges that restrict its applications in practice including large memory requirement and unsatisfactory reconstruction performance. To address these challenges, in this paper, we propose a classification guided deep convolutional network for image compressed sensing (CCSNet), which includes a sampling sub-network and a reconstruction sub-network. In the sampling sub-network, multiple convolutional layers are used to sample the original image, which significantly reduces the parameters of the sampling matrix while causes performance degradation moderately compared against existing convolution based sampling methods. In the reconstruction sub-network, a novel two-branch architecture is proposed to improve the adaptability of the model to various textures in natural images. The first branch, named the classification branch, is to classify the sampled measurements of the original image to one of the predefined textural classes. The second branch, named the reconstruction branch, consists of multiple sub-branches, which are responsible for reconstructing the original images belonging to the corresponding textural classes. By jointly utilizing two sub-networks, the entire network can be trained in the form of end-to-end metric with a joint loss function. Experimental results demonstrate that the proposed method provides a significant quality improvement in terms of PSNR compared against state-of-the-art methods.

Astaxanthin is a pigment with various functions. Free astaxanthin is obtained mainly through saponification methods, which could result in many byproducts. Enzymatic methods using lipases have been used in a few cases, while there are no reports on the use of esterases for the production of free astaxanthin. Herein we present the screening and identification of a novel esterase (Est3-14) from a marine mud metagenomic library. Est3-14 is pH-sensitive and keeps good stability in alkaline buffers (residual activity 94%, pH 8.0, 4 °C, and 36 h). Meanwhile, Est3-14 keeps a good stability in the medium temperature condition (residual activity 56.7%, pH 8.0, 40 °C, and 84 h). Est3-14 displayed high hydrolysis activity to prepare free all- trans-astaxanthin in biphasic systems. Furthermore, under optimal conditions (0.5 mL ethanol, 6 mL 0.1 M Tris-HCl buffer, pH 8.0, 0.5% (w/v) H. pluvialis oil, 40 °C), the hydrolytic conversion ratio was 99.3% after 36 h.

Gaussian random matrix (GRM) has been widely used to generate linear measurements in compressed sensing (CS) of natural images. However, there actually exist two disadvantages with GRM in practice. One is that GRM has large memory requirement and high computational complexity, which restrict the applications of CS. Another is that the CS measurements randomly obtained by GRM cannot provide sufficient reconstruction performances. In this paper, a Deep neural network based Sparse Measurement Matrix (DSMM) is learned by the proposed convolutional network to reduce the sampling computational complexity and improve the CS reconstruction performance. Two sub networks are included in the proposed network, which are the sampling sub-network and the reconstruction sub-network. In the sampling sub-network, the sparsity and the normalization are both considered by the limitation of the storage and the computational complexity. In order to improve the CS reconstruction performance, a reconstruction sub-network are introduced to help enhance the sampling sub-network. So by the offline iterative training of the proposed end-to-end network, the DSMM is generated for accurate measurement and excellent reconstruction. Experimental results demonstrate that the proposed DSMM outperforms GRM greatly on representative CS reconstruction methods, Comment: 5 pages, accepted by ICIP 2018

The compressed sensing (CS) has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconstruction models have been proposed and obtained superior performance. However, these methods suffer from blocking artifacts or ringing effects at low sampling ratios in most cases. To address this problem, we propose a deep convolutional Laplacian Pyramid Compressed Sensing Network (LapCSNet) for CS, which consists of a sampling sub-network and a reconstruction sub-network. In the sampling sub-network, we utilize a convolutional layer to mimic the sampling operator. In contrast to the fixed sampling matrices used in traditional CS methods, the filters used in our convolutional layer are jointly optimized with the reconstruction sub-network. In the reconstruction sub-network, two branches are designed to reconstruct multi-scale residual images and muti-scale target images progressively using a Laplacian pyramid architecture. The proposed LapCSNet not only integrates multi-scale information to achieve better performance but also reduces computational cost dramatically. Experimental results on benchmark datasets demonstrate that the proposed method is capable of reconstructing more details and sharper edges against the state-of-the-arts methods., Comment: 5 pages. Accepted by ICASSP2018

In this article, the combined effect of the mutual coupling of two closely located RFID tags and the inherent non-linearity of the impedance of the microchip of a tag is studied. The conclusion of the study is that these factors together might result in a counter-intuitive condition where some of the tags suddenly become inactive while the reader increases the transmit power.

The development of electronic bulk commodity trading market is booming in China at present. The role of supply chain finance (SCF) financing business is increasingly important in commodities trading. It can solve the problem of financing difficulties, reduce financing costs and raise the efficiency of the trade by supply chain finance. With the flourish of Internet technology, such as E-commerce, Internet of things, big data, cloud computing and so on, supply chain finance has turned into the online financial supply chain from offline to online. As a new financial pattern, Internet Finance (ITFIN) is also appeared because of the organic collection of Internet technology and the financial function. This paper analyzes the pattern of Internet finance, the situation of electronic bulk commodity trading market and developmental history of bulk commodity chain finance in China. Finally it proposes the financial innovative pattern of SCF + ITFIN.

Different from general algorithms of haze removal and low lighting image enhancement, which only use the information of image to process, this paper adds a reference image to get more information for the algorithm and focuses on enhancing region of interest of an image based on the reference one. With the reference image, the haze one can be divided into Region of Interest (RoI) and Region of no Interest (non-RoI). Furthermore, the reference image can provide more useful information for computing the transmission map and atmospheric light. For the non-RoI region, a more robust transmission map and minimizing reconstruction error cost function based method to estimate atmospheric light has been proposed. Because the atmospheric light is a global variable, the optimized one is also suitable for the RoI region. With the global optimized atmospheric light, an optimized transmission map can be got for the RoI region. The RoI region can be enhanced via the optimal transmission map and atmosphere light. Theoretical analysis gives eloquent proof proving that the proposed method is definitely better than the traditional dark-channel-prior-based methods due to our better transmission map and atmosphere light. Extensive experiments also show the expected results.

In this paper, the hierarchical frame based video compressed sensing (CS) framework is proposed, which outperforms the traditional framework through the better exploitation of frames correlation with reference frames, the unequal sample subrates setting among frames in different layers and the reduction of the error propagation. By considering the spatial and temporal correlations of the video sequence, a spatial-temporal sparse representation based recovery is proposed for this framework. The similar blocks in both the current frame and these recovered reference frames are composed as a spatial-temporal group, which is defined as the unit of the sparse representation. By exploiting the low dimensional subspace description of each group, the video CS recovery is converted as a low-rank matrix approximation problem, which can be solved by exploiting the hard thresholding and the gradient descent. Experimental results show that the proposed method achieves better performance against both the state-of-art still-image CS recovery algorithms and the existing residual domain based video CS reconstruction approaches.

Gaussian random matrix (GRM) has been widely used to generate linear measurements in compressive sensing (CS) of natural images. However, in practice, there actually exist two problems with GRM. One is that GRM is non-sparse and complicated, leading to high computational complexity and high difficulty in hardware implementation. The other is that regardless of the characteristics of signal the measurements generated by GRM are also random, which results in low efficiency of compression coding. In this paper, we design a novel local structural measurement matrix (LSMM) for block-based CS coding of natural images by utilizing the local smooth property of images. The proposed LSMM has two main advantages. First, LSMM is a highly sparse matrix, which can be easily implemented in hardware, and its reconstruction performance is even superior to GRM at low CS sampling sub rate. Second, the adjacent measurement elements generated by LSMM have high correlation, which can be exploited to greatly improve the coding efficiency. Furthermore, this paper presents a new framework with LSMM for block-based CS coding of natural images, including measurement generating, measurement coding and CS reconstruction. Experimental results show that the proposed framework with LSMM for block-based CS coding of natural images greatly enhances the existing CS coding performance when compared with other state-of-the-art image CS coding schemes.

Image interpolation is one of the most elementary imaging research topics. A number of image interpolation methods have been developed and tested on uncompressed images in the literature. However, a lot of videos have already been stored or have to be transmitted in compressed format due to the storage limitation or the bandwidth limitation. The existed image interpolation methods may not be efficient when directly applied to compressed images or videos. Inspired by the success of the intra prediction in HEVC and the edge-directed image interpolation methods, a directional intra frame interpolation for HEVC compressed video is proposed. The main idea is to use the directional prediction information in compressed low-resolution video bitstreams to estimate the associated high-resolution video. For intra frames, the prediction direction information is taken into account as context in the directional interpolation. When a pixel is decompressed with a small prediction residual, the interpolation is performed along its block direction. The interpolation weight for each block direction is off-line trained by the Wiener filter based on the representative video sequences. For each pixel with a large prediction residual, a piecewise autoregressive model is used as a regularization term into the interpolation function. Extensive experiments demonstrate that the proposed method achieves better performance than the traditional methods such as Bicubic, KR, LAZA, NEDI and SAI.

With the rapid development of digital video technology, frame rate up conversion is widely used. In this paper, a novel motion vector refinement method for frame rate up conversion on depth based 3D video is proposed. Our method involves two major stages in frame rate up conversion which are motion estimation and motion vector filtering. In the motion estimation process, the depth constraint to block matching algorithm is introduced in the bi-directional motion estimation method to obtain the motion vectors. In the motion vector filtering process, a depth-guided filter is designed to enhance the consistence of motions in the same depth plane. The refined motion vectors are used for frame interpolation. Experimental results show that the proposed method achieves 0.45 dB gain in terms of PSNR on average and improves the visual quality of the frame rate up-converted video.

In this paper, a practical video coding scheme is developed to realize state-of-the-art video coding efficiency with lower encoder complexity at low bit-rate, while supporting standard compliance and error resilience. Such an architecture is particularly attractive for wireless visual communications. At the encoder, multiple descriptions of a video sequence are generated in the spatio-temporal domain by temporal multiplexing and spatial adaptive downsampling. The resulting side descriptions are interleaved with each other in temporal domain, and still with conventional square sample grids in spatial domain. As such, each side description can be compressed without any change to existing video coding standards. At the decoder, each side description is first decompressed, and then reconstructed to original resolution with the help of the other side description. In this procedure, the decoder recover the original video sequence in a constrained least squares regression process, using 2D or 3D piecewise autoregressive model according to different prediction modes. In this way, the spatial and temporal correlation is sufficiently explored to achieve superior quality. Experiment results demonstrate the proposed video coding scheme outperforms H.264 in rate-distortion performance at low bit-rates and achieves superior visual quality at medium bit-rates as well.

In this paper, we propose a novel image interpolation algorithm, which is formulated via combining both the local autoregressive (AR) model and the nonlocal adaptive 3-D sparse model as regularized constraints under the regularization framework. Estimating the high-resolution image by the local AR regularization is different from these conventional AR models, which weighted calculates the interpolation coefficients without considering the rough structural similarity between the low-resolution (LR) and high-resolution (HR) images. Then the nonlocal adaptive 3-D sparse model is formulated to regularize the interpolated HR image, which provides a way to modify these pixels with the problem of numerical stability caused by AR model. In addition, a new Split-Bregman based iterative algorithm is developed to solve the above optimization problem iteratively. Experiment results demonstrate that the proposed algorithm achieves significant performance improvements over the traditional algorithms in terms of both objective quality and visual perception, Comment: 4 pages, 5 figures, 2 tables, to be published at IEEE Visual Communications and Image Processing (VCIP) 2012

In this paper, a mode-dependent intra frame interpolation method is proposed for H.264/AVC compressed video. The intra prediction mode information is taken into account in the interpolation filter design. For each intra prediction mode, an optimal Wiener filter is trained based on the representative video sequences. Therefore the trained filter is adaptive to the intra prediction mode. Furthermore, the quantization parameter is also explored as context information for filter selection. Extensive experiments demonstrate that the proposed method achieves better performance than the traditional methods such as Bicubic, Bilinear, LAZA and NEDI, while keeping low computational complexity.