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Lufenuron is an effective benzoylurea insecticide that inhibits the synthesis of chitin and regulates the growth of insects. However, little is known about the effects of lufenuron treatment on the development of Spodoptera frugiperda (J. E. Smith). In this study, we assessed the toxicity of lufenuron on S. frugiperda and evaluated the effects of lufenuron treatment on the growth and development of S. frugiperda. The results showed that lufenuron exhibits high insecticidal activity against S. frugiperda, with the LC

An Fe3O4/carboxymethyl cellulose (Fe3O4/CMC) magnetic biosorbent was prepared using the ion-imprinting technology, where La(III) was used as the template ion. The morphology and structure of Fe3O4/CMC were characterized by SEM, FTIR and XRD. It is found that nano Fe3O4 with inverse spinel structure can distribute in CMC and endow the composite with good magnetic properties. The adsorption performance such as adsorption capacity, influence of pH and initial concentration were fully explored. The prepared Fe3O4/CMC is revealed to have good adsorption properties with Qmax of 61.5 mg g−1, in line with the pseudo-second-order kinetic model. When handling the multi-ion coexistence solution of Cu(II), Ni(II) and Cd(II), Fe3O4/CMC shows high selective adsorption for La(III). Meanwhile, cycling experiments find that the adsorption capacity is only slightly reduced (less than 5%) after 5-time reuse. Good adsorption properties, high selectivity and easy recovery give the newly-synthesized Fe3O4/CMC biosorbent broad application potential in the treatment of La(III)-containing wastewater.

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This paper proposes a perceptual hash algorithm based on the combination of Scale-invariant feature transform (SIFT) and cluster analysis (K-means). SIFT is used for extracting local stabilized feature points of images. K-means is used for compressing feature data and obtain image perceptual hash. Assess the similarity between video frame and frame by means of Hamming distance among perceptual hash value. Through the analysis of experimental data, the results show that the algorithm has good robustness and success rate.

Face super-resolution has attracted much attention in recent years. Many algorithms have been proposed. However, these approaches only perform well under the condition that the input is noiseless or has small noise. These existing approaches think the measure of between LR and HR is Euclidean distance. Euclidean distance cannot measure well the condition that the image's pixel values tend to have random fluctuations of varying magnitudes or the image is represented as sparse high-dimensional data. In this paper, we propose a novel sparse representation and Wasserstein distance based face super-resolution approach that seem the measure of between LR and HR as Wasserstein distance. Specifically, we introduce the fused Lasso to the least squares representation of the input LR image in order to obtain a stable sparse representation, especially when the noise level of the input LR image is high. Experiments are carried out on the benchmark FERET face dataset. Visual and quantitative comparisons show that the proposed face super-resolution method achieves comparable performance to the state-of-the-art methods under noiseless condition, and yields superior super-resolution results when the input LR face image is contaminated by strong noise.

Cloud computing security issues are increasingly serious. Behavioral trust is the trust relationship established by the entity based on its past experience of interaction. It can be either direct interaction or indirectly recommended by third parties. User behavior trust assessment using analytic hierarchy process, by collecting historical behavior information to obtain the user's behavior trust evidence, according to the characteristics of trust evidence is divided into different trust attributes which, through the analytic hierarchy process and the relative weight of each trust attribute calculated by the user Behavioral trust value. Combined with IPv6 protocol, using IPv6 extended header, the evaluation value is placed in the header for transmission and verification, making this article practical.

Multi-join query optimization is an important technique for designing and implementing database manage system. It is a crucial factor that affects the capability of database. This paper proposes a new algorithm to solve the problem of multi-join query optimization based on parallel ant colony optimization. In this paper, details of the algorithm used to solve multi-join query optimization problem have been interpreted, including how to define heuristic information, how to implement local pheromone update and global pheromone update and how to design state transition rule. After repeated iteration, a reasonable solution is obtained. Compared with genetic algorithm, the simulation result indicates that parallel ant colony optimization is more effective and efficient.

Digital watermarking technology is of great importance to protect the copyright of the owners and authenticate the security of the media. As digital images are vulnerable to some common attacks during transmission, it is very necessary to design a watermarking algorithm which can resist all kinds of common attacks. Nowadays, most of the watermarking algorithms published rely much on the locations of the pixels for watermark embedding, which results in less robustness. And some algorithms took some measures to increase the ability to resist attacks, but the measures taken limited the algorithm in watermark embedding capacity. In this paper, a robust watermarking algorithm based on convolution neural network (CNN) is proposed. We introduce discrete wavelet transform (DWT) technology and singular value decomposition (SVD) technology, to achieve the embedding process of watermark. The network is established in the spatial domain based on the pixels' relationships of watermark, host image and watermarked image. After that, The pixels of the watermarked image are lightly modified with the network. In addition, some attacks are taken to the watermarked image. Simulation shows that proposed algorithm has good performance, the watermark extracted can be clearly identified.

The Bayesian theory provides a new solution to image super-resolution reconstruction. In view of the poor robustness to noise and motion estimation in the vast majority of superresolution reconstruction algorithms. In this paper, we propose an image super-resolution reconstruction algorithm based on Bayesian representation. In the proposed algorithm, uncharted super-resolution images, motion parameters and unknown model parameters are utilized for modeling in a hierarchical Bayesian framework. We adopt degenerate distribution to derive the estimation of analytic solutions and applied the solutions to the super-resolution reconstruction which also enables the proposed algorithm robust to noises. The experimental results show that the proposed image super-resolution reconstruction algorithm based on Bayesian representation can achieve higher (or similar) performance than the state of-the-art methods.

Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In the beginning of 2017, deep forest is put forward to make up the blank of the decision tree in the field of deep learning. Deep forests have much less parameters than deep neural network and the advantages of higher classification accuracy. In this paper, we propose a novel pedestrian detection approach that combines the flexibility of a part-based model with the fast execution time of a deep forest classifier. In this proposed combination, the role of the part evaluations is taken over by local expert evaluations at the nodes of the decision tree. We first do feature select based on extreme learning machines to get feature sets. Afterwards we use the deep forest to classify the feature sets to get the score which is the results of the local experts. We tested the proposed method with well-known challenging datasets such as TUD and INRIA. The final experimental results on two challenging pedestrian datasets indicate that the proposed method achieves the state-of-the-art or competitive performance.

The pressure meter is used to do field pressure test on the 15 drill holes in the 4 deep overburden layers where high earth-rock dam is planned to be built, totally 102 reasonable experiment data is obtained with the maximum depth of 85.0m and the maximum test pressure of 4MPa, which is a relatively accurate reflection of the engineering mechanical properties of deep overburden layer as well as its distribution rules. According to the comparison between compression modulus, the laboratory compression test and the field pressure meter test results of deep overburden layer are analyzed. It's found that the difference between the two is obvious, and with the increasing depth of overburden layer, the compression modulus value by laboratory test is significantly smaller than that by the field pressure meter test. It's difficult to accurately determine the engineering mechanical properties and the distribution rules of deep overburden layer by the laboratory test. While the pressure meter test, as an effective method to measure the engineering mechanical properties of deep overburden layer, can avoid the loss of structural strength and the influence of scale effect caused by the disturbance of sampling, therefore it can better reflect such characteristics of the overburden layer as complex structural layers, discontinuous rock stratum, alternation of different sizes of gravels, the phenomena of discontinuous grading of open work boulder, the lenses easily to be horizontally aligned or irregular dissection, etc.