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In this paper, a new three-dimensional chaotic system is proposed for image encryption. The core of the encryption algorithm is the combination of chaotic system and compressed sensing, which can complete image encryption and compression at the same time. The Lyapunov exponent, bifurcation diagram and complexity of the new three-dimensional chaotic system are analyzed. The performance analysis shows that the chaotic system has two positive Lyapunov exponents and high complexity. In the encryption scheme, a new chaotic system is used as the measurement matrix for compressed sensing, and Arnold is used to scrambling the image further. The proposed method has better reconfiguration ability in the compressible range of the algorithm compared with other methods. The experimental results show that the proposed encryption scheme has good encryption effect and image compression capability.

In the actual image processing process, we often encounter mixed images that contain multiple valid messages. Such images not only need to be transmitted safely, but also need to be able to achieve effective separation at the receiving end. This paper designs a secure and efficient encryption and separation algorithm based on this kind of mixed image. Since chaotic system has the characteristics of initial sensitivity and pseudo-randomness, a chaos matrix is introduced into the compressed sensing framework. By using sequence signal to adjust the chaotic system, the key space can be greatly expanded. In the algorithm, we take the way of parallel transmission to block the data. This method can realize the efficient calculation of complex tasks in the image encryption system and improve the data processing speed. In the decryption part, the algorithm in this paper can not only realize the restoration of images, but also complete the effective separation of images through the improved restoration algorithm.

The network security transmission of digital images needs to solve the dual security problems of content and appearance. In this paper, a visually secure image compression and encryption scheme is proposed by combining compressed sensing (CS) and regional energy. The plain image is compressed and encrypted into a secret image by CS and zigzag confusion. Then, according to the regional energy, the secret image is embedded into a carrier image to obtain the final visual secure cipher image. A method of hour hand printing (HHP) scrambling is proposed to increase the pixel irrelevance. Regional energy embedding reduce the damage to the visual quality of carrier image, and the different embedding positions between images greatly enhances the security of the encryption algorithm. Furthermore, the hyperchaotic multi-character system (MCS) is utilized to construct measurement matrix and control pixels. Simulation results and security analyses demonstrate the effectiveness, security and robustness of the propose algorithm.

The estimation of yield parameters based on early data is helpful for agricultural policymakers and food security. Developments in unmanned aerial vehicle (UAV) platforms and sensor technology help to estimate yields efficiency. Previous studies have been based on less cultivars (<10) and ideal experimental environments, it is not available in practical production. Therefore, the objective of this study was to estimate the yield parameters of soybean (Glycine max (L.) Merr.) under lodging conditions using RGB information. In this study, 17 time point data throughout the soybean growing season in Nanchang, Jiangxi Province, China, were collected, and the vegetation index, texture information, canopy cover, and crop height were obtained by UAV-image processing. After that, partial least squares regression (PLSR), logistic regression (Logistic), random forest regression (RFR), support vector machine regression (SVM), and deep learning neural network (DNN) were used to estimate the yield parameters. The results can be summarized as follows: (1) The most suitable time point to estimate the yield was flowering stage (48 days), which was when most of the soybean cultivars flowered. (2) The multiple data fusion improved the accuracy of estimating the yield parameters, and the texture information has a high potential to contribute to the estimation of yields, and (3) The DNN model showed the best accuracy of training (R 2 =0.66 rRMSE=32.62%) and validation (R 2 =0.50, rRMSE=43.71%) datasets. In conclusion, these results provide insights into both best estimate period selection and early yield estimation under lodging condition when using remote sensing.

Constitutive photomorphogenesis 9 signalosome (CSN) consists of a total of eight subunits (CSN1-CSN8) in mammalian cells. CSN6 may promote carcinogenesis by positively regulating v-myc avian myelocytomatosis viral oncogene homolog (Myc) and MDM2 proto-oncogene stability, and is regarded as a potential target for cancer therapy. Quercetin has a substantial anticancer effect on various human cancer cells. The present study investigated the effects of quercetin on HT-29 human colorectal cancer cell viability, apoptosis and cell cycle arrest using an MTT assay, flow cytometry, transmission electron microscopy and western blotting. It was determined that quercetin inhibited HT-29 cell viability in a dose-dependent manner. Cell shrinkage, chromatin condensation and nuclear collapse were observed in the 50, 100 and 200 µM quercetin groups. The exposure of HT-29 cells to quercetin led to significant cell cycle arrest in the S-phase. Western blot analysis revealed that quercetin reduced the protein expression levels of phosphorylated-Akt and increased CSN6 protein degradation; therefore, affecting the expression levels of Myc, p53, B-cell lymphoma 2 (Bcl-2) and Bcl-2 associated X protein. The overexpression of CSN6 reduced the effect of quercetin treatment on HT-29 cells, suggesting that quercetin-induced apoptosis may involve the Akt-CSN6-Myc signaling axis in HT-29 cells.

Invasion of fibroblast-like synoviocytes (FLSs) is critical in the pathogenesis of rheumatoid arthritis (RA). The metalloproteinases (MMPs) and activator of Toll-like receptor 4 (TLR4)/nuclear factor-κB (NF-κB) pathway play a critical role in RA-FLS invasion induced by lipopolysaccharide (LPS). The present study aimed to explore the anti-invasive activity of celastrol on LPS-stimulated human RA-FLSs, and to elucidate the mechanism involved. We investigated the effect of celastrol on LPS-induced FLS migration and invasion as well as MMP expression and explored the upstream signal transduction. Results showed that celastrol suppressed LPS-stimulated FLS migration and invasion by inhibiting MMP-9 expression and activity. Furthermore, our results revealed that celastrol inhibited the transcriptional activity of MMP-9 by suppressing the binding activity of NF-κB in the MMP-9 promoter, and suppressed the TLR4/MyD88/NF-κB pathway. Administration of celastrol (0.5 mg/kg and 1 mg/kg, intraperitoneally) daily for 3 weeks in a collagen-induced arthritis rat model markedly alleviated the clinical signs, synovial hyperplasia and inflammatory cell infiltration of joints. In conclusion, celastrol might inhibit FLS migration and invasion induced by LPS by suppressing TLR4/NF-κB-mediated MMP-9 expression, providing a theoretical foundation for the clinical treatment of RA with celastrol.

In this paper, a new image encryption transmission algorithm based on the parallel mode is proposed. This algorithm aims to improve information transmission efficiency and security based on existing hardware conditions. To improve efficiency, this paper adopts the method of parallel compressed sensing to realize image transmission. Compressed sensing can perform data sampling and compression at a rate much lower than the Nyquist sampling rate. To enhance security, this algorithm combines a sequence signal generator with chaotic cryptography. The initial sensitivity of chaos, used in a measurement matrix, makes it possible to improve the security of an encryption algorithm. The cryptographic characteristics of chaotic signals can be fully utilized by the flexible digital logic circuit. Simulation experiments and analyses show that the algorithm achieves the goal of improving transmission efficiency and has the capacity to resist illegal attacks.

Bending control is one of the main methods of shape control for the hot rolled plate. However, the existing bending force setting models based on traditional mathematical methods are complex and have low control accuracy, which leads to poor strip exit shapes. Aiming at the problem of complex bending force setting of the traditional algorithm, an improved whale swarm optimization algorithm and twin support vector machine-based bending force model for hot rolled strip steel (LWOA-TSVR) is proposed. Based on the hot rolling field production data of a steel plant, the research group established the bending force prediction model by using the nonlinear approximation ability of the twin support vector machine. The introduction of the Levy flight improvement algorithm improves the generalization ability, prediction accuracy, and convergence speed of the whale swarm optimization algorithm with the help of the convergence of coefficient vectors, solves the problem of a random selection of the parameters of the traditional whale swarm optimization algorithm and optimizes the ability of the whale swarm algorithm to jump out of the local optimum. Based on the actual rolling database, the hit rate of the proposed method reaches 91% (from −5 to 5 KN), which fully meets the requirements of the detection accuracy on the actual production line. The model is not only able to overcome the local search to obtain the global optimal solution, but also has the advantages of fast convergence and higher prediction accuracy. A comparison of the model with twin support vector machines and traditional whale swarm algorithms shows that the prediction accuracy is higher. The experimental results also show that this model has advantages over existing bending force prediction models in terms of improving the accuracy of the strip shape control and providing theoretical guidance for practical bending force settings.

As the main method of information transmission, it is particularly important to ensure the security of speech communication. Considering the more complex multipath channel transmission situation in the wireless communication of speech signals and separating or extracting the source signal from the convolutional signal are crucial steps in obtaining source information. In this paper, chaotic masking technology is used to guarantee the transmission safety of speech signals, and a fast fixed-point independent vector analysis algorithm is used to solve the problem of convolutional blind source separation. First, the chaotic masking is performed before the speech signal is sent, and the convolutional mixing process of multiple signals is simulated by impulse response filter. Then, the observed signal is transformed to the frequency domain by short-time Fourier transform, and instantaneous blind source separation is performed using a fast fixed-point independent vector analysis algorithm. The algorithm can preserve the high-order statistical correlation between frequencies to solve the permutation ambiguity problem in independent component analysis. Simulation experiments show that this algorithm can efficiently complete the blind extraction of convolutional signals, and the quality of recovered speech signals is better. It provides a solution for the secure transmission and effective separation of speech signals in multipath transmission channels.

With the rapid development of information technology in today’s society, the security of transmission and the storage capacity of hardware are increasingly required in the process of image transmission. Compressed sensing technology can achieve data sampling and compression at the rate far lower than that of the Nyquist sampling theorem and can effectively improve the efficiency of information transmission. Aiming at the problem of weak security of compressed sensing, this study combines the cryptographic characteristics of chaotic systems with compressed sensing technology. In the actual research process, the existing image encryption technology needs to be applied to the hardware. This paper focuses on the combination of image encryption based on compressed sensing and digital logic circuits. We propose a novel technology of parallel image encryption based on a sequence generator. It uses a three-dimensional chaotic map with multiple stability to generate a measurement matrix. This study also analyzes the effectiveness, reliability, and security of the parallel encryption algorithm for source noise pollution with different distribution characteristics. Simulation results show that parallel encryption technology can effectively improve the efficiency of information transmission and greatly enhance its security through key space expansion.