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In the information age, network security has gradually become a potentially huge problem, especially in the medical field, it is essential to ensure the accuracy and safety of images, and patient information needs to be included with minimal change. Combined log-polar transform (LPT) and discrete cosine transform (DCT), a novel robust watermarking algorithm for medical images, is proposed. It realized the lossless embedding of patient information into medical images. In the process of feature extraction and watermark embedding, the proposed algorithm reflects the characteristics of LPT, scale invariance and rotation invariance, and retains the advantages of DCT's ability to resist conventional attacks and robustness. As it adopts zero-watermarking embedding technology, it solves the defects caused by the traditional watermark embedding technology to modify the original image data and guaranteed the quality of medical images. The good experimental results show the effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]

Studying the progress and trend of the novel coronavirus pneumonia (COVID-19) transmission mode will help effectively curb its spread. Some commonly used infectious disease prediction models are introduced. The hybrid model is proposed, which overcomes the disadvantages of the logistic model's inability to predict the number of confirmed diagnoses and the drawbacks of too many tuning parameters of the SEIR (Susceptible, Exposed, Infectious, Recovered) model. The realization and superiority of the prediction of the proposed model are proven through experiments. At the same time, the influence of different initial values of the parameters that need to be debugged on the hybrid model is further studied, and the mean error is used to quantify the prediction effect. By forecasting epidemic size and peak time and simulating the effects of public health interventions, this paper aims to clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviours are critical to slow down the epidemic. [ABSTRACT FROM AUTHOR]

In the continuous development of computer network technology, multimedia technology and information technology, digitization has become the main means of displaying information, thus facilitating the storage, copying and dissemination of digital multimedia information. In this context, there are no restrictions on arbitrary editing, copying, modification, and dissemination of digital images, music, etc., which leads to various social problems such as information security, copyright disputes, and piracy. With the advancement of networks and multimedia, digital watermarking technology has received worldwide attention as an effective method of copyright protection. Improving the anti-geometric attack ability of digital watermarking algorithms using image feature-based algorithms have received extensive attention. This paper proposes a novel robust watermarking algorithm based on SURF-DCT perceptual hashing (Speeded Up Robust Features and Discrete Cosine Transform), namely blind watermarking. The algorithm firstly uses the affine transformation with a feature matrix and chaotic encryption technology to preprocess the watermark image, enhance the confidentiality of the watermark, and perform block and DCT coefficients extraction on the carrier image, and then uses the positive and negative quantization rules to modify the DCT coefficients. The embedding of the watermark is completed, and the blind extraction of the watermark realized. Experiments show that the algorithm has good invisibility and strong robustness against conventional and geometric attacks and can effectively protect the security of images with NC value more than 90%. [ABSTRACT FROM AUTHOR]

• A novel zero-watermarking technique for medical images based on a ResNet101 depth residual neural network is presented here. • The trained CNN extracts the image features, resulting in the output of the full connection layer (fc_1000). • Subsequently, the profound characteristics undergo additional conversions through DWT and DCT transforms, creating a feature vector via a hash function. • In the image authentication phase, numerous processes use the same technique to recover the watermark data. • The results of the tests show that the scheme is reliable, robust, and safe and can successfully extract watermark data. This paper suggests a way to move forward with medical images based on the discrete wavelet transform (DWT), ResNet101-DCT (discrete cosine transform), and zero watermarking. In addition, we extract deep medical image attributes using ResNet101, a pre-training network. Furthermore, deep features undergo a transformation via DWT and DCT, then a perceptual hash function is employed to generate the feature vector. The watermark is encrypted by chaotic scrambling the original watermark, an exclusive-OR (XOR) gate into the medical image, and generating and storing the logical key vector. Overall, the feature extraction technique is employed to extract the significant characteristics of the medical image under testing and develop the feature vector. Finally, the feature vector is subjected to an XOR maneuver with the logical key vector, deriving the encrypted watermark. Once the encrypted watermark has been obtained and decrypted, the restored watermark can be used to determine the medical image's ownership and watermark information by calculating the normalized correlation (NC) coefficient. Most NC values are greater than 0.50. The algorithm also shows good robustness against conventional and geometric attacks. [Display omitted] [ABSTRACT FROM AUTHOR]

With the continuous progress and development in the field of Internet technology, the area of medical image processing has also developed along with it. Specially, digital watermarking technology plays an essential role in the field of medical image processing and greatly improves the security of medical image information. A medical image watermarking algorithm based on an accelerated‐KAZE discrete cosine transform (AKAZE‐DCT) is proposed to address the poor robustness of medical image watermarking algorithms to geometric attacks, which leads to low security of the information contained in medical images. First, the AKAZE‐DCT algorithm is used to extract the feature vector of the medical image and then combined with the perceptual hashing technique to obtain the feature sequence of the medical image; then, the watermarking image is encrypted with logistic chaos dislocation to get the encrypted watermarking image, which ensures the security of the watermarking information; finally, the watermarking is embedded and extracted with the zero‐watermarking technique. The experimental results show that the algorithm can effectively extract the watermark under conventional and geometric attacks, reflecting better robustness and invisibility, and has certain practicality in the medical field compared with other algorithms. [ABSTRACT FROM AUTHOR]

Magnetic fluid (MF) is a kind of magnetically controlled colloid, which may serve as an active medium in support and lubrication systems. This paper presents a design of micro magnet arrays for MF lubrication associated with fluid/gas supports. By using electrochemical machining and electroplating techniques, thick micro magnet arrays with sheet and ring cells were fabricated in 316 stainless steel substrate. The support and lubrication behaviors of MF absorbed on the magnet arrays were tested and compared. Results confirm that MF fixed on the magnet arrays possesses a certain load carrying capacity. Due to the supporting capacity, low friction coefficient of 0.003 can be achieved. Under high load condition, the magnetic surfaces only show friction reduction at the higher speeds. • Magnetic fluid is magnetically controlled colloid served as lubricant. • It can generate a magnetostatic force under an external magnetic field. • Micro magnet arrays were fabricated on a rubbing surface. • Support capacity of magnetic fluid covered on the magnet arrays was investigated. • Ultra low friction coefficient can be achieved due to the magnetic fluid support. [ABSTRACT FROM AUTHOR]

To solve the problem of poor robustness of existing traditional DCT‐based medical image watermarking algorithms under geometric attacks, a novel deep learning‐based robust zero‐watermarking algorithm for medical images is proposed. A Residual‐DenseNet is designed, which took low‐frequency features after discrete cosine transformation of medical images as labels and applied skip connections and a new objective function to strengthen and extract high‐level semantic features that can effectively distinguish different medical images and binarise them to get robust hash vectors. Then, these hash vectors are bound with the chaotically encrypted watermark to generate the corresponding keys to complete the generation of watermark. The proposed algorithm neither modified the original medical image in the watermark generation stage nor required the original medical image in the watermark extraction stage. Moreover, the proposed algorithm is also suitable for multiple watermarks. Experimental results show that the proposed algorithm has good robust performance under both conventional and geometric attacks. [ABSTRACT FROM AUTHOR]

Contaminationof heavy metals from mill tailings is a global problem.China’s Jinchuan Group Limited has stored approximately onebillion tons of mill tailings which contain toxic levels of copper,nickel, and other metal compounds. To solve the problem, this workextended the principle of hydrometallurgical extraction to the recoveryof copper/nickel from Jinchuan tailings and designed an environmental-friendlyrecovering process. Synergetic leaching by sulfuric acid and ferricion was proposed to achieve the efficient leach of copper, nickel,and other metals in Jinchuan tailings. Novel silica-based 2-aminomethylpyridineand iminodiacetic acid functionalized adsorbent were used to selectivelyrecover copper and nickel from the leachate of Jinchuan tailings,respectively. The stable adsorption capacity on semi-industrial scaleoperation after 700 continuous adsorption-regeneration runs promisesthe achievement of copper and nickel recovery from Jinchuan tailings. [ABSTRACT FROM AUTHOR]

Terrestrial laserscanning (TLS), also called ground-based LiDAR (Light Detection And Ranging) is a relatively new method which revolutionised geomorphological research in many domains. However, detailed studies of tidal flats by TLS have not been described in the literature yet. This study aims to fill this methodological gap by the application of TLS at two different locations on the coast of Jiangsu Province, Eastern China, and an assessment of the usability of this method for geomorphological research in such environments. The acquired point clouds are first processed to remove erroneous and noisy points. Subsequently, point clouds are computed to produce polygonal meshes and grid-based digital terrain model (DTM) more commonly used by the scientific community. The accuracy of the measurements is assessed by an analysis of elevation deviations for flat and horizontal concrete blocks. High quality point clouds with point densities of up to 4,000 points/m 2 were acquired for a distance of up to 200 m. The data allowed for the detection of small landforms such as tidal channels, creeks and ripples in centimetre and decimetre scale. The point clouds had an average error of approximately 3 mm, however for some few points errors of up to 1.8 cm were detected. Based on the results it can be concluded that TLS can be a useful additional method for geomorphological research on tidal flats due to its ability to describe the landforms from high density point clouds. Repeated scanning could therefore provide data to quantitatively and qualitatively describe geomorphological changes over wider areas and thereby improve the understanding of sedimentation and erosion on tidal flats. [ABSTRACT FROM AUTHOR]

A silica-based 2-aminomethylpyridine functionalized adsorbentwassynthesized to extract copper. Characterization of the adsorbent wascarried out to determine the surface morphology, specific surfacearea, pore size, and grafting ratio of 2-aminomethylpyridine groups.The adsorption selectivity of the adsorbent for Cu(II) ions was 1353times higher than that for Fe(III) ions. The adsorption capacity forCu(II) ions maintained a stable value of 0.53 mmol/g with and withoutperturbation of Fe(III) ions. Density functional theory (DFT) calculationsrevealed a mechanism of selective adsorption of copper, through theformation of 2-aminomethylpyridine-Cu(II) complexes. Semi-industrialscale experiments revealed that Cu(II) ions in the acidic leachateof low-grade copper ore could be efficiently extracted by the adsorbenteven in perturbation of substantial amounts of Fe(III) and other metalions. Based on the adsorbent, a new process for copper hydrometallurgicalextraction was developed, in which the adverse environmental impactsin traditional solvent extraction processes could be avoided. [ABSTRACT FROM AUTHOR]