Due to the potential security problem about key management and distribution for the symmetric image encryption schemes, a novel asymmetric image encryption method is proposed in this paper, which is ...based on the elliptic curve ElGamal (EC-ElGamal) cryptography and chaotic theory. Specifically, the SHA-512 hash is first adopted to generate the initial values of a chaotic system, and a crossover permutation in terms of chaotic index sequence is used to scramble the plain-image. Furthermore, the generated scrambled image is embedded into the elliptic curve for the encrypted by EC-ElGamal which can not only improve the security but also can help solve the key management problems. Finally, the diffusion combined chaos game with DNA sequence is executed to get the cipher image. The experimental analysis and performance comparisons demonstrate that the proposed method has high security, good efficiency, and strong robustness against the chosen-plaintext attack which make it have potential applications for the image secure communications.
Emotion classification based on brain-computer interface (BCI) systems is an appealing research topic. Recently, deep learning has been employed for the emotion classifications of BCI systems and ...compared to traditional classification methods improved results have been obtained. In this paper, a novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network (CNN), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding. Then the data with reduced redundancy are used as the input features of a DNN for classification task. The public datasets of DEAP and SEED are used for testing. Experimental results show that the proposed network is more effective than conventional CNN methods on the emotion recognitions. For the DEAP dataset, the highest recognition accuracies of 89.49% and 92.86% are achieved for valence and arousal, respectively. For the SEED dataset, however, the best recognition accuracy reaches 96.77%. By combining the CNN, SAE, and DNN and training them separately, the proposed network is shown as an efficient method with a faster convergence than the conventional CNN.
BACKGROUND Risk of coronavirus disease 2019 (COVID-19) hospitalization is robustly linked to cardiometabolic health. We estimated the absolute and proportional COVID-19 hospitalizations in US adults ...attributable to 4 major US cardiometabolic conditions, separately and jointly, and by race/ethnicity, age, and sex. METHODS AND RESULTS We used the best available estimates of independent associations of cardiometabolic conditions with a risk of COVID-19 hospitalization; nationally representative data on cardiometabolic conditions from the National Health and Nutrition Examination Survey 2015 to 2018; and US COVID-19 hospitalizations stratified by age, sex, and race/ethnicity from the Centers for Disease Control and Prevention's Coronavirus Disease 2019-Associated Hospitalization Surveillance Network database and from the COVID Tracking Project to estimate the numbers and proportions of COVID-19 hospitalizations attributable to diabetes mellitus, obesity, hypertension, and heart failure. Inputs were combined in a comparative risk assessment framework, with probabilistic sensitivity analyses and 1000 Monte Carlo simulations to jointly incorporate stratum-specific uncertainties in data inputs. As of November 18, 2020, an estimated 906 849 COVID-19 hospitalizations occurred in US adults. Of these, an estimated 20.5% (95% uncertainty interval UIs, 18.9-22.1) of COVID-19 hospitalizations were attributable to diabetes mellitus, 30.2% (UI, 28.2-32.3) to total obesity (body mass index ≥30 kg/m
), 26.2% (UI, 24.3-28.3) to hypertension, and 11.7% (UI, 9.5-14.1) to heart failure. Considered jointly, 63.5% (UI, 61.6-65.4) or 575 419 (UI, 559 072-593 412) of COVID-19 hospitalizations were attributable to these 4 conditions. Large differences were seen in proportions of cardiometabolic risk-attributable COVID-19 hospitalizations by age and race/ethnicity, with smaller differences by sex. CONCLUSIONS A substantial proportion of US COVID-19 hospitalizations appear attributable to major cardiometabolic conditions. These results can help inform public health prevention strategies to reduce COVID-19 healthcare burdens.
It is a challenge for any optical method to measure objects with a large range of reflectivity variation across the surface. Image saturation results in incorrect intensities in captured fringe ...pattern images, leading to phase and measurement errors. This paper presents a new adaptive digital fringe projection technique which avoids image saturation and has a high signal to noise ratio (SNR) in the three-dimensional (3-D) shape measurement of objects that has a large range of reflectivity variation across the surface. Compared to previous high dynamic range 3-D scan methods using many exposures and fringe pattern projections, which consumes a lot of time, the proposed technique uses only two preliminary steps of fringe pattern projection and image capture to generate the adapted fringe patterns, by adaptively adjusting the pixel-wise intensity of the projected fringe patterns based on the saturated pixels in the captured images of the surface being measured. For the bright regions due to high surface reflectivity and high illumination by the ambient light and surfaces interreflections, the projected intensity is reduced just to be low enough to avoid image saturation. Simultaneously, the maximum intensity of 255 is used for those dark regions with low surface reflectivity to maintain high SNR. Our experiments demonstrate that the proposed technique can achieve higher 3-D measurement accuracy across a surface with a large range of reflectivity variation.
•A novel method is proposed to identify Alzheimer's disease.•An optimized convolutional neural network is developed.•Neural network parameters are reduced for computing efficiency.•Experimental ...results show good performance on accuracy.
To diagnose Alzheimer's disease (AD), neuroimaging methods such as magnetic resonance imaging have been employed. Recent progress in computer vision with deep learning (DL) has further inspired research focused on machine learning algorithms. However, a few limitations of these algorithms, such as the requirement for large number of training images and the necessity for powerful computers, still hinder the extensive usage of AD diagnosis based on machine learning. In addition, large number of training parameters and heavy computation make the DL systems difficult in integrating with mobile embedded devices, for example the mobile phones. For AD detection using DL, most of the current research solely focused on improving the classification performance, while few studies have been done to obtain a more compact model with less complexity and relatively high recognition accuracy. In order to solve this problem and improve the efficiency of the DL algorithm, a deep separable convolutional neural network model is proposed for AD classification in this paper. The depthwise separable convolution (DSC) is used in this work to replace the conventional convolution. Compared to the traditional neural networks, the parameters and computing cost of the proposed neural network are found greatly reduced. The parameters and computational costs of the proposed neural network are found to be significantly reduced compared with conventional neural networks. With its low power consumption, the proposed model is particularly suitable for embedding mobile devices. Experimental findings show that the DSC algorithm, based on the OASIS magnetic resonance imaging dataset, is very successful for AD detection. Moreover, transfer learning is employed in this work to improve model performance. Two trained models with complex networks, namely AlexNet and GoogLeNet, are used for transfer learning, with average classification rates of 91.40%, 93.02% and a less power consumption.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Uterine sarcomas are rare malignant tumors of the uterus with a high degree of malignancy. Their clinical manifestations, imaging examination findings, and laboratory test results overlap with those ...of uterine fibroids. No reliable diagnostic criteria can distinguish uterine sarcomas from other uterine tumors, and the final diagnosis is usually only made after surgery based on histopathological evaluation. Conservative or minimally invasive treatment of patients with uterine sarcomas misdiagnosed preoperatively as uterine fibroids will shorten patient survival. Herein, we will summarize recent advances in the preoperative diagnosis of uterine sarcomas, including epidemiology and clinical manifestations, laboratory tests, imaging examinations, radiomics and machine learning-related methods, preoperative biopsy, integrated model and other relevant emerging technologies.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
There is a great demand for the endogenous development of green finance in the new era. Green bankers can boost the supply of such growth based on bankers’ spirit and green feelings. Apart from the ...common features of entrepreneurship, bankers’ spirit also has some distinctive qualities, including rationality in innovation, awareness of risk management, and social responsibility. Moreover, green bankers possess a special feature—green feelings. They are committed to green finance and are sensitive enough to seize the business opportunities in it. Green bankers can contribute actively to the development of green financial services by grasping prospective development opportunities, implementing product innovation and institutional innovation, and building social networks and cultivating green culture. However, there is insufficient motivation for the development of bankers’ spirit and green feelings in China, and administrators also play a negative role in the cultivation of green bankers. Therefore, it is necessary to cultivate green bankers progressively. First, those with bankers’ spirit are supposed to become bankers. Second, bankers’ green feelings are cultivated. Third, the incentive and restraint mechanisms should be established and improved. Finally, an external environment conducive to the development of green bankers is created.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In this paper, a novel image watermarking method is proposed which is based on discrete wave transformation (DWT), Hessenberg decomposition (HD), and singular value decomposition (SVD). First, in the ...embedding process, the host image is decomposed into a number of sub-bands through multi-level DWT, and the resulting coefficients of which are then used as the input for HD. The watermark is operated on the SVD at the same time. The watermark is finally embedded into the host image by the scaling factor. Fruit fly optimization algorithm, one of the natural-inspired optimization algorithms is devoted to find the scaling factor through the proposed objective evaluation function. The proposed method is compared to other research works under various spoof attacks, such as the filter, noise, JPEG compression, JPEG2000 compression, and sharpening attacks. The experimental results show that the proposed image watermarking method has a good trade-off between robustness and invisibility even for the watermarks with multiple sizes.
Self-oscillation absorbs energy from a steady environment to maintain its own continuous motion, eliminating the need to carry a power supply and controller, which will make the system more ...lightweight and promising for applications in energy harvesting, soft robotics, and microdevices. In this paper, we present a self-oscillating curling liquid crystal elastomer (LCE) beam-mass system, which is placed on a table and can self-oscillate under steady light. Unlike other self-sustaining systems, the contact surface of the LCE beam with the tabletop exhibits a continuous change in size during self-sustaining curling, resulting in a dynamic boundary problem. Based on the dynamic LCE model, we establish a nonlinear dynamic model of the self-oscillating curling LCE beam considering the dynamic boundary conditions, and numerically calculate its dynamic behavior using the Runge-Kutta method. The existence of two motion patterns in the LCE beam-mass system under steady light are proven by numerical calculation, namely self-curling pattern and stationary pattern. When the energy input to the system exceeds the energy dissipated by air damping, the LCE beam undergoes self-oscillating curling. Furthermore, we investigate the effects of different dimensionless parameters on the critical conditions, the amplitude and the period of the self-curling of LCE beam. Results demonstrate that the light source height, curvature coefficient, light intensity, elastic modulus, damping factor, and gravitational acceleration can modulate the self-curling amplitude and period. The self-curling LCE beam system proposed in this study can be applied to autonomous robots, energy harvesters, and micro-instruments.
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Cervical cancer (CC) patients with lymph node metastasis (LNM) have a poor prognosis. Clarification of the detailed mechanisms underlying LNM may provide potential clinical therapeutic targets for CC ...patients with LNM. However, the molecular mechanism of LNM in CC is unclear. In the present study, we demonstrated that fatty acid synthase (FASN), one of the key enzymes in lipid metabolism, had upregulated expression in the CC samples and was correlated with LNM. Moreover, multivariate Cox proportional hazards analysis identified FASN as an independent prognostic factor of CC patients. Furthermore, gain-of-function and loss-of-function approaches showed that FASN promoted CC cell migration, invasion, and lymphangiogenesis. Mechanistically, on the one hand, FASN could regulate cholesterol reprogramming and then activate the lipid raft-related c-Src/AKT/FAK signaling pathway, leading to enhanced cell migration and invasion. On the other hand, FASN induced lymphangiogenesis by secreting PDGF-AA/IGFBP3. More importantly, knockdown of FASN with FASN shRNA or the inhibitors C75 and Cerulenin dramatically diminished LNM in vivo, suggesting that FASN plays an essential role in LNM of CC and the clinical application potential of FASN inhibitors. Taken together, our findings uncover a novel molecular mechanism in LNM of CC and identify FASN as a novel prognostic factor and potential therapeutic target for LNM in CC.
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