Floodpolat6300

In addition, the effects of the Gaussian filtering are better than the self-snake model based image enhancement method, which indicates that the image denoising methods are more suitable for performance improvement of the deep face recognition system rather than the image enhancement techniques under the application of the practical cases.People are exploring new ideas based on artificial intelligent infrastructures for immediate processing, in which the main obstacles of widely-deploying deep methods are the huge volume of neural network and the lack of training data. To meet the high computing and low latency requirements in modeling remote smart tongue diagnosis with edge computing, an efficient and compact deep neural network design is necessary, while overcoming the vast challenge on modeling its intrinsic diagnosis patterns with the lack of clinical data. To address this challenge, a deep transfer learning model is proposed for the effective tongue diagnosis, based on the proposed similar-sparse domain adaptation (SSDA) scheme. Concretely, a transfer strategy of similar data is introduced to efficiently transfer necessary knowledge, overcoming the insufficiency of clinical tongue images. Then, to generate simplified structure, the network is pruned with transferability remained in domain adaptation. Finally, a compact model combined with two sparse networks is designed to match limited edge device. Extensive experiments are conducted on the real clinical dataset. The proposed model can use fewer training data samples and parameters to produce competitive results with less power and memory consumptions, making it possible to widely run smart tongue diagnosis on low-performance infrastructures.The aim of this study is to estimate the effects of some acoustic parameters on thermal lesions of atherosclerotic plaques in high-intensity focused ultrasound (HIFU) fields. A fluid-solid thermal coupling model is presented for describing the temperature elevation and thermal ablation of atherosclerotic plaque. A finite element approach is used to solve the coupling equations in cylindrical coordinates. The model considers the effect of the wall thickness of large arteries. The extent of the tissue lesion is determined by the accumulated thermal lesion with Arrhenius integral equation at each location. The results show the lesion size of atherosclerotic plaque is positively correlated to the excited frequency and acoustic output power with heating time. The computational model indicates HIFU may present a novel option for thermal ablation of atherosclerotic plaques with a completely non-invasive treatment paradigm.Communication is a key element of everyone's life. Nowadays, we most often use internet connections for communication. We use the network to communicate with our family, make purchases, we operate home appliances, and handle various matters related to public administration. Moreover, the Internet enables us to participate virtually in various official meetings or conferences. There is a risk of losing our sensitive data, as well as their use for malicious purposes. In this situation, each connection should be secured with an appropriate security protocol. These protocols also need to be regularly checked for vulnerability to attacks. In this article, we consider a tool that allows to analyze different executions of security protocols as well as simulate their operation. This research enables the gathering of knowledge about the behaviour of the security protocol, taking into account various time parameters (time of sending the message, time of generation confidential information, encryption and decryption times, delay in the network, lifetime) and various aspects of computer networks. The conducted research and analysis of the obtained results enable the evaluation of the protocol security and its vulnerability to attacks. Our approach also assumes the possibility of setting time limits that should be met during communication and operation of the security protocol. We show obtained results on Andrew and Needham Schroeder Public Key protocols examples.Ipomoea cairica (L.) sweets are an invasive weed which has caused serious harm to the biodiversity and stability of the ecosystem. It is very important to accurately and rapidly identifying and monitoring Ipomoea cairica (L.) sweets in the wild for managements taking the necessary strategies to control the Ipomoea cairica (L.) sweets to rapidly grow in the wild. However, current approaches mainly depend on manual identification, which result in high cost and low efficiency. Satellite and manned aircraft are feasible assisting approaches, but the quality of the images collected by them is not well since the ground sampling resolution is low and cloud exists. In this study, we present a novel identifying and monitoring framework and method for Ipomoea cairica (L.) sweets based on unmanned aerial vehicle (UAV) and artificial intelligence (AI). In the proposed framework, we low-costly collected the images with 8256 × 5504 pixels of the monitoring area by the UAV and the collected images are split into more small sub-images with 224 × 224 pixels for identifying model. For identifying Ipomoea cairica (L.) sweets, we also proposed a novel deep convolutional neural network which includes 12 layers. Finally, the Ipomoea cairica (L.) sweets can be efficiently monitored by painting the area containing Ipomoea cairica (L.) sweets. In our experiments, we collected 100 raw images and generated 288000 samples, and made comparison with LeNet, AlexNet, GoogleNet, VGG and ResNet for validating our framework and model. The experimental results show the proposed method is excellent, the accuracy is 93.00% and the time cost is 7.439 s. The proposed method can achieve to an efficient balance between high accuracy and low complexity. Our method is more suitable for the identification of Ipomoea cairica (L.) sweets in the wild than other methods.In this paper, we formulate and analyze an HTLV/HIV dual infection model taking into consideration the response of Cytotoxic T lymphocytes (CTLs). The model includes eight compartments, uninfected CD4+T cells, latent HIV-infected cells, active HIV-infected cells, free HIV particles, HIV-specific CTLs, latent HTLV-infected cells, active HTLV-infected cells and HTLV-specific CTLs. The HIV can enter and infect an uninfected CD4+T cell by two ways, free-to-cell and infected-to-cell. Infected-to-cell spread of HIV occurs when uninfected CD4+T cells are touched with active or latent HIV-infected cells. In contrast, there are two modes for HTLV-I transmission, (ⅰ) horizontal, via direct infected-to-cell touch, and (ⅱ) vertical, by mitotic division of active HTLV-infected cells. We analyze the model by proving the nonnegativity and boundedness of the solutions, calculating all possible steady states, deriving a set of key threshold parameters, and proving the global stability of all steady states. The global asymptotic stability of all steady states is proven by using Lyapunov-LaSalle asymptotic stability theorem. We performed numerical simulations to support and illustrate the theoretical results. In addition, we compared between the dynamics of single and dual infections.In this paper we propose a data driven realization and model order reduction (MOR) for linear fractional-order system (FoS) by applying the Loewner-matrix method. Given the interpolation data which obtained by sampling the transfer function of a FoS, the minimal fractional-order state space descriptor model that matching the interpolation data is constructed with low computational cost. Based on the framework, the commensurate order α of the fractional-order system is estimated by solving a least squares optimization in terms of sample data in case of unknown order-α. In addition, we present an integer-order approximation model using the interpolation method in the Loewner framework for FoS with delay. Finally, several numerical examples demonstrate the validity of our approach.

To improve the understanding of the molecular mechanism of vitiligo is necessary to predict and formulate new targeted gene therapy strategies.

GSE65127, GSE75819, GSE53146 and GSE90880 were collected, and obtained four groups of differentially expressed genes (DEGs) by limma R package. Through weighted gene co-expression network analysis (WGCNA), the co-expression of genes with large variance in GSE65127 and GSE75819 was identified. Enrichment analysis of intersection gene between module genes and DEGs with the same up-regulated or down-regulated in GSE65127 and GSE75819 was performed. In addition, ssGSEA was used to identify the immune infiltration of vitiligo in four datasets.

A total of 3083 DEGs and 16 modules were identified from GSE65127, and 5014 DEGs and 6 modules were screened from GSE75819. Finally, 77 important DEGs were identified. Enrichment analysis showed that 77 DEGs were mainly involved in spliceosome etc. The results of GSVA showed that melanogenesis, Fc gamma R-mediated phagocytosis, leishmaniasis, Wnt pathway and glycolipid metabolism were important KEGG pathways. The genes involved in these pathways were identified as key genes (MARCKSL1, MC1R, PNPLA2 and PRICKLE2). check details The AUC values of MC1R were the highest. Furthermore, different immune cells had different infiltration in vitiligo. There was a high correlation between immune cells and key genes.

MC1R was found as a key gene in vitiligo and involved in the melanogenesis. The immune cells were different infiltration in vitiligo. These results suggested that key genes may be used as markers of vitiligo, and were associated with immune cell, especially MC1R.

MC1R was found as a key gene in vitiligo and involved in the melanogenesis. The immune cells were different infiltration in vitiligo. These results suggested that key genes may be used as markers of vitiligo, and were associated with immune cell, especially MC1R.This research discusses an interesting topic, using artificial intelligence methods to predict the score of powerlifters. We collected the characteristics of powerlifters, and then used the reservoir computing extreme learning machine to build a predictive model. In order to further improve the prediction results, we propose a method to optimize the reservoir computing extreme learning machine using the whale optimization algorithm. Experimental results show that our proposed method can effectively predict the score of powerlifters with the coefficient of determination value is 0.7958 and root-mean-square error of prediction value is 16.73. This provides a reliable basis for experts to judge the results before the competition.With the wide application of unmanned ground vehicles (UGV) in a complex environment, the research on the obstacle avoidance system has gradually become an important research part in the field of the UGV system. Aiming at the complex working environment, a sensor detection system mounted on UGV is designed and the kinematic estimation model of UGV is studied. In order to meet the obstacle avoidance requirements of UGVs in a complex environment, a fuzzy neural network obstacle avoidance algorithm based on multi-sensor information fusion is designed in this paper. MATLAB is used to simulate the obstacle avoidance algorithm. By comparing and analyzing the simulation path of UGV's obstacle avoidance motion under the navigation control of fuzzy controller and fuzzy neural network algorithm, the superiority of the proposed fuzzy neural network algorithm was verified. Finally, the superiority and reliability of the obstacle avoidance algorithm are verified through the obstacle avoidance experiment on the UGV experimental platform.