Notes

Label-free single-cell solitude allowed simply by microfluidic effect printing as well as real-time cell phone reputation.
Finally, two object detection models with and without our data augmentation method are applied to verify the effectiveness of our method. The experimental results demonstrate that our method can achieve the data augmentation for prohibited item X-ray pseudocolor images in X-ray security inspection effectively.With the increasing complexity, scale, and intelligentization of modern equipment, the maintenance cost of equipment is increasing day by day. Moreover, once an unexpected major failure occurs, it will cause loss and damage to production, economy, and safety. Based on the considerations of system reliability and safety, fault prediction has gradually become a hot topic in the field of reliability. As a new branch of machine learning, deep learning realizes deep abstract feature extraction and expression of complex nonlinear relations by stacking deep neural networks and makes its methods solve bad problems in many traditional machine learning fields. The improvement and excellent results have been achieved. This article first introduces the model structure and working principle of the classic deep learning model noise reduction autoencoder and combines the feature extraction results of the experimental data of electromechanical sensor equipment and the model characteristics to analyze that this type of model failure.With the gradual expansion of the book logistics market and the year-on-year increase in book publications, the incidence of book reverse logistics continues to increase, and the problem of book companies' inventory backlog has become increasingly prominent. To effectively alleviate the current backlog of book returns and exchanges, this paper constructs a two-party game model of "book publisher-book retailer," analyzes the evolution process of book publishers and book retailers' participation strategies and the influence of parameter changes on stable strategies through theoretical analysis and numerical simulation, and draws the following conclusions. (1) Whether book publishers and book retailers choose to participate in the reverse logistics optimization of book returns and exchanges is closely related to their benefits and costs, and it also depends on whether the other party participates in the reverse logistics optimization of books. (2) When the cost of participating in book reverse logistics reaches a certain condition, the probability of both parties participating in the optimization is the greatest.Understanding cross-domain traffic scenarios from multicamera surveillance network is important for environmental perception. Most of existing methods select the source domain which is most similar to the target domain by comparing entire domains for cross-domain similarity and then transferring the motion model learned in the source domain to the target domain. The cross-domain similarity between overall different scenarios with similar local layouts is usually not utilized to improve any automatic surveillance tasks. However, these local commonalities, which may be shared across multiple traffic scenarios, can be transferred across scenarios as prior knowledge. To address these issues, we present a novel framework for cross-domain traffic scene understanding by integrating deep learning and topic model. This framework leverages the labeled samples with activity attribute labels from the source domain to annotate the target domain, where each label represents the local activity of some objects in the scene. When labeling the activity attributes of the target domain, there is no need to select the source domain, which avoids the phenomenon of performance degradation or even negative transfer due to wrong source domain selection. The effectiveness of the proposed framework is verified by extensive experiments carried out using public road traffic data.The aim of this study is to discover the impact of software security and e-mail security on overall cybersecurity among the students of Imam Abdulrahman Bin Faisal University in Dammam. Another main purpose to conduct this study is to know the level of knowledge students have in the developing countries about the cybersecurity and how much are they mindful of cyber-attacks and the level of awareness among the university students. Two important hypotheses were studied to discover their importance in awareness of cybersecurity. One is software security, and the other is e-mail security. A total of 11 relevant questions were drafted, and then these questions were distributed among the university students, and around 390 responded to the questionnaires. Statistical analysis was performed on the responses using tools. Initial tests such as validity and reliability test, feasibility test of a variable, correlation test, multicollinearity test, multiple regression, and Heteroskedasticity test were conducted using SPare or application updates. Furthermore, students' awareness of email security is also good.Hypersoft set is a novel area of interest which is able to tackle the real-world scenarios where classification of parameters into their respective sub-parametric values in the form of overlapping sets is mandatory. It employs a new approximate mapping which considers such sets in the form of sub-parametric tuples as its domain. The existing soft set-like structures are insufficient to tackle such kind of situations. This research intends to establish a novel concept of parameterization of fuzzy set under hypersoft set environment with uncertain components of intuitionistic fuzzy set and neutrosophic set. Two novel structures, i.e., fuzzy parameterized intuitionistic fuzzy hypersoft set (fpifhs-set) and fuzzy parameterized neutrosophic hypersoft set (fpnhs-set), are developed by employing algebraic techniques like theoretic, analytical, pictorial, and algorithmic techniques. After characterizing the elementary properties and set-theoretic operations of fpifhs-set and fpnhs-set, two novel algorithms are proposed to solve real-life decision-making COVID-19 problem. The results of both algorithms are compared with related already established models through certain evaluating features to judge the advantageous aspects of the proposed study. The generalization of the proposed models is discussed by describing some of their particular cases.In the past few years, the Internet has become more and more popular, and more and more people use the Internet. A lot of new software has been developed on the Internet, so the network traffic has increased rapidly. A company once clearly stated that, in the future, the network volume in our country will reach nearly 5 zb. In addition, the network is comprehensively unified, and the learning of the Internet is strengthened by the way of suggesting errors. It is also necessary to learn from the external environment to make the finite value of the network as good as possible. Reinforcement learning is about solving many difficult problems. Use reinforcement learning to promote and change this state, so that the advantages of the network can be fully developed. Regarding the environment and actions at the time as the basic mapping requirements, and the network technology strategy can be best developed. In order to solve the QoS optimization scheme of the current mainstream heuristic algorithm in the software-defined network scene, the software-defined network QoS optimization algorithm is proposed. First, the network resources and status information are unified into the network model, and then, the long- and short-term memory network is used to improve the algorithm's flow perception ability. Finally, based on the deep reinforcement learning algorithm, a dynamic traffic scheduling strategy that satisfies the QoS objective is constructed. Among them, QoS refers to the main functions of the entire network system, especially for some new users, but for many old users, this system can represent a certain application, for example, whether a certain software can get timely, whether it can work smoothly when processing videos, and whether it can be interrupted when making a voice call for improvement.This paper focuses on the phenomenon of "big data killing" implied in e-commerce and discusses how to take the government as the lead to coordinately supervise the price discrimination behavior of e-commerce companies towards loyal customers. First, the four-party evolutionary game model of the government regulatory department, e-commerce platform, e-commerce company, and consumer is built. Second, the stability of the strategy choice of each game subject is analyzed. On this basis, the evolutionary stable strategy in the system based on First Law of Lyapunov is explored. Finally, the influences of key elements on system evolution are simulated and analyzed by MATLAB2021. Results demonstrate that (1) the government supervision mechanism can effectively supervise the price discrimination of e-commerce company based on big data to loyal customers; (2) when the government chooses the strict supervision strategy, reducing the information supervision cost of the e-commerce platform and the strict supervision cost of the government enable the government and the e-commerce platform to coordinate supervision and make the e-commerce company incline to choose the nondifferential pricing strategy; (3) when the government chooses the loose supervision strategy, reducing the information supervision cost of the e-commerce platform and increasing the probability of consumer discovering differential pricing and the penalties for differential pricing of e-commerce company enable the e-commerce platform and consumer to coordinate supervision, and make the e-commerce company incline to choose the nondifferential pricing strategy. The results of this study can provide theoretical guidance for the government and companies to make beneficial strategic decisions in the development of e-commerce.With the rapid development of multimedia and Internet technology, English news text summary technology has received widespread attention as a way to quickly obtain English news text content. The existing method of English news text summarization based on graph model usually takes English news text as the vertices of the graph, and the relationship between the two vertices is represented by the edge. Although it has achieved good results, it cannot quickly sort out the English Complex relationships between news texts. In order to solve this problem, this paper uses the hypergraph model to model the relationship between English news text, and conducts in-depth research on the application of the hypergraph model in the field of English news text summarization. The high popularity of the Internet has brought about earth-shaking changes to the news industry, which makes the news on the Internet a great way for netizens to get news. However, the public cannot pick out satisfactory events from a large amount of news. In order to solve this problem, news event discovery technology that can help users quickly discover and understand hot news is produced. In addition, user personalized recommendation technology will rely on customer operation habits to provide customers with hot events of interest. The personalized news recommendation method adopted in this article has the advantages of integrating discovery and personalized news recommendation, and provides users with a better experience. KYA1797K supplier Compared with the traditional hierarchical clustering algorithm, the algorithm proposed in this paper significantly improves the accuracy.
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