Application and Analysis of Education and Teaching Mode Based on 5G and Smart Technology

The development of information technology has brought tremendous changes to our country’s education. Based on the 5G + Internet, the article proposes a brand-new intelligent education model and proposes an ST analysis method, which mainly studies the teacher and students in the classroom. Class performance, based on Yebes network, proposed a learning decision method and application. The research results of the article show the following: (1) The article compares the monthly test scores of three variables under two different teaching modes. The results show that the performance of the online teaching mode is generally better than that of the traditional teaching mode, and the performance of the experimental class has increased more, with an average growth score of 4.83, indicating that there are significant differences in students’ learning and cognitive abilities under different teaching models. (2) The article compares the students’ knowledge mastery under two different teaching modes. The results show that under the traditional teaching mode, the students’ knowledge mastery is low, and the complete mastery rate is only 15%. In the network multimedia teaching mode, the students’ knowledge mastery rate has been greatly improved, the complete mastery rate is as high as 45%, and the students’ mastery of knowledge has been extremely improved, indicating that the network multimedia teaching mode can stimulate students’ learning interest more, improve learning efficiency. (3) Studying the differences in the source of curriculum resources of three different types of teachers, and the results show that the proportion of curriculum resources downloaded through the Internet is the largest; in the investigation of the impact of multimedia teaching on the classroom, the cooperation rate of students when multimedia teaching is not used, classroom practice accuracy and classroom completion rate are low, but after using multimedia teaching, students’ cooperation rate and classroom practice accuracy rate have been greatly improved, among which the accuracy rate of the experimental class is as high as 62.4%, and the students’ thinking ability is also good. Great improvement.

Cooperating elephants mitigate competition until the stakes get too high

Cooperation is ubiquitous in the animal kingdom as it aims to maximize benefits through joint action. Selection, however, may also favor competitive behaviors that could violate cooperation. How animals mitigate competition is hotly debated, with particular interest in primates and little attention paid thus far to nonprimates. Using a loose-string pulling apparatus, we explored cooperative and competitive behavior, as well as mitigation of the latter, in semi-wild Asian elephants (Elephas maximus). Our results showed that elephants first maintained a very high cooperation rate (average = 80.8% across 45 sessions). Elephants applied “block,” “fight back,” “leave,” “move side,” and “submission” as mitigation strategies and adjusted these strategies according to their affiliation and rank difference with competition initiators. They usually applied a “fight back” mitigation strategy as a sanction when competition initiators were low ranking or when they had a close affiliation, but were submissive if the initiators were high ranking or when they were not closely affiliated. However, when the food reward was limited, the costly competitive behaviors (“monopoly” and “fight”) increased significantly, leading to a rapid breakdown in cooperation. The instability of elephant cooperation as a result of benefit reduction mirrors that of human society, suggesting that similar fundamental principles may underlie the evolution of cooperation across species.

APPLICATION OF DEEP LEARNING

In the last decade, there has been a significant increase in the number of papers related to machine learning and the application of machine learning in various fields of science. Belmonte et al. observed that between 2010 and 2018, the growth in the number of papers related to machine learning topics and big data was exponential. They analysed 4240 scientific publications from the Web of Science citation database [1]. Xu et al., in the analysis of publications in the International Journal of Machine Learning and Cybernetics, noted that from 2010 to 2017, the number of publications, the cooperation rate, the total number of authors, and the degree of cooperation had shown an increasing trend [2]. Dokic et al. analysed the publication of papers in which deep learning is applied in the field of agriculture and noticed that the first papers were published in 2014, and in the second half of the second decade of the 21st century, exponential growth in the number of published papers was observed [3]. The objectives of this paper are primarily to analyze the literature related to the application of deep learning in apple growing, to propose the division of these papers depending on the area, and to analyze the observed trends in publishing papers related to this topic. In the analysis, only papers published in scientific journals were considered, and the condition is that they be found in the citation databases of the Web of Science or Scopus. The second section gives a brief overview of deep learning and its development and a presentation of the importance of apple growing in agriculture. The third section is an overview of papers that use deep learning methods and solve some problems in growing apples. The third section is divided into four parts, depending on the area the paper deals with. The fourth section is a discussion and conclusion.

Payoff-dependence learning ability resolves social dilemmas

Understanding the appearance and maintenance of cooperation behavior is one of the most interesting challenges in natural and social sciences. Evolutionary game is a useful tool to study this issue. Here, we consider a basic strategy updating rule: the probability of a player updating its strategy is affected by the learning ability, which is determined by payoffs and an aspiration parameter [Formula: see text]. For positive [Formula: see text], learning ability is directly proportional to player’s own payoff. When [Formula: see text] equals 0, it returns to traditional situation. It is found that increasing the value of [Formula: see text] can promote the cooperation. With the increase of [Formula: see text], the player’s learning ability is continuously enhanced, and the probability of changing strategies is also increased. This paper verifies the influence of the introduced selection parameter [Formula: see text] on the cooperation rate from different aspects. We tested this hypothesis through the Monte Carlo simulation, and demonstrated that introducing [Formula: see text] changed the network of interaction effectively, therefore changing the effect of the adoption of the strategy on the uncertainty of cooperation evolution. This paper analyzed the results of the payoff-dependence learning ability of different players when they imitate the strategies of their opponents, which can effectively promote the evolution of cooperation.

Linked Method of Open Government Data by Datasets Oriented

In order to make public data more useful, it is necessary to provide relevant data sets that meet the needs of users. We introduce the method of linkage between datasets. We provide a method for deriving linkages between fields of structured datasets provided by public data portals. We defined a dataset and connectivity between datasets. The connectivity between them is based on the metadata of the dataset and the linkage between the actual data field names and values. We constructed the standard field names. Based on this standard, we established the relationship between the datasets. This paper covers 31,692 structured datasets (as of May 31, 2020) among the public data portal datasets. We extracted 1,185,846 field names from over 30,000 datasets. We extracted 1,185,846 field names from over 30,000 datasets. As a result of analyzing the field names, the field names related to spatial information were the most common at 35%. This paper verified the method of deriving the relation between data sets, focusing on the field names classified as spatial information. For this reason, we have defined spatial standard field names. To derive similar field names, we extracted related field names into spaces such as locations, coordinates, addresses, and zip codes used in public datasets. The standard field name of spatial information was designed and derived 43% cooperation rate of 31,692 datasets. In the future, we plan to apply similar field names additionally to improve the data set cooperation rate of the spatial information standard.

Optimal Tag-Based Cooperation Control for the “Prisoner’s Dilemma”

A long-standing problem in biology, economics, and social sciences is to understand the conditions required for the emergence and maintenance of cooperation in evolving populations. This paper investigates how to promote the evolution of cooperation in the Prisoner’s Dilemma game (PDG). Differing from previous approaches, we not only propose a tag-based control (TBC) mechanism but also look at how the evolution of cooperation by TBC can be successfully promoted. The effect of TBC on the evolutionary process of cooperation shows that it can both reduce the payoff of defectors and inhibit defection; although when the cooperation rate is high, TBC will also reduce the payoff of cooperators unless the identified rate of the TBC is large enough. An optimal timing control (OTC) of switched replicator dynamics is designed to consider the control costs, the cooperation rate at terminal time, and the cooperator’s payoff. The results show that the switching control (SC) between an optimal identified rate control of the TBC and no TBC can properly not only maintain a high cooperation rate but also greatly enhance the payoff of the cooperators. Our results provide valuable insights for some clusters, for example, logistics parks and government, to regard the decision to promote cooperation.

Survey Methods of the 2018 International Tobacco Control (ITC) Japan Survey

This paper describes the methods of the Wave 1 (2018) International Tobacco Control (ITC) Japan Survey. The respondents were adults aged 20 years and older in one of four user groups: (1) cigarette-only smokers who smoked at least monthly and used heated tobacco products (HTPs) not at all or less than weekly, (2) HTP-only users who used HTPs at least weekly and smoked cigarettes not at all or less than monthly, (3) cigarette-HTP dual users who smoked at least monthly and used HTPs at least weekly, and (4) non-users who had never smoked or who smoked less than monthly and used HTPs less than weekly. Eligible respondents were recruited by a commercial survey firm from its online panel. Respondents were allocated proportionally to sample strata based on demographic, geographic, and user type specifications benchmarked to a national reference. Survey weights, accounting for smoking/HTP use status, sex, age, education, and geography, were calibrated to benchmarks from a nationally representative survey in Japan. Response rate was 45.1% and cooperation rate was 96.3%. The total sample size was 4615 (3288 cigarette smokers, 164 exclusive HTP users, 549 cigarette-HTP dual users, and 614 non-users). The 2018 ITC Japan Survey sampling design and survey data collection methods will allow analyses to examine prospectively the use of cigarettes and HTPs in Japan and factors associated with the use of both products and of transitions between them.

Smoking Cessation and Vaping Cessation Attempts among Cigarette Smokers and E-Cigarette Users in Central and Eastern Europe

Our aim is to assess the smoking cessation and vaping cessation activity, including quit attempts and willingness to quit among university students in Central and Eastern Europe, as well as to investigate personal characteristics associated with smoking cessation and vaping cessation attempts. Data were collected by questionnaire which included 46 questions on cigarette and e-cigarette use. Questionnaires were obtained from 14,352 university students (aged 20.9 ± 2.4 years; cooperation rate of 72.2%). For the purposes of this analysis, only data from exclusive cigarette smokers (n = 1716), exclusive e-cigarette users (n = 129), and dual users (216) were included. Of all cigarette smokers, 51.6% had previously tried to quit smoking and 51.5% declared a willingness to quit cigarette smoking in the near future. Among all e-cigarette users only 13.9% had ever tried to quit using the e-cigarette and 25.2% declared a willingness to give up using e-cigarette in the near future. The majority of the group did not use pharmacotherapy to quit cigarette (87.5%) or e-cigarette (88.9%) use. Our results indicate that while most university students have some desire to quit conventional smoking, those who use e-cigarettes do not have the same desire.

Parental opinions regarding consent for observational research of no or minimal risk in the pediatric intensive care unit

Background The aim of the study was to determine opinions and knowledge regarding the process of obtaining informed consent to participate in observational research in pediatric intensive care. Methods Survey 1 asked decision makers what model(s) of consent was acceptable for each type of observational research both before and after background information. Survey 2 asked decision makers about the experience of being asked for consent to observational research, and knowledge regarding the consent process both before and after background information. Results Cooperation rate was 100/117 (85%). The proportion in favor of any of the offered alternatives to signed informed consent for observational research, after receiving all the background information, was 74-80%, lowest for observational prospective research with a minimal risk intervention 37/50 (74%; 95% CI 60–84%). The proportion who agreed they felt overwhelmed by being approached for consent to observational research was 26 (52%; 95% CI 39-65%). Most respondents (from 60 to 74%) felt they understood the concepts regarding observational research; however, after reading background information, most (from 60 to 74%) felt their understanding had improved “a great deal”. Conclusion Understanding of risk, practical difficulties, consent bias, and Research Ethics Board safeguards was poor. Future study is needed to confirm our finding that most agreed with alternative methods of consent for observational research.