Exploration of the Construction and Path Selection of the Deep Integration Model of Industry-Education in Universities

Against the backdrop of continuous development in the field of education,universities are encouraged to innovate their talent cultivation systems and objectives.The deep integration of industry and education has emerged as an effective strategy,aligning with the basic requirements of the new engineering education initiative and exerting a positive impact on socioeconomic development.However,an analysis of the current state of industry-education integration in universities reveals several issues that require optimization,affecting the ultimate effectiveness of integration.To optimize this phenomenon and achieve high-quality development,universities need to further explore the construction of a deep integration model of industry and education,adhering to corresponding principles to form a comprehensive system.On this basis,pathways for deep industry-education integration can be summarized.

Exploration of Innovation Strategy in the Deep Integration of Information Technology and Education and Teaching

The rapid development of information technology provides a new opportunity and impetus for the reform of education and teaching.Deep integration of information technology and education and teaching is the only way to promote the modernization of education.Starting from the connotation of the deep integration of information technology and education and teaching,this paper analyzes the existing problems in the current integration process,and puts forward innovative strategies from the aspects of concept,resources,model,and evaluation.Through measures such as building a smart teaching environment,enriching high-quality teaching resources,innovating teaching organization models,and establishing multiple evaluation systems,we will realize the deep integration of information technology and teaching,promote the reform of teaching and learning methods,improve the quality of personnel training,and provide strong support for the modernization of education.

Research on the Deep Integration Mechanism of National Fitness Campaign and College Physical Education Teaching:Taking Guangdong University of Science and Technology as an Example

By using the methods of literature review,questionnaire survey,and expert interview,this paper discusses and analyzes the deep integration mechanism of national fitness campaign and college physical education teaching,in order to promote the development of college physical education teaching reform,expand ideas,explore new fields,and provide novel development directions.The purpose is to enhance the national physical quality,improve the public service system,enhance the health awareness of the citizens,and provide guidelines for promoting the development of sports.

Query Translation on the Fly in Deep Web Integration

To facilitate users to access the desired information, many researches have dedicated to the Deep Web （i.e. Web databases） integration. We focus on query translation which is an important part of the Deep Web integration. Our aim is to construct automatically a set of constraints mapping rules so that the system can translate the query from the integrated interface to the Web database interfaces based on them. We construct a concept hierarchy for the attributes of the query interfaces, especially, store the synonyms and the types （e.g. Number, Text, etc.） for every concept At the same time, we construct the data hierarchies for some concepts if necessary. Then we present an algorithm to generate the constraint mapping rules based on these hierarchies. The approach is suitable for the scalability of such application and can be extended easily from one domain to another for its domain independent feature. The results of experiment show its effectiveness and efficiency.

Development Strategies for Z County E-sports Town in Chongqing:Integration of E-sports,Tourism,and Agriculture under New Policies

Chongqing Z County E-sports Town was once included in the“first batch of sports and leisure characteristic town pilot project list”issued by the General Administration of Sports of China.After several years of development,the e-sports industry in Z County e-sports town has struggled with issues such as a limited market,low popularity,poor business performance,and a lack of income generation capabilities.These challenges have severely hindered the town’s development,resulting in its exclusion from the recent“Chongqing characteristic town list.”Under the requirements of new policies,it can no longer promote itself,attract investment,or undertake project construction under the name of a“characteristic town”and must undergo redevelopment.This paper employs case analysis and comparative investigation methods to conduct an in-depth empirical study on the development of Z County E-sports Town.It is proposed that under the new policy environment,the town should foster the deep integration of its existing e-sports industry and culture with tourism and agriculture,transforming Z County E-sports Town into a new characteristic tourist destination.The findings of this paper offer a reference for the development of similar towns facing stagnation and provide case support for the construction of tourist destinations featuring e-sports.

Xue Ling:Culture-Tourism Integration to Deepen China-UK People-to-People Ties

Xue Ling has been long engaged in international cultural and arts exchanges for decades.In February 2019,she assumed office as director of China National Tourist Office(CNTO),London,embarking on a new career to increase China-UK cultural exchanges and promote China's tourist resources.

Ontology-based schema matching method in web query interface integration

Deep web data integration needs to do schema matching on web query interfaces and obtain the mapping table.By introducing semantic conflicts into web query interface integration and discussing the origins and categories of the semantic conflicts,an ontology-based schema matching method is proposed.The process of the method is explained in detail using the example of web query interface integration in house domain.Conflicts can be detected automatically by checking semantic relevance degree,then the categories of the conflicts are identified and messages are sent to the conflict solver,which eliminates the conflicts and obtains the mapping table using conflict solving rules.The proposed method is simple,easy to implement and can be flexibly reused by extending the ontology to different domains.

Digital Twin for Integration of Design-Manufacturing-Maintenance:An Overview

Traditional design,manufacturing and maintenance are run and managed independently under their own rules and regulations in an increasingly time-and-cost inefective manner.A unifed platform for efcient and intelligent designmanufacturing-maintenance of mechanical equipment and systems is highly needed in this rapidly digitized world.In this work,the defnition of digital twin and its research progress and associated challenges in the design,manufacturing and maintenance of engineering components and equipment were thoroughly reviewed.It is indicated that digital twin concept and associated technology provide a feasible solution for the integration of design-manufacturingmaintenance as it has behaved in the entire lifecycle of products.For this aim,a framework for information-physical combination,in which a more accurate design,a defect-free manufacturing,a more intelligent maintenance,and a more advanced sensing technology,is prospected.

Distributed optimization of electricity-Gas-Heat integrated energy system with multi-agent deep reinforcement learning

The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES)has the characteristics of strong coupling,non-convexity,and nonlinearity.The centralized optimization method has a high cost of communication and complex modeling.Meanwhile,the traditional numerical iterative solution cannot deal with uncertainty and solution efficiency,which is difficult to apply online.For the coordinated optimization problem of the electricity-gas-heat IES in this study,we constructed a model for the distributed IES with a dynamic distribution factor and transformed the centralized optimization problem into a distributed optimization problem in the multi-agent reinforcement learning environment using multi-agent deep deterministic policy gradient.Introducing the dynamic distribution factor allows the system to consider the impact of changes in real-time supply and demand on system optimization,dynamically coordinating different energy sources for complementary utilization and effectively improving the system economy.Compared with centralized optimization,the distributed model with multiple decision centers can achieve similar results while easing the pressure on system communication.The proposed method considers the dual uncertainty of renewable energy and load in the training.Compared with the traditional iterative solution method,it can better cope with uncertainty and realize real-time decision making of the system,which is conducive to the online application.Finally,we verify the effectiveness of the proposed method using an example of an IES coupled with three energy hub agents.

Tasks-Oriented Joint Resource Allocation Scheme for the Internet of Vehicles with Sensing, Communication and Computing Integration

With the development of artificial intelligence(AI)and 5G technology,the integration of sensing,communication and computing in the Internet of Vehicles(Io V)is becoming a trend.However,the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management problems.In view of the above challenges,this paper proposes a tasks-oriented joint resource allocation scheme(TOJRAS)in the scenario of Io V.First,this paper proposes a system model with sensing,communication,and computing integration for multiple intelligent tasks with different requirements in the Io V.Secondly,joint resource allocation problems for real-time tasks and delay-tolerant tasks in the Io V are constructed respectively,including communication,computing and caching resources.Thirdly,a distributed deep Q-network(DDQN)based algorithm is proposed to solve the optimization problems,and the convergence and complexity of the algorithm are discussed.Finally,the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme,compared to the existing ones.The exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%,and our proposed resource allocation scheme improves the m AP performance by about 0.15 under resource constraints.

Analysis of BIM application relationship with integration degree of construction environment

As the world pays more attention to low-earbon and sustainable development,there's a growing trend in architecture,engineering and construction fields to construct greener,more energy efficient and more sustainable buildings with the help of building information modeling(BIM).As BIM application becomes popular in industry,more and more scholars and engineers point out that an integrated construction environment is better for BIM's fully functional capability.This paper illustrates the relationship between BIM application and an integrated construction environment based on case survey data.It is found that even though an integration construction environment is not necessary in BIM application,the statistical results showed that DBB(design-bid-build)and CM(construction management)modes have great differences in BIM's application implementation scope and implementation deepness,while DB and CM modes do not.

Numerical Simulation of Installation Process and Uplift Resistance for An Integrated Suction Foundation in Deep Ocean

A concept design, named integrated suction foundation, is proposed for a tension leg platform（TLP） in deep ocean. The most important improvement in comparing with the traditional one is that a pressure-resistant storage module is designed. It utilizes the high hydrostatic pressure in deep ocean to drive water into the module to generate negative pressure for bucket suction. This work aims to further approve the feasibility of the concept design in the aspect of penetration installation and the uplift force in-place. Seepage is generated during suction penetration, and can have both positive and negative effects on penetration process. To study the effect of seepage on the penetration process of the integrated suction foundation, finite element analysis（FEA） is carried out in this work. In particular, an improved methodology to calculate the penetration resistance is proposed for the integrated suction foundation with respect to the reduction factor of penetration resistance. The maximum allowable negative pressure during suction penetration is calculated with the critical hydraulic gradient method through FEA. The simulation results of the penetration process show that the integrated suction foundation can be installed safely. Moreover, the uplift resistance of the integrated suction foundation is calculated and the feasibility of the integrated suction foundation working on-site is verified. In all, the analysis in this work further approves the feasibility of the integrated suction foundation for TLPs in deep ocean applications.

Device Physics Research for Submicron and Deep Submicron Space Microelectronics Devices and Integrated Circuits

Device physics research for submicron and deep submicron space microelectronics devices and integrated circuits will be described in three topics.1.Thin film submicron and deep submicron SOS / CMOS devices and integrated circuits.2.Deep submicron LDD CMOS devices and integrated circuits.3.C band and Ku band microwave GaAs MESFET and III-V compound hetrojunction HEM T and HBT devices and integrated circuits.

MUS Model:A Deep Learning-Based Architecture for IoT Intrusion Detection

In the face of the effective popularity of the Internet of Things(IoT),but the frequent occurrence of cybersecurity incidents,various cybersecurity protection means have been proposed and applied.Among them,Intrusion Detection System(IDS)has been proven to be stable and efficient.However,traditional intrusion detection methods have shortcomings such as lowdetection accuracy and inability to effectively identifymalicious attacks.To address the above problems,this paper fully considers the superiority of deep learning models in processing highdimensional data,and reasonable data type conversion methods can extract deep features and detect classification using advanced computer vision techniques to improve classification accuracy.TheMarkov TransformField(MTF)method is used to convert 1Dnetwork traffic data into 2D images,and then the converted 2D images are filtered by UnsharpMasking to enhance the image details by sharpening;to further improve the accuracy of data classification and detection,unlike using the existing high-performance baseline image classification models,a soft-voting integrated model,which integrates three deep learning models,MobileNet,VGGNet and ResNet,to finally obtain an effective IoT intrusion detection architecture:the MUS model.Four types of experiments are conducted on the publicly available intrusion detection dataset CICIDS2018 and the IoT network traffic dataset N_BaIoT,and the results demonstrate that the accuracy of attack traffic detection is greatly improved,which is not only applicable to the IoT intrusion detection environment,but also to different types of attacks and different network environments,which confirms the effectiveness of the work done.

Towards Integrated Testing Approach: An Application of Cognitive Science and Deep Learning Principle

The use of multiple-choice(MC)question types has been one of the most contentious issues in language testing.Much has been said and written about the use of MC over the years.However,no attempt has ever been made to introduce any innovation in test item types.The researchers proposed a jumbled words test item(JW)based on cognitive science and deep learning principles,and addressed the feasibility of replacing the type of multiple-choice(MC)question with JW to meet the ongoing rapid development of language testing practice.Two research questions were proposed ad hoc,focusing on the co-relationship between JW and MC scores.RASCH-GZ was used to perform item analyses(Rasch,1960).The item difficulty parameters thus obtained were used to compare the two different test items.The sample data metric includes 40 Chinese participants.The findings revealed that correlation analysis revealed that the performance of the same group of subjects taking both JW and MC was not relevant(Pearson Corr=0).This is primarily due to the total elimination of guessing factors inherent in test-takers during JW test performance.Three factors were specified for the design of the JW test:compute program,test difficulty,and score acceptability.These all have three dimensions.Data collected through questionnaires were analyzed using EFA in SPSS V.24.0.KMOs(=0.867)were found to be approximately one and significance at 0.000(0.05),indicating that the construct of theuestionnaire thus designed has better validity for factor analysis.Three important conclusions were obtained,the implications of which could provide impetus for our testing counterparts to practice more precisely and correctly,potentially reshaping our overall language testing practice.Limitations and recommendations for future research were also discussed.

Deep Web数据集成研究综述

随着World Wide Web(WWW)的飞速发展,Deep Web中蕴含了海量的可供访问的信息,并且还在迅速地增长.这些信息要通过查询接口在线访问其后端的Web数据库.尽管丰富的信息蕴藏在Deep Web中,由于Deep Web数据的异构性和动态性,有效地把这些信息加以利用是一件十分挑战性的工作.Deep Web数据集成至今仍然是一个新兴的研究领域,其中包含有若干需要解决的问题.总体来看,在该领域已经开展了大量的研究工作,但各个方面发展并不均衡.文中提出了一个Deep Web数据集成的系统架构,依据这个系统架构对Deep Web数据集成领域中若干关键研究问题的现状进行了回顾总结,并对未来的研究发展方向作了较为深入的探讨分析.

一种基于语义及统计分析的DeepWeb实体识别机制

分析了常见的实体识别方法,提出了一种基于语义及统计分析的实体识别机制(deep Web entity identification mechanism based on semantics and statistical analysis,简称SS-EIM),能够有效解决Deep Web数据集成中数据纠错、消重及整合等问题.SS-EIM主要由文本匹配模型、语义分析模型和分组统计模型组成,采用文本粗略匹配、表象关联关系获取以及分组统计分析的三段式逐步求精策略,基于文本特征、语义信息及约束规则来不断精化识别结果;根据可获取的有限的实例信息,采用静态分析、动态协调相结合的自适应知识维护策略,构建和完善表象关联知识库,以适应Web数据的动态性并保证表象关联知识的完备性.通过实验验证了SS-EIM中所采用的关键技术的可行性和有效性.

面向Deep Web的动态竞争情报智能采集策略

企业间竞争互动的高强度与高速度，突显出竞争情报的时效性，动态竞争情报是企业在复杂多变的环境下取得成功的关键。Web资源可分为surface Web和Deep Web。Surface Web由静态网页构成，Deep Web信息资源由动态页面、商业数据库、实时数据和企业内部数据库组成，具有质量高、实时性强、易于深度分析的特点，是企业动态竞争情报的重要来源，但常规网络信息采集工具不能直接获得这些信息。针对动态竞争情报采集中存在的信息源选择、信息抽取、信息分析中存在的障碍，提出面向Deep Web的动态竞争情报智能采集策略，详细探讨了动态数据源的智能选择、查询结果的智能抽取、智能化的数据集成和智能分析策略。

Deep Web集成服务的不确定模式匹配

随着Deep Web的迅猛发展,从高度自治、异构及动态变化的Web数据库中,为用户提供高质量的数据逐渐成为当前Deep Web集成服务的一个研究热点.在大部分Web数据库只能通过查询接口为用户提供服务的前提下,如何建立用户请求与集成查询接口模式之间以及集成查询接口模式与Web数据库查询接口模式之间的匹配关系,是Deep Web集成服务中进行合理的用户请求转换的关键.之前的相关工作都是寻找最佳的匹配结果,回避匹配的不确定性,丢弃了可能有价值的其他匹配结果.文中首先剖析了请求转换中模式匹配的不确定性,提出了数字类型的相似度计算方法,给出了进行数字类型的模式匹配的有效的剪枝方法以及数据类型驱动的模式匹配优化方法,并在此基础上提出了一种基于相似度计算的不确定性模式匹配方法,最后通过大量的实验证明了该方法的有效性.

基于结果模式的Deep Web数据抽取

高效、准确地获取Deep Web数据是实现Deep Web数据集成系统的关键问题,然而重复语义标注、嵌套属性的存在是Deep Web数据抽取效率和准确率难以提升的瓶颈问题.因此提出基于结果模式的Deep Web数据抽取机制,将数据抽取工作分为结果模式生成和数据抽取两个阶段,属性语义标注放在结果模式生成阶段来完成,有效解决了重复语义标注问题;同时针对嵌套属性问题,提出一种有效的解决方法.与同类成果相比,基于结果模式的数据抽取方法提高了数据抽取的准确率及效率,并且为Deep Web数据集成奠定了良好的基础.