A study on fast post-processing massive data of casting numerical simulation on personal computers

When castings become complicated and the demands for precision of numerical simulation become higher,the numerical data of casting numerical simulation become more massive.On a general personal computer,these massive numerical data may probably exceed the capacity of available memory,resulting in failure of rendering.Based on the out-of-core technique,this paper proposes a method to effectively utilize external storage and reduce memory usage dramatically,so as to solve the problem of insufficient memory for massive data rendering on general personal computers.Based on this method,a new postprocessor is developed.It is capable to illustrate filling and solidification processes of casting,as well as thermal stess.The new post-processor also provides fast interaction to simulation results.Theoretical analysis as well as several practical examples prove that the memory usage and loading time of the post-processor are independent of the size of the relevant files,but the proportion of the number of cells on surface.Meanwhile,the speed of rendering and fetching of value from the mouse is appreciable,and the demands of real-time and interaction are satisfied.

Recent Strategies for the Development of Biosourced-Monomers, Oligomers and Polymers-Based Materials: A Review with an Innovation and a Bigger Data Focus

After setting the ground of the quantum innovation potential of biosourced entities and outlining the inventive spectrum of adjacent technologies that can derive from those, the current review highlights, with the support of Bigger Data approaches, and a fairly large number of articles, more than 250 and 10,000 patents, the following. It covers an overview of biosourced chemicals and materials, mainly biomonomers, biooligomers and biopolymers;these are produced today in a way that allows reducing the fossil resources depletion and dependency, and obtaining environmentally-friendlier goods in a leaner energy consuming society. A process with a realistic productivity is underlined thanks to the implementation of recent and specifically effective processes where engineered microorganisms are capable to convert natural non-fossil goods, at industrial scale, into fuels and useful high-value chemicals in good yield. Those processes, further detailed, integrate: metabolic engineering involving 1) system biology, 2) synthetic biology and 3) evolutionary engineering. They enable acceptable production yield and productivity, meet the targeted chemical profiles, minimize the consumption of inputs, reduce the production of by-products and further diminish the overall operation costs. As generally admitted the properties of most natural occurring biopolymers (e.g., starch, poly (lactic acid), PHAs.) are often inferior to those of the polymers derived from petroleum;blends and composites, exhibiting improved properties, are now successfully produced. Specific attention is paid to these aspects. Then further evidence is provided to support the important potential and role of products deriving from the biomass in general. The need to enter into the era of Bigger Data, to grow and increase the awareness and multidimensional role and opportunity of biosourcing serves as a conclusion and future prospects. Although providing a large reference database, this review is largely initiatory, therefore not mimicking previous classic reviews but putting them in a multiplying synergistic prospective.

Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies

Cloud computing plays a significant role in modern information technology, providing organizations with numerous benefits, including flexibility, scalability, and cost-efficiency. However, it has become essential for organizations to ensure the security of their applications, data, and cloud-based networks to use cloud services effectively. This systematic literature review aims to determine the latest information regarding cloud computing security, with a specific emphasis on threats and mitigation strategies. Additionally, it highlights some common threats related to cloud computing security, such as distributed denial-of-service (DDoS) attacks, account hijacking, malware attacks, and data breaches. This research also explores some mitigation strategies, including security awareness training, vulnerability management, security information and event management (SIEM), identity and access management (IAM), and encryption techniques. It discusses emerging trends in cloud security, such as integrating artificial intelligence (AI) and machine learning (ML), serverless computing, and containerization, as well as the effectiveness of the shared responsibility model and its related challenges. The importance of user awareness and the impact of emerging technologies on cloud security have also been discussed in detail to mitigate security risks. A literature review of previous research and scholarly articles has also been conducted to provide insights regarding cloud computing security. It shows the need for continuous research and innovation to address emerging threats and maintain a security-conscious culture in the company.

THE STRATEGIES OF INTEGRATING SPATI AL DATA ANALYSIS AND GIS

The gap between SDA(Spatial Data Analysis)and GIS(Geographical Information Systems )existed for a long time.Presently this problem still remains in spite of a lot of theore tical and practical studies which tr y to find the solu-tion for it.The research background and current situation about how to in tegrate SDA and GIS are introduced at first.The main idea of this article is to make su re what is the best scheme to bridge th e gap between SDA and GIS and how to design it.There are a lot of factors to influ ence the standards to assess such a sc heme,for instance,the attitude of users and GIS developers,the framework and related functions of current available GI S software in the market and so on.But the two most important ones of them are effic iency and flexibility of the scheme i tself.Efficiency can be measured by the conve-nient extent and temporal length when it is used for carrying out SDA.Flex ibility means users can define their own SDA methods.The best integration schem e should satisfy the two standards at the same time.A group of functions,which can be combined to implement any SDA meth od,are defined in order to design such an integration scheme.The functio ns are divided into five classes according to their properties.

Novel modelling strategies for high‑frequency stock trading data

Full electronic automation in stock exchanges has recently become popular,generat-ing high-frequency intraday data and motivating the development of near real-time price forecasting methods.Machine learning algorithms are widely applied to mid-price stock predictions.Processing raw data as inputs for prediction models(e.g.,data thinning and feature engineering)can primarily affect the performance of the prediction methods.However,researchers rarely discuss this topic.This motivated us to propose three novel modelling strategies for processing raw data.We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks.In these experiments,our strategies often lead to statistically significant improvement in predictions.The three strategies improve the F1 scores of the SVM models by 0.056,0.087,and 0.016,respectively.

Reliability Strategies for Data Transmission in Wireless Mesh Network

Wireless Mesh Network (WMN) is a new-type wireless network. Its core idea is that any of its wireless equipment can act as both an Access Point (AP) and a router. Each node in the network can send and receive signals as well as directly communicate with one or several peer nodes. One important issue to be considered in wireless Mesh networks is how to secure reliable data transmission in multi-hop links. To solve the problem, the 3GPP system architecture proposes two functionalities: ARQ and HARQ. This paper presents two HARQ schemes, namely hop-by-hop and edge-to-edge, and three ARQ schemes: hop-by-hop, edge-to-edge, and last-hop. Moreover, it proposes three solutions for WMNs from the perspective of protocol stock design: layered cooperative mechanism, relay ARQ mechanism and multi-hop mechanism.

Real-Time Data Transmission with Data Carrier Support Value in Neighbor Strategic Collection in WSN

An efficient trust-aware secure routing and network strategy-based data collection scheme is presented in this paper to enhance the performance and security of wireless sensor networks during data collection.The method first discovers the routes between the data sensors and the sink node.Several factors are considered for each sensor node along the route,including energy,number of neighbours,previous transmissions,and energy depletion ratio.Considering all these variables,the Sink Reachable Support Measure and the Secure Communication Support Measure,the method evaluates two distinct measures.The method calculates the data carrier support value using these two metrics.A single route is chosen to collect data based on the value of data carrier support.It has contributed to the design of Secure Communication Support(SCS)Estimation.This has been measured according to the strategy of each hop of the route.The suggested method improves the security and efficacy of data collection in wireless sensor networks.The second stage uses the two-fish approach to build a trust model for secure data transfer.A sim-ulation exercise was conducted to evaluate the effectiveness of the suggested framework.Metrics,including PDR,end-to-end latency,and average residual energy,were assessed for the proposed model.The efficiency of the suggested route design serves as evidence for the average residual energy for the proposed framework.

A Study on the Existing Cybersecurity Policies and Strategies in Combating Increased Cybercrime in Zambia

The incidence of cybercrime in Zambia is rising, with perpetrators exhibiting a growing tendency to focus on corporate entities and private citizens. The Zambian government has tried to resolve the issue of network protection dangers through the execution of different approaches and procedures. Notwithstanding, the adequacy of these actions has been restricted. The present research investigates Zambia’s extant cybersecurity policies and strategies and delineates several domains where enhancements can be made. The research provides several suggestions on how the Zambian government can enhance its efforts to address cybercrime. This research employs a qualitative approach to investigate the extent of cybersecurity policies and strategies in Zambia by analyzing secondary data. The study seeks to offer valuable insights into the efficacy of Zambia’s cybersecurity framework in addressing the escalating menace of cybercrime by scrutinizing pertinent literature, government reports, and academic articles. The results of this study provide valuable insights into the difficulties encountered by the nation and propose suggestions for improving current policies and strategies.

Research on the Development Strategy of Smart Tourism in Hainan Under the Background of Big Data

Hainan is a major tourist province.It is urgent to promote the transformation and upgrading of Hainan’s tourism industry from a traditional service industry to a modern service industry by means of informatization.Smart tourism is a brand-new tourism form and operation mode of tourism transformation and upgrading.Integrating big data technology will make smart tourism more accurate in three aspects:tourism management,tourism service,and tourism marketing,and further enhance the satisfaction of the tourism experience.This paper studies the development status of smart tourism in Hainan,deeply summarizes its existing problems and causes,and puts forward the development strategy of smart tourism in Hainan to promote the healthy development of the tourism industry in Hainan.

Research on Enterprise Human Resource Management Reform Strategy in the Era of Big Data

In the 21st century,with the development of the Internet,mobile devices,and information technology,society has entered a new era:the era of big data.With the help of big data technology,enterprises can obtain massive market and consumer data,realize in-depth analysis of business and market,and enable enterprises to have a deeper understanding of consumer needs,preferences,and behaviors.At the same time,big data technology can also help enterprises carry out human resource management innovation and improve the performance and competitiveness of enterprises.Of course,from another perspective,enterprises in this era are also facing severe challenges.In the face of massive data processing and analysis,it requires superb data processing and analysis capabilities.Secondly,enterprises need to reconstruct their management system to adapt to the changes in the era of big data.Enterprises must treat data as assets and establish a perfect data management system.In addition,enterprises also need to pay attention to protecting customer privacy and data security to avoid data leakage and abuse.In this context,this paper will explore the thinking of enterprise human resource management innovation in the era of big data,and put forward some suggestions on enterprise human resource management innovation.

Research on Innovation Strategy of Supply Chain Management of Agricultural Enterprises Under the Background of Big Data

With the rapid development and widespread application of Big Data technology, the supply chain management of agricultural products enterprises is facing unprecedented reform and challenges. This study takes the perspective of Big Data technology and collects relevant information on the application of supply chain management in 100 agricultural product enterprises through a survey questionnaire. The study found that the use of Big Data can effectively improve the accuracy of demand forecasting, inventory management efficiency, optimize logistics costs, improve supplier management efficiency, enhance the overall level of supply chain management of enterprises, and propose innovative strategies for supply chain management of agricultural products enterprises based on this. Big Data technology brings a new solution for agricultural products enterprises to enhance their supply chain management level, making the supply chain smarter and more efficient.

Legal Risks and Governance Strategies for Open Public Data in China

With advancements in information technology and the increasing demand for data-driven governance,the openness of public data has become essential for global governance and social innovation.However,legal risks related to privacy protection,data security,intellectual property,liability allocation,and legal adaptability pose significant challenges to data governance in China.This paper analyzes these risks and proposes three strategies:enhancing the legal framework through clear data classification and accountability mechanisms,establishing regulatory bodies to monitor data usage,and promoting public education on data privacy.These strategies aim to address gaps in legal discourse and guide effective data governance,contributing to the secure development of open data initiatives in China and beyond.

Leveraging Predictive Analytics for Strategic Corporate Communications: Enhancing Stakeholder Engagement and Crisis Management

This study explores the integration of predictive analytics in strategic corporate communications, with a specific focus on stakeholder engagement and crisis management. Our mixed-methods approach, which combines a comprehensive literature review with case studies of five multinational corporations, allows us to investigate the applications, challenges, and ethical implications of leveraging predictive models in communication strategies. While our findings reveal significant potential for enhancing personalized content delivery, real-time sentiment analysis, and proactive crisis management, we stress the need for careful consideration of challenges such as data privacy concerns and algorithmic bias. This emphasis on ethical implementation is crucial in navigating the complex landscape of predictive analytics in corporate communications. To address these issues, we propose a framework that prioritizes ethical considerations. Furthermore, we identify key areas for future research, thereby contributing to the evolving field of data-driven communication management.

基于Data Curation的高校机构知识库研究

高校机构知识库引进Data Curation理念可以使高校机构知识库发挥优势、避免局限的同时使其更好地服务于教学与科研,为图书馆的发展带来新的机遇与挑战。文章通过对高校机构知识库进行DC的策略分析为高校机构知识库的构建与发展提供建议与思考。

数据中心多能互补分布式能源系统设计与运行优化研究

为实现数据中心的低碳转型和能效提升,提出了一种集成多种可再生能源和储能设备的多能互补分布式能源系统作为其供能系统。建立了包含生命周期成本、碳排放、能耗、电网购电率和热量浪费率等指标的优化目标体系。制定了两种考虑负荷特征的运行策略,采用改进的多目标蝗虫优化算法对系统容量配置进行求解。为了分析优化目标和运行策略对系统优化的影响,通过组合不同优化目标和运行策略形成了多种优化设计方案,采用层次分析法和熵权法确定评价指标的权重,并利用多准则妥协解排序法进行评价排序。针对青海省某数据中心的案例,获得其能源系统的优化设计与运行方案。研究表明:运行策略主要影响吸收式制冷机和地源热泵的容量,而优化目标主要影响储能设备的容量;在相同的运行策略下,增加优化目标可提升系统的综合性能;对于不同的方案,生命周期成本、碳排放、能耗等基础优化目标值变化较小,而电网购电率和热量浪费率变化较大;通过实施五目标优化,即在基础优化目标的基础上增加热量浪费率、电网购电率,系统的综合性能得到了显著提升,其中热量浪费率和电网购电率分别降低了62.30%和25.92%,可再生能源发电占比增加了2%。

基于强化学习的云环境下大数据能效策略模型

大数据需要大量的云资源进行数据处理和分析,运行起来需要消耗较多的能源,在处理大数据的云环境中,资源数量和任务数量呈指数级增长,导致云数据中心的功耗增加。基于此,提出了一种基于强化学习的云环境下大数据能效策略模型,该模型利用DPSO和DQN的集成来更好地估计和校正数据维数缺陷。将所提出的模型与传统的DQN和负载感知算法加以比较。结果表明,随着任务数量的增加,所提模型在大数据处理方面的性能优于传统DQN和负载感知算法,为绿色云环境下的资源配置提供了一种节能方案。

面向工业物联网的策略隐藏属性基加密方案

针对工业物联网中的生产和监管数据易泄露与数据获取中断的问题,提出支持策略隐藏的属性基加密方案。将属性信息分为属性名和属性值进行方案构造,属性值在构造过程中并未暴露并且上传到云服务器的访问策略仅由属性名构成,工业物联网设备在获取生产信息时不会完整的访问策略,防止了不法分子对设备信息盗取进而导致访问策略及生产信息的泄露。同时,授权多个半可信云服务器,当某个半可信云服务器不能正常工作时可快速根据服务器密钥更新用户私钥,使得其他半可信云服务器接替其工作,保证物联网设备读取数据的连续性。此外,引入属性认证,在撤销过程中无需重更新密文,适用于产生大量数据的物联网环境的撤销。经过安全性分析和性能分析,该方案能抵抗选择明文攻击,且系统初始化、用户密钥生成、加密、解密都具有较高的运行效率。

可扩展的数据集成引擎DataTurbo的设计与实现

针对分布式异构数据共享存在的"信息孤岛"问题,设计并实现了一个用于解决分布式数据迁移、集成、融合的平台DataTurbo。DataTurbo利用可扩展功能构件支持贴近用户语义的策略描述,结合灵活的调度引擎,弥补了很多商业工具仅支持规范化的数据访问接口以及功能局限的弱点,成为一个综合的平台。重点剖析了DataTurbo系统的框架结构,强调功能层下面的引擎支撑层的设计与实现,重点描述了核心调度逻辑和功能构件的规范。DataTurbo现已成功部署并服务于广州市番禺区数据中心,能承担大量异构数据的交换与同步,适合政府各级部门、企业各级机构的数据集成、共享与数据中心的构建。

大数据赋能职业院校数字化建设研究

在职业教育领域,大数据技术的应用具有深远的意义。如何有效整合和利用大数据资源,推动职业院校的数字化建设,提升教育教学的质量和效率,已成为当前职业教育发展的重要课题。为此,相关人员需结合大数据,采取有效措施推动职业院校的数字化建设。文章将探究大数据赋能职业院校数字化建设的有效策略。

国外Data Commons对我国图书馆数据共享空间建设的启示

[目的/意义]国外的数据共享空间已在各个领域得到了广泛实践和应用,尤其在辅助科学研究、提升用户服务和改善数据管理方面表现突出,对我国图书馆数据共享空间建设具有重要的启示。[方法/过程]文章梳理并分析了国内外图书馆数据共享空间的理论和实践现状,以美国国家农业图书馆数据共享空间为例,对其平台建设、数据管理和用户服务模式等方面进行剖析,最后得到我国图书馆数据共享空间建设的启示。[结果/结论]从强化数据对等意识、鼓励数据发现及复用和创新融合新方法等方面论述我国图书馆数据共享空间建设的价值,并提出包括总体计划、平台建设、数据管理和用户服务等建设策略。