Rehabilitation of Sepsis Patients with Acute Kidney Injury Based on Intelligent Medical Big Data

The objective of this study was to explore rehabilitation of patients with acute kidney injury (AKI) treated with Xuebijing injection by using intelligent medical big data analysis system. Based on Hadoop distributed processing technology, this study designed a medical big data analysis system and tested its performance. Then, this analysis system was used to systematically analyze rehabilitation of sepsis patients with AKI treated with Xuebijing injection. It is found that the computing time of this system does not increase obviously with the increase of cases. The results of systematic analysis showed that the glomerular filtration rate (59.31 ± 3.87% vs 44.53 ± 3.53%) in the experimental group was obviously superior than that in the controls after one week of treatment. The levels of urea nitrogen (9.32 ± 2.21 mmol/L vs. 14.32 ± 0.98 mmol/L), cystatin C (1.65 ± 0.22 mg/L vs. 2.02 ± 0.13 mg/L), renal function recovery time (6.12 ± 1.66 days vs. 8.66 ± 1.17 days), acute physiology and chronic health evaluation system score (8.98 ± 2.12 points vs. 12.45 ± 2.56 points), sequential organ failure score (7.22 ± 0.86 points vs. 8.61 ± 0.97 points), traditional Chinese medicine (TCM) syndrome score (6.89 ± 1.11 points vs. 11.33 ± 1.23 points), and ICU time (16.43 ± 2.37 days vs. 12.15 ± 2.56 days) in the experimental group were obviously lower than those in the controls, and the distinctions had statistical significance ( P < 0.05 ). The significant efficiency (37.19% vs. 25.31%) and total effective rate (89.06% vs. 79.06%) in the experimental group were obviously superior than those in the controls, and distinction had statistical significance ( P < 0.05 ). In summary, the medical big data analysis system constructed in this study has high efficiency. Xuebijing injection can improve the renal function of sepsis patients with kidney injury, and its therapeutic effect is obviously better than that of Western medicine, and it has clinical application and promotion value.

Application of virtualisation environment for data security in operational data processing systems

This paper presents a concept, developed and tested by the authors, of a virtualisation environment enabling the protection of aggregated data through the use of high availability (HA) of IT systems. The presented solution allows securing the central database system and virtualised server machines by using a scalable environment consisting of physical servers and disk arrays. The authors of this paper focus on ensuring the continuity of system operation and on minimising the risk of failures related to the availability of the operational data analysis system.

The Quality of E-learning on The Website

The goal of the current study is to learn more about different online apps that can be used in E-learning. The study also aims at shedding light on more important methods using applications on the web in the quality of E-learning. Applications are taken from the website: Classroom, Edmodo, Moodle, Zoom, Free Conference call, Meet, WebEx Meet, Telegram, WhatsApp, Viber, and YouTube. These applications are used in E-learning. The study focuses on various types of university education, and it is made public through social media. The study uses Google Forms approach that consists of a set of questions that are answered entirely and at random. The study was conducted on 288 samples during the 18 hours ending on 29/ 05/2020 at 13:50:30. The results were a questionnaire of seeing which website applications are mostly used in the quality of E-learning. Google, statistical package for the social sciences (SPSS), and Excel data analysis system is scrutinized by using the questionnaire results. Now E-learning uses more interactively in universities than any other time during the different web applications for creating conferences, seminars, classes for students.

A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications

The emergence of new technologies and the era of IoT which will be based on compute-intensive applications. These applications will increase the traffic volume of today’s network infrastructure and will impact more on emerging Fifth Generation (5G) system. Research is going in many details, such as how to provide automation in managing and configuring data analysis tasks over cloud and edges, and to achieve minimum latency and bandwidth consumption with optimizing task allocation. The major challenge for researchers is to push the artificial intelligence to the edge to fully discover the potential of the fog computing paradigm. There are existing intelligence-based fog computing frameworks for IoT based applications, but research on Edge-Artificial Intelligence (Edge-AI) is still in its initial stage. Therefore, we chose to focus on data analytics and offloading in our proposed architecture. To address these problems, we have proposed a prototype of our architecture, which is a multi-layered architecture for data analysis between cloud and fog computing layers to perform latency- sensitive analysis with low latency. The main goal of this research is to use this multi-layer fog computing platform for enhancement of data analysis system based on IoT devices in real-time. Our research based on the policy of the OpenFog Consortium which will offer the good outcomes, but also surveillance and data analysis functionalities. We presented through case studies that our proposed prototype architecture outperformed the cloud-only environment in delay-time, network usage, and energy consumption.

Key Technologies of Media Data In-Depth Analysis System Based on Artificial Intelligence-Based Big Data

At present, big data related technologies are developing rapidly, and major companies provide big data analysis services. However, the big data analysis system formed by the combination method cannot sense each other and lacks cooperation, resulting in a certain amount of waste of resources in the big data analysis system. In order to find the key technology of the data analysis system and conduct in-depth analysis of the media data, this paper proposes a scheduling algorithm based on artificial intelligence (AI) to implement task scheduling and logical data block migration. By analyzing the experimental results, we know that the performance of LAS (Logistic-Block Affinity Scheduler) is improved by 23.97%, 16.11%, and 10.56%, respectively, compared with the other three algorithms. Based on real new media data, this article analyzes the content of media data and user behavior in depth through big data analysis methods. Compared with other methods, the algorithm model in this paper optimizes the accuracy of hot topic extraction, which has important implications for media data mining. In addition, the analysis results of the emotional characteristics, audience characteristics, and hot topic communication characteristics obtained by the research also have practical value. This method improves the recall rate and F value by 5% and 4.7%, respectively, and the overall F value of emotional judgment is about 88.9%.

Labor Education in Primary and Secondary Schools in the New Era Based on Modern Information Technology

With the rapid development of information technology represented by artificial intelligence (AI), there has been a great impact on educational methods, educational behaviors, and educational models. It can even be said that there is a fundamental change. Especially under the influence of the pandemic, educational behaviors have undergone radical changes. The education management information system in primary and secondary schools strengthens the service of labor education for primary and secondary school students, with a data analysis system for labor education. A survey on labor education in 103 primary and secondary schools in the region has been carried out. Information technology can well improve the form of labor education in primary and secondary schools and promote the development of labor education. Big data can be used to objectively analyze the problems of labor education in primary and secondary schools, thus proposing a new era of labor education data analysis system and management for primary and secondary schools.

Application of Analytic Hierarchy Process and Weighted Product Methods in Determining the Best Employees

<p><strong>A</strong><strong>bstract</strong><strong>.</strong> Employees are one of the company's assets that must be managed properly. Therefore the selection of the best employees is now needed. The problem faced in determining the best and qualified employees is that there are still no standards in assessing only one person subjectively in determining the best employee, which consequently lacks appropriate or objective results. To provide rewards for the best employees, we need a system to support the decisions of the best employees who deserve to receive rewards to be on target. The purpose of this research is to design and build a decision support system application in determining the best employees using the analytic hierarchy process and weighted product methods. Stages of software development of the Software Development Life Cycle (SDLC) uses a waterfall, that is data analysis, system design, construction, coding, testing and implementation. The results of this process are in the form of calculation applications that have been obtained from the analytic hierarchy process and weighted product methods in determining the best employee. The result gives an accuracy rate of 82.3%.</p><p><strong>Keywords</strong><strong>: </strong>analytic hierarchy process, weighted product, decision support system, employees</p>

An Introduction to the HLanData Project: a Step Forward in the Harmonization of Spatial Information Throughout Europe

Nowadays spatial information is becoming more and more accessible for various purposes due to local, national and European initiatives. This paper is addressed to one such initiative Hlandata, whose purpose is to make a significant step forward in the harmonization and use of land cover (LC) and land use (LU) geographic data and its related data bases over Europe. The project was developed using the best experiences from previous geographic data harmonization activities with the goal of demonstrating the feasibility of European level harmonization of land information related datasets. The three pilot projects “LU-LC Data Analysis System for intermediatelevel users”, “Harmonized and Interoperable Land Information Systems” and “Stratification of Waste Dumps” were developed and tested within the project and demonstrated advantages from user oriented value-added services emphasizing data search, exploration and analysis.