Big Data Monitoring Research Articles

IN-MEMORY INTELLIGENT COMPUTING

Context. Processed big data has social significance for the development of society and industry. Intelligent processing of big data is a condition for creating a collective mind of a social group, company, state and the planet as a whole. At the same time, the economy of big data (Data Economy) takes first place in the evaluation of processing mechanisms, since two parameters are very important: speed of data processing and energy consumption. Therefore, mechanisms focused on parallel processing of large data within the data storage center will always be in demand on the IT market.
 Objective. The goal of the investigation is to increase the economy of big data (Data Economy) thanks to the analysis of data as truth table addresses for the identification of patterns of production functionalities based on the similarity-difference metric.
 Method. Intelligent computing architectures are proposed for managing cyber-social processes based on monitoring and analysis of big data. It is proposed to process big data as truth table addresses to solve the problems of identification, clustering, and classification of patterns of social and production processes. A family of automata is offered for the analysis of big data, such as addresses. The truth table is considered as a reasonable form of explicit data structures that have a useful constant – a standard address routing order. The goal of processing big data is to make it structured using a truth table for further identification before making actuator decisions. The truth table is considered as a mechanism for parallel structuring and packing of large data in its column to determine their similarity-difference and to equate data at the same addresses. Representation of data as addresses is associated with unitary encoding of patterns by binary vectors on the found universe of primitive data. The mechanism is focused on processorless data processing based on read-write transactions using in-memory computing technology with significant time and energy savings. The metric of truth table big data processing is parallelism, technological simplicity, and linear computational complexity. The price for such advantages is the exponential memory costs of storing explicit structured data.
 Results. Parallel algorithms of in-memory computing are proposed for economic mechanisms of transformation of large unstructured data, such as addresses, into useful structured data. An in-memory computing architecture with global feedback and an algorithm for matrix parallel processing of large data such as addresses are proposed. It includes a framework for matrix analysis of big data to determine the similarity between vectors that are input to the matrix sequencer. Vector data analysis is transformed into matrix computing for big data processing. The speed of the parallel algorithm for the analysis of big data on the MDV matrix of deductive vectors is linearly dependent on the number of bits of the input vectors or the power of the universe of primitives. A method of identifying patterns using key words has been developed. It is characterized by the use of unitary coded data components for the synthesis of the truth table of the business process. This allows you to use read-write transactions for parallel processing of large data such as addresses.
 Conclusions. The scientific novelty consists in the development of the following innovative solutions: 1) a new vector-matrix technology for parallel processing of large data, such as addresses, is proposed, characterized by the use of read-write transactions on matrix memory without the use of processor logic; 2) an in-memory computing architecture with global feedback and an algorithm for matrix parallel processing of large data such as addresses are proposed; 3) a method of identifying patterns using keywords is proposed, which is characterized by the use of unitary coded data components for the synthesis of the truth table of the business process, which makes it possible to use the read-write transaction for parallel processing of large data such as addresses. The practical significance of the study is that any task of artificial intelligence (similarity-difference, classification-clustering and recognition, pattern identification) can be solved technologically simply and efficiently with the help of a truth table (or its derivatives) and unitarily coded big data . Research prospects are related to the implementation of this digital modeling technology devices on the EDA market. KEYWORDS: Intelligent

A development Management Process to implement smart Sports in Higher Education Institutions in Heilongjiang Province, the People’s Republic of China

Background and Aim: Currently, education is entering a rapid stage of development in smart education. Starting in 2015, research on the comprehension and application of smart education has received notable recognition in academic circles. It has actively and effectively contributed to advancing a fresh phase of educational reform and enhancing educational progress. This research aims to construct the developing strategies to implement smart sports in higher education institutions in Heilongjiang Province. Materials and Methods: The participants in this research can be divided into four groups, which are as follows: (1) 20 teachers and 400 students were selected to participate in answering the questionnaires; (2) 7 experts were invited to in-depth interviews; (3) 19 experts were invited to conduct two-round Delphi study; and (4) 9 experts were invited to discuss and confirm the developing strategies to implement smart sports in higher education institutions in Heilongjiang province, based on POLC framework. Descriptive statistics, such as the calculation of the mean and standard deviation, are employed to assess the questionnaire data. The consensus data was analyzed by using the median and interquartile range. The criteria for analysis were a median of ≥ 3.50 and an interquartile range of ≤ 1.50. Results: the factors for the developing strategies were divided into four aspects as follows: 1. Planning, which includes Development Strategy which consists of 5 factors; 2. Organizing includes basic support which consists of 6 factors, and physical education curriculum integration setup and implementation which consists of 9 factors; 3. Leading includes a Sports Learning Environment which consists of 5 factors; and 4. Controlling includes Sports big data monitoring and evaluation which consists of 6 factors and Sports services and management which consist of 7 factors. Conclusion: The final development strategies for implementing smart sports in higher education institutions in Heilongjiang Province were acceptable.

The Threat of Consumer Autonomy under Personalised Recommendations

The twenty-first century is the century of digital technology. Algorithms, big data monitoring, and other technologies are widely applied to the business sector, creating a new business model of surveillance capitalism. This business model is mainly characterized by personalized recommendations, which collect, analyze, and predict consumer preferences to customize advertisements for targeted consumers, guiding and shaping their consumption decisions for profit. Based on existing literature, this paper analyses the impact of personalized recommendations on consumer decision-making in the business model, intending to explore the threat of consumer autonomy under surveillance capitalism. The paper argues that personalized recommendations have a covert intervention in consumers’ decision-making and reflexivity: they deprive consumers of the opportunity to make changes to their personalities and reinforce their inherent preferences. Personalized recommendations also result in discrimination against vulnerable groups and differential pricing, influencing people without their knowledge. In addition, personalized recommendations threaten consumer autonomy by weakening their ability to reflect. To safeguard consumer autonomy, people should take the initiative to improve their digital literacy, reflect on the ideology of instrumental rationality, and actively participate in platform interactions.

Design and implementation of load intensity monitoring platform supported by big data technology in stage training for women’s sitting volleyball

This study aims to discuss the load intensity monitoring in the training process of sitting volleyball, to help coaches understand the training status of athletes, and to provide a scientific basis for the follow-up training plan. Through big data technology, the physiological changes of athletes can be more accurately grasped. This includes classification and summary of exercise load intensity and experimental study of the relationship between heart rate and rating perceived exertion (RPE). Through monitoring the training process of a provincial women’s sitting volleyball team, it is found that there is a significant positive correlation between athletes’ RPE and average heart rate. This result shows that by monitoring the change in heart rate and RPE of athletes, athletes' training state and physical condition can be more accurately understood. The results reveal that through the use of big data technology and monitoring experiments, it is found that heart rate and RPE are effective monitoring indicators, which can scientifically reflect the load intensity during sitting volleyball training. The conclusions provide coaches with a more scientific basis for making training plans and useful references for sports involving people with disabilities.

Online Big-Data Monitoring and Assessment Framework for Internal Combustion Engine with Various Biofuels

Article Online Big-Data Monitoring and Assessment Framework for Internal Combustion Engine with Various Biofuels Ming Zhang 1,*, Vikas Sharma 2, Zezhong Wang 1, Yu Jia 1, Abul Kalam Hossain 1, and Yuchun Xu 1 1 College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK 2 School of Architecture, Technology and Engineering, University of Brighton, Brighton BN2 4GJ, UK * Correspondence: m.zhang21@aston.ac.uk Received: 14 December 2022 Accepted: 26 April 2023 Published: 30 May 2023 Abstract: As the primary power source for automobiles, the internal combustion (IC) engines have been widely used and served millions of people worldwide. With increasingly stringent environmental regulations, biofuels have been obtained more attentions and are being used as alternative fuel to power IC engines. However, there are currently no standard solutions or well-established monitoring and assessment methods that can effectively evaluate the IC engine’s performance with biofuels. The expectation for biofuels is to keep the engine’s lifetime as long as the conventional fuels, or even longer. Otherwise, their usage would be unnecessary because they would reduce the lifecycle of the engine and also cause more waste and pollution. To address this challenge, we initially designed two biofuels: waste cooking oil biofuel (WCOB) and lamb fat biofuel (LFB). Then we proposed an online big-data monitoring and assessment framework for IC engines operating with various types of fuel. We conducted comprehensive experiments and comparisons based on the proposed framework. The results indicate that LFB performs best under all the performance indicators.

Fast Real-Time Data Process Analysis Based on NoSQL for IoT Pavement Quality Management Platform

The quality of road pavements is highly impacted by environmental variables, such as temperature, humidity, and weather; and construction-related variables, such as material quality and time. In this paper, an advanced data collection and analysis system based on big data/cloud was proposed for the use of IoT location-based smart platforms for pavement quality big data at road pavement sites. For the big data platform, a relational database management system (RDBMS) for a general alphanumeric data-based infrastructure for IoT-based systems was designed based on distributed/parallel processing to enable rapid and big data collection and analysis. The structure was established based on a NoSQL-based database to enable real-time high-speed collection and analysis, and the big data platform was developed as a data collection and visualization infrastructure. When the big data system was studied using data analysis methods, the proposed system demonstrated improvements in data collection performance and analysis speed, indicating that analysis results could be derived in real time. Specifically, the data collection processing (create) speed of the NoSQL-based system (0.405 ms) was significantly higher than that of the compared existing system (21.146 ms). Real-time processing capacity was also verified based on quality big data generated on actual road pavements, and the proposed system was proven suitable for the real-time monitoring (the data collection processing) of road pavement quality big data.

Error analysis in big data analysis

Practice errors in medicine are among the situations that disrupt patient-physician communication, reduce work efficiency, and eventually lead to the formation of an atmosphere of distrust. Various aspects of the subject have been examined in the symposiums we have held with Prof Dr. Ethem Geçim for 14 years. We shared our information at medikolegalduzlem.com. Big Data analysis is important in system audits. It is inevitable to reach erroneous results if the data belonging to these are made carelessly and negligently. The main problems encountered in big data analysis are: 1. Dependency: Computer dependency is at the forefront of algorithm and process monitoring. 2. Chaotic situation: The problem of systematic change is observed in the supervised learning machine. In the unsupervised learning machine, the definition of the identity of grouping is problematic. In the learning machine system of the reward-punishment system, there is a case study based on random data, not systematic. It is very difficult to reach an inductive conclusion from this situation. There is absolutely a need to organize a constant, standard data flow. 3. Loss of control: Steps that need to be checked are skipped in big data monitoring. Injustice occurs. 4. Waiver of rights: The expert is content with the data given by the machine as a dependent. There is a problem with the development of innovative processes. In that case, it is useful to pay attention to the following points in the regulation on data ethics: 1. Identity: In the big data analysis of the expert, it is important to classify and group the personal data and to accept it in court. In this respect, it is necessary to control whether the expert is legitimate, sufficient, and effective. 2. Confidentiality: The problem of privacy of the person being examined is important. The approach of the telemedicine application, which we call image-based analysis, to this problem is subject to legal and ethical evaluation. 3. Ownership: There are inadequacies in the regulation of the infrastructure applied in the field of health. The purchase of each tool and its accessible application also needs regulation. 4. Reputation: Audit problematic of non-standard practices of practitioners is evident. As a result, there is a need to evaluate error definition, error measurement, error effect, and error tolerability in big data analysis.

Coastal Flood Risk and Smart Resilience Evaluation under a Changing Climate

Coastal areas are highly vulnerable to flood risks, which are exacerbated by the changing climate. This paper provides a comprehensive review of the literature on coastal flood risk assessment and resilience evaluation and proposes a smart-resilient city framework based on pre-disaster, mid-disaster, and post-disaster evaluations. First, this paper systematically reviews the origin of the resilience concept and the development of flood resilience. Next, it introduces the social-acceptable risk criteria and the flood resilience level for different flood phases. Then, a coastal flood resilience management system for smart cities is proposed, covering 3 phases of flood disasters (before, during, and after). Risk assessment is essential in pre-disaster scenarios because it provides a comprehensive understanding of the potential hazards and vulnerabilities of an area or system. Big data monitoring during disasters is an essential component of effective emergency management and response that can allow for more informed decisions and thus quicker, more effective responses to disasters, ultimately saving lives and minimizing damage. Data-informed loss assessments are crucial in providing a rapid, accurate understanding of post-disaster impact. This understanding, in turn, is instrumental in expediting recovery and reconstruction efforts by aiding decision-making processes and resource allocation. Finally, the impacts of climate change on the 3 phases of flood disasters are summarized. The development of more resilient coastal communities that are better equipped to withstand the impacts of climate change and adapt to changing environmental conditions is crucial. To address coastal compound floods, researchers should focus on understanding trigging factor interactions, assessing economic and social risks, improving monitoring and emergency response systems, and promoting interdisciplinary research with data openness. These strategies will enable comprehensive and holistic management of coastal flood risks in the context of climate change.

Smart aquaponics (SMARTAP project): small/ medium farm economic improvement model, environmental sustainability, monitoring and simplified management of big data

ISHS XXXI International Horticultural Congress (IHC2022): International Symposium on Urban Horticulture for Sustainable Food Security (UrbanFood2022) Smart aquaponics (SMARTAP project): small/ medium farm economic improvement model, environmental sustainability, monitoring and simplified management of big data

The research of the contribution of big data in enhancing power battery safety

The use of oil and gas is the main cause behind the increased impact of severe weather over the past two decades. In the use of oil and natural gas, fuel powered vehicles account for a large part. Therefore, at present, countries are vigorously promoting the settlement of new energy vehicles. With the support of national policies, new energy manufacturers are springing up. There are also many large factories with good qualifications, but there are also many small factories mixed with other factories. With the increase of new energy vehicles, more and more traffic accidents caused by new energy vehicles, especially the self-destruction of new energy vehicles caused by power batteries. Therefore, this paper attempts to make suggestions and analysis on the production, charging, driving and handling of power batteries after accidents through big data monitoring.

KFIML: Kubernetes-Based Fog Computing IoT Platform for Online Machine Learning

The massive onsite data produced by the Internet of Things (IoT) can bring valuable information and immense potentials, thus empowering a new wave of emerging applications. However, with the rapid increase of onsite IoT data streams, it has become extremely challenging to develop a scalable computing platform and provide a comprehensive workflow for processing IoT data streams with lower latency and more intelligence. To this end, we present a Kubernetes-based scalable fog computing platform (KFIML), integrating big data streaming processing with machine learning (ML)-based applications. We also provide a comprehensive IoT data processing workflow, including data access and transfer, big data processing, online ML, long-term storage, and monitoring. The platform is feasibly validated on a clustered testbed, which comprises a master node, IoT broker servers, worker nodes, and a local database server. By leveraging the lightweight orchestration system, namely Kubernetes, we can readily scale and manage containerized software frameworks on our testbed. The big data processing layer utilizes the advanced data flow frameworks such as Apache Flink, to support both streaming processing and statistical analysis with low latency. In addition, the specified long short-term memory (LSTM)-based ML pipelines are employed on the online ML layer, to enable the real-time predictive analysis of IoT data streams. The experiments on a real-world smart grid use case demonstrate that the container-based KFIML platform can be well-scaled with Kubernetes to efficiently perform big data processing increased onsite IoT data streams with lower latency and conduct ML-based applications.

Aging Time Prediction Model Analysis and Numerical Simulation of Random Degradation Equipment Based on Big Data Linkage Technology.

In this study, we focus on the relevance of remaining life prediction of randomly degraded equipment in the context of big data monitoring and the core issue of quantifying uncertainty in remaining life prediction. We analyze the limitations and common problems of current research. To address the limitations and common problems, a solution for predicting the remaining life of randomly degraded devices with multisource sensing monitoring in the context of big data is proposed, and the feasibility and effectiveness of the idea are verified using battery data. Finally, multiple machine learning methods, such as support vector machines, random forests, recurrent neural networks, and convolutional neural networks, are combined to predict the remaining life of batteries, and these four machine learning methods perform well in the work of battery remaining life prediction and solve the key scientific problems.

An online longitudinal study about search index reflexing public attention of vaccinate against COVID-19.

BackgroundThe coronavirus disease of 2019 (COVID-19) has had catastrophic effects worldwide. Mounting efforts for vaccination against COVID-19 have achieved tremendous progress. Online searching is a voluntary behavior of people might reflect the public attention and awareness.. Screening and analyzing the details of vaccine related searches may help the government to grasp the trend of public opinion and provide a reference for vaccination strategies and future efforts to protect public health.MethodsThree terms related to COVID-19 and COVID-19 vaccine as well as daily relative search volumes (RSV) were retrieved in the Baidu Index (BDI) from 1 January 2020 to 1 July 2021 in China. Besides the national total data, those of the individual provinces/cities/region of Beijing, Shanghai, Guangdong, Heilongjiang, Sichuan, and Tibet were also included. Vaccine-related policies were also gathered during this period. The vaccination rates within China were derived from the National Health Commission of the People’s Republic of China, from 23 March 2021 to 1 July 2021. The searching index was calculated by the searching volume and curve graphs were used to demonstrate the variation and the related trend of the RSV and vaccination rates.ResultsA total of 548 days’ BDI data were retrieved. The national and provincial curves of the BDI exhibited similar fluctuating upward trends, with 5 obvious rises, especially in COVID-19 vaccine searching volume. The vaccination number was correlated with the searching volume growth of COVID-19 vaccine and vaccine uptake (r=0.382, P<0.001; r=0.256, P=0.010). Relevant vaccination events corresponded to the variation searching trend and were attributed to or were influenced by the searching variation.ConclusionsPublic awareness about vaccination against COVID-19 was related to the implementation of vaccine policies. Positive vaccine-related policy and high public awareness about vaccination could play a vital role in maximizing the vaccination uptake. Advanced internet data grabbing could consolidate public information in an efficient and timely manner. These findings would support efforts to utilize the big data monitoring of the public opinion to forecast and guide the public health policies. Dynamic monitoring as well as prevention and timely adjustment under this supervision could be expected.

A Novel Intelligent Hybrid Optimized Analytics and Streaming Engine for Medical Big Data.

Medical data processing is exponentially increasing day by day due to the frequent demand for many applications. Healthcare data is one such field, which is dynamically growing day by day. In today's scenario, an enormous amount of sensing devices and data collection units have been employed to generate and collect medical data all over the world. These healthcare devices will result in big real-time data streams. Hence, healthcare-based big data analytics and monitoring have gained hawk-eye importance but needs improvisation. Recently, machine and deep learning algorithms have gained importance to analyze huge amounts of medical data, extract the information, and even predict the future insights of diseases and also cope with the huge volume of data. But applying the learning models to handle big/medical data streams remains to be a challenge among the researchers. This paper proposes the novel deep learning electronic record search engine algorithm (ERSEA) along with firefly optimized long short-term memory (LSTM) model for better data analytics and monitoring. The experimentations have been carried out using Apache Spark using the different medical respiratory data. Finally, the proposed framework results are contrasted with existing models. It shows the accuracy, sensitivity, and specificity like 94%, 93.5%, and 94% for less than 5 GB dataset, and also, more than 5 GB it provides 94%, 92%, and 93% to prove the extraordinary performance of the proposed framework.

ADOPTION OF VIBRATION OF FUZZY PARTIAL FRACTIONAL-ORDER EQUATION IN MONITORING OF NOISE BIG DATA UNDER INFINITE IMPULSE RESPONSE DIGITAL FILTER ALGORITHM

In order to study the application of fuzzy partial fractional-order equation based on infinite impulse response (IIR) digital filter in noise big data monitoring, in this study, first, the noise level is calculated through IIR filter to obtain the sound signal sequence after C weight and A weight; the weight equivalent continuous sound level of C, A, and Z is calculated. Then, the vibration condition of fuzzy partial fractional-order equation is studied; the vibration acceleration signal is converted into the vibration velocity and displacement signal; the corresponding effective values are calculated according to the root mean square (RMS), and the noise monitoring platform is designed from hardware and software aspects. Finally, the calibrated sound level value is compared with the standard signal value generated by the digital signal generator; the sequence of sound signal after weight of the original signal, the actual acceleration signal, velocity signal, and displacement signal are calculated; the acceleration signal, velocity signal, and theoretical signal values are compared at different frequencies. The results show that the error between the calculated and calibrated sound level and the standard signal value is very small, and the results of weight counting of C and A are good and meet the requirement of calculation; the error between the measured value of actual acceleration, velocity, and displacement signal and the theoretical value is small, which can realize the accurate acquisition of vibration signal; the error between the actual measured value and the theoretical value of acceleration and velocity at different frequencies is small, about 1% and 10%, respectively. Therefore, the fuzzy partial fractional-order equation based on IIR digital filter can effectively monitor the big noise data.

Network Traffic Obfuscation System for IIoT-Cloud Control Systems

One of the latest technologies enabling remote control, operational efficiency upgrades, and real-time big-data monitoring in an industrial control system (ICS) is the IIoT-Cloud ICS, which integrates the Industrial Internet of Things (IIoT) and the cloud into the ICS. Although an ICS benefits from the application of IIoT and the cloud in terms of cost reduction, efficiency improvement, and real-time monitoring, the application of this technology to an ICS poses an unprecedented security risk by exposing its terminal devices to the outside world. An adversary can collect information regarding senders, recipients, and prime-time slots through traffic analysis and use it as a linchpin for the next attack, posing a potential threat to the ICS. To address this problem, we designed a network traffic obfuscation system (NTOS) for the IIoT-Cloud ICS, based on the requirements derived from the ICS characteristics and limitations of existing NTOS models. As a strategy to solve this problem wherein a decrease in the traffic volume facilitates traffic analysis or reduces the packet transmission speed, we proposed an NTOS based on packet scrambling, wherein a packet is split into multiple pieces before transmission, thus obfuscating network analysis. To minimize the ICS modification and downtime, the proposed NTOS was designed using an agent-based model. In addition, for the ICS network traffic analyzer to operate normally in an environment wherein the NTOS is applied, a rule-based NTOS was adopted such that the actual traffic flow is known only to the device that is aware of the rule and is blocked for attackers. The experimental results verified that the same time requested for response and level of difficulty of analysis were maintained by the application of an NTOS based on packet scrambling, even when the number of requests received by the server per second was reduced. The network traffic analyzer of the ICS can capture the packet flow by using the pre-communicated NTOS rule. In addition, by designing an NTOS using an agent-based model, the impact on the ICS was minimized such that the system could be applied with short downtime.

Mobile-Learning-Based Exploration of English Reading Teaching Reform for Non-English Majors in China

Under the development of the information age, traditional Chinese teaching model of college English reading courses can no longer meet the needs of the times, and needs to be reformed urgently. From the perspective of mobile learning, this paper tries to combine modern educational technology with classroom teaching by using an online English reading learning platform, and explores the construction of a new teaching model. This model tries to form effective teaching supervision and assessment through the combination of online and offline teaching model, in-class and after-class teaching model, as well as real-time and dynamic big data monitoring, which breaks the closed classroom teaching environment, enriches the teaching content and means, complements and improves the teaching method of traditional Chinese college English reading courses. The aim of this study is to enrich teaching content, reform traditional teaching model and construct a new teaching model for English reading courses through the teaching practice of the online English reading platform, and further optimize the use of the platform by collecting and analyzing effective assessment and feedback, so as to make full use of the online reading platform and improve the mobile reading teaching model.&amp;nbsp;

Preventive Effect of Egg Membrane Protein Supplementation on Dragon Boat Sports Injury under the Monitoring of Artificial Intelligence and Big Data

Dragon boat is an excellent traditional national sport in China, with excellent fitness results. Based on the country’s strong support for traditional national sports, the dragon boat has developed into a traditional national sport with specific influence and popularity among the masses. Eggshell membrane (ESM) has the effect of relieving arthritis, joint pain, and joint stiffness. The supplementary ESM’s preventive effect on sports injuries has attracted people’s attention. Based on this, this article builds a sports injury prediction model under artificial intelligence and big data monitoring to accurately predict the injuries suffered by athletes, provide help to reduce athletes’ injuries during training, and promote the development of dragon boat sports. This article selects 20 members of a dragon boat team from a university and randomly divides them into a control group (supplement of equal amounts of whey protein), a normal group, an ESM low-dose group (26 mg/kg bw/d), and an ESM high-dose group (52 mg/kg bw/d), with 5 people in each group. The subjects were subjected to downhill exercise and immune stimulation experiments. Among them, the subjects in the control group performed downhill exercise for 12 hours, and they were tested for lactic acid (LAC), lactate dehydrogenase (LDH), urea, malondialdehyde (MDA), hemoglobin (Hb), blood creatinine (CRE), and other related physical and chemical indicators. The other three groups of subjects did not take low-dose and high-dose ESM for 7 consecutive days and were injected with lipopolysaccharide on the 8th day for immune stimulation and tested for interferon (IFN-γ), tumor necrosis factor (TNF-α), interleukins (IL-1β, IL-6, and IL-10), and other related cytokines. The results show that oral egg membrane protein (ESM) can reduce subjects’ serum LAC, LDH, urea, MDA, Hb, and CRE concentration. In addition, the ESM can reduce the expression of proinflammatory factors IL-1β and TNF-α, while increasing the expression of anti-inflammatory factor IL-10, which provides clinical data for the clinical use of ESM as a supplement for sports injury or for relieving arthritis symptoms. The study shows that egg membrane protein has a protective effect against sports injury and may be regulated by inflammatory factors.

Application of big data information system in early diagnosis, treatment, and nursing of cervical cancer infected by human papillomavirus

AbstractIn order to study the application value of big data information monitoring system in early diagnosis, treatment, and nursing of human papillomavirus (HPV) infected cervical cancer, firstly, the traditional ID3‐based decision tree model was optimized through the minimum sample number of different leaf nodes. According to the optimization model, the diagnosis model of HPV‐infected cervical cancer was constructed and applied to the monitoring system of big data of cervical cancer. Diagnosis model and information monitoring system (DMIMS) was compared with Decision tree based on decision support degree (DTBDS). Then, 876 HPV‐infected cervical cancer patients diagnosed in our hospital from January 30, 2018 to January 30, 2019 were defined as the experimental group, and 670 HPV‐infected cervical cancer patients diagnosed in our hospital from January 30, 2017 to January 30, 2018 were defined as the control group. Only the experimental group used cervical cancer data and information diagnosis system. Finally, the biopsy rate after examination, the coincidence rate with pathology diagnosis, the detection rate of precancerous lesion of HPV infection, and the detection rate of HPV infection of cervical cancer were compared between the two groups. The results showed that the classification accuracy of DMIMS (95%) was higher than that of DTBDS (67%) (p &lt; 0.05). The biopsy rate of the experimental group was significantly lower than that of the control group (p &lt; 0.05); the coincidence rate between biopsy and pathological diagnosis, detection rate of HPV precancerous lesion, and detection rate of HPV infected cervical cancer were significantly higher in the experimental group than in the control group (p &lt; 0.05), which showed that the big data monitoring system of cervical cancer played an important role in the detection of HPV infection cervical cancer, which can make the patients with precancerous lesions and early cervical cancer get timely diagnosis, treatment, and nursing.

Measure Social Metrics with Sodatech: a Monitoring and Analysis Platform of Big Data

Social Network Services (SNS) nowadays require marketing research study using Big Data tools. In this paper we present Sodatech platform: a monitoring and analysis tool of big data to help researchers and SNS community managers to measure effectiveness of social media marketing activities. Sodatech platform provides three functions: collect massive data from social network; analyze marketing metrics with big data tools; display intuitive data in a dynamic and interactive interface.