Big Amount Research Articles

TECHNOLOGICAL AND SCIENTIFIC DEVELOPMENTS TOWARDS USE OF BIG DATA IN HEALTH DATA MANAGEMENT – AN OVERVIEW

The healthcare industry has been using data and technology driven innovation for a long time now with Evidence Based Medicine (EBM) being at the helm. Effectively using technology is vital towards successful data management in healthcare. A convenient approach that can collect, store, evaluate, and analyze health data will be beneficial to all the stakeholders in the healthcare delivery chain. With the advancement, clubbed with the availability and affordability of technology, it has become an effective tool for providing the right- care backed with accurate, consolidated evidence. Such approach not only helps in improving the quality of care, it also helps in early detection of diseases and effective treatment, Big Data is providing the necessary data management tools for the same. A simple Extract-Transform-Load (ETL) procedure using Hadoop can be used. As Big data analyses large amounts of data to uncover hidden patterns, correlations and other insights, its role in making the right, predictive decisions for quality care of the patients and better health planning is possible. Big data technologies such as Hadoop bring significant cost advantages when it comes to storing large amounts of data and combined with the speed of Hadoop and in-memory analytics, with the ability to analyze new sources of data, we are able to analyze information instantly – and make decisions which is very essential in the healthcare provider segment. Hadoop does not only store, but also process any and many file data: big or small, can be plain text files or binary files like images and even multiple different version of some particular data format across different time periods. It consists of three core components: a distributed file system, a parallel programming framework, and a resource/job management system. In healthcare, patient records, health plans, insurance information and other types of information is extremely difficult to manage – but, this very data is full of key insights, the need is for an effective data management and analytics that can be pragmatic. By analyzing large amounts of information – both structured and unstructured – rapidly, health care providers can provide lifesaving diagnoses. Keywords: Big Data, EBM, ETL, Hadoop

Towards an AI‐driven business development framework: A multi‐case study

AbstractArtificial intelligence (AI) and the use of machine learning (ML) and deep learning (DL) technologies are becoming increasingly popular in companies. These technologies enable companies to leverage big quantities of data to improve system performance and accelerate business development. However, despite the appeal of ML/DL, there is a lack of systematic and structured methods and processes to help data scientists and other company roles and functions to develop, deploy and evolve models. In this paper, based on multi‐case study research in six companies, we explore practices and challenges practitioners experience in developing ML/DL models as part of large software‐intensive embedded systems. Based on our empirical findings, we derive a conceptual framework in which we identify three high‐level activities that companies perform in parallel with the development, deployment and evolution of models. Within this framework, we outline activities, iterations and triggers that optimize model design as well as roles and company functions. In this way, we provide practitioners with a blueprint for effectively integrating ML/DL model development into the business to achieve better results than other (algorithmic) approaches. In addition, we show how this framework helps companies solve the challenges we have identified and discuss checkpoints for terminating the business case.

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends.

Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

IoT-Enabled Vehicle Speed Monitoring System

Millions of people lose their lives each year worldwide due to traffic law violations, specifically, over speeding. The existing systems fail to report most of such violations due to their respective flaws. For instance, speed guns work in isolation and cannot measure speed of all vehicles on roads at all spatial points. They can only detect the speed of the vehicle the line of sight of the camera. A solution is to deploy a huge number of speed guns at different locations on the road to detect and report vehicles that are over speeding. However, this solution is not feasible because it demands a large amount of equipment and computational resources to process such a big amount of data. In this paper, a speed detection framework is developed to detect vehicles’ speeds with only two speed guns, which can report speed even when the vehicle is not within the camera’s line of sight. The system is specifically designed for an irregular traffic scenario such as that of Pakistan, where it is inconvenient to install conventional systems. The idea is to calculate the average speed of vehicles traveling in a specific region, for instance, between two spatial points. A low-cost Raspberry Pi (RPi) module and an ordinary camera are deployed to detect the registration numbers on vehicle license plates. This hardware presents a more stable system since it is powered by a low consumption Raspberry Pi that can operate for hours without crashing or malfunctioning. More specifically, the entrance and exit locations and the time taken to get from one point to another are recorded. An automatic alert to traffic authorities is generated when a driver is over speeding. A detailed explanation of the hardware prototype and the algorithms is given, along with the setup configurations of the hardware prototype, the website, and the mobile device applications.

A GENERALIZED SYSTEM FOR MAPPING RELATIONAL AND NON-RELATIONAL (NoSQL) DATABASES

Nowadays systems form mapping relational and non-relational (NoSQL) databases are getting more and more popularity. Because today most businesses faced a reality, where they have to work with a big amount of data in order to make decisions that can affect to their place in the market. The data produced in today's world is growing day by day, which makes it hard to process and analyze it. Traditional relational databases cannot handle the work with big amount of data, while non-relational database is suiting more to work with it. Therefore it is important to make a generalized system which can map relational database to non-relational database. In this paper we introduce a novel methodology for migrating relational database to non-relational database based on two existing works.

Assessing the Environmental Performances of Urban Roundabouts Using the VSP Methodology and AIMSUN

In line with globally shared environmental sustainability goals, the shift towards citizen-friendly mobility is changing the way people move through cities and road user behaviour. Building a sustainable road transport requires design knowledge to develop increasingly green road infrastructures and monitoring the environmental impacts from mobile crowdsourced data. In this view, the paper presents an empirically based methodology that integrates the vehicle-specific power (VSP) model and microscopic traffic simulation (AIMSUN) to estimate second-by-second vehicle emissions at urban roundabouts. The distributions of time spent in each VSP mode from instantaneous vehicle trajectory data gathered in the field via smartphone were the starting point of the analysis. The versatility of AIMSUN in calibrating the model parameters to better reflect the field-observed speed-time trajectories and to enhance the estimation accuracy was assessed. The conversion of an existing roundabout within the sample into a turbo counterpart was also made as an attempt to confirm the reproducibility of the proposed procedure. The results shed light on new opportunities in the environmental performance evaluation of road units when changes in design or operation should be considered within traffic management strategies and highlighted the potential of the smart approach in collecting big amounts of data through digital communities.

“LAZARUS” THE NORTH KOREAN HACKER GROUP

The Democratic People’s Republic of Korea (DPRK) is famous for the poverty, destitution and backwardness, however in spite of these negative features it is among the most advanced cyber warfare countries. In the daily news quite often can be read about cyber-attacks against different states, media institutions or banks where the experts assume that the DPRK supported hacker group the “LAZARUS” is behind the attacks. According to the latest news the group in connection with stealing a big amount of crypto currency and money laundering got into the limelight. The state officially denies the existence of the group however cyber analysts and security experts found direct and circumstantial evidences that prove the connection between the North Korean state and the hacker group.

Estimate the Performance of Cloudera Decision Support Queries

Hive and Impala queries are used to process a big amount of data. The overwriting amount of information requires an efficient data processing system. When we deal with a long-term batch query and analysis Hive will be more suitable for this query. Impala is the most powerful system suitable for real-time interactive Structured Query Language (SQL) query which are added a massive parallel processing to Hadoop distributed cluster. The data growth makes a problem with SQL Cluster because the execution processing time is increased. In this paper, a comparison is demonstrated between the performance time of Hive, Impala and SQL on two different data models with different queries chosen to test the performance. The results demonstrate that Impala outperforms Hive and SQL cluster when it comes to analyze data and processing tasks. Using two benchmark datasets, TPC-H and statistical computing, we compare the performance of Hive, Impala, and SQL clusters 2009 Statistical Graphics Data Expo.

Baseline Methods for the Parameter Estimation of the Generalized Pareto Distribution.

In the parameter estimation of limit extreme value distributions, most employed methods only use some of the available data. Using the peaks-over-threshold method for Generalized Pareto Distribution (GPD), only the observations above a certain threshold are considered; therefore, a big amount of information is wasted. The aim of this work is to make the most of the information provided by the observations in order to improve the accuracy of Bayesian parameter estimation. We present two new Bayesian methods to estimate the parameters of the GPD, taking into account the whole data set from the baseline distribution and the existing relations between the baseline and the limit GPD parameters in order to define highly informative priors. We make a comparison between the Bayesian Metropolis–Hastings algorithm with data over the threshold and the new methods when the baseline distribution is a stable distribution, whose properties assure we can reduce the problem to study standard distributions and also allow us to propose new estimators for the parameters of the tail distribution. Specifically, three cases of stable distributions were considered: Normal, Lévy and Cauchy distributions, as main examples of the different behaviors of the tails of a distribution. Nevertheless, the methods would be applicable to many other baseline distributions through finding relations between baseline and GPD parameters via studies of simulations. To illustrate this situation, we study the application of the methods with real data of air pollution in Badajoz (Spain), whose baseline distribution fits a Gamma, and show that the baseline methods improve estimates compared to the Bayesian Metropolis–Hastings algorithm.

An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study.

IntroductionIdentifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning.Material and methodsWe conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation.Results1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality.ConclusionsMachine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.

Особенности культивирования российских и зарубежных сортов жимолости съедобной (Lonicera edulis Turcz.) in vitro

Introduction. The demand for honeysuckle berries and planting material is growing. Clonal micropropagation is the most effective method for industrial plantations. The research objective was to study the effect of cytokinins and auxins on Russian and Canadian honeysuckle microshoots and roots.
 Study objects and methods. The study featured regenerated honeysuckle (Lonicera edulis Turcz.) of three Russian cultivars (Bakcharsky Velikan, Doch Velikana, Yugana) and two Canadian cultivars (Boreal Beauty, Boreal Beast). The experiment focused on the effect of sterilizing agents and sterilization time on the viability of honeysuckle explants at the stage of culture introduction in vitro. The effect of the growth regulator Cytodef in the QL nutrient medium on organogenesis was studied at the stage of micropropagation proper, the effect of auxin IBA on plant root formation – at the stage of rooting in vitro.
 Results and discussion. The greatest viability of honeysuckle explants (80–94%) was registered in the samples affected by Lizoformin 3000 (5%) and silver nitrate (0.2%) as sterilizing agents with a sterilization time of 10 min at the stage of in vitro culture introduction. The biggest quantity (8.8 pcs.) and total length (40.1 cm) of microshoots were observed when the content of cytokinin Cytodef in the culture medium QL was 0.3 mg/L at the stage micropropagation proper. The Boreal Beast cultivar had the largest total length of shoots (29.0 cm). The biggest quantity (5.5 pcs.) and total length (30.8 cm) of roots resulted from 0.5 mg/L of auxin IBA at the stage of rooting in vitro. Coconut substrate produced the highest survival rate (92–99%) at the stage of adaptation to non-sterile conditions in vivo, with the greatest number of leaves (8.1–10.2 pcs.) observed in Canadian cultivars.
 Conclusion. Cytodef and IBA proved to be effective growth-regulating substances for microplants of Russian and Canadian honeysuckle cultivars in vitro, which makes them promising for berry plantations.

Multiple gastrointestinal magnetic foreign bodies in children

Objective: To introduce the analysis of the clinical picture, treatment strategy and complications among children having multiple foreign magnetic bodies (FB).Methods: Retrospective analysis of the treatment of 796 children from 2013 to September 2021 having stomach and intestine FB. Comprehensive study included plan radiography of the abdomen, fiberoptic gastroduodenoscopy, etc.Results: Magnets were detected in 33 (4.15%) out of 796 cases. 9 patients swallowed multiple foreign magnetic bodies (FB) within time interval. The parts fixed in different parts of gastrointestinal tract (GIT) and required surgical removal. All patients had multiple perforations of different GIT parts. It required suturing gut wall, bowel resection applying gastrointestinal anasto mosis and celiogrhaphy, resection applying anastomosis and by leaving the programmed laparostomy. Every patient recovered. Algorithm of supervision of children having FB GIT.Conclusions: Multiple foreign magnetic bodies of GIT among children in the current moment are the most dangerous foreign objects accompanied with big amount of complications. They are observed among children of any age and gender and require complicated and long surgical interventions. Multiple foreign magnetic bodies of GIT can be suspected in case of severe surgical pathology along with the lack of passage of radiopaque FB in GIT. Urgent surgical intervention is required in case of detecting it.

Denial of Human Superiority Over Nature as the Denial of the Value of Nature

Ecology as a science today, mainly rejects anthropocentrism in favour of nonhuman-centred ethics. Such rejection is propagated as a proper valuing of nature, while the human-centred eco-theories are considered to be the theoretic basis for the exploitation and destruction of nature by humans. The main purpose of some nonhuman-centred ecologic theories is to reduce the growth of the human’s population because the people’s existence, totally, is seen as a cause of ecological disasters, and even social problems. The aim of the article is to show that human beings are, in reality, the only living organisms on Earth, able to take care about nature as it deserves. The main problem is incorrect behaviour with nature, not a big amount of people living on the Earth. The ecological disasters, at the same time, are connected not only with humans’ irresponsible conduct, but with the natural forces that are independent from human activity, but that does not deprive people from the task to take responsibility for their environmental behaviour.

Calcium carbonate nanoparticles of quail’s egg shells: Synthesis and characterizations

Abstract Avian eggshell is a natural biomaterial that has been used as an alternative natural source of CaCO3 and is accessible in big amounts from egg manufacturing. This study was planned to estimate CaCO3 in quail’s eggshell because it has a probable use in the progress of a novel choice of many applications. Physical properties: mineralogical documentation of the natural eggshell nanoparticles were approved using XRD and FTIR to explore the chemical bond or molecular structure of the materials. Micrographs were obtained using FESEM/EDX and TEM to identify the morphology and size of nanoparticles. The results showed that quail eggshell was soft, with white to light sand color, and a smooth texture which allows good deposition of different color spots, from black to brown spots. The resulted of eggshells signifies almost 8.4% w/w of the overall weight (12.2) gram of quail egg and 91.60% w/w of the micropowder to the full weight of (0.94) gram of quail eggshell. The results presented that calcium is the main element in an eggshell; frequently occurs in a formula of CaCO3 and the crystal construction was almost pure calcite. FTIR spectra for quail eggshell demonstrated the existence of the out of plane bending, the asymmetric stretching, and the plane bending styles of the carbonate groups, specific of normal dolomite, situated at 873 cm–1, 1405 cm–1, and 710 cm–1, respectively. The FESEM and TEM for nanoparticles were shown calcite CaCO3 nanoparticles with an ordinary size of ≤ 100 nm for FESEM and with a variety size of ≤ 50 nm for TEM. Unfortunately, eggshell is an egg product manufacturing deposit. These incomes will let fast developments in proportional studies of the organic elements of avian eggshell and their purposeful consequences by usages of eggshell in nourishment and medicine which can be applied for many resolutions that diminish their consequence on environmental contamination.

К вопросу о направлениях совершенствования уголовного законодательства России, охраняющего общественные отношения в сфере экономической деятельности

The article analyses the changes in Chapter 22 of the Criminal Code of the Russian Federation on the basis of RF Federal Laws No. 500-FZ of December 27, 2019, No. 73-FZ of April 1, 2020, and No. 352-FZ of October 27, 2020. As it has been established, these changes are not enough to improve Chapter 22, since they affect only an insignificant part of its norms. The changes under discussion have expanded the scope of Article 191 of the Criminal Code of the Russian Federation, since the new version provides for liability for not only illegal traffic in precious metals, natural precious stones or pearls, but also for illegal traffic in amber, jade or other semiprecious stones. In addition, its Part 1 now contains an administrative prejudice. Similar to Part 1 of Article 191 of the Criminal Code of the Russian Federation, the above changes have resulted in administrative prejudice in Part 1 of Article 193 of the Criminal Code of the Russian Federation. Its new edition provides for wider evaluative features, such as “on a massive scale” and “in especially big amount”. As a result, its scope has narrowed, which can be considered as the liberalization of criminal legislation that protects public relations in economic activity. The legislator has precisely determined the “massive” and “especially big” amounts in relation to Articles 198, 199, 199.1, 199.3, 199.4 of the Criminal Code of the Russian Federation. Chapter 22 of the Criminal Code of the Russian Federation has been supplemented by Article 200.7, which provides for liability for bribery of an arbitrator, which indicates the “expanded” criminal law intervention in economic activity. The purpose of this “expansion” is to eliminate the legislative gap in the Chapter under discussion. Despite the changes, the problem of improving the Chapter has not been resolved, as it can be seen from the shortcomings of the legislative structures of Articles 169, 178, 183, 189, 190, 194 of the Criminal Code of the Russian Federation. The comparative analysis of the other norms that make up Chapter 22 of the Criminal Code of the Russian Federation has shown that most of these norms provide for criminal liability for acts that, by their legal nature, do not constitute an encroachment on public relations in economic activity. Thus, in order to improve the criminal legislation protecting these social relations, the norms prohibiting the commission of acts that are not encroachments on these relations should be “transferred” from Chapter 22 of the Criminal Code of the Russian Federation to its other Chapters. As a result, it has become possible to establish correctly the range of crimes against social relations in economic activity. They include crimes prohibited by Articles 171, 171.2, 171.3, 171.4, 172, 172.1, 172.2, 172.3, 178, 183, 195, 196, 197 of the Criminal Code of the Russian Federation. By their legal nature, these acts are various manifestations of unfair competition. Therefore, the considered improvement should include the criminalization of new forms of its manifestation, such as discrediting the business reputation of legal entities, misleading consumers, and illegal issuance of all types of loans. The decriminalization of certain acts prohibited by the norms that constitute Chapter 22 of the Criminal Code of the Russian Federation can be considered as a way to improve the criminal legislation protecting public relations in economic activity.

Study of Mechanical Performance of BN Grafted Graphene Oxide Hybrid Aerogel for Polypropylene Composites

Boron nitride‐strengthened polymer matrix composites based on 3‐dimensional porous materials are a considerable problem to develop. GO‐BN aerogel (GO‐BN) has been generated using a unique method for manufacturing large‐scale 3D BNs and graphene oxide (GO) aerogels. Propylene was then added to the aerogel and polymerized in situ to create GO–BN/PP nanocomposites. Afterwards, the outcomes show that BNs and GO‐BN were successfully established and that the 3D outlines using GO‐BN aerogel have outstanding mechanical properties. Around 1% of the nanocomposite was used in its construction. This aerogel’s thermal conductivity was 0.135 W/mK, and its mechanical properties were greatly improved over those of pristine PP, with increases of 10.12 percent in tensile strength and 48.4 percent in flexural strength and 61.5 percent in compression strength. They may now be produced in big quantities using this simple preparation procedure.

Digital image processing for urban sprawl classification in google earth

Maps of land use and land cover are necessary for studying how the earth's surface changes over time and how human activities affect their surroundings the expanding pool of available resources of remote sensing, particularly the wellarchived Sentinel-2B images with resolution of 10 metres were used to conduct the research. For land use and land cover maps, supervised categorization is used. However, ground truth is required to attain high classification accuracy. There is a need for high-quality samples in big quantities. It takes time and effort to collect ground truth samples. When it comes to ground truth, it might be costly and sometimes impossible to obtain. This work presented a method for using Google Earth Engine products as ground truth instead of manually labelling ground truth samples and proceeding with classification with region of interest to produce land use and land cover maps for 2021 in this study area, Amravati, on the Google Earth Engine platform in this paper. The accuracy test is carried out on randomly generated samples from various places, with an overall accuracy of roughly 83.4 percent.

MANAGERIAL COMPETENCE DEVELOPMENT IN THE CONTEXT OF PHILOSOPHY OF EDUCATION.

The aim: To analyze the problem of university management in the cultural and philosophical aspects. Materials and methods: The research was conducted in the form of a written questionnaire. Students were supposed to give a single answer to each question. The survey was conducted in September 2017-2018 and 2018-2019 academic year at Ternopil National Medical University. It encompassed 274 second-year Ukrainian students of medical, dental and pharmaceutical faculties and Institute of Nursing and 228 first, second and third year foreign students, who have come from the USA and study at the faculty of foreign students and International School of Medicine. Although the number of foreign students who participated in the survey was small, we believe that the number of respondents was sufficient enough to draw conclusions about the general trends in the issue under study. Results: Analysis of responses given by Ukrainian and American students allowed us to single out certain trends and specific differences in their outlook and professionally relevant persuasions and attitudes. A positive peculiarity is that the main motive that prompted Ukrainian students to choose a medical profession was that they believe it to be humane and noble (36%). Despite this, many students consider other important criteria when choosing a profession; particularly that it is prestigious and well-paid (21 % and 13 % respectively). The negative sign is that relatively big amount of students in the comparison with Western countries (the USA in particular), indicate that the occupational choice was influenced by their parents' desire (14 %). Conclusions: The role of pedoetology as one of the priorities of pedagogical science and practice, which should generalize managerial aspects, has been proved. The experience of university education of the USA in the questions of educational management has been analyzed. An overview of the basic concepts relevant to the management of American higher education has been conducted. The features of university management in the USA in modern conditions have been characterized. The differences in the field of professionally significant value orientations between Ukrainian and American students have been investigated. The emphasis is given to the role of teachers' philosophical culture in successful teaching and learning.

A Survey of Machine Learning for Big Data Processing

Today’s world is a data-driven one, with data being produced in vast amounts as a result of the rapid growth of technology that permeates every aspect of our lives. New data processing techniques must be developed and refined over time to gain meaningful insights from this vast continuous volume of produced data in various forms. Machine learning technologies provide promising solutions and potential methods for processing large quantities of data and gaining value from it. This study conducts a literature review on the application of machine learning techniques in big data processing. It provides a general overview of machine learning algorithms and techniques, a brief introduction to big data, and a discussion of related works that have used machine learning techniques in a variety of sectors to process big amounts of data. The study also discusses the challenges and issues associated with the usage of machine learning for big data.

Guest Editorial: Deep Learning For Genomics

The six papers in this special section focus on deep learning for genomics. Thanks to the development of high-throughput technologies, a huge amount of omics data is being produced relative to DNA and RNA sequences and (and also) abundance at individual subject or even at individual cell level. In particular, the genomics field is rich in data thanks to the rapid reduction in the cost of genetic sequencing. On the other hand, deep learning is transforming the field of many machine learning applications, such as computer vision and natural language processing, by effectively leveraging on big amount of data and is now emerging as a promising approach for many genomics modeling tasks. The scope of this special section is to discuss novel algorithms, methodologies and applications of deep learning to genomic studies with focus on their potentialities and challenges.