scholarly journals Formation of Human Capital in the Digital Economy

2020 ◽  
pp. 19-35
Author(s):  
Leonid Hr. Melnyk ◽  
Oleksandr M. Matsenko ◽  
Vladyslav S. Piven ◽  
Oleksandr M. Derykolenko ◽  
Maksym V. Kyrylenko
Keyword(s):  
Human Capital ◽  
Large Data ◽  
Digital Economy ◽  
Creative Industries ◽  
Data Sets ◽  
Property Relations ◽  
Main Thing ◽  
Main Components ◽  
Economic Formation ◽  
The Impact

The article studies the role and various manifestations of human capital in the functioning and development of the digital economy. The key context of the research is the phenomenon of reproduction of the components of human capital, including the processes of production and consumption. As an important prerequisite for reproduction, the phenomenon of streamlining the three basic origins of functioning of economic systems is analyzed: material, informational and synergetic. The relationship and interaction of these origins play an important role in the formation of various types of capital, including human capital. The article reveals the content and directions of investing in the main components of the reproductive cycle of the economic system: demand, property relations, performers, governing bodies, infrastructure, and natural factors. The article focuses on the analysis of the trialectic foundations of the reproduction of human capital through the formation and interaction of the essential origins of man: biological, social, and labor. The man-consumer of the digital economy is fundamentally different from the human-consumer of previous eras. The main thing is that the vast majority of personal needs become an end in itself, rather than a means of obtaining material benefits in the future. Man-producer will increasingly move from the impact on material objects of labor (change of shapes, sizes, properties) to the impact on information (development of creative industries, the selection of useful information from large data sets). Based on the analysis of the substantive basis of the three industrial revolutions (third, fourth, and fifth), transformational changes are predicted, which should take place during the phase of transition to a new socio-economic formation. The main ones are sustainization of human consciousness, digitalization, and networkization of competencies; formation of the ability to live in the conditions of Internet of Things; sociologization of development, etc.

Transformation of education as a metatrend of the digital economy

2020 ◽  
Vol 16 (10) ◽  
pp. 1960-1979
Author(s):  
N.A. Egina ◽  
E.S. Zemskova
Keyword(s):  
Human Capital ◽  
Social Transformation ◽  
Network Effects ◽  
Digital Economy ◽  
Network Education ◽  
Economic Progress ◽  
Positive Externalities ◽  
Communication Processes ◽  
Education Network ◽  
The Impact

Subject. The study focuses on the impact of the digital economy determinants of the education transformation. Objectives. The article provides our own approach treating the education capital as a specific asset of the digital economy, which has an acceleration effect and sets up new trends in education through integrative networks. Methods. The study is based on principles of the systems integration, cross-disciplinary and multidisciplinary approaches. Results. The socio-economic progress was found to be determined with properties of human capital, which are solely specific to the digital economy. In new circumstances, it gets more important for actors of global, national, corporate and social networks to more actively cooperate within distributed networks in order to train high professionals, who would have skills in information networks. Thus, they would raise a new form of human capital – the capital of network education (network-based education capital). We describe positive externalities that arise when the educational sector joins communication processes. We illustrate how educational forms evolves, which are typical of a certain phase of the socio-economic development. The education capital was discovered to grow into a specific asset generating the quasi-rent and working as a social ladder only provided more actors are involved into the network. Conclusions and Relevance. Studying the evolution of educational forms through the cross-disciplinary method, we discovered the need for a system approach, which would help substantiate its transformation in the time of the digital economy, and the emergence of network-based education. These are technologies and tools of the digital economy that become unique factors generating the acceleration effect of the educational capital and ensuring the use of diverse network effects for the formation of intellectual capital and their social transformation.

FORMATION OF HUMAN CAPITAL IN THE DIGITAL TRANSFORMATION PARADIGM

2020 ◽  
Vol 2 (5) ◽  
pp. 115-119
Author(s):  
M. V. SAVINA ◽  
◽  
A. A. STEPANOV ◽  
I.A. STEPANOV ◽  
◽  
...  
Keyword(s):  
Human Capital ◽  
Digital Age ◽  
Digital Transformation ◽  
Educational Process ◽  
Knowledge And Skills ◽  
Main Components ◽  
The Impact ◽  
Modern Era

The article highlights the problems of the impact of "digitalization" of society on the formation and transformation of human capital, and above all, the development of new competencies, knowledge and skills. The main components of human capital in the modern era, the features of the formal and informal educational process are clarified and disclosed. The necessity of minimizing the precariat class is proved. The main directions of qualitative improvement of human capital adequate to the challenges of the digital age and globalization are defined.

Multi-Variable, High Order, Performance Models (2005C)

2005 ◽  
Author(s):  
David Japikse ◽  
Oleg Dubitsky ◽  
Kerry N. Oliphant ◽  
Robert J. Pelton ◽  
Daniel Maynes ◽  
...  
Keyword(s):  
Data Processing ◽  
Large Data ◽  
High Order ◽  
Large Data Sets ◽  
Data Sets ◽  
Performance Models ◽  
Statistical Accuracy ◽  
Evaluation Methodologies ◽  
New Models ◽  
The Impact

In the course of developing advanced data processing and advanced performance models, as presented in companion papers, a number of basic scientific and mathematical questions arose. This paper deals with questions such as uniqueness, convergence, statistical accuracy, training, and evaluation methodologies. The process of bringing together large data sets and utilizing them, with outside data supplementation, is considered in detail. After these questions are focused carefully, emphasis is placed on how the new models, based on highly refined data processing, can best be used in the design world. The impact of this work on designs of the future is discussed. It is expected that this methodology will assist designers to move beyond contemporary design practices.

Visual Communication in Times of Crisis: The Fukushima Nuclear Accident

Leonardo ◽  
2012 ◽  
Vol 45 (2) ◽  
pp. 113-118 ◽  
Author(s):  
Rama C. Hoetzlein
Keyword(s):  
News Media ◽  
Visual Communication ◽  
Large Data ◽  
Nuclear Accident ◽  
Large Data Sets ◽  
Fukushima Nuclear Accident ◽  
Data Sets ◽  
On Line ◽  
The Impact ◽  
Information Aesthetics

This paper follows the development of visual communication through information visualization in the wake of the Fukushima nuclear accident in Japan. While information aesthetics are often applied to large data sets retrospectively, the author developed new works concurrently with an ongoing crisis to examine the impact and social aspects of visual communication while events continued to unfold. The resulting work, Fukushima Nuclear Accident—Radiation Comparison Map, is a reflection of rapidly acquired data, collaborative on-line analysis and reflective criticism of contemporary news media, resolved into a coherent picture through the participation of an on-line community.

The applying of machine learning methods to improve the quality of well casing

2020 ◽  
pp. 81-93
Author(s):  
D. V. Shalyapin ◽  
D. L. Bakirov ◽  
M. M. Fattakhov ◽  
A. D. Shalyapina ◽  
A. V. Melekhov ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
New Approach ◽  
Geological Conditions ◽  
Expert Assessments ◽  
The Impact ◽  
The Relationship

The article is devoted to the quality of well casing at the Pyakyakhinskoye oil and gas condensate field. The issue of improving the quality of well casing is associated with many problems, for example, a large amount of work on finding the relationship between laboratory studies and actual data from the field; the difficulty of finding logically determined relationships between the parameters and the final quality of well casing. The text gives valuable information on a new approach to assessing the impact of various parameters, based on a mathematical apparatus that excludes subjective expert assessments, which in the future will allow applying this method to deposits with different rock and geological conditions. We propose using the principles of mathematical processing of large data sets applying neural networks trained to predict the characteristics of the quality of well casing (continuity of contact of cement with the rock and with the casing). Taking into account the previously identified factors, we developed solutions to improve the tightness of the well casing and the adhesion of cement to the limiting surfaces.

scholarly journals Analysing the Impact of Large Data Imports in OpenStreetMap

2021 ◽  
Vol 10 (8) ◽  
pp. 528
Author(s):  
Raphael Witt ◽  
Lukas Loos ◽  
Alexander Zipf
Keyword(s):  
Large Data ◽  
Heterogeneous Data ◽  
Geographical Information ◽  
Data Sets ◽  
Heterogeneous Data Source ◽  
External Data ◽  
Time Period ◽  
Global Mapping ◽  
Data Source ◽  
The Impact

OpenStreetMap (OSM) is a global mapping project which generates free geographical information through a community of volunteers. OSM is used in a variety of applications and for research purposes. However, it is also possible to import external data sets to OpenStreetMap. The opinions about these data imports are divergent among researchers and contributors, and the subject is constantly discussed. The question of whether importing data, especially large quantities, is adding value to OSM or compromising the progress of the project needs to be investigated more deeply. For this study, OSM’s historical data were used to compute metrics about the developments of the contributors and OSM data during large data imports which were for the Netherlands and India. Additionally, one time period per study area during which there was no large data import was investigated to compare results. For making statements about the impacts of large data imports in OSM, the metrics were analysed using different techniques (cross-correlation and changepoint detection). It was found that the contributor activity increased during large data imports. Additionally, contributors who were already active before a large import were more likely to contribute to OSM after said import than contributors who made their first contributions during the large data import. The results show the difficulty of interpreting a heterogeneous data source, such as OSM, and the complexity of the project. Limitations and challenges which were encountered are explained, and future directions for continuing in this field of research are given.

Models of uniformity for grain boundary distributions

2009 ◽  
Vol 42 (5) ◽  
pp. 783-792 ◽  
Author(s):  
A. Morawiec
Keyword(s):  
Density Function ◽  
Large Data ◽  
Large Data Sets ◽  
Polycrystalline Materials ◽  
Data Sets ◽  
Boundary Density ◽  
Definition Of ◽  
The Impact ◽  
Orientation Determination ◽  
Strict Definition

Progress in experimental methods of serial sectioning and orientation determination opens new opportunities to study inter-crystalline boundaries in polycrystalline materials. In particular, macroscopic boundary parameters can now be measured automatically. With sufficiently large data sets, statistical analysis of interfaces between crystals is possible. The most basic and interesting issue is to find out the probability of occurrence of various boundaries in a given material. In order to define a boundary density function, a model of uniformity is needed. A number of such models can be conceived. It is proposed to use those derived from an assumed metric structure of the interface manifold. Some basic metrics on the manifold are explicitly given, and a number of notions and constructs needed for a strict definition of the boundary density function are considered. In particular, the crucial issue of the impact of symmetries is examined. The treatments of homo- and hetero-phase boundaries differ in some respects, and approaches applicable to each of these two cases are described. In order to make the abstract matter of the paper more accessible, a concrete boundary parameterization is used and some examples are given.

scholarly journals Data Science Methods for Psychology

Psychology ◽  
2020 ◽  
Author(s):  
Jeffrey Stanton
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Data Science ◽  
Large Data ◽  
Large Data Sets ◽  
Predictive Analysis ◽  
Data Sets ◽  
The Impact

The term “data science” refers to an emerging field of research and practice that focuses on obtaining, processing, visualizing, analyzing, preserving, and re-using large collections of information. A related term, “big data,” has been used to refer to one of the important challenges faced by data scientists in many applied environments: the need to analyze large data sources, in certain cases using high-speed, real-time data analysis techniques. Data science encompasses much more than big data, however, as a result of many advancements in cognate fields such as computer science and statistics. Data science has also benefited from the widespread availability of inexpensive computing hardware—a development that has enabled “cloud-based” services for the storage and analysis of large data sets. The techniques and tools of data science have broad applicability in the sciences. Within the field of psychology, data science offers new opportunities for data collection and data analysis that have begun to streamline and augment efforts to investigate the brain and behavior. The tools of data science also enable new areas of research, such as computational neuroscience. As an example of the impact of data science, psychologists frequently use predictive analysis as an investigative tool to probe the relationships between a set of independent variables and one or more dependent variables. While predictive analysis has traditionally been accomplished with techniques such as multiple regression, recent developments in the area of machine learning have put new predictive tools in the hands of psychologists. These machine learning tools relax distributional assumptions and facilitate exploration of non-linear relationships among variables. These tools also enable the analysis of large data sets by opening options for parallel processing. In this article, a range of relevant areas from data science is reviewed for applicability to key research problems in psychology including large-scale data collection, exploratory data analysis, confirmatory data analysis, and visualization. This bibliography covers data mining, machine learning, deep learning, natural language processing, Bayesian data analysis, visualization, crowdsourcing, web scraping, open source software, application programming interfaces, and research resources such as journals and textbooks.

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