scholarly journals Universal Digital Twin – the impact of heat pumps on social inequality

2021 ◽  
pp. 100079
Author(s):  
Thomas Savage ◽  
Jethro Akroyd ◽  
Sebastian Mosbach ◽  
Michael Hillman ◽  
Franziska Sielker ◽  
...  
Keyword(s):  
Social Inequality ◽  
Heat Pumps ◽  
Digital Twin ◽  
The Impact

On the Impact of Heat Pumps Electric Load on the Power Consumption of Lampedusa

2020 ◽  
Author(s):  
Marina Bonomolo ◽  
Mariano Giuseppe Ippolito ◽  
Giuliana Leone ◽  
Rossano Musca ◽  
Vincenzo Porgi ◽  
...  
Keyword(s):  
Power Consumption ◽  
Heat Pumps ◽  
Electric Load ◽  
The Impact

Investigating the Impact of Human in-the-Loop Digital Twin in an Industrial Maintenance Context

2020 ◽  
Author(s):  
Ali Al-Yacoubb ◽  
Will Eaton ◽  
Melanie Zimmer ◽  
Achim Buerkle ◽  
Dedy Ariansyaha ◽  
...  
Keyword(s):  
Digital Twin ◽  
Human In The Loop ◽  
Industrial Maintenance ◽  
The Impact

scholarly journals Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

2021 ◽  
Vol 11 (10) ◽  
pp. 4602
Author(s):  
Farzin Piltan ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Variable Structure ◽  
Vibration Signal ◽  
Crack Size ◽  
Approximation Technique ◽  
Digital Twin ◽  
Average Accuracy ◽  
Machine Learning Approach ◽  
The Impact ◽  
Signal Approximation

In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%.

Assessing the impact of automation in pharmaceutical quality control labs using a digital twin

2022 ◽  
Vol 62 ◽  
pp. 270-285
Author(s):  
Tiago Coito ◽  
Miguel S.E. Martins ◽  
Bernardo Firme ◽  
João Figueiredo ◽  
Susana M. Vieira ◽  
...  
Keyword(s):  
Quality Control ◽  
Digital Twin ◽  
Pharmaceutical Quality ◽  
The Impact

Wzbogacona rzeczywistość a nierówności społeczne

2021 ◽  
Vol 66 (2) ◽  
pp. 80-93
Author(s):  
Piotr Nieradka ◽  
Keyword(s):  
Social Inequality ◽  
Digital Divide ◽  
Social Inequalities ◽  
Individual Characteristics ◽  
Advanced Technology ◽  
Original Research ◽  
Augmented Reality Technology ◽  
Digital Gap ◽  
The One ◽  
The Impact

This paper focuses on the technology of extended reality, whereby the aim is to draw attention to selected issues related to technology and solutions in the extended reality area in the context of social inequalities. The first part of the article discusses the essence of extended reality technology, paying attention to its place in the modern economy, along with a description of such solutions. The remainder of the study focuses on the issue of social inequality, with particular emphasis on the impact of extended reality on the problem of the digital gap and digital divide. The article presents the results of original research undertaken on a group of 88 respondents with both CAWI and equipment supporting augmented reality technology, of which 39.36% declared testing with this type of equipment in the past. The article also presents the results of original research with the use of XR equipment. XR-related solutions are currently still perceived as luxury goods, despite both the increasing availability for users and undoubtedly unique advantages. These include realism of generated experiences and the phenomenon of immersion in the synthetic world. The analysis of the role of such solutions in the context of social inequality provides a conclusion about their dichotomous nature. On the one hand, by popularizing the XR technology, the possibility of overcoming barriers and inequalities resulting from individual characteristics or social position is created, which leads to the improvement in the life situations of some people who have been struggling with exclusion so far. On the other hand, it provides the conclusion that such advanced technology entering everyday life has the potential to create another digital divide, which in the long term will result in increasing digital disproportions and exclusion.

The Impact of Socio-Economic Position on Perceived Oppression: Using Social Support as a Mediator

2021 ◽  
Vol 36 (2) ◽  
pp. 1-19
Author(s):  
Lee Nakyung ◽  
Im Tobin
Keyword(s):  
Social Support ◽  
Social Inequality ◽  
Cultural Imperialism ◽  
Hierarchical Regression ◽  
Economic Downturn ◽  
New Paradigm ◽  
Network Ties ◽  
Distribution Of Resources ◽  
Economic Position ◽  
The Impact

Within the continuing economic downturn, our society has been facing rapid dichotomization and bi-polarization between groups which reinforces social inequality. Young (2011) states that social inequality should be viewed as a concept of oppression, not by the distribution of resources, which has been considered as a general indicator of social inequality. Following her argument, she suggests exploitation, marginalization, powerlessness, cultural imperialism, and violence as sub-concepts of oppression. In this sense, this study uses the distribution of resources as an independent variable and the ‘perceived oppression’ presented by Young as a dependent variable to identify the role of social support as a factor that makes the difference between social-structural inequality and contextual inequality. Through hierarchical regression analyses and bootstrapping methods this paper looks at how the two different perspectives on inequality are related, and how the social support mediates the relationship between socio-economic position and perceived oppression. By shedding light on the meaning of socio-economic isolation of individuals, this study will contribute to the academia in searching for the alternatives to strengthen the stability of our society where the new paradigm of communication is being used to form network ties and corresponding sense of supports.

AI Assisted High Fidelity Multi-Physics Digital Twin of Industrial Gas Turbines

2021 ◽  
Author(s):  
Senthil Krishnababu ◽  
Omar Valero ◽  
Roger Wells
Keyword(s):  
Gas Turbine ◽  
Data Storage ◽  
Gas Turbines ◽  
Building Blocks ◽  
Data Driven ◽  
High Fidelity ◽  
Test Field ◽  
Operating Characteristics ◽  
Digital Twin ◽  
The Impact

Abstract Data driven technologies are revolutionising the engineering sector by providing new ways of performing day to day tasks through the life cycle of a product as it progresses through manufacture, to build, qualification test, field operation and maintenance. Significant increase in data transfer speeds combined with cost effective data storage, and ever-increasing computational power provide the building blocks that enable companies to adopt data driven technologies such as data analytics, IOT and machine learning. Improved business operational efficiency and more responsive customer support provide the incentives for business investment. Digital twins, that leverages these technologies in their various forms to converge physics and data driven models, are therefore being widely adopted. A high-fidelity multi-physics digital twin, HFDT, that digitally replicates a gas turbine as it is built based on part and build data using advanced component and assembly models is introduced. The HFDT, among other benefits enables data driven assessments to be carried out during manufacture and assembly for each turbine allowing these processes to be optimised and the impact of variability or process change to be readily evaluated. On delivery of the turbine and its associated HFDT to the service support team the HFDT supports the evaluation of in-service performance deteriorations, the impact of field interventions and repair and the changes in operating characteristics resulting from overhaul and turbine upgrade. Thus, creating a cradle to grave physics and data driven twin of the gas turbine asset. In this paper, one branch of HFDT using a power turbine module is firstly presented. This involves simultaneous modelling of gas path and solid using high fidelity CFD and FEA which converts the cold geometry to hot running conditions to assess the impact of various manufacturing and build variabilities. It is shown this process can be executed within reasonable time frames enabling creation of HFDT for each turbine during manufacture and assembly and for this to be transferred to the service team for deployment during field operations. Following this, it is shown how data driven technologies are used in conjunction with the HFDT to improve predictions of engine performance from early build information. The example shown, shows how a higher degree of confidence is achieved through the development of an artificial neural network of the compressor tip gap feature and its effect on overall compressor efficiency.

scholarly journals Can the implementation of the Europe 2020 Strategy goals be efficient? The challenge for achieving social equality in the European Union

Equilibrium ◽  
2017 ◽  
Vol 12 (3) ◽  
Author(s):  
Michaela Stanickova
Keyword(s):  
European Union ◽  
Income Inequality ◽  
Labour Market ◽  
Social Inequality ◽  
The European Union ◽  
Social Equality ◽  
Europe 2020 ◽  
Europe 2020 Strategy ◽  
The Impact ◽  
The Eu

Research background: Economic crisis hit all the European Union Member States hard, with the impact of crisis varying considerably. The low growth performance in the EU has increased concerns regarding an increasing wage dispersion, income inequality at large, and social exclusion in line with poverty. Inequality should be seen as a cornerstone of both sustainable and inclusive growth under the Europe 2020 Strategy. Social inequality in the EU is a real problem, which hampers sustainable economic growth. Purpose of the article: The purpose of this study is to introduce evaluation of social development convergence and divergence trends between the EU Member States in the context of the Europe 2020 Strategy. The study gives an outline of the issues of the labour market and income disparities and poverty. Policymakers must be clear about what social objectives they are aiming to achieve, therefore special attention is paid to headline national goals of the Europe 2020 Strategy. Methods: The main task of this study is to assess social dimension and inequalities problems in the EU27 by applying Data Envelopment Analysis method, resp. time-series dynamic efficiency analysis in the form of output-oriented Malmquist Productivity Index. This study contains changes of key social equality indicators related to the Europe 2020 Strategy and compares objectives and general outlines of period 2010-2015, as well as the impact on national economics and living conditions. Findings & value added: Results contain elements of typology premises of the EU28 and point to a large diversity in inequality patterns, as the Author observes both increases and decreases in inequality at the EU level. Recent changes in social inequality have been associated with the business cycle, particularly with the accessibility of the labour market and, of course, with income inequality. Additionally, the development challenges are discussed for improvement of the socioeconomic well-being of the EU and to avoid social disparities.

scholarly journals Analyzing the Manufacturing Supply Chain Performance for Urgent Item During COVID-19 Outbreak

2021 ◽  
Vol 9 (1) ◽  
pp. 15-31
Author(s):  
Ali Arishi ◽  
Krishna K Krishnan ◽  
Vatsal Maru
Keyword(s):  
Raw Materials ◽  
Early Stage ◽  
Supply And Demand ◽  
Network Models ◽  
Combine System ◽  
Neural Network Models ◽  
Digital Twin ◽  
Twin Model ◽  
The Impact ◽  
Reaction Patterns

As COVID-19 pandemic spreads in different regions with varying intensity, supply chains (SC) need to utilize an effective mechanism to adjust spike in both supply and demand of resources, and need techniques to detect unexpected behavior in SC at an early stage. During COVID-19 pandemic, the demand of medical supplies and essential products increases unexpectedly while the availability of recourses and raw materials decreases significantly. As such, the questions of SC and society survivability were raised. Responding to this urgent demand quickly and predicting how it will vary as the pandemic progresses is a key modeling question. In this research, we take the initiative in addressing the impact of COVID-19 disruption on manufacturing SC performance overwhelmed by the unprecedented demands of urgent items by developing a digital twin model for the manufacturing SC. In this model, we combine system dynamic simulation and artificial intelligence to dynamically monitor SC performance and predict SC reaction patterns. The simulation modeling is used to study the disruption propagation in the manufacturing SC and the efficiency of the recovery policy. Then based on this model, we develop artificial neural network models to learn from disruptions and make an online prediction of potential risks. The developed digital twin model is aimed to operate in real-time for early identification of disruptions and the respective SC reaction patterns to increase SC visibility and resilience.

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