scholarly journals The INSESS-COVID19 Project. Evaluating the Impact of the COVID19 in Social Vulnerability While Preserving Privacy of Participants from Minority Subpopulations

2021 ◽  
Vol 11 (7) ◽  
pp. 3110
Author(s):  
Karina Gibert ◽  
Xavier Angerri
Keyword(s):  
Social Vulnerability ◽  
Data Science ◽  
Policy Design ◽  
Data Driven ◽  
Informed Decision Making ◽  
Technological Infrastructure ◽  
Moderate Number ◽  
Reliable Diagnosis ◽  
Multivariate Relationships ◽  
The Impact

In this paper, the results of the project INSESS-COVID19 are presented, as part of a special call owing to help in the COVID19 crisis in Catalonia. The technological infrastructure and methodology developed in this project allows the quick screening of a territory for a quick a reliable diagnosis in front of an unexpected situation by providing relevant decisional information to support informed decision-making and strategy and policy design. One of the challenges of the project was to extract valuable information from direct participatory processes where specific target profiles of citizens are consulted and to distribute the participation along the whole territory. Having a lot of variables with a moderate number of citizens involved (in this case about 1000) implies the risk of violating statistical secrecy when multivariate relationships are analyzed, thus putting in risk the anonymity of the participants as well as their safety when vulnerable populations are involved, as is the case of INSESS-COVID19. In this paper, the entire data-driven methodology developed in the project is presented and the dealing of the small subgroups of population for statistical secrecy preserving described. The methodology is reusable with any other underlying questionnaire as the data science and reporting parts are totally automatized.

scholarly journals Dealing with Randomness and Concept Drift in Large Datasets

Data ◽  
2021 ◽  
Vol 6 (7) ◽  
pp. 77
Author(s):  
Kassim S. Mwitondi ◽  
Raed A. Said
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Data Science ◽  
Concept Drift ◽  
Real Life ◽  
Quality Education ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Address Data ◽  
The Impact

Data-driven solutions to societal challenges continue to bring new dimensions to our daily lives. For example, while good-quality education is a well-acknowledged foundation of sustainable development, innovation and creativity, variations in student attainment and general performance remain commonplace. Developing data -driven solutions hinges on two fronts-technical and application. The former relates to the modelling perspective, where two of the major challenges are the impact of data randomness and general variations in definitions, typically referred to as concept drift in machine learning. The latter relates to devising data-driven solutions to address real-life challenges such as identifying potential triggers of pedagogical performance, which aligns with the Sustainable Development Goal (SDG) #4-Quality Education. A total of 3145 pedagogical data points were obtained from the central data collection platform for the United Arab Emirates (UAE) Ministry of Education (MoE). Using simple data visualisation and machine learning techniques via a generic algorithm for sampling, measuring and assessing, the paper highlights research pathways for educationists and data scientists to attain unified goals in an interdisciplinary context. Its novelty derives from embedded capacity to address data randomness and concept drift by minimising modelling variations and yielding consistent results across samples. Results show that intricate relationships among data attributes describe the invariant conditions that practitioners in the two overlapping fields of data science and education must identify.

Should Attitudinal Views toward Innovation Development Play a Role in Policy in Peripheral EU Regions?: New Evidence from Vouchers Program in Northern Ireland

2018 ◽  
Vol 6 (2) ◽  
pp. 121-137
Author(s):  
Sean M. McDonald ◽  
Remi C. Claire ◽  
Alastair H. McPherson
Keyword(s):  
Economic Development ◽  
Northern Ireland ◽  
Policy Design ◽  
Regional Economic Development ◽  
Innovation Development ◽  
Regional Economic ◽  
Eu Regions ◽  
Future Policy ◽  
The Impact ◽  
New Evidence

The impact and effectiveness of policies to support collaboration for Research & Development (R&D) and Innovation is critical to determining the success of regional economic development. (O’Kane, 2008) The purpose of this paper is to evaluate the level of success of the Innovation Vouchers Program operated by Invest Northern Ireland (Invest NI) from 2009 to 2013 and address if attitudinal views towards innovation development should play in a role in future policy design in peripheral EU regions. 

What is the Business with AI? Preparing Future Decision Makers and Leaders

2020 ◽  
Author(s):  
Helena S. Wisniewski
Keyword(s):  
Data Science ◽  
Decision Makers ◽  
Management Skills ◽  
Business Courses ◽  
College Of Business ◽  
College Of Engineering ◽  
Investment Firm ◽  
Business Curricula ◽  
The University ◽  
The Impact

With companies now recognizing how artificial intelligence (AI), digitalization, the internet of things (IoT), and data science affect value creation and the maintenance of a competitive advantage, their demand for talented individuals with both management skills and a strong understanding of technology will grow dramatically. There is a need to prepare and train our current and future decision makers and leaders to have an understanding of AI and data science, the significant impact these technologies are having on business, how to develop AI strategies, and the impact all of this will have on their employees’ roles. This paper discusses how business schools can fulfill this need by incorporating AI into their business curricula, not only as stand-alone courses but also integrated into traditional business sequences, and establishing interdisciplinary efforts and collaborative industry partnerships. This article describes how the College of Business and Public Policy (CBPP) at the University of Alaska Anchorage is implementing multiple approaches to meet these needs and prepare future leaders and decision makers. These approaches include a detailed description of CBPP’s first AI course and related student successes, the integration of AI into additional business courses such as entrepreneurship and GSCM, and the creation of an AI and Data Science Lab in partnership with the College of Engineering and an investment firm.

scholarly journals Crise Económica em Portugal: Evolução da Incidência de Depressão e Correlação com o Desemprego

2020 ◽  
Vol 33 (13) ◽  
Author(s):  
Inês Laplanche Coelho ◽  
Mafalda Sousa-Uva ◽  
Nuno Pina ◽  
Sara Marques ◽  
Carlos Matias-Dias ◽  
...  
Keyword(s):  
Social Vulnerability ◽  
Ecological Study ◽  
Preventive Measures ◽  
Portuguese Population ◽  
Sentinel Network ◽  
Unemployment Rates ◽  
Positive Correlation ◽  
National Statistics ◽  
Using Data ◽  
The Impact

Introduction: Previous studies have found an increase in the incidence rate of depression between 2007 – 2013 in Portugal, with a positive correlation with the unemployment rate, namely, in men. So, it was hypothesized that this increase is related with the situation of economic crisis. This study aimed to investigate if the correlation between unemployment rates and the incidence of depression is maintained in the post-crisis period of economic recovery in Portugal (2016 – 2018).Material and Methods: An ecological study was carried out, using data from the General Practitioners Sentinel Network concerning depression incidence (first episodes and relapses) and data from the National Statistics Institute on unemployment rates in the Portuguese population. The correlation coefficient was estimated using linear regression and the results were disaggregated by sex.Results: Between 2016 and 2018, there was a consistent decrease in the incidence of depression in both sexes. During the 1995 – 2018 period, a positive correlation was observed between unemployment and depression, with a coefficient of 0.833 (p = 0.005) in males and of 0.742 (p = 0.022) in females.Discussion: The reduction in the incidence of depression in both sexes observed between 2016 – 2018 corroborates a positive correlation between unemployment and depression in the Portuguese population, previously observed between 2007 – 2013.Conclusion: This study highlights the need to monitor the occurrence of mental illness in the Portuguese population, especially in moments of greatest social vulnerability in order to establish preventive measures, as a way to mitigate the impact of future economic crises.

scholarly journals The Impact of Energy Consumption on the Three Pillars of Sustainable Development

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1372
Author(s):  
Silviu Nate ◽  
Yuriy Bilan ◽  
Danylo Cherevatskyi ◽  
Ganna Kharlamova ◽  
Oleksandr Lyakh ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Energy Consumption ◽  
Energy Density ◽  
Ecological Footprint ◽  
Policy Design ◽  
Disability Adjusted Life ◽  
The Social ◽  
Low Efficiency ◽  
Disability Adjusted Life Year ◽  
The Impact

The paper analyzes the impact of energy consumption on the three pillars of sustainable development in 74 countries. The main methodological challenge in this research is the choice of a single integral indicator for assessing the social component of sustainable development. Disability-adjusted life year (DALY), ecological footprint, and GDP (Gross domestic product) are used to characterize the social, ecological, and economical pillars. The concept of physics, namely the concept of density (specific gravity), is used. It characterizes the ratio of the mass of a substance to its volume, i.e., reflects the saturation of a certain volume with this substance. Thus, to assess the relationship between energy consumption and the three foundations of sustainable development, it is proposed to determine the energy density of the indicators DALY, the ecological footprint, and GDP. The reaction to changes in energy consumption is described by the elasticity of energy density functions, calculated for each of the abovementioned indicators. The state of the social pillar is mostly dependent on energy consumption. As for the changes in the ecological pillar, a 1% reduction in energy consumption per capita gives only a 0.6% ecological footprint reduction, which indicates a low efficiency of reducing energy consumption policy and its danger for the social pillar. The innovative aspect of the research is to apply a cross-disciplinary approach and a calculative technique to identify the impact that each of the pillars of sustainable development imposes on energy policy design. The policy of renewable energy expansion is preferable for all sustainable development pillars.

scholarly journals Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 99 ◽  
Author(s):  
Yueqi Gu ◽  
Orhun Aydin ◽  
Jacqueline Sosa
Keyword(s):  
Los Angeles ◽  
A Priori ◽  
Safety Data ◽  
Spatial Optimization ◽  
Optimal Number ◽  
Multidisciplinary Optimization ◽  
Data Driven ◽  
Fault System ◽  
Design Data ◽  
The Impact

Post-earthquake relief zone planning is a multidisciplinary optimization problem, which required delineating zones that seek to minimize the loss of life and property. In this study, we offer an end-to-end workflow to define relief zone suitability and equitable relief service zones for Los Angeles (LA) County. In particular, we address the impact of a tsunami in the study due to LA’s high spatial complexities in terms of clustering of population along the coastline, and a complicated inland fault system. We design data-driven earthquake relief zones with a wide variety of inputs, including geological features, population, and public safety. Data-driven zones were generated by solving the p-median problem with the Teitz–Bart algorithm without any a priori knowledge of optimal relief zones. We define the metrics to determine the optimal number of relief zones as a part of the proposed workflow. Finally, we measure the impacts of a tsunami in LA County by comparing data-driven relief zone maps for a case with a tsunami and a case without a tsunami. Our results show that the impact of the tsunami on the relief zones can extend up to 160 km inland from the study area.

DATA-DRIVEN DISCOVERY OF MATERIAL STATES IN COMPOSITES UNDER FATIGUE LOADS

2021 ◽  
Author(s):  
MUTHU RAM ELENCHEZHIAN ◽  
VAMSEE VADLAMUDI ◽  
RASSEL RAIHAN ◽  
KENNETH REIFSNIDER
Keyword(s):  
Domain Knowledge ◽  
Data Science ◽  
Damage Tolerance ◽  
Electrochemical Impedance ◽  
Research Work ◽  
Rate Of Change ◽  
Data Driven ◽  
Develop Model ◽  
Damage Development ◽  
Fatigue Loads

Our community has a widespread knowledge on the damage tolerance and durability of the composites, developed over the past few decades by various experimental and computational efforts. Several methods have been used to understand the damage behavior and henceforth predict the material states such as residual strength (damage tolerance) and life (durability) of these material systems. Electrochemical Impedance Spectroscopy (EIS) and Broadband Dielectric Spectroscopy (BbDS) are such methods, which have been proven to identify the damage states in composites. Our previous work using BbDS method has proven to serve as precursor to identify the damage levels, indicating the beginning of end of life of the material. As a change in the material state variable is triggered by damage development, the rate of change of these states indicates the rate of damage interaction and can effectively predict impending failure. The Data-Driven Discovery of Models (D3M) [1] aims to develop model discovery systems, enabling users with domain knowledge but no data science background to create empirical models of real, complex processes. These D3M methods have been developed severely over the years in various applications and their implementation on real-time prediction for complex parameters such as material states in composites need to be trusted based on physics and domain knowledge. In this research work, we propose the use of data-driven methods combined with BbDS and progressive damage analysis to identify and hence predict material states in composites, subjected to fatigue loads.

Into the Vicious Cycle of Precarity: Labour Market, Precarious Work, Social Vulnerability and Youth: The case of Greece within the EU context

2021 ◽  
Vol 7 (12) ◽  
pp. 474-496
Author(s):  
Nikos Papadakis ◽  
Maria Drakaki ◽  
Sofia Saridaki ◽  
Vassilis Dafermos
Keyword(s):  
European Union ◽  
Young People ◽  
Labour Market ◽  
Social Vulnerability ◽  
The State ◽  
The European Union ◽  
Development Education ◽  
Ongoing Research ◽  
Precarious Work ◽  
The Impact

Ιn the last decade, there has been a widespread expansion of both precarious work and precarious forms of employment (such as temporary and low-qualified jobs, seasonal and part-time jobs etc.), in which a growing share of young people work. The impact of precarious work on young people is likely to be permanent, while it seems to affect (even over-determine) their life courses. Non-smooth and early transitions into labour market are very likely to worsen progressively their long-term life chances (Lodovici & Semenza, 2012: 7). Undoubtedly, the long-lasting global economic Crisis and the subsequent Recession, has heavily affected the state of play in the labour market worldwide, provoking severe modifications both in the field of employment and countries’ social cohesion. Based on the above mentioned, the paper deals with precarious work in general, while it emphasizes precarious work among youth. It initially captures, briefly, the state of play in terms of the impact of the Crisis on the widening of the phenomenon of precarious work and then it focuses on theoretical insights and critical conceptual definitions concerning precariousness in the labour market. Further, based on secondary quantitative -data analysis, it analyses the key- parameters and facets of precarious work (focusing on youth) in the European Union and, mainly, in Greece. Additionally, it briefly presents parameters of the impact of the COVID-19 pandemic on precariousness in Greece. Finally, the paper explores the correlation between precarious work and social vulnerability, especially among young people. The present paper is based on an ongoing Research Project. More specifically, this research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning 2014-2020» in the context of the project “Precarious Work and Youth in today’s Greece: secondary quantitative analysis, qualitative filed research and research-based policy proposals” (MIS 5048510).

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