A multivariate approach to the identification of initial smart  specialisations of Polish voivodeships

Bal-Domańska, Beata; Sobczak, Elżbieta; Stańczyk, Elżbieta

doi:10.24136/eq.2020.034

Cited by 12 publications

(7 citation statements)

References 41 publications

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“…Smart production management, remote sensing and immersive visualization systems, virtual simulation algorithms, and data-driven planning technologies [97][98][99][100] articulate autonomous manufacturing processes. Cyber-physical production and robotized manufacturing systems [101][102][103][104][105] integrate cloud computing technologies, data mining and predictive modeling algorithms [106][107][108], and virtual mapping tools.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence-based decision-making algorithms, biometric sensor and immersive 3D technologies, and event modeling and forecasting tools articulate big-data-driven cognitive manufacturing. Decision intelligence and modeling tools, visual imagery technologies, and spatial computing algorithms assist interconnected virtual services in cyber-physical management systems [106][107][108]. Predictive modeling tools and visual perception algorithms enable ambient sound recognition software across IoT sensing networks.…”

Section: Deployment Of Citygml In Iortmentioning

confidence: 99%

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Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things

Andronie

Lăzăroiu

Iatagan

et al. 2023

IJGI

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The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising “Internet of Robotic Things” + “big data management algorithms”, “deep learning-based object detection technologies”, and “geospatial simulation and sensor fusion tools”. As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Deployment Of Citygml In Iortmentioning

confidence: 99%

Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things

Andronie

Lăzăroiu

Iatagan

et al. 2023

IJGI

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“…717-734) and how science and the economy interact (OECD 2000, pp. 168-172;Bal-Domańska, Sobczak, and Stańczyk 2020;Gorączkowska 2020).…”

Section: Maloneyunclassified

The Innovation Gap of National Innovation Systems in the European Union

Dworak

Grzelak

2023

CER

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The main aim of the paper is to assess the innovation gap between the national innovation systems (NIS) of the European Union (EU) and the average level of innovation of EU economies. The study takes into account NIS identified in the literature, i.e., (a) developed systems and (b) developing systems. In the theoretical part of the paper, the literature in the fields of NIS and the innovation gap is reviewed, the definitions and selected classifications of NIS around the world are presented, and the concept of the innovation gap between countries is defined. In the empirical part, the level of innovation in EU economies is assessed using Hellwig’s synthetic development indicator. In order to measure the level of innovation in individual NISs, arithmetic means of national values of the synthetic measure of development (innovation) are used. The innovation gap is calculated as the quotient between the level of innovation of individual NISs analyzed in the study and the average level of innovation in EU economies. The study covered 2010 and 2021. The paper formulates the following research hypothesis: the level of innovation in EU economies is determined by the type of NIS. Consequently, developing system countries are less innovative and, thus, are characterized by an innovation gap in relation to the EU average. The results of the study confirm the hypothesis. The relationship between the innovation level of the EU economies and the type of NIS, as well as the assessment of the innovation gap between the national innovation systems of the EU and the average level of innovation of the EU economies, constitute the value‑added of the paper.

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“…The rankings mentioned above are only concerned with the environmental dimension. Nevertheless, this dimension is also included within broader studies that rank sustainable cities [ 54 , 55 , 56 ], smart cities [ 57 , 58 , 59 , 60 ], or smart sustainable cities [ 33 , 61 ]. In such cases, the environmental dimension is one of several elements considered alongside, for instance, ICT, living, economy, mobility, people, and government.…”

Section: Rankings and Indicators Of Environmental Sustainabilitymentioning

confidence: 99%

Environmental Sustainability Assessment of the European Union’s Capital Cities

Czupich

Łapińska

Bartoš

2022

IJERPH

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The growing flow of people into cities causes several challenges for their functioning. This brings the need to ensure, for example, efficient transport, sustainable waste, and appropriate energy policy. Particularly capital cities are exposed to the above-mentioned risks, due to their large numbers and densities of inhabitants. Therefore, the state of the environment in cities should be monitored systematically. The research aim of this article is to evaluate the level of environmental sustainability in capitals of European Union countries. A synthetic indicator was made up of diagnostic variables, using quantitative and qualitative indicators relating to the quality of the environment. Based on the ranking, results reveal that Europe is spatially divided according to the level of environmental quality. The best results were achieved by capital cities of the northern European countries. The analysis presented here has some application potential. It can serve to identify challenges to improving the quality of the environment, and to raise public awareness of the importance of changing individual behaviour (e.g., use of public transport).

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A multivariate approach to the identification of initial smart specialisations of Polish voivodeships

Cited by 12 publications

References 41 publications

Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things

Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things

The Innovation Gap of National Innovation Systems in the European Union

Environmental Sustainability Assessment of the European Union’s Capital Cities

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