How to search?
advanced search

Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 4
items per page: 25 50 75
Sort by:

Deep learning classification and recognition method for milling surface roughness combined with simulation data

Lingli Lu Huaian Yi Aihua Shu Jianhua Qin Enhui Lu
Metrology and Measurement Systems | 2023 | vol. 30 | No 1 | 117-138 | DOI: 10.24425/mms.2023.144401
Keywords Milling surface classification deep neural network simulation
Download PDF Download RIS Download Bibtex

Abstract

To address the problem that a deep neural network needs a sufficient number of training samples to have a good prediction performance, this paper firstly used the Z-Map algorithm to generate a simulated profile of the milling surface and construct an optical simulation model of surface imaging to supplement the training sample size of the neural network. Then the Deep CORAL model was used to match the textures of the simulated samples and the actual samples across domains to solve the problem that the simulated samples were not in the same domain as the actual milling samples. Experimental results have shown that high texture matching could be achieved between optical simulation images and actual images, laying the foundation for expanding the actual milled workpiece images with the simulation images. The deep convolutional neural model Xception was used to predict the classification of six classes of data sets with the inclusion of simulation images, and the accuracy was improved from 86.48% to 92.79% compared with the model without the inclusion of simulation images. The proposed method solves the problem of the need for a large number of samples for deep neural networks and lays the foundation for similar methods to predict surface roughness for different machining processes.
Go to article

Authors and Affiliations

Lingli Lu
1
Huaian Yi
1
Aihua Shu
1
Jianhua Qin
1
Enhui Lu
2
  1. School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, People’s Republic of China
  2. School of Mechanical Engineering, Yangzhou University, Yangzhou, 225009, People’s Republic of China

Temporal isolation between two strongly differentiated stocks of the Greenland halibut ( Reinhardtius hippoglossoides Walbaum, 1792) from the Western Barents Sea

Barbara Wojtasik Agnieszka Kijewska Monika Mioduchowska Barbara Mikuła Jerzy Sell
Polish Polar Research | 2021 | vol. 42 | No 2 | 117-138 | DOI: 10.24425/ppr.2021.136603
Keywords Arctic allozymes discrete stocks genetic diversity heavy metals
Download PDF Download RIS Download Bibtex

Abstract

Identification of discrete stocks of Greenland halibut is an important aspect of proper fisheries exploitation. Available literature data indicated a lack of significant inter‑area differences between Greenland halibut populations from the Northeast Atlantic. To define the population diversity, two aspects were taken into account: enzyme‑genetic diversity and the concentration of heavy metals in tissues. Seven allozyme loci variations were used to characterize the genetic structure of four populations of Greenland halibut from the Western Barents Sea region. The samples were collected from the spawning area in the period when this species took migration to spawn. The sample RH4 was significantly different from the other samples collected from the same location (RH2 and RH3) and depth for over two days. Another sample (RH8), collected from the nearby area 6 days later was similar to the samples RH2, RH3. We noticed a significant divergence between the sample RH4 and the three remaining samples, where the value of the index FST fluctuated about 0.40 and approximately 0.01 between three similar populations. This genetic fluctuation negates the thesis of a panmictic character of the Western Atlantic population. Feeding groups of Greenland halibut are moving along the Barents Sea shelf and they are exposed to different heavy metals concentrations according to the food preferences or the exact place of feeding. We identified similar concentrations of heavy metals, i.e., Zn, Cu, Cd, and Pb in all samples. Trace metal analysis of aquatic organisms from the Barents Sea can provide important information on the degree of environmental contamination, and the potential impact of seafood consumption.
Go to article

Authors and Affiliations

Barbara Wojtasik
1
Agnieszka Kijewska
2
Monika Mioduchowska
1 3
Barbara Mikuła
4
Jerzy Sell
1
  1. University of Gdansk, Department of Genetics and Biosystematics, Gdansk, Poland
  2. Institute of Oceanology, Polish Academy of Sciences, Sopot, Poland
  3. University of Gdansk, Department of Marine Plankton Research, Gdynia, Poland
  4. University of Silesia, Department of Analytical Chemistry, Faculty of Chemistry, Katowice, Poland

Social Cohesion in Decentralized Ukraine: From Old Practices to New Order

Oleksandra Deineko
Studia Socjologiczne | 2021 | No 1 | 117-138 | DOI: 10.24425/sts.2021.136281
Keywords social cohesion amalgamated hromada hromadap decentralization Ukrainian society
Download PDF Download RIS Download Bibtex

Abstract

The article presents a systematic study of social cohesion phenomenon at the level of amalgamated hromadas as a key local entity of decentralization reform in Ukraine. Building on the analysis of the 26 semi-structured interviews conducted in amalgamated hromadas of two border regions of Ukraine – Kharkiv and Chernivtsi, the author has identified social cohesion components, their interconnection as well as positive and negative factors of social cohesion strengthening at community level. Relying on Chan’s empirical model and perceived perspective of social cohesion, hromada amalgamation is conceptualized as a transformation process of avoiding ‘old practices’ to form ‘new order’. In the process, the establishing of democratic tools, local activist growth, reducing gaps between center and periphery, formation of common sociocultural space are emphasized. Strengthening social cohesion components at the hromada level are stated to become a sure basis for ‘a giant leap’ of Ukraine’s democratisation in the nearest future.
Go to article

Bibliography

1. Aasland, Aadne, and Oleksii Lyska. 2016. Local democracy in Ukrainian cities: civic participation and responsiveness of local authorities. Post-Soviet Affairs, 32, 2: 152–175.
2. Babenko, Svetlana. 2002. Life Strategies and Social Practices: Sociocultural Potential of the Post-Soviet Society Transformation. Methodology, theory and practice of sociological analysis of society, 73, 2: 57–64.
3. Bader, Max. 2020. Decentralization and a Risk of Local Elite Capture in Ukraine. In: H. Shelest and M. Rabinovych, eds. Decentralization, Regional Diversity, and Conflict. The Case of Ukraine. Basingstocke: Palgrave McMillan, 259–282.
4. Berger-Schmitt, Regina. 2002. Considering social cohesion in quality of life assessments: Concept and measurement. Social indicators research, 58, 1-3: 403–428.
5. Bollen, K. A. and Hoyle, R. H. 1990. Perceived cohesion: A conceptual and empirical examination. Social forces, 23, 3: 479–504.
6. Bottoni, Gianmaria. 2018. A multilevel measurement model of social cohesion. Social Indicators Research, 136, 3: 835–857.
7. Chan, Joseph, Ho-Pong To, and Elaine Chan. 2006. Reconsidering social cohesion: Developing a definition and analytical framework for empirical research. Social indicators research, 75, 2: 273–302. 8.
Council of Europe. 2008. Report of high-level task force on social cohesion: Towards an active, fair, and socially cohesive Europe. Strasbourg: Council of Europe.
9. Delhey, Jan, Klaus Boehnke, Georgi Dragolov, Zsófia S Ignácz, Mandi Larsen, Jan Lorenz, and Michael Koch. 2018. Social Cohesion and its Correlates: A Comparison of Western and Asian Societies. Comparative Sociology, 17, 3-4: 426–455.
10. Dickes, Paul, and Marie Valentova. 2013. Construction, validation and application of the measurement of social cohesion in 47 European countries and regions. Social indicators research, 113, 3: 827–846.
11. Dragolov, Georgi, Zsófia Ignácz, Jan Lorenz, Jan Delhey, and Klaus Boehnke. 2013. Social cohesion radar measuring common ground: An international comparison of social cohesion methods report. http://aei.pitt.edu/74134/1/Social_cohesion_radar.pdf, accessed 30.09.2020.
12. Fonseca, Xavier, Stephan Lukosch, and Frances Brazier. 2019. Social cohesion revisited: a new definition and how to characterize it. Innovation: The European Journal of Social Science Research, 32, 2: 231–253.
13. Golovakha, Evgeniy I., and Panina, Nataliya V. 2006. Main Stages and Trends in the Transformation of Ukrainian Society: from Perestroika to the Orange Revolution. Sociology: Theory, Methods, Marketing 3: 32–51.
14. Jenson, Jane. 1998. Mapping social cohesion: The state of Canadian research. Ottawa: Canadian policy research networks.
15. Kutsenko, Olga. 2004. Phases and Ways of Systemic Transformations: Similarities and Differences in the Former Countries of State Socialism. In: O. Kutsenko, S. Babenko, eds. Postcommunist Transformations: Vectors, Dimensions, Content. Kharkiv: V.N. Karazin Kharkiv National University Publisher, 251–274.
16. Law of Ukraine ‘About Voluntary Amalgamation of Territorial Hromadas’. 2015. https://zakon.rada.gov.ua/laws/show/157-19#Text, accessed 30.09.2020.
17. Mansfield, Edward D., and Jack Snyder. 2002. Democratic Transitions, Institutional Strength, and War. International Organization, 56, 2: 297–337.
18. Monitoring of the Decentralization Process and Local Self-Government Reform. 2020. https://decentralization .gov.ua/mainmonitoring, accessed 30.09.2020.
19. Oleinekova, Olga 2020. Decentralization Reform: An Effective Vehicle for Modernization and Democratization in Ukraine? In: H. Shelest and M. Rabinovych, eds. Decentralization, Regional Diversity, and Conflict. The Case of Ukraine. Basingstocke: Palgrave McMillan, 311–338.
20. Order of the Cabinet of Ministers of Ukraine ‘On Approval of the Concept of Reforming Local Self-Government and Territorial Organization of Power in Ukraine’. 2014. https://zakon5.rada.gov.ua/laws/show/333-2014-%D1%80, accessed 30.09.2020.
21. Order of the Cabinet of Ministers of Ukraine ‘Concept of Reforming Local Self-Government and Territorial Structure of Power’. 2014. https://zakon.rada.gov.ua/laws/show/333-2014-%D1%80#Text, accessed 25.11.2020.
22. Perspectives on Global Development 2012: Social Cohesion in a Shifting World. OECD Publishing. https://www.oecd-ilibrary.org/development/perspectives-on-global-development-2012_persp_glob_dev-2012-en, accessed 30.09.2020.
23. Rabinovych, M., Shelest, H. 2020. Introduction: Regional Diversity, Decentralization, and Conflict in and around Ukraine. In: H. Shelest and M. Rabinovych, eds. Decentralization, Regional Diversity, and Conflict. The Case of Ukraine. Basingstocke: Palgrave McMillan.
24. Rajulton, F., Ravanera, Z. R. and Beaujot, R. 2007. Measuring social cohesion: An experiment using the Canadian national survey of giving, volunteering, and participating. Social Indicators Research, 80, 3: 461–492.
25. Riabchuk, Mykola. 2015. The ‘Two Ukraines’ Reconsidered: The End of Ukrainian Ambivalence? Studies in Ethnicity and Nationalism, 15, 1: 138–156.
26. Resolution of the Verkhovna Rada of Ukraine ‘State Strategy for Regional Development 2015 - 2020’. 2014. https://zakon.rada.gov.ua/laws/show/385-2014-%D0%BF#Text, accessed 23.11.2020.
27. Report ‘Сonflicts in the Amalgamated Territorial Hromadas: Types, Reasons, Forms, Consequences’. 2018. Association of Impowering Self-Organization of Population.Kropyvnytskyi.
28. Romanova, Valentyna, and Andreas Umland. 2019. Decentralising Ukraine: Geopolitical Implications. Survival, 61, 5: 99–112.
29. Rotterdam Resilient Strategy. 2020. https://www.resilientrotterdam.nl/en/download, accessed 30.11.2020.
30. Tkachuk, A.F. 2018. From a Perspective Plan to the Community Development Strategy (Through the Prism of the Experience of the Amalgamated Territorial Hromadas in Khmelnytskyi Region). Kiev: Juston Publishing House LLC.
31. Zhurzhenko, Tatiana. 2014. Divided Nation? Reconsidering the Role of Identity Politics in the Ukraine Crisis. Die Friedens-Warte, 89, 1/2: 249–267.
32. Ukrainian society 1992–2013. Current state and dynamics of changes. Sociological monitoring. Institute of Sociology of the NAS of Ukraine. 2013. https://i-soc.com.ua/assets/files/monitoring/soc-mon-2013.pdf, accessed 27.11.2020.
33. Umland, Andreas. 2019. International Implications of Ukraine’s Decentralization. VOXUkraine, January 30. https://voxukraine.org/en/international-implications-of-ukraine-s-decentralization/, accessed 27.11.2020.
34. Zhalilo Y.A, Shevchenko O.V., Romanova V.V. 2019. Decentralization of Power: Agenda for the Medium Term. Kyiv: National Institute for Strategic Studies.
Go to article

Authors and Affiliations

Oleksandra Deineko
1
ORCID: ORCID
  1. V.N. Karazin Kharkiv National University

Is the information contained in coin designs fully reliable?. = Czy informacje zawarte w stemplach monet są w pełni wiarygodne?

Stanisław Suchodolski
Wiadomości Numizmatyczne | Rok LII | Zeszyt 2 (186) | 117-138
Download PDF Download RIS Download Bibtex

Authors and Affiliations

Stanisław Suchodolski

This page uses 'cookies'. Learn more