Identity, autonomy and conflict in republics of Russia and Ukraine

2008 ◽  
Vol 41 (1) ◽  
pp. 79-91 ◽  
Author(s):  
Karina V. Korostelina
Keyword(s):  
Early Warning ◽  
Ethnic Relations ◽  
Social Identities ◽  
Public Institutions ◽  
European Research ◽  
Ethnic Stereotypes ◽  
Early Warning Model ◽  
National Minorities ◽  
Potential Problems ◽  
Warning Model

This paper discusses the results of the survey conducted in co-operation with the European Research Center for Migration and Ethnic Relations, concerning identity in the Autonomous Republics of Russia and Ukraine. The survey queried 6522 residents of such republics as Bashkortostan, Karelia, Komi, Sakha (Yakutia), and Tatarstan in Russia, and Crimea in Ukraine. It examined the construction of social identities, common narratives regarding threats and deprivations, confidence in public institutions, the prevalence of views toward national minorities as ‘fifth columns’, ethnic stereotypes, ethnocentrism, and other conflict indicators. An early warning model, built on the basis of the results, measured the potential for conflict based on these factors, and found that it was most pronounced in Bashkortostan and Crimea, and to a lesser extent in Tatarstan. Conflict was less likely in Sakha, Karelia, and Komi, although there were still certain indicators that suggested potential problems, including moderate support for independence in these republics.

scholarly journals The Level of Public Acceptance to the Development of a Coastal Flooding Early Warning System in Jakarta

2021 ◽  
Vol 13 (2) ◽  
pp. 566
Author(s):  
Nelly Florida Riama ◽  
Riri Fitri Sari ◽  
Henita Rahmayanti ◽  
Widada Sulistya ◽  
Mohamad Husein Nurrahmat
Keyword(s):  
Path Analysis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Coastal Flooding ◽  
Coastal Areas ◽  
Public Acceptance ◽  
Community Attitudes ◽  
Early Warning Model ◽  
Warning Model

Coastal flooding is a natural disaster that often occurs in coastal areas. Jakarta is an example of a location that is highly vulnerable to coastal flooding. Coastal flooding can result in economic and human life losses. Thus, there is a need for a coastal flooding early warning system in vulnerable locations to reduce the threat to the community and strengthen its resilience to coastal flooding disasters. This study aimed to measure the level of public acceptance toward the development of a coastal flooding early warning system of people who live in a coastal region in Jakarta. This knowledge is essential to ensure that the early warning system can be implemented successfully. A survey was conducted by distributing questionnaires to people in the coastal areas of Jakarta. The questionnaire results were analyzed using cross-tabulation and path analysis based on the variables of knowledge, perceptions, and community attitudes towards the development of a coastal flooding early warning system. The survey result shows that the level of public acceptance is excellent, as proven by the average score of the respondents’ attitude by 4.15 in agreeing with the establishment of an early warning system to manage coastal flooding. Thus, path analysis shows that knowledge and perception have a weak relationship with community attitudes when responding to the coastal flooding early warning model. The results show that only 23% of the community’s responses toward the coastal flooding early warning model can be explained by the community’s knowledge and perceptions. This research is expected to be useful in implementing a coastal flooding early warning system by considering the level of public acceptance.

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

Dynamic early-warning model of dam deformation based on deep learning and fusion of spatiotemporal features

2021 ◽  
pp. 107537
Author(s):  
Wenlong Chen ◽  
Xiaoling Wang ◽  
Dawei Tong ◽  
Zhijian Cai ◽  
Yushan Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Warning ◽  
Spatiotemporal Features ◽  
Early Warning Model ◽  
Dam Deformation ◽  
Warning Model

scholarly journals An Early Warning Model for School Dropout: a Case Study in E-learning Class

2019 ◽  
Author(s):  
Felipe Braz ◽  
Fernanda Campos ◽  
Victor Stroele ◽  
Mário Dantas
Keyword(s):  
Early Warning ◽  
School Dropout ◽  
Early Warning Model ◽  
E Learning ◽  
Warning Model
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