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A complex dynamical network (CDN) can be considered as the composition system with the nodes subsystem (NS) and the links subsystem (LS), and both subsystems are coupled with each other. Therefore, the dynamics of NS affects the dynamics of LS, and vice versa. In this paper, two vector differential equation (VDE)s are employed to describe the dynamics of NS and LS, respectively, in which the dynamics of NS is proposed as the VDE with the second derivative term (SDT) for more realistic engineering applications. Compared with the existing literature, the key advantage is that the dynamics of links is utilized to assist synthesizing the tracking controller of NS. The results show that when the state of LS tracks asymptotically the synthesized auxiliary tracking goal (ATG), the state of NS tracks asymptotically the given reference signal. Observing the eventual topology of CDN, all eventual links between nodes depend on the proposed ATG when NS achieving the tracking target, in which one special case is that if all target signals determined by ATG are not zero, all nodes are not isolated. Finally, the effectiveness of the proposed control scheme is verified by a numerical simulation example. [ABSTRACT FROM AUTHOR]

• Bibliometric techniques identify the productivity and influence on research. • Fuzzy systems are an area of study with potential for further research. • Zadeh is known as the most influential author in fuzzy research in North American. • The United States is the most productive and influential country in fuzzy research. The objective of this paper is to apply bibliometric techniques to analyze the evolution of fuzzy systems research (FSR) in the United States of America and Canada. The aim is to visualize the progression of FSR since Zadeh's first publication of fuzzy sets and the regional impact that these ideas have in the scientific community. This work obtains data retrieved from the Web of Science (WoS) scientific database. Using a hybrid methodology designed to maximize the obtention of representative results, we analyze 20,317 documents published between 1965 and 2020. The main findings show that Zadeh is the most influential author and that Pedrycz is the most productive author at FSR. Journals such as Fuzzy Sets and Systems (FSS) and IEEE Transactions on Fuzzy Systems (TFS) publish most of the FSR articles. The results show a greater contribution by the United States of America compared to Canada in FSR. This analysis contributes to visualization and evolution in the field of study of FSR in diverse regions. [ABSTRACT FROM AUTHOR]

This paper aims to incorporate a high order diffusion term into a SIQR epidemic model with transient prophylaxis and lasting prophylaxis. The existence and uniqueness of the global positive solution is proven and we find a condition ensuring the extinction of an infectious disease. The existence of a stationary distribution for the stochastic epidemic model is investigated as well. Numerical simulations are conducted to support our theoretical results and an example of application with COVID-19 data from Canada is used to estimate the transmission rate and reproduction number associated with the stochastic model, while constructing a model fitting the data. • A stochastic SIQR epidemic model with higher-order perturbation is investigated. • The existence and uniqueness of the global positive solution are studied. • A stochastic threshold is established for extinction. • COVID-19 data from Canada are used to estimate the transmission rate. [ABSTRACT FROM AUTHOR]

• An approach for cleaning and reconciliation of tags in open data portal (ODP) categories using natural language processing methods. • A data structure for connecting ODPs – cross-portal metadata alignment structure generated using ODPs' tags. • Formal context reduction based on semantic similarity measure through means of natural language processing. • Cross-portal dataset categorization - determining appropriate category for a dataset using the metadata alignment structure. • Quality assessment using datasets obtained from Canada's and New Zealand's ODP. Due to openness and transparency initiatives, a vast amount of data is being made publicly available. This data has great significance for business and society. However, it also led to challenges that need to be overcome for this data to reach its full potential. In this paper, we are focusing on the problem of connecting open data portals (ODPs) through metadata alignment. We investigate the available metadata accompanying datasets, especially the part related to categories datasets belong to and tags that closely describe datasets. The methodology we propose is relying on Formal Concept Analysis for the creation of the hierarchical structure used for determining the similarity of tags' usage in different ODPs. We propose such a structure to be used for open data portal metadata alignment. Further, we apply semantic similarity measures to reduce the complexity of the cross-portal data structure while preserving all its characteristics. We demonstrate how our approach can be used for determining dataset category across multiple ODPs aligned using the data structure our approach generates. We envision our approach to improve cross-portal search and metadata enrichment through open data categorization. Lastly, the quality of our approach was tested using datasets obtained from Canada's and New Zealand's ODPs. [ABSTRACT FROM AUTHOR]

• A fully automated, real-time forecasting model for COVID-19 transmission to help frontline health workers and government policy makers. • Use of Artificial intelligence (AI) and Deep Learning to model Infectious diseases without loosing temporal components. • One of the early studies to use LSTM networks to predict the COVID-19 transmission. • We showed the trends of different countries and compared them with Canadian data to predict the future infections. On March 11 th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14 th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases. [ABSTRACT FROM AUTHOR]

Abstract: This paper re-investigates whether rational bubbles existed in the G-7 stock markets during the period of January 2000–June 2009 using the newly developed Fourier unit root test and a nonparametric rank test for cointegration. The empirical results from our Fourier unit test indicate that the null hypothesis of I(1) unit root in stock prices can be rejected for Canada, France, Italy and the UK. However, the empirical results from the rank test reveal that rational bubbles did not exist in the G-7 stock markets during the sample period. [Copyright &y& Elsevier]

Abstract: This paper investigates the volatility of the rates of output growth for the U.S., Canada and the UK. We empirically characterize the volatility of the growth rate of real GDP and, at the same time, we hope we can successfully identify business cycle turning points. The empirical results show that there have been structural changes in the volatility of output growth for these countries. While the Markov Switching heteroscedasticity model can capture this feature very well for all three countries, the modified Markov Switching heteroscedasticity model introduced here not only performs extremely well in modeling the volatility behavior of the growth rate of real GDP, but, at the same time, it also successfully identifies business cycle peak and trough dates. [Copyright &y& Elsevier]