The numbers of confirmed cases of new coronavirus (Covid-19) are increased daily in different countries. To determine the policies and plans, the study of the relations between the distributions of the spread of this virus in other countries is critical. In this work, the distributions of the spread of Covid-19 in Unites States America, Spain, Italy, Germany, United Kingdom, France, and Iran were compared and clustered using fuzzy clustering technique. At first, the time series of Covid-19 datasets in selected countries were considered. Then, the relation between spread of Covid-19 and population's size was studied using Pearson correlation. The effect of the population's size was eliminated by rescaling the Covid-19 datasets based on the population's size of USA. Finally, the rescaled Covid-19 datasets of the countries were clustered using fuzzy clustering. The results of Pearson correlation indicated that there were positive and significant between total confirmed cases, total dead cases and population's size of the countries. The clustering results indicated that the distribution of spreading in Spain and Italy was approximately similar and differed from other countries.

The present study aims to determine the impact of green innovation (GI) on the overall performance of an organization while keeping the variable of environmental management (EM) as a moderator. We used a dataset consisting of four data years, from 2014 to 2017, of A-share companies listed on the Shanghai Stock Exchange (SSE). The concept of green innovation refers to the use of advancements in technology that enable savings in energy, along with the recycling of waste material. When advanced technology is utilized in the production process, the products are referred to as green products and the whole process of adopting such technologies and product design is referred to as “Corporate Environmental Management”. Such innovations improve the overall financial performance of companies as it enables them to improve their social image by reducing their carbon footprint and ensures their long-term sustainability. The main issue is the limited focus and attention given to the topic, from the perspective of companies. This research focuses on the impact of green innovation and the importance of environmental management for the sustainability of companies. Our findings suggest that the relationship between green innovation and the performance of the company is positive and verifies the existence of moderating effects of environmental management on the relationship between green innovation and firm performance. Implications are given to academia and practitioners.

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Intrusion Detection Systems (IDS) are designed to provide security into computer networks. Different classification models such as Support Vector Machine (SVM) has been successfully applied on the network data. Meanwhile, the extension or improvement of the current models using prototype selection simultaneous with their training phase is crucial due to the serious inefficacies during training (i.e. learning overhead). This paper introduces an improved model for prototype selection. Applying proposed prototype selection along with SVM classification model increases attack discovery rate. In this article, we use fuzzy rough sets theory (FRST) for prototype selection to enhance SVM in intrusion detection. Testing and evaluation of the proposed IDS have been mainly performed on NSL-KDD dataset as a refined version of KDD-CUP99. Experimentations indicate that the proposed IDS outperforms the basic and simple IDSs and modern IDSs in terms of precision, recall, and accuracy rate.

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