Effect of Media News on Radicalization of Attitudes to Immigration

Recent years have witnessed a growing aversion to immigration worldwide and, at the same time, radicalization of public opinion on the issue. This paper explores the relationship between media news and individual attitudes to immigration. We run an empirical analysis whereby an index capturing individuals’ pro-immigration attitude, measured in 19 countries, is regressed over indexes capturing the coverage and tone of media news about immigration. We find that pro-immigration attitudes are negatively correlated with media coverage and the negative tone of news. However, this correlation is significant only for those with high trust in the media. In the case of low trust, higher coverage of immigration and a negative news slant make previous preferences and beliefs vis-à-vis immigration more extreme, yielding a lower pro-immigration index for those politically on the right, while the opposite applies to those on the left. The pro-immigration index is constructed by means of fuzzy methods to account for the many aspects defining attitudes to immigration.

Problematic Cellular Automata Segmentation and Clusterization of a Region’s Geoinformation Space

We consider the problems of clustering and segmentation for objects in the geoinformation space using the cellular automata theory, both classical and non-orthogonal ones. We clarify the terminology associated with the use of hybrid software and hardware for processing information coming from sources of different physical nature. This research is based on the geometric clusterization methods of multidimensional real or virtual spaces. As illustrative examples we consider two and three-dimensional variants, which, from our point of view, does not reduce the results’ significance in relation to the space of a greater dimension. Based on the formation conditions of the geoinformation space model as a semantic system, the use of semantic interoperability of its properties and corresponding subspaces is justified. It is shown that the unified geographic information space (UGIS) can be the data source for the formation procedures of various problem-oriented clusters used to manage socio-economic objects. As a variant of the UGIS formed subspaces this study uses a digital plan-diagram that has proven its effectiveness during previous works on the analysis of territories during their space monitoring. We also pay attention to the use of fuzzy methods and models in the processing of fuzzy source data and the clusters formation. Specific examples of clustering and segmentation using classical and non-orthogonal cellular automata are given.

Deterministic and stochastic models of infection spread and testing in an isolated contingent

The mathematical SIR model generalisation for description of the infectious process dynamics development by adding a testing model is considered. The proposed procedure requires the expansion of states’ space dimension due to variables that cannot be measured directly, but allow you to more adequately describe the processes that occur in real situations. Further generalisation of the SIR model is considered by taking into account randomness in state estimates, forecasting, which is achieved by applying the stochastic differential equations methods associated with the application of the Fokker – Planck – Kolmogorov equations for posterior probabilities. As COVID-19 practice has shown, the widespread use of modern means of identification, diagnosis and monitoring does not guarantee the receipt of adequate information about the individual’s condition in the population. When modelling real epidemic processes in the initial stages, it is advisable to use heuristic modelling methods, and then refine the model using mathematical modelling methods using stochastic, uncertain-fuzzy methods that allow you to take into account the fact that flow, decision-making and control occurs in systems with incomplete information. To develop more realistic models, spatial kinetics must be taken into account, which, in turn, requires the use of systems models with distributed parameters (for example, models of continua mechanics). Obviously, realistic models of epidemics and their control should include models of economic, sociodynamics. The problems of forecasting epidemics and their development will be no less difficult than the problems of climate change forecasting, weather forecast and earthquake prediction.

Ranking the Return on Assets of Tehran Stock Exchange by New Method Based on Z-numbers Data

Since real-world data is often inaccurate and working with fuzzy data and Z-numbers are very important and necessary, in the real world we need to rank and compare data. In this paper, we introduce a new method for ranking Z-numbers. This ranking algorithm is based on centroid point.We evaluate distance between centroid point, and based on this distance, we rank the Z-numbers.We use this method in two practical examples. First in ranking the return on assets of Tehran stock exchange, and second, in ranking of factors affecting the productivity of tourism security.The advantage of this method over conventional fuzzy methods is considering uncertainty, and allocating credit in the opinion of experts to estimate fuzzy parameters.

Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods

Even though forecasting methods have advanced in the last few decades, economists still face a simple question: which prediction method gives the most accurate results? Econometric forecasting methods can deal with different types of time series and have good results, but in specific cases, they may fail to provide accurate predictions. Recently, new techniques borrowed from the soft computing area were adopted for economic forecasting. Starting from the importance of economic forecasts, we present an experimental study where we compared the accuracy of some of the most used econometric forecasting methods, namely the simple exponential smoothing, Holt and ARIMA methods, with that of two new methods based on the concept of fuzzy time series. We used a set of time series extracted from the Eurostat database and the R software for all data processing. The results of the experiments show that despite not being fully superior to the econometric techniques, the fuzzy time series forecasting methods could be considered as an alternative for specific time series.

Evaluation of Clustering Algorithms on HPC Platforms

Clustering algorithms are one of the most widely used kernels to generate knowledge from large datasets. These algorithms group a set of data elements (i.e., images, points, patterns, etc.) into clusters to identify patterns or common features of a sample. However, these algorithms are very computationally expensive as they often involve the computation of expensive fitness functions that must be evaluated for all points in the dataset. This computational cost is even higher for fuzzy methods, where each data point may belong to more than one cluster. In this paper, we evaluate different parallelisation strategies on different heterogeneous platforms for fuzzy clustering algorithms typically used in the state-of-the-art such as the Fuzzy C-means (FCM), the Gustafson–Kessel FCM (GK-FCM) and the Fuzzy Minimals (FM). The experimental evaluation includes performance and energy trade-offs. Our results show that depending on the computational pattern of each algorithm, their mathematical foundation and the amount of data to be processed, each algorithm performs better on a different platform.

Applicability of Fuzzy and Fuzzy Analytic Hierarchy Process (Fuzzy AHP) Methods to Determine the Optimum Soil Depth in Land Suitability Evaluation for Irrigated Rice

<p>The conventional Boolean logic models of land suitability assessment disregard the continuity concepts of the soil and landscape which might cause inaccurate evaluation and classification. To overcome this uncertainty and consequent constraints, the fuzzy set theories were introduced. Therefore, the current study was undertaken to estimate the optimum soil depth that is used in land suitability evaluation for irrigated rice through the fuzzy sets theory and analytic hierarchy process (fuzzy AHP) in Guilan Province, Iran. The square root and quantitative land suitability evaluation methods were employed to calculate traditional land suitability indices (for depths, 0-25, 0-50, 0-75, and 0-100 cm). Also, fuzzy and fuzzy AHP methods were used to explore new land indices. The Sarma similarity indices were used to compare the results of traditional and fuzzy methods for different soil depths. The results showed that the compatibility percentage between the representative pedons (0-100 cm) and the findings of this research (0-50 and 0-75 cm) were remarkable. Furthermore, the highest compatibility percentage of land suitability class was related to the comparison of these two former depths and 0 to 100 cm depths in each of the two used fuzzy methods. Besides, except for 0-25 cm depths, actual yield revealed a significant and positive correlation with the rest three soil pedon depths. These findings show that considering 0 to 50 cm soil depth might be a relevant alternative as the optimal depth to evaluate land suitability for rice in paddy fields in the Guilan rice-growing area. </p>

“One Out–All Out” Principle in the Water Framework Directive 2000—A New Approach with Fuzzy Method on an Example of Greek Lakes

The “One Out–All Out” (OOAO) principle imposed by the WFD selects the worst ecological status assessed by different biological quality elements (BQEs). Since it is a precautionary rule that can lead to problems of underestimation of the overall status, its amendment has been a matter of debate for WFD 20+. The use of fuzzy methods that express the functional relationships between variables in ecology and management has been gaining more ground recently. Here is attempted the inclusion of a fuzzy regression among the frequently monitored BQE (phytoplankton) and the outcome of OOAO application in six Greek lakes. The latter was determined by the comparison of four BQE indices in order to assess the extent to which BQEs might underpin the optimal/actual qualitative classification of a waterbody. This approach encompasses the uncertainty and the possibility to broaden the acceptable final EQR based on the character and status of each lake. We concluded that the fuzzy OOAO is an approach that seems to allow a better understanding of the WFD implementation and case-specific evaluation, including the uncertainty in classification as an asset. Moreover, it offers a deeper understanding through self-learning processes based on the existing datasets.