Being able to evaluate risks is an important task in many areas of human activity: economics, ecology, etc. Usually, environmental risk assessment is carried out on the basis of multiple and sometimes conflicting factors. Using multiple criteria decision-making (MCDM) methodology is one of the possible ways to solve the problem. Methodologies of analytic hierarchy process (AHP) are the most commonly used MCDM methods, which combine subjective and personal preferences in risk assessment process. However, AHP involves human subjectivity, which introduces vagueness type of uncertainty and requires the usage of decision making under those uncertainties. In this paper it was considered to deal with uncertainty by using the fuzzy-based techniques. However, nowadays there exist multiple Fuzzy AHP methodologies developed by different authors. In this paper, these Fuzzy AHP methodologies will be compared, and the most appropriate Fuzzy AHP methodology for the application in case of environmental risks assessment will be offered on the basis of this comparison.

-Microarray technology has been the leading research direction in medicine, pharmacology, genome studies and other related areas over the past years. This technology enables researches to simultaneously study activity expression of tens of thousands of genes. After the experimental data have been processed, arrays of numerical values of gene expressions are obtained that are the basis for receiving relevant information and new knowledge. This paper briefly overviews the basics of microarray technology as well as task classes that could be solved using microarray data. The existing approaches to clustering gene expression sets are discussed. It is shown that the fuzzy cmeans clustering method appears the most appropriate for that purpose. Due to that, the problem of choosing an optimal size of fuzziness parameter arises. Three widespread techniques for solving the problem are considered and their comparative analysis is provided.

Risk assessment is an important task in many areas of human activity: economic, technical, ecological etc. Preliminary data adequacy in risk assessments is carried out on the basis of statistical methods and experts' evaluation on potential losses and probabilities of the event. But in many cases, risk assessment must be carried out under the conditions of lack of initial information or uncertainty of information. For that reason, special risk assessment approaches (methods) are necessary. One of them is the usage of fuzzy logic approach. In this paper, fuzzy logic approach is used to manage this uncertainty in information concerning accidental releases of toxic chemicals at chemical plants. This approach can be used by plant risk advisers in Latvia to make right decisions in the situations where chemical releases can harm not only the environment but also human health.

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Ecological risks are characterized by a high degree of uncertainty about the chances of unfavorable event outcomes and the losses associated with those outcomes. Subjective expert judgment is widely used when baseline data are insufficient. This introduces additional uncertainties in the results of risk analyses. In order to successfully model the existing uncertainties, this paper presents a methodology for ecological risk analysis that is based on input evaluations in the form of intuitionistic fuzzy values (IFVs). The advantage of this approach is the ability to model a wide range of uncertainties in ecological risk analysis tasks.