Radiation protection and estimate of commercial aircrew effective doses in Bosnia and Herzegovina.

Cosmic rays are the primary source of the daily exposure of aircrew and passengers to ionising radiation. This study aims to estimate the effective doses of ionising radiation for aircraft crews in Bosnia and Herzegovina by taking into consideration factors such as flight duration and altitude, as well as the geographical position of airports. The CARI-7 algorithm and neural network method were used in the analysis of data obtained from the Sarajevo International Airport. The results show that the estimated annual effective doses in 2021 range from 0.06 to 10 mSv for flights to and from Belgrade and Dubai, respectively. Both linear regression and neural network models were developed to predict the effective dose based on flight duration, average altitude, latitude and maximum altitude. The findings reveal that flight duration is the most statistically significant factor, followed by average altitude, latitude and maximum altitude.

Dimensionality reduction of independent influence factors in the objective evaluation of quality of experience.

Big Data analytics and Artificial Intelligence (AI) technologies have become the focus of recent research due to the large amount of data. Dimensionality reduction techniques are recognized as an important step in these analyses. The multidimensional nature of Quality of Experience (QoE) is based on a set of Influence Factors (IFs) whose dimensionality is preferable to be higher due to better QoE prediction. As a consequence, dimensionality issues occur in QoE prediction models. This paper gives an overview of the used dimensionality reduction technique in QoE modeling and proposes modification and use of Active Subspaces Method (ASM) for dimensionality reduction. Proposed modified ASM (mASM) uses variance/standard deviation as a measure of function variability. A straightforward benefit of proposed modification is the possibility of its application in cases when discrete or categorical IFs are included. Application of modified ASM is not restricted to QoE modeling only. Obtained results show that QoE function is mostly flat for small variations of input IFs which is an additional motive to propose a modification of the standard version of ASM. This study proposes several metrics that can be used to compare different dimensionality reduction approaches. We prove that the percentage of function variability described by an appropriate linear combination(s) of input IFs is always greater or equal to the percentage that corresponds to the selection of input IF(s) when the reduction degree is the same. Thus, the proposed method and metrics are useful when optimizing the number of IFs for QoE prediction and a better understanding of IFs space in terms of QoE.