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Thirty five Slovak households were selected for an investigation of indoor environmental quality. Measuring of indoor air physical and chemical factors and a questionnaire survey was performed during May 2017. The range of permissible operative temperature was not met in 11% of objects. Relative humidity met the legislative requirements in all monitored homes. Concentrations of total volatile organic compounds (TVOCs) were significantly higher in the apartments than in the family houses. The average TVOC levels in the apartments and family houses were 519.7 µg/m3 and 330.2 µg/m3, respectively. Statistical analysis confirmed the effect of indoor air temperature, relative humidity and particulate matter (PM0.5 and PM1) on the levels of TVOCs. Higher TVOC levels were observed also in homes where it is not a common practice to open windows during cleaning activities. Other factors that had a statistically significant effect on concentrations of volatile organic compounds were heating type, attached garage, location of the apartment within residential building (the floor), as well as number of occupants. Higher TVOC concentrations were observed in indoor than outdoor environment, while further analysis showed the significant impact of indoor emission sources on the level of these compounds in buildings. The questionnaire study showed a discrepancy between objective measurement and subjective assessment in the household environment, and pointed to insufficient public awareness about volatile organic compounds (VOCs).

We address the following problem: given a set of complex images or a large database, the numerical and computational complexity and quality of approximation for neural network may drastically differ from one activation function to another. A general novel methodology, scaled polynomial constant unit activation function ''SPOCU,'' is introduced and shown to work satisfactorily on a variety of problems. Moreover, we show that SPOCU can overcome already introduced activation functions with good properties, e.g., SELU and ReLU, on generic problems. In order to explain the good properties of SPOCU, we provide several theoretical and practical motivations, including tissue growth model and memristive cellular nonlinear networks. We also provide estimation strategy for SPOCU parameters and its relation to generation of random type of Sierpinski carpet, related to the [pppq] model. One of the attractive properties of SPOCU is its genuine normalization of the output of layers. We illustrate SPOCU methodology on cancer discrimination, including mammary and prostate cancer and data from Wisconsin Diagnostic Breast Cancer dataset. Moreover, we compared SPOCU with SELU and ReLU on large dataset MNIST, which justifies usefulness of SPOCU by its very good performance.

People who live in buildings are exposed to harmful effects of indoor air pollution for many years. Therefore, our research is aimed to investigate the indoor air quality in family houses. The measurements of indoor air temperature, relative humidity, total volatile organic compounds (TVOC), particulate matters (PM) and sound pressure level were carried out in 25 houses in several cities of the Republic of Macedonia. Mean values of indoor air temperature and relative humidity ranged from 18.9 °C to 25.6 °C and from 34.1% to 68.0%, respectively. With regard to TVOC, it can be stated that excessive occurrence was recorded. Mean values ranged from 50 μg/m3 to 2610 μg/m3. Recommended value (200 μg/m3) for human exposure to TVOC was exceeded in 32% of houses. Mean concentrations of PM2.5 (particular matter with diameter less than 2.5 µm) and PM10 (diameter less than 10 µm) are determined to be from 16.80 µg/m3 to 30.70 µg/m3 and from 38.30 µg/m3 to 74.60 µg/m3 individually. Mean values of sound pressure level ranged from 29.8 dB(A) to 50.6 dB(A). Dependence between characteristics of buildings (Year of construction, Year of renovation, Smoke and Heating system) and data from measurements (Temperature, Relative humidity, TVOC, PM2.5 and PM10) were analyzed using R software. Van der Waerden test shows dependence of Smoke on TVOC and PM2.5. Permutational multivariate analysis of variance shows the effect of interaction of Renovation and Smoke.

Numerous occurrence of Echinothrips americanus Morgan, 1913, a relatively newly established pest in European greenhouses, on a wide spectrum of cultivated plants throughout Slovakia has encouraged us to present current data on its food ecology. Analysing 1,650 plant species, the poinsettia thrips was found on 106 species (48 families) including 19 showing sporadic, 75 moderate and 17 high infestation. Larvae were detected on 25 plant taxa from 13 families, most of them being new hosts. Mature clusters of Acalypha hispida, showing moderate to high infestation level and carrying both larvae and adults, were selected for more detailed statistical evaluation. Preference of adults for upper and lower leaf surface tends to be without statistical significance, the larvae on the contrary were strongly related to the lower side (Pearson's v 2 = 13.3552, P = 0.0013). Concerning the age, increased occurrence was apparent on younger leaves for both larvae and adults. For larvae the nonparametric test gives the statistical difference between three age categories of leaves with the following values: Kruskal-Wallis v 2 = 6.6384, P = 0.03618. The statistical significance of age seems to be more crucial for younger shoots, as proved by the statistics for both larvae (Kruskal-Wallis v 2 = 8.4852, P = 0.0144) and adults (Kruskal-Wallis v 2 = 7.1049, P = 0.0287). Different food preferences of larvae and adults may result in specific approach when applying an effective biological or chemical control. Narrower tolerance of larvae could make them more manageable target in plant protection instead of efforts to control the whole thrips population.

The main problem of ecological data modelling is their interpretation and its correct understanding. This problem cannot be solved solely by a big data collection. To sufficiently understand ecosystems we need to know how these processes behave and how they respond to internal and external factors. Similarly, we need to know the behavior of processes that are involved in the climate system and the biosphere of the earth. In order to characterize precisely the behavior of individual elements and ecosystems we need to use deterministic, stochastic and chaotic behavior. Unfortunately, the chaotic part of systems is typically completely ignored in almost all approaches. Ignoring of chaotical part leads to many biased outcomes. To overcome this gap we model chaotic system behavior by random iterated function system which provides a generic guidelines for such data management. This also allows to replicate a complexity and chaos of ecosystem.

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