Cybernetic formulation of control in ecosystems

Hierarchical control formulations are given for a simple predator-prey model: self-optimization of one subsystem, natural multi-goal optimization, man's multiparameter control of an ecosystem and man's control of a self-optimizing ecosystem. Natural multigoal optimization suggests how several levels of an ecosystem can be optimized simultaneously. The idea of management of a self-optimizing ecosystem takes into account the reactions of the ecosystem to man's activity.     

Natural control mechanisms in models of aquatic ecosystems

Based on cybernetic categories of natural control mechanisms, four generations of ecosystem models are distinguished: feed-forward, feedback, self-adaptation and self-organization models. The analysis of the natural control mechanisms in aquatic ecosystems suggests that different processes are controlled in different ways, and, although the four mechanisms were identified in historical sequence, they all operate simultaneously. The concept of self-organization of an ecosystem is introduced and specified for a model of an aquatic pelagic ecosystem. The concept of the ecosystem as a multilayer, multigoal and multiechelon hierarchical system with hierarchy of the levels of biological organization is also introduced.     

Colonization of surfaces by phenolic compounds utilizing microorganisms

The aim of the present study is to determine optimal adhesive interaction of phenolic compounds utilizing Candida maltosa and Rhodococcus erythropolis when adhering to kaolin, silicone, synthetic foil (Steriking R40) and fluorinated silicones, comparing cell and support surface hydrophobicity. In parallel, the interfering effect of detergents was investigated. Data obtained show that the less hydrophobic supports display high initial cell adhesion when contacted with the cell type with a lower surface hydrophobicity (yeast cell) but most stable yeast biofilms are those formed on highly hydrophobic fluorinated silicones. On the other hand, support hydrophobicity has no effect on bacterial cell detachment; however, bacterial biofilms are denser when growing on more hydrophobic supports. Both detergents interfere (independently on the cell type) with the early and late phases of biofilm development.     

Winter arsenic pollution in 10 forest ecosystems in the mountainous border regions of the Czech Republic

Environmental Science and Pollution Research - Arsenic (As) concentrations and deposition fluxes were measured in snow and rime at 10 mountain-top sites near the borders between the Czech Republic...     

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.

Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).

Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.

Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.