Sławomir Francik

The neuroprotective effect of cornus MAS on brain tissue of Wistar rats.

Cornelian cherry (Cornus mas) is a valuable source of phenolic antioxidants. Flavonoid derivatives as nonenzymatic antioxidants are important in the pathophysiology of many diseases including neurological disorders (e.g., Alzheimers disease) or heart disease. In this study, we examined the effect of an addition of freeze-dried fruit of cornelian cherry on three types of diets: control diet, fructose diet, and diet enriched in fats (high-fat diet). This effect was studied by determining the following antioxidant parameters in both brain tissue and plasma in rats: catalase, ferric reducing ability of plasma, paraoxonase, protein carbonyl groups, and free thiol groups. Results indicate that both fructose diet and high-fat diet affect the antioxidant capacity of the organism. Furthermore, an addition of cornelian cherry resulted in increased activity of catalase in brain tissue, while in plasma it caused the opposite effect. In turn, with regard to paraoxonase activity in both brain tissue and plasma, it had a stimulating effect. Adding cornelian cherry to the tested diets increased the activity of PON in both tested tissues. Moreover, protective effect of fruits of this plant was observed in the process of oxidation of proteins by decreasing levels of protein carbonyl groups and thiol groups in brain tissue as well as in plasma.

Aronia melanocarpa Treatment and Antioxidant Status in Selected Tissues in Wistar Rats

Aronia juice is considered to be a source of compounds with high antioxidative potential. We conducted a study on the impact of compounds in the Aronia juice on oxidative stress in plasma and brain tissues. The influence of Aronia juice on oxidative stress parameters was tested with the use of a model with a high content of fructose and nonsaturated fats. Therefore, the activity of enzymatic (catalase, CAT, and paraoxonase, PON) and nonenzymatic (thiol groups, SH, and protein carbonyl groups, PCG) oxidative stress markers, which indicate changes in the carbohydrate and protein profiles, was marked in brain tissue homogenates. Adding Aronia caused statistically significant increase in the CAT activity in plasma in all tested diets, while the PON activity showed a statistically significant increase only in case of high fat diet. In animals fed with Aronia juice supplemented with carbohydrates or fat, statistically significant increase in the PON activity and the decrease in the CAT activity in brain tissue were observed. In case of the high fat diet, an increase in the number of SH groups and a decrease in the number of PCG groups in brain tissue were observed.

Bis(4,4′-dimethyl-2,2′-bipyridine)oxidovanadium(IV) Sulfate Dehydrate: Potential Candidate for Controlling Lipid Metabolism?

Vanadium is a trace element mainly connected with regulation of insulin metabolism which is particularly important in diabetes. In recent years, organic complexes of vanadium seem to be more interesting than inorganic salts. Nevertheless, the effect of vanadium on lipid metabolism is still a problematic issue; therefore, the main purpose of this study was to investigate the effect of 3 organic complexes of vanadium such as sodium (2,2′-bipyridine)oxidobisperoxovanadate(V) octahydrate, bis(2,2′-bipyridine)oxidovanadium(IV) sulfate dehydrate, and bis(4,4′-dimethyl-2,2′-bipyridine)oxidovanadium(IV) sulfate dihydrate in conjunction with high-fat as well as control diet in nondiabetes model on the following lipid parameters: total cholesterol, triglycerides, and high density lipoprotein as well as activity of paraoxonase 1. All of these parameters were determined in plasma of Wistar rats. The most significant effect was observed in case of bis(4,4′-dimethyl-2,2′ bipyridine)oxidovanadium(IV) sulfate dehydrate in rats fed with high-fat diet. Based on our research, bis(4,4′-dimethyl-2,2′-bipyridine)oxidovanadium(IV) sulfate dihydrate should be the aim of further research and perhaps it will be an important factor in the regulation of lipid metabolism.

Present trends in research on application of artificial neural networks in agricultural engineering

Abstract The objective of the paper was to carry out a bibliometric quantitative analysis of publications concerning the application of artificial neural networks in the research area - agriculture and a bibliometric quantitative analysis and subject analysis with regard to agricultural engineering. A number of scientific publications devoted to the ANN found in the data base of the Web of Science - in documents published to 2015 was a basis for the quantitative analysis. Research on the use of artificial neural networks in the research area - agriculture is extending systematically. Moreover, a rapidly growing number of citations prove a continuous increase in the scientists’ interest in possibilities of the ANN applications. The quantitative analysis of scientific publications in 5 selected scientific journals and thematically related to agricultural engineering (indexed in the Web of Science) allowed a statement that 236 scientific articles from 1996- 2015 were related to the ANN application. The biggest number of publications was reported in Computers and Electronics in Agriculture - 118 articles. In 2011-2015 there was a growing trend in dynamics of publishing of scientific papers devoted to the ANN application to agricultural engineering. Thus, we may assume that the research related to application of the artificial neural networks to agricultural engineering will be continued and their scope and number will be still growing. The thematic analysis of the most often quoted publications from 2011-2015 in the journal Computers and Electronics in Agriculture, proved that they concern both the issues related to the classification problem as well as to modelling processes and systems. We should suppose that the subjects related to modelling of drying processes and application of neural networks for image analysis will grow dynamically in the following years.

Application of artificial neural networks in modelling the contact area of grain seeds

Abstract The objective of the research was to create a model which defines the relation between a fundamental contact area of a seed and the pressure force, water content in a seed and its geometrical dimensions with application of artificial neural networks (SSN). Computer program Statistica Neural Networks v. 6.0. was used for formation of a neural model. Tests were carried out on Roma wheat seed and Dańkowskie Złote rye with six various water contents: 0.11 0.15 0.19 0.23 0.28 0.33 (kg·kg-1 dry mass). Caryopses were loaded with eight values of compression force - from 41 N to 230 N. Multiplicity of iterations was 5. Seed material was moistened to obtain a specific water content. Each seed was loaded with compression force with respectively growing values: 41N, 68N, 95N, 122N, 149N, 176N, 203N and 230N. A four-layer network of Perceptron type with 10 neurons in the first and 8 neurons in the second hidden layer was selected as a model which the best defines the contact area of grain seeds loaded with axial force at various moisture levels. This network has 4 inputs (water content, pressure force, thickness and length of caryopses) and one output (elementary contact area of rye and wheat seeds). Comparison of the neural model with empirical formulas obtained from nonlinear estimation proved a considerable higher precision of the first one.

Modeling and Simulation of Biomass Drying Using Artificial Neural Networks

Willow (Salix viminalis) is a moist material after the crops. Therefore, the content of water in this type of material has to be lowered by drying before any further mechanical or thermal processing, in order to increase its calorific value. The process of drying is energy-intensive. Thus it is advisable to search for optimal methods and parameters of drying. The optimisation requires evolving a model that is based on the crucial parameters of the process. One of the possible solutions is to apply models of Artificial Neural Networks. Artificial Neural Networks belong to the group of methods of artificial computational intelligence and are often used in modelling various phenomena and processes. The aim of this work was to develop models using Artificial Neural Networks to describe the process of convective drying of the willow woodchips. As a result of presented work we obtained neural models describing alterations of water content, changes of the temperature and the mass of the chips. The presented models are highly accurate. We used experimentally obtained data in order to validate the models. It is important to underline that the data were not applied while the artificial neural networks were being developed. Subsequently, the models were used to simulate the process of drying what allowed us to define the optimal parameters of drying willow woodchips characterised by different moisture content.

Influence of Storing Miscanthus x gigantheus on Its Mechanical and Energetic Properties

The purpose of the study was to analyze the effect of storage of a Giant miscanthus on its mechanical and energy properties. The research material came from an experimental plantation of the Faculty of Production Engineering and Energetics of the University of Agriculture in Cracow. It was compared to plant material stored under cover where it was protected from atmospheric agents such as rain, snow and wind. The studies compared energy properties such as calorific value, moisture content and ash content. The second part of the research was used to determine selected mechanical properties—unit destructive force. Measured calorific value in two groups was: storage = Yes: 16,568 [kJ/kg], storage = No: 15,897 [kJ/kg]. Mean values of Pj (unitary destructive force) for stored and not-stored miscanthus were different. For X1—storage = Yes Pj = 48.27 [N] and for storage = No Pj = 40.73 [N].

The Dynamic Model of Willow Biomass Production

The paper presents methods and tools for building computer simulation models for supporting decision-making activities. The cultivation of energy crops for biomass production is a continuous process in which delays are very important along with other factors the values of which may denote a function of many parameters. The harvest of energy plants is a discrete process, so the simulation must combine both of these processes. The paper presents the method of constructing a model for growing energy crops and interprets conclusions resulting from the operation of the model, for changing values of the decision parameters.

The Research into Determination of the Particle-Size Distribution of Granular Materials by Digital Image Analysis

The granular materials, in particular fragmented biomass and cereal grains, are widely used in the power industry, the food industry, and agriculture. These materials are subjected to various processing methods and automated transport processes, which encourages implementation of fast tools (methods) for evaluation of the basic physical properties of these materials. The particle-size distribution is one of the basic characteristics of granular materials. The classic measurement methods are very time-consuming. Digital Image Analysis (DIA) provides new possibilities for quick, real-time measurements. The paper presents the study of the possibility of using DIA to determine the particle-size distribution of granular materials. The results obtained with DIA showed correlations with the actual particle-size composition at the values ranging from 0.63 to 0.89 R 2 .

Analysis of Possible Application of Olive Pomace as Biomass Source

The aim of the study was to determine the use of stored olive pomace as a solid biomass for energy purposes. The samples were collected from olive pomace pile stored for three years on Agricultural University in Cracow campus. Samples were taken from three heights and then standardized for each of them. Work has been done to compare energy properties such as calorific value, depending on the sampling depth of the material from the prism.