It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).

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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., Alzheimer's 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.

The biomass is regarded as a part of renewable energy sources (RES), which can satisfy energy demands. Biomass obtained from plantations is characterized by low bulk density, which increases transport and storage costs. Briquetting is a technology that relies on pressing biomass with the aim of obtaining a denser product (briquettes). In the production of solid biofuels, the technological as well as material variables significantly influence the densification process, and as a result influence the end quality of briquette. This process progresses differently for different materials. Therefore, the optimal selection of process’ parameters is very difficult. It is necessary to use a decision support tool—decision support system (DSS). The purpose of the work was to develop a decision support system that would indicate the optimal parameters for conducting the process of producing Miscanthus and willow briquettes (pre-comminution, milling and briquetting), briquette parameters (durability and specific density) and total energy consumption based on process simulation. Artificial neural networks (ANNs) were used to describe the relationship between individual parameters of the briquette production process. DSS has the form of a web application and is opened from a web browser (it is possible to open it on various types of devices). The modular design allows the modification and expansion the application in the future.

Solid biofuels can be defined as processed and unprocessed biomass. By definition it can be divided into: natural fuels (as obtained) and synthetic fuels (after mechanical and chemical treatment). Raw materials for the production of solid biofuels may include: wood, stalk plants, peat, sewage sludge and grains of cereals. These raw materials can be used directly as fuel or as a half-finished product for further production. The aim of the work was to analyze trends and research topics in the production of solid biofuels. This analysis was made using bibliometric techniques. Bibliometric analyzes allow to indicate the research topics, authors, as well as research institutions that significantly influence a given discipline. The research and analysis were carried out on scientific articles taken from the Scopus database in 2014-2018. The downloaded data have been cleaned and processed in the VOSviewer program. This program allows to analyze the frequency of occurrence of keywords in years and to present results in graphic form. Next, a detailed analysis of the content of the publications and classification according to selected criteria was carried out. The main countries that carry out research in this area are: Spain, Italy, Brazil, the Czech Republic and China. The main research areas were: Energy, Environmental Science, Agricultural and Biological Sciences, Chemical Engineering and Engineering. The most popular research topics throughout the research period were: biomass (raw materials, properties), biomass agglomeration processes (briquetting, pelleting), energy properties research, thermal biomass treatment (torrefaction, gasification and others), research on production and biochar properties and other.