We present the results of our experimental work concerning the sheet resistance, R,, temperature characteristics of resistance and relative power spectral density of l/f noise, S. versus the volume fraction ofwnductingmmponent , U , for RuOrglassmmposites.We eliminate the contributions of motact-film-resistive-~m interfaces to the measured characteristics. We find that the composites investigated can be mapped onto the threecomponent 3Drandomresistornetworl; (RRN) formedfrom wellconductingmetallic bonds, poorlyconductingmetal-insulator-meial (MIM) bondsand thosenotconducting. We provide the physical interpretation 01 the network's parameters: h,. h,, b , , i.e. the ratio of con. ductances of poorly and well conducting bonds, the ratio of their l/f noise relative power spectral densities and the fraction of metallic bonds in the set of all conducting bonds, respectively. The electrical transport characteristics of RuOrglass thick resistive films are interpreted with the help of bicritical behaviour of such a percolation network. We find qualitative agreement between S(R,) experimental data for a certain region of U and that from 3D RRN computer simulations using the Monte Carlo real space renormalization group algorithm.

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In this paper, we present a high-accuracy model for blueberry yield prediction, trained using structurally innovative data sets. Blueberries are blooming plants, valued for their antioxidant and anti-inflammatory properties. Yield on the plantations depends on several factors, both internal and external. Predicting the accurate amount of harvest is an important aspect in work planning and storage space selection. Machine learning algorithms are commonly used in such prediction tasks, since they are capable of finding correlations between various factors at play. Overall data were collected from years 2016–2021, and included agronomic, climatic and soil data as well satellite-imaging vegetation data. Additionally, growing periods according to BBCH scale and aggregates were taken into account. After extensive data preprocessing and obtaining cumulative features, a total of 11 models were trained and evaluated. Chosen classifiers were selected from state-of-the-art methods in similar applications. To evaluate the results, Mean Absolute Percentage Error was chosen. It is superior to alternatives, since it takes into account absolute values, negating the risk that opposite variables will cancel out, while the final result outlines percentage difference between the actual value and prediction. Regarding the research presented, the best performing solution proved to be Extreme Gradient Boosting algorithm, with MAPE value equal to 12.48%. This result meets the requirements of practical applications, with sufficient accuracy to improve the overall yield management process. Due to the nature of machine learning methodology, the presented solution can be further improved with annually collected data.