The aim of this paper is to answer the question: Are the Łódź Hills useful for electrical energy production from wind energy or not? Due to access to short-term data related to wind measurements (the period of 2008 and 2009) from a local meteorological station, the measure – correlate – predict approach have been applied. Long-term (1979‒2016) reference data were obtained from ECWMF
ERA-40 Reanalysis. Artificial neural networks were used to calculate predicted wind speed. The obtained average wind speed and wind power density was 4.21 ms⁻¹ and 70 Wm⁻¹, respectively, at 10 m above ground level (5.51 ms⁻¹, 170 Wm⁻¹ at 50 m). From the point of view of Polish wind conditions, Łódź Hills may be considered useful for wind power engineering.

Weather forecasting requires knowledge of the laws of atmospheric movement. Apart from classic fluid mechanics, we must consider the rotational motion of our planet, the differential heating of its surface through the absorption of solar radiation, as well as water evaporation and condensation processes.

In individual dogs, despite good quality of raw sperm, some parameters are significantly changed after thawing, which cannot be predicted. We therefore investigated whether motility parameters objectively obtained by CASA, membrane integrity (MI), cell morphology or a combination are suitable to improve the prediction of bad post-thaw quality. For this purpose 250 sperm analysis protocols from 141 healthy stud dogs, all patients introduced for sperm cryopreservation, were evaluated and a Classification and Regression Tree (CART) -analysis performed. The sperm was routinely collected, analysed, and frozen by using a modified Uppsala system. After thawing, data were routinely examined by using CASA, fluorescent microscopy for membrane integrity (MI) and Hancock’s fixation for evaluation of cell morphology. Samples were sorted by post-thaw progressive motility (P) in good (P > / = 50%, n=135) and bad freezers (P<50%, n=115). Among bad freezers, 73.9% showed in addition post-thaw total morphological abberations of >40% and/or MI <50%.

Bad freezers were significantly older than good freezers (p<0.05). Progressive motility (P), velocity curvilinear (VCL), mean coefficient (STR), and linear coefficient (LIN) were potential predictors for post-thaw sperm quality since specifity was best (85.8%) and sensitivity (75.4 %) and accuracy (80.4 %) good. For these objectively measured raw sperm parameters, cut-off values were calculated allowing prediction of bad post-thaw results with high accuracy: P = 83.1 % VCL = 161.3 µm/sec, STR = 0.83 %, and LIN = 0.48 %. Raw sperm samples with values below these cut off values will have below average post-thaw quality with a probability of 85.8%. We conclude that VCL, P, STR and LIN are potential predictors of the outcome of sperm cryopreservation, when combined.

The existing traffic noise prediction models in road intersections relate mainly to the typical solutions of intersection geometry and traffic organisation. There are no models for large and more complex intersections such as signalised roundabouts. This paper presents the results of studies on the development of a traffic noise prediction model for this type of intersection. The model was developed using a multiple regression method based on the results of field measurements of traffic parameters and noise levels in the vicinity of signalised roundabouts in Poland. The obtained model consists of two groups of variables affecting noise levels at the intersection. The first group determines in detail the influence of traffic and geometry of the closest entry. The second group shows the influence of more distant noise sources (traffic at the three remaining entries of the intersection) and the influence of the dimensions of the entire intersection. The developed model was verified through additional field measurements, as well as compared to the results of two methods of traffic noise prediction: the French ‘NMPB-Routes-2008’ and the German ‘RLS-90’. The obtained results confirmed a higher accuracy of calculations performed using the developed model in the range of: −1.2 dB ÷ +1.0 dB, while the ‘NMPB-Routes-2008’ and ‘RLS-90’ calculate precision were respectively: −2.8 dB ÷ +1.3 dB, and +0.8 dB ÷ +5.2 dB. Therefore, the developed model allows for a more accurate prediction of noise levels in the vicinity of signalised roundabouts in a flat terrain without buildings and noise barriers.

The continuous growth of smart communities and ever-increasing demand of sending or storing videos, have led to consumption of huge amount of data. The video compression techniques are solving this emerging challenge. However, H.264 standard can be considered most notable, and it has proven to meet problematic requirements. The authors present (BPMM) as a novel efficient Intra prediction scheme. We can say that the creation of our proposed technique was in a phased manner; it's emerged as a proposal and achieved impressive results in the performance parameters as compression ratios, bit rates, and PSNR. Then in the second stage, we solved the challenges of overcoming the obstacle of encoding bits overhead. In this research, we try to address the final phase of the (BPMM) codec and to introduce our approach in a global manner through realization of decoding mechanism. For evaluation of our scheme, we utilized VHDL as a platform. Final results have proven our success to pass bottleneck of this phase, since the decoded videos have the same PSNR that our encoder tells us, while preserving steady compression ratio treating the overhead. We aspire our BPMM algorithm will be adopted as reference design of H.264 in the ITU.

Electromagnetic mill installation for dry grinding represents a complex dynamical system that requires specially designed control system. The paper presents model-based predictive control which locates closed loop poles in arbitrary places. The controller performs as gain scheduling prototype where nonlinear model – artificial recurrent neural network, is parameterized with additional measurements and serves as a basis for local linear approximation. Application of such a concept to control electromagnetic mill load allows for stable performance of the installation and assures fulfilment of the product quality as well as the optimization of the energy consumption.

This study was conducted to predict the yield and biomass of lentil (Lens culinaris L.) affected by weeds using artificial neural network and multiple regression models. Systematic sampling was done at 184 sampling points at the 8-leaf to early-flowering and at lentil maturity. The weed density and height as well as canopy cover of the weeds and lentil were measured in the first sampling stage. In addition, weed species richness, diversity and evenness were calculated. The measured variables in the first sampling stage were considered as predictive variables. In the second sampling stage, lentil yield and biomass dry weight were recorded at the same sampling points as the first sampling stage. The lentil yield and biomass were considered as dependent variables. The model input data included the total raw and standardized variables of the first sampling stage, as well as the raw and standardized variables with a significant relationship to the lentil yield and biomass extracted from stepwise regression and correlation methods. The results showed that neural network prediction accuracy was significantly more than multiple regression. The best network in predicting yield of lentil was the principal component analysis network (PCA), made from total standardized data, with a correlation coefficient of 80% and normalized root mean square error of 5.85%. These values in the best network (a PCA neural network made from standardized data with significant relationship to lentil biomass) were 79% and 11.36% for lentil biomass prediction, respectively. Our results generally showed that the neural network approach could be used effectively in lentil yield prediction under weed interference conditions.

This paper investigates the application of a novel Model Predictive Control structure for the drive system with an induction motor. The proposed controller has a cascade-free structure that consists of a vector of electromagnetics (torque, flux) and mechanical (speed) states of the system. The long-horizon version of the MPC is investigated in the paper. In order to reduce the computational complexity of the algorithm, an explicit version is applied. The influence of different factors (length of the control and predictive horizon, values of weights) on the performance of the drive system is investigated. The effectiveness of the proposed approach is validated by some experimental tests.

The Lithuanian national standard of electric resistance is maintained as the basis for calibration and measurement capabilities published in the key comparison database of the International Bureau of Weights and Measures (BIPM). The stability and uncertainty of the resistance value measurements, performed since 2004 using the calibrated values of the standard resistors to predict their future behaviour as well as influence of environmental conditions, are discussed. Also discussed is the recovery of a standard resistor which had undergone a mechanical disturbance. It is concluded that the standard resistors operated by the Lithuanian National Electrical Standards Laboratory feature stable drift of resistance, which is well predicted by means of linear regression.