The objectives of the study were to evaluate the correctness of the body posture of female soccer players in the frontal plane from the back based on selected body points in two static positions (habitual and actively corrected) using a non-contact optical measurement method. Forty-two young women (aged 16–20) playing soccer in a sports club in Poland were examined and compared with controls. The spatial coordinates (x, y, z) of the selected body points were determined. Four points (Oc_L, Oc_R, Pv_Land Pv_R) were extracted and used to calculate vectors $$\overline{{\boldsymbol{Oc}}}={\boldsymbol{O}}{{\boldsymbol{c}}}_{{\boldsymbol{L}}}-{\boldsymbol{O}}{{\boldsymbol{c}}}_{{\boldsymbol{R}}}$$Oc¯=OcL−OcR and $$\overline{{\boldsymbol{Pv}}}={\boldsymbol{P}}{{\boldsymbol{v}}}_{{\boldsymbol{L}}}-{\boldsymbol{P}}{{\boldsymbol{v}}}_{{\boldsymbol{R}}}$$Pv¯=PvL−PvR for analysis. The results show that median of the pelvic line angle was positive (Pv_Rwas lower than Pv_L) in both groups. For the habitual posture, the absolute value of the difference between the 25th and 75th percentiles in the pelvic line was almost three times greater among the soccer players than the controls (ratio between soccer players and controls: 2.93). Static postural imbalances in female soccer players require diagnosis of the sacroiliac joints with analysis of lumbar-pelvic system support and inhibition in the context of myofascial connection integration. Exercises can be implemented to stabilize the lumbar-pelvis complex as prophylaxis for spinal overload during the training cycle.

This paper addresses the influence of the sputtering time and hence thickness of thin copper (Cu) layers on the grain size, surface morphology and electrical properties. Cu layers 54-853 nm thick were deposited by DC magnetron sputtering at room temperature from a Cu target with a sputtering power of 2.07 W × cm^-2 in an argon atmosphere at a pressure of 8 × 10^-3 mbar. The structural and electrical properties were determined on the basis of four-contact probe measurements, stylus profilometry, atomic force microscopy (AFM), scanning electron microscopy (SEM) with an X-ray microanalysis (EDS) detector, and X-ray diffraction (XRD). The results of the conducted experiments show that the structure of thin copper layers can significantly change depending on the thickness and deposition process parameters. Three characteristic areas of structural changes and growth of copper crystallites/grains were distinguished. Ra and the RMS roughness linearly increase with increasing film thickness, while the crystallite size significantly changes only for copper films thicker than 600 nm. In addition, the resistivity of the Cu film is reduced to approximately 2 μΩ × cm for films with a thickness on the order of 400 nm, and a further increase in their thickness does not have a significant effect on their resistivity. This paper also determines the bulk resistance for the Cu layers under study and estimates the reflection coefficient at the grain boundaries.

This study presents the development process of a digital twin for a complex dynamic object using Artificial Neural Networks. A separately excited DC motor is considered as an example, which, despite its well-known electromechanical properties, remains a non-trivial object for neural network modeling. It is shown that describing the motor using a generalized neural network with various configurations does not yield satisfactory results. The optimal solution was based on a separation into two distinct nonlinear autoregressive with exogenous inputs (NARX) artificial neural networks with cross-connections for the two main machine variables: one for modeling the armature current with exogenous inputs of voltage and armature speed, and another for modeling the angular speed with inputs of voltage and armature current. Both neural networks are characterized by a relatively small number of neurons in the hidden layer and a time delay of no more than 3 time steps. This solution, consistent with the physical understanding of the motor as an object where electromagnetic energy is converted into thermal and mechanical energy (and vice versa), allows the model to be calibrated for the ideal no-load mode and subsequently account for the influence of torque loads of various natures and changes in the control object parameters over a wide range. The study demonstrates that even for modeling an object such as a DC electric drive with cascaded control, reducing errors at the boundaries of the known operating range requires generating test signals covering approximately 120% of the nominal speed range and 250–400% of the nominal current. Analysis of various test signals revealed that training with a sequence of step changes and linear variations across the entire operating range of armature current and speed provides higher accuracy compared to training with random or uniform signals. Furthermore, to ensure the neural network model’s functionality under varying load torque, a mechanical load observer was developed, and a model architecture incorporating an additional input for disturbance was proposed. The SEDCM_NARX_LOAD neural network model demonstrates a theoretically justified response to load application, although dynamic and static errors arise. In the experiment, the current error was 7.4%, and the speed error was 0.5%. The practical significance of the research lies in the potential use of the proposed model for simulating dynamic and static operational modes of electromechanical systems, tuning controllers, and testing control strategies without employing a physical motor.

The integration of solar generation into national energy balances is associated with a wide range of technical, economic, and organizational challenges, the solution of which requires the adoption of innovative strategies for energy system management. The inherent variability of electricity production, driven by fluctuating climatic conditions, complicates system balancing processes and necessitates the reservation of capacities from conventional energy sources to ensure reliability. Under modern market conditions, the pricing of generated electricity is commonly based on day-ahead forecasts of day energy yield, which significantly affects the economic performance of solar power plants. Consequently, achieving high accuracy in day-ahead electricity production forecasting is a critical and highly relevant task. To address this challenge, a physico-statistical model has been developed, in which the analytical approximation of daily electricity generation is represented as a function of a random variable—cloud cover—modeled by a β-distribution. Analytical expressions were derived for calculating the mathematical expectation and variance of daily electricity generation as functions of the β-distribution parameters of cloudiness. The analytical approximation of daily generation deviates from the exact value, obtained through hourly integration, by an average of 3.9%. The relative forecasting error of electricity production, when using the mathematical expectation of cloudiness compared to the analytical approximation of daily generation, reaches 15.2%. The proposed forecasting method, based on a β-parametric cloudiness model, enhances the accuracy of day-ahead production forecasts, improves the economic efficiency of solar power plants, and contributes to strengthening the stability and reliability of power systems with a substantial share of solar generation.

Niniejszy artykuł analizuje integrację instalacji fotowoltaicznych (PV) z elektrowniami szczytowo‑pompowymi (ESP) jako formą magazynowania energii i narzędziem ograniczania ryzyka blackoutu. Rosnący udział PV w wytwarzaniu energii elektrycznej zwiększa wahania bilansu mocy; nadprodukcja w słoneczne dni może prowadzić do wzrostu częstotliwości i napięcia w sieci oraz – przy niskiej inercji – uruchamiać mechanizmy ochronne, skutkujące kaskadowymi wyłączeniami źródeł. Na podstawie przeglądu literatury i raportów, studium przypadku blackoutu na Półwyspie Iberyjskim (28 kwietnia 2025 r.) oraz odniesienia do funkcjonowania Krajowego Systemu Elektroenergetycznego (KSE) wykazano, że ESP – dzięki dużej inercji, usługom częstotliwościowym i zdolności do czarnego startu – stanowią kluczowy zasób bezpieczeństwa energetycznego państwa. Zwrócono także uwagę na rolę sztucznej inteligencji (AI) w prognozowaniu generacji, automatyzacji sterowania i koncepcji „samoleczących się” sieci. Wyniki wskazują, że strategicznym priorytetem jest równoległe zwiększanie mocy PV i pojemności magazynowania w skali GWh, zwłaszcza poprzez rozwój ESP oraz zapewnienie mechanizmów umożliwiających wykorzystanie tych zasobów w usługach systemowych. Wzmacnia to odporność KSE i, poprzez powiązania transgraniczne, stabilność zintegrowanego systemu energetycznego UE.