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Following paper presents a research aiming to increase the efficiency of an Unmanned Aerial System (UAS) fleet during mission execution. The developed system allows to reduce the mission time by considering the fleet diversity. System is based on a multilevel optimization, applying an innovative trajectory generation method. The Mixed Integer Linear Programming (MILP) algorithm was adopted, so the trajectories for individual platform were optimal (shortest). The developed solution considers not only the area but also the UASs performance and sensors capabilities. The mission operator with the dedicated GUI could control and designate the platforms for various tasks. The method was tested on various platforms with different performance. The method proved its efficiency reducing the mission time up to 15%. Finally, the system was successfully tested in real environment with the use of three various platforms.

Abstract Firm location studies traditionally assume that this is business that attracts other business to a given location. They focus on knowledge transfers between the most productive and innovative firms and analyse agglomeration economies arising from matching, learning and sharing mechanisms. We argue that the population density also attracts firms to the place. Therefore, we extend this stream of research by using the concept of economies of density to discuss the effects of the attraction of firms by population. Our study explores the existence of the hierarchical causal mechanism that occurs when 2nd line firms understood as non-innovative, less productive retail and service businesses together with human settlement create an environment that attracts 1st line firms - the most innovative and productive businesses. We show that population density and business agglomeration jointly affect the location of firms in different sectors and should be considered as complementary rather than substitutable attraction factors.

Streptococcus pyogenes is responsible for 20–30% of pharyngitis in children and 5–15% in adults. The ineffective treatment of group A Streptococcus (GAS) infections can result in postinfectious sequelae. This study aims to evaluate the frequency of GAS pharyngitis and assess the management of patients with pharyngitis and antibiotic use. We conducted a multicenter, retrospective analysis of medical records from nine primary care centers in Poland. The study enrolled 1949 medical records of patients (children 67.4%, adults 32.6%). An infection of Streptococcus pyogenes, based on a rapid strep test, was diagnosed in 830 patients (42.6%). In the comprehensive study group of 1949 patients, 1054 (54.1%) were given antibiotics. Notably, 224 patients had a negative rapid strep test result but still received antibiotic treatment, underscoring the complexity of treatment decisions. The most commonly used antibiotics were oral penicillin V in 431 cases (41%) and amoxicillin in 219 cases (20.8%). We observed no significant difference between positive rapid strep test results and patients’ sociodemographic data and comorbidities. The prevalence of GAS was 42.6% in the analyzed records of patients with pharyngitis, and 54.1% were prescribed antibiotics. Antibiotics were overprescribed for sore throats. Strategies are needed to promote rational antibiotic use.

Fungal infections constitute a significant challenge and continue to be a predominant cause of treatment failure in pediatric leukemia cases. Despite the implementation of antifungal prophylaxis, these infections contribute to approximately 20% of cases in children undergoing treatment for acute lymphoblastic leukemia (ALL). The aim of this study is to highlight the diagnostic and therapeutic challenges associated with invasive fungal infections (IFIs). We also present a review of the epidemiology, risk factors, treatment, and a clinical presentation of IFI in patients with ALL. This case report details the clinical course of confirmed Candida albicans (C. albicans) and Aspergillus spp. infections during the consolidation phase of ALL treatment in a 5-year-old pediatric patient. This male patient did not experience any complications until Day 28 of protocol II. Then, the patient’s condition deteriorated. Blood culture detected the growth of C. albicans. Despite the implementation of targeted therapy, the boy’s condition did not show improvement. The appearance of respiratory symptoms necessitated a computed tomography (CT) of the chest, which revealed multiple nodular densities atypical for C. albicans etiology. In spite of ongoing antifungal treatment, the lesions depicted in the CT scans showed no regression. A lung biopsy ultimately identified Aspergillus species as the source of the infection. Overcoming fungal infections poses a considerable challenge; therefore, an accurate diagnosis and the prompt initiation of targeted therapy are crucial in managing these infections in patients with leukemia.

High-quality short-term forecasts of electrical energy generation in solar power plants are crucial in the dynamically developing sector of renewable power generation. This article addresses the issue of selecting appropriate (preferred) methods for forecasting energy generation from a solar power plant within a 15 min time horizon. The effectiveness of various machine learning methods was verified. Additionally, the effectiveness of proprietary ensemble and hybrid methods was proposed and examined. The research also aimed to determine the appropriate sets of input variables for the predictive models. To enhance the performance of the predictive models, proprietary additional input variables (feature engineering) were constructed. The significance of individual input variables was examined depending on the predictive model used. This article concludes with findings and recommendations regarding the preferred predictive methods.

The fixation of carbon dioxide with epoxides is one of the most attractive methods for the green utilisation of this greenhouse gas and leads to many valuable chemicals. This process is characterised by 100% atom efficiency; however, an efficient catalyst is required to achieve satisfactory yields. Metal–organic frameworks (MOFs) are recognised as being extremely promising for this purpose. Nevertheless, many of the proposed catalysts are based on ions of rare elements or elements not entirely safe for the environment; this is notable with commercially unavailable ligands. In an effort to develop novel catalysts for CO2 fixation on an industrial scale, we propose novel MOFs, which consist of aluminium ions coordinated with commercially available 1,4-naphthalene dicarboxylic acid (Al@NDC) and their nanocomposites with gold nanoparticles entrapped inside their structure (AlAu@NDC). Due to the application of 4-amino triazole and 5-amino tetrazole as crystallization mediators, the morphology of the synthesised materials can be modified. The introduction of gold nanoparticles (AuNPs) into the structure of the synthesised Al-based MOFs causes the change in morphology from nano cuboids to nanoflakes, simultaneously decreasing their porosity. However, the homogeneity of the nanostructures in the system is preserved. All synthesised MOF materials are highly crystalline, and the simulation of PXRD patterns suggests the same tetragonal crystallographic system for all fabricated nanomaterials. The fabricated materials are proven to be highly efficient catalysts for carbon dioxide cycloaddition with a series of model epoxides: epichlorohydrin; glycidol; styrene oxide; and propylene oxide. Applying the synthesised catalysts enables the reactions to be performed under mild conditions (90 °C; 1 MPa CO2) within a short time and with high conversion and yield (90% conversion of glycidol towards glycerol carbonate with 89% product yield within 2 h). The developed nanocatalysts can be easily separated from the reaction mixture and reused several times (both conversion and yield do not change after five cycles). The excellent performance of the fabricated catalytic materials might be explained by their high microporosity (from 421 m2 g−1 to 735 m2 g−1); many catalytic centres in the structure exhibit Lewis acids’ behaviour, increased capacity for CO2 adsorption, and high stability. The presence of AuNPs in the synthesised nanocatalysts (0.8% w/w) enables the reaction to be performed with a higher yield within a shorter time; this is especially important for less-active epoxides such as propylene oxide (two times higher yield was obtained using a nanocomposite, in comparison with Al-MOF without nanoparticles).

Streptococcus pyogenes is responsible for 20–30% of pharyngitis in children and 5–15% in adults. The ineffective treatment of group A Streptococcus (GAS) infections can result in postinfectious sequelae. This study aims to evaluate the frequency of GAS pharyngitis and assess the management of patients with pharyngitis and antibiotic use. We conducted a multicenter, retrospective analysis of medical records from nine primary care centers in Poland. The study enrolled 1949 medical records of patients (children 67.4%, adults 32.6%). An infection of Streptococcus pyogenes, based on a rapid strep test, was diagnosed in 830 patients (42.6%). In the comprehensive study group of 1949 patients, 1054 (54.1%) were given antibiotics. Notably, 224 patients had a negative rapid strep test result but still received antibiotic treatment, underscoring the complexity of treatment decisions. The most commonly used antibiotics were oral penicillin V in 431 cases (41%) and amoxicillin in 219 cases (20.8%). We observed no significant difference between positive rapid strep test results and patients' sociodemographic data and comorbidities. The prevalence of GAS was 42.6% in the analyzed records of patients with pharyngitis, and 54.1% were prescribed antibiotics. Antibiotics were overprescribed for sore throats. Strategies are needed to promote rational antibiotic use. [ABSTRACT FROM AUTHOR]

High-quality short-term forecasts of electrical energy generation in solar power plants are crucial in the dynamically developing sector of renewable power generation. This article addresses the issue of selecting appropriate (preferred) methods for forecasting energy generation from a solar power plant within a 15 min time horizon. The effectiveness of various machine learning methods was verified. Additionally, the effectiveness of proprietary ensemble and hybrid methods was proposed and examined. The research also aimed to determine the appropriate sets of input variables for the predictive models. To enhance the performance of the predictive models, proprietary additional input variables (feature engineering) were constructed. The significance of individual input variables was examined depending on the predictive model used. This article concludes with findings and recommendations regarding the preferred predictive methods. [ABSTRACT FROM AUTHOR]

Fungal infections constitute a significant challenge and continue to be a predominant cause of treatment failure in pediatric leukemia cases. Despite the implementation of antifungal prophylaxis, these infections contribute to approximately 20% of cases in children undergoing treatment for acute lymphoblastic leukemia (ALL). The aim of this study is to highlight the diagnostic and therapeutic challenges associated with invasive fungal infections (IFIs). We also present a review of the epidemiology, risk factors, treatment, and a clinical presentation of IFI in patients with ALL. This case report details the clinical course of confirmed Candida albicans (C. albicans) and Aspergillus spp. infections during the consolidation phase of ALL treatment in a 5-year-old pediatric patient. This male patient did not experience any complications until Day 28 of protocol II. Then, the patient's condition deteriorated. Blood culture detected the growth of C. albicans. Despite the implementation of targeted therapy, the boy's condition did not show improvement. The appearance of respiratory symptoms necessitated a computed tomography (CT) of the chest, which revealed multiple nodular densities atypical for C. albicans etiology. In spite of ongoing antifungal treatment, the lesions depicted in the CT scans showed no regression. A lung biopsy ultimately identified Aspergillus species as the source of the infection. Overcoming fungal infections poses a considerable challenge; therefore, an accurate diagnosis and the prompt initiation of targeted therapy are crucial in managing these infections in patients with leukemia. [ABSTRACT FROM AUTHOR]

Abstract Mesenchymal stem cells (MSCs) have natural immunoregulatory functions that have been explored for medicinal use as a cell therapy with limited success. A phase Ib study was conducted to evaluate the safety and immunoregulatory mechanism of action of MSCs using a novel ex vivo product (SBI‐101) to preserve cell activity in patients with severe acute kidney injury. Pharmacological data demonstrated MSC‐secreted factor activity that was associated with anti‐inflammatory signatures in the molecular and cellular profiling of patient blood. Systems biology analysis captured multicompartment effects consistent with immune reprogramming and kidney tissue repair. Although the study was not powered for clinical efficacy, these results are supportive of the therapeutic hypothesis, namely, that treatment with SBI‐101 elicits an immunotherapeutic response that triggers an accelerated phenotypic switch from tissue injury to tissue repair. Ex vivo administration of MSCs, with increased power of testing, is a potential new biological delivery paradigm that assures sustained MSC activity and immunomodulation.

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: There is an unmet medical need for pruritus associated with chronic kidney disease, a distressing complication characterized by generalized and persistent itch affecting 20% to 40% of patients undergoing hemodialysis. Here we report the results of a phase 2 trial evaluating the efficacy and safety of a novel peripherally restricted kappa opioid receptor agonist, difelikefalin, in adult patients undergoing hemodialysis with pruritus. Methods: In this study, 174 hemodialysis patients with moderate-to-severe pruritus were randomly assigned to receive difelikefalin (0.5, 1.0, or 1.5 μg/kg) or placebo intravenously thrice weekly after each hemodialysis session for 8 weeks in a double-blind, controlled trial. The primary endpoint was the change from baseline at week 8 in the weekly mean of the 24-hour Worst Itching Intensity Numerical Rating Scale score. The secondary efficacy endpoint was the change in itch-related quality of life measured by the Skindex-10 questionnaire. Other endpoints included safety, sleep quality, and additional measures including the 5-D itch scale. Results: A significant reduction from baseline in itch intensity scores at week 8 favored all difelikefalin doses combined versus placebo (P = 0.002). Difelikefalin also showed improvement over placebo in Skindex-10, 5-D itch, and sleep disturbance scores (P ≤ 0.005). Overall, 78% of patients receiving difelikefalin reported treatment-emergent adverse events versus 42% of patients given placebo, with diarrhea, dizziness, nausea, somnolence, and fall being the most frequent (≥5%). Conclusion: In this trial, difelikefalin effectively reduced itching intensity and improved sleep and itch-related quality of life. Keywords: chronic kidney disease, CKD-aP, CR845, hemodialysis, kappa opioid receptor agonist, uremic pruritus

The FDTD (Finite Difference Time Domain) method has proven to be effective in modeling high-frequency electromagnetic problems in telecommunications industry. Recently it has been successfully applied in microwave power engineering. In order to accurately model scenarios typical in this field one has to deal with the movement of objects placed inside cavities. This paper describes a simple algorithm that makes it possible to take into account object rotation -- important in simulations of domestic microwave ovens. Results of example simulations are presented and an experimental verification of the simulation tool is performed. [ABSTRACT FROM AUTHOR]

The main objective of this study was to conduct multi-stage and multi-variant prognostic research to assess the impact of e-mobility development on the Polish power system for the period 2022–2027. The research steps were as follows: forecast the number of electric vehicles (using seven methods), forecast annual power demand arising solely out of the operation of the forecast number of electric vehicles, forecast annual power demand with and without the impact of e-mobility growth (using six methods), forecast daily profiles of typical days with and without the impact of e-mobility growth (using three methods). For the purpose of this research, we developed a unique Growth Dynamics Model to forecast the number of electric vehicles in Poland. The application of Multi-Layer Perceptron (MLP) to the extrapolation of non-linear functions (to the forecast number of electric vehicles and forecast annual power demand without the impact of e-mobility growth) is our original, unique proposal to use the Artificial Neural Network (ANN). Another unique, innovative proposal is to include Artificial Neural Networks (Multi-Layer Perceptron and Long short-term memory (LSTM)) in an Ensemble Model for simultaneous extrapolation of 24 non-linear functions to forecast daily profiles of typical days without taking e-mobility into account. This research determined the impact of e-mobility development on the Polish power system, both in terms of annual growth of demand for power and within particular days (hourly distribution) for two typical days (summer and winter). Under the (most likely) balanced growth variant of annual demand for power, due to e-mobility, such demand would grow by more than 4%, and almost 7% under the optimistic variant. Percentage growth of power demand in terms of variation according to time of day was determined. For instance, for the balanced variant, the largest percentage share of e-mobility was in the evening “peak” time (about 6%), and the smallest percentage was in the night “valley” (about 2%).

This review paper describes the state of the art in measurements of ferromagnetic linewidth, permeability tensor and saturation magnetization employing electrodynamic theory of ferromagnetic resonance. It is shown that the electrodynamic theory allows significant improvements of measurement accuracy of these parameters with respect to the commonly used perturbation and magnetostatics theories. Contrary to the perturbation method the electrodynamic theory is not limited to small samples. It allows determination of the resonance frequencies and Q-factors for arbitrary size spherical and cylindrical gyromagnetic samples in suitably chosen metal enclosures. Results obtained with the electrodynamic theory for very small samples are identical to those obtained with perturbation and magnetostatics theories. Results of measurements of the ferromagnetic linewidth, permeability tensor and saturation magnetization at microwave frequencies are presented.

The ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation. Forecasts for the next 24 h play an important role in this process. They are also used in energy market transactions. Even a small improvement in the quality of these forecasts translates into more security of the system and savings for the economy. Using two wind farms for statistical analyses and forecasting considerably increases credibility of newly created effective prediction methods and formulated conclusions. In the first part of our study, we have analysed the available data to identify potentially useful explanatory variables for forecasting models with additional development of new input data based on the basic data set. We demonstrate that it is better to use Numerical Weather Prediction (NWP) point forecasts for hourly lags: −3, 2, −1, 0, 1, 2, 3 (original contribution) as input data than lags 0, 1 that are typically used. Also, we prove that it is better to use forecasts from two NWP models as input data. Ensemble, hybrid and single methods are used for predictions, including machine learning (ML) solutions like Gradient-Boosted Trees (GBT), Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), K-Nearest Neighbours Regression (KNNR) and Support Vector Regression (SVR). Original ensemble methods, developed for researching specific implementations, have reduced errors of forecast energy generation for both wind farms as compared to single methods. Predictions by the original ensemble forecasting method, called “Ensemble Averaging Without Extremes” have the lowest normalized mean absolute error (nMAE) among all tested methods. A new, original “Additional Expert Correction” additionally reduces errors of energy generation forecasts for both wind farms. The proposed ensemble methods are also applicable to short-time generation forecasting for other renewable energy sources (RES), e.g., hydropower or photovoltaic (PV) systems.

The paper combines theoretical models of housing and business locations and shows that they have the same determinants. It evidences that classical, behavioural, new economic geography, evolutionary and co-evolutionary frameworks apply simultaneously, and one should consider them jointly when explaining urban structure. We use quantitative tools in a theory-guided factors induction approach to show the complexity of location models. The paper discusses and measures spatial phenomena as distance-decaying gradients, spatial discontinuities, densities, spillovers, spatial interactions, agglomerations, and as multimodal processes. We illustrate the theoretical discussion with an empirical case of interacting point-patterns for business, housing, and population. The analysis reveals strong links between housing valuation and business location and profitability, accompanied by the related spatial phenomena. It also shows that assumptions concerning unimodal spatial urban structure, the existence of rational maximisers, distance-decaying externalities, and a single pattern of behaviour, do not hold. Instead, the reality entails consideration of multimodality, a mixture of maximisers and satisfiers, incomplete information, appearance of spatial interactions, feed-back loops, as well as the existence of persistence of behaviour, with slow and costly adjustments of location.

The fixation of carbon dioxide with epoxides is one of the most attractive methods for the green utilisation of this greenhouse gas and leads to many valuable chemicals. This process is characterised by 100% atom efficiency; however, an efficient catalyst is required to achieve satisfactory yields. Metal–organic frameworks (MOFs) are recognised as being extremely promising for this purpose. Nevertheless, many of the proposed catalysts are based on ions of rare elements or elements not entirely safe for the environment; this is notable with commercially unavailable ligands. In an effort to develop novel catalysts for CO2 fixation on an industrial scale, we propose novel MOFs, which consist of aluminium ions coordinated with commercially available 1,4-naphthalene dicarboxylic acid (Al@NDC) and their nanocomposites with gold nanoparticles entrapped inside their structure (AlAu@NDC). Due to the application of 4-amino triazole and 5-amino tetrazole as crystallization mediators, the morphology of the synthesised materials can be modified. The introduction of gold nanoparticles (AuNPs) into the structure of the synthesised Al-based MOFs causes the change in morphology from nano cuboids to nanoflakes, simultaneously decreasing their porosity. However, the homogeneity of the nanostructures in the system is preserved. All synthesised MOF materials are highly crystalline, and the simulation of PXRD patterns suggests the same tetragonal crystallographic system for all fabricated nanomaterials. The fabricated materials are proven to be highly efficient catalysts for carbon dioxide cycloaddition with a series of model epoxides: epichlorohydrin; glycidol; styrene oxide; and propylene oxide. Applying the synthesised catalysts enables the reactions to be performed under mild conditions (90 °C; 1 MPa CO2) within a short time and with high conversion and yield (90% conversion of glycidol towards glycerol carbonate with 89% product yield within 2 h). The developed nanocatalysts can be easily separated from the reaction mixture and reused several times (both conversion and yield do not change after five cycles). The excellent performance of the fabricated catalytic materials might be explained by their high microporosity (from 421 m2 g−1 to 735 m2 g−1); many catalytic centres in the structure exhibit Lewis acids' behaviour, increased capacity for CO2 adsorption, and high stability. The presence of AuNPs in the synthesised nanocatalysts (0.8% w/w) enables the reaction to be performed with a higher yield within a shorter time; this is especially important for less-active epoxides such as propylene oxide (two times higher yield was obtained using a nanocomposite, in comparison with Al-MOF without nanoparticles). [ABSTRACT FROM AUTHOR]

Abstract Resonance in a ferromagnetic sphere, known in the body of literature as the mode of uniform precession, has recently been proven to be magnetic plasmon resonance (MPR). This finding has prompted research which is presented in this paper on the relation between the Q-factor at the MPR and the ferromagnetic resonance (FMR) linewidth ΔH, which is a parameter of magnetized gyromagnetic materials. It is proven in this paper that ΔH can be unequivocally determined from the Q-factor measured at the MPR, if all losses in the resonance system are properly accounted for. It can be undertaken through a rigorous but simple electrodynamic study involving the transcendental equation, as proposed in this paper. The present study also reveals that electric losses have a substantially reduced impact on ΔH due to the large magnetic to electric energy storage ratio at the MPR. Theoretical results are supported by measurements of the Q-factors on a monocrystalline yttrium iron garnet (YIG) sphere.

In this article, an optimal method of unmanned aerial vehicle selection for following a moving ground target is presented. The most suitable unmanned aerial vehicle from an aircraft operating fleet is selected for tracking task. The aircraft choice is done taking regarding the unmanned aerial vehicle distribution in the mission zone, individual performance of fleet platforms, and maneuverability of a ground platform. The unmanned aerial vehicle fleet members’ flying qualities are various, so an optimization process is used to assign an aircraft to follow the ground target. A target trajectory prediction is embedded in tracking algorithm. The simulation results demonstrated the effectiveness of algorithms developed, and in-flight demonstration proved the possibility of realization of computed trajectories by the unmanned aerial vehicle.

The ability to forecast electricity generation for a small wind turbine is important both on a larger scale where there are many such turbines (because it creates problems for networks managed by distribution system operators) and for prosumers to allow current energy consumption planning. It is also important for owners of small energy systems in order to optimize the use of various energy sources and facilitate energy storage. The research presented here addresses an original, rarely predicted 48 h forecasting horizon for small wind turbines. This topic has been rather underrepresented in research, especially in comparison with forecasts for large wind farms. Wind speed forecasts with a 48 h horizon are also rarely used as input data. We have analyzed the available data to identify potentially useful explanatory variables for forecasting models. Eight sets with increasing data amounts were created to analyze the influence of the types and amounts of data on forecast quality. Hybrid, ensemble and single methods are used for predictions, including machine learning (ML) solutions like long short-term memory (LSTM), multi-layer perceptron (MLP), support vector regression (SVR) and K-nearest neighbours regression (KNNR). Original hybrid methods, developed for research of specific implementations and ensemble methods based on hybrid methods’ decreased errors of energy generation forecasts for small wind turbines in comparison with single methods. The “artificial neural network (ANN) type MLP as an integrator of ensemble based on hybrid methods” ensemble forecasting method incorporates an original combination of predictors. Predictions by this method have the lowest mean absolute error (MAE). In addition, this paper presents an original ensemble forecasting method, called “averaging ensemble based on hybrid methods without extreme forecasts”. Predictions by this method have the lowest root mean square error (RMSE) error among all tested methods. LSTM, a deep neural network, is the best single method, MLP is the second best one, while SVR, KNNR and, especially, linear regression (LR) perform less well. We prove that lagged values of forecasted time series slightly increase the accuracy of predictions. The same applies to seasonal and daily variability markers. Our studies have also demonstrated that using the full set of available input data and the best proposed hybrid and ensemble methods yield the lowest error. The proposed hybrid and ensemble methods are also applicable to other short-time generation forecasting in renewable energy sources (RES), e.g., in photovoltaic (PV) systems or hydropower.

The article deals with the problem of automation and decision-making support in forming sustainable assortments of plant species for landscaped areas of cities, taking into account environmental factors, conditions of growing, properties of plant species, and landscaping practices adopted in the region. The automation of plant species and composition planning for planted territories requires formation of a knowledge base and output algorithms, as well as consideration of specific landscaping practices and techniques adopted in the region. It also requires the application of new modeling methods in combination with methods of spatial analysis, which together with the database of available regional plant species forms a recommendation system and provides decision support in the tasks of landscaping for city planners. The feature of the proposed method based on network analysis is the combination and composition of plant species, as well as the properties of invasive species, which require the use of graph models to form a knowledge base. The proposed method can be useful for forming sustainable assortments of plant species for landscaping squares, boulevards, small courtyards in the city, technical areas and other cases that do not involve the creation of individual complex landscape design. As part of the testing of the proposed method, the authors have formed a unique information base of species. The use of such a database, together with the method of selecting the species composition based on their resistances and interspecies compatibility will ensure the expansion of the diversity of species in the created ecosystem, which will make green areas more resistant to external anthropogenic factors and diseases, as well as preventing the spread of species invasive to the region. [ABSTRACT FROM AUTHOR]

Purpose: This research aimed to evaluate the biomechanical impact on a 3-year-old child's head during collisions with unmanned aerial vehicles (UAVs), focusing on the effects of UAV mass, impact velocity, and impact direction, using the Head Injury Criterion (HIC) for assessment. Methods: Experiments simulated impacts with UAVs of varying masses (249, 500 and 900 g) and velocities (19.0, 24.0 and 29.0 m/s) from different directions. HIC values were measured for each scenario and analyzed in relation to the Abbreviated Injury Scale to determine potential injury severity. Results: The findings showed that both the UAV's mass and impact velocity have a significant influence on the HIC value, with higher figures indicating a greater risk of serious injury. For the UAVs weighing 249 g and 500 g, frontal impacts resulted in the highest HIC values; however, for the UAV weighing 900 g, the highest HIC value occurred for the back hit. Moreover, injury risk was found to escalate non-linearly with increased velocity, especially for heavier UAVs. Conclusions: The study emphasizes the critical influence of UAV mass and impact velocity on the severity of head injuries in children. Increased mass and velocity correlated with higher HIC values, indicating a greater likelihood of severe injury. Frontal impacts were particularly hazardous for lighter UAVs, while rear impacts were more dangerous for heavier UAVs. These findings support the need for stringent regulations on UAV operational parameters, focusing on speed and mass limitations, to mitigate the risk of severe head injuries in children. [ABSTRACT FROM AUTHOR]

This paper presents the work completed under a research project titled "Design of a mobile platform for the support of forensic testing of scenes with potential CBRN hazards". The study focuses on operation of the mobile platform control algorithm, determination of the mobile platform position and preparation of the mobile platform system for integration with a video navigation system. The sensors installed on the mobile platform are intended as emergency backup systems in the event of loss of communication between the platform and its operator. The results of the test drive sessions completed to verify the control algorithm performance are also given.

The ferromagnetic linewidth obtained via different rectangular cavity ferromagnetic resonance experimental data processing algorithms has been compared. Approaches based on perturbation theory closed-form formulas, resonance frequency dispersion, Q -factor, transmitted power, and an electrodynamic transcendental equation (TDE) have been considered. The results were compared with direct Q -factor measurements in a subwavelength cavity and an electron paramagnetic resonance spectrometer. Three spherical samples, of approximately 1 mm in diameter and different saturation magnetization values, were investigated: monocrystalline gallium-substituted YIG (Ga:YIG, 50 Gs), polycrystalline aluminum-substituted YIG (Al:YIG, 380 Gs), and polycrystalline zirconium–calcium-substituted YIG (Zr,Ca:YIG,1826 Gs). A new approach to processing rectangular cavity data, based on the TDE, has been proposed and validated. Using the proposed approach, it is possible to obtain the accurate value of the linewidth in regimes where strong coupling between the sample and the cavity does not occur but the small perturbation assumption is no longer valid either. There is also no need to decouple the sample from the cavity. The conclusions of this work are useful for resonant characterization of not only magnetic spheres but also other shapes. [ABSTRACT FROM AUTHOR]