Search

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).

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%).

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 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.