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Generating clean energy is necessary due to the increasing demand resulting from the development of human civilization and the global economy. The generation of clean energy is justified by the need to reduce the negative impact on the natural environment. Every action aimed at converting one form of energy into another carries consequences related to the efficiency of the transformation process and the physicochemical properties characterizing the systems of its utilization. Special attention should be paid to hydrogen, which is a fuel with high activity in terms of penetration through the structures of conventional construction materials, and there is a high probability of significant emissions of this fuel from its storage and transportation processes. This situation worsens the overall efficiency of the hydrogen utilization process but also necessitates the identification of the consequences of hydrogen emissions into the atmosphere. Therefore, a comprehensive assessment of energy conversion for the purposes of hydrogen production and storage in terms of environmental impact is necessary. The conducted analyses are presented in this article.

One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions. The classification model is intended to provide a probabilistic output, through which the outputs of the regression models are combined. The number of used input parameters is only five. They are related to the distance, the antenna heights, and the statistics of the terrain profile and line-of-sight obstacles. The proposed approach allows creation of a generalized model that is valid for various types of areas and terrains, different antenna heights, and line-of-sight and non line-of-sight propagation conditions. An experimental dataset is provided by measurements for a variety of relief types (flat, hilly, mountain, and foothill) and for rural, urban, and suburban areas. The experimental results show an excellent performances in terms of a root mean square error of a prediction as low as 7.3 dB and a coefficient of determination as high as 0.702. Although the study covers only one operating frequency of 433 M Hz , the proposed model can be trained and applied for any frequency in the decimeter wavelength range. The main reason for the choice of such an operating frequency is because it falls within the range in which many wireless systems of different types are operating. These include Internet of Things (IoT), machine-to-machine (M2M) mesh radio networks, power efficient communication over long distances such as Low-Power Wide-Area Network (LPWAN)—LoRa, etc. [ABSTRACT FROM AUTHOR]

This work presents the development and testing of an experimental web-based SDR (software-defined radio) monitoring system for indirect solar activity detection, which has the ability to estimate and potentially predict various events in space and on earth, including solar flares, coronal mass ejections, and geomagnetic storms. The proposed system can be used to investigate the effect of solar activity on the propagation of very-low-frequency (VLF) signals. The advantages and benefits of the given approach are as follows: increasing measurement accuracy and eventual solar activity identification by combining measurements from multiple spatially distributed SDRs. The verification process involves carrying out several experiments comparing data from the GOES satellite system and the Dunksin SuperSID system with information received by the SDR monitoring system. Then, utilizing Pearson correlation coefficients, the measured data from the SDRs, along with those from the GOES satellite system and the Dunsing monitoring station, are investigated. At the time of a solar flare, the correlation value is above 90% for most of the stations used. Combining the signal-to-noise ratio via summation also shows an improvement in the results, with a correlation above 98%. [ABSTRACT FROM AUTHOR]

The kinetic constants determined for three enzymes of different origins - fungal, bacterial, and mammal, have been compared in the process of hydrogen peroxide disproportionation as a model reaction. In order to stabilize the enzyme, it has been immobilized on waterinsoluble glucan whilst the studied process has been carried out in both the absence and the presence of low molecular weight aliphatic alcohols over a wide range of concentrations. In this study, purified catalases originating from the fungus Penicilium chrysogenum, bacteria Micrococcus lysodeikticus, and mammalian from the bovine liver, have been used. Their activities were determined by means of a spectrophotometric method, following the decay of the absorbance at 240 nm at a constant temperature, over the temperature range from 0 to 25°C. The catalytic processes were performed in the presence of methyl and ethyl alcohols in concentrations from 1% to 10%. For catalase immobilization, the water-insoluble glucan URE13-300 was used as a carrier when determining heterogeneous catalytic enzyme activity. The best and most stable was found to be the enzyme from P. chrysogenum, both immobilized and in the native state. It was found that the immobilized enzyme retains its activity over the temperature range - from 0 to 25°C. Its apparent kinetic constants were calculated to be: Km =109.4 mM and Vmax =14.42 µmol l-1 s-1. Preservation of the catalytic activity above 90% of the initial one was found in the presence of ethanol and methanol at concentrations not exceeding 3%. [ABSTRACT FROM AUTHOR]

Nowadays pollution of aquatic ecosystems with pesticides causes acute and chronic poisoning of fish, leading to serious damage to vital organs, such as the liver. Common carp (Cyprinus carpio Linnaeus, 1758) is a popular edible fish favored for culture due to its rapid growth, hardiness and reproduction in confined waters. The purpose of the present study was to investigate the negative effects of cypermethrin (CYP) and chlorpyrifos (CPF), based on their maximum allowable concentrations (Directive 2013/39/EU) on histochemical and biochemical biomarkers in the liver of common carp. The histochemical analysis included Periodic acid-Shiff stainig (PAS reaction) and Sudan Black B staining, while in the biochemical study different hepatic enzyme activities such as lactate dehydrogenase (LDH), aspartate aminotransferase (ASAT) and alanine aminotransferase (ALAT). The negative effects of the tested pesticides on fish were expressed with liver changes in the amount of glycogen and lipids, and enzyme changes of LDH, ASAT and ALAT, caused by the acute and chronic exposure to cypermethrin and chlorpyrifos under laboratory conditions. The results from such experimental set ups could be used in the legislation of protection water bodies from contamination, in areas near intensive application of plant protection products and also in implementing the Directive 2013/39/EU and Water Frame Directive by using multi-biomarker approaches. [ABSTRACT FROM AUTHOR]

The paper provides methodology to determine requirements in the frequency domain in designing a mushroom EBG structure to improve antenna array radiation characteristics. With integrating the structure in front of the antenna radiating surface, a gain increase and a change in beamwidth is observed. The proposed methodology has been tested. Studies with different dielectric substrates in the mushroom--like EBG structure design are provided to prove the proposed concept. Improved radiation parameters of a scanning antenna system (for 2.4 GHz ISM frequency band) consisting of an EBG structure and an antenna array are shown. [ABSTRACT FROM AUTHOR]