Slovak University of Technology in Bratislava

The aim of this review is to explore the relationship between the architecture of microservices and antisocial behavior. Microservice architecture is a software design approach that involves decomposing a system into small, independent services that can offer benefits such as flexibility and scalability. Expected result is to analyze dataset of statuses, which defines the length of time the status displays on the user’s device. Because people spend time on social networks at every opportunity, even for hours. When combined with the time-valued, we try to gain a deeper understanding of social networks and users behavior. They are forced into this behavior through the feedback loop mechanism of the neurotransmitter Dopamine. Main goal is to understand how to minimize the impact of antisocial behavior on social networks and what exactly makes users spend a disproportionate amount of time on these platforms.

Alternating current (AC) losses in superconducting tapes (SCTs) are one of the main concerns for their use in applications where costs of heat dissipation need to be reduced, such as pulse magnets. Modification of the tapes by striation has been shown to significantly reduce AC losses, and several filamentization methods have been developed. Up to two orders of magnitude decrease in AC losses was reported in previous works with laser ablation, mechanical grooving, or chemical etching. However, a different approach may have to be used to achieve similar results for those SCTs (or their applications after striation), which are more prone to degradation by the side effects of the mentioned methods. We proposed a procedure based on chemo-mechanical etching, which avoids problems arising from thermal degradation and mechanical pressure. Our method intended for the REBCO (RE = rare earth metal, B = Ba, C = Cu, O = oxygen) based high-temperature SCTs yielded low and reproducible AC losses, low cost, retention of the relative critical current up to 93%, and preservation of the mechanical properties of the tape substrate.

Hydrotalcite samples were prepared in the form of powder and/or sol under different conditions and characterised by various techniques. A suitable system of liquid carriers consisting of perfluoroheptane, isopropanol, and water (PFH-IPA-H 2 O) was chosen to apply HTlcs as deacidifying agents on paper. The areas of miscibility and immiscibility in the PFH-IPA-H 2 O system were determined at a temperature of 25 °C. The properties of HTlcs dispersed in the prepared solvent were measured. The size of the particles was determined by optical microscopy with image analysis. The average particle size ranges from 1 µm to 2 µm. The settling speed of particles in the prepared colloidal systems was monitored using turbidimetry. Sols in the mixture of solvents had uniformly dispersed particles that settled slowly. The effect of the prepared colloidal HTlcs dispersions on the properties of the paper, specifically the pH of its surface, was also tested. Hydrotalcites in the form of a sol with a ratio of magnesium to aluminium of 5:1 were found to be promising candidates for deacidification. The use of surfactant additives in the preparation of HTlcs did not positively affect the properties of the paper.

In this research work, we subjected the Sleipner steel to pack-boronizing within the temperature range of 1173–1323 K, lasting from 1 to 10 h. Our study involved assessing the steel’s microstructure by examining interphase morphology and measuring the layers’ thicknesses through scanning electron microscopy. To determine the phase composition of the boronized layers, we employed X-ray diffraction analysis. Furthermore, we investigated the redistribution of certain elements during the boronizing process using EDS mapping and EDS point analysis. The boride layers were found to consist of FeB and Fe 2 B phases. We conducted microhardness testing using the Vickers method on the diffusion zone, Fe 2 B, and FeB. Lastly, we utilized a diffusion model to evaluate the activation energies of boron in FeB and Fe 2 B, and we presented the results in terms of activation energies.

The paper considers scientific principles of technological procedure development for the selection and assignment of parameters of velocity machining by blade tool at gear milling with due regard to the required parameters of the gear surface layer. Optimum micro-cutting conditions are determined when full participation in stock removal of all mill teeth is provided. Technological regulation of selection and setting of machining parameters is spread for gear hobbing of the spur gear wheels with fine-modular and coarse-modular hardness (220...320 HV) and after heat treatment (HRC46...63). Correlation with essential parameters of the surface layer, cutting depth, feed, speed and radius of rounding of the cutting edge of milling cutters’ teeth is established. It is scientifically grounded, tested, and proved at what modes of cutting, the radius of rounding of cutting edge of the cutting tool is made metal removal at oncoming and passing high-speed micro milling. The process of determining of minimal sliding angle, or maximum value (without the use of coolant and with coolant), in which there is no micro-cutting process taking into account variation of variable parameters of cutting conditions and geometrical parameters of hobbing cutters is modelled. For the first time, we ascertained the relation between the sliding angle and the maximum contact angle of the mill tooth with the work surface, which allowed determine the efficiency of the shaping process through coefficient (without coolant and with coolant).

Based on the results obtained in the previous research, the values of meshing characteristics of profile shifted quasi-involute arc-tooth-trace gears is calculated. The influence of the values of profile shift coefficient and the angle of tooth trace on meshing characteristic distribution on the tooth flank surface along tooth trace is defined. The results can be used for design of profile shifted quasi-involute arc-tooth-trace gears, cut by Gleason-type cutters with different profile angle value.

The paper presents the results of modelling and investigation of the gear cutting process using the power skiving method. The investigations are based on the developed geometrical model of undeformed chips. The approach is based on the original method of forming an integral three-dimensional model on the basis of discrete cross-sections of tool tooth in their successive positions on the cutting path, taking into account the shape and size of the gap previously formed by this tooth. The formulae for calculating the cutting force and the forces acting on the work gear and tool are derived as a function of the angular position of the cutter. The dependence of this force on the angle of tool shaft setting and the angle of inclination of gear teeth is studied. The formulae take into account how the cutting intensity changes due to the peculiarities of the kinematics of power skiving technology and the influence of the actual geometric parameters of the tool on the cutting force. Based on the obtained dependencies, a methodology has been developed for the selection of the axial feed rate and spindle set angle to ensure a compromise between the required productivity and the accuracy of the gear profile.

The paper focuses on analyzing and designing a mounting system for the modified rear powertrain of the E-up Boost. The initial section delves into the theoretical aspects, specifically examining the differing sources of vibration between internal combustion engines and electric powertrains. Following this, the subsequent section presents an in-depth analysis of the powertrain mounting system using ADAMS View. After the analysis is carried out, a robust mounting system is designed and verified through structural analysis.

Composting is one of the efficient and effective methods of disposal and recovery of biodegradable waste. A favorable and intensified course of the composting process can be achieved by optimal composition of the composted material (moisture, content of organic substances and nutrients, C:N ratio, etc.), and also by optimizing the conditions under which composting takes place (temperature, pH, structure and aeration of the material, etc.). The paper contains a methodology for calculating the composition of composted material and also a methodology for solving the forced aeration process of composted material. More specifically, some process parameters of the intensified composting of a defined amount of kitchen biowaste, taking place in closed composting reactors, are presented and analyzed. Using the material balance of the composting process, the connections between the desired composition of the matured compost, the composition of the composted material and the conditions under which the composting takes place are pointed out. The specified connections enable the composting process to be optimized appropriately.

The bulk semi-insulating GaAs material was used for preparation of pad radiation detectors with circular contacts of 1 mm diameter. The spectrometric properties of a semiconductor detector depend on the quality of the base material and on the deposited metallization. Another factor affecting the detector spectrometry is the applied bias controlling the electric collection field. With increasing bias, the charge collection efficiency of particular detector grows. However, this spectrometric property should be changing with detector thickness, which affects the intensity of electric collection field at constant bias applied through the detector sandwich structure. In this paper we have studied the electrical and spectrometric properties of semi-insulating GaAs detectors as a function of their thickness. The measured saturation reverse current was in the range of 3 – 30 nA, increasing with decreasing detector thickness at a substrate resistivity of about of 10 ⁷ Ωcm. The maximal obtainable charge collection efficiency evaluated from ²⁴¹ Am gamma spectra grew with decreasing detector thickness from 50% for a 450 µm thick detector to 80% for a 230 µm thick detector.

The particle detector based on a low concentration 4H-SiC epitaxial layer shows promising properties for the detection of various types of ionizing radiation. The wide bandgap energy of the 4H-SiC semiconductor material (3.23 eV at room temperature) allows the detector to operate reliably at room temperature and at elevated temperatures up to several hundred degrees Celsius. The 80 >m thick 4H-SiC epitaxial layer grown on a 350 >m 4H-SiC substrate was used for detector preparation. The active area of the detector was defined by a 3 mm Schottky contact. The current-voltage measurement shows a reverse current of less than 30 pA up to 1000 V. Capacitance-voltage measurements show that the epitaxial layer is completely depleted at bias voltages above 250 V. The detector has been tested with neutrons of energies from 370 keV up to 14.9 MeV. Neutrons were produced by three nuclear reactions p-T, d-D and d-T. The measured spectra clearly identified the elastic and inelastic scattering at silicon and carbon atoms of detector material.

It is demonstrated here that the concept of variable activation energy is mathematically not fully correct. Further it is shown that general rate equation is a formal mathematical tool for the description of thermoanalytical kinetic data. The temperature function, k ( T ), is not the rate constant in general and the conversion function, f ( α ), may not reflect the mechanism in case of complex processes. Both, k ( T ) and f (α), are functions enabling to describe the kinetic hypersurface. For the complex processes, the physical meaning of parameters occurring in both functions is unclear. Hence, no mechanistic conclusions should be drawn from the values of an individual kinetic parameter; particularly, just from the values of activation energy. The conclusions can be drawn from the quantities with a clear physical meaning such as the values of isoconversional times, isoconversional temperatures, conversion, reaction rate, etc., i.e., the quantities that can be accessible experimentally. These quantities can be recovered and modeled from known kinetic parameters. It is proved here that the right temperature function may not be necessarily the Arrhenius equation for a complex process.

In the present work, a nanocomposite, based on embedding Co-doped CeO2 nanoparticles into graphitic carbon nitride (g-C3N4), was applied to functionalize commercial glassy carbon paste. This is the first application of the electrochemical sensor, developed through the proposed procedure, in electrochemical sensing. The sensor was utilized for the electrochemical determination of organophosphate pesticide fenitrothion (FNT). Cyclic voltammetry identified reversible oxidation of FNT (oxidation at 0.18 V and reduction at 0.13 V) and additional reduction at −0.62 V vs. Ag/AgCl in HCl solution (pH = 1). Theoretical calculations were carried out to model and elucidate experimentally observed redox processes. Special attention was devoted to modeling experimental conditions, and based on the obtained results, a detailed redox mechanism of the investigated analyte was proposed. This represents the first complete and unambiguous elucidation of the FNT redox mechanism, supported by joined experimental and theoretical data. Square wave voltammetry (SWV) was utilized for quantification, whereby the FNT oxidation peak was chosen for monitoring the analyte concentration. The developed sensor provided a nanomolar detection limit (3.2 nmol L−1), a wide linear concentration range (from 0.01 to 13.7 μmol L−1), and good precision, repeatability, and selectivity towards FNT. Practical application possibility was explored by testing the sensor performance for examining tap water and apple samples. Recovery tests, conducted during the FNT-spiked sample assays, showed a great application capability of the developed sensor for real-time monitoring of FNT traces in environmental samples.

Diabetic retinopathy (DR) can cause irreversible eye damage, even blindness. The prognosis improves with early diagnosis. According to the International Classification of Diabetic Retinopathy Severity Scale (ICDRSS), DR has five stages. Modern, cost‐effective techniques for automatic DR screening and staging of fundus images are based on deep learning (DL). To obtain higher classification accuracy, the combination of several diverse individual DL models into one ensemble could be used. A new approach to model diversity in an ensemble is proposed by manipulating the training input data involving original and four variants of preprocessed image datasets. There are publicly available datasets with labels for all five stages, but some contain poor‐quality images. In contrast, this algorithm was trained on images from a six‐class DDR dataset, including the class of poor‐quality ungradable images, to enhance the classification performance. The solution was evaluated on the APTOS dataset, containing only ICDRSS classes. Classification results of the ensemble model were presented on two different ensemble convolutional neural network (CNN) models, based on Xception and EfficientNetB4 architectures using two fusion approaches. Our proposed ensemble models outperformed all other single deep learning architectures regarding overall accuracy and Cohen's Kappa, with the best results using the EfficientNetB4 architecture.

The authors of this study investigated the use of machine learning (ML) and feature engineering (FE) techniques to accurately determine FAO reference evapotranspiration ( ETo ) with a minimal number of climate variables being measured. The recommended techniques for areas with insufficient measurements are based solely on daily temperature readings. Various ML methods were tested to evaluate how sophisticated an ML algorithm is for this task necessary. The main emphasis was on feature engineering, which involves converting raw variables into inputs better suited for ML algorithms, resulting in improved results. FE methods for estimating evapotranspiration include approximations of clear-sky solar radiation based on altitude and Julian day, approximate relative humidity and wind velocity, a categorical month variable, and variables interactions. The authors confirmed that the ability of ML in such tasks is not solely dependent on choosing the suitable algorithm but also on this frequently ignored step. The results of computational experiments are presented, accompanied by a comparison of the proposed method against standard ETo empiric equations. Machine learning methods, mainly due to the transformation of raw variables using FE, provided better results than traditional empirical methods and sophisticated ML algorithms without FE. In addition, the authors tested the applicability of the developed models in the broader area to evaluate the possibility of their generalizability. The potential of this approach to deliver improved predictions, reduced input requirements, and increased efficiency holds interesting promise for optimizing water management strategies, irrigation planning, and decision-making within the agricultural sector.

The application of biochar is considered to be a beneficial strategy for improving soil ecosystem services. The objectives of this study are to evaluate the differences in the soil erosion of silt loam soil with or without the application of biochar and to compare the impact of the application of biochar on soil erosion for different agricultural practices, namely, bare soil, silage corn, and sown peas. Specifically, the physically-based EROSION 3D model was used to estimate the soil erosion of small plots of sloping agricultural land. In considering various combinations of agricultural practices and rainfalls with different durations and intensities, several scenarios were used to assess the impact of the application of biochar on soil erosion. The results of this study demonstrate that the highest mean values of mean soil erosion in the case study area were simulated without using any biochar on bare soil. The values of the mean soil erosion were reduced with the use of biochar. The effect of the application of biochar was shown for all types of agricultural practices; above all, it reduced soil erosion that occurred above high values (over 30 t ha –1 ). Although the application and reapplication of biochar showed promise in reducing soil erosion, further research is needed to gain a deeper understanding of its total effects.

The aim of the study is to analyse changes and predict the course of mean monthly water temperatures of the Danube River at various locations for the future. The first part of the study involves conducting a statistical analysis of the annual and monthly average air temperatures, water temperatures, and discharges along the Danube River. The study examines long-term trends, changes in the trends, and multiannual variability in the time series. The second part of the study focuses on simulating the average monthly water temperatures using Seasonal Autoregressive Integrated Moving Average (SARIMA) models and nonlinear regression models (NonL), based on two RCP based incremental mean monthly air temperature scenarios. To assess the impact of future climate on stream temperatures, the historical long-term average of the monthly water temperature (1990–2020) was compared with scenarios S1 (2041–2070) and S2 (2071–2100). The simulation results from the two stochastic models, the SARIMA and NonL, showed that in scenario S1, the Danube River’s average monthly water temperature is projected to increase by 0.81/0.82°C (Passau), 0.55/0.71°C (Bratislava), and 0.68/0.56°C (Reni). In scenario S2, the models predict higher increases: 2.83/2.50°C (Passau), 2.06/2.46°C (Bratislava), and 2.52/1.90°C (Reni). Overall, the SARIMA model proved to be more stable and effective in simulating the increase in monthly water temperatures in the Danube River.

Climate change is presently a widely discussed subject in relation to alterations in water storage capacity and the components of the hydrological balance within catchment areas. This research study was directed at two main objectives: 1. The indirect estimation of long-term mean annual runoff using an empirical model; 2. The determination of changes in the annual runoff regime of fifty Danube sub-basins. Monthly areal precipitation, discharges, and air temperature data from 1961 to 1990 were collected for selected headwater sub-basins of the Danube River. In the first part, Turc-type empirical equations for the estimation of the long-term average annual runoff R in the Danube basin were employed. The parameters of the empirical equations were determined through nonlinear regression. Given the underestimation of the actual (territorial, balance) evapotranspiration ET values determined from the balance equation, the precipitation totals were corrected by +10%. With a 10% increase in precipitation, the values of balance ET reached the values ET determined by the Budyko–Zubenok–Konstantinov method. In the second part, fifty equations for the estimation of changes in the average annual runoff, depending on increases in the air temperature and changes in the annual precipitation separately for each of the 50 sub-basins, were established. In conclusion, the results suggest that, on average, a 100 mm increase in the average annual rainfall in the Danube River headwater sub-basins, will cause a 50 mm increase in outflow, and a 1 °C increase in the average annual air temperature will lead to a 12 mm decrease in runoff.

Intense floods represent a challenge to risk management. While they are multivariate in their nature, they are often studied in practice from univariate perspectives. Classical frequency analyses, which establish a relation between the peak flow or volume and the frequency of exceedance, may lead to improper risk estimations and mitigations. Therefore, it is necessary to study floods as multivariate stochastic events having mutually correlated characteristics, such as peak flood flow, corresponding volume and duration. The joint distribution properties of these characteristics play an important role in the assessment of flood risk and reservoir safety evaluation. In addition, the study of flood hydrographs is useful because of the inherent dependencies among their practice-relevant characteristics present on-site and in the regional records. This study aims to provide risk analysts with a consistent multivariate probabilistic framework using a copula-based approach. The framework respects and describes the dependence structures among the flood peaks, volumes, and durations of observed and synthetic control flood hydrographs. The seasonality of flood generation is respected by separate analyses of floods in the summer and winter seasons. A control flood hydrograph is understood as a theoretical/synthetic discharge hydrograph, which is determined by the flood peak with the chosen probability of exceedance, the corresponding volume, and the time duration with the corresponding probability. The framework comprises five steps: 1. Separation of the observed hydrographs, 2. Analysis of the flood characteristics and their dependence, 3. Modelling the marginal distributions, 4. A copula-based approach for modelling joint distributions of the flood peaks, volumes and durations, 5. Construction of synthetic flood hydrographs. The flood risk assessment and reservoir safety evaluation are described by hydrograph analyses and the conditional joint probabilities of the exceedance of the flood volume and duration conditioned on flood peak. The proposed multivariate probabilistic framework was tested and demonstrated based on data from two contrasting catchments in Slovakia. Based on the findings, the study affirms that the trivariate copula-based approach is a practical option for assessing flood risks and for reservoir safety.