First of all, the smelting equipment is the most important component of a foundry’s main production process and therefore requires constant reproduction. This is ensured by timely and high-quality maintenance and repair, the cost of which is 8–12% of the total costs. The technical and economic conditions of the enterprise itself depend on this, as the productivity of workers during production is directly related to the technical condition of the equipment and its downtime for repairs. An important factor in ensuring a melting furnace’s reproduction is a replacement of the worn lining, which leads to downtime of the smelting furnace and reduces the efficiency of its operation. The amount of torque required depends directly on the compound used. The quality of the manufacturing and sintering process of the lining, which provides the necessary durability, is affected by the heat capacity of the materials used when they are affected by the melting temperature of the alloys. In the present work, using the BRUKER D8 ADVANCE diffractometer, the Shimadzu XRF-1800 spectrometer and the STA 449 F1 Jupiter synchronous thermal analyzer, we probed the changes in the heat capacity of quartzite and PKMVI-3 under the action of temperatures of 200–1550 °C. This technology allows the manufacture of a lining that maintains high stability during operations at 1550–1600 °C melting modes.

Increasing the productivity and reliability of mining infrastructure facilities is an important task in achieving future goals. Mining dump trucks are an important part of coal mine infrastructure. In this article, to determine the reliability indicators in a brush–commutator unit and the residual life of electric motor brushes, a mathematical model for processing statistical data has been developed, which allows for the classification of types of failures and, unlike existing models, the determination of the life of the brushes according to the maximum extent of their wear. A method for predicting the residual life of an electric brush in a DC electric motor is presented, which contains a list of controlled reliability indicators, included a mathematical model. The described model improves the accuracy of the prediction and detection of DC motor failures. The derivation of the general formula for calculating the residual life of electric brushes is given. Based on the proposed mathematical model, we studied and calculated the reliability of the brush–commutator unit, the minimum height of the brush during operation, the average rate of its wear, the standard deviation and the mathematical expectation of brush wear. A nomogram of the failure-free operation time of the brush–commutator unit in a DC electric motor was modeled using the height of the brush during operation. Output parameters after the implementation of this monitoring system include the reliability of the electric motor operation. At the same time, diagnostic characteristics are improved twofold, and the residual life of the brush-switching unit is increased by 28–30%.

Mining operations have a significant negative impact on the surrounding ecosystems. The operation of mines and quarries creates a large amount of waste that accumulate and are practically unrecyclable in the environment. The involvement of these wastes in economic activity is an extremely urgent task. This can make the economy more sustainable and reduce its influence on ecosystems. This work presents the attempts of using quarry wastes as a fertilizer applied for growing tea crops. The novelty of this research involves revealing the quarry wastes as a fertilizer when growing Camellia sinensis (L.) O. Kuntze and assessing changes in the productivity of this plant when applying these calcium wastes. The waste of a quarry intended for extracting crushed stone was studied in this article. The composition of the waste was analyzed. Fertilizers used for manuring the soil were prepared based on the waste. Two experimental sites were selected. One of them was a control, where Camellia sinensis (L.) O. Kuntze was grown without using fertilizers. Fertilizers obtained from the waste were applied on the second site. The experimental work proceeded for 10 years. When discussing the results, special attention was paid to climatic conditions. This was caused by the need to show that it was the use of the fertilizer that influenced the change in the yield, not the climatic conditions. As a result of using calcium fertilizers based on the waste, the productivity of Camellia sinensis (L.) O. Kuntze was increased. The application of the fertilizers based on the quarry wastes was shown to provide an increase in the yield. The possibility of using calcium fertilizers to overcome unfavorable agroclimatic conditions during the tea cultivation was also demonstrated. To assess the climatic impact of applying new fertilizers, three-dimensional modeling in the “gnuplot v.5.4” software was used. As a result, an increase in the average annual precipitation, from 1000 to 1980 mm/year, in the range of the average annual air temperature, from 14 to 16 °C, was found to lead to an increase (when using a new fertilizer) in the yield of Camellia sinensis (L.) O. Kuntze up to 4.8 times (from 20 to 95 centner/ha). The results have shown that applying fertilizers based on the quarry wastes is also possible in unfavorable climatic conditions.

Floods, caused by intense rainfall or typhoons, overwhelming urban drainage systems, pose significant threats to urban areas, leading to substantial economic losses and endangering human lives. This study proposes a methodology for flood assessment in urban areas using a multiclass classification approach with a Deep Neural Network (DNN) optimized through hyperparameter tuning with genetic algorithms (GAs) leveraging remote sensing data of a flood dataset for the Ibadan metropolis, Nigeria and Metro Manila, Philippines. The results show that the optimized DNN model significantly improves flood risk assessment accuracy (Ibadan-0.98) compared to datasets containing only location and precipitation data (Manila-0.38). By incorporating soil data into the model, as well as reducing the number of classes, it is able to predict flood risks more accurately, providing insights for proactive flood mitigation strategies and urban planning.

This study presents a method for classifying landslide triggers and sizes using climate and geospatial data. The landslide data were sourced from the Global Landslide Catalog (GLC), which identifies rainfall-triggered landslide events globally, regardless of size, impact, or location. Compiled from 2007 to 2018 at NASA Goddard Space Flight Center, the GLC includes various mass movements triggered by rainfall and other events. Climatic data for the 10 years preceding each landslide event, including variables such as rainfall amounts, humidity, pressure, and temperature, were integrated with the landslide data. This dataset was then used to classify landslide triggers and sizes using deep neural networks (DNNs) optimized through genetic algorithm (GA)-driven hyperparameter tuning. The optimized DNN models achieved accuracies of 0.67 and 0.82, respectively, in multiclass classification tasks. This research demonstrates the effectiveness of GA to enhance landslide disaster risk management.

When creating products using magnetoresistive materials based on magnetic and non-magnetic metals, one is focused on achieving giant magnetoresistance. Such an effect, obtained at the turn of 1980–90-ies years of the last century in superlattices, reached 10–80 % with the intensity of tens of kE at 4.5 K. By methods of physics of magnetic phenomena the regularities of interaction between ferromagnetics and antiferromagnetics, constituting alloys, are determined for strengthening the design of spin valves. Insights into the formation of anisotropy in manganese and permalloy bilayers are detailed. Differences of layer creation causally related to the thermomagnetic activation mode of the process have been determined. The use of alloys in antiferromagnetic nanostructures can indeed lead to the formation of unidirectional anisotropy with changing values of the exchange shift of the hysteresis loop. It has been shown that nanostructures containing alloys exhibit improved magnetism and exchange interaction properties. These results confirm the potential of alloys in creating materials with improved magnetic phenomena and possible applications in various fields such as electronics and magnetic devices.