Introduction. The new “Infrastructure for Life” project aims to renovate housing, public spaces, and develop convenient public transportation routes. Along with chemical air pollution, motor vehicle traffic is becoming a significant source of noise pollution that threatens public health. Special acoustic barriers and green spaces are used to control noise. However, existing methods of noise protection for courtyard spaces are not always effective and/or require significant material costs. To overcome these limitations, the use of small architectural forms (SAFs) has been proposed. These forms can serve as noise reduction measures and also be elements of landscaping. The aim of this research is to provide a calculated justification for the possibility of using engineering and technical landscaping elements, namely SAFs, to reduce the noise from adjacent residential infrastructure, particularly motor vehicles, on recreational and leisure areas in residential districts. Materials and Methods. The empirical basis of the study consisted of data from regulatory documents, scientific publications, and field observations. The research object was a small architectural form, a semi-enclosed gazebo with a solid rear wall, which could be used for noise protection of adjacent courtyard spaces and landscaped areas. For comparison, we also considered the following design solutions: no barrier, a standard noise barrier, a strip of green space, and a small railway barrier. Acoustic calculations were performed in accordance with current standards using specialized software, including Ecolog-Noise, Calculation of Sound Insulation, and Calculation of Traffic Flow Noise. Results. Calculations of noise impact and airborne sound insulation were performed for various design options, and the results were presented in tables and graphical materials. The frequency characteristic of airborne sound insulation for a solid SAF wall was determined graphically in the form of a broken line. The airborne sound insulation index for the rear wall of the SAF was 29 dB, which indicated its potential use as a local noise protection element under the considered calculation conditions. Calculations of traffic noise characteristics showed that the equivalent sound level of the motor traffic flow was 59.7 dBA, while the maximum permissible level for a residential recreation area was 45 dBA. The graphical representation of the noise propagation calculation results was presented as isolines of acoustic discomfort zones at the normalization height of 1.5 m. Additionally, a 3D graphical distribution of noise was prepared for visualization purposes. Discussion. The results showed that the calculated noise reduction was primarily determined by the presence of a solid enclosing surface between the acoustic impact source and the protected area. In the conditions considered, a standard noise-proof screen and a gazebo-type SAF proved to be the most effective, while green spaces, a small acoustic screen and the absence of any protective measures did not provide the required level of noise protection. Conclusions. The use of enclosing structures, including acoustic barriers and SAFs, can significantly reduce the noise level and decrease the acoustic discomfort zone. The need to develop affordable and effective solutions for reducing noise levels in residential areas is determined by sanitary and hygienic legislation. The calculated data demonstrate the prospects of using semi-enclosed SAFs with solid enclosing elements as an additional means of local noise protection. However, the final assessment of their effectiveness requires further verification for other structural solutions, planning conditions, and noise load scenarios.
Introduction. Increasing the ballistic resistance of armor materials is a pressing scientific and technical challenge driven by the need to develop protective materials that can effectively resist high-speed impacts. Homogeneous isotropic steels, which provide protection through a combination of hardness and toughness, have limited effectiveness under high-speed stress of approximately 103 m/s. These materials typically undergo brittle fracture or complete penetration without significant energy dissipation. A promising alternative approach is the use of heterogeneous and anisotropic materials with a more complex, organized structure that can alter crack propagation trajectories and increase fracture energy intensity. Natural ferrite-martensitic composites (NFMCs), formed in steels quenched from the intercritical temperature range, are an example of such materials. NFMC steels, produced by quenching from the intercritical temperature range, are currently being actively researched. It has been shown that changing the quenching temperature affects the volume ratio of ductile ferrite and high-strength martensite layers, which in turn influences the mechanical properties of the material. However, existing studies have been conducted primarily under static tension or bending conditions. When subjected to ballistic loading with extremely high strain rates, the mechanisms of crack arrest may differ significantly. In this context, the geometric parameters of the structure such as the orientation, length, and thickness of the layers, as well as their ratios, become more important than the strength properties of individual phases. Currently, there is a lack of systematic data on which specific structural characteristics of NFMC are crucial for ballistic resistance, and what optimal values should be. Therefore, the aim of this research is to determine the geometric parameters of NFMC steel structures that ensure effective use in armor protection systems. Materials and Methods. The study was conducted on 14G2 steel samples measuring 140×70×7 mm. The NFMC structure was obtained by quenching the steel from the intercritical temperature range with an initial banded ferrite-pearlite structure. Bullet resistance tests were performed by shooting targets of this material at a distance of 50 m from a Dragunov sniper rifle with 7.62 mm cartridges with a heat-strengthened core at a bullet velocity of approximately 103 m/s. The geometric parameters of the structure were assessed using a metallographic method on a Neophot 21 microscope using ToupView software to obtain a quantitative assessment. Results. The microstructure of the studied natural ferritic-martensitic composite, which was a composite material with reinforcing fibers of discrete length, was shown. The results of determining the characteristic parameters of the steel composite geometry in accordance with GOST R 54 570–2011 were presented. The misorientation angle in the structure was 7÷11°. The volume fraction of martensite was 28.37% when quenched from a temperature of 735℃. The average width of the plates of the strengthening phase "h" ̅" = 0.0051" mm and the average free path "λ" ̅_"⊥" corresponding to the width of ferrite plates "c" ̅" = 0.0131" mm were measured. The ratio c/h was 2.57. The calculated critical length of the martensite layer in NFMC from the condition of equilibrium of normal and shear stresses in it, in NFMC with a total rolling reduction of 70% was 18.3 μm. The data on the distribution of the size of martensite layers in 14G2 steel with the NFMC structure were shown. Discussion . The obtained data on geometric parameters of the structure of 14G2 steel, quenched from the intercritical temperature range (735℃) and having NFMC organization, meet the requirements for the composite material. In a layered composite with optimal geometry, as a crack propagates successively from one layer to another along the interface and the crack tip approaches it, delamination can form, hindering the initial crack's propagation. Existing ferrite-martensite interfaces with a length greater than the critical value act as an effective barrier to crack propagation. Conclusion . It was found that high ballistic resistance of the natural ferrite-martensite composite was achieved with an optimal combination of geometric parameters of the composite material structure. At the same time, achieving such an NFMC optimum as armor protection can be ensured by the correct hot rolling technology (total reduction of 70% and higher) and choosing the optimal quenching temperature in the intercritical range.
Introduction. Vibration in multi-purpose drilling-milling-boring machines affects equipment reliability, production noise levels, and operator safety. Literature discusses sources of vibration, damping mechanisms, and methods for measuring vibration characteristics, while regulatory documents establish permissible levels of vibration acceleration and vibration velocity. However, the frequency dependencies of vibration energy loss factors for individual machine components, which are necessary for engineering vibration forecasting, remain insufficiently studied. The aim of this research is to evaluate vibration energy loss factors in the components of multi-purpose drilling-milling-boring machines and analyze their effect on the level of vibration affecting the operator, thereby substantiating measures to improve occupational safety. The objectives included conducting octave-band measurements, performing regression analysis of the data, and selecting relationships with the lowest standard deviation. Materials and Methods . This study utilized an integrated approach combining experimental measurements and mathematical data processing methods. Experiments were conducted on a dedicated test bench and directly on the machine using a torque hammer to excite vibrations. Vibration acceleration was recorded in octave frequency bands using modern measuring equipment. Vibrational energy loss coefficients (η) were calculated using a modified formula that took into account vibration acceleration levels. To summarize the experimental data and construct predictive models, regression analysis was used, including approximation by nonlinear functions and polynomials of varying degrees. The quality of the approximation was assessed using the minimum standard deviation criterion. Results. It was experimentally established that the loss coefficients for cast iron housing parts in the frequency range from 125 to 8000 Hz varied within the range of (7.8–8.8) ·10 –3 , demonstrating a weak frequency dependence. For engineering calculations, constant value of η ≈ 8·10 –3 could be adopted. Regression analysis revealed that the best approximating relationship for the gearbox housing was a sixth-order polynomial. Specific relationships were obtained for the cutting units: for boring and drilling, the best fit was provided by a seventh-order polynomial, and for the milling unit, by a fifth-order polynomial. The resulting mathematical models accurately described the behavior of loss coefficients within the studied frequency range. Discussion. The analysis of the results confirmed that the dissipative properties of machine structural elements were not constant and depended significantly on the type of technological operation and frequency range. The identified analytical relationships allowed us to move from point estimates at fixed frequencies to continuous energy loss prediction, which was critical for analyzing the dynamic behavior of the machine at its natural frequencies. This opens up opportunities for targeted design. Knowing the frequency spectrum of the most hazardous vibration modes, we can optimize damping specifically in these areas. For example, we can select materials with increased internal friction, or use composite vibration-absorbing coatings in specific structural areas. Conclusion. This study provides tools for quantitative assessment of vibrational energy loss coefficients for the main vibration sources in multi-purpose machine tools. The potential practical significance of this work lies in the possibility of using the derived regression relationships to develop active and passive vibration control algorithms, design damping systems, and select materials. Implementation of these findings will reduce noise and vibration levels in work areas, minimize the risk of occupational illnesses for operators, and improve overall occupational safety. Additionally, it will extend equipment life by reducing the vibration loads on components. The practical significance of this work lies in its potential for use in developing vibration control algorithms, which will significantly reduce noise levels in work areas.
Introduction . Inspecting the overhead crane runways in production workshops and warehouses is a challenging and dangerous task. The risks for specialists are associated with the high altitude at which the runways are located and the lack of walkways along them. At the same time, standard visual and dimensional inspection is characterized by a low inspection speed. The scientific literature contains numerous studies on the potential of artificial intelligence (AI) to ensure occupational safety: methods for monitoring occupational risks and preventing accidents, the relationship between accident rates and the competencies of crane operators, the detection of defects in removable load‑handling devices using computer vision tools, as well as remote monitoring of crane safety based on video data from IP cameras. However, these solutions do not address the inspection of crane runways, which have specific features, primarily their considerable length (often up to 200 meters or more). The application of AI requires video-analytical monitoring along the entire length of the runway and training a neural network to recognize local defects and geometric deviations, which existing methods do not provide. Today, crane runway inspection is conducted through visual and dimensional control methods, including direct inspection and surveying using total stations and theodolites. While these methods are accurate, they are labor-intensive and time-consuming. Therefore, there is a need for the use of AI to improve safety and speed while maintaining accuracy in identifying faults in crane runways. The aim of this research is to develop a method for remote inspection of the overhead crane runways located at height in industrial facilities. This will help minimize the exposure of workers to hazardous and harmful working conditions while maintaining accuracy, speed, and reliability of the inspection results. Materials and Methods . Data on crane runway defects collected during inspections at industrial facilities was used as the basis for the study. The methodology for identifying defects in crane runways was based on GOST R 56944–2016 . Computer vision neural networks were trained using open libraries for Python language. A modernized pre-trained YOLOv8 neural network was used to detect defects. Results. A method for remote detection of defects on overhead crane runways was developed using an unmanned aerial vehicle designed by the authors (a quadcopter with a protective frame, equipped with a Livox MID‑40 lidar, an Orbbec Gemini 2 depth camera, a 4K RGB camera, and a DWM1000 positioning system supporting TWR and TDOA). Based on survey data collected in 2024 from 352 overhead cranes with a total runway length of approximately 14 kilometers, a modernized YOLOv8 neural network for computer vision was trained. This resulted in the creation of a complete three-dimensional point cloud that covered the runways, crane beams and supports, as well as reference to column lines and centers. The three-dimensional model allowed for the automatic identification and classification of local defects, as well as the estimation of their sizes with accuracy of one millimeter. Automated geometry assessment showed that the deviations in the runway markings in the model in question did not exceed the permitted values according to GOST R 56944–2016 (40 millimeters in one section and 10 millimeters on adjacent columns), which confirmed the effectiveness of the method. Discussion. The results obtained indicate that the authors’ goal has been achieved — the development of a method for remote inspection of overhead crane runways. This was made possible by conducting a significant number of surveys, which provided a diverse range of defects for training neural networks. A comparison with previous studies has shown the uniqueness of the proposed approach to inspecting overhead crane runways. Methods based on artificial intelligence and unmanned aerial vehicles (UAVs) have previously been used to monitor personnel, assess removable lifting attachments, and inspect tower cranes outdoors. However, these methods were not suitable for detecting local defects in overhead crane runways inside production facilities. The main limitation of the developed method was the flight time of the UAV (no more than 20 minutes), which was due to the low battery capacity. This capacity could not be increased without increasing the maximum size of the device (0.5 meters) in the confined conditions of enclosed spaces. The new method's results were positive, as it enabled inspection of tracks along their entire length from a close distance. By building a 3D model using photogrammetry, it was possible to assess the size of defects and reduce labor intensity and duration of the survey by at least half. These benefits made its further development and practical implementation worthwhile. Conclusion . The main outcome of the study was the development of a method for remote inspection of overhead crane runways located at height in industrial facilities. During the research, neural networks were trained to detect defects, and an algorithm was created for inspection. This involved creating a three-dimensional model that allowed for automated assessment of geometric deviations in the runways in both longitudinal and transverse planes, as well as the identification of local defects. The key benefit of this method was that it eliminated the need for experts to climb to heights, ensuring their safety. Additionally, it allowed for the inspection of hard-to-reach areas and the detection of previously invisible defects, reducing the likelihood of future emergency situations. Further research in this field will focus on improving the system's ability to automatically identify the condition of load-bearing metal structures and possible defects in cranes.
Introduction. Thousands of oil spills are recorded in Russia every year, many of which occur in remote regions such as Western Siberia, the Arctic zone, and the Far East. Oil production at all stages has a negative impact on the lithosphere, hydrological regime, and biodiversity. This impact increases with the scale of petrochemical and mining activities. An analysis of the literature shows that various sorbents and mobile solutions are used to eliminate spills, but there are still challenges with high transportation costs, limited shelf life, difficulty in selecting the right material for a specific type of contamination, and a lack of technological modules that can be delivered to hard-to-reach areas. The aim of this research was to transfer the previously developed technology for producing a composite sorbent to a mobile vehicle, and to determine the design and technical parameters of a mobile technological module (MTM) for the prompt elimination of oil spills. Materials and Methods. The study was based on a stationary scheme previously developed by the authors for producing a composite sorbent from dichloroethane, cetylamine surfactant, vermiculite, and shredded polystyrene foam. The process involved converting polystyrene foam to a viscous state with a solvent, adding mineral filler, and drying the mixture. To design a mobile technological module, we calculated the production cycle time, the weight of the complex, and the area occupied by the equipment. The calculations took into account elements made of PTFE-4 fluoroplastic according to TU 6-05-810-88[2] , a BYD NYP-3.6 gear pump, a Greenworks G24HG 24V heat gun, a capacitor, containers, mixers, a drying chamber, chutes, and hydraulic fittings. UAZ 23632 was chosen as a mobile vehicle because of its off-road capabilities, cargo capacity, and cargo platform dimensions. Results. It was established that the composite sorbent could be produced in mobile conditions using a previously developed technological process, which included dissolving polystyrene foam, adding a filler, and drying the formed mass. The production cycle for a batch of 20 sorbent sheets measuring 210×297×5 mm took five hours when drying at a temperature of 22°C, and was reduced to four hours by using a heat gun. The total mass of the main elements of the mobile complex, excluding the sorbent components, amounted to 70 kg, and the working mass of the module with precursors reached 78 kg. The area occupied by the main equipment elements was 8900 cm[2] , which corresponded to the area of a EUR-pallet (approximately 0.9 m[2] ). The calculations confirmed that it was possible to place all components of the mobile technological module on a UAZ 23632 vehicle. During the study, it was also taken into account that the mineral filler could be reused for up to three cycles, after which the loss of sorption properties amounted to 35%. Discussion. The research results demonstrated that converting the stationary production process for a composite sorbent into a mobile format was technically feasible and met the goal of providing a rapid response to emergency oil spills in hard‑to‑reach areas. Unlike solutions focused on delivering ready‑made sorbents, the proposed approach involved transporting the components and producing the material directly near the spill site, which reduced the dependence on logistics and storage conditions. The comparison with literature data confirmed the relevance of this approach. Mobile technologies made it possible to customize the properties of the sorbent, reducing transportation costs and minimizing waste through material recovery. However, the limitations of this study included its design-based nature: the functionality of the module was assessed based on mathematical modeling and comparison with similar equipment. The practical significance of the results was that they confirmed the possibility of installing a compact production complex on an off-the-road vehicle without changing the basic process flow. Conclusion. During the study, the key parameters of the mobile technological module were calculated: duration of the production cycle, weight of the equipment, and the occupied area. It has been shown that a module weighing 78 kg and with an area of approximately 0.9 m[2] can be mounted on a UAZ 23632 vehicle and used to produce a composite sorbent directly at the site of an emergency spill. This solution makes it possible to increase the efficiency of pollution cleanup, reduce logistics costs, and lower the environmental impact by reducing the number of transport trips and the amount of recycled polystyrene foam waste. A mobile technological module can be recommended as a component of equipment for eliminating emergency oil spills in hard‑to‑reach areas.
Introduction. Traditional water body monitoring systems often demonstrate insufficient effectiveness in promptly locating the source of an emergency or non-stationary discharge, which hinders timely management decisions. This problem is further exacerbated by the rapid spread of pollutants in conditions where observational data is incomplete, delayed, or fragmented. These circumstances emphasize the need for methods that can not only detect pollution but also reconstruct the coordinates of its source based on its concentration field. The literature provides a wide range of approaches based on advection-diffusion equations, numerical hydrodynamic modeling, and satellite data analysis. However, the integration of computer vision methods with hydrodynamic models to solve the inverse problem of source identification remains under-researched. Theoretically, such integration is justified by the possibility of automated delineation of water area boundaries through semantic segmentation and a physically meaningful description of passive impurities transport. However, there is a scientific gap due to the lack of verified computational schemes that combine these two approaches. The aim of this study is to develop and verify an approach that integrates computer vision and hydrodynamic modeling to accurately identify the source of a negative impact. Materials and Methods. The methodology included two interconnected modules. The first computer vision module performed semantic segmentation of satellite images or aerial photography data using convolutional neural networks. The result was a binary mask of the water area; its external contours were extracted using OpenCV with morphological post‑processing. The second module implemented a two-dimensional numerical advection-diffusion model based on the finite volume method with a steady velocity field. The inverse identification algorithm generated a set of virtual candidate points along the perimeter of the water body. For each point, a direct calculation of the concentration field was performed, after which a candidate was selected using the root-mean-square error, ensuring the best fit to the observed distribution. Validation was carried out on three synthetic scenarios (pH, dissolved oxygen, free chlorine) in 300 computational experiments. Results. Three hundred computational experiments were conducted using randomly assigned source coordinates. The computer vision module accurately generated a water area mask in all trials. The inverse identification algorithm, which iterated through candidates in 50-meter increments, identified the true source with absolute accuracy in 285 cases (95%). It was found that 12 out of 15 erroneous cases were due to the source being located less than 1 m from the water area boundary, where the influence of turbulent diffusion and boundary conditions led to the “blurring” of the pollution trace. The key limitations of the method were identified: errors in the two-dimensional hydrodynamic approximation for stratified water bodies, and a decrease in accuracy under sharply non-stationary hydrodynamics. Visualization of the results confirmed the high quality of spatial localization of the source. Discussion. The results obtained confirmed that the integration of these methods created a synergistic effect. Computer vision ensured the speed and objectivity of spatial data processing, while hydrodynamic modeling provided physical and mathematical validity for the analysis. This approach overcame the key limitation of traditional monitoring by allowing us not only to detect pollution, but also to determine its causes. This is consistent with current trends in predictive analytics. Conclusion. The developed concept forms the foundation for creating decision-support operational systems and “digital twins” of water bodies. Its implementation in environmental monitoring practices creates the prerequisites for transitioning from reactive response to proactive risk management, and can contribute to enhancing the validity and effectiveness of measures to ensure environmental safety of water resources. Prospects for further research lie in adapting methods for working with real-time online monitoring data and different types of water bodies.
Introduction. The 2020s have brought the issue of electromagnetic radiation (EMR) in video conferencing (VC) to the forefront. Such services have become widespread due to pandemic‑related self‑isolation, falling smartphone prices, and the increased mobility of students and workers. Human tissues heat up locally during conversations and video calls because the device emits non‑ionizing electromagnetic radio waves. Their effect on the body has been described in several studies, but only the situation of audio calls is considered. There is no data on how harmful EMR is in VC. Accordingly, it is impossible to substantiate recommendations for video communication safety, in particular for “protection by distance”. This study aims to fill this gap by determining the level of EMR emitted by smartphones during video conferences. Materials and Methods. The radiation was measured using a PZ‑41 device. The manufacturer was Special Design Bureau PiTON, located in Nizhny Novgorod. The antenna recorded the maximum and average values of the energy flux density (EFD). Conditions: EFD — 0.26–100,000 µW/cm 2 , frequency used to determine EFD — 2450 MHz, and the averaging time for the parameter before it was displayed on the screen — 1 minute. Smartphones with iOS and Android operating systems with and without Wi‑Fi were tested. Measurements were taken at distances of 0 and 15 cm from the top and bottom speakers. Five experiments were conducted in each case. Results. During video calls, EMR was higher than during a conversation. For each device, the maximum and average values of EFD in the video call mode with Wi‑Fi enabled and disabled were summarized in tables. We obtained 40 indicators for each of the four cases (two devices and two modes), with minimum and maximum values: 0.365 and 9.732; 3.813 and 72.136; 0.01 and 0.633; 0.781 and 30.271. We noted the data with interference modeling and poor Internet connection. For each experiment, we derived average values. EFD from iOS turned out to be higher than from Android. EFD avg excess with Wi-Fi was indistinguishable (0.6) in 0 cm from the top speaker and more than 85 in 15 cm from the bottom one. The absolute values were low: 0.549 and 0.985 and 0.854 and 0.010, respectively. Discussion. Protection by distance worked at a distance of 15 cm from the speaker with Wi‑Fi (similar EFD max and EFD avg values were obtained). Interference both reduced and increased EFD. There was no pattern. Unstable Internet doubled the average EFD. Wi‑Fi reduced the EFD by a factor of 3.1–26.9 for iOS and by a factor of 3.8–167.5 for Android (due to the router’s short range, the smartphone did not need a powerful transmitter). For iOS, high EFDs were recorded at the bottom speaker; for Android — at the top one. Android demonstrated the maximum reduction in EFD avg : with Wi‑Fi 15 cm away from the bottom speaker, the value decreased by a factor of 115; at the top speaker, by a factor of 167. Conclusion. Device holders distance the smartphone from the user and enhance the VC safety. A failure in Internet connection increases radiation. In the future, it would be advisable to study other smartphone models and work out the regulation of EMF during calls.
Introduction. The relevance of this work stems from the need to move away from general decarbonization plans and towards differentiated climate strategies, since countries that emit greenhouse gases (GHGs) differ in terms of their energy balance, level of industrialization, population size, carbon intensity of their economy, and the export of fuel and energy resources. The same emission reduction goals may require different technological, investment, and regulatory solutions. Unification approaches are not effective in this context. The literature mainly focuses on analyzing carbon intensity, low-carbon development scenarios, and individual energy indicators. The fragmentary nature of the known approaches prevents the creation of a typology of GHG-emitting countries based on a combination of economic, demographic, energy, and climatic characteristics. The presented scientific work fills this gap. The aim of this study was to form and interpret the typology of key GHG-emitting countries based on a comprehensive analysis of economic, demographic, energy, and climatic indicators. According to this typology, decarbonization conditions and transitional climate risks within clusters were identified. Materials and Methods. The research was based on a statistical database for more than 40 GHG-emitting countries. To ensure comparability, data preprocessing, z -score normalization of features, clustering by Ward’s hierarchical method, and the combined t-SNE + k-means approach were used. The optimal number of clusters was determined using the elbow method and the silhouette coefficient, and the clustering quality was determined by the Davies–Bouldin index. Results. Significant cross-country differences in specific greenhouse gas emissions per capita, the carbon intensity of GDP at PPP, the structure of generation capacity, and specific emissions per unit of electricity produced have been identified. Based on the results of hierarchical clustering and t-SNE + k-means , four groups of states were identified: - two largest emitters with large-scale and diversified energy production; - four exporters of fuel and energy resources; - eight carbon-intensive industrial and developing countries; - 31 relatively energy-efficient and low-carbon economies. Discussion. Cluster analysis suggested that the absolute amount of GHGs emissions was not the only factor to consider when choosing a climate strategy. Countries with similar carbon footprints could differ significantly in terms of economic scale, the proportion of coal generation, export orientation of the energy complex, industrialization level, energy efficiency, and final energy consumption structure. Each formed cluster required different priorities and approaches for adapting decarbonization strategies, namely: reduction of coal generation and modernization of networks for the largest emitters; reduction of emissions in mining and processing; CCUS and export diversification for resource economies; improvement of industrial energy efficiency and modernization of generation for industrial countries; elimination of residual emissions and accounting for imported carbon footprint for low-carbon economies. The limitations of the study were related to differences in national statistical reporting, incompleteness of some indicators, and sensitivity of clustering to the choice of variables. Conclusion. The proposed cluster approach makes it possible to move from ranking countries based on emissions to identifying groups with similar profiles of economic, demographic, energy, and climate indicators. The results of clustering can be used in further analysis of key emitting countries to identify the most suitable technologies and legislative measures for reducing greenhouse gas emissions. Additionally, the results can be used to assess the effectiveness of implementing and adapting decarbonization strategies.
Introduction. Providing safe drinking water to the population is a crucial task for health protection and sustainable development, as its quality directly influences the level of morbidity and mortality. The UN and WHO have stated that insufficient efficiency of water treatment systems contribute to the emergence and spread of infectious diseases, causing up to 1.4 million deaths worldwide each year. At the same time, the issue of comprehensive comparative assessment of water treatment technologies in terms of the overall risk to public health remains underdeveloped, considering both chemical and microbiological hazards. This gap in scientific knowledge necessitates research that focuses not only on meeting water quality standards but also on an integrated assessment of the effects of various technological schemes on human health. In this study, we aim to conduct a comparative assessment of the effectiveness of drinking water treatment technologies used in centralized water supply systems in terms of the overall risk to public health. This will made it possible to choose the best solution for water treatment in practice.Materials and Methods. The information base for the study consisted of current regulatory documents that establish requirements for drinking water quality and technological processes for its preparation, such as the “Methodology for developing a register of BAT for water supply and sanitation systems”1; Russian and international standards, and guidelines for assessing public health risks, scientific articles and monographs on filtration, coagulation, clarification, sorption, oxidation, and disinfection of water. The assessment of source water quality was conducted according to the main groups of indicators: organoleptic, generalized, sanitary-microbiological, parasitological, as well as sanitary-chemical. Mathematical modeling and statistical data processing methods were used to quantify and compare different water treatment schemes. The calculation was performed in accordance with the approaches described in MR 2.1.4.0289–222.Based on the classification of water supply sources by water quality, we analyzed the recommended sets of technological operations:for the first class — pre-filtration with optional reagent treatment and mandatory disinfection;for the second class — filtration (in the presence of phytoplankton, microfiltration) with coagulation, settling and subsequent disinfection;for the third class — additional stage of purification including clarification, oxidation, sorption and repeated disinfection.The study was performed using standard methods of laboratory analysis of water quality and specialized software for modeling and risk assessment.Results. The effectiveness of the current treatment technology (mechanical purification, coagulation, and chlorination) and the proposed multistage scheme (including ultrafiltration, sorption, and combined disinfection) were evaluated. Mathematical modeling of changes in water quality parameters for three scenarios of water treatment was performed. Using special software, a model experiment and an assessment of quality changes were conducted for four groups of parameters (organoleptic, generalized, sanitary-microbiological and parasitological, and sanitary-chemical). According to MR 2.1.4.0289–223, the values of integrated risk and the effectiveness of its reduction as a result of water treatment were calculated. The results were statistically processed. Based on the data on sanitary and hygienic monitoring and calculation of the overall risk to public health, the source water was found to have excesses in several indicators. It was established that the proposed multi-stage method provided more thorough purification and significantly reduced the negative impact on health across all groups of parameters (organoleptic, generalized, sanitary-microbiological and sanitary-chemical).Discussion. A comparative analysis of the effectiveness of the two water treatment methods revealed a significant advantage of the multi-stage purification process. The proposed integrated approach fully ensured that water quality met the regulatory requirements for maximum permissible values through a combination of ultrafiltration, sorption and combined disinfection. The multi-stage purification scheme ensured not only complete microbiological and chemical safety, but also high organoleptic water parameters, enhancing the overall reliability of the water supply system.Conclusion. The paper provides a comparative assessment of the effectiveness of two water treatment technologies for the centralized water supply system in Penza. Based on the methodology for calculating the overall risk to public health, it was found that the source water from the Surskoye reservoir had a high risk level. The current purification method (coagulation and chlorination) has been shown to reduce the risk to an average level, leaving the water supply system vulnerable. In contrast, the proposed multi-stage method (ultrafiltration, sorption, UV disinfection, and chloroamination) demonstrated very high efficiency (82%) in reducing the cumulative risk to negligible value. These results support the advantages of a multi-stage approach and can serve as a foundation for upgrading water treatment systems to increase their reliability and safety for the public.
Introduction. Fatigue failure is one of the main causes of failure of metal structures subjected to variable loads. Initially, this damage is not visible as cracks, but it leads to the accumulation of microdefects and the redistribution of internal stresses. Currently, it is not possible to monitor the progression of these defects in large structures with a significant surface area. To detect such processes in a timely manner, highly sensitive inspection methods are required that can identify potential areas of failure with a high degree of accuracy during the early stages of structural operation. Such methods do not currently exist, and our research aims to solve this problem to a certain extent. One promising approach is the monitoring of changes in the strength of a permanent magnetic field, which reflects the evolution of material state. The current study aims to investigate the potential of spatial analysis of magnetic response to identify instability zones during fatigue loading, where the likelihood of failure is high, as well as to analyze changes in steel structure.Materials and Methods. The study focused on samples made of 09G2S steel, subjected to loading to fracture on a servohydraulic testing machine INSTRON-8801. Magnetic measurements were taken at 12 points along the sample using an IKN-2M-8 instrument. Changes in the resulting strength of the permanent magnetic field were recorded at different stages of fatigue loading. All measurements were repeated at least three times to ensure the reliability of the results.Results. It has been found, that at the stage of relative operating time Ni/Np = 0.4–0.5, anomalous changes in the magnetic field strength corresponding to the fracture nucleus were recorded at certain points. Additionally, a characteristic area of signal stabilization was observed in the range Ni/Np = 0.8–0.9. This could be explained by the temporary relaxation of stresses prior to destruction. The obtained data demonstrate the local variability of the magnetic response and confirm the sensitivity of this method to the early stages of material degradation.Discussion. The conducted research has shown that spatial analysis of changes in the strength of a permanent magnetic field can be used to locate fracture nuclei in ferromagnetic steels. This dataset can be used as a basis for training samples for intelligent monitoring systems, including neural network algorithms that focus on predicting the remaining life and automatically assessing the technical condition of structures. This is particularly important for welded structures with a high number of welds.Conclusion. The introduction of energy into a system inevitably leads to a reorganization of the structure of the material in order to adapt to external forces. This reorganization is accompanied by a change in the material's magnetic field. By recording these changes, it is possible to interpret the measurement results in terms of possible destruction, as the most efficient way for the system to utilize the supplied energy is through the formation of new surfaces, or cracks.
Introduction. In industrial filtration systems, one of the main challenges is reducing the filter capacity due to the accumulation of retained particles and the formation of sediment layer on the filter baffle. This results in increased hydraulic resistance, increased energy consumption, and forced service stops. Extending the lifespan of filter elements while maintaining productivity is a crucial technological challenge. This involves methods such as the regeneration of hydrodynamic filters, including the rotation of the filter element and the use of vibration effects. However, current research focuses on these methods individually, with no theoretical models for the combined effect of centrifugal and vibrational forces. Experimental data on the synergy between these forces has not been collected, and criteria for optimizing this combined effect have not been established considering operating parameters and the adhesive properties of sediment. The aim of this research was to develop a computational method for optimizing the combined centrifugal-vibration effect, based on an analytical and experimental study of its impact on the regeneration efficiency of hydrodynamic filters.Materials and Methods. The research was conducted on a laboratory test bench with a hydrodynamic vibrating filter equipped with a cylindrical filter baffle made of a combined porous mesh metal (fineness of 10 µm), which could perform independent rotational and vibrational movements. To describe the condition for sediment particle detachment, an analytical model was developed based on the balance of forces acting on a particle on a rotating and vibrating surface. This allowed us to evaluate the effectiveness of filter regeneration based on operating parameters. The experiments were conducted using aqueous suspensions of electrocorundum (200–250 µm) and silicon carbide (60–80 µm) with a volume concentration of 0.1%. The regeneration mode involved a simultaneous increase in the rotational speed of the baffle to 1000 rpm and vibration with an amplitude of 1 mm at a variable frequency of 50, 60 and 70 Hz with the filtrate outlet closed to eliminate the change of pressure.Results. Quantitative dependencies of the regeneration efficiency on rotational speed, vibration amplitude and frequency were experimentally determined. An analytical model of force balance was developed, which allowed predicting the degree of purification for any combination of these parameters. Verification of the model showed that the discrepancy between the calculated and experimental data did not exceed 15–20%, confirming its suitability for engineering calculations. Based on the model, a computational optimization method was proposed that provided a choice of a combination of operating parameters at which the required level of cleaning was achieved with minimal energy consumption and permissible mechanical loads on the structure.Discussion. The low efficiency of purely centrifugal regeneration (2–20%) was explained by the fact that for fine particles, the ratio of adhesive forces to inertial forces was significantly higher than for coarse particles. This was consistent with the Derjaguin classical theory of adhesion. The synergistic effect of the combined effect was due to the addition of radial centrifugal force by tangential shear stresses generated by vibration, which ensured a more complete destruction of adhesive bonds in the sediment layer. The discrepancy between the model and the experiment in the range of 15–20% was mainly due to uncertainty in determining the adhesion characteristics of the particle –filter baffle pair. However, this level of accuracy was acceptable for the engineering selection of operating parameters. The obtained patterns were qualitatively consistent with the known literature data on the individual effects of rotation and vibration on sediment removal, but for the first time, they quantitatively describe their combined effect. One limitation of the study was the validation of the model for aqueous suspensions only, which required additional research to extend it to viscous and non-Newtonian media.Conclusion. It has been experimentally proven that the combination of centrifugal and vibrational effects can increase the regeneration efficiency of the hydrodynamic filter baffle by 60–80%, compared to 2–20% with rotation alone. An analytical model has been developed based on the balance of forces, and verified experimentally with an error of no more than 20%. This model is suitable for engineering calculations of optimal regeneration modes. It is demonstrated that the key parameter determining the accuracy of the forecast is the adhesion properties of particles, which require experimental determination for each system. The results provide a scientific basis for designing continuous self-cleaning filtration devices. A promising direction for future research is the adaptation of this technique to rheologically complex industrial environments, as well as optimizing energy consumption in the vibration system.
Introduction. The use of artificial neural networks (ANNs) to diagnose the technical condition of automotive equipment is an active area of research. However, existing work mainly focuses on evaluating individual units, such as the engine, without a comprehensive analysis of the interconnected systems of a car. This creates a gap in the field of the development of intelligent systems that can take into account the state of the chassis, braking, and steering systems at the same time. The aim of this study is to develop an intelligent decision-making support system (IDMSS) based on ANNs that can comprehensively assess the technical condition of a vehicle by combining expert knowledge and data on damage to different components.Materials and Methods. Defective indicators, determined on the basis of regulatory documents, and manuals on operation, maintenance and repair, were used to defect car parts and assemblies. The research was based on the methodology of neural network modeling. To train the ANN, an array of 100 samples was used, formed on the basis of:statistical data;expert surveys of specialists from the Automotive Equipment Maintenance and Repair Center at Don State Technical University;analysis of big data from online sources.Defective parameters of 13 main vehicle systems, operational factors and even the psycho-emotional state of the driver were considered. The training array included damage parameters for frame parts, axles, suspension, wheels, brake, and steering systems. To compare the effectiveness, three multilayer perceptrons (MLPs) architectures with different numbers of neurons in hidden layers, activation functions, and the BFGS learning algorithm were created and trained.Results. The best results were shown by the MLP 8-24-3 neural network (8 input, 24 hidden, 3 output neurons). Its performance on the training sample was 93.75%, on the test sample — 90%. The accuracy of classification by category of technical condition reached 100% for the category “operation permitted”, 94.74% for “operation permitted with restrictions”, and 82.35% for “operation prohibited”. Sensitivity analysis revealed that the parameters of the frame (X1) and axles (X2) had the greatest influence on the classification.Discussion. The developed ANN has demonstrated high efficiency in a comprehensive assessment of the vehicle's technical condition, going beyond the diagnosis of individual units. It has been established that the weighting coefficients of the neural network can serve as a quantitative measure of the relationship and mutual influence of the details of various systems on the overall safety. The results obtained confirm the practical applicability of the approach for creating flexible IDMSSs in the field of maintenance and diagnostics.Conclusion. The research contributes to the development of data mining methods for transport systems, offering a new approach to integrating heterogeneous parameters and expertise into a single neural network model. It is an important step towards improving the reliability and safety of automotive equipment. An intelligent system based on expert experience and statistical data is a promising tool for automating assessment and decision-making processes. Further development of the system may include expanding the database and improving learning algorithms, which will increase its accuracy and efficiency.
Introduction. Modernization of production facilities, with increased automation and complexity of technological processes, leads to a greater psychophysiological burden on workers and a higher likelihood of errors. This, in turn, increases the risk of occupational injuries. The increasing number of workplace accidents underscores the economic and social importance of accident prevention, as injuries reduce productivity and increase compensation costs. Modern approaches to occupational risk management require a systematic assessment of not only the probability of an incident and the severity of its consequences, but also the state of protective mechanisms — safety barriers that limit the impact of hazardous factors. Haddon's methodology, originally developed for transportation safety, can be used to identify weak links and analyze the sequence of incidents. Its barrier-oriented principles are theoretically applicable to industrial environments. However, existing research on barrier models in industry is fragmented and does not provide a unified tool for quantifying the effectiveness of barriers and their contribution to reducing injury risks. Therefore, the aim of this study is to develop a method for applying a barrier-oriented approach based on the Haddon model for a comprehensive quantitative assessment of personnel injury risks.Materials and Methods. A barrier safety model was used to solve the problem of reducing occupational injuries. The study consisted of three parts. The first was a comprehensive analysis of the requirements of Russian legislation in the field of occupational risk assessment, as well as scientific publications on the use of a barrier-oriented approach. The second was the description of the methodology for determining the likelihood of a hazard based on the results of an assessment of the reliability of safety barriers. The assessment of safety barriers was conducted according to checklists using the adapted Haddon model. Finally, an illustration of practical application of barrier approach using model example was provided.Results. A methodology for using a barrier-oriented approach to assess injury risks has been developed. A method for quantifying the impact of current hazards has been defined, taking into account the reliability of safety barriers. Risk levels for the hazard realization have been determined. Both the methodological principles proposed in this study and those already applied have been considered, indicating their advantages and limitations. An example of calculating the probability of hazards occurring when lifting and moving goods using hoisting devices has been given.Discussion. The presented methodology for applying the barrier-oriented approach allows us to take into account the influence of organizational factors and human factor on the safety of production processes and to obtain quantitative estimates of the possibility of hazard occurrence. Additionally, this approach provides a comprehensive assessment of safety barriers, considering not only their presence and effectiveness, but also reliability indicators — efficiency and sustainability of operation. This creates a basis for simplifying the process of prioritizing injury prevention measures and optimizing occupational risk management systems.Conclusion. The main results of the research include a practical way to calculate the probability of hazardous production factors, as well as recommendations for gradual implementation of the developed methodology into the practice of occupational safety and health management. The practical significance of this work lies in its potential for integration of the proposed approach with operational monitoring tools in the field of occupational safety and health and in its applicability to solving problems related to worker injury risk management in various production conditions.
Introduction. The human factor is the cause of 70–80% of industrial accidents. This is the reason for scientific interest in this topic. Researchers are studying the issues of assessing occupational injury risks based on individual employee qualities. However, nonparametric methods are not used when analyzing the relationship between these qualities and hazardous incidents. At the same time, parametric statistical approaches for processing non-numeric information are unreasonable without first checking the distribution of variables for normality. The presented scientific work is intended to correct the situation. The aim is to identify and statistically substantiate the relationship between individual factors and realized production risks.Materials and Methods. The authors observed the staff of Gazprom Transgaz Surgut LLC and created a questionnaire. They anonymously interviewed 569 workers and measured their level of responsibility (according to 34 statements) and emotionality (according to 26 statements). Eight variables were used in data processing: “injury”, “age”, “education”, “length of service in the company”, “total years of service”, “profession”, “responsibility”, and “emotionality”. The statements of 206 people (36.2%) with experience of injuries and occupational diseases and 363 (63.8%) without such experience were summarized. The correlation of independent variables and dependent variable (“injury”) was studied using the contingency tables. Estimates of the Pearson’s chi-square and the level of its statistical significance were supplemented by calculations of the intensity and direction of the relationship of variables (gamma coefficient).Results. High internal consistency of the statements (Cronbach's alpha 0.923) and high content validity of the questionnaire have been proven. The significance (р < 0.001) for all variables allowed us to reject the null hypothesis about the subordination of the studied set of features to a normal distribution. Median frequencies of the “degree of responsibility” and “emotionality” variables for the group without injuries were 2 and 3, respectively, and noticeably higher in the group with injuries (5 and 5). The group differences in the statements were statistically significant (р < 0.05). Employees with realized risks had higher than average ranks in terms of qualitative severity of responsibility and emotionality. There was no significant difference in socio-demographic indicators for the grouping feature “injury” (р > 0.05). The Pearson’s chi-square value was 78.704 for the “responsibility — injury” pair and 35.350 for the “emotionality — injury” pair. Gamma was 0.514 and 0.359, respectively. Spearman's coefficient was 0.344 for responsibility and injury, 0.242 for emotionality and injury. The significance of all three criteria was <0.001.Discussion. The outcome of risks was determined by individual rather than socio-demographic characteristics of the employees. This was indicated by:high median frequencies of “responsibility” and “emotionality” variables in the “injury” group,average ranks among respondents with realized hazardous events (р< 05).The risk increased with increasing severity of signs of emotional instability and low responsibility. The relationship between “responsibility” and “injury” in gamma was stronger than in Spearman’s, therefore, gamma better accounted for nonlinear monotonic trends and showed a more significant monotonic average relationship.Conclusion. Responsibility and emotionality are significant determinants of hazardous incidents. The research results will allow us to develop occupational safety and select staff more effectively. In the future, it is possible to build personalized (targeted) approaches to work with employees, and depending on their individual characteristics, predict the occurrence of hazardous events.
Introduction. Studies of fire risks associated with overhead power lines (OHPLs) consider combustible materials, terrain, and meteorological conditions. The mechanisms of fire occurrence and spread have been studied, and quantitative risk modeling is being developed based on incident statistics. However, these scenarios rely on arbitrary or poorly defined sets of initial factors, making it difficult to create unified risk management systems. This scientific work aims to fill this gap by creating a unified classification of fire hazard factors for overhead power lines that takes into account the causes, environment, and development of fires. A scenario-based risk matrix for OHPLs is built on this foundation.Materials and Methods. The basis of the study was a method for assessing fire risk, which considers fire from overhead power lines as a result of the interaction between three key components: the ignition source, combustible medium and fire propagation conditions. Through an analysis of the relevant literature, these components were broken down, classified, and the principles for systematizing them were identified.Results. Ignition sources, combustible medium, and fire propagation conditions were presented as axes in the scenario matrix of fire risk associated with overhead power lines. These factors were classified and structured using author-created diagrams. The first one included the types of short circuits, heating, and ignition mechanisms. In the second, four classes of materials were differentiated by their sensitivity to fire. The third one described three categories of fire propagation conditions. The risk level and critical ignition energy were mathematically represented. The final matrix aggregated four classes of material: high-sensitive, medium-sensitive, low-sensitive, and specific. Fire spread conditions were divided into favorable, moderate, and unfavorable. Taking into account the ignition sources (interphase and single-phase), the risk levels were determined: low, medium, high, and critical.Discussion. The matrix combined 24 typical scenarios of the studied hazard (two groups of sources × four classes of materials × three categories of propagation conditions). Five scenarios (approximately 21%) were critical. As a rule, they occurred with a combination of high-energy emergency conditions, high- and medium-sensitive materials and adverse weather conditions. The matrix can be used in the transition from a qualitative description of OHPLs to a quantitative assessment of the probability of a fire and its consequences. This innovation will be beneficial for modeling OHPL incidents, refining safety measures, and improving risk assessment. Scenarios can be ranked based on importance, allowing for a more efficient allocation of resources for protective measures.Conclusion. The new approach, in contrast to the traditional one, makes it possible to overcome the limitations of the fragmented hazard assessment and systematically analyze fire scenarios related to overhead power lines. This allows us to justify decisions on modernizing and strengthening the protection of individual network sections, i.e., to focus investments on infrastructure elements and typical situations that fire risks depend on to a greater extent. Future research in this area is expected to:supplement accident statistics and the amount of experimental data on the energy characteristics of ignition sources;provide a quantitative parameterization of the function that represents the risk level for each scenario;set numerical thresholds for four risk levels.
Introduction. Ensuring the safety of lifting equipment is closely linked to the reliability of steel ropes operating under variable loads and in aggressive environments. Increased design complexity, higher operational intensity, and larger machine lifting capacities lead to increased human-made risks and economic losses. Traditional methods, such as static safety factors and visual inspections, are ineffective in the face of digitalization and increased operational intensity. According to regulatory authorities, 20% of accidents involving lifting equipment are caused by rope defects, with more than 5,000 injury incidents recorded annually. The literature describes statistical defect analysis, tribological models of wire wear that take into account friction and lubricant degradation, and hierarchical modeling of rope as a system. However, there are still some serious systemic problems: models are not fully integrated into practice, theoretical knowledge is not always applied in engineering methods, and predictive models do not allow for a comprehensive analysis of operational factors. To address these issues, the aim of this work is to develop a predictive model for assessing the reliability of steel ropes at the design stage. This model takes into account regulatory requirements in order to prevent sudden failures and optimize operations.Materials and Methods. The study was based on the proposed hierarchical decomposition of rope reliability by degradation levels, which allowed for the algorithmic implementation of the “weakest link” principle for sequential systems. The modeling object was a 6×36 WS FC (two lay rope type) steel rope according to GOST 7668–80 used in gantry crane mechanisms. RD ROSEK 012–97 standards were adapted to the design tasks using a polynomial approximation method of discrete criteria into continuous limit state functions. To assess reliability at various hierarchical levels, a combination of Kelvin-Voigt, Archard, and Weller models, as well as the Weibull, Poisson, and normal distributions, was applied. Mathematical data processing and probability calculations were implemented in MS Excel and Mathcad. The model was verified by comparing predicted curves with the estimated service life according to the ISO 16625 methodology for M5 and M6 modes.Results. Based on the RD ROSEK 012–97 rejection standards, generalized limit states for 6×36 WS FC rope (GOST 7668) were determined. Analytical functions were derived for the relationship between the permissible number of breaks, wear, and corrosion, as well as the dependence of cross-sectional area loss on accumulated defects for M1–M8 modes. A comprehensive predictive reliability model was developed that integrates probabilistic processes of wire breakage accumulation, wear kinetics, and rheological degradation of the core into a single calculation model.Discussion. The proposed approach aims to bridge the gap between theoretical knowledge and operational practice, by considering the synergy of degradation mechanisms. It resolves the contradiction between the parallel development of defects and the sequential approach (“weakest link model”), using the principle of criticality in any limit state. Unlike additive methods, this approach incorporates the concept of dynamically dependent parameters. The rheology of the material alters the contact conditions between wires, accelerating fatigue damage accumulation. Using this approach as an analytical tool during design ensures high accuracy in predictions. However, due to the heterogeneity of models, it is necessary to develop a specific criterion for assessing overall error.Conclusion. The model is designed to be used during the design phase of lifting equipment to predictively assess reliability and minimize the risk of sudden rope failure in accordance with GOST 7668–80. It takes into account regulatory requirements and provides a 37% more conservative forecast compared to ISO 16625. Future development plans include extending the model to other rope design groups and integrating it into engineering practice.
Introduction. Pollution of aquatic ecosystems by petroleum products, including the transboundary transport of pollutants from ships' ballast water, requires improvement of cleaning methods. Existing shipboard ballast water management systems are not sufficiently effective in removing dissolved and emulsified hydrocarbons. A promising solution is the use of sorption materials. However, choosing the optimal sorbent for specific pollutants is a challenging task that requires scientific research. In this study, we aimed to demonstrate a quantum chemical modeling technique to predict the effectiveness of the “sorbent — pollutant” interaction using cellulose and typical oil components as examples.Materials and Methods. A fragment of cellulose (cellobiose) and contaminant molecules: benzene, phenol, and naphthalene were used as a model system. These substances were chosen due to their chemical structure and ability to simulate real environmental pollution. Preliminary optimization of the geometry and calculation of energy parameters were performed using the semi-empirical PM3 method in the GAMESS program. To verify the results, the density functional theory with the B3LYP functional and the 6-31G(d) basis was used. The adsorption energy was calculated as the difference between the total energies of the complex and the isolated components. The active interaction centers were identified based on the analysis of geometric parameters, boundary molecular orbitals (HOMO/LUMO), and charge transfer.Results. The key electronic characteristics of pollutants were calculated, showing that naphthalene had the highest polarizability (HOMO-LUMO gap 8.43 eV), and phenol had a significant dipole moment (1.14 D). Geometrically and energetically optimal configurations were determined for the cellobiose-benzene complex. It was established that sorption was provided by the formation of weak hydrogen bonds (O...H-C) with distances of 1.85-1.91 Å. The adsorption energy for the most stable configuration was 21.27 kJ/mol, which corresponded to a stable non-covalent interaction. Criteria for the stability of adsorption complexes (energy, structural, electronic) were formulated for the development of preliminary heuristic rules in the decision support system for the selection of sorbents.Discussion. The developed quantum chemical modeling technique made it possible to quantify the energy and mechanisms of intermolecular interaction in the "sorbent — pollutant" system. It was shown that native cellulose was able to effectively retain nonpolar aromatic hydrocarbons due to dispersion forces and weak hydrogen bonds. The calculated parameters can serve as the basis for a scientifically sound selection of components for ballast water filters and other purification systems, taking into account the type of pollutant, as well as for integration into information and analytical decision support systems.Conclusion. The results of the work can be integrated into information and analytical decision support systems for the selection of sorbents for ballast water treatment, as well as serve as a basis for further research of modified forms of cellulose.
Introduction. Modern unmanned aerial vehicles (UAVs) are widely used for monitoring territories, aerial photography, and logistics. Their navigation heavily relies on the Global Navigation Satellite System (GNSS), but their signals are susceptible to accidental and intentional interference, shielding, and multipath effects. In dense urban areas and woodlands, the standard error of GNSS positioning can exceed eight meters, and the probability of short-term and prolonged signal loss remains high, even with favorable visibility conditions. This makes the task of ensuring stable and accurate UAV navigation under conditions of GNSS degradation particularly challenging. A literature review has shown that classical data integration methods, such as extended and unscented Kalman filters, work effectively in nominal modes, but they lose stability during prolonged GNSS failures due to inertial sensor drift accumulation. New architectures based on deep learning (e.g. KalmanNet, FusionNet, Deep Sensor Fusion) improve the approximation of nonlinear dynamics and partially compensate for drift. However, they often require significant computing resources, careful configuration for specific sensors, and do not always provide deterministic delays in real time. Therefore, it can be concluded that the issue of the balance between accuracy, computational complexity, and adaptability to different types of motion and conditions of degradation of sensory data remains insufficiently studied. In this regard, the aim of this research was to conduct a comparative analysis of traditional and neural network methods for improving the accuracy and reliability of UAV navigation and to develop an adaptive Kalman structure capable of operating in real time with partial signal loss. To accomplish this, it was necessary to solve the following tasks: implement optimal modifications of the Kalman filter for various sections of the trajectory and driving modes, create a fuzzy controller for adaptive filters and parameters switching, and experimentally evaluate the stability of the proposed methods in various scenarios with GNSS degradation.Materials and Methods. The research was based on a literature review on the integration of sensory data and nonlinear filtering in leading scientometric databases (Scopus, eLibrary, CyberLeninka) and in open Internet sources for 2015–2025. Mathematical modeling was conducted in the MATLAB environment. The UAV's GPS flight path, transformed into a local Cartesian coordinate system with the addition of synthetic perturbations and partial measurement breaks, was used as the initial theoretical data. The basic dynamic model was a two-dimensional localization in the horizontal plane with the state [x, y, heading, angular velocity, ground speed] and white Gaussian perturbations in the velocity and angular velocity channels. The Euler method was used for discretization. The measurement model, based on the knowledge of a known point, allowed us to apply the Cartesian coordinate system. EKF, UKF/SRCDKF and Particle Filter were implemented and compared as reference algorithms for nonlinear filtering. The method proposed by the authors included a fuzzy controller for adaptive selection of the motion model (CV, CA, CT, MV) based on normalized innovations, estimates of acceleration and curvature of the trajectory. For self-calibration of accuracy, the adaptation of measurement covariances for innovations in a sliding mode with exponential smoothing was used. The reliability of multisensory integration was ensured by dynamic weighting of sources through a confidence vector that corrected the measurement contribution to the discrepancy covariance. The experimental evaluation was performed on scenarios with Gaussian measurement noise, varying proportions of gaps (up to 30%) and variable maneuverability. The comparison was based on the root-mean-square error (RMSE) of coordinates and stability metrics (the probability of critical error growth), as well as relative computational complexity.Results. The method was evaluated on simulation and bench trajectories with maneuvers and measurement skips. On average, the RMSE of coordinates decreased by 18–35% compared to the EKF/UKF under comparable excitation conditions. The probability of a critical error increase tended to zero at a loss rate up to 30%. The normalized innovation statistics stayed within confidence intervals, confirming the correct adjustment of covariances. Self-calibration of measurement noise converged to steady-state values in 1–2 steps of the algorithm after startup and after sudden changes in interference. Ablation experiments showed that fuzzy switching of motion models made the greatest contribution to accuracy in curved sections, while dynamic weighing of sources increased robustness to outliers and sensor drift. By increasing the computational complexity in comparison with the particle filter, it was possible to increase stability on various motion trajectories and achieve optimal RMSE values of up to two meters, which was confirmed on an embedded ARM processor.Discussion. The gain in accuracy and stability was due to a combination of a locally adequate kinematic model and online adaptation of sensor confidence, which reduced systematic biases and prevented covariance overclocking. Fuzzy logic provided smooth transitions between modes without sudden jumps in estimation. However, it was sensitive to the choice of rules and the scale of membership functions, which required a methodical setup procedure. Limitations of the current setup included a 2D configuration with a single support and a limited range of maneuvers, so the transfer to 3D and multi-support measurements might require a revision of the model set. Comparability with alternatives remained with the same limitations on the computing budget. With unlimited resources, heavier methods partially reduced the gap. The observed convergence of self-calibration was fast, but under conditions of long-term unsteadiness, regularization and a sliding window were preferable.Conclusion. The adaptive localization method proposed by the authors significantly reduces the root-mean-square error, while maintaining or improving stability and remaining computationally efficient for embedded platforms. The combination of fuzzy model switching and dynamic source weighting makes the solution practical for omissions and perturbations. Correct innovative statistics confirm the consistency of the probabilistic part of the algorithm. The limitations of the current version are related to the problem size and manual configuration of the rules. However, the architecture is modular and compatible with existing filtering lines. Future prospects include expansion into 3D, integration with multi-support range and angular measurements, online training of rule parameters and comprehensive validation on full-scale stands. Overall, the results suggest that the method is well-suited for application in mobile robotics and autonomous navigation systems.
Introduction. Over the past decades, the issue of household and industrial waste management in Russia and worldwide has gained increased importance due to growing urbanization, increased consumption, and limited landfill space. The traditional disposal model — landfilling — is associated with environmental and public health risks and low levels of resource recovery. Alternative strategies are being developed, among which the key ones are separate collection and mechanical-biological treatment (MBT), as well as thermal disposal methods. A literature review shows that international studies (Europe, Japan) demonstrate the positive effects of a combination of separate collection and MBT, while Russian studies emphasize the barriers: insufficient infrastructure for separate collection, low public participation, and municipal financial constraints. The analysis highlights the importance of an integrated approach, where technological solutions are accompanied by organizational measures and economic incentives. Currently, there is a lack of comprehensive assessments of the MTB implementation, taking into account various separate collection and local logistics scenarios. Existing studies are often limited to technical and technological aspects or present calculations at the level of individual pilot sites, without assessing the systemic economic impacts or providing a detailed sensitivity analysis of key parameters. Therefore, the aim of this study is to identify optimal MSW sorting options for subsequent incineration, based on combustion heat and pollutant emissions.Materials and Methods. The author collected and analyzed statistical data on the functioning of the MSW management system at the regional level using publicly available information from the Federal State Statistics Service and the Federal Service for Supervision of Natural Resources (Form 2-TP (waste))1 as sources. Data and reports from IFC (International Finance Corporation)2 and the public legal company Russian Environmental Operator (REO)3 were studied and analyzed. To achieve the set goals, the author has developed a research algorithm, including the use of regression analysis, the construction of a ranked series of the specific heat of MSW combustion by morphological composition with subsequent division into groups I and II, the formation of alternative waste sorting options by changing the proportion of MSW groups (I and II) in the conditionally accepted scenarios, followed by solving a multi-criteria problem of choosing the optimal option for MSW sorting for the purposes of further incineration. The research was conducted in MS Excel and Statistica software.Results. An analysis of statistical data on the solid municipal waste management system and composition revealed that the morphological composition for the purposes of further incineration remained relatively stable over the past 5 years with minor seasonal fluctuations. A clear correlation was observed between population size and waste accumulation volume, as well as between waste volume and the level of waste sorting. Simulation modeling of a feasible set of alternative MSW sorting options made it possible to determine the optimal balance between the economic efficiency of incineration and environmental pollution. In this setting, batches were simulated for subsequent incineration with different ratios of solid municipal waste from groups I and II in the ratio of 10/90, 30/70, 50/50, 70/30, 90/10, respectively. Optimal options for MSW sorting for further incineration were determined by calculating a function that determined the degree of approximation of each of the sorting alternatives under consideration to the ideal value (ideal point), which was based on the values of minimum emissions (fly ash, sulfur oxide, nitrogen oxide, carbon monoxide) and the maximum value of the calorific value (the energy value of MSW based on the morphological composition). The sensitivity coefficients to changes in the basic values of the sorting parameters and the gross emission of pollutants were calculated. Optimal options for MSW sorting from the perspective of further incineration were obtained based on the calculation of combustion heat and pollutant emissions (including carbon footprint indicators).Discussion. The main result of the study is a developed approach to the efficient sorting of municipal solid waste for subsequent thermal recycling. The solution to this problem was based on principles that allowed for a classical solution to the direct and dual problem of achieving maximum efficiency from the municipal solid waste incineration process while minimizing the negative impact on the environment. The obtained results will enable the effective separation and sorting of municipal solid waste despite the production constraints of incineration plants at municipal solid waste landfills. The analysis of the morphological composition of municipal solid waste for subsequent sorting and thermal recycling made it possible to identify waste separation options with the highest energy value based on heat of combustion of individual components and characterized by a minimum amount of pollutant emissions.Conclusion. The analysis of statistical data from the municipal solid waste management system revealed the prevalence of landfill disposal and the ineffectiveness of the sorting process. Regression models revealed increasing trends in the volume of waste removed and sorted, necessitating further targeted and effective sorting for recycling purposes. Simulation modeling of alternative MSW sorting options and the solution of an optimization problem made it possible to identify effective MSW sorting options for further thermal disposal, with minimal pollutant emissions and maximum combustion heat values. In conclusion, during the research, optimal options for municipal solid waste sorting were obtained based on the morphological composition of batches for maximum efficiency of thermal disposal and minimum emissions of pollutants into the atmosphere.
Introduction. Global climate warming is exacerbating the problem of landscape fires due to increased evaporation of moisture from combustible materials, more frequent dry thunderstorms, extended fire season, and shifting boundaries of landscape zones. These processes pose a particular threat to the forest regions of Russia, especially the Republic of Sakha (Yakutia). Domestic and international researchers have found that year-to-year variations in average monthly surface air temperature (MAT) have a significant impact on variations in fire risks. The authors have previously proved the existence of a positive reverse causality between the indicators of forest fire frequency and temperature anomalies of the following year in many regions of Siberia. However, the significance of this connection at the level of individual uluses of Yakutia, the territories of which coincide with the areas of responsibility of fire departments, has not previously been assessed. This creates a gap in scientific knowledge. The aim of this research is to fill this gap by assessing the significance of this relationship for all uluses of the republic and checking its resistance to time shifts of the analyzed series.Materials and Methods. The research was conducted using data from 2000–2024. ERA5 reanalysis was used as a source of information on MAT distribution at a height of 2 m above the studied territories, corresponding to the nodes of the 0.25° grid. Information from the Remote Monitoring Information System of the Federal Forestry Agency was used as factual material on the number of landscape fires in the territory of each ulus (district) of Yakutia and the total area of its sites covered by fire each year during the specified period. For each of the 34 uluses (districts) of Yakutia, we calculated the average MAT for their entire territory for the months from May to July, taking into account MAT information corresponding to points for which such information was provided in ERA5. The statistical relationships between interannual changes in the average MAT for the period under review and variations in forest fires indicators throughout Yakutia (one year ahead) were studied using correlation analysis for various time periods lasting 10–20 years. Linear trends were removed from the time series before performing the analysis. The significance of correlation coefficients was assessed using the Student's criterion with a confidence level of 95% or higher. The stability of these relationships was verified by examining their consistency when the series were shifted by one year and when the analyzed segments were varied between 10 and 20 years in length.Results. The uluses (districts) of Yakutia were identified, where statistical relationships between interannual changes in the average MAT for May–July, with one-year-ahead variations in the number of landscape fires and the area of fire-affected areas of Yakutia from 2000 to 2023, were found to be significant at a confidence level ≥95%. These include uluses (districts) located both in the northern and western parts of the republic's territory, as well as in its central part. The stability of the identified relationships was proved to time shifts of the analyzed periods by units of years into the past and future, as well as to changes in the duration of time series segments within 10–20 years. It was also established that during the period from 2001 to 2023, the relationships under consideration gradually strengthened: the number of uluses and districts with reliability of conclusions ≥95% increased more than twice, and the number of territories with reliability ≥99% rose from zero to 16. The relationship between changes in MAT for the identified uluses (districts) and variations in the number of fires in the territory of Yakutia were more reliable than the relationships with variations in the area of its parts affected by fire.Discussion. The results confirmed the existence of uluses (districts) on the territory of Yakutia, for which the influence on the interannual changes in MAT exerted by variations in the indicators of forest fires throughout Yakutia, which were 1 year ahead of them in time, was significant. The novelty consisted in identifying all uluses (districts) for which the connections between these processes were significant and resistant to time shifts. The revealed stability of the discovered relationships indicated the fundamental nature of the dependence: contamination of snow by particles of fire aerosols deposited on it reduced the albedo of the underlying surface covered with it, which accelerated melting, increased the temperature and evaporation rate, increasing fire risk. During the period under study, these relationships strengthened, which indicated the influence of climate warming on the activation of the positive reverse causality under consideration. Therefore, with further warming of the climate of Yakutia, they would increase even more. Variations in the number of landscape fires throughout Yakutia had a stronger effect on MAT changes for the identified uluses (districts) than variations in the burned area. The results obtained made it possible to use the indicators of forest fires throughout Yakutia of the previous year as predictors for long-term MAT forecasts for its identified areas (uluses).Conclusion. Uluses (districts) in Yakutia have been identified, for which the statistical relationship between changes in forest burning rates throughout the republic and annual MAT variations in May — July are significant. With a reliability of ≥95% for such uluses (districts) for the period from 2015 to 2024, 23 were identified, and with a reliability of ≥99%, 16 were identified. The stability of these relationships to time shifts and the duration of time series have been proved. It is established that for 2001–2024, the identified relationships have significantly strengthened, which indicates the activation of the positive reverse causality in question in the region. The tasks set in the work have been solved: the locations of the uluses (districts) of Yakutia have been determined, as well as the months for which the considered relationships are the strongest and most stable. It is also shown that the number of fires on the entire territory of Yakutia serves as a more informative predictor of the prognostic models of the studied process for its uluses (districts) than the burned area. The results of the study suggest that it is possible to use the results of monitoring forest fires in Yakutia to create forecasts for the upcoming year for the identified uluses (districts). This is particularly important for optimizing fire management strategies in a changing climate, as the months of May — July account for the peak of forest fires.
