Modeling Methods Research Articles

Structural constraints limit the regime of optimal flux in autocatalytic reaction networks

Autocatalytic chemical networks play a predominant role in a large number of natural systems such as in metabolic pathways and in ecological networks. Despite recent efforts, the precise impact of thermodynamic constraints on these networks remains elusive. In this work, we present a theoretical framework that allows specific bounds on the thermodynamic affinity and on the concentrations of autocatalysts in mass-action autocatalytic networks. These bounds can be obtained solely from the stoichiometry of the underlying chemical reaction network, and are independent from the numerical values of kinetic parameters. This property holds in the specific regime where all the fluxes of the network are tightly coupled and maximal. Our method is applicable to large networks, and can be used to complement constraints-based modeling methods of metabolic networks, which typically do not provide predictions about thermodynamic properties or concentration ranges of metabolites.

A REVIEW OF ANIMAL MODELS FOR BONE FRACTURE NONUNION AND THEIR ROLE IN STUDYING BIOLOGICAL THERAPY EFFICACY

The bone healing impairment, such as non-union fractures after injuries of long bones, lead to loss of working capacity and result in significant financial costs, which emphasizes the socioeconomic significance of the problem. However, it is not known which method of modeling the non-union bone fractures is more optimal for further research into the effectiveness of biological therapy aimed at treating bone healing impairment. For a detailed study of methods of non-union fracture treatment of, it is necessary to determine the developed animal models. The objective was to analyze the existing animal models of fracture nonunion in long bones in vivo and to consider the possibility of their further use to evaluate the effectiveness of the use of modern biotechnologies for the in the management of fracture nonunion. It was found that the majority of developed animal models of atrophic long bone non-union were created using small animals, namely rats, mice, and rabbits. A more common method of modeling bone non-union is performing an osteotomy with the formation of a defect of different widths between the bone fragments and subsequent removal of the periosteum proximal and distal to the osteotomy site; damage to the endosteum or removal of bone marrow. Also, in such animal models, researchers use a silicone spacer, a polysulfone plate, or a latex-silicone foil to physically prevent fracture union. In these animal models, studies using mesenchymal stromal cells, platelet-rich plasma or bone morphogenetic protein-2 (BMP-2) have already been conducted for the management of non-union bone fractures. At the same time, the clinical results of the application of various biological therapies are ambiguous, which determines the conduct of further experimental studies, in particular, in vivo. However, there are disagreements about which in vivo modeling methods give a reproducible result and prevent bone union, which determines the need for further analysis of existing modeling tools for conducting research in this direction.

Machine learning-based uncertainty analysis in power system planning: Insights and pathways for decarbonization

This study performs an extensive uncertainty analysis of decarbonization pathways for the Canadian power system. By leveraging machine learning (ML) techniques, we represent complexities and nonlinear behaviors of the power system, which are challenging to observe using traditional modeling methods due to computational limitations. We further utilize K-means clustering and statistical methods to identify correlations, investigate the effects of uncertainty in key parameters (capital costs, demand, and carbon tax), and identify opportunities for diversification of the power generation mix. Key findings include the increased flexibility of gas combined cycle (generation mean 4.8 TWh, installed capacity mean 4.8 GW) over gas simple cycle (generation mean 22.3 GWh, 0 installed capacity) in response to carbon tax, highlighting its role in a low-carbon future. Variations in solar and wind deployment rates, influenced by capacity factors and location-specific costs, underscore the necessity of a flexible grid system. This analysis reveals important correlations between renewable deployment, and transmission expansion and gas combined cycle reliance. The results also highlight minimal sensitivity of hydroelectric power generation (∼ 43 % of scenarios) to changes in input parameters. The novelty of our work lies in proposing a comprehensive approach which uses ML techniques to go beyond the capabilities of existing models, specifically by performing extensive uncertainty analysis and provides key insights into system behavior, causative factors, and an expanded breadth of potential pathways. Taken together, this proposed approach lays a foundation for strategic planning and policy formulation.

Structural constraints limit the regime of optimal flux in autocatalytic reaction networks

Autocatalytic chemical networks play a predominant role in a large number of natural systems such as in metabolic pathways and in ecological networks. Despite recent efforts, the precise impact of thermodynamic constraints on these networks remains elusive. In this work, we present a theoretical framework that allows specific bounds on the thermodynamic affinity and on the concentrations of autocatalysts in mass-action autocatalytic networks. These bounds can be obtained solely from the stoichiometry of the underlying chemical reaction network, and are independent from the numerical values of kinetic parameters. This property holds in the specific regime where all the fluxes of the network are tightly coupled and maximal. Our method is applicable to large networks, and can be used to complement constraints-based modeling methods of metabolic networks, which typically do not provide predictions about thermodynamic properties or concentration ranges of metabolites.

ДОКТРИНА ВЗАИМОСВЯЗЕЙ ЭЛЕМЕНТОВ КОНКУРЕНТОСПОСОБНОСТИ В ЦЕЛЯХ ОБЕСПЕЧЕНИЯ ИННОВАЦИОННОСТИ БИЗНЕС-СТРУКТУР В СФЕРЕ МЕБЕЛЬНОГО РЫНКА

The article examines the relationships between the elements of competitiveness in order to ensure the innovativeness of business structures. The doctrine, as a system of beliefs and basic principles, serves as the basis for developing a competitiveness strategy in order to enhance the competitive advantages of enterprises. Competitiveness is a multidimensional property, namely, product innovation, plays a big role in achieving a high status in a competitive market. It is necessary to study each element of competitiveness in order to ensure the innovativeness of business structures. Competition forces business structures to improve the quality of their products, use new product components, innovative technologies and innovations in design and sales systems. Business structures in the furniture market need to study the interrelationships of the elements of competitiveness and their impact on the final result. In order to substantiate the doctrine of the interrelations of elements to ensure the innovativeness of business structures, a quality management system for innovations of business structures in the furniture market was defined, built on the blocks of ensuring innovation and quality, quality management and quality improvement, including elements of competitiveness to ensure the innovativeness of business structures: development of a sustainable regulatory framework, creation of the most favorable investment climate and effective competition policy, promoting the innovative activities of business structures, simplifying barriers to doing business in the quality management system of innovations of business structures in the field furniture market. During the research, methods of structural, systemic and functional, quantitative and qualitative analysis, graphic and mathematical methods, and methods of structural and functional modeling were used.

Optimization of vision transformer-based detection of lung diseases from chest X-ray images.

Recent advances in Vision Transformer (ViT)-based deep learning have significantly improved the accuracy of lung disease prediction from chest X-ray images. However, limited research exists on comparing the effectiveness of different optimizers for lung disease prediction within ViT models. This study aims to systematically evaluate and compare the performance of various optimization methods for ViT-based models in predicting lung diseases from chest X-ray images. This study utilized a chest X-ray image dataset comprising 19,003 images containing both normal cases and six lung diseases: COVID-19, Viral Pneumonia, Bacterial Pneumonia, Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), and Tuberculosis. Each ViT model (ViT, FastViT, and CrossViT) was individually trained with each optimization method (Adam, AdamW, NAdam, RAdam, SGDW, and Momentum) to assess their performance in lung disease prediction. When tested with ViT on the dataset with balanced-sample sized classes, RAdam demonstrated superior accuracy compared to other optimizers, achieving 95.87%. In the dataset with imbalanced sample size, FastViT with NAdam achieved the best performance with an accuracy of 97.63%. We provide comprehensive optimization strategies for developing ViT-based model architectures, which can enhance the performance of these models for lung disease prediction from chest X-ray images.

Formulation of nanoemulsion parijoto fruit extract (Medinilla Speciosa) with variation of tweens stabilizers

Nanotechnology has substantial potential for development due to its ability to modify surface characteristics and particle size, facilitating enhanced absorption of functional food compounds and controlled release of active substances to mitigate adverse effects. Nanoemulsion, a stable colloidal system formed by blending oil, emulsifier, and water, was identified as nanotechnology with promising applications. However, investigations into the impact of surfactants on characteristic nanoemulsions need to be more varied. This research gap necessitated further exploration in the advancement of nanotechnology-based foods. The parijoto fruit (Medinilla speciosa), an indigenous plant species in Indonesia, has yet to undergo extensive scrutiny for its potential use as a functional and nutraceutical food. Anthocyanins, a principal compound in the parijoto fruit, had exhibited efficacy in reducing the risk of cardiovascular disease diabetes, demonstrating anti-inflammatory and antioxidant properties. This study aimed to investigate the characteristics of nanoemulsion formulations derived from parijoto fruit extract and to evaluate an optimum condition with various tween surfactants. The findings from this investigation could furnish valuable insights for the further advancement of anthocyanin nanoemulsions from parijoto fruit extract. The results comprised the characterization of nanoemulsion particle size, polydispersity index, ζ-potential, conductivity, pH, and viscosity. Through mathematical modeling and statistical methods, RSM optimizes nanoemulsion by examining the relationships and interactions between independent and response variables. Furthermore, the characterization of nanoemulsion encompassed ζ-potential, polydispersity, particle size, conductivity, pH, and viscosity. Elevated surfactant concentrations resulted in diminished particle sizes and more uniform size distribution, albeit reaching a plateau where surfactant aggregation and micelle formation ensued. Increased concentrations of surfactant type, concentration, and parijoto extract impacted the physical characteristics of nanoparticle size and polydispersity. The optimal process conditions for nanoemulsion consisting of the type of Tween used are Tween 80, Tween concentration of 12%, and parijoto fruit extract concentration of 7.5%, yielding a desirability value of 0.74, categorizing it as moderate.

THE FREIGHT FORWARDING OPERATIONS WITH BULK CARGO IMPROVEMENT

Abstract. The article considers the technology of bulk cargo transportation option by accelerated trains, based on two separate routes that allow delivering goods to the countries of the European Union by land in the West of our country and through the ports of the Black Sea coast. Approaches that allow not only to choose the best option for the bulk cargo transporting by accelerated trains technology, but also improving the vehicles use under the conditions of limited resources are considered. The rationality of measures to improve the quality of freight transportation organization is not only the economic effect from freight charges, but also the time of delivery. The analysis of the generalized economic-mathematical model of container trains, that includes the main criteria and parameters taking into account the technical equipment of the chain and the technology of transit flows, when service channels of different capacity in can exist each part of the logistic transport chain, with different costs and effects on the development of road traffic. The model of a freight container train proposed in this section improves the technology for the goods in containers transportation by the railways. During the delivery and export of goods by vehicles model development, it is necessary to take into account the fact that under present conditions, the volume of small-scale transport is almost zero. This is because they are expensive (high transport fee), have low product safety and long delivery time. Almost all of them are now transported by road, so it is not recommended to consider them as a promising direction in the further development of services for the import and export of goods. After calculating the savings from car turnover by containers acceleration, it was found that shipping containers in a block container train is much more efficient than in a block group, as cargo delivery times are faster, ranging from 8.05 to 9.08 days. We get savings from acceleration of car turnover by containers for a year within 430 - 450 thousand UAH. Economic consequences of introduction of container trains in Ukraine for 5 years of operation is 1 965 128 UAH for the route (S1) and 1 979 142 UAH for the route (S2). The proposed approaches prospect of development may be clarified by predicting the volumes of transport and the costs of components associated with the transport of goods in containers, by using stochastic modeling methods and artificial intelligence.

Devices and Methods for Dosimetry of Personalized Photodynamic Therapy of Tumors: A Review on Recent Trends.

Significance: Despite the widespread use of photodynamic therapy in clinical practice, there is a lack of personalized methods for assessing the sufficiency of photodynamic exposure on tumors, depending on tissue parameters that change during light irradiation. This can lead to different treatment results. Aim: The objective of this article was to conduct a comprehensive review of devices and methods employed for the implicit dosimetric monitoring of personalized photodynamic therapy for tumors. Methods: The review included 88 peer-reviewed research articles published between January 2010 and April 2024 that employed implicit monitoring methods, such as fluorescence imaging and diffuse reflectance spectroscopy. Additionally, it encompassed computer modeling methods that are most often and successfully used in preclinical and clinical practice to predict treatment outcomes. The Internet search engine Google Scholar and the Scopus database were used to search the literature for relevant articles. Results: The review analyzed and compared the results of 88 peer-reviewed research articles presenting various methods of implicit dosimetry during photodynamic therapy. The most prominent wavelengths for PDT are in the visible and near-infrared spectral range such as 405, 630, 660, and 690 nm. Conclusions: The problem of developing an accurate, reliable, and easily implemented dosimetry method for photodynamic therapy remains a current problem, since determining the effective light dose for a specific tumor is a decisive factor in achieving a positive treatment outcome.

Simulating Climatic Patterns and Their Impacts on the Food Security Stability System in Jammu, Kashmir and Adjoining Regions, India

This study investigated the historical climate data and future projections under the SSP5-8.5 scenario for Jammu, Kashmir (J&K), and its adjoining regions in India. Agriculture is a critical economic pillar of this region, making it highly vulnerable to climate change. This study focused on temperature and precipitation trends. Statistical analysis and modeling methods, including cloud computing, were employed to predict changes and assess their impact on agricultural productivity and water resources. The results indicated that by 2100, the mean maximum and minimum temperatures are projected to increase by approximately 2.90 °C and 2.86 °C, respectively. Precipitation variability is expected to rise, with a mean increase of 2.64 × 10−6 mm per day. These changes have significant consequences for crop yield, water stress, and ecosystem dynamics. An analysis of Gross Primary Productivity (GPP) as a proxy for agricultural productivity using linear regression revealed a concerning trend. Although the total GPP of the study area remained stable over time, it declined by −570 g yr−1 in 2010, coinciding with a 1 °C temperature rise. Projections based on the expected 3 °C temperature increase by 2100 suggest a total GPP loss of −2500 g yr−1. These findings highlight the urgent need for proactive adaptation measures, including sustainable agricultural practices, improved water management, and enhanced socioeconomic infrastructure, to mitigate the impact of climate change and ensure long-term resilience and food security in the region.

Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance.

Temperature mapping methods for thermoelastic analyses of the ARIEL spacecraft payload module

The ARIEL mission is a European space project that aims to detect exoplanets with a spacecraft orbiting around the L2 point of the Sun-Earth system. The main payload consists of a Cassegrain telescope composed of mirrors that reflect and concentrate the incoming light from the deep space observations to finally guide it to the detectors. As in many other space missions, a dedicated complex assessment is established during the design phase to evaluate the impact on the optical performance caused by thermoelastic effects, which involves the coordinated work of the thermal, structural, and optical engineers. Despite that well-known and standardized processes and tools are established separately in each involved area, there is a lack of standardization about the way of exchanging the data between them, where additional calculations are required in some cases. This work focuses on the temperature mapping, which is the intermediate step between thermal and structural analyses, where temperatures are transferred to the structural model. The main difficulty of this process is related to the differences in modelling methods and approaches between both models, being necessary the development of an adequate algorithm to find the most accurate transfer of temperatures. This paper shows two different options for temperature mapping, detailing the proposed flowcharts. One of these methods requires the performance of an additional thermal conductive analysis, where a new improved procedure has been implemented in this work to solve some computational issues that made its application for large models difficult or even unfeasible. Both temperature mapping methods have been applied to the payload module of the ARIEL spacecraft, comparing the output results in terms of temperatures, stresses, forces, and displacements to evaluate their differences.

Deep learning with local spatiotemporal structure preserving for soft sensor development of complex industrial processes

Data-driven soft sensors have emerged as indispensable tools for predicting quality variables in complex industrial processes because of their cost-effectiveness and ease of maintenance. In particular, soft sensors based on deep learning have been utilized in extensive research and successful applications in recent years. However, traditional deep learning methods capture hierarchical data features by minimizing global fitting errors, neglecting the local structural characteristics implied in the original data. In this paper, we propose a new deep learning method for soft sensor development. Utilizing autoencoders as the foundational architecture of our network, a new semisupervised strategy is adopted for layerwise pretraining optimization. On the one hand, more representative data features are extracted by maintaining the local spatiotemporal structure of the data; on the other hand, layer-by-layer supervised learning is employed to identify the critical features that are aligned with the ultimate task, which aids in obtaining the optimal network parameters and improving the resulting prediction accuracy. Subsequently, a local spatiotemporal structure-preserving stacked semisupervised autoencoder (LSP-SuAE) is established. To evaluate the feasibility and effectiveness of the proposed approach, experiments are carried out in a real industrial process. A soft sensor based on the LSP-SuAE is developed to predict the rate of ethylbenzene conversion during the dehydrogenation of styrene. The experimental results demonstrate that, compared to five other common or similar data-driven modeling methods, LSP-SuAE exhibits higher prediction accuracy and better stability.

Employing deep reinforcement learning to maximize lower limb blood flow using intermittent pneumatic compression.

Intermittent pneumatic compression (IPC) systems apply external pressure to the lower limbs and enhance peripheral blood flow. We previously introduced a cardiac-gated compression system that enhanced arterial blood velocity (BV) in the lower limb compared to fixed compression timing (CT) for seated and standing sub7 jects. However, these pilot studies found that the CT that maximized BV was not constant across individuals and could change over time. Current CT modelling methods for IPC are limited to predictions for a single day and one heartbeat ahead. However, IPC therapy for may span weeks or longer, the BV response to compression can vary with physiological state, and the best CT for eliciting the desired physiological outcome may change, even for the same individual. We propose that a deep reinforcement learning (DRL) algorithm can learn and adaptively modify CT to achieve a selected outcome using IPC. Herein, we target maximizing lower limb arterial BV as the desired out19 come and build participant-specific simulated lower limb environments for 6 participants. We show that DRL can adaptively learn the CT for IPC that maximized arterial BV. Compared to previous work, the DRL agent achieves 98% ± 2 of the resultant blood flow and is faster at maximizing BV; the DRL agent can learn an "optimal" policy in 15 minutes ± 2 on average and can adapt on the fly. Given a desired objective, we posit that the proposed DRL agent can be implemented in IPC systems to rapidly learn the (potentially time-varying) "optimal" CT with a human-in-the-loop.

The control unit of the combined photovoltaic/thermal system

During the last years the design and implementation of energy-saving solutions remains a relevant and integral task for implementation the route of decreasing carbon emissions and reduce the negative impact on the environment. Using combined photovoltaic/thermal systems is limited by their high price connected mainly with the complexity of their control systems, even taking into account availability of government programs for financial support for the use of alternative energy. The novelty of the proposed work consists in the development of a mathematical model and the analytical study of the control system of combined photovoltaic/thermal system for research the operation of control and logic elements and their response to transient and emergency modes during such systems operation. Aim of the work - operational analysis, analytical and optimization studies of the control system of the combined photovoltaic/thermal system to ensure reliable operation in transient and emergency modes and significantly simplify the design of such a system. In this article was implemented mathematical modeling methods and using specialized software. As a result of the work the mathematical model of the control system of combined photovoltaic/thermal system which operates with variable set of control and logic elements was developed. A structural solution of the control system is proposed for further implementation and field testing. The response criteria of the control system to transient and emergency operation modes of the combined photovoltaic/thermal system have been developed. Practical value. It was established the permissible and critical values of a number of parameters characterizing the operation of combined photovoltaic/thermal system in normal mode and deviations from which indicate the presence of an emergency situation are determined. The proposed algorithms of the control system will make it possible to increase the reliability of combined photovoltaic/thermal system and reduce the risk of emergency situations during field tests and further operation.

Long-term health in individuals born preterm or with low birth weight: A cohort study.

To measure the association of prematurity and non-preterm low birth weight (LBW) with several long-term health outcomes. We selected adult participants from the Constances cohort. Associations between preterm birth (<37 weeks versus ≥37 weeks) and outcomes were measured using modified Poisson regression with adjustment for participant age and parental history. We used the same modeling methods to measure the association between LBW (i.e., <sex-specific 10th percentile) and outcomes in participants born ≥ 37 weeks. We tested for an interaction between exposures and sex. Among 30,295 participants, preterm birth (5.2%) was associated with (RR[CI95]): obesity (1.25[1.08-1.46]), hypertriglyceridemia (1.23[1.07-1.42]), high LDL-cholesterol (1.16[1.05-1.28]), high blood pressure (HBP) (1.22[1.08-1.36]), metabolic syndrome (1.35[1.06-1.71]), non-alcoholic fatty liver disease (1.26[1.08-1.47]), allergic and atopic symptoms (1.06[1.01-1.12]), and lack of tertiary education (1.11[1.02-1.20]). Women had a significantly higher risk of hypertriglyceridemia and metabolic syndrome. In non-preterm participants, LBW was associated with prediabetes/diabetes (1.30[1.12-1.52]), HBP (1.22[(1.12-1.33]) and lack of tertiary education (1.13[1.07-1.20]), whereas the risk of obesity (0.83[0.73-0.95]) and abdominal obesity (0.84[0.76-0.93]) was reduced. Preterm birth and non-preterm LBW are both risk factors for several adult outcomes. However, regarding excess fat storage, their long-term effect seems to be in the opposite direction. Preterm birth is associated with a higher long-term risk of obesity, whereas low birth weight is not. This study improves the understanding of the common idea that low birth weight is associated with a long-term risk of obesity, whereas it might depend on the cause of low birth weight. These findings provide new insights into the difficult distinction between the long-term adverse health effects of preterm birth and low birth weight.

The privacy-explainability trade-off: unraveling the impacts of differential privacy and federated learning on attribution methods.

Since the advent of deep learning (DL), the field has witnessed a continuous stream of innovations. However, the translation of these advancements into practical applications has not kept pace, particularly in safety-critical domains where artificial intelligence (AI) must meet stringent regulatory and ethical standards. This is underscored by the ongoing research in eXplainable AI (XAI) and privacy-preserving machine learning (PPML), which seek to address some limitations associated with these opaque and data-intensive models. Despite brisk research activity in both fields, little attention has been paid to their interaction. This work is the first to thoroughly investigate the effects of privacy-preserving techniques on explanations generated by common XAI methods for DL models. A detailed experimental analysis is conducted to quantify the impact of private training on the explanations provided by DL models, applied to six image datasets and five time series datasets across various domains. The analysis comprises three privacy techniques, nine XAI methods, and seven model architectures. The findings suggest non-negligible changes in explanations through the implementation of privacy measures. Apart from reporting individual effects of PPML on XAI, the paper gives clear recommendations for the choice of techniques in real applications. By unveiling the interdependencies of these pivotal technologies, this research marks an initial step toward resolving the challenges that hinder the deployment of AI in safety-critical settings.

STUDYING THE IMPACT OF THE VOLUME OF WORK AT THE RAILCAR TRANSFER POINT ON THE NEED FOR TRACK STORAGE CAPACITY FOR BOGIES

Purpose. In the context of military aggression by moscow, export cargo flows, which were largely transported by sea from Ukraine's Black Sea ports, were directed towards western land crossings. At the same time, exporters were trying not only to find new logistics routes but also to increase exports to European countries. This resulted in longer queues at land borders, both road and rail, and the turnover of railcars and their individual components became uncontrollable and unpredictable. The reason for the downtime of cargo in railroad cars at the borders was the limited processing capacity of railcar interchange points and the transshipment capacity of cargo terminals at border stations. One of the elements of the cargo border crossing system, the increase of which will increase the volume of export cargo flow, is the points of change of wagons from 1435 mm gauge to 1520 mm gauge. Therefore, the aim of this study is to determine the required track capacity for storing 1520 mm and 1435 mm gauge bogies under conditions of daily uneven transportation and the stochastic nature of the turnover of cars on the territory of European railways. Methodology. Simulation modeling, mathematical statistics, and inventory management methods were used to study the impact of the workload of a railcar transfer point on track capacity. Results. As a result of the research, the numerical characteristics of a random variable of the number of cars transferred to the transfer point during the day, as well as the value of car turnover in Europe, were obtained. A methodology for determining the required track capacity for storing bogies based on simulation modeling of the operation of the railcar interchange points was developed. The dependence of the guaranteed stock of bogies of 1435 mm gauge at the station on the daily number of cars serviced at the railroad car transfer point was obtained. Practical significance. The obtained dependences of the required capacity of the tracks for storing bogies on the daily number of cars arriving at the transfer point will allow us to check and, if necessary, bring the existing track development of border stations into line with the calculated volumes of work.

Development of a Tabletop Hologram for Spatial Visualization: Application in the Field of Architectural and Urban Design

Architects, engineers, and designers normally visualize architectural, urban planning, urban design, or landscape design projects in different ways to present their ideas. At present, the two most widely utilized and accessible methods for spatial visualization are digital 3D modeling and physical 3D modeling. Despite their popularity, both approaches have intrinsic limitations. These shortcomings are progressively being mitigated through advancements in technology and digitalization. In this study, we propose the utilization of hologram technology as an innovative approach to overcome the limitations of both modeling methods mentioned. This research addresses two main points: the seamless integration of hologram production into the standard workflow of architectural and urban design projects, and the experimental creation of a tabletop hologram prototype using the most advanced stereoscopic visualization capabilities—CHIMERA hologram printer. The experiment’s results indicate that tabletop holograms’ visualization quality can potentially replace traditional methods in the near future. The process of creating holograms can be incorporated into the standard workflow of architectural and urban design projects and utilized in specific contexts.

RESEARCH OF CHANGES IN THE POWER OF CAR FLOW AT THE SHUTTING STATION DURING THE PERIOD OF MILITARY ACTIONS

It is impossible to solve the problem of improving the system of organizing car flows in trains at the current level of information support without creating an information and management system that will integrate into a single information and computing space the most important optimization tasks covering all levels of managing the process of freight transportation on railway transport, such as such as the calculation of the routing of car flows from loaded and empty cars, the calculation of the plan for the formation of freight trains at the training ground of the main technical stations, the calculation of the plan for the formation of local trains in the areas of local work, and the construction of adjusted operational plans for the operation of stations. The aggression of the Russian Federation against Ukraine fundamentally changed the system of railway transport of Ukraine. The volume of cargo transportation by rail transport during military operations in the east of Ukraine decreased by 37% in 2022, but already in 2023, an increase in the transportation of some types of cargo began, in particular, grain cargo, mineral construction materials, dangerous bulk cargoes, military equipment. Transportation of goods by railways of Ukraine, especially during military operations, is not carried out evenly over a long period of time and has fluctuations in the capacity of car flows. The updated directions of their tracking, which depend on the technical condition of the railway infrastructure for the current period, lead to a change in the direction of tracking of car flows and, as a consequence, their organization into trains according to the formation plan at technical stations. The conducted studies established that the capacities of car flows are random values, therefore it is advisable to use mathematical statistics methods for their description, and modeling methods for forecasting work with them. Those car flows that arrive at the sorting station most of all fall under the geometric law of distribution of a random variable depending on the power of the direction of arrival, which has a significant impact on the accumulation parameter at the station. Fluctuations in the capacity of car flows are quite significant and depend on a sufficiently large number of factors. For the operational planning of the work of sorting stations, it is necessary to take the characteristics of the current state or a sample of data for short periods (a decade, a week), that is, it is necessary to constantly analyze the data from the ASK of the VP UZ - there is, therefore, a study of the capacity of car flows in the technical station during the period of changing their directions is an actual task and can be used to adjust the dispatcher routing and the plan for the formation of single-group and group freight trains.