bluuebunny/crossref_metadata_2025 huggingface dataset:
METHODS FOR ASSESSING THE RISKS OF ACTIVE POWER IMBALANCES IN LOCAL POWER SYSTEMS
Authors: Teptia Yevhenii , Komar Viacheslav , Teptia Vira
Published: December 2024

The current trends in the development of Ukraine's energy sector, driven by global climate change and Ukraine's accession to the global community's efforts to reduce greenhouse gas emissions, have contributed to the development of renewable energy sources. The introduction of renewable energy sources into distribution power grids could not but affect the structure and operation of the latter. After the integration of renewable energy sources, distribution networks are transformed from predominantly radial ones to those with two-way flows and consumers can be supplied with power both from their own dispersed energy sources (RES) and from centralized sources - power system substations. In recent years, the share of renewable energy sources in the total generation in distribution networks has reached 20-30%. And this share will continue to grow. Therefore, distribution system operators are facing new challenges related to the efficiency of networks containing dispersed energy sources. After all, given the change in weather factors and the instability of generation from sources such as solar and wind power plants, the nature of generation of some RES is probabilistic and not always predictable. The development of renewable energy has led to new challenges and the need to find new models and methods to improve the efficiency of distribution power grids and local power systems. The full-scale invasion of the Russian Federation and the destruction of energy facilities led to interruptions in electricity supply. Further attacks on power plants and substations could exacerbate the power shortage and lead to an energy crisis and large material damage. As an alternative, renewable energy sources can support electricity supply companies and provide some consumers with electricity under martial law in difficult conditions of the electricity system. The volatility of some renewable energy sources has a negative impact on the balance reliability and sustainability of the power system. When forecasting RES generation, there are risks of active power imbalances in local power systems. It is possible to reduce the probability of imbalances and, as a result, penalties for RES owners by developing methods and tools for planning the modes of local power systems with RES. The purpose of this paper is to study the problem of risks in forecasting RES generation schedules. The paper solves the urgent task of improving methods and tools for optimizing the normal modes of power systems and distribution power grids with RES. The existing risk assessment methods that can be used to analyze the occurrence of active power imbalances in local power systems when planning operating modes in the modern energy market are analyzed
OPTIMAL CONDITIONS FOR THE OPERATION OF REACTIVE POWER SOURCES IN A MICROGRID
Authors: Polishchuk Andriy
Published: December 2024

Recently, the issue of implementing microgrids to ensure reliable power supply to critical consumers during forced power outages has become urgent in Ukraine. These systems not only increase energy independence, but also ensure the continued operation of essential facilities such as hospitals, water and heat utilities, especially under conditions of unstable centralised power supply. At the same time, these enterprises are significant consumers of reactive power due to the use of motors and pumps. This poses additional challenges to the stable operation of microgrids, as reactive power must be effectively managed to maintain optimal voltage parameters and minimise losses in electrical networks. This work addresses the current task of improving methods and means for optimising reactive power flows in microgrids by coordinating the operation of reactive power sources, which will contribute to reducing energy losses in microgrids and improving voltage quality. The subject of the research is the process of automated control of a set of reactive power sources (RPS) in the microgrid. Coordinating their operation will help reduce energy losses in the microgrid and improve voltage quality. To solve this problem, the principle of predictive control and the method of "ideal" current distribution (based on energy losses) were applied. Using the "ideal" current distribution model, the non-linear optimisation problem of reactive power flows in the distribution network was reduced to a much simpler problem of finding the current distribution in an equivalent circuit with active resistances.
APPLICATION OF MACHINE LEARNING FOR OPTIMIZATION OF LOGISTICS PROCESSES
Authors: Alieksieiev Maksym , Nebava Mykola
Published: December 2024

The article considers the current features of the logistics component and key current trends in the field of logistics. Particular attention is paid to the analysis of problems that are successfully solved with the help of machine optimization of logistics routes, accurate demand forecasting, efficient management of warehouse inventory management, predictive maintenance, product quality control, optimization of last-mile delivery optimization, risk management, and environmental sustainability. In this paper analyzes the most common and effective approaches used in machine learning, including artificial neural networks and decision trees. The advantages of using artificial neural networks in demand forecasting are described, the essence of the of the decision tree methodology, and the advantages of their application, in particular, the possibility of making of making informed management decisions, risk reduction, support for strategic planning, simplicity and simplicity and clarity of use, as well as the non-parametric nature of this method, which allows avoiding mistakes, associated with incorrect assumptions about the distribution of data. The practical application of artificial neural networks and decision trees in business is analyzed separately, which made it possible to demonstrate their effectiveness in solving real problems in the modern logistics system. The article describes the CCD methodology, which covers various key stages, such as data collection, cleaning and data processing, extraction of useful information, and interpretation of the identified patterns. Important attention is paid to the results that demonstrate a significant improvement in forecasting accuracy, as well as a significant reduction in the corresponding errors. In addition, a decision tree-based regression model is considered, which, due to its ability to effectively work with nonlinear data, allows to increase the overall productivity of an enterprise by optimizing decision-making processes and improving the efficiency of of resource management.
ANALYSIS OF THE PARAMETERS OF THE AUTOMATED CONTROL SYSTEM FOR MANAGING THE CABLE PRODUCTION PROCESS
Authors: Tyzhuk Denys , Filippova Maryna
Published: December 2024

An important element of most technical systems are communication channels containing communication lines, in which conductive cables are usually used: radio frequency, coaxial and data transmission cables. Their production is a continuous multi-operational process that has all the properties of a complex system. Most cable companies use programmable logic controllers with conventional controllers to control line speed during cable extrusion. These traditional control rollers have difficulty maintaining a constant line speed, causing surface defects on the extruded cables and affecting the quality of the manufactured cables. To overcome these problems, it is necessary to analyze the controlled parameters and establish the causes of defects. During the production of the cable, it is necessary to apply the controller of the adaptive neural system of the fuzzy conclusion to ensure a constant speed of the line during the process of cable extrusion. An adaptive neuro-fuzzy inference system is a multi-level network combination of artificial neural network learning algorithms and fuzzy logic inference systems to map certain input parameters to the output. It uses a common interface and the ability to learn artificial neural networks to improve the knowledge of systems. The most used model is the Takagi-Sugeno model because it is computationally efficient and works well with optimization and adaptation methods. The work described the process of applying insulation to the wire by the extrusion method, and proposed a line speed control system. As a result of the analysis of parameters that are decisive for improving the quality of cable production, an extrusion speed control system was proposed. This will make it possible to further implement and simulate the work of the fuzzy inference adaptive neurosystem controller, which will be developed in the future. After its development and modeling, a comparison with existing analogues will be made
OPTIMIZATION OF THE PROCESS OF ASSESSMENT OF OPEN FEEDBACKS OF RESPONDENTS BY MEANS OF ARTIFICIAL INTELLIGENCE
Authors: Gryshchuk Tetiana
Published: December 2024

In this work, the characteristics of the task of assessment of respondents' answers, the main types of answers to questions, ISSN 1997-9266. Вісник Вінницького політехнічного інституту. 2014. № 1 102 the advantages of using open-ended questions are emphasized, and the main problems of evaluating answers to such questions with a large number of respondents, taking into account their characteristics, are considered. The report discusses various methods of presenting a verbal response in vector form. Various metrics for calculating the proximity between individual answers are considered and a method for calculating the distance between respondents' answers based on the modified Jacquard distance is proposed. The proposed distance takes n-gram proximity into account. A mathematical model of the evaluation process is proposed in the form of an optimization problem, which consists in finding the minimum sequence of respondents' answers, which the expert needs to evaluate, so that the average distance between unrated answers and rated answers does not exceed the threshold value. A method of solving the optimization problem using artificial intelligence methods, namely clustering methods, has been developed. The best and worst case of input data for the problem are described. The main idea of the proposed method is the step-by-step clustering of unrated answers of respondents and the selection of a characteristic answer from the largest cluster for manual evaluation by an expert. The concept of the respondent's characteristic answer is formulated and the method of its selection based on the normal distribution law of distances within the cluster is developed. It is proposed to automatically evaluate unrated answers by finding the minimum distance to rated answers. The results of the conducted experimental studies, which consisted in the selection of the data set, the vectorization method, and the clustering method, are given. Comparative tables and graphs of the use of various methods are given. The conducted studies empirically proved the adequacy of the developed mathematical models and methods and their independence from the specifics of the context of questions and answers and the characteristics of the respondents, such as the country of origin or residence of the respondent, the language of communication, and personal characteristics. The developed models and methods can be used in distance learning systems as support for the teacher's work
STUDY OF SCENARIOS OF COOPERATIVE LEARNING OF DISTRIBUTED SYSTEMS COORDINATORS
Authors: Dubovoi Volodymyr , Dzyubenko Yaroslav , Yukhymchuk Maria
Published: December 2024

Distributed systems are becoming more and more common in energy, telecommunications, transportation, etc. Centralized management of such systems is unreliable in war, climatic disasters, cyber attacks and other critical circumstances. Decentralization allows to increase the stability and reliability of distributed systems, but it requires management coordination. Coordinators determine the optimal setting for local control systems, for which they provide forecasting, clustering, estimation of uncertain parameters, system modeling, and optimization modules. For the implementation of coordination methods, learning systems are preferred, as they are able to flexibly adjust to the peculiarities of managing objects in different conditions. However, the training of coordinators is complicated by the need to train many coordinators. This paper examines five neural coordinator training scenarios. The scenarios differ depending on the structure of the neural coordinators: a segmented network according to the structure of the simulation model of the coordinator or an integrated network, as well as on the learning strategy: train all the coordinators of the decentralized system in parallel or only one and clone the results; cooperative training of a group of coordinators. Experimental studies of the proposed method of training neural network coordinators, implemented on Python TensorFlow, were carried out. The distributed system of linear objects and the connection of the heat conduction type were investigated. RMSE over a given number of training epochs was used as a criterion for learning efficiency. The study showed greater effectiveness of cooperative learning of segmented coordinators, however, the effectiveness depends on the connectivity index of the objects of the distributed system.
OPTIMIZATION OF DECENTRALIZED LAST-MILE DELIVERY USING ARTIFICIAL INTELLIGENCE
Authors: Leshchenko Yulia , Moroz Ihor , Yukhymchuk Maria , Lesko Vladyslav
Published: December 2024

In today's e-commerce environment and the ever-growing demand for fast and efficient delivery services, optimizing the last mile is becoming critical to improving the competitiveness of logistics companies. One of the most effective solutions is to implement a decentralized delivery model that uses local centers or micro hubs to optimize routes. This solution reduces the time of delivery to the end consumer, reducing the load on central warehouses, and increases the flexibility and reliability of the logistics system. Artificial intelligence (AI) plays a key role in improving the efficiency of decentralized delivery. By analyzing large amounts of data in real time, AI helps to optimize delivery routes, reducing fuel costs and reducing the number of vehicles, which at the same time has a positive impact on the environment. Machine learning algorithms allow routes to be automatically adapted to changes in traffic, weather conditions, and traffic levels, which is especially important in large cities with heavy traffic. In addition, AI ensures efficient inventory management in local centers, which allows for better planning of replenishment of goods and reduces storage costs. Lastmile management with AI also contributes to the improvement of customer service. The system can take into account individual consumer preferences, for example, determine the optimal delivery time for customer convenience, and provide accurate information about the time of arrival of the courier. This not only increases customer satisfaction, but also reduces the likelihood of repeated deliveries in case the recipient is not at the location. Thus, the decentralized delivery model optimized by AI not only reduces operational costs and improves the efficiency of logistics processes, but also contributes to a more environmentally friendly and customer-oriented service.
YOLO ALGORITHM USING IN DECISION SUPPORT SYSTEMS BASED ON THE USE OF NEURAL NETWORKS
Authors: Marchuk Andrii
Published: December 2024

In recent years, computer technologies, particularly computer vision technologies, have played an increasingly important role in medical diagnostics, including otoscopy—the process of visualizing the external auditory canal and tympanic membrane to detect ear pathologies. However, otoscopic images have several characteristics that complicate automatic classification, such as variations in lighting, orientation, and the presence of artifacts. These factors necessitate the development of reliable deep learning models for accurate disease classification. This paper examines modern approaches to address this issue. It discusses the use of convolutional neural networks (CNNs) for object detection in medical images, specifically photographs and videos obtained through otoscopic examinations, to facilitate diagnostic procedures. An analysis of the OTONet architecture, specifically designed for classifying otoscopic images, as well as research utilizing quantum machine learning (QML), is conducted. The paper explores a hybrid quantumclassical CNN architecture that employs both quantum and classical convolutional layers for extracting hidden features from images. The use of deep learning architectures, such as CNNs, integrated with the YOLO algorithm, is also discussed as a promising approach to automating diagnostics using otoscopic images. The training process of the model is described, which involves function approximation based on examples and the use of one-hot encoding for convenient handling of categories. The network predicts objects by dividing the image into a grid of cells and assigning bounding boxes to each object, taking into account their position, size, and confidence level. This approach allows for the effective resolution of complex classification and object detection tasks with high accuracy. This line of research holds significant potential for further development in the medical field, providing effective solutions for medical image processing and improving diagnostic quality.
CLUSTERING METHODS FOR USER SEGMENTATION IN RECOMMENDER SYSTEMS
Authors: Pakula Anton , Garmash Volodymyr
Published: December 2024

With the development of information technologies and the constant growth of digital content, the issue of providing highquality personalized recommendations is becoming an important aspect of modern information systems. The development and implementation of recommender systems that can effectively adapt suggestions to individual user preferences is a key area of research in data mining. Recently, more and more attention has been paid to algorithms that provide more accurate data segmentation to improve the quality of recommendations. In particular, clustering methods allow grouping users based on similar behavioral characteristics, which positively affects the accuracy of the recommendations provided and contributes to their relevance. In the course of this study, modern approaches to the use of clustering methods in the context of creating recommender systems are considered. Considerable attention is paid to the principles of adapting recommendations based on the analysis of user behavior and preferences, as well as the possibilities of improving the accuracy of forecasts through the use of data grouping. Segmentation approaches that allow creating consistent and accurate forecasts for different categories of users, taking into account their unique features and preferences, are considered. The prospects of using different data processing strategies and techniques to improve the quality of recommendations are analyzed. The results of the study emphasize the importance of choosing an appropriate data analysis method to ensure the relevance of the content received by users. It has been determined that effective clustering can significantly improve not only the accuracy of recommendations but also the overall user experience, as it helps to identify hidden patterns in behavior and preferences, which in turn contributes to a deeper understanding of user needs and increases the likelihood of their satisfaction with the service. Thus, this study has significant potential for the development and improvement of recommender systems in various fields.
TRANSFER OF GENETIC ALGORITHM PARAMETERS FOR GROUND ROBOT MOTION TASKS TO UAVS FOR MOTION OPTIMIZATION
Authors: Kulyk Yaroslav , Baranovska Anastasiia
Published: December 2024

This article explores the possibilities of using genetic algorithms to optimize the trajectory of unmanned aerial vehicles (UAVs) in order to improve the accuracy and efficiency of air quality assessment under various conditions. One of the main objectives is to ensure adaptive autonomous navigation of UAVs in dynamic environments, where parameters related to air pollution can change in real-time under the influence of external factors, such as weather conditions, geographical features, or levels of anthropogenic impact. Changes in environmental conditions, such as weather conditions and the presence of obstacles, increase the requirements for efficient algorithms for safe and efficient UAV movement. The use of genetic algorithms is a promising approach because they are able to effectively solve complex optimization problems in dynamic environments where traditional methods may be less effective. Genetic algorithms, due to their ability to search for optimal solutions in complex data spaces, can be effectively used to determine the optimal routes for collecting information about air pollution. They allow UAVs to adapt their trajectories to the current environmental conditions, taking into account factors such as wind direction and speed, pollution levels in different areas, and the presence of natural or artificial obstacles in urban or rural environments. Thanks to this approach, the algorithms provide coordinated work within a group of UAVs, which allows for the division of monitoring zones, the collection of more accurate data, and faster responses to changes in the environment. The article also discusses how genetic algorithms can improve the process of data collection and processing for further air quality analysis. The optimization of trajectories reduces the energy consumption of UAVs, increases the volume and quality of collected data, which in turn enhances the accuracy of assessments of harmful substance concentrations in the air. This makes genetic algorithms a promising and effective tool for increasing the autonomy and overall efficiency of unmanned systems in the context of air quality monitoring in various environments, such as large cities, industrial zones, agricultural areas, or nature reserves.
COMPARATIVE ANALYSIS OF REVIEWER ASSIGNMENT ALGORITHMS FOR PHD-THESES COMMITTEES
Authors: Petrychko Mykola , Shtovba Serhiy
Published: December 2024

In Ukraine, PhD theses are defended in one-time committees, which are formed manually. This can take a lot of time, and at the same time, the expertise of the selected members can be questionable, not to mention the corruption risks. Therefore, the automation of the process of assigning committees is important. In this work, a comparative analysis of algorithms for assigning reviewers based on assignment quality criteria and resource costs for finding members of the one-time committee is carried out. The subject of a thesis and the subject of the articles of potential committee members are represented by categorical distributions over research specialties from the ANZSRC-2020 research classification system. As a similarity criterion, improved Chekanovsky metric is used, which additionally takes into account the kinship of different research specialties using Jaccard index. The comparison of the efficiency of the algorithms for the assignment of committees is carried out experimentally on a dataset of 67 PhD theses. The NAQA information system was used to create the dataset. Experiments were conducted for the following optimization algorithms: brute force; brute force on a truncated set of candidates (only candidates with a certain minimum level of similarity are considered); greedy algorithm; greedy algorithm with elitism; isolated assignment (the most similar candidates are considered optimal). Thanks to optimization, it was possible to improve the composition of onetime committees by an average of 13-34%, depending on the algorithm used. This improvement occurred with a small candidate pool. An increase in the number of candidates will lead to a greater effect of applying the proposed algorithms for assigning opponents. As a result of the experiments, it was established that a good balance between the quality of assignment and resource consumption is achieved by a greedy algorithm without elitism and a brute force on a truncated set of candidates. At the same time, according to any algorithm, the newly created one-time committees are not worse, but usually better, than the one-time committees offered by the institution
DESIGNING AN IMAGE CLASSIFIER FOR FAST SEARCH IN LARGE-SCALE DATABASES
Authors: Povoroznyuk Anatoly , Filatov Valerii , Filatova Аnna
Published: December 2024

The rapid increase in the volume of information on the Internet necessitates the development of effective methods for its realtime processing in information systems. In particular, an important aspect is the clustering of news information, which considers the morphological analysis of texts and visual content. This creates a relevant challenge in clustering images accompanying textual information on various web resources, such as news portals, social networks, and informational websites. Image clustering allows for more efficient structuring of large data sets and simplifies searching for the necessary information. The subject of this study is the creation of an image classifier that is resistant to the rapid growth of information in databases. This is especially important in conditions where the amount of information is increasing at a high rate, and there is a need to quickly and efficiently process large data arrays. The study aims to enhance the performance of searching for identical images in large databases, where the rate of new information addition can reach 10–12 thousand images per day. This requires the development of a specialized image classifier that can provide fast and accurate clustering despite the intense growth in the volume of information. To achieve this goal, various modern methods are used, including mathematical modeling, contentbased image retrieval, image processing techniques, and decision-making methods. One of the main tools applied in the study is the two-dimensional discrete cosine transform, which allows for efficient image encoding and data compression without loss of important information. The research results demonstrate that the developed image classifier is indeed resistant to the increase in the amount of information in databases. The analysis of the classifier’s properties showed that the proposed solution ensures high data processing speed and minimal computational requirements. Experiments have proven that image clustering using this approach is relatively fast and resource-efficient. The developed classifier can significantly improve the efficiency of information systems, particularly in the context of the constant growth of information flows, making it an important tool for processing large image databases.
IMPROVING TASKS DISTRIBUTION USING A
Authors: Kvyetnyy Roma , Bohach Ilona , Slobodyan Roman
Published: December 2024

This research is dedicated to the development and implementation of an approach aimed at enhancing the efficiency of task allocation among support service agents using Artificial Intelligence (AI). With the growing volume of data and the increasing complexity of customer issues, the traditional approach of manually assigning tasks has become less effective, leading to delays in issue resolution, overloading of agents, and inefficient use of resources. Modern companies actively seek innovative solutions to optimize this process, with AI becoming a key tool for achieving the desired outcomes. The foundation of this approach is the principle of automating task distribution based on the analysis of employees' qualifications, experience, and current workload. AI is capable of automatically processing input data about tasks and identifying the most suitable executor. This reduces the time needed to resolve a task and improves its quality. The approach consists of seven sequential stages: from gathering information about employees and tasks to a retrospective evaluation of task performance. At each stage, AI uses specifically designed instructions to help it execute tasks, make decisions regarding distribution, and predict potential risks. The approach provides for collecting information about employees, analyzing the task, identifying the necessary skills to complete it, matching these skills to employee profiles, predicting potential risks, and offering recommendations for their avoidance. The final stage is a retrospective evaluation of task performance effectiveness and feedback from users. For each stage, specific AI instructions have been developed, which include examples of input data, queries, and expected results. This ensures the flexibility and accuracy of the model's operation at all stages of the process. As a result, the proposed approach allows companies to more effectively distribute tasks among employees, considering their experience, qualifications, and current workload. It enhances the quality of customer service, increases job satisfaction, and optimizes the use of company resources.
TIME SERIES FORECASTING USING INDICATOR ARRAYS IN PYTHON
Authors: Kabachiy Vladyslav , Maslii Roman , Kulyk Yaroslav
Published: December 2024

An analysis of methods of forecasting financial time series was carried out, models using intelligent technologies were considered. A proprietary model for forecasting time series using arrays of indicators in Python is proposed. Time series forecasting is the application of a model to predict future values based on previously observed values. And although regression analysis is often used to test the theory that the current value of one or more independent time series affects the current value of another time series, this time series analysis is not called "time series analysis" but focuses on comparing the values of one time series in different moments of time. The rate of the EUR/USD currency pair for 08/03/19 - 03/03/20 was taken as test data. After reviewing existing methods for time series forecasting using Python, the LSTM model was chosen. After analyzing the obtained results, the received signal forecast can be used as a decision support system. The accuracy of forecasting is primarily achieved due to the use of the LSTM model, normalization of the input dataset, the use of auxiliary technical indicators and the selection of parameters for the forecasting model. After reviewing existing methods for time series forecasting using Python, the LSTM model was chosen. Using LSTM, time series forecasting models can predict future values based on past sequential data. This provides greater accuracy for demand forecasters, leading to better business decisions. NVIDIA's cuDNN solves the computational problem. From experience, cuDNN can deliver 10+ times faster than standard use of CUDA directly. Based on the fact that the predicted Close price values are not accurate enough, signals were designed to recommend buy/sell positions. This signal represents a value between 0 and 1, where 0 is a buy and 1 is a sell.
METHOD OF TERMINOLOGICAL KNOWLEDGE BASES DATA SYNCHRONIZATION
Authors: Yarovyi Andrii , Kudriavtsev Dmytro
Published: December 2024

Method for data synchronization in terminological knowledge bases is proposed, based on the use of a neural network and classification by subject area topics. Text data synchronization is one of the key tasks for efficient data processing, which involves systematizing knowledge based on its semantic component and addresses the problem of understanding context based on already known data. To solve this problem, a comprehensive approach is used, which includes a set of solutions and synchronization algorithms at all levels of data processing, including semantic analysis, software and hardware-level algorithms, and the use of optimized data models for specific tasks. During the research, an algorithm for optimal data distribution in terminological knowledge bases was developed based on the semantic value of terms. The main criteria for the distribution of textual data in terminological knowledge bases were identified. The task of updating data in terminological knowledge bases during their population was examined. The problem of conflicting term synchronization based on semantic affiliation to related subject areas was formulated and described. Algorithms for term comparison based on semantic analysis, cosine similarity, the Jaccard method, and the term frequency-inverse document frequency (TF-IDF) method were considered. A description of the synchronization method in accordance with the created models and data structures is provided. The advantages and disadvantages of known methods for text data synchronization using data processing algorithms and methods are described, with examples given of data storage and retrieval tasks. The effectiveness of the synchronization method is demonstrated using examples. Data structures were optimized for efficient storage and retrieval of text data for text classification tasks. A prototype of terminological knowledge bases was created, and the synchronization method was applied using the example of the call center subject area.
Hydrogeological uniqueness of groundwater in the Cherkashinsky area of the Tobolsk district of the Tyumen region
Authors: Aleksandrov A. S. , Beshentsev V. A.
Published: December 2024

Intensive development of the Tyumen region, where the largest West Siberian megabasin is located (the Tyumen groundwater deposit is the largest in Russia), violates the natural state of ecosystems, in particular groundwater, which is the basis of an ecosystem vital to the health of the planet. Currently, the significance of groundwater is often underestimated. Professor Robert Reinecke from Johannes Gutenberg University Mainz emphasizes that groundwater should be considered as a crucial ecosystem. This is because groundwater is a major ecosystem itself, also it plays a critical role in the earth surface. So what is the hydrogeological uniqueness of the groundwater of the Cherkashinsky area in the Tobolsk district of the Tyumen region?

Mineral and thermal groundwater, historically termed "medicinal water," are essential. In the early 20th century, "mineral water" was synonymous with "medicinal water." Today, groundwater is utilized not only as a mineral resource for extracting valuable components like bromine and iodine but also as technical water. Technical water have special requirements. This is due to the need to assess the geochemical compatibility of formation fluids and injected fluids, because forecasting equilibrium can help prevent problems of salt deposition in oil fields. The difficult procedure for determining the compatibility of injected water and formation water complicates the operational use of hydrochemical data, in especially in monitoring calcite precipitate.

The aim of this article is to present the groundwater composition and evaluate the geochemical compatibility of formation fluids and injected fluids in the Cherkashinsky area. Studying this science topic, we mostly used method of atomic-absorption analysis and the geochemical compatibility of formation fluids and injected fluids. This study examines the value of concentration and potential accumulation of micro-components (bromine and iodine) in groundwater, defined by the geological and hydrogeological conditions of this area. Additionally, we have identified a computational method of compatibility of mixed waters based on calcite and gypsum. Also, we showed the dependency of calcium salt solubility in a multi-component mixture on temperature, pressure, and the partial pressure of carbon dioxide.
Optimization of the development process for complex deposits of the Tyumen suite using flow-diverging systems
Authors: Vafin R. V. , Gilyazetdinov R. A. , Sharipov F. A.
Published: December 2024

Interest in the industrial exploitation of hard-to-recover reserves is growing due to the depletion of the major sources of the country's resource base. These resources are presented by long-developed and multi-layered fields. In most cases oil flow rate from wells in these fields do not exceed five tons daily. This situation affects both extraction costs and the operational performance of the deposits. Currently, under the existing technical and economic conditions, further development of these reserves is becoming unprofitable. This is due to the necessity of planning, designing, and implementing complex geological and technological measures, along with interpreting the extensive data obtained from geophysical surveys.

The aim of this study is to optimize oil extraction processes by injecting flow-diverging compositions in the development of complex and low-yield deposits in the West Siberian oil and gas province associated with the Tyumen suite.

To achieve this aim, we summarized experiences from the studied field and similar fields with comparable geological conditions. We also studied the history of operations involving flowdiverging systems in various tectonic and stratigraphic units. Additionally, we conducted a basic interpretation of geophysical study results from similar fields to identify analogues for creating isolation screens By analyzing existing geological and field data, we verified criteria to perform processing by flow-diverging compositions in the studied field.

Based on the analysis of measures for the implementation of physical and chemical methods to increase oil recovery, we defined a list of recommended basic compositions for initial implementation of this method in the geological conditions under consideration. Taking into account the geological and technological parameters of well operation, we developed a targeted program for injecting flow-diverging compositions. This program will help exploit remaining liquid hydrocarbon reserves and establish primary criteria for selecting candidate wells. We concluded that the results obtained can be replicated in similar fields to optimize the development of low-yield deposits of the Tyumen suite.
Mutually according concept for biostratigraphic data and clinoform model of western siberian tithonian-barremian deposits
Authors: Grishkevich V. F. , Nezhdanov A. A. , Oleynik E. V.
Published: December 2024

There is a unique situation for past decades with Western Siberia Tithonian-Barremian deposit’s regional schemes: the clinoform model of their structure is generally accepted in regional oil exploring organizations, and at the same time, there exists inconsistency between this model and scientific interpretations of current biofacilitie’s complex.

Main goal of this work is initialization of public scientific discussion.

Superstitious biots. The Bazhenov bituminous formation is an integral clinoform model part — central basin deep fine-grained sediments (fondothem by Rich). It is proposed to take clinoform strata structure as conceptual basement of paleobiotic analysis to overcome existing discrepancies. West Siberian paleobasin lateral filling led to a constant replacement of its paleogeographic and environmental condition zones. Superstite (endemic) biotic complexes could be controled by ecological niches, sliding in age along with lithological boundaries. The ecological niche for ammonoids could be poorly ventilated normal salinity waters underlying the brackish waters of the halocline protruding beyond the shelf edge. Absolute age criterion. There is a regionally consistent marker — a tufite layer — at the Upper Bazhenov subformation bottom part. Microanalysis of its zircon grains estimated U‒Pb age as 140.2–141.11 million years, which is in good agreement with the clinoform model.

The biofacilities revision might be operated looking for posible coexisting endemic superstite and panboreal (cosmopolitan) ammonites and other stratifying organisms.

The currently accumulated complex of biostratigraphic data does not directly contradict the clinoform model. A working regional scheme of West Siberian Tithonian-Barremian deposits with a developed agreed upon correlation part and with a general biostratigraphic part’s intermediate version might be submitted for consideration of Russin Interdepartmental Stratigraphic Committee.
Practical results of scattered waves using for prediction of fractures zones and evaluation of faults conductivity concerning oil and gas field
Authors: Kuznetsov V. I.
Published: December 2024

The main task in the modern development of the oil industry in in each area is creating a rational strategy for the exploration and hydrocarbon production. This strategy ensures an optimal balance between the required production level and explored reserves. In this regard, oil and gas geophysics requirements are constantly increasing. Geological exploration is being reoriented to search for complex oil and gas traps. Importance of the issues related to the uneven lateral distribution of reservoir properties — primarily porosity and permeability of rocks — is increasing. Standard methodological techniques used in seismic studies no longer provide reliable solutions of geological tasks.

This article focuses on testing technology of fracture zone prediction in geological sections and assessing conductivity for fluids of tectonic disturbances at oil and gas fields. The solution of this task will significantly increase the efficiency of pools development concerning oil and gas fields characterized by complicated geological structure. Typically, evaluation of faults and fractures conductivity is accomplished on the basis of geological and production data. Fault conductivity and fracture zone data in explicit form is not used neither in geological modeling nor in hydrodynamic modeling. This study presents an attempt to apply the diffuse seismic wave field to predict fracture zones in the geological environment and the properties of faults at a real site. Modelling was carried out to assess the potential of this approach, confirming the technology's viability. Further, real data was processed and compared with field data. Real data was processed and compared with field data.
Increasing the efficiency of development of reservoirs with low-pressure natural gas using ejector devices
Authors: Apasov R. T. , Elizarenko I. I. , Nigmatullin E. H. , Eraliev Sh. B. , Sivkova M. V. , Zinchenko Ju. K. , Pershin N. A.
Published: December 2024

An analysis of the current conditions of development of unique natural gas fields in Western Siberia reveals that these resources are entering the final development phase. This phase is characterized by critically low pressures insufficient to transport gas to the wellhead. At present time, the share of low-pressure gas reserves increases, but techniques and technologies do not provide efficient and cost effective production. The aim of this study is to develop a universal decision to enhance the extraction process of low-pressure natural gas from the depleted Cenomanian production horizon based on commercial introduction a gas ejector. A gas ejector is device in which the total pressure of the gas flow boosts through turbulent mixing with a higher-pressure flow. This necessitates an analysis of three installation options for the ejector: in the production gas well, at the wellhead, and in the gas collection network. A comprehensive analysis has determined the optimal geometric parameters and operating characteristics of the ejector based on numerical calculations. Modelling of the ejector operation process in hydrodynamic simulator was performed and technological indicators of object development were calculated. Additionally, a technical and economic justification for the design solution has been provided, and the most effective placement of the gas ejector in low-pressure gas fields has been selected.
Tinjauan Literatur Sistematis tentang Alat Bantu Digital untuk Pengajaran dan Pembelajaran Geometri: Tren Penelitian, Kumpulan Data, dan Analisis Data
Authors: Adirakasiwi Alpha Galih , Juandi Dadang , Fatimah Siti
Published: December 2024

Alat bantu digital diperlukan dalam pembelajaran geometri karena mampu memberikan visualisasi yang lebih konkret terhadap konsep-konsep abstrak, meningkatkan pemahaman siswa, serta mendukung proses pembelajaran yang lebih interaktif dan efektif. Teknologi ini memungkinkan siswa untuk mengeksplorasi konsep geometris secara dinamis dan mendalam melalui simulasi, animasi, atau perangkat lunak khusus. Selain itu, alat bantu digital dapat membantu guru menyampaikan materi dengan cara yang lebih menarik, fleksibel, dan sesuai dengan kebutuhan siswa. Metode penelitian yang dipilih dalam penelitian ini adalah metode SLR (Systematic Literature Review). Penelitian ini menggunakan analisis konten pada sejumlah artikel yang telah diterbitkan dalam jurnal terindeks Scopus dengan quartile Q1- Q4 sejak tahun 2013 hingga 2023, dengan alat bantu digital pengajaran dan pembelajaran geometri sebagai fokus utama penelitian. Penelitian ini menunjukkan bahwa dalam delapan tahun terakhir, jumlah publikasi yang berfokus pada alat bantu digital telah meningkat. Di antara publikasi-publikasi tersebut, penelitian yang paling dominan adalah penelitian kualititatif. Metode analisis data yang paling sering digunakan case study. Berdasarkan hasil ini, rekomendasi yang dapat disampaikan adalah pengembangan lebih lanjut terhadap alat bantu digital untuk mendukung proses pembelajaran geometri, termasuk eksplorasi metode pembelajaran dan pengajaran baru khususnya matematika dan evaluasi efektivitasnya dalam berbagai konteks pendidikan.
Persepsi Etnomatematika dalam Pembelajaran Matematika
Authors: Marlissa Inggrid , Juandi Dadang , Turmudi Turmudi
Published: December 2024

Kajian ini bertujuan untuk membahas mengenai persepsi guru dan siswa mengenai etnomatematika. Penelitian etnomatematika di Indonesia telah menghasilkan dua pendekatan yang saling melengkapi, yaitu sebagai alat bantu pembelajaran dan sebagai kerangka kerja penelitian. Pendekatan pembelajaran etnomatematika bertujuan untuk memperkaya pemahaman siswa akan konsep-konsep matematis melalui integrasi dengan unsur-unsur budaya lokal, sementara pendekatan penelitian etnomatematika berupaya mengungkap dan mendeskripsikan manifestasi konsep-konsep matematis dalam berbagai praktik budaya. Studi-studi etnomatematika seringkali mengungkap adanya penggunaan konsep matematis yang implisit dalam aktivitas sehari-hari masyarakat, sehingga berkontribusi pada pemahaman yang lebih komprehensif tentang bagaimana pengetahuan matematis dibangun dan diwariskan secara lintas generasi dalam suatu budaya. Penelitian ini menggunakan pendekatan deskriptif kualitatif untuk mendeskripsikan implementasi konsep etnomatematika pada guru dan siswa SMP di Kota Merauke. Melalui analisis data yang diperoleh dari kajian pustaka, angket, dan wawancara mendalam, penelitian ini menyimpulkan bahwa potensi pengembangan etnomatematika di wilayah tersebut masih terbuka lebar. Namun demikian, kajian lebih lanjut masih diperlukan untuk menggali potensi dan tantangan dalam pengembangan pendekatan pembelajaran yang lebih kontekstual.