Data Structure Research Articles

Bridging theory and data: A computational workflow for cultural evolution

Cultural evolution applies evolutionary concepts and tools to explain the change of culture over time. Despite advances in both theoretical and empirical methods, the connections between cultural evolutionary theory and evidence are often vague, limiting progress. Theoretical models influence empirical research but rarely guide data collection and analysis in logical and transparent ways. Theoretical models themselves are often too abstract to apply to specific empirical contexts and guide statistical inference. To help bridge this gap, we outline a quality-assurance computational workflow that starts from generative models of empirical phenomena and logically connects statistical estimates to both theory and real-world explanatory goals. We emphasize and demonstrate validation of the workflow using synthetic data. Using the interplay between conformity, migration, and cultural diversity as a case study, we present coded and repeatable examples of directed acyclic graphs, tailored agent-based simulations, a probabilistic transmission model for longitudinal data, and an approximate Bayesian computation model for cross-sectional data. We discuss the assumptions, opportunities, and pitfalls of different approaches to generative modeling and show how each can be used to improve data analysis depending on the structure of available data and the depth of theoretical understanding. Throughout, we highlight the significance of ethnography and of collecting basic cultural and demographic information about study populations and call for more emphasis on logical and theory-driven workflows as part of science reform.

Digitalization and Dynamic Criticality Analysis for Railway Asset Management

The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment.

Lateritic Ni–Co Prospectivity Modeling in Eastern Australia Using an Enhanced Generative Adversarial Network and Positive-Unlabeled Bagging

AbstractThe surging demand for Ni and Co, driven by the acceleration of clean energy transitions, has sparked interest in the Lachlan Orogen of New South Wales for its potential lateritic Ni–Co resources. Despite recent discoveries, a substantial knowledge gap exists in understanding the full scope of these critical metals in this geological province. This study employed a machine learning-based framework, integrating multidimensional datasets to create prospectivity maps for lateritic Ni–Co deposits within a specific Lachlan Orogen segment. The framework generated a variety of data-driven models incorporating geological (rock units, metamorphic facies), structural, and geophysical (magnetics, gravity, radiometrics, and remote sensing spectroscopy) data layers. These models ranged from comprehensive models that use all available data layers to fine-tuned models restricted to high-ranking features. Additionally, two hybrid (knowledge-data-driven) models distinguished between hypogene and supergene components of the lateritic Ni–Co mineral systems. The study implemented data augmentation methods and tackled imbalances in training samples using the SMOTE–GAN method, addressing common machine learning challenges with sparse training data. The study overcame difficulties in defining negative training samples by translating geological and geophysical data into training proxy layers and employing a positive and unlabeled bagging technique. The prospectivity maps revealed a robust spatial correlation between high probabilities and known mineral occurrences, projecting extensions from these sites and identifying potential greenfield areas for future exploration in the Lachlan Orogen. The high-accuracy models developed in this study utilizing the Random Forest classifier enhanced the understanding of mineralization processes and exploration potential in this promising region.

A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm

For the protection of information security, link prediction, as a basic problem of network science, has important application significance. However, most of the existing link prediction algorithms rely on the node information of the graph structure, which is not applicable in some graph structure data involving privacy. At the same time, most of the algorithms only consider the general graph structure and do not fully consider the high-order information in the graph. Because of this, this paper proposes an algorithm called hypergraph-based link prediction with self-attention (HWA) to solve the above problems. The algorithm can obtain hypergraphs without knowing the attribute information of hypergraph nodes and combines the graph convolutional network (GCN) framework to capture node feature information for link prediction. Experiments show that the HWA algorithm proposed in this paper, combined with the GCN framework, shows better link prediction performance than other graph-based neural network benchmark algorithms on eight real networks. This further verifies the validity and reliability of the model in this paper and provides new protection ideas and technical means for information security.

Alterations of gray matter asymmetry in internet gaming disorder.

Structural asymmetry is a subtle but pervasive property of the human brain, which has been found altered in various psychiatric and neurocognitive disorders. However, little is known regarding potential alterations of structural asymmetry underlying internet gaming disorder (IGD). Therefore, this study aimed to investigate the structural features of gray matter asymmetry in IGD. High-resolution structural magnetic resonance imaging data were collected from 104 individuals with IGD and 104 recreational game users (RGUs). We applied a whole-brain voxel-based asymmetry (VBA) approach to determine the asymmetrical aberrations of gray matter in relation to IGD. Furthermore, the local abnormalities of structural asymmetry were employed as features to examine the effect of classification using a support vector machine (SVM). The results indicated that individuals with IGD as compared to RGUs showed asymmetrical alterations of gray matter in the medial prefrontal cortex (mPFC), orbitofrontal cortex, precuneus, middle temporal gyrus, superior parietal lobule and inferior temporal gyrus, regions implicated in hedonic motivation, self-reflection, information integration and visuospatial attention processing. Moreover, these atypical asymmetrical features can distinguish IGD subjects from RGUs with high accuracy. These results suggested that disrupted structural asymmetry of motivational reward, visuospatial and default mode circuits might be potential biomarkers for identifying pathological gaming dependence. These findings extended our understanding of structural underpinnings of IGD and provided new insights for developing effective interventions to alleviate compulsive gaming usage.

Enhancing Recovery of Structural Health Monitoring Data Using CNN Combined with GRU

Structural health monitoring (SHM) plays a crucial role in ensuring the safety of infrastructure in general, especially critical infrastructure such as bridges. SHM systems allow the real-time monitoring of structural conditions and early detection of abnormalities. This enables managers to make accurate decisions during the operation of the infrastructure. However, for various reasons, data from SHM systems may be interrupted or faulty, leading to serious consequences. This study proposes using a Convolutional Neural Network (CNN) combined with Gated Recurrent Units (GRUs) to recover lost data from accelerometer sensors in SHM systems. CNNs are adept at capturing spatial patterns in data, making them highly effective for recognizing localized features in sensor signals. At the same time, GRUs are designed to model sequential dependencies over time, making the combined architecture particularly suited for time-series data. A dataset collected from a real bridge structure will be used to validate the proposed method. Different cases of data loss are considered to demonstrate the feasibility and potential of the CNN-GRU approach. The results show that the CNN-GRU hybrid network effectively recovers data in both single-channel and multi-channel data loss scenarios.

Some isomers of boron-nitrogen doped phenanthrene – DFT treatment

Phenanthrene is an even alternant aromatic hydrocarbon. In adjacent positions simultaneously boron and nitrogen doped phenanthrene and some of their derivatives have found some technological applications. In the present study, isomers of phenanthrene perturbed as mentioned above have been considered within the restrictions of density functional theory at the level of B3LYP/6-311++G(d,p) level. All the isomeric structures presently considered are thermally favored and electronically stable at the standard states. Various structural and quantum chemical data have been collected and discussed, including UV-VIS spectra. Also the NICS (0) data have been obtained for the isomers.

Mathematical foundation of High-Dimensional Data Analysis: Leveraging Topology and Geometry for Enhanced Model Interpretability in AI

One of the most important challenges for modern AI and machine learning is the analysis of high-dimensional data. Traditional methods face serious complications in such cases due to high complexity of datasets: the curse of dimensionality, overfitting, and lack of transparency of model behavior. In this paper, we adopt a novel approach to analyze high-dimensional data; topological and geometric techniques will be exploited, taking advantage of better model interpretability and deeper insights into the structure. Precisely, we discuss Topological Data Analysis, mainly Persistent Homology (Edelsbrunner et al., 2002), which allows the extraction of topological features-like loops and connected components that enable the extracting knowledge about the global structure of data. We also see how some concepts of differential geometry and Riemannian geometry (Do Carmo, 1976) can be used to cast light on manifold data structure lying at the heart of any attempt at modeling intrinsic patterns in high-dimensional spaces. We will review how these mathematical pillars, combined with state-of-the-art techniques for dimensionality reduction like t-SNE, UMAP, Principal Component Analysis, are able to provide interpretable and low-dimensional representations of high-dimensional data that can be used to understand models and make decisions. Case studies are also included, which explain the practical working of these methods in AI systems and show how much complex models can be made transparent using these, especially in domains that are very critical, such as healthcare (Caruana et al., 2015), finance (Chen et al., 2018), and autonomous systems ( Wang et al., 2019). We also discuss some of the difficulties in using these methods for practical applications: computational complexity; the need for large-scale data processing (Bengio et al., 2007); and integration of topological and geometric intuition with the rest of the machine learning pipeline (Zhu et al., 2020). We conclude with possible future directions of research toward fine-tuning these methods and exploring their broader applicability to AI in its quest for more robust, interpretable, and reliable AI models. Given this work, we focus on how linking topology, geometry, and AI bears great promise for solving one of today's critical challenges: model interpretability in high-dimensional data analysis.

Blood Management System Using Java with GUI and Array List-based Database

This research paper presents the development of a Blood Management System (BMS) using Java with a graphical user interface (GUI), ArrayList as a data structure for managing records, and file handling for persistence. The system provides an efficient and user-friendly platform for blood donation management, focusing on functionalities accessible to an administrative user (admin) only. Key features include donor data management (adding, updating, searching, deleting), stock management of blood groups, and essential application controls like login/logout and exit functionalities. This system aims to streamline the management of blood banks by providing quick access to critical information such as donor location, blood group, and stock availability. The source code for the Blood Management System is accessible at https://github.com/BloodBankManagment.

PENGGUNAAN STRATA TAJUK OLEH SATWA BURUNG DI DESA LORULUN KECAMATAN WERTAMRIAN, KABUPATEN KEPULAUAN TANIMBAR

This research aims to determine the use of canopy strata for birds and determine bird activity based on the use of canopy strata in Lorulun Village, Wertamrian District, Tanimbar Islands Regency. Bird watching in Lorulun Village uses the point count method which is made in five plots. To collect vegetation structure data vertically, namely using a canopy stratification approach, the technical implementation is by recording the types of birds found. The data obtained in this research was then analyzed descriptively qualitatively and quantitatively. The results of the research carried out were that there were 20 types of bird species found at the research location with distribution in strata A, there were 7 species of birds, Strata B, there were 7 species of birds, Strata C, there were 8 species of birds, Strata D, there was 1 species of birds. birds, Strata E there are 2 types of bird animals in the use of per strata, while in the use of vegetation strata there are bird species that use more than one stratum, 16 types of bird animals and activity patterns of bird species in each stratum include: rest activities, social activities and activities. Eat. Including the activity patterns of bird species in each strata, including: resting activities, social activities and eating activities.

Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence

The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like healthcare, finance, natural language processing, and robotics. However, challenges such as computational demands, data dependency, and model interpretability remain. This paper discusses the benefits, limitations, and future potential of ML and DL and also provides a review study of a hybrid model makes use of both techniques (machine learning & deep learning) advantages to solve complicated problems more successfully than one could on its own. To boost performance, increase efficiency, or address scenarios where either ML or DL alone would not be able to manage, this approach combines deep learning structures with conventional machine learning techniques.

Concept model and realization of object orientied system with metaprogramming

The purpose of the research is to develop a conceptual model of an object oriented system and implement it using a functional language with metaprogramming support. Methods. A conceptual model of an object oriented system was developed based on a dynamic object system with message passing and dynamic dispatch. The developed object oriented system includes functions for creating and deleting classes, creating and deleting object instances of a class, functions for working with object properties, functions for adding methods to a class and passing messages. Inheritance is also possible, when a child class will have the properties of the parent class and its own properties. Results. The object oriented system was implemented using a set of macros and helper functions. The basic data structure was a hash table with key access capabilities, which was also implemented using macros. When a class is created, an instance constructor is automatically generated, setting the initial values of the object’s fields. Inheritance is implemented as the inclusion of fields of a parent class in a child object. Polymorphism is implemented as dynamic dispatch: when processing a message, a handler method is searched. The resulting object oriented system, together with the hash table module, occupies 97 lines. Conclusion. As a result of the work, a conceptual model of an object oriented system was created and implemented, which includes the main functions of the object oriented programming paradigm. It has been shown that macros can be used to create a compact object oriented system without the need to add any new functions or capabilities to the functional language interpreter.

Structural basis for C-degron selectivity across KLHDCX family E3 ubiquitin ligases.

Specificity of the ubiquitin-proteasome system depends on E3 ligase-substrate interactions. Many such pairings depend on E3 ligases binding to peptide-like sequences - termed N- or C-degrons - at the termini of substrates. However, our knowledge of structural features distinguishing closely related C-degron substrate-E3 pairings is limited. Here, by systematically comparing ubiquitylation activities towards a suite of common model substrates, and defining interactions by biochemistry, crystallography, and cryo-EM, we reveal principles of C-degron recognition across the KLHDCX family of Cullin-RING ligases (CRLs). First, a motif common across these E3 ligases anchors a substrate's C-terminus. However, distinct locations of this C-terminus anchor motif in different blades of the KLHDC2, KLHDC3, and KLHDC10 β-propellers establishes distinct relative positioning and molecular environments for substrate C-termini. Second, our structural data show KLHDC3 has a pre-formed pocket establishing preference for an Arg or Gln preceding a C-terminal Gly, whereas conformational malleability contributes to KLHDC10's recognition of varying features adjacent to substrate C-termini. Finally, additional non-consensus interactions, mediated by C-degron binding grooves and/or by distal propeller surfaces and substrate globular domains, can substantially impact substrate binding and ubiquitylatability. Overall, the data reveal combinatorial mechanisms determining specificity and plasticity of substrate recognition by KLDCX-family C-degron E3 ligases.

RASP v2.0: an updated atlas for RNA structure probing data.

RNA molecules function in numerous biological processes by folding into intricate structures. Here we present RASP v2.0, an updated database for RNA structure probing data featuring a substantially expanded collection of datasets along with enhanced online structural analysis functionalities. Compared to the previous version, RASP v2.0 includes the following improvements: (i) the number of RNA structure datasets has increased from 156 to 438, comprising 216 transcriptome-wide RNA structure datasets, 141 target-specific RNA structure datasets, and 81 RNA-RNA interaction datasets, thereby broadening species coverage from 18 to 24, (ii) a deep learning-based model has been implemented to impute missing structural signals for 59 transcriptome-wide RNA structure datasets with low structure score coverage, significantly enhancing data quality, particularly for low-abundance RNAs, (iii) three new online analysis modules have been deployed to assist RNA structure studies, including missing structure score imputation, RNA secondary and tertiary structure prediction, and RNA binding protein (RBP) binding prediction. By providing a resource of much more comprehensive RNA structure data, RASP v2.0 is poised to facilitate the exploration of RNA structure-function relationships across diverse biological processes. RASP v2.0 is freely accessible at http://rasp2.zhanglab.net/.

Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.

The investigation of molecular interactions between ligands and their target molecules is becoming more significant as protein structure data continues to develop. In this study, we introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural network designed to study protein-ligand interactions based on the 3D structural data of protein-ligand complexes. Unlike 1D protein sequences or 2D ligand graphs, the 3D graph representation offers a more precise portrayal of the complex interactions between proteins and ligands. Research studies have shown that our fusion model, PLA-STGCNnet, outperforms individual algorithms in accurately predicting binding affinity. The advantage of a fusion model is the ability to fully combine the advantages of multiple different models and improve overall performance by combining their features and outputs. Our fusion model shows satisfactory performance on different data sets, which proves its generalization ability and stability. The fusion-based model showed good performance in protein-ligand affinity prediction, and we successfully applied the model to drug screening. Our research underscores the promise of fusion spatial-temporal graph neural networks in addressing complex challenges in protein-ligand affinity prediction. The Python scripts for implementing various model components are accessible at https://github.com/ligaili01/PLA-STGCN.

Mining contextually meaningful subgraphs from a vertex-attributed graph.

Networks have emerged as a natural data structure to represent relations among entities. Proteins interact to carry out cellular functions and protein-Protein interaction network analysis has been employed for understanding the cellular machinery. Advances in genomics technologies enabled the collection of large data that annotate proteins in interaction networks. Integrative analysis of interaction networks with gene expression and annotations enables the discovery of context-specific complexes and improves the identification of functional modules and pathways. Extracting subnetworks whose vertices are connected and have high attribute similarity have applications in diverse domains. We present an enumeration approach for mining sets of connected and cohesive subgraphs, where vertices in the subgraphs have similar attribute profile. Due to the large number of cohesive connected subgraphs and to overcome the overlap among these subgraphs, we propose an algorithm for enumerating a set of representative subgraphs, the set of all closed subgraphs. We propose pruning strategies for efficiently enumerating the search tree without missing any pattern or reporting duplicate subgraphs. On a real protein-protein interaction network with attributes representing the dysregulation profile of genes in multiple cancers, we mine closed cohesive connected subnetworks and show their biological significance. Moreover, we conduct a runtime comparison with existing algorithms to show the efficiency of our proposed algorithm.

Enhancing ID-based Recommendation with Large Language Models

Large Language Models (LLMs) have recently garnered significant attention in various domains, including recommendation systems. Recent research leverages the capabilities of LLMs to improve the performance and user modeling aspects of recommender systems. These studies primarily focus on utilizing LLMs to interpret textual data in recommendation tasks. However, it's worth noting that in ID-based recommendations, textual data is absent, and only ID data is available. The untapped potential of LLMs for ID data within the ID-based recommendation paradigm remains relatively unexplored. To this end, we introduce a pioneering approach called “LLM for ID-based Recommendation” (LLM4IDRec). This innovative approach integrates the capabilities of LLMs while exclusively relying on ID data, thus diverging from the previous reliance on textual data. The basic idea of LLM4IDRec is that by employing LLM to augment ID data, if augmented ID data can improve recommendation performance, it demonstrates the ability of LLM to interpret ID data effectively, exploring an innovative way for the integration of LLM in ID-based recommendation. Specifically, we first define a prompt template to enhance LLM's ability to comprehend ID data and the ID-based recommendation task. Next, during the process of generating training data using this prompt template, we develop two efficient methods to capture both the local and global structure of ID data. We feed this generated training data into the LLM and employ LoRA for fine-tuning LLM. Following the fine-tuning phase, we utilize the fine-tuned LLM to generate ID data that aligns with users’ preferences. We design two filtering strategies to eliminate invalid generated data. Thirdly, we can merge the original ID data with the generated ID data, creating augmented data. Finally, we input this augmented data into the existing ID-based recommendation models without any modifications to the recommendation model itself. We evaluate the effectiveness of our LLM4IDRec approach using three widely-used datasets. Our results demonstrate a notable improvement in recommendation performance, with our approach consistently outperforming existing methods in ID-based recommendation by solely augmenting input data.

Incentivized BiLSTM with Triplet Attention for Predecting Congestion in Network Traffic

In the world of online live video streaming, network traffic congestion poses a serious risk to user experience and service quality. Several factors contributing to the congestion include insufficient bandwidth, an increase in user demand, as well as ineffective data routing. The incapacity of the prediction algorithms to Adjust to modifications in network circumstances and challenges in detecting long-range relationships may restrict their ability to effectively estimate congestion in dynamic contexts when it comes to online streaming video. In order to overcome these constraints, this study created an Incentivized-RF-Triplet ASTM (“Incentivized learning-based triplet attention enabled rat fierce hunting optimized Bidirectional Long Short-Term Memory) for network traffic congestion prediction in online streaming video. A reward” scheme built into the Incentivized learning mechanism pushes the algorithm to prioritize online live video streaming congestion prediction. The sequential structure of network traffic data is handled by the BiLSTM architecture, that is well-known for capturing temporal dependencies. By utilizing the triplet Attention method, the model is better able to identify relevant regions within the input data as well as identify congestion patterns more successfully. The RFSO method incorporates social behavior with selection and searching qualities to further improve the classifier's parameters. Therefore, the characteristics of the model can be tuned more effectively and robustly, improving its effectiveness in congestion detection. The outcomes of the experiment show how well the Incentivized-RF-Triplet ASTM technique works to precisely estimate traffic congestion for the Darpa99week1 dataset. 95.97% accuracy, 96.08% specificity, and 0.22 mean square error are reported.

Genetic Influence of the Brain on Muscle Structure: A Mendelian Randomization Study of Sarcopenia.

The association between brain and sarcopenia has not been clarified. We aim to investigate the causal association between brain structure, function, gene expression and sarcopenia-related traits. All participants were Europeans. GWAS data of Brain Imaging Data Structure (BIDs) was from the UK Biobank. Gene expression in 13 brain regions was acquired from the GTEx Consortium. The sarcopenia-related traits, including appendicular lean mass (ALM), whole body lean mass (WBLM), grip strength and sarcopenia diagnosed by the European Working Group on Sarcopenia in Older People (EWGSOP) or Foundation for the National Institutes of Health (FNIH), were from the IEU website. The inverse variance weighted (IVW), MR Egger, weighted median, weighted mode and Wald ratio methods were used for a two-sample Mendelian randomization (MR) analysis between brain structures and sarcopenia-related traits. The summary-data-based MR (SMR) was used to investigate brain genes causally influencing sarcopenia. We calculated the F-value, odds ratio with 95% CI and p-value. For the sensitivity analysis, the heterogeneity I2 statistic, Cochrane's Q test, Egger's intercept test, MR-PRESSO and the leave-one-out sensitivity test were used. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, protein-protein interaction (PPI), transcription factor interaction prediction and miRNA interaction prediction analysis were used to reveal potential signalling pathways and mechanisms. We included 3144 imaging phenotypes from 8428 participants in BIDs data. One hundred forty-one BIDs were identified to causally influence ALM, 160 BIDs showed significant causal effect on the WBLM, and 86 BIDs showed significant causal effect on grip strength. There were 48 or 32 BIDs causally associated with sarcopenia diagnosed by the EWGSOP or FNIH criteria respectively. After FDR correction, there were 35 BIDs showing causal effect on the ALM, 28 BIDs showing causal effect on the WBLM and 7 BIDs showing causal effect on grip strength (p < 0.05). Twelve to forty-eight genes in different brain regions showed causal effect on all the five sarcopenia-related traits. MMP24-AS1, HLA-DRB6, HLA-DQA2, DDX42, BAG6, NUSAP1, LINC00940, NME1-NME2 and AS3MT in the amygdala region showed detrimental effect on all the five sarcopenia-related traits, whereas HLA-DRB1, HLA-DQB1-AS1 and C6ORF3 showed protective effect (p < 0.05). The gene enrichment analysis indicated these screened genes was mainly enriched in immune-related signalling. We discovered the causal effect of BIDs and brain gene expression on sarcopenia. The positive genes were mainly enriched in immune-related signalling, suggesting an immune-based cross-organ regulation mechanism of brain-muscle axis.

Methods of Balancing Technological Systems of Multiproduct Production

The functioning of the machine-building industry has its specifics, particularly periodic changes in the range (size, configuration, and others) of manufactured products. In addition, it is essential to consider the need to reduce the time spent on the production of each unit. Almost continuous changes in technology, failures in the supply of raw materials, uncoordinated logistics, and many other factors often cause significant and unproductive costs, leading to an increase in the technological stage. The most promising direction to reduce the technological time of manufacturing products by multiproduct enterprises is to reduce the waiting time owing to the uniform distribution of each technological transition according to the state of the available workshop equipment (plant, production area, enterprise). This study proposes a novel model of technological systems that enables the adaptation of technological processes for part manufacturing and comprises data structures that define their technical capabilities. The proposed algorithm facilitates a reduction in downtime and an increase in equipment utilization factor. It is possible to optimize the technological processes that change the structure of each production operation to adapt to the existing technology. Testing this methodology demonstrated a significant increase of 8% in the process utilization rate of machinery.