Model Construction Process Research Articles

ASAS-NANP symposium: Mathematical Modeling in Animal Nutrition: The power of identifiability analysis for dynamic modeling in animal science:a practitioner approach.

Constructing dynamic mathematical models of biological systems requires estimating unknown parameters from available experimental data, usually using a statistical fitting procedure. This procedure is usually called parameter identification, parameter estimation, model fitting, or model calibration. In animal science, parameter identification is often performed without analytic considerations on the possibility of determining unique values of the model parameters. These analytical studies are related to the mathematical property of structural identifiability, which refers to the theoretical ability to recover unique values of the model parameters from the measures defined in an experimental setup and use the model structure as the sole basis. The structural identifiability analysis is a powerful tool for model construction because it informs whether the parameter identification problem is well-posed (i.e., the problem has a unique solution). Structural identifiability analysis is helpful to determine which actions (e.g., model reparameterization, choice of new data measurements, and change of the model structure) are needed to render the model parameters identifiable (when possible). The mathematical technicalities associated with structural identifiability analysis are very sophisticated. However, the development of dedicated, freely available software tools enables the application of identifiability analysis without needing to be an expert in mathematics and computer programming. We refer to such a non-expert user as a practitioner for hands-on purposes. However, a practitioner should be familiar with the model construction and software implementation process. In this paper, we propose to adopt a practitioner approach that takes advantage of available software tools to integrate identifiability analysis in the modeling practice in the animal science field. The application of structural identifiability implies switching our regard of the parameter identification problem as a downstream process (after data collection) to an upstream process (before data collection) where experiment design is applied to guarantee identifiability. This upstream approach will substantially improve the workflow of model construction toward robust and valuable models in animal science. Illustrative examples with different levels of complexity support our work. The source codes of the examples were provided for learning purposes and to promote open science practices.

Research on Welding Quality Traceability Model of Offshore Platform Block Construction Process

Quality traceability plays an essential role in assembling and welding offshore platform blocks. The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry. Currently, quality management remains in the era of primary information, and there is a lack of effective tracking and recording of welding quality data. When welding defects are encountered, it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data. In this paper, a composite welding quality traceability model for offshore platform block construction process is proposed, it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm. By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm, the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems. Furthermore, the model and the quality traceability algorithm are checked by cases in actual working conditions. Verification analyses suggest that the proposed early-warning model for welding quality and the algorithm for optimizing backtracking requests are effective and can be applied to the actual construction process.

Применение интеллектуального анализа процессов в управлении проектами

Deals with the intellectual analysis of processes (Process Mining), which has recently gained popularity in various organizations. It is based on the construction of business process models in a specific area (for example, in the field of project management) based on event logs, providing a more accurate understanding of the actions occurring in business processes for the purpose of their subsequent analysis and improvement. The article defines process mining, event logs, lists the main tasks, algorithms and view models. The authors propose a methodology that can be used in the application of process analysis in the field of project management. The authors also highlight the main business processes in project management, for which it is advisable to build models and analyze them.

Logistic regression in cancer research: A narrative review of the concept, analysis, and interpretation

Logistic regression is a fundamental statistical technique employed in predictive modeling. It transforms a linear combination of input variables into a probability value, allowing the available data to predict the likelihood of an event occurring. Interpretation involves understanding the coefficients of the model, odds ratios, and the impact of predictor variables on the outcome. Various performance metrics, such as the receiver operating characteristic curve, the area under the curve, and R-squared (measure of the percentage of total variation in the dependent variable that is accounted for by the independent variable), aid in assessing the model accuracy. We conducted an extensive search in the PubMed database for relevant articles published in English between January 2013 and August 2023 using the keywords, “logistic regression,” “binary logistic regression,” “logistic regression in cancer research,” “logistic regression analysis,” and “logistic regression result interpretation.” Of the 118 articles retrieved by the original search, we excluded 103 and included 15 in the review; we manually added six more articles considered classic examples of logistic regression and regression statistics. The review encompasses a wide spectrum of cancer research applications, from tumor classification and prognosis to risk assessment and response prediction. The article takes a step-by-step approach, guiding readers through the data preparation, model construction, and interpretation processes in the context of logistic regression.

University Financial Early Warning Model Based on Fuzzy Comprehensive Evaluation

With the reform of the higher education system, schools and universities have transitioned from risk‐free management to current risk management, and financial risk has become a concern that every college and university must address. In recent years, how to effectively forewarn colleges and universities for financial risk, as well as how to prevent and control financial risk at colleges and universities, has been a hot topic. Firstly, this study analyzes the process of model construction, including introducing the basic information of the model, determining the factor level, establishing the weight set and the alternative set, the first‐grade fuzzy comprehensive evaluation, and the second‐grade fuzzy comprehensive evaluation. Secondly, with the financial data of our school in 2020 and 2021 as samples, the fuzzy comprehensive evaluation and early warning model of university financial risk is used to comprehensively evaluate its financial status. Finally, according to the causes and types of financial risks in universities and combined with the analysis results of the fuzzy comprehensive evaluation model of our school’s financial situation, the study gives the relevant measures to prevent and control the financial risks in universities and carries on a detailed analysis and explanation of each measure. The construction and implementation of a financial early warning model in colleges and universities can effectively avoid and reduce the financial risk of colleges and universities, which has certain research value.

Theory and practice of organizational and technological design in construction

To achieve the effectiveness of the integrated process of organizational and technological design in construction, it is necessary to use the latest achievements of science in this area, taking into account the experience of their implementation. The main function of organizational and technological design is to model the implementation of construction processes (taking into account the identification and analysis of previous work), determine the options for connections and their priority, and, ultimately, select the most rational organizational and technological solutions that ensure the readiness of facilities and the construction organization for implementation. works. This choice is carried out with the help of a multi-criteria, multi-level assessment of such decisions based on computational and information modeling of construction processes, the choice of organization methods and technology for the production of works, the creation of an information base to provide construction with all types of resources, using information and software and the development of organizational and technological documentation. In Russia, in recent years, project management in the construction industry has become increasingly widespread. This is due to the modern processes of globalization, development and the emergence of common properties of the organizational structures of Russian and foreign construction companies to achieve the effectiveness of the functioning of such organizations. The unification of the target functions of construction organizations makes it possible to obtain a comprehensive, global management structure for efficient construction enterprises, which leads to the development of project management uniformity and the necessary development of organizational and technological design as a necessary stage in the construction of residential, social, cultural or other facilities.

Modeling of the stability of elements of attached equipment of the rock-destroying working item of the construction machine

The materials of the article are devoted to the issue of simulation modeling of the stability of the elements of the attached equipment of the rock-crushing work equipment of the construction machine for earthworks. Today, an urgent task is to perform earthworks in a short time with mobile machines. Such a need exists in the formation of trenches, in particular, in the laying of main pipelines. A feature of the need for rapid formation of trenches for the laying of main pipelines is the mobility of the basic equipment. In order to create competitive technical solutions in the field of construction mechanization, in particular, when performing earthworks, it is necessary to perform project work on improving existing solutions and creating new ones using simulation modeling methods.
 The developed rock-destroying working body for earthworks works in conditions of dynamic destruction. Its design is developed taking into account the possibility of using it on existing equipment. Thus, it is proposed to use the base machine of the 2nd power class with a diameter of a disc rock-destroying organ of 600 mm when it works in soils of category IV.
 To implement the synthesis of the drive of the rock-crushing working body with the existing equipment of the base machine, a modular frame structure is proposed, which provides the functions of "fixation", "immersion", "movement" of the working equipment.
 A computer geometric model of the attached equipment was created, in the formation of which the approaches of form formation with implicitly expressed dimensions and parameters of geometric arrays were used, which allows to optimize the design process taking into account feedback as a result of simulation modeling of processes and experimental studies, and to use it in the formation of an information model construction process during earthworks.

Research on a Regional Landslide Early-Warning Model Based on Machine Learning—A Case Study of Fujian Province, China

China’s landslide disasters are serious, and regional landslide disaster early-warning is one of the important means of disaster prevention and mitigation. The traditional regional landslide disaster early-warning model, however, is limited by the complex landslide induction mechanism, limited data accumulation, and insufficient big data analysis methods, and has problems such as limited early-warning accuracy and insufficient refinement. In this paper, a machine learning method was introduced into the field of regional landslide disaster warning. From the model construction process of training sample-set construction, sample learning and training, model parameter optimization, model preservation, warning output, and so on, a method for constructing a regional landslide early-warning model based on machine learning was systematically proposed. In the sample learning and training, 80% of the training sample-set was used as the training set, and 20% was used as the test set for five-fold cross validation. The Bayesian Optimization algorithm was used to optimize the model parameters, and the accuracy, ROC curve, and AUC value were used to verify the model accuracy and model generalization ability. With China’s Fujian province as an example, based on nine years of geological and meteorological data (2010–2018), geological environment factors, factors of hazard-affected bodies and historical disaster situations, and rainfall-induced factors in four categories, a total of 26 indicators were used as input characteristic parameters. Six machine learning algorithms were adopted to improve model training; the results showed that the Random Forest algorithm performed the best, giving an accuracy of 92.3%, and was the model with the best generalization ability (AUC was 0.955). The second best was the Artificial Neural Network model, with an accuracy of 0.937 and an AUC of 0.935. Next were the Nearest Neighbor model, the Logistic Regression model, and the Support Vector Machine; the poorest results were from the Decision Tree model. Finally, the typical rainfall-type landslide disaster process in Fujian Province was selected as an example to verify the Random Forest algorithm model. The results showed that compared with the early-warning results of the original explicit statistical model, the hit rate of the new model was 6 times, or equal to that of the original model, and the landslide density in the early-warning area of the new model was 1.6–1.7 times that of the original model. Preliminary verification showed that the new model based on the Random Forest method has obvious advantages, a higher hit rate and a smaller warning area, and can achieve more accurate warnings. The follow-up will continue to track the new landslide disaster situation in the study area and carry out model verification and correction.

Multi-objective interval portfolio optimization modeling and solving for margin trading

Margin trading is an important part of stock market. Compared with traditional channel business, it brings excess profits to both financial institutions and investors. As an important optimization problem in the field of finance, portfolio optimization with the purpose of spreading risks and improving returns is the essence of margin trading. In this paper, aiming at achieving the portfolio optimization for multi-period margin trading, we propose a multi-objective interval portfolio optimization method, including model construction and optimization. Based on real business rules, the process of model construction consists of three steps. First, an interval estimation method of securities’ returns and risks is designed according to market feedback to keep portfolios remaining robust under the market fluctuation. Second, a multi-objective problem of maximizing return and minimizing risk is constructed to seek portfolios with balanced risks and returns. Third, a rolling investment mechanism is formed based on the portfolio’s actual return of each period to maintain effective continuous investment. For the model optimization, a multi-objective interval optimization evolutionary algorithm with knee based decision-making is developed. It obtains knee solutions for different risk segments to meet the investment needs of investors with different risk preferences, so that their investment decisions do not need to rely on quantitative expression of their preferences. Furthermore, adapting to the interval model, an interval optimization method is developed to enhance the robustness of knee points. Experimental results on several benchmark problems and the real portfolio optimization problem demonstrate the superiority of the proposed model and algorithm.

Performance of quantum kernel on initial learning process

For many manufacturing companies, the production line is very important. In recent years, the number of small-quantity, high-mix products have been increasing, and the identification of good and defective products must be carried out efficiently. At that time, machine learning is a very important issue on shipping inspection using small amounts of data. Quantum machine learning is one of most exciting prospective applications of quantum technologies. SVM using kernel estimation is one of most popular methods for classifiers. Our purpose is to search quantum advantage on classifier to enable us to classifier in inspection test for small size datasets. In this study, we made clear the difference between classical and quantum kernel learning in initial state and propose analysis of learning process by plotting ROC space. To meet the purpose, we investigated the effect of each feature map compared to classical one, using evaluation index. The simulation results show that the learning model construction process between quantum and classical kernel learning is different in initial state. Moreover, the result indicates that the learning model of quantum kernel is the method to decrease the false positive rate (FPR) from high FPR, keeping high true positive rates on several datasets. We demonstrate that learning process on quantum kernel is different from classical one in initial state and plotting to ROC space graph is effective when we analyse the learning model process.

Soil organic matter gain by reduced tillage intensity: Storage, pools, and chemical composition

Soil organic matter (SOM) loss due to intensive cultivation is the focus of studies on climate change and food security. Dropping tillage intensity has been widely reported as a potential tool for SOM increase; however, the chemical composition, storage mechanisms, and vertical distribution of SOM gain are not fully understood, especially in calcareous soils. This study aimed to analyze the increased SOM conditions among pools and depths in an eighteen-year-long field experiment comparing SOM under conventional moldboard plowing (PT), deep cultivation (DC), and no tillage (NT) systems on a base-saturated Endocalcic Chernozem (Loamic). Soil samples were collected from the 0–10 cm and 30–40 cm soil layers and divided into mineral phase-associated organic matter (<63 µm, stable pool; MPAOM) and aggregate related organic matter (>63 µm, labile pool). The aggregate related organic matter was further divided into particulate organic matter (POM, <1.0 g cm-3) and aggregates-associated organic matter (AAOM, >1.0 g cm-3). Results indicated an overall increase in soil organic carbon (SOC) concentration in the topsoil in the order of PT<DC<NT. The surplus did not manifest as POM but as either MPAOM or AAOM fractions. This increase likely resulted from the surplus above-ground plant residue input, as the SOC content in the 30–40 cm layer did not change. The additional SOM, stabilized in the soil, did not affect SOM composition between depths and fractions, suggesting preferential SOM binding by the fine fraction and aggregates. This suggests preferential binding of the more aromatic and less complex SOM to the fine fraction, even for the surplus SOM in recent years. This fractionation maintains or even increases the difference between organic carbon pools (soil fractions) in terms of SOM composition. In addition, vertical differentiation as a result of tillage intensity mitigation was established by an increase in the stratification of aromaticity. These results suggest the key role of dissolved SOM movement in the profile as a potential driving force for the differentiation of aromaticity with depth. The results also emphasize the role of local conditions on OM composition changes, establishing the complexity of the process and difficulties of holistic model construction.

Updating studies of past life and ancient ecologies using defossilized organismal proxies

The fossil record represents the world’s largest historical dataset of biodiversity. However, the biomechanical and ecological potential of this dataset has been restricted by various unique barriers obstructing experimental study. Fossils are often partial, modified by taphonomy, or lacking modern analogs. In the past, these barriers confined many studies to descriptive and observational techniques. Fortunately, advances in computer modeling, virtual simulations, model fabrication, and physical experimentation now allow ancient organisms and their biomechanics to be studied like never before using “Defossilized Organismal Proxies” (DOPs). Although DOPs are forging new approaches integrating ecology, evolutionary biology, and bioinspired engineering, their application has yet to be identified as a unique, independent methodological approach. We believe that techniques involving DOPs will continue revolutionizing paleontology and how other related fields interact with and draw insights from life’s evolutionary history. As the field of paleontology moves forward, identifying the framework for this novel methodological development is essential to establishing best practices that maximize the scientific impact of DOP-based experiments. In this perspective, we reflect on current literature innovating the field using DOPs and establish a workflow explaining the processes of model formulation, construction, and validation. Furthermore, we present the application of DOP-based techniques for non-specialists and specialists alike. Accelerating technological advances and experimental approaches present a host of new opportunities to study extinct organisms. This expanding frontier of paleontological research will provide a more holistic view of ecology, evolution, and natural selection by breathing new life into the fossil record.

A systematic review of predictive models for hospital-acquired pressure injury using machine learning.

To summarize the use of machine learning (ML) for hospital-acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction process of ML models to provide references for establishing high-quality ML predictive models. As an adverse event, HAPI seriously affects patient prognosis and quality of life, and causes unnecessary medical investment. At present, the performance of various scales used to predict HAPIs is still unsatisfactory. As a new statistical tool, ML has been applied to predict HAPIs. However, its performance has varied in different studies; moreover, some deficiencies in the model construction process were observed in each study. Systematic review. Relevant articles published between 2010-2021 were identified in the PubMed, Web of Science, Scopus, Embase and CINHAL databases. Study selection was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis guidelines. The quality of the included articles was assessed using the prediction model risk of bias assessment tool. Twenty-three studies out of 1793 articles were considered in this systematic review. The sample size of each study ranged from 149-75353; the prevalence of pressure injuries ranged from 0.5%-49.8%. ML showed good performance for HAPI prediction. However, some deficiencies were observed in terms of data management, data pre-processing and model validation. ML, as a powerful decision-making assistance tool, is helpful for the prediction of HAPIs. However, existing studies have been insufficient in terms of data management, data pre-processing and model validation. Future studies should address these issues to establish ML models for HAPI prediction that can be widely used in clinical practice. This review highlights that ML is helpful in predicting HAPI; however, in the process of data management, data pre-processing and model validation, some deficiencies still need to be addressed. The ultimate goal of integrating ML into HAPI prediction is to develop a practical clinical decision-making tool. A complete and rigorous model construction process should be followed in future studies to develop high-quality ML models that can be applied in clinical practice.

A six-factor asset pricing model of China's stock market from the perspective of institutional investors' dominance

PurposeChina's stock market, which serves as an example of emerging markets, is steadily maturing in the context of globalization. In order to analyze the pricing mechanism of China's stock market, this paper builds a six-factor model to address the market features that are structurally efficient but not entirely efficient.Design/methodology/approachThis study updates the Fama–French factor model's construction process to account for the unique features of China's stock market before creating a model that incorporates size, volume, value, profitability, and profit-income factors based on institutional investors' trading behavior and research preferences. The SWS three-tier sector stock index's monthly and quarterly data for the years 2016–2021 are used as samples for this study.FindingsThe results imply that China's stock market is structurally efficient and exhibits high levels of rationality in the region dominated by institutional investors. Specifically, big-size and high-volume portfolios that perform well in terms of liquidity can receive trading premiums. Growth-style sectors prevail at present, and investing in sectors with strong profitability and reliable financial reporting data can produce better returns.Practical implicationsThe research on China's stock market can be extended to improve the understanding of the development process of similar emerging markets, thereby promoting their improvement. To enhance the development of emerging markets, the regulators should attach great importance to the role of local institutional investors in driving the market to maturity. It is crucial to adopt a structured approach to examine the market pricing mechanism throughout the middle stage of the transition from developing to mature markets.Originality/valueThis study offers a structured viewpoint on asset pricing in growing emerging markets by combining the multi-factor pricing model approach with behavioral finance theories.

NONLINEAR REGRESSION MODELS FOR ESTIMATING THE DURATION OF SOFTWARE DEVELOPMENT IN JAVA FOR PC BASED ON THE 2021 ISBSG DATA

Context. The problem of estimating the duration of software development in Java for personal computers (PC) is important because, first, failed duration estimating is often the main contributor to failed software projects, second, Java is a popular language, and, third, a personal computer is a widespread multi-purpose computer. The object of the study is the process of estimating the duration of software development in Java for PC. The subject of the study is the nonlinear regression models to estimate the duration of software development in Java for PC.&#x0D; Objective. The goal of the work is to build nonlinear regression models for estimating the duration of software development in Java for PC based on the normalizing transformations and deleting outliers in data to increase the confidence of the estimation in comparison to the ISBSG model for the PC platform.&#x0D; Method. The models, confidence, and prediction intervals of nonlinear regressions to estimate the duration of software development in Java for PC are constructed based on the normalizing transformations for non-Gaussian data with the help of appropriate techniques. The techniques to build the models, confidence, and prediction intervals of nonlinear regressions are based on normalizing transformations. Also, we apply outlier removal for model construction. In general, the above leads to a reduction of the mean magnitude of relative error, the widths of the confidence, and prediction intervals in comparison to nonlinear models constructed without outlier removal application in the model construction process.&#x0D; Results. A comparison of the model based on the decimal logarithm transformation with the nonlinear regression models based on the Johnson (for the SB family) and Box-Cox transformations as both univariate and bivariate ones has been performed.&#x0D; Conclusions. The nonlinear regression model to estimate the duration of software development in Java for PC is constructed based on the decimal logarithm transformation. This model, in comparison with other nonlinear regression models, has smaller widths of the confidence and prediction intervals for effort values that are bigger than 900 person-hours. The prospects for further research may include the application of bivariate normalizing transformations and data sets to construct the nonlinear regression models for estimating the duration of software development in other languages for PC and other platforms, for example, mainframe.

Evaluation of Coalbed Methane Content by Using Kernel Extreme Learning Machine and Geophysical Logging Data

Accurate evaluation of coalbed methane (CBM) content plays a momentous role in the identification and efficient development of favorable exploitation blocks of CBM resources, but there are still many technical challenges in the exploration and development of onshore CBM fields. With the development and application of geophysical logging technology, using geophysical logging data to predict the gas content of CBM reservoirs has been proven to be an effective and feasible solution. However, the complex logging response of the CBM reservoirs makes it difficult to characterize the relationship between the gas content and the logging curve response by a simple linear relationship. In this paper, kernel extreme learning machine (KELM), a machine learning method, is combined with the geophysical logging data to predict the vertical variation curve of gas content in CBM wells. In this paper, the laboratory data on coal rock gas content from 12 CBM wells in the Southern Shizhuang block are selected, and a CBM content prediction model based on the KELM method is constructed by selecting the log curves, combining cross-validation and grid-seeking to determine the hyperparameters, and validating the prediction model using the test dataset and a new well in the same block. The application of the model on the test dataset was remarkable, and the vertical variation of CBM content obtained by applying it to the new well was consistent with the laboratory results, which proved the correctness and generalizability of the model. The results of this paper show that the CBM content evaluation model based on the KELM method and geophysical logging data is applicable to the 3# coal seam in the target block and can be used to predict the vertical CBM content of CBM wells; compared with the extreme learning machine (ELM) method and the backpropagation neural network (BPNN) method, the KELM method requires fewer hyperparameters to be explored when constructing the CBM content evaluation model, and the model construction is simple and has high prediction accuracy. At the same time, the CBM content model constructed by the KELM method differs for different blocks, coal seams at different depths, and different response ranges of geophysical logging data. The construction of a CBM content prediction model using the KELM method and logging curves is an effective means of characterizing CBM resources, and the model construction process and evaluation criteria studied in this paper can be used to help other blocks evaluate the CBM content, providing guidance for further exploration and development of CBM fields with practical application.

PRINCIPLES OF BUILDING AN INFORMATION MODEL OF A SEPARATE CONSTRUCTION PROCESS

The world practice of construction processes information modeling is considered. The principles of constructing an information model of a separate construction process, isolated from the system of a calendar or network schedule for the creation of construction products, are proposed. The features of technological processes of construction and installation works are highlighted depending on the level of mechanization. The classification of technological processes according to the criterion “degree of mechanization” is given. The necessity of using the work front as the main element of the information model of a separate construction process is proved. The structure of the information model of a separate construction process has been developed, which is: a source of initial data, a computing device and a description of the result. The boundaries of the application of the separate construction process information model are defined. The prospects of scientific research and the features of information modeling applied to individual construction processes are shown.

A CONTINUAL IMPROVEMENT COST OF QUALITY MODEL FOR SERVICES

ABSTRACT A continual improvement cost of quality (CICOQ) model for services is developed. Before developing the model, the main potential targets of continual improvement efforts in any service organization are presented, explained, and diagrammatically illustrated. The purpose of this is to assist service organizations in identifying and selecting the next and most effectual target of improvement at the end of each continual improvement cycle. The steps of the PDSA cycle and their corresponding action items are also presented. Also presented are the quality management tools that can be utilized when implementing some of the action items. Additionally, the major differences among the action items of some of the PDSA cycle for services and the PDSA cycle for manufacturing are highlighted. Furthermore, our PDSA cost items are presented and classified into Prevention-Appraisal-Failure (PAF) categories. Finally, based on the PAF classification, our model construction process and CICOQ model for services are presented. Unlike the quality-level axis of the COQ and CICOQ models for manufacturing, which follow the ratio scale format, the quality-level axis of our CICOQ model for services follows the ordinal scale format. This is due to the intangibility of services and the tangibility of products. To the best of our knowledge, the model is the first CICOQ model ever developed for planning and implementing continual improvement programmes in services and for assessing, managing, monitoring, and controlling quality-related costs in service organizations. Keywords Cost item, customer benefit package, PAF classification, PDSA cycle for services, quality levels, service delivery system.

Modeling of Gas Flows in Underground Gas Storage Facilities

The object of this study is the underground gas storage facilities of Ukraine, with the aim of developing a systematic mathematical model to describe the flow of gas in complex underground gas storage systems. This model will enable to development of effective methods and algorithms for calculating the modes of operation of such systems. The proposed systematic mathematical model connects the model of the structure of underground gas storage in terms of graph theory with variable topology and various mathematical models of gas flows in facilities with concentrated and distributed parameters. We set the models of gas flows in UGS facilities in the form of analytical relations, differential equations and algorithmically described systems. In order to automate the process of model construction (for given boundary conditions), we developed a system of formation, analysis and synthesis of graphs for underground gas storage facilities, as well as a set of information and mathematical models of facilities.

Effect of Anisotropic Electrical Conductivity Induced by Fiber Orientation on Ablation Characteristics of Pulsed Field Ablation in Atrial Fibrillation Treatment: A Computational Study.

Pulsed field ablation (PFA) is a promising new ablation modality for the treatment of atrial fibrillation (AF); however, the effect of fiber orientation on the ablation characteristics of PFA in AF treatment is still unclear, which is likely an essential factor in influencing the ablation characteristics. This study constructed an anatomy-based left atrium (LA) model incorporating fiber orientation and selected various electrical conductivity and ablation targets to investigate the effect of anisotropic electrical conductivity (AC), compared with isotropic electrical conductivity (IC), on the ablation characteristics of PFA in AF treatment. The results show that the percentage differences in the size of the surface ablation area between AC and IC are greater than 73.71%; the maximum difference in the size of the ablation isosurface between AC and IC at different locations in the atrial wall is 3.65 mm (X-axis), 3.65 mm (Z-axis), and 4.03 mm (X-axis), respectively; and the percentage differences in the size of the ablation volume are greater than 6.9%. Under the condition of the pulse, the amplitude is 1000 V, the total PFA duration is 1 s, and the pulse train interval is 198.4 ms; the differences in the temperature increase between AC and IC in LA are less than 2.46 °C. Hence, this study suggests that in further exploration of the computational study of PFA in AF treatment using the same or similar conditions as those used here (myocardial electrical conductivity, pulse parameters, and electric field intensity damage threshold), to obtain more accurate computational results, it is necessary to adopt AC rather than IC to investigate the size of the surface ablation area, the size of the ablation isosurface, or the size of the ablation volume generated by PFA in LA. Moreover, if only investigating the temperature increase generated by PFA in LA, adopting IC instead of AC for simplifying the model construction process is reasonable.