Python Language Research Articles

Magnetic resonance imaging-based deep learning for predicting subtypes of glioma

PurposeTo explore the value of deep learning based on magnetic resonance imaging (MRI) in the classification of glioma subtypes.MethodsThis study retrospectively included 747 adult patients with surgically pathologically confirmed gliomas from a public database and 64 patients from our hospital. Patients were classified into IDH-wildtype (IDHwt) (490 cases), IDH-mutant/1p19q-noncodeleted (IDHmut-intact) (105 cases), and IDH-mutant/1p19q-codeleted (IDHmut-codel) (216 cases) based on their pathological findings, with the public database of patients were divided into training and validation sets, and patients from our hospital were used as an independent test set. The models were developed based on five categories of preoperative T1-weighted, T1-weighted gadolinium contrast-enhanced, T2-weighted and T2-weighted fluid-attenuated inversion recovery (T1w, T1c, T2w and FLAIR) magnetic resonance imaging (MRI) of four sequences and mixed imaging of the four sequences, respectively. The receiver operating characteristic curve (ROC), area under the curve (AUC) of the ROC were generated in the jupyter notebook tool using python language to evaluate the accuracy of the models in classification and comparing the predictive value of different MRI sequences.ResultsIDHwt, IDHmut-intact and IDHmut-codel were the best classified in the model containing only FLAIR sequences, with test set AUCs of 0.790, 0.737 and 0.820, respectively; and the worst classified in the model containing only T1w sequences, with test set AUCs of 0.621, 0.537 and 0.760, respectively.ConclusionWe have developed a set of models that can effectively classify glioma subtypes and that work best when only the FLAIR sequence model is included.

Software Implementation of the Epps-Pulley Criterion in Matlab Modeling Environment

Purpose. Modeling systems and programming platforms provide broad opportunities for the use of statistical tools in research activities. Since the normal distribution is one of the most common distribution laws, the criterion for checking the sample for normality is in high demand among statistical assessment tools, among which the Epps-Pulley test has the status as one of the most powerful tests to check the deviation of the distribution from the normal one. There are a number of implementations of this test in the R and Python languages. However, this test is not implemented in one of the most popular Matlab modeling software. Thus, the purpose of this study is to develop a software implementation of the Epps-Pulley criterion in the Matlab environment and verify the correctness of the performed calculations.Materials and methods. We implemented the calculation of EppsPulley statistics by two methods – classical, using cycles, and matrix-vector, using linear algebra operations. The classical method requires calculating the intermediate values necessary to obtain the criterion statistics using two independent cycles, the second cycle being a double one, in which one cycle is nested within the other. The matrix-vector method requires fewer lines of code by performing calculations using linear algebra operations on matrices and vectors. We obtained critical statistical values for the sample size ranging from 8 to 1000 elements with two-dimensional linear interpolation of tabular values. We used an approximation by a beta function of the third kind for a sample of over 1000 elements.Results. An assessment of the computational efficiency of the methods showed that the cyclic approach is about three times higher than the matrix-vector approach in terms of consumed time, which is presumably associated with the processing of insignificant elements in triangular matrices when performing component-by-component operations. The correctness of the software implementation of the Epps-Pulley criterion was tested on several examples, which confirmed the compliance of the calculated values of the criterion statistics, as well as the critical values of statistics, with known data. We carried out a criterion statistical evaluation based on the empirical values of the error of the first kind. We obtained the error values correspondence to the specified significance levels. We performed comparative estimates of the Epps-Pulley test with the Anders-Darling and Shapiro-Wilk tests in terms of the criterion empirical power. Evaluation results are tabulated. The software implementation of the Epps-Pulley test is published on the MATLAB Central Internet resource and is available for free use.

New Approach: Tabular Fuzzy Arithmetic of the LR Type by Jomatopfe

This paper presents a fuzzy approach of LR types, also called LR-type tabular fuzzy arithmetic capable of handling or computing simultaneously the kernels and supports of trapezoidal fuzzy numbers, instead of doing it separately (Advantage of this approach) in order to minimize the tedious steps of alpha-cut based approaches. On the theoretical level, the aim of this article is to explain in a concise and clear manner some basic concepts of fuzzy logic that seem to continue to complicate authors and readers (researchers) in this field, given the important place of this theory in Artificial Intelligence today. Some user interfaces have been created in this article, with the python language, in order to automatically calculate certain results; and especially to minimize the calculation time, in particular of membership degrees, kernels and supports. Trapezoidal fuzzy numbers are transformed into LR form in order to allow a comparative study between the alpha-cut approach with the Jomatopfe LR-type tabular fuzzy arithmetic presented in this paper. We successfully demonstrated the transition from the trapezoidal fuzzy form to the LR type form and vice versa. After a comparative study between the alpha-cut approach and the tabular fuzzy arithmetic of the LR type, Jomatopfe's LR type arithmetic significantly reduced the complexity of the calculation processes compared to classical methods, which require distinct steps for each element. This fuzzy tabular altimetry is not to be confused with other fuzzy tables operations on membership degrees.

ANALISIS KOMPARATIF BAHASA PEMROGRAMAN UNTUK PEMULA IMPLEMENTASI SISTEM PENDUKUNG KEPUTUSAN DENGAN METODE SIMPLE ADDITIVE WEIGHTING

Selecting the right programming language for beginners is a significant challenge in software development. This research aims to analyze a comparison of programming languages Python, JavaScript, Java, C++, and PHP using the Simple Additive Weighting (SAW) method to provide an objective recommendation for beginners. The evaluation is based on six key criteria: ease of learning, job opportunities, execution speed, versatility, community & ecosystem, and maintenance cost, with weights assigned according to the importance of each criterion. Data were obtained through literature review, user surveys, and expert judgment analysis. The results show that Python received the highest preference score (1.13), followed by JavaScript (1.08), Java (1.18), C++ (1.27), and PHP (1.34). Python excelled in terms of ease of learning and versatility, while JavaScript led in community and ecosystem aspects. The study concludes that Python is the most suitable programming language for beginners, especially for foundational learning and general application development. These findings have implications for the development of programming curricula and recruitment strategies in the information technology industry.

Importance of Glomerular Filtration Rate Determination and its Role in Drug-Dose Adjustments with Special Reference to Oncology

Glomerular filtration Rate (GFR) measured in steady state is the best marker of kidney function. Glomerular filtration rate can be determined using exogenous markers like inulin, iothalamate, or iohexol (mGFR), or estimated using endogenous markers like creatinine and cystatin C (eGFR). Several equations are available, CKD-EPI equation is the recommended equation as per current guidelines. CKD-EPI creatinine equation refit (without the race variable) – 2021, and CKD-EPI creatinine - cystatin C equation refit (without the race variable)-2021 are the most recent recommendations from the National Kidney Foundation and the American Society of Nephrology. Dose modifications of many drugs are needed in kidney dysfunction. In chronic kidney disease, as the kidney function is stable, determination of GFR is more accurate and can be used to modify the drug doses. Since the kidney function is not stable in acute kidney injury, determination of GFR is more difficult and it is not accurate. This makes it difficult to use it for determining dose modifications of drugs. Therapeutic drug level monitoring, and adjusting the dose based on pharmacokinetics/pharmacodynamics knowledge of the drug could be helpful in such cases. In oncology, accurate GFR measurement is crucial for adjusting the doses of chemotherapeutic agents to avoid toxicity and ensure efficacy, as cancer patients often have compromised kidney function due to the disease or its treatment. In this manuscript, we review all these aspects to provide a comprehensive understanding of GFR measurement and its implications in drug-dose adjustments, with a particular focus on how accurate GFR assessment is essential for optimizing chemotherapy dosing in oncology patients to minimize toxicity and maximize therapeutic efficacy. Frequently, medical professionals need to depend on online calculators to get the value of eGFR, as these equations can be complicated. Scientific calculators can be used too, but that is not a common practice in nephrology clinics. The authors have shown in this paper how to use Microsoft Excel to create personal calculators, so that eGFR may be calculated by medical professionals and dialysis staff even when internet is not available due to institutional policies or otherwise. The ‘Appendix’ section given at the end of the paper shows how to create these personal calculators in Microsoft excel. Calculators for CKD-EPI equations can also be developed in excel, however the calculations are more complex, so I prefer using the programming language python to develop these calculators. In this paper, the authors have shown only the excel based calculators for CG equation and MDRD-4 (re-expressed) equation.

การพัฒนาแชทบอทเพื่อส่งเสริมการท่องเที่ยวชุมชนช่างศิลป์จังหวัดนครปฐม

The objectives of this research are to 1) develop an information system to promote tourism through the Chatbot application system in tourist attractions of the artisan community in Nakhon Pathom Province 2) study tourists' satisfaction with the Chatbot with Python language to promote tourism through the application system. LINE Chatbot in Nakhon Pathom Province The data was collected from a sample of 423 tourists and members of online social groups, which is a specific sample group. Statistics used in data analysis include frequency, percentage, mean, and standard deviation. The research results found that the study of tourist behavior in using chatbots in 4 areas: meeting needs, being able to work. according to function, ease of use, and efficiency It was found that tourists' satisfaction in using chatbots was at a high level, with an average of 4.20. The aspect that tourists are most satisfied with is the ease of use. has an average of 4.23 in terms of meeting the needs and system performance They have an average of 4.20 and the ability to work according to their duties. has an average of 4.18, respectively.

Towards a more objective and high-throughput spheroid invasion assay quantification method

Multicellular spheroids embedded in 3D hydrogels are prominent in vitro models for 3D cell invasion. Yet, quantification methods for spheroid cell invasion that are high‐throughput, objective and accessible are still lacking. Variations in spheroid sizes and the shapes of the cells within render it difficult to objectively assess invasion extent. The goal of this work is to develop a high-throughput quantification method of cell invasion into 3D matrices that minimizes sensitivity to initial spheroid size and cell spreading and provides precise integrative directionally-dependent metrics of invasion. By analyzing images of fluorescent cell nuclei, invasion metrics are automatically calculated at the pixel level. The initial spheroid boundary is segmented and automated calculations of the nuclear pixel distances from the initial boundary are used to compute common invasion metrics (i.e., the change in invasion area, mean distance) for the same spheroid at a later timepoint. We also introduce the area moment of inertia as an integrative metric of cell invasion that considers the invasion area as well as the pixel distances from the initial spheroid boundary. Further, we show that principal component analysis can be used to quantify the directional influence of a stimuli to invasion (e.g., due to a chemotactic gradient or contact guidance). To demonstrate the power of the analysis for cell types with different invasive potentials and the utility of this method for a variety of biological applications, the method is used to analyze the invasiveness of five different cell types. In all, implementation of this high‐throughput quantification method results in consistent and objective analysis of 3D multicellular spheroid invasion. We provide the analysis code in both MATLAB and Python languages as well as a GUI for ease of use for researchers with a range of computer programming skills and for applications in a variety of biological research areas such as wound healing and cancer metastasis.

RECOGNIZING A TRANSFORMER OIL GAUGE IN AN IMAGE USING A PRE-TRAINED MOBILENETV2 FPN LITE MODEL

Oil gauges allow you to monitor the amount of oil in transformers, which is necessary to prevent its overheating and related negative consequences, such as deterioration of the insulating and cooling properties of the oil, increase in pressure inside the device and possible accidents. Most of the oil pointers do not have an electric output to indicate the measurement results, which complicates the process of reading the readings of the device. One of the options for solving this problem may be the use of intelligent methods of image analysis, which have recently been actively used in many scientific and practical fields. The purpose of the study was to develop and evaluate the effectiveness of an intelligent system for determining the presence of an arrow transformer oil gauge in an image using a pre-trained MobileNetV2 FPN Lite model. Materials and methods. In the course of the study, theoretical and empirical methods were used. The study considers the pointer oil gauges of the MS-1 and MS-2 models. To develop a machine learning model for image recognition, the Python language was chosen, the open-source TensorFlow Object Detection API platform, which is part of TensorFlow, was used, which implements the creation, training, and testing of neural network object detection models. Research results. The process of generating a training set using augmentation methods and the process of data labeling are described. The process of selecting hyperparameters for model training was considered and analyzed. The hyperparameter “number of neurons” was used with the same value as in the pre-trained model. ReLU was chosen as the activation function, and the momentum optimization method was used. The batch size was 4, and the number of epochs was assumed to be 2000. The Accuracy, Recall, Precision, and F1-score metrics were calculated, and the quality and efficiency of the resulting model were evaluated based on them. The model did a good job of avoiding false positives, which confirms the high value of the Precision = 1.0 metric. However, it incorrectly recognized a significant number of non-oil gauge objects, as reflected by the low Recall = 0.687 value. The reason for such indicators of the Recall and F1-score metrics is the imbalance of data for training. To eliminate this drawback in further research, it is necessary to prepare a larger number of unique images of this device, displaying it at different angles of inclination, in different weather conditions, in different lighting. Conclusions. A model of an intelligent system for determining the presence of a transformer oil gauge in an image using a pre-trained MobileNetV2 FPN Lite model has been built. The model was trained to determine the device and its boundaries on the image, but the accuracy of the model is insufficient for its use in real conditions. Increasing the training, validation and test sample size can solve this problem. In general, the proposed method of recognizing transformer oil indicator on the image has shown its performance.

Фармацевтический справочник преимуществ лекарственных препаратов.

Introduction. Since each drug from one pharmacological group may have its own unique and clinically significant characteristics, there is a need to develop a program that would reflect the advantages of these drugs. Objective. To develop an information system that contains brief information about the advantages of various drugs, with the ability to access for healthcare professionals. Materials and methods. Information on the advantages of some drugs was collected. Microsoft Office Excel software, Google disk cloud storage, PyCharm software development environment, and Python language were used. Results and discussion. A chatbot was created in the Telegram messenger, which provides information about the unique and clinically significant characteristics of these drugs. Conclusion. The developed chatbot can be used by healthcare professionals to obtain information about the advantages of drugs. At the same time, it needs to be further filled with information and brought to the state level.

Searching for oil storage tank layout by solving NP-complete combinatorial optimisation problem

In the process of designing a new tank farm or reconstructing an old one, we often face the task of finding the optimal layout of hydrocarbon storage tanks. It is necessary to comply with both the customer's wishes and the requirements of normative documents regarding restrictions in the location of petroleum product storage facilities, and it must be correlated with the size of the site where the tanks are to be constructed. This results in an optimisation problem that can be solved in various ways. In this research, we shall consider two methods for solving such an NP-complete combinatorial optimisation problem: the knapsack problem and the bin-packing problem. For this purpose, we shall solve the discussed problem using both methods analytically, and then form programme algorithms for the solution based on the Python language. Let us compare both methods in real cases of tank placement and analyse their efficiency.

Управление энергопотреблением IIoT-устройств в электроэнергетических системах на основе нейро-нечетких моделей

The article presents the results of a study on optimizing the energy consumption of Industrial Internet of Things devices providing telemetry and included in the control loops of electric power systems. The relevance of the study lies in the emerging need to increase the battery life of mobile telemetry devices of industrial and technological systems, which helps to reduce the costs of their maintenance and support in working condition. An algorithm for controlling the parameters of the processor core of mobile devices with ARM architecture processors is proposed, which ensures higher energy efficiency of such devices used in the Industrial Internet of Things of the EPS. The novelty of the obtained results is the proposed algorithm for software control of the configuration of the ARM processor core parameters of mobile telemetry devices of the EPS, ensuring its higher energy efficiency, which is achieved by using data mining methods in its structure – a bidirectional neural network of long short-term memory and a fuzzy logical inference system. The choice of this network architecture is due to its ability to identify relationships in temporal sequences of device parameters by viewing the sequence in two directions at once – from the beginning to the end and vice versa. The network operates in the energy consumption plan classification mode, the results of which are then fed to the input of the fuzzy logic inference system to predict the optimal parameters of the ARM processor, which together forms a neuro-fuzzy model for managing the energy consumption of IIoT devices. Using machine learning libraries in the Python language in the Google Colab environment, model experiments were conducted, as a result of which the classification accuracy using a bidirectional neural network exceeded 0.8, and the standard deviation was 0.058 when predicting the parameters of the ARM processor based on the fuzzy logic inference system.

Research on Case-Based Teaching of Digital Image Processing Implemented with Python Language

Digital image processing is a course with strong theoretical backgrounds and also has wide applications in reality. However, traditional teaching method only focuses on delivering abstract theories, which leads to a decrease in students' interest in the course. To resolve this problem, this paper studies the characteristics of basic image processing algorithms from the perspective of combining theory with practice. We investigate the characteristics of fundamental image processing algorithms. We implement some teaching cases using Python.

System for forecasting the lake dynamics in Russian Arctic using satellite images based on NextGIS Web

The article discusses the important geoecological problem of predicting the dynamics of thermokarst lakes in the Russian Arctic as intensive sources of natural greenhouse gas emissions, which is considered as one of the factors of current climate change. The purpose of the work is to consider the development of a system for forecasting the dynamics of lake areas using entropy-randomized machine learning algorithms and tools of the NextGIS Web geographic information system. The procedure for processing information to predict the dynamics of lakes is considered. Data from remote measurements of the areas of thermokarst lakes in the Arctic zone of Russia, obtained from Landsat satellite images over the past several decades, and climate data determined by reanalysis of meteorological data for the same period are used as retrospective information for forecasting. The system is implemented on the basis of the NextGIS Web geographic information system, which allows the inclusion of randomized modeling applications using the Python language.

Evaluating CNN Architectures for the Automated Detection and Grading of Modic Changes in MRI: A Comparative Study.

Modic changes (MCs) classification system is the most widely used method in magnetic resonance imaging (MRI) for characterizing subchondral vertebral marrow changes. However, it shows a high degree of sensitivity to variations in MRI because of its semiquantitative nature. In 2021, the authors of this classification system further proposed a quantitative and reliable MC grading method. However, automated tools to grade MCs are lacking. This study developed and investigated the performance of convolutional neural network (CNN) in detecting and grading MCs based on their maximum vertical extent. In order to verify performance, we tested CNNs' generalization performance, the performance of CNN with that of junior doctors, and the consistency of junior doctors after AI assistance. A retrospective analysis of 139 patients' MRIs with MCs was conducted and annotated by a spine surgeon. Of the 139 patients, MRIs from 109 patients were acquired using Philips scanners from June 2020 to June 2021, constituting Dataset 1. The remaining 30 patients had MRIs obtained from both Philips and United Imaging scanners from June 2022 to March 2023, forming Dataset 2. YOLOv8 and YOLOv5 were developed in PyCharm using the Python language and based on the PyTorch deep learning framework, data enhancement and transfer learning were applied to enhance model generalization. The model's performance was compared with precision, recall, F1 score, and mAP50. It also tested generalizability and compared it with the junior doctor's performance on the second data set (Dataset 2). Post hoc, the junior doctor graded Dataset 2 with CNN assistance. In addition, the region of interest was displayed using the class activation mapping heat map. On the unseen test set, the YOLOv8 and YOLOv5 models achieved precision of 81.60% and 61.59%, recall of 80.90% and 67.16%, mAP50 of 84.40% and 68.88%, and F1 of 0.81 and 0.60 respectively. On Dataset 2, YOLOv8 and junior doctor achieved precision of 95.1% and 72.5%, recall of 68.3% and 60.6%. In the AI-assisted experiment, agreement between the junior doctor and the senior spine surgeon significantly improved from Cohen's kappa of 0.368-0.681. YOLOv8 in detecting and grading MCs was significantly superior to that of YOLOv5. The performance of YOLOv8 is superior to that of junior doctors, and it can enhance the capabilities of junior doctors and improve the reliability of diagnoses.

Hydraulic Modeling of Water Distribution Networks: Hardy-Cross Method Implemented in Python

Distribution network is a part of the water supply system that, formed by pipes and accessory parts, aims to provide potable water to consumers continuously with recommended quantity and pressure (TSUTIYA, 2006). However, due to the exponentially growing demand for potable water, the design of network layouts has become increasingly complex nowadays. In this regard, due to regional parameters such as topography and supply size, the use of meshed networks is feasible, in other words, networks that can supply a point through different paths due to their ring or block shape. Thus, this study aims to evaluate the performance of a meshed distribution network of a fictitious layout developed by undergraduate Civil Engineering students at the Federal University of Alagoas (UFAL) through system modeling in a Python program using the Hardy-Cross method. Furthermore, the effectiveness of the computational program was validated by comparing its results with those provided by the EPANET software. The methodology was based on the bibliographic review of issues related to solving meshed networks using the Hardy-Cross method, where a fictitious problem was modeled with parameters specified in NBR 12218/2017. Thus, this method was implemented in a computational program in Python language, and from this, the results related to the established layout were obtained. Additionally, the same layout was built in the EPANET software, and its results were used to validate the computational program. Therefore, it was possible to conclude that the computational program presented satisfactory results when compared to the EPANET software data, obtained due to recent performance advances in the Python language, which adds greater ease of use, result accuracy, modeling problem speed, and expands the possibilities of evaluating network performance. Finally, it was found that both modeling approaches are in accordance with the reference values established by TSUTIYA (2006) and recommended by NBR 12218/2017.

Finite element method applied to plane frames

The calculation of displacements and forces in structural models is of great relevance in structural design and provides information about the behavior of the structure under different loading conditions. Due to the complexity of real problems and the demands of the production sector, the solution of a structural model becomes unfeasible without the aid of computational tools. The direct stiffness method, also known as matrix structural analysis, can be considered as a particular case of the finite element method. This article presents a computer software for structural analysis of plane frames using the direct stiffness method. The software considers the presence of hinged nodes and elastic supports in the structural model. The computational code was developed in Python language. Excel files are used as data input and output. Results include information such as nodal displacements, internal forces, and support reactions in the structure. Variations of internal forces along the frame axis, i.e. internal force diagrams, are presented in graphical manner. The influence of considering rigid or elastic supports on the results is investigated in the article.

Reliability-Based Boundary Element Models for plane linear elasticity

In Structural Engineering, one of the main objectives is to find robust solutions that ensure structural safety without sacrificing cost-effectiveness. To achieve this goal, one strategy involves considering the uncertainties inherent in projects, taking into account the variability of design parameters, rather than relying solely on characteristic values provided by semi-probabilistic approaches. This probabilistic paradigm allows for estimating the probability of failure of structural elements and systems, providing robustness to design, contributing to decision-making process, and reducing risks associated with both overly conservative and expensive projects, and economical yet unsafe projects. This study aims to implement the coupling between mechanical and reliability models to analyze the structural behavior of plates in linear regime. Mechanical models based on the Boundary Element Method (BEM) and reliability models based on Monte Carlo Simulation (MCS) and First Order Reliability Method (FORM) will be developed and validated, by using Python language. The random variables considered may include mechanical parameters such as elasticity modulus and Poisson's ratio, dimensions of structural elements, as well as applied loads. The objective is to evaluate the probability of occurrence of usual limit states in structural analysis, such as the violation of allowable displacement and stress values.

Pre-processing for isogeometric analysis of laminated composites

Composites are materials resulting from the mixture of two or more components, usually a polymer matrix reinforced with synthetic fibers, used to build stronger structures capable of withstanding heavy loads and unfavorable environmental conditions. Laminated shells are made of layers of fiber-reinforced composite material, stacked in a specific sequence to ensure good structural performance. The analysis of these structures is predominantly done using the Finite Element Method, a conventional approach used to solve these problems, approximating both the displacements and the geometry of the structure using polynomial functions. A more recent alternative is Isogeometric Analysis (IGA), which uses rational functions to approximate the displacements and geometry of the structure. This ensures that the geometry of the problem is exact, regardless of the level of discretization used. The aim of this work is to study the use of NURBS surfaces in the context of isogeometric analysis of laminated structures. The theoretical and practical aspects of these representations will be studied, with emphasis on the pre-processing stage of the numerical analysis. In this context, the pre-processing of the numerical model of the composite material structure is carried out in the academic software FEMEP (Finite Element Method Educational Computer Program), written in the Python language. The software's graphical interface has been extended to handle models of laminated composite plates. The numerical model is processed using the FAST tool, where the pre-processing program writing routines have been implemented. In the post-processing stage, the simulation results are visualized using the Nlpos tool. The system developed was used in examples of laminated plates, demonstrating the system's capabilities.

Matrix structural analysis of beams on elastic supports: implementation in Python

Technological resources have made it possible to model natural phenomena and engineering problems more accurately, thus allowing better results. This article presents the computational implementation of a procedure for matrix structural analysis of beams. Both rigid and elastic supports were considered to represent the contact between the structural model and the external environment. A computational code was created in Python language. The matrix method allows a detailed analysis of the structural response. The developed code calculates stiffness matrices and force vectors of beam elements, which are assembled into a global stiffness matrix and a global force vector, and the equilibrium equation system is then solved. Results such as nodal displacements and slopes, support reactions, and internal forces are obtained. Different support conditions, namely fixed, hinged, and elastic supports, as well as different loading conditions, were investigated for beams. Results highlight that the presence of elastic supports can significantly affect the overall structural behavior of the system. By incorporating these effects into the analysis, more accurate predictions of structural response can be obtained, leading to safer and more economical solutions. The article provides a basis for further investigation of the behavior of complex structural systems. The use of Python language can be justified as it provides a versatile and efficient platform for conducting structural analysis and is suitable for future advances in the field of structural engineering.

Numerical Study of the Effects of Residual Stress in Welded Pipe Joints

In general, welding processes can be understood as the establishment of the union of parts, generally metallic, using a heat source to promote the fusion of materials, with or without the presence of pressure efforts. Studies related to the distributions and levels of residual resistance arising from the thermal cycles involved in welding processes are of vital importance for evaluating and guaranteeing the integrity of welded pipes. This work proposes the implementation of a routine in PYTHON language to automate the production of thermomechanical coupling welding models using the ABAQUS® CAE finite element tool, considering the heat distribution model of the welding source according to Goldak et al. (1984), better known as the double ellipsoid model. The studies are produced considering an ambient temperature of 20 °C and another with preheating of the tubes to 300 °C. The results achieved by this study are related solely to analyzes of the residual stress fields in a thin walled tube joint manufactured in AISI 304 stainless steel, welded using TIG (Tungsten Inert Gas) processes in a single pass. Thus, this study seeks to analyze the hoop and axial residual stress distributions on the internal or external walls of post-welded tubes in angular positions 0°, 90°, 180° and 270°, whether or not there is uniform preheating of the pre-welding pipes. The results show that, in general, residual stresses differ slightly with the angular position, being relatively high when compared to the yield stress of the steel, and therefore have a considerable impact on reducing the mechanical strength of the welded joint. It should also be noted that when considering the preheating of the pipes there is a significant decrease in the peak temperatures reached in the transfer process, in the cooling rates and in the residual stress levels of the joints, especially in the most critical regions, located in the proximity from the center of the weld beads.