Integrated Development Environment Research Articles

From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows

AbstractWe present a comprehensive and user-friendly framework built upon the integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry.

Transforming Software Development Through Generative AI : A Systematic Analysis of Automated Development Practices

This article presents a comprehensive analysis of the transformative impact of Generative Artificial Intelligence (GenAI) on software development practices. Through systematic evaluation of implementations across multiple development environments, this article demonstrates how GenAI technologies significantly enhance four critical areas of the software development lifecycle: code generation, automated testing, intelligent code review, and predictive failure analysis. The findings indicate a 45% reduction in initial code development time through automated code generation, 60% improvement in test coverage through AI-driven test case creation, and 30% decrease in post-deployment issues through predictive failure analysis. The article employs a mixed-methods approach, combining quantitative analysis of development metrics from 50 software projects with qualitative assessments from 200 professional developers across diverse organizational contexts. Results reveal that organizations implementing GenAI-assisted development workflows experience a mean productivity increase of 37% while maintaining or improving code quality metrics. Additionally, the article identifies key integration challenges and provides a framework for effective GenAI adoption in software development environments. These findings contribute to the growing body of knowledge on AI-assisted software development and offer practical insights for organizations seeking to optimize their development processes through GenAI integration.

Neuroendocrine tumors of the stomach and pancreas. Possibilities of application of radiomics. Problems and solutions.

Background. Radiomics is promising tool contributing to the diagnosis and treatment of neuroendocrine neoplasms of various localizations. This method is often used for differential diagnosis of gastrointestinal neoplasms from other neoplasms of this localization.Aim. To demonstrate difficulties in imaging of gastrointestinal neuroendocrine tumors, as well as possibilities of radiomics in differential diagnosis.Materials and methods. Data of 12 patients with morphologically confirmed gastric neoplasms (6 - with neuroendocrine tumor, 6 - with adenocarcinoma) and data of 22 patients with morphologically confirmed pancreatic neoplasms (11 - with neuroendocrine tumor, 11 - with adenocarcinoma) who underwent CT examination of abdominal cavity organs with intravenous contrast before treatment at the Russian Scientific Center of Roentgenoradiology were used in this study. Radiomic features were extracted from the area of manually segmented gastric and pancreatic tumors in the native phase of the CT scans. Processing of the features and statistical analysis were performed using Microsoft Office Excel and free open-source software development environment for the R programming language - R-Studio.Results. Typical and atypical imaging features of gastric and pancreatic neuroendocrine tumors, peculiarities of contrast, localization and structure of neoplasms were demonstrated on the examples of CT scans. There were 15 radiomic features that were statistically significantly different in patients with neuroendocrine tumors and gastric adenocarcinomas. In the group of patients with neuroendocrine tumors and pancreatic adenocarcinomas, statistically significant differences were observed in 14 radiomic features.Conclusion. Neuroendocrine tumors of the stomach and pancreas are rare neoplasms that are not only clinically silent in most cases, but also difficult to visualize due to their small size and poor contrast characteristics. Texture analysis is a promising tool for the differential diagnosis of gastrointestinal neoplasms from other neoplasms of this localization, especially considering the difficulties of biopsy.

Internet of things and long range-based bridge slope early detection systems

<p><span>This research proposes an internet of things and long range (LoRa)-based bridge slope status monitoring and warning system that is wireless, low-cost, and user-friendly, with continuous data sent. Bridge inspection officers can easily obtain bridge slope data via a web browser on a cell phone. The design uses Arduino integrated development environment software and an ITGMPU accelerometer sensors, TTGO ESP32, cellphones, successfully identified tilt angle variations from 0.11° to 15.2° were the research's outputs, and and they were continuously transmitted to the bridge inspection officer's mobile phone. Measurements of throughput, quality of service (QoS), and latency characteristics have been made to assess the internet network's performance. The network system performance statistics show an average measured network delay of 1.2 seconds, a throughput of 85 bps, and a QoS of 0%. Consequently, the system performs well and the internet network performance falls into the very good range.</span></p>

Enhancing educational efficiency: Generative AI chatbots and DevOps in Education 4.0

AbstractThe emergence of new technologies has developed a new industrial need for engineering graduate students. This modern industry needs a modern educational framework to support further development. This research revolves around developing the learning methodological approach for university courses that aligns with Education 4.0 and can fulfill the essential knowledge requirements for future engineers in Industry 4.0. We used a constructive approach as a baseline methodology already proven in the field of engineering education. On the other hand, we modified this approach by implementing Industry 4.0 standards, such as Agile methodology, DevOps tools, and Artificial intelligence chatbots. In this way, we managed to establish one more step in the digital transformation of education as one of the paramount cornerstones of Industry 4.0. As a result, during the course, Agile methodology and DevOps tools created a learning environment very similar to the real software development environment in companies. The efficiency of the delivery of the learning material also increased by implementation and integration of AI chatbots. This was tracked by the number of projects developed during course implementation. The study showed that in the field of education, the implementation of novel approaches developed in the industry to increase efficiency can be implemented in the educational environment. It also showed that the implementation of those methodologies does not hinder but improves the efficiency of the educational cycle.

Open Geospatial Engine: A Cloud-based Spatiotemporal Computing Platform

Abstract. The quantity and diversity of Earth spatiotemporal big data have significantly increased in recent years, providing the potential to comprehensively analyse complex spatiotemporal problems from new perspectives. However, integrating, managing, and applying multi-source heterogeneous Earth spatiotemporal big data remains a challenge. To address this issue, this study proposes a cloud-based spatiotemporal computing platform, the Open Geospatial Engine (OGE), for the unified organization and joint analysis of Earth spatiotemporal big data in multiple dimensions and scales. The framework of this platform comprises three modules: data management, computing engine, and service interface. The data management module adopts the GeoCube model to effectively integrate and manage multi-source heterogeneous spatiotemporal data through multi-dimensional aligned tiles. The computing engine module seamlessly maps the GeoCube model to the cloud environment by extending Spark RDD, making the GeoCube model capable of high-performance distributed computing. Following OGC specifications, the service interface module integrates and shares data, operators, and spatiotemporal analysis models through extensible APIs in an interactive development environment. Combined with a series of high-performance optimization techniques, OGE simplifies data queries and the construction of complex analytical applications by integrating these three modules. The applicability of OGE is demonstrated by case studies involving multi-dimensional queries and joint analysis of long-time-series and heterogeneous spatiotemporal data.

Generated power forecast of dye-sensitized solar plant with deep neural network

ABSTRACT The enormous amount of solar power and its state-of-the-art capturing technology that produces electricity, increases the grid interconnection rate of photovoltaic (PV) plants. Prior knowledge of the aperiodic characteristics of solar energy received by the Earth’s surface is essential for PV plants to ensure reliable operation and stable, secure grid connections. Power generation under diffused sunlight by PV plants with the most widely used first-generation solar cells has significant limitations. A dye-sensitized solar plant (DSSP), utilizes dye-sensitized solar cell (DSSC) technology, remains active in low light conditions. Though the activeness enables such plants to generate electricity throughout the year while receiving diffuse sunlight, unlike conventional solar technologies the plants have a requirement of knowledge of future power generation. This could be the first paper to report on the generated power forecast for such plants. The study has utilized a machine learning approach with a proposed DSSP model for the forecast. Spyder, an open-source integrated development environment, is the test workbench for the experimentation. The results show the robustness and reliability of the method, regardless of the weather conditions in the test area. The DSSP, along with the prediction model, will moderately overcome the shortcomings of power generation under diffuse daylight with intermittent solar radiation and grid connections.

Exploring Learning Dynamics: A Comparative Study of IDEs and Command-Line Interfaces for Learning Java Programming language in the Department of Computer Science, Jigawa State College of Education and Legal Studies Ringim

This study explored the learning dynamics associated with Integrated Development Environments (IDEs) and Command-Line Interfaces (CLIs) for teaching and learning java programming language in the Department of Computer Science at the College of Education and Legal Studies, Ringim. The study evaluates the effectiveness of different programming tools (IDEs and CLIs) in enhancing the learning experience of Java programming students. It seeks to understand how these tools impact learners' confidence, skill development, and overall programming proficiency. The results revealed distinct advantages and challenges associated with each environment, underscoring the importance of context and learner preferences in selecting the appropriate tool for Java programming education. The study provides valuable recommendations for educators on optimizing programming instruction to better meet the needs of diverse learners.

Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development

Crowdsourcing Software Development (CSD) platforms, i.e., TopCoder, function as intermediaries connecting clients with developers. Despite employing systematic methodologies, these platforms frequently encounter high task abandonment rates, with approximately 19% of projects failing to meet satisfactory outcomes. Although existing research has focused on task scheduling, developer recommendations, and reward mechanisms, there has been insufficient attention to the support of platform moderators, or copilots, who are essential to project success. A critical responsibility of copilots is estimating project duration; however, manual predictions often lead to inconsistencies and delays. This paper introduces an innovative machine learning approach designed to automate the prediction of project duration on CSD platforms. Utilizing historical data from TopCoder, the proposed method extracts pertinent project attributes and preprocesses textual data through Natural Language Processing (NLP). Bidirectional Encoder Representations from Transformers (BERT) are employed to convert textual information into vectors, which are then analyzed using various machine learning algorithms. Zero-shot learning algorithms exhibit superior performance, with an average accuracy of 92.76%, precision of 92.76%, recall of 99.33%, and an f-measure of 95.93%. The implementation of the proposed automated duration prediction model is crucial for enhancing the success rate of crowdsourcing projects, optimizing resource allocation, managing budgets effectively, and improving stakeholder satisfaction.

Code Completion Using Markov Chains

Modern software engineers use many tools to speed up the development process. Many of them use integrated development environments (IDEs), which provide services such as text editors, debuggers and even intelligent code completion. This paper is dedicated to the development of a model for predicting variants of program source code termination. To improve the accuracy of the model, we used combinations of Markov chains constructed using different ways of calculating the current context of the program: linear and with AST. The linear way of computing the context is an analysis of the tokenized representation of the source code. The second method, on the other hand, uses a representation of the source code in the form of an abstract syntax tree. Combining the different models preserves more semantic information about the code, also adding the ability to support custom code writing style features. In order to compare the different models, a new dataset has been created specifically for the Pascal language. A detailed comparison of the working mechanisms as well as the prediction accuracy on the collected data is given. The proposed model showed high enough accuracy of predictions with minimal computation costs, which allows using it in integrated development environments.

Design and research of serial communication system based on virtual instrument technology

Abstract In the measurement and automation control system, serial communication has been connected between the acquisition hardware (lower computer) and the upper computer between the easiest to realize and the most economical way. In view of the traditional instrument itself is relatively bulky, inconvenient to carry, this paper adopts the virtual instrument development environment to realize the realization of serial communication, in order to complete the subsequent data acquisition. When compared with other software development environment, LabWindows/CVI platform comes with a function library to make the program writing simple and flexible. In this paper, we designed the realization method of serial communication based on virtual instrument Labwindows/CVI environment, completed the design and realization of serial communication between PC and lower computer (microcontroller) based on Labwindows/CVI environment, and applied it to the example of serial communication with microcontroller to realize the data communication function. Practical results prove that the serial communication system based on virtual instrument technology has the advantages of friendly user interface, high stability, convenience and reliability, and can reliably and accurately complete the communication, and provide strong technical support for the system to follow up data acquisition.

Design and implementation of an electronic voting system for optimal electoral process

Electronic voting is a tool for increasing the credibility and efficiency of elections in several democracies around the world. In Nigeria, many elections have conducted sub-optimally due to the manual methods employed in voting and transmission of election results. In such cases, electoral officials have been accused of colluding with politicians to alter election results while the results are being transmitted from polling units to results collation centres. To address this problem, this paper proposes and presents a framework for electronic voting and transmission of election results. The proposed system is designed and implemented using a C++-based Visual Studio Integrated Development Environment (IDE) and a MATLAB/Simulink application. This setup allowed for the simulation of the voting process, encryption of voting data, transmission of voting results and decryption of election results. The simulation-based results obtained for the proposed system was compared with that of the existing system. The results indicate that for sample sizes ranging from 2,000 to 7,000 voters, the proposed system accurately accredited an average of 85% of voters, compared to 73% for the existing system. Specifically, for 2,500, 4,000, 5,500, and 7,000 accredited voters, the existing system had 684, 1,095, 1,505, and 1,916 failed voters respectively. In comparison, there was a significant reduction in the number of failed voters for the proposed system and this stood at 364, 582, 801, and 1,019 respectively. Additionally, the quality of verified feedback for 3,000, 4,500, 6,000, and 7,000 accredited voters showed that the proposed system had quality scores of 0.362, 0.138, 0.019, and 0.000, compared to 0.549, 0.197, 0.024, and 0.001 for the existing system. In conclusion, the proposed system has proven to be highly effective, credible, secure, and transparent in safeguarding the integrity of the electoral process and it is therefore recommended for hardware implementation in subsequent general elections.

Managing and editing UML documents: structure, integration, automation

Unified Modeling Language (UML) plays an important role in the modern software development process by offering standardized methods for visual modeling, analysis, and design of complex systems. This paper discusses the main aspects of storing, editing, and integrating UML documents with integrated development environments (IDEs). The main focus is on comparing the two main formats for storing UML models – XML Metadata Interchange (XMI) and JavaScript Object Notation (JSON). XMI, being an official standard developed by Object Management Group, provides a high degree of detail and compatibility with various UML tools, making it preferable for large and complex projects that require support for the full UML specification. At the same time, JSON is simple and flexible, which makes it suitable for projects where speed of development and ease of integration are important, although it is inferior to XMI in describing complex aspects of UML models. The paper analyzes in detail the methods of editing UML documents, including manual editing and automated approaches, using APIs and scripts. Manual editing is useful when you need to make small changes to the structure of a UML document, but it can be time-consuming and error-prone when working with large projects. Automation, on the other hand, provides effective tools for mass editing and generating UML elements, which significantly speeds up development and minimizes the likelihood of errors, especially since modern tools such as StarUML offer the necessary tools for programmatically modifying UML documents by integrating them into development processes. Combining of UML documents with integrated development environments also plays a key role in increasing development efficiency, as this allows automating code generation based on UML models, maintaining synchronization between code and diagrams, and facilitating visualization and documentation of architectural solutions. Despite the significant advantages, there are challenges associated with limited support for all UML features and potential conflicts between models and code. This only emphasizes the need for further research in this area. Prospects for further research include the development of new methods and tools for working with UML documents, improving their integration with development environments, and using artificial intelligence to automate the analysis and design of UML models. Attention should also be paid to the using of UML with continuous integration processes, which can significantly increase the flexibility and adaptability of software development. Thus, the study of the structure of storing, editing, and integrating UML documents is an important area for optimizing the design and development of modern software systems.

Application of INTDOSKIT tool for assessment of uncertainties on dose coefficients for ingestion of uranium by workers

The International Commission on Radiological Protection (ICRP) has developed the Human Alimentary Tract Model (HATM) to calculate radiation doses from the ingestion of radionuclides for the protection of workers and the public. In parallel, the ICRP's Occupational Intake of Radionuclides (OIR) series provides biokinetic models and dose coefficients based on a reference human, primarily for regulatory purposes. Although these coefficients are not usually checked for uncertainties, the investigation of such uncertainties is crucial to ensure their reliability in radiation protection. This study uses INTDOSKIT, a software tool developed using the R programming language and RStudio as the Integrated Development Environment (IDE), to calculate doses and explore uncertainties following ingestion of U-238 by workers. Two scenarios were investigated: Case I, using the latest HATM model and the systemic uranium model from ICRP publications 100 and 137 respectively, and Case II, based on data from literature, using older ICRP models. In both cases, intake of U-238 in its soluble form (Type F) was modeled and the results were validated using the ICRP OIR dataviewer software. Following validation, uncertainty and sensitivity analyses were performed. In Case I, uncertainties were assigned to the particle transport parameters in both the systemic model model and HATM as well as the uranium uptake fraction into the blood. While in Case II they were limited to the uptake fraction (f1) and the systemic uranium model. A Monte Carlo simulation of 60,000 runs was performed for both cases, sampling model parameters from their respective probability distributions to generate dose distributions. The influence of each parameter on these distributions was also analyzed. Probability distributions were inferred to the calculated dose values using the maximum likelihood estimation method and the Kolmogorov-Smirnov goodness-of-fit. The results showed that for both cases the committed effective dose coefficient, e (50), followed a lognormal distribution. Case I had a geometric mean of 3.2E-08 Sv/Bq and GSD of 2.0, while case II had a slightly lower geometric mean of 3.1E-08 Sv/Bq and GSD of 1.9. Sensitivity analysis showed that the main contributor to dose uncertainty was the fraction of uranium absorbed from the small intestine into the blood. Both cases showed similar trends, with slightly higher results in case I. Overall, this study demonstrates the effectiveness of INTDOSKIT in calculating dose coefficients and analyzing uncertainties. It suggests that while the ICRP reference values remain useful for protection, the incorporation of additional statistical measures and distribution characteristics could further enhance radiation protection strategies.

Recent advances in Machine Learning based Advanced Driver Assistance System applications

In recent years, the rise of traffic in modern cities has demanded novel technology to support the drivers and protect the passengers and other third parties involved in transportation. Thanks to rapid technological progress and innovations, many Advanced Driver Assistance Systems (A/DAS) based on Machine Learning (ML) algorithms have emerged to address the increasing demand for practical A/DAS applications. Fast and accurate execution of A/DAS algorithms is essential for preventing loss of life and property. High-speed hardware accelerators are vital for processing the high volume of data captured by increasingly sophisticated sensors and complex mathematical models’ execution of modern deep learning (DL) algorithms. One of the fundamental challenges in this new era is to design energy-efficient and portable ML-enabled platforms for vehicles to provide driver assistance and safety. This article presents recent progress in ML-driven A/DAS technology to offer new insights for researchers. We covered standard ML models and optimization approaches based on widely accepted open-source frameworks extensively used in A/DAS applications. We have also highlighted related articles on ML and its sub-branches, neural networks (NNs), and DL. We have also reported the implementation issues, bench-marking problems, and potential challenges for future research. Popular embedded hardware platforms such as Field Programmable Gate Arrays (FPGAs), central processing units (CPUs), Graphical Processing Units (GPUs), and Application Specific Integrated Circuits (ASICs) used to implement A/DAS applications are also compared concerning their performance and resource utilization. We have examined the hardware and software development environments used in implementing A/DAS applications and reported their advantages and disadvantages. We provided performance comparisons of usual A/DAS tasks such as traffic sign recognition, road and lane detection, vehicle and pedestrian detection, driver behavior, and multiple tasking. Considering the current research dynamics, A/DAS will remain one of the most popular application fields for vehicular transportation shortly.

Investigating age and ethnicity as novel high-risk phenotypes in mucinous ovarian cancer: retrospective study in a multi-ethnic population

ObjectivesPrimary mucinous ovarian carcinoma represents 3% of ovarian cancers and is typically diagnosed early, yielding favorable outcomes. This study aims to identify risk factors, focussing on the impact of age...

Collaborative dynamics in open source software development: Unveiling the influence of team interaction and the role of project manager

AbstractAs the world becomes increasingly reliant on open‐source software (OSS) development platforms, collaborative communication networks are becoming more complex, resulting in non‐trivial effects on project performance. This study examines the impact of communication network characteristics on project performance, measured in terms of the number of issues closed within OSS projects. Additionally, this research examines how this project outcome is affected by project managers' active participation in these communication networks. Using a panel dataset of 1599 projects spanning 104 weeks, with a total of 120,243 observations, we find that the density of communication network has an inverted U‐shaped relationship with project performance, that is, as the density of the communication network formed by team member interactions increases, project performance initially increases but then starts to decrease. Meanwhile, a project manager's participation in the communication network has no direct positive impact on project performance, but it flattens the inverted U‐shaped relationship, that is, it reduces the rate of decrease in project performance after the density of the communication network formed by project team members crosses the inflection point. These results provide valuable insights into the dynamics of communication networks in OSS projects and can help develop strategies for improving project performance.

Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems.

Chemical reaction networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level definitions of CRNs enables to use various simulation approaches, including deterministic and stochastic methods, from the same model. However, existing Python tools for simulation of CRN typically wrap external C/C++ libraries for model definition, translation into equations and/or numerically solving them, limiting their extensibility and integration with the broader Python ecosystem. In response, we developed Poincaré and SimBio, two novel Python packages for simulation of dynamical systems and CRNs. Poincaré serves as a foundation for dynamical systems modeling, while SimBio extends this functionality to CRNs, including support for the Systems Biology Markup Language (SBML). Poincaré and SimBio are developed as pure Python packages enabling users to easily extend their simulation capabilities by writing new or leveraging other Python packages. Moreover, this does not compromise the performance, as code can be just-in-time compiled with Numba. Our benchmark tests using curated models from the BioModels repository demonstrate that these tools may provide a potentially superior performance advantage compared to other existing tools. In addition, to ensure a user-friendly experience, our packages use standard typed modern Python syntax that provides a seamless integration with integrated development environments. Our Python-centric approach significantly enhances code analysis, error detection, and refactoring capabilities, positioning Poincaré and SimBio as valuable tools for the modeling community. Poincaré and SimBio are released under the MIT license. Their source code is available on GitHub (https://github.com/maurosilber/poincare and https://github.com/hgrecco/simbio) and can be installed from PyPI or conda-forge.

Exploring Use of Chatgpt for Integrated Design Environments: A Quality Model for Chatgpt

Exploring Use of Chatgpt for Integrated Design Environments: A Quality Model for Chatgpt

Implementation of Integrated Development Environment for Machine Vision-Based IEC 61131-3

IEC 61131-3 is an international standard for developing standardized software for automation and control systems. Machine vision systems are a prominent technology in the field of computer vision and are widely used in various industries, such as manufacturing, robotics, healthcare, and automotive, and are often combined with AI technologies. In industrial automation systems, software developed for defect detection or product classification typically involves separate systems for automation and machine vision programs, leading to increased system complexity and unnecessary resource wastage. To address these limitations, this study proposes an IEC 61131-3-based integrated development environment for programmable machine vision. We selected 11 APIs commonly used in machine vision systems, evaluated their functions in an IEC 61131-3 compliant development environment, and measured the performance of representative machine vision applications. This approach demonstrates the feasibility of developing PLC and machine vision programs within a single-controller system. We investigated the impact of controller performance on function execution.