Research background: Multi-modal synthetic data fusion and analysis, simulation and modelling technologies, and virtual environmental and location sensors shape the industrial metaverse. Visual digital twins, smart manufacturing and sensory data mining techniques, 3D digital twin simulation modelling and predictive maintenance tools, big data and mobile location analytics, and cloud-connected and spatial computing devices further immersive virtual spaces, decentralized 3D digital worlds, synthetic reality spaces, and the industrial metaverse. Purpose of the article: We aim to show that big data computing and extended cognitive systems, 3D computer vision-based production and cognitive neuro-engineering technologies, and synthetic data interoperability improve artificial intelligence-based digital twin industrial metaverse and hyper-immersive simulated environments. Geolocation data mining and tracking tools, image processing computational and robot motion algorithms, and digital twin and virtual immersive technologies shape the economic and business management of extended reality environments and the industrial metaverse. Methods: Quality tools: AMSTAR, BIBOT, CASP, Catchii, R package and Shiny app citationchaser, DistillerSR, JBI SUMARI, Litstream, Nested Knowledge, Rayyan, and Systematic Review Accelerator. Search period: April 2024. Search terms: “digital twin industrial metaverse” + “artificial Intelligence of Things systems”, “multisensory immersive extended reality technologies”, and “algorithmic big data simulation and modelling tools”. Selected sources: 114 out of 336. Published research inspected: 2022–2024. PRISMA was the reporting quality assessment tool. Dimensions and VOSviewer were deployed as data visualization tools. Findings & value added: Simulated augmented reality and multi-sensory tracking technologies, explainable artificial intelligence-based decision support and cloud-based robotic cooperation systems, and ambient intelligence and deep learning-based predictive analytics modelling tools are instrumental in augmented reality environments and in the industrial metaverse. The economic and business management of the industrial metaverse necessitates connected enterprise production and big data computing systems, simulation and modelling technologies, and virtual reality-embedded digital twins.
Alejandro Pradas-Gomez, Arindam Brahma, Ola Isaksson
Engineering analysis automation in product development relies on rigid interfaces between tools, data formats and documented processes. When these interfaces change, as they routinely do as the product evolves in the engineering ecosystem, the automation support breaks. This paper presents a DUCTILE (Delegated, User-supervised Coordination of Tool- and document-Integrated LLM-Enabled) agentic orchestration, an approach for developing, executing and evaluating LLM-based agentic automation support of engineering analysis tasks. The approach separates adaptive orchestration, performed by the LLM agent, from deterministic execution, performed by verified engineering tools. The agent interprets documented design practices, inspects input data and adapts the processing path, while the engineer supervises and exercises final judgment. DUCTILE is demonstrated on an industrial structural analysis task at an aerospace manufacturer, where the agent handled input deviations in format, units, naming conventions and methodology that would break traditional scripted pipelines. Evaluation against expert-defined acceptance criteria and deployment with practicing engineers confirm that the approach produces correct, methodologically compliant results across 10 repeated independent runs. The paper discusses the paradigm shift and the practical consequences of adopting agentic automation, including unintended effects on the nature of engineering work when removing mundane tasks and creating an exhausting supervisory role.
Abstract Effective inventory replenishment and routing are crucial for minimizing supply chain costs and enhancing operational efficiency. In this paper, we focus on the integrated optimization of inventory replenishment and routing problems in Vendor Managed Inventory (VMI) mode and further propose an enhanced Multi-Task Proximal Policy Optimization (MTPPO) with deep reinforcement learning. The proposed model refines inventory replenishment strategies by learning from inventory status and retailer location data. Routing strategies are optimized by utilizing a Graph Isomorphism Network (GIN) to analyze the network data of retailers and formulate routing strategies based on delivery requirements and retailer network information. By jointly optimizing inventory and routing problems, the total cost is reduced. Further, experimental results demonstrate that the MTPPO outperforms heuristic algorithms, reducing inventory costs by 8.58% and total costs by 6.18%.
Abstract This research paper focuses on the design, implementation, and evaluation of a digital amplifier system, engineered to incorporate MOSFETs and H-bridge configurations. The primary function of this digital amplifier is to super-impose alternating voltage onto a direct current (DC) power supply. A key aspect of the study highlights the strategic implementation of a Second-Order Generalized Integrator (SOGI) model. This SOGI model is seamlessly integrated within the digital amplifier’s control framework to precisely generate and maintain the desired voltage waveform characteristics. The primary objective of super-imposing this alternating output voltage onto the DC power supply is to facilitate a comprehensive evaluation of a voltage class B system’s immunity to DC-side ripple. By subjecting the system to this super-imposed voltage, the research aims to rigorously assess its ability to maintain reliable performance even when subjected to fluctuations and disturbances on the DC power supply line. This setup enables testing and assessment of the electrical performance, safety, and durability of voltage Class B systems and components. The digital amplifier system was extensively tested and validated under both low voltage (4 V) and high voltage (400 V) conditions. The results demonstrate the system’s capability to generate a sinusoidal output voltage with a magnitude of up to 40 V, operating across a wide frequency range from 10 Hz to 150 kHz. The incorporation of the SOGI model enabled effective processing of single-phase AC signals and permits the real-time adjustment of frequencies. The system successfully super-imposed the generated sinusoidal voltage onto the main DC power supply, delivering the combined signal to the load. The amplifier’s performance was validated under high voltage conditions (up to 400 V DC input), showcasing its stability and reliability across diverse operating parameters. This work lays the foundation for advancing power electronic technologies and enabling their deployment in a wide range of industrial applications.
Electrical engineering. Electronics. Nuclear engineering, Information technology
Abhik Roychoudhury, Corina Pasareanu, Michael Pradel
et al.
Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (AI). We argue that successfully deploying AI software engineers requires a level of trust equal to or even greater than the trust established by human-driven software engineering practices. The recent trend toward LLM agents offers a path toward integrating the power of LLMs to create new code with the power of analysis tools to increase trust in the code. This opinion piece comments on whether LLM agents could dominate software engineering workflows in the future and whether the focus of programming will shift from programming at scale to programming with trust.
Vahid Garousi, Zafar Jafarov, Aytan Mövsümova
et al.
Artificial Intelligence (AI) tools such as GitHub Copilot and ChatGPT are increasingly used in software engineering (SE) for tasks such as code, test, and documentation generation. However, engineers often face uncertainty about when to trust, refine, or discard AI-generated artifacts. We present a pragmatic workflow, complemented by a four-quadrant decision model, that formalizes how developers iteratively prompt, inspect, refine, and, when needed, fall back to manual work. The workflow and decision model were derived from a grey literature review and field observations across three industrial settings in Türkiye and Azerbaijan. Two real-world scenarios demonstrate the workflow's practical value, showing how engineers navigate key decision points when using AI. Our approach offers lightweight, structured guidance to support more deliberate and quality-aware use of AI tools in everyday SE tasks.
Artificial Intelligence (AI) technology such as Large Language Models (LLMs) have become extremely popular in creating code. This has led to the conjecture that future software jobs will be exclusively conducted by LLMs, and the software industry will cease to exist. But software engineering is much more than producing code -- notably, \emph{maintaining} large software and keeping it reliable is a major part of software engineering, which LLMs are not yet capable of.
Engineering education providers should foresee the potential of digital transformation of teaching and skill-developing activities so that graduating engineers can find themselves highly aligned with the demands and attributes needed by prospective industrial employers. The advancement of industrial revolutions towards hybridisation of the enabling technologies recognised by Industry 4.0, Society 5.0, and Industry 5.0 have transformed the components of the engineering higher education system remarkably. Future workforce requirements will demand an employee’s multidisciplinary skill mix and other professional qualities. Implementing human-centric decision-making based on insights from the Digital Twin (DT) systems, sustainability, and lean systems is necessary for further economic growth. Recent barriers identified by the Australian Council of Engineering Deans, the development of teaching capabilities, and affordable and digitally transformed learning facilities by education providers were all considered. This paper explores the role of Digital Twins (DTs) in enhancing engineering higher education by incorporating Industry 4.0 components and other industrial advances. By reviewing curricula, pedagogy, and the evolving skill requirements for engineering graduates, this study identifies key benefits of DTs, such as cost-effectiveness, resource management, and immersive learning experiences. This paper also outlines challenges in implementing DT-based labs, including IT infrastructure, data quality, privacy, and security issues. The findings indicate that engineering education should embrace DTs to foster multidisciplinary skills and human-centric decision-making to meet future workforce demands. Collaboration with industry is highlighted as a crucial factor in the successful transformation of teaching practices and in offering real-world experiences. The COVID-19 pandemic has expedited the adoption of DT technologies, demonstrating their utility in minimising educational disruptions. While this paper acknowledges the high potential of DTs to prepare engineering students for future industry demands, it also emphasises the need for professional development among educators to ensure effective and balanced implementation.
The green hydrogen economy is expected to play a crucial role in carbon neutrality, but industrial-scale water electrolysis requires improvements in efficiency, operation costs, and capital costs before broad deployment. Electrolysis operates at a high current density and involves the substantial formation of gaseous products from the electrode surfaces to the electrolyte, which may lead to additional resistance and a resulting loss of efficiency. A detailed clarification of the bubble departure phenomena against the electrode surface and the surrounding electrolytes is needed to further control bubbles in a water electrolyzer. This study clarifies how electrolyte properties affect the measured bubble detachment sizes from the comparisons with analytical expressions and dynamic simulations. Bubble behavior in various electrolyte solutions and operating conditions was described using various physical parameters. A quantitative relationship was then established to connect electrolyte properties and bubble departure diameters, which can help regulate the bubble management through electrolyte engineering.
Stanislav Eroshenko, Evgeniy Shmakov, Dmitry Klimenko
et al.
This paper explores the application of conceptual hydrological models in optimizing the operation of hydroelectric power plants (HPPs) in steppe regions, a crucial aspect of promoting low-carbon energy solutions. The study aims to identify the most suitable conceptual hydrological model for predicting reservoir inflows from multiple catchments in a steppe region, where spring runoff dominates the annual water volume and requires careful consideration of snowfall. Two well-known conceptual models, HBV and GR6J-CemaNeige, which incorporate snow-melting processes, were evaluated. The research also investigated the best approach to preprocessing historical data to enhance model accuracy. Furthermore, the study emphasizes the importance of accurately defining low-water periods to ensure reliable HPP operation through more accurate inflow forecasting. A hypothesis was proposed to explore the relationship between atmospheric circulation and the definition of low-water periods; however, the findings did not support this hypothesis. Overall, the results suggest that combining the conceptual models under consideration can lead to more accurate forecasts, underscoring the need for integrated approaches in managing HPP reservoirs and promoting sustainable energy production.
Engineering machinery, tools, and implements, Technological innovations. Automation
Aurora Ramírez, José Raúl Romero, Carlos García-Martínez
et al.
Although metaheuristics have been widely recognized as efficient techniques to solve real-world optimization problems, implementing them from scratch remains difficult for domain-specific experts without programming skills. In this scenario, metaheuristic optimization frameworks are a practical alternative as they provide a variety of algorithms composed of customized elements, as well as experimental support. Recently, many engineering problems require to optimize multiple or even many objectives, increasing the interest in appropriate metaheuristic algorithms and frameworks that might integrate new specific requirements while maintaining the generality and reusability principles they were conceived for. Based on this idea, this paper introduces JCLEC-MO, a Java framework for both multi- and many-objective optimization that enables engineers to apply, or adapt, a great number of multi-objective algorithms with little coding effort. A case study is developed and explained to show how JCLEC-MO can be used to address many-objective engineering problems, often requiring the inclusion of domain-specific elements, and to analyze experimental outcomes by means of conveniently connected R utilities.
Taiwo A. Adebiyi, Nafeezat A. Ajenifuja, Ruda Zhang
Digital twin (DT) technology has received immense attention over the years due to the promises it presents to various stakeholders in science and engineering. As a result, different thematic areas of DT have been explored. This is no different in specific fields such as manufacturing, automation, oil and gas, and civil engineering, leading to fragmented approaches for field-specific applications. The civil engineering industry is further disadvantaged in this regard as it relies on external techniques by other engineering fields for its DT adoption. A rising consequence of these extensions is a concentrated application of DT to the operations and maintenance phase. On another spectrum, Building Information Modeling (BIM) is pervasively utilized in the planning/design phase, and the transient nature of the construction phase remains a challenge for its DT adoption. In this paper, we present a phase-based development of DT in the Architecture, Engineering, and Construction industry. We commence by presenting succinct expositions on DT as a concept and as a service, and establish a five-level scale system. Furthermore, we present separately a systematic literature review of the conventional techniques employed at each civil engineering phase. In this regard, we identified enabling technologies such as computer vision for extended sensing and the Internet of Things for reliable integration. Ultimately, we attempt to reveal DT as an important tool across the entire life cycle of civil engineering projects, and nudge researchers to think more holistically in their quest for the integration of DT for civil engineering applications.
Context: Requirements Engineering for AI-based systems (RE4AI) presents unique challenges due to the inherent volatility and complexity of AI technologies, necessitating the development of specialized methodologies. It is crucial to prepare upcoming software engineers with the abilities to specify high-quality requirements for AI-based systems. Goal: This research aims to evaluate the effectiveness and applicability of Goal-Oriented Requirements Engineering (GORE), specifically the KAOS method, in facilitating requirements elicitation for AI-based systems within an educational context. Method: We conducted an empirical study in an introductory software engineering class, combining presentations, practical exercises, and a survey to assess students' experience using GORE. Results: The analysis revealed that GORE is particularly effective in capturing high-level requirements, such as user expectations and system necessity. However, it is less effective for detailed planning, such as ensuring privacy and handling errors. The majority of students were able to apply the KAOS methodology correctly or with minor inadequacies, indicating its usability and effectiveness in educational settings. Students identified several benefits of GORE, including its goal-oriented nature and structured approach, which facilitated the management of complex requirements. However, challenges such as determining goal refinement stopping criteria and managing diagram complexity were also noted. Conclusion: GORE shows significant potential for enhancing requirements elicitation in AI-based systems. While generally effective, the approach could benefit from additional support and resources to address identified challenges. These findings suggest that GORE can be a valuable tool in both educational and practical contexts, provided that enhancements are made to facilitate its application.
Alexander A. Solovyov , Tatyana A. Dugina , Svetlana E. Karpushova
et al.
The article aims to determine the possibilities of product quality management in industry 4.0 based on digital institutions. Based on international experience for 2022, the authors applied the regression analysis method to compile an econometric model that proves that ensuring digital competitiveness requires state and public management of product quality in industry 4.0 with the help of digital institutions. It has also been proved that digital competitiveness in local and global markets is determined by different institutions. The theoretical significance of the authors’ conclusions is that they have formed a new institutional understanding of product quality in industry 4.0. The contribution of the article to the literature consists in the development of the scientific provisions of the TQM concept through the formation of a new, broad understanding of the quality of Industry 4.0 products in the unity of the completeness of the manufacturer’s technical capabilities and the perceived value and usefulness of this product, as well as through a reconsideration of the approach to measuring and managing the quality of products industry 4.0 in order to strengthen their digital competitiveness in accordance with the new understanding of quality. The practical significance of the article is related to the fact that the monitoring has revealed a favorable institutional environment for quality management of products in industry 4.0 in Russia. The institutional perspective of improving the product quality of industry 4.0 in Russia has also been opened up. The managerial significance is expressed in the fact that the developed institutional approach to product quality management in industry 4.0 will improve the efficiency of this management and ensure a more complete capacity utilization of medium- and high-tech industries, as well as an increase in the export of their products. The proposed approach can be applied in any country of the world, as it has been developed taking into account a broad analysis of international experience.
In software development, code comments play a crucial role in enhancing code comprehension and collaboration. This research paper addresses the challenge of objectively classifying code comments as "Useful" or "Not Useful." We propose a novel solution that harnesses contextualized embeddings, particularly BERT, to automate this classification process. We address this task by incorporating generated code and comment pairs. The initial dataset comprised 9048 pairs of code and comments written in C, labeled as either Useful or Not Useful. To augment this dataset, we sourced an additional 739 lines of code-comment pairs and generated labels using a Large Language Model Architecture, specifically BERT. The primary objective was to build classification models that can effectively differentiate between useful and not useful code comments. Various machine learning algorithms were employed, including Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gradient Boosting, Random Forest, and a Neural Network. Each algorithm was evaluated using precision, recall, and F1-score metrics, both with the original seed dataset and the augmented dataset. This study showcases the potential of generative AI for enhancing binary code comment quality classification models, providing valuable insights for software developers and researchers in the field of natural language processing and software engineering.
Data-driven engineering refers to systematic data collection and processing using machine learning to improve engineering systems. Currently, the implementation of data-driven engineering relies on fundamental data science and software engineering skills. At the same time, model-based engineering is gaining relevance for the engineering of complex systems. In previous work, a model-based engineering approach integrating the formalization of machine learning tasks using the general-purpose modeling language SysML is presented. However, formalized machine learning tasks still require the implementation in a specialized programming languages like Python. Therefore, this work aims to facilitate the implementation of data-driven engineering in practice by extending the previous work of formalizing machine learning tasks by integrating model transformation to generate executable code. The method focuses on the modifiability and maintainability of the model transformation so that extensions and changes to the code generation can be integrated without requiring modifications to the code generator. The presented method is evaluated for feasibility in a case study to predict weather forecasts. Based thereon, quality attributes of model transformations are assessed and discussed. Results demonstrate the flexibility and the simplicity of the method reducing efforts for implementation. Further, the work builds a theoretical basis for standardizing data-driven engineering implementation in practice.