Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

Bastiaan Heeren, Fabiano Dalpiaz, Mazyar Seraj et al.

Software engineering educators strive to continuously improve their courses and programs. Understanding the current state of practice of software engineering higher education can empower educators to critically assess their courses, fine-tune them by benchmarking against observed practices, and ultimately enhance their curricula. In this study, we aim to provide an encompassing analysis of higher education on software engineering by considering the higher educational offering of an entire European country, namely the Netherlands. We leverage a crowd-sourced analysis process by considering 10 Dutch universities and 207 university courses. The courses are analysed via knowledge areas adopted from the SWEBOK. The mapping process is refined via homogenisation and internal consistency improvement phases, and is followed by a data analysis phase. Given its fundamental nature, Construction and Programming is the most covered knowledge area at Bachelor level. Other knowledge areas are equally covered at Bachelor and Master level (e.g., software engineering models), while more advanced ones are almost exclusively covered at Master level. We identify three clusters of tightly coupled knowledge areas: (i) requirements, architecture, and design, (ii) testing, verification, and security, and (iii) process-oriented and DevOps topics. Dutch universities generally cover all knowledge areas uniformly, with minor deviations reflecting institutional research strengths. Our results highlight correlations among key knowledge areas and their potential for enhancing integrated learning. We also identify underrepresented areas, such as software engineering economics, which educators may consider including in curricula. We invite researchers to use our research method in their own geographical region, in order to contrast software engineering education programs across the globe.

Salahuddin Alawadhi, Noorhan Abbas

Integrating Retrieval Augmented Generation (RAG) with Large Language Models (LLMs) has shown the potential to provide precise, contextually relevant responses in knowledge intensive domains. This study investigates the ap-plication of RAG for ABB circuit breakers, focusing on accuracy, reliability, and contextual relevance in high-stakes engineering environments. By leveraging tailored datasets, advanced embedding models, and optimized chunking strategies, the research addresses challenges in data retrieval and contextual alignment unique to engineering documentation. Key contributions include the development of a domain-specific dataset for ABB circuit breakers and the evaluation of three RAG pipelines: OpenAI GPT4o, Cohere, and Anthropic Claude. Advanced chunking methods, such as paragraph-based and title-aware segmentation, are assessed for their impact on retrieval accuracy and response generation. Results demonstrate that while certain configurations achieve high precision and relevancy, limitations persist in ensuring factual faithfulness and completeness, critical in engineering contexts. This work underscores the need for iterative improvements in RAG systems to meet the stringent demands of electrical engineering tasks, including design, troubleshooting, and operational decision-making. The findings in this paper help advance research of AI in highly technical domains such as electrical engineering.

Wenjun Xia, Chuang Niu, Ge Wang

Computed tomography (CT) is a major medical imaging modality. Clinical CT scenarios, such as low-dose screening, sparse-view scanning, and metal implants, often lead to severe noise and artifacts in reconstructed images, requiring improved reconstruction techniques. The introduction of deep learning has significantly advanced CT image reconstruction. However, obtaining paired training data remains rather challenging due to patient motion and other constraints. Although deep learning methods can still perform well with approximately paired data, they inherently carry the risk of hallucination due to data inconsistencies and model instability. In this paper, we integrate the data fidelity with the state-of-the-art generative AI model, referred to as the Poisson flow generative model (PFGM) with a generalized version PFGM++, and propose a novel CT framework: Flow-Oriented Reconstruction Conditioning Engine (FORCE). In our experiments, the proposed method shows superior performance in various CT imaging tasks, outperforming existing unsupervised reconstruction approaches.

Vint Lee, Sohom Roy

For small-scale liquid rockets, pressure-fed systems are commonly favoured due to their simplicity and low weight. In such systems, accurate regulation of both tank and injector pressures over a wide range of upstream pressures is critical $-$ more accurate regulation allows for higher engine efficiency and minimal tank mass, thus improving flight performance. However, existing methods such as dome-loaded pressure regulators are inflexible, or require extensive characterization to function accurately. These methods also suffer from limited orifice size, droop, and slow reaction times, making them unsuitable for throttling by adjusting pressures in flight, which are increasingly important as propulsively landing rockets become more common. To overcome these challenges, we designed an electronic pressure regulator (eReg), a multi-input multi-output system utilising closed loop feedback to accurately control downstream pressures. Our design is simple, low-cost and robust: with a single ball valve actuated by a motor, we regulate both gaseous pressurant and cryogenic liquid propellant at high flow rates (1.14 kg/s of liquid; 0.39 kg/s of gas) and upstream pressures (310 bar). Using 2 eRegs to regulate propellant tank pressures, and 2 eRegs for regulating propellant flow to the engine, we demonstrated our system's ability, in a static fire test, to regulate pressures accurately (within 0.2 bar) while simultaneously throttling our engine. To the best of our knowledge, this is the first time any undergraduate team has successfully throttled a liquid bipropellant engine.

Alvaro M. Aparicio-Morales, Enrique Moguel, Luis Mariano Bibbo et al.

Quantum computing represents a revolutionary computational paradigm with the potential to address challenges beyond classical computers' capabilities. The development of robust quantum software is indispensable to unlock the full potential of quantum computing. Like classical software, quantum software is expected to be complex and extensive, needing the establishment of a specialized field known as Quantum Software Engineering. Recognizing the regional focus on Latin America within this special issue, we have boarded on an in-depth inquiry encompassing a systematic mapping study of existing literature and a comprehensive survey of experts in the field. This rigorous research effort aims to illuminate the current landscape of Quantum Software Engineering initiatives undertaken by universities, research institutes, and companies across Latin America. This exhaustive study aims to provide information on the progress, challenges, and opportunities in Quantum Software Engineering in the Latin American context. By promoting a more in-depth understanding of cutting-edge developments in this burgeoning field, our research aims to serve as a potential stimulus to initiate pioneering initiatives and encourage collaborative efforts among Latin American researchers.

Paul Ralph, Miikka Kuutila, Hera Arif et al.

While the methodological rigor of computing research has improved considerably in the past two decades, quantitative software engineering research is hampered by immature measures and inattention to theory. Measurement-the principled assignment of numbers to phenomena-is intrinsically difficult because observation is predicated upon not only theoretical concepts but also the values and perspective of the research. Despite several previous attempts to raise awareness of more sophisticated approaches to measurement and the importance of quantitatively assessing reliability and validity, measurement issues continue to be widely ignored. The reasons are unknown, but differences in typical engineering and computer science graduate training programs (compared to psychology and management, for example) are involved. This chapter therefore reviews key concepts in the science of measurement and applies them to software engineering research. A series of exercises for applying important measurement concepts to the reader's research are included, and a sample dataset for the reader to try some of the statistical procedures mentioned is provided.

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.

Ricardo D. Caldas

Resilient cyber-physical systems comprise computing systems able to continuously interact with the physical environment in which they operate, despite runtime errors. The term resilience refers to the ability to cope with unexpected inputs while delivering correct service. Examples of resilient computing systems are Google's PageRank and the Bubblesort algorithm. Engineering for resilient cyber-physical systems requires a paradigm shift, prioritizing adaptability to dynamic environments. Software as a tool for self-management is a key instrument for dealing with uncertainty and embedding resilience in these systems. Yet, software engineers encounter the ongoing challenge of ensuring resilience despite environmental dynamic change. My thesis aims to pioneer an engineering discipline for resilient cyber-physical systems. Over four years, we conducted studies, built methods and tools, delivered software packages, and a website offering guidance to practitioners. This paper provides a condensed overview of the problems tackled, our methodology, key contributions, and results highlights. Seeking feedback from the community, this paper serves both as preparation for the thesis defense and as insight into future research prospects.

Johan Linåker, Björn Regnell, Hussan Munir

In recent years Open Innovation (OI) has gained much attention and made firms aware that they need to consider the open environment surrounding them. To facilitate this shift Requirements Engineering (RE) needs to be adapted in order to manage the increase and complexity of new requirements sources as well as networks of stakeholders. In response we build on and advance an earlier proposed software engineering framework for fostering OI, focusing on stakeholder management, when to open up, and prioritization and release planning. Literature in open source RE is contrasted against recent findings of OI in software engineering to establish a current view of the area. Based on the synthesized findings we propose a research agenda within the areas under focus, along with a framing-model to help researchers frame and break down their research questions to consider the different angles implied by the OI model.

Marc Verriere, Nicolas Schunck, Irene Kim et al.

From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z=118), it is estimated that as many as about 3000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but they play an essential role in astrophysical events such as supernova explosions or neutron star mergers that are presumed to be at the origin of most heavy elements in the Universe. Understanding the structure, reactions, and decays of nuclei across the entire chart of nuclides is an enormous challenge because of the experimental difficulties in measuring properties of interest in such fleeting objects and the theoretical and computational issues of simulating strongly-interacting quantum many-body systems. Nuclear density functional theory (DFT) is a fully microscopic theoretical framework which has the potential of providing such a quantitatively accurate description of nuclear properties for every nucleus in the chart of nuclides. Thanks to high-performance computing facilities, it has already been successfully applied to predict nuclear masses, global patterns of radioactive decay like $β$ or $γ$ decay, and several aspects of the nuclear fission process such as, e.g., spontaneous fission half-lives. Yet, predictive simulations of nuclear spectroscopy or of nuclear fission, or the quantification of theoretical uncertainties and their propagation to applications, would require several orders of magnitude more calculations than currently possible. However, most of this computational effort would be spent into generating a suitable basis of DFT wavefunctions. Such a task could potentially be considerably accelerated by borrowing tools from the field of machine learning and artificial intelligence. In this paper, we review different approaches to applying supervised and unsupervised learning techniques to nuclear DFT.

Mahdi Fahmideh, John Grundy, Aakash Ahmed et al.

Many scientific and practical areas have shown increasing interest in reaping the benefits of blockchain technology to empower software systems. However, the unique characteristics and requirements associated with Blockchain Based Software (BBS) systems raise new challenges across the development lifecycle that entail an extensive improvement of conventional software engineering. This article presents a systematic literature review of the state-of-the-art in BBS engineering research from a software engineering perspective. We characterize BBS engineering from the theoretical foundations, processes, models, and roles and discuss a rich repertoire of key development activities, principles, challenges, and techniques. The focus and depth of this survey not only gives software engineering practitioners and researchers a consolidated body of knowledge about current BBS development but also underpins a starting point for further research in this field.

Rohit Gupta, Sieglinde Kranz, Nikolaus Regnat et al.

Domain-Specific Languages (DSLs) help practitioners in contributing solutions to challenges of specific domains. The efficient development of user-friendly DSLs suitable for industrial practitioners with little expertise in modelling still is challenging. For such practitioners, who often do not model on a daily basis, there is a need to foster reduction of repetitive modelling tasks and providing simplified visual representations of DSL parts. For industrial language engineers, there is no methodical support for providing such guidelines or documentation as part of reusable language modules. Previous research either addresses the reuse of languages or guidelines for modelling. For the efficient industrial deployment of DSLs, their combination is essential: the efficient engineering of DSLs from reusable modules that feature integrated documentation and guidelines for industrial practitioners. To solve these challenges, we propose a systematic approach for the industrial engineering of DSLs based on the concept of reusable DSL Building Blocks, which rests on several years of experience in the industrial engineering of DSLs and their deployment to various organizations. We investigated our approach via focus group methods consisting of five participants from industry and research qualitatively. Ultimately, DSL Building Blocks support industrial language engineers in developing better usable DSLs and industrial practitioners in more efficiently achieving their modelling.

Mole Li, Alan Grigg, Charles Dickerson et al.

Software Product Line Engineering has attracted attention in the last two decades due to its promising capabilities to reduce costs and time to market through reuse of requirements and components. In practice, developing system level product lines in a large-scale company is not an easy task as there may be thousands of variants and multiple disciplines involved. The manual reuse of legacy system models at domain engineering to build reusable system libraries and configurations of variants to derive target products can be infeasible. To tackle this challenge, a Product Line Systems Engineering process is proposed. Specifically, the process extends research in the System Orthogonal Variability Model to support hierarchical variability modeling with formal definitions; utilizes Systems Engineering concepts and legacy system models to build the hierarchy for the variability model and to identify essential relations between variants; and finally, analyzes the identified relations to reduce the number of variation points. The process, which is automated by computational algorithms, is demonstrated through an illustrative example on generalized Rolls-Royce aircraft engine control systems. To evaluate the effectiveness of the process in the reduction of variation points, it is further applied to case studies in different engineering domains at different levels of complexity. Subject to system model availability, reduction of 14% to 40% in the number of variation points are demonstrated in the case studies.

P. Baczyk, W. Satula, J. Dobaczewski et al.

We extend the nuclear Density Functional Theory (DFT) by including proton-neutron mixing and contact isospin-symmetry-breaking (ISB) terms up to next-to-leading order (NLO). Within this formalism, we perform systematic study of the nuclear mirror and triple displacement energies, or equivalently of the Isobaric Multiplet Mass Equation (IMME) coefficients. By comparing results with those obtained within the existing Green Function Monte Carlo (GFMC) calculations, we address the fundamental question of the physical origin of the ISB effects. This we achieve by analyzing separate contributions to IMME coefficients coming from the electromagnetic and nuclear ISB terms. We show that the ISB DFT and GFMC results agree reasonably well, and that they describe experimental data with a comparable quality. Since the separate electromagnetic and nuclear ISB contributions also agree, we conclude that the beyond-mean-field electromagnetic effects may not play a dominant role in describing the ISB effects in finite nuclei.

Simone Romano, Giuseppe Scanniello, Davide Fucci et al.

Background: Noise, defined as an unwanted sound, is one of the commonest factors that could affect people's performance in their daily work activities. The software engineering research community has marginally investigated the effects of noise on software engineers' performance. Aims: We studied if noise affects software engineers' performance in (i) comprehending functional requirements and (ii) fixing faults in the source code. Method: We conducted two experiments with final-year undergraduate students in Computer Science. In the first experiment, we asked 55 students to comprehend functional requirements exposing them or not to noise, while in the second experiment 42 students were asked to fix faults in Java code. Results: The participants in the second experiment, when exposed to noise, had significantly worse performance in fixing faults in the source code. On the other hand, we did not observe any statistically significant difference in the first experiment. Conclusions: Fixing faults in source code seems to be more vulnerable to noise than comprehending functional requirements.

Manuel Mazzara, Kevin Khanda, Ruslan Mustafin et al.

In this paper we offer an overview on the topic of Microservices Science and Engineering (MSE) and we provide a collection of bibliographic references and links relevant to understand an emerging field. We try to clarify some misunderstandings related to microservices and Service-Oriented Architectures, and we also describe projects and applications our team have been working on in the recent past, both regarding programming languages construction and intelligent buildings.

Fedor Dzerzhinskiy, Leonid D. Raykov

A later translation (2015) of the article in Russian published in 1990. The article proposes an approach to defining a set of basic notions for subject area of software engineering discipline. The set of notions is intended to serve as a basis for detection and correction of some widespread conceptual mistakes in the efforts aimed at improving the quality and work productivity in creation and operation of software.

Amit K. Chopra, Munindar P. Singh

Following established tradition, software engineering today is rooted in a conceptually centralized way of thinking. The primary SE artifact is a specification of a machine -- a computational artifact -- that would meet the (elicited and) stated requirements. Therein lies a fundamental mismatch with (open) sociotechnical systems, which involve multiple autonomous social participants or principals who interact with each other to further their individual goals. No central machine governs the behaviors of the various principals. We introduce Interaction-Oriented Software Engineering (IOSE) as an approach expressly suited to the needs of open sociotechnical systems. In IOSE, specifying a system amounts to specifying the interactions among the principals as protocols. IOSE reinterprets the classical software engineering principles of modularity, abstraction, separation of concerns, and encapsulation in a manner that accords with the realities of sociotechnical systems. To highlight the novelty of IOSE, we show where well-known SE methodologies, especially those that explicitly aim to address either sociotechnical systems or the modeling of interactions among autonomous principals, fail to satisfy the IOSE principles.

John M. Jowett

The Relativistic Heavy Ion Collider at BNL has been exploring the energy frontier of nuclear physics since 2001. Its performance, flexibility and continued innovative upgrading can sustain its physics output for years to come. Now, the Large Hadron Collider at CERN is about to extend the frontier energy of laboratory nuclear collisions by more than an order of magnitude. In the coming years, its physics reach will evolve towards still higher energy, luminosity and varying collision species, within performance bounds set by accelerator technology and by nuclear physics itself. Complementary high-energy facilities will include fixed-target collisions at the CERN SPS, the FAIR complex at GSI and possible electron-ion colliders based on CEBAF at JLAB, RHIC at BNL or the LHC at CERN.

M. J. Vicente Vacas, D. Cabrera, Q. B. Li et al.

We study the interaction of the $Θ^+$ pentaquark with nuclear matter associated to the $KN$ decay channels and to the two meson cloud. We find that the potential is attractive and could be strong enough to lead to the existence of $Θ^+$ nuclear bound states.