Engine sounds originate from sequential exhaust pressure pulses rather than sustained harmonic oscillations. While neural synthesis methods typically aim to approximate the resulting spectral characteristics, we propose directly modeling the underlying pulse shapes and temporal structure. We present the Pulse-Train-Resonator (PTR) model, a differentiable synthesis architecture that generates engine audio as parameterized pulse trains aligned to engine firing patterns and propagates them through recursive Karplus-Strong resonators simulating exhaust acoustics. The architecture integrates physics-informed inductive biases including harmonic decay, thermodynamic pitch modulation, valve-dynamics envelopes, exhaust system resonances and derived engine operating modes such as throttle operation and deceleration fuel cutoff (DCFO). Validated on three diverse engine types totaling 7.5 hours of audio, PTR achieves a 21% improvement in harmonic reconstruction and a 5.7% reduction in total loss over a harmonic-plus-noise baseline model, while providing interpretable parameters corresponding to physical phenomena. Complete code, model weights, and audio examples are openly available.
Cyber-Physical Systems (CPS) produce behavior through execution on substrates coupling computation with physical processes. However, usual engineering approaches do not treat execution semantics as first-class engineering entities. Formal verification reasons about model behaviors under fixed semantic assumptions that are not revisable and do not account for physical execution constraints. Simulation-based validation explores scenarios under execution semantics that are implicitly determined by the simulation engine. In both cases, physical constraints of the execution substrate are addressed as implementation details rather than as semantic boundary conditions. In this article, it is hypothesized that making execution semantics explicit as first-class engineering entities is necessary and sufficient to bridge the gap between verified model behaviors and validated executed behaviors in CPS. To test this hypothesis, Modeling and Simulation Based Engineering (MSBE) is proposed: a methodology grounded in the Theory of Modeling and Simulation. MSBE formalizes execution conditions as four components: execution semantics, activity (behaviorally meaningful changes), admissibility constraints (physical bounds), and specified properties (behavioral guarantees). MSBE organizes engineering around an iterative cycle alternating formal execution, experimental execution, verification, and activity-mediated validation. Executability is defined as stabilization of execution conditions and the induced admissible model space. The cycle is applied to four CPS classes (human-centric, biophysical, technological, and digital twins). These applications show that the framework generalizes beyond CPS to any system whose behavior depends on explicitly defined execution conditions. Modeling and Simulation-Based Engineering
Sebe Vanbrabant, Gustavo Rovelo Ruiz, Davy Vanacken
While the increased integration of AI technologies into interactive systems enables them to solve an increasing number of tasks, the black-box problem of AI models continues to spread throughout the interactive system as a whole. Explainable AI (XAI) techniques can make AI models more accessible by employing post-hoc methods or transitioning to inherently interpretable models. While this makes individual AI models clearer, the overarching system architecture remains opaque. This challenge not only pertains to standard XAI techniques but also to human examination and conversational XAI approaches that need access to model internals to interpret them correctly and completely. To this end, we propose conceptually representing such interactive systems as sequences of structural building blocks. These include the AI models themselves, as well as control mechanisms grounded in literature. The structural building blocks can then be explained through complementary explanatory building blocks, such as established XAI techniques like LIME and SHAP. The flow and APIs of the structural building blocks form an unambiguous overview of the underlying system, serving as a communication basis for both human and automated agents, thus aligning human and machine interpretability of the embedded AI models. In this paper, we present our flow-based approach and a selection of building blocks as MATCH: a framework for engineering Multi-Agent Transparent and Controllable Human-centered systems. This research contributes to the field of (conversational) XAI by facilitating the integration of interpretability into existing interactive systems.
Large Language Models (LLMs) have transformed software engineering, but their application to physical engineering domains remains underexplored. This paper evaluates LLMs' capabilities in high-powered rocketry design through RocketBench, a benchmark connecting LLMs to high-fidelity rocket simulations. We test models on two increasingly complex design tasks: target altitude optimization and precision landing challenges. Our findings reveal that while state-of-the-art LLMs demonstrate strong baseline engineering knowledge, they struggle to iterate on their designs when given simulation results and ultimately plateau below human performance levels. However, when enhanced with reinforcement learning (RL), we show that a 7B parameter model outperforms both SoTA foundation models and human experts. This research demonstrates that RL-trained LLMs can serve as effective tools for complex engineering optimization, potentially transforming engineering domains beyond software development.
Often software engineering classes have the student concentrate on designing and planning the project but stop short of actual student team development of code. This leads to criticism by employers of new graduates that they are missing skills in working in teams and coordinating multiple overlapping changes to a code base. Additionally, students that are not actively experiencing team development are unprepared to understand and modify existing legacy-code bases written by others. This paper presents a new approach to teaching undergraduate software engineering that emphasizes not only software engineering methodology but also experiencing development as a member of a team and modifying a legacy code base. Our innovative software engineering course begins with learning the fundamentals of software engineering, followed by examining an existing framework of a social media application. The students are then grouped into multiple software teams, each focusing on a different aspect of the app. The separate teams must define requirements, design, and provide documentation on the services. Using an Agile development approach, the teams incrementally add to the code base and demonstrate features as the application evolves. Subsequent iterations of the class pick up the prior students code base, providing experience working with a legacy code base. Preliminary results of using this approach at the university are presented in this paper including quantitative analysis. Analysis of student software submissions to the cloud-based code repository shows student engagement and contributions over the span of the course. Positive student evaluations show the effectiveness of applying the principles of software engineering to the development of a complex solution in a team environment. Keywords: Software engineering, teaching, college computer science, innovative methods, agile.
Quantum computing, particularly in the area of quantum optimization, is steadily progressing toward practical applications, supported by an expanding range of hardware platforms and simulators. While Software Engineering (SE) optimization has a strong foundation, which is exemplified by the active Search-Based Software Engineering (SBSE) community and numerous classical optimization methods, the growing complexity of modern software systems and their engineering processes demands innovative solutions. This Systematic Literature Review (SLR) focuses specifically on studying the literature that applies quantum or quantum-inspired algorithms to solve classical SE optimization problems. We examine 77 primary studies selected from an initial pool of 2083 publications obtained through systematic searches of six digital databases using carefully crafted search strings. Our findings reveal concentrated research efforts in areas such as SE operations and software testing, while exposing significant gaps across other SE activities. Additionally, the SLR uncovers relevant works published outside traditional SE venues, underscoring the necessity of this comprehensive review. Overall, our study provides a broad overview of the research landscape, empowering the SBSE community to leverage quantum advancements in addressing next-generation SE challenges.
Field-effect transistors (FETs) predominantly utilize electrons for signal processing in modern electronics. In contrast, phonon-based field-effect transistors (PFETs)-which employ phonons for active thermal management-remain markedly underdeveloped, with effectively reversible thermal conductivity modulation posing a significant challenge. Herein, we propose a novel PFET architecture enabling reversible thermal conductivity modulation. This design integrates a substrate in the central region with a two-dimensional (2D) material to form an engineered junction, exploiting differences in out-of-plane acoustic phonon properties to regulate heat flow. Molecular dynamics simulations of a graphene (Gr)/hexagonal boron nitride (h-BN) junction demonstrate a substantial thermal conductivity reduction up to 44-fold at 100 K. The effect is maintained at room temperature and across diverse substrates, confirming robustness. This work establishes a new strategy for dynamic thermal management in electronics.
This systematic literature review aims to investigate the impact of artificial intelligence (AI) on the labour force in software engineering, with a particular focus on the skills needed for future software engineers, the impact of AI on the demand for software engineering skills, and the future of work for software engineers. The review identified 42 relevant publications through a comprehensive search strategy and analysed their findings. The results indicate that future software engineers will need to be competent in programming and have soft skills such as problem-solving and interpersonal communication. AI will have a significant impact on the software engineering workforce, with the potential to automate many jobs currently done by software engineers. The role of a software engineer is changing and will continue to change in the future, with AI-assisted software development posing challenges for the software engineering profession. The review suggests that the software engineering profession must adapt to the changing landscape to remain relevant and effective in the future.
Acoustic data serves as a fundamental cornerstone in advancing scientific and engineering understanding across diverse disciplines, spanning biology, communications, and ocean and Earth science. This inquiry meticulously explores recent advancements and transformative potential within the domain of acoustics, specifically focusing on machine learning (ML) and deep learning. ML, comprising an extensive array of statistical techniques, proves indispensable for autonomously discerning and leveraging patterns within data. In contrast to traditional acoustics and signal processing, ML adopts a data-driven approach, unveiling intricate relationships between features and desired labels or actions, as well as among features themselves, given ample training data. The application of ML to expansive sets of training data facilitates the discovery of models elucidating complex acoustic phenomena such as human speech and reverberation. The dynamic evolution of ML in acoustics yields compelling results and holds substantial promise for the future. The advent of electronic stethoscopes and analogous recording and data logging devices has expanded the application of acoustic signal processing concepts to the analysis of bowel sounds. This paper critically reviews existing literature on acoustic signal processing for bowel sound analysis, outlining fundamental approaches and applicable machine learning principles. It chronicles historical progress in signal processing techniques that have facilitated the extraction of valuable information from bowel sounds, emphasizing advancements in noise reduction, segmentation, signal enhancement, feature extraction, sound localization, and machine learning techniques...
This paper reports on a study of transferring a desktop-based model-based engineering tool to a web application. The study has been conducted in the WEBMODEL project where the well-established technology stack around the Eclipse platform and the Eclipse Modeling Framework was lifted into a cloud-based environment. As results, a modeling language independent tooling kernel for web-based modeling tools and a minimal prototypical web-based implementation of the AutoFOCUS 3 model-based engineering tool are presented. Furthermore, the report documents experiences and implementation advises gained during the implementation.
Mohamed Ladaoui Benferhat, Samira Debache Benzagouta, Abdelouahab Bouttout
et al.
This paper aims at evaluating the acoustical quality of historical Ottoman masjids in Algeria. Measurements were carried out according to current international standards, allowing calculation of acoustical parameters including reverberation time, speech transmission index and Clarity. Three Ottoman masjids located in Algiers (Jedid, Ali-Bitchin, and Safir) were selected. The main purpose of the study was to evaluate the acoustics of the prayer hall in relation to its worship use, assuming as a reference studies of existing masjids. Results showed that two of three worship spaces are generally reverberant under unoccupied conditions (with mid frequency reverberation times of about 3 s), while the other has reverberation time of about 1 s at medium frequencies. Speech intelligibility under unoccupied conditions is between poor and fair for Jedid and Ali-Bitchin masjids, while in Safir masjid values were generally above 0.6. Presence of architectural elements made C50 values quite scattered and characterized by a clear non-symmetrical distribution. Calculations to consider the effect of occupancy were also performed, resulting in significantly drier acoustics with improved clarity.
Manuel De Stefano, Fabiano Pecorelli, Dario Di Nucci
et al.
Quantum computing is no longer only a scientific interest but is rapidly becoming an industrially available technology that can potentially overcome the limits of classical computation. Over the last years, all major companies have provided frameworks and programming languages that allow developers to create their quantum applications. This shift has led to the definition of a new discipline called quantum software engineering, which is demanded to define novel methods for engineering large-scale quantum applications. While the research community is successfully embracing this call, we notice a lack of systematic investigations into the state of the practice of quantum programming. Understanding the challenges that quantum developers face is vital to precisely define the aims of quantum software engineering. Hence, in this paper, we first mine all the GitHub repositories that make use of the most used quantum programming frameworks currently on the market and then conduct coding analysis sessions to produce a taxonomy of the purposes for which quantum technologies are used. In the second place, we conduct a survey study that involves the contributors of the considered repositories, which aims to elicit the developers' opinions on the current adoption and challenges of quantum programming. On the one hand, the results highlight that the current adoption of quantum programming is still limited. On the other hand, there are many challenges that the software engineering community should carefully consider: these do not strictly pertain to technical concerns but also socio-technical matters.
Software engineers need relevant and up-to-date architectural knowledge (AK), in order to make well-founded design decisions. However, finding such AK is quite challenging. One pragmatic approach is to search for AK on the web using traditional search engines (e.g. Google); this is common practice among software engineers. Still, we know very little about what AK is retrieved, from where, and how useful it is. In this paper, we conduct an empirical study with 53 software engineers, who used Google to make design decisions using the Attribute-Driven-Design method. Based on how the subjects assessed the nature and relevance of the retrieved results, we determined how effective web search engines are to find relevant architectural information. Moreover, we identified the different sources of AK on the web and their associated AK concepts.
There is an ongoing debate in computer science how algorithms should best be studied. Some scholars have argued that experimental evaluations should be conducted, others emphasize the benefits of formal analysis. We believe that this debate less of a question of either-or, because both views can be integrated into an overarching framework. It is the ambition of this paper to develop such a framework of algorithm engineering with a theoretical foundation in the philosophy of science. We take the empirical nature of algorithm engineering as a starting point. Our theoretical framework builds on three areas discussed in the philosophy of science: ontology, epistemology and methodology. In essence, ontology describes algorithm engineering as being concerned with algorithmic problems, algorithmic tasks, algorithm designs and algorithm implementations. Epistemology describes the body of knowledge of algorithm engineering as a collection of prescriptive and descriptive knowledge, residing in World 3 of Popper's Three Worlds model. Methodology refers to the steps how we can systematically enhance our knowledge of specific algorithms. In this context, we identified seven validity concerns and discuss how researchers can respond to falsification. Our framework has important implications for researching algorithms in various areas of computer science.
Hennie Huijgens, Ayushi Rastogi, Ernst Mulders
et al.
In 2014, a Microsoft study investigated the sort of questions that data science applied to software engineering should answer. This resulted in 145 questions that developers considered relevant for data scientists to answer, thus providing a research agenda to the community. Fast forward to five years, no further studies investigated whether the questions from the software engineers at Microsoft hold for other software companies, including software-intensive companies with different primary focus (to which we refer as software-defined enterprises). Furthermore, it is not evident that the problems identified five years ago are still applicable, given the technological advances in software engineering.
Alberto Gutierrez-Torre, Josep Ll. Berral, David Buchaca
et al.
Maritime traffic emissions are a major concern to governments as they heavily impact the Air Quality in coastal cities. Ships use the Automatic Identification System (AIS) to continuously report position and speed among other features, and therefore this data is suitable to be used to estimate emissions, if it is combined with engine data. However, important ship features are often inaccurate or missing. State-of-the-art complex systems, like CALIOPE at the Barcelona Supercomputing Center, are used to model Air Quality. These systems can benefit from AIS based emission models as they are very precise in positioning the pollution. Unfortunately, these models are sensitive to missing or corrupted data, and therefore they need data curation techniques to significantly improve the estimation accuracy. In this work, we propose a methodology for treating ship data using Conditional Restricted Boltzmann Machines (CRBMs) plus machine learning methods to improve the quality of data passed to emission models. Results show that we can improve the default methods proposed to cover missing data. In our results, we observed that using our method the models boosted their accuracy to detect otherwise undetectable emissions. In particular, we used a real data-set of AIS data, provided by the Spanish Port Authority, to estimate that thanks to our method, the model was able to detect 45% of additional emissions, of additional emissions, representing 152 tonnes of pollutants per week in Barcelona and propose new features that may enhance emission modeling.
Mohit Kumar Ahuja, Mohamed-Bachir Belaid, Pierre Bernabé
et al.
Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI). To deem an AI system as trustworthy, it is crucial to assess its behaviour and characteristics against a gold standard of Trustworthy AI, consisting of guidelines, requirements, or only expectations. While AI systems are highly complex, their implementations are still based on software. The software engineering community has a long-established toolbox for the assessment of software systems, especially in the context of software testing. In this paper, we argue for the application of software engineering and testing practices for the assessment of trustworthy AI. We make the connection between the seven key requirements as defined by the European Commission's AI high-level expert group and established procedures from software engineering and raise questions for future work.