Crossing-fault tunnel is vulnerable especially when the fault dislocation happens. The propagation of fault rupture affects the deformation on ground surface and failure on underground structures and it is vital for crossing-fault tunnel to reveal dislocation mechanism with fault rupture propagation. Thus, based on different fault parameters such as width of fault fracture zone and fault dip, three experimental tests were carried out to obtain the propagation mechanism of fault rupture and its impact on ground deformation and failure of tunnel with a self-designed large-scaled model box. The test results firstly show that the fault parameters effect the pattern of fault rupture. Under the propagation of fault rupture, four sub-regions were identified on ground surface as stability region in footwall, coordination region, severe deformation region and stability region in hanging wall. The parameter influence from fault fracture zone will be sensitive for tunnel and the tunnel near fault fracture zone is most damaged. Typical damage types transversely can be divided into four categories according to the degree of damage and moderate damage or serious damage should be avoided as far as possible to guarantee the normal operation of the tunnel.
Architectural engineering. Structural engineering of buildings, Structural engineering (General)
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
Recent advances in large language models (LLMs) have enabled software engineering agents to tackle complex code modification tasks. Most existing approaches rely on execution feedback from containerized environments, which require dependency-complete setup and physical execution of programs and tests. While effective, this paradigm is resource-intensive and difficult to maintain, substantially complicating agent training and limiting scalability. We propose SWE-World, a Docker-free framework that replaces physical execution environments with a learned surrogate for training and evaluating software engineering agents. SWE-World leverages LLM-based models trained on real agent-environment interaction data to predict intermediate execution outcomes and final test feedback, enabling agents to learn without interacting with physical containerized environments. This design preserves the standard agent-environment interaction loop while eliminating the need for costly environment construction and maintenance during agent optimization and evaluation. Furthermore, because SWE-World can simulate the final evaluation outcomes of candidate trajectories without real submission, it enables selecting the best solution among multiple test-time attempts, thereby facilitating effective test-time scaling (TTS) in software engineering tasks. Experiments on SWE-bench Verified demonstrate that SWE-World raises Qwen2.5-Coder-32B from 6.2\% to 52.0\% via Docker-free SFT, 55.0\% with Docker-free RL, and 68.2\% with further TTS. The code is available at https://github.com/RUCAIBox/SWE-World
Inclusive mobility is an essential pillar of sustainable urban development, ensuring equitable access to public transport for all categories of users, including people with disabilities, the elderly and other vulnerable groups. The study analyses the policies and strategies adopted by several European countries, with a focus on Germany, Sweden, France, Spain and Romania. It highlights good practices, such as accessible infrastructure, digitalisation of services and integration of public transport. At the same time, major challenges are identified, such as lack of funding, implementation gaps and resistance to change. Innovative solutions, such as modular capsules for the transport of people with disabilities, are also explored. The study provides recommendations for improving inclusive mobility in Romania, highlighting the need for a coherent national strategy, investments in modernisation and digitalisation.
Architectural engineering. Structural engineering of buildings, Engineering design
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.
Cristina Martinez Montes, Birgit Penzenstadler, Robert Feldt
The well-being of software engineers is increasingly under strain due to the high-stress nature of their roles, which involve complex problem-solving, tight deadlines, and the pressures of rapidly evolving technologies. Despite increasing recognition of mental health challenges in software engineering, few studies focus on the factors that sustain or undermine well-being. Existing research often overlooks the interaction between personal, collaborative, and organisational influences on this unique population. This study fills this gap by investigating the specific factors affecting the well-being of software engineers. We conducted 15 qualitative interviews and complemented them with a confirmatory cross-country survey to validate and extend our findings to a broader population. Our mixed-methods approach provides a robust framework to identify key factors influencing well-being, including personal perceptions of well-being, interpersonal and collaborative dynamics, workplace support and recognition, organisational culture, and specific stressors inherent to software engineering. By offering a detailed, context-specific exploration of these factors, our study builds on existing literature and provides actionable insights for improving well-being in software engineering. We conclude with policy recommendations to inform organisational strategies and develop targeted interventions that address the specific challenges of this field, contributing to more sustainable and supportive work environments.
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.
Engineering design risks could cause unaffordable losses, and thus risk assessment plays a critical role in engineering design. On the other hand, the high complexity of modern engineering designs makes it difficult to assess risks effectively and accurately due to the complex two-way, dynamic causal-effect risk relations in engineering designs. To address this problem, this paper proposes a new risk assessment method called token fuzzy cognitive map (Token-FCM). Its basic idea is to model the two-way causal-risk relations with the FCM method, and then augment FCM with a token mechanism to model the dynamics in causal-effect risk relations. Furthermore, the fuzzy sets and the group decision-making method are introduced to initialize the Token-FCM method so that comprehensive and accurate risk assessments can be attained. The effectiveness of the proposed method has been demonstrated by a real example of engine design for a horizontal directional drilling machine.
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.
A cidade de Campos dos Goytacazes figura como centro urbano do maior município em extensão territorial do estado do Rio de Janeiro, com mais de quinhentos mil habitantes, vivenciou nas décadas de 2000 e 2010 uma forte expansão urbana marcada por uma intensa atividade imobiliária. Dentre diversos fatores que alavancaram este processo, o mais significativo foi a autonomia financeira decorrente de repasses de royalties e participações especiais da extração do petróleo. O debate proposto neste trabalho é de como uma cidade com orçamento público elevado, condições geomorfológicas de uma extensa planície sem grandes restrições e de grande potencial ambiental propícia a uma expansão urbana planejada, enfrenta crescimento que resulta em fragmentação e estratificação do solo urbano, consolidando uma maneira de ocupação do solo marcada pela segregação socioespacial. A principal ferramenta de análise é a espacialização de dados por meio de mapas temáticos para
a leitura das transformações urbanas ao longo do tempo, explicitando aspectos socioespaciais e morfológicos.
Architectural engineering. Structural engineering of buildings
Allysson Allex Araújo, Marcos Kalinowski, Daniel Graziotin
This paper explores the intricate challenge of understanding and measuring software engineer behavior. More specifically, we revolve around a central question: How can we enhance our understanding of software engineer behavior? Grounded in the nuanced complexities addressed within Behavioral Software Engineering (BSE), we advocate for holistic methods that integrate quantitative measures, such as psychometric instruments, and qualitative data from diverse sources. Furthermore, we delve into the relevance of this challenge within national and international contexts, highlighting the increasing interest in understanding software engineer behavior. Real-world initiatives and academic endeavors are also examined to underscore the potential for advancing this research agenda and, consequently, refining software engineering practices based on behavioral aspects. Lastly, this paper addresses different ways to evaluate the progress of this challenge by leveraging methodological skills derived from behavioral sciences, ultimately contributing to a deeper understanding of software engineer behavior and software engineering practices.
Abstract The maximum section column of townhouses in Takayama can be classified into type A [earthen floor type] and type B [living space type]. The dimensions of maximum section columns tended to increase as the plane scale increased and the frontage widened. The atrium area tended to expand in the early Meiji period. We observed that the form of a maximum section column standing in one straight line at a rectangle atrium became the form of a maximum section column standing crossing‐axis in the plane of the structure of ridge direction and the span at irregularly shaped atrium, together complicating the plane and atrium shapes.
Architecture, Architectural engineering. Structural engineering of buildings
Eriks Klotins, Michael Unterkalmsteiner, Tony Gorschek
Background - Startup companies are becoming important suppliers of innovative and software intensive products. The failure rate among startups is high due to lack of resources, immaturity, multiple influences and dynamic technologies. However, software product engineering is the core activity in startups, therefore inadequacies in applied engineering practices might be a significant contributing factor for high failure rates. Aim - This study identifies and categorizes software engineering knowledge areas utilized in startups to map out the state-of-art, identifying gaps for further research. Method - We perform a systematic literature mapping study, applying snowball sampling to identify relevant primary studies. Results - We have identified 54 practices from 14 studies. Although 11 of 15 main knowledge areas from SWEBOK are covered, a large part of categories is not. Conclusions - Existing research does not provide reliable support for software engineering in any phase of a startup life cycle. Transfer of results to other startups is difficult due to low rigor in current studies.
Industrial process engineering and PLC program development have traditionally favored Function Block Diagram (FBD) programming over classical imperative style programming like the object oriented and functional programming paradigms. The increasing momentum in the adoption and trial of ideas now classified as 'No Code' or 'Low Code' alongside the mainstream success of statistical learning theory or the so-called machine learning is redefining the way in which we structure programs for the digital machine to execute. A principal focus of 'No Code' is deriving executable programs directly from a set of requirement documents or any other documentation that defines consumer or customer expectation. We present a method for generating Function Block Diagram (FBD) programs as either the intermediate or final artifact that can be executed by a target system from a set of requirement documents using a constrained selection algorithm that draws from the top line of an associated recommender system. The results presented demonstrate that this type of No-code generative model is a viable option for industrial process design.
Scaffold-free tissue engineering strategies aim to recapitulate key aspects of normal developmental processes as a means of generating highly biomimetic grafts. Cartilage and fibrocartilaginous tissues have successfully been engineered by bringing together large numbers of cells, cellular aggregates or microtissues and allowing them to self-assemble or self-organize into a functional graft. Despite the promise of such approaches, considerable challenges still remain, such as engineering scaled-up tissues with predefined geometries, ensuring robust fusion between adjacent cellular aggregates or microtissues, and directing the (re)modelling of such biological building blocks into a unified scaled-up graft with hierarchical matrix organisation mimetic of the native tissue. In this study, we first demonstrate the benefits of engineering cartilage via the fusion of multiple cartilage microtissues compared to conventional scaffold-free approaches where (millions of) individual cells are allowed to aggregate and generate a cartilaginous graft. Key advantages include the engineering of a tissue with a richer extracellular matrix, a more hyaline-like cartilage phenotype and a final graft which better matched the intended geometry. A major drawback associated with this approach is that individual microtissues did not completely (re)model and remnants of their initial architectures where still evident throughout the macrotissue. In an attempt to address this limitation, the enzyme chondroitinase ABC (cABC) was employed to accelerate structural (re)modelling of the engineered tissue. Temporal enzymatic treatment supported robust fusion between adjacent microtissues, enhanced microtissue (re)modelling and supported the development of a more biomimetic tissue with a zonally organised collagen architecture. Additionally, we observed that cABC treatment modulated matrix composition (rebalancing the collagen:glycosaminoglycans ratio), tissue phenotype, and to a lesser extent, tissue mechanics. Ultimately, this work demonstrates that microtissue self-organisation is an effective method for engineering scaled-up cartilage grafts with a pre-defined geometry and near-native levels of ECM accumulation. Importantly we have demonstrated that key limitations associated with tissue engineering using multiple cellular aggregates, microtissues or organoids can be alleviated by temporal enzymatic treatment during graft development.
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.
Recent innovations in self-supervised representation learning have led to remarkable advances in natural language processing. That said, in the speech processing domain, self-supervised representation learning-based systems are not yet considered state-of-the-art. We propose leveraging recent advances in self-supervised-based speech processing to create a common speech analysis engine. Such an engine should be able to handle multiple speech processing tasks, using a single architecture, to obtain state-of-the-art accuracy. The engine must also enable support for new tasks with small training datasets. Beyond that, a common engine should be capable of supporting distributed training with client in-house private data. We present the architecture for a common speech analysis engine based on the HuBERT self-supervised speech representation. Based on experiments, we report our results for language identification and emotion recognition on the standard evaluations NIST-LRE 07 and IEMOCAP. Our results surpass the state-of-the-art performance reported so far on these tasks. We also analyzed our engine on the emotion recognition task using reduced amounts of training data and show how to achieve improved results.
In this study, comparative investigations of a typical steel moment frame and a steel frame-braced core-tube structure, designed following different seismic codes, are conducted to evaluate the significant differences between the Chinese and the United States (US) seismic codes for steel buildings. The study outcomes can enhance the understanding of the differences in steel structure design between two countries, especially the differences in seismic performance. Specifically, the design outputs of the structural components, material consumptions, dynamic characteristics, and seismic loads were analyzed. Subsequently, finite element (FE) models based on the design outputs were established to assess the seismic performance and collapse margin. Furthermore, the local buckling effect of the steel components can be considered in the FE model, which can effectively predict the buckling-induced strength deterioration under excessive deformation. The comparison results reveal that, under the same design conditions in this study, both the strength requirement and deflection limit are critical factors that control the typical buildings design following the Chinese codes. However, the strength requirement is the primary factor that controls the design outcomes following the US codes. Moreover, the collapse resistance of the steel moment frames from both codes are similar, but the material consumption based on the Chinese design code is comparatively higher. In contrast, the material consumption of the steel frame-braced core-tube structures are comparable, but the collapse resistance of the US design code is better. Furthermore, the collapse analyses indicate that the local buckling effect could significantly reduce the collapse resistance.