Manu Sasidharan, Manuel Herrera, Gokcen Yilmaz
et al.
Bridges are vital connections within transport networks, but scour-induced failures can severely disrupt network connectivity, increase user travel delays, and reduce reliability. The goal of this paper is to prioritize bridge scour management actions to improve transport network performance, defined here by connectivity and delay. This paper introduces a novel risk-informed decision-support framework that aids long-term programming and real-time operational decision processes. This framework couples bridge-level monitoring with network-level prioritization based on predicted transport-user impacts and early-warning triggers. It quantifies expected travel delays and network connectivity under different flood scenarios, guiding maintenance and protection investments toward bridges with the largest performance consequences. The framework is applied to a case study on UK railway bridges where warning times to failure are estimated and proactive bridge closures are simulated to assess operational impacts. The results inform the risk-aware prioritization of bridges for operational measures. This risk-informed approach extends traditional scour management by explicitly tying asset interventions to user-oriented performance outcomes and by supporting long-term programming and real-time operational decisions under uncertainty.
Dectot--Le Monnier de Gouville Esteban, Mohammad Hamdaqa, Moataz Chouchen
YARA has established itself as the de facto standard for "Detection as Code," enabling analysts and DevSecOps practitioners to define signatures for malware identification across the software supply chain. Despite its pervasive use, the open-source YARA ecosystem remains characterized by ad-hoc sharing and opaque quality. Practitioners currently rely on public repositories without empirical evidence regarding the ecosystem's structural characteristics, maintenance and diffusion dynamics, or operational reliability. We conducted a large-scale mixed-method study of 8.4 million rules mined from 1,853 GitHub repositories. Our pipeline integrates repository mining to map supply chain dynamics, static analysis to assess syntactic quality, and dynamic benchmarking against 4,026 malware and 2,000 goodware samples to measure operational effectiveness. We reveal a highly centralized structure where 10 authors drive 80% of rule adoption. The ecosystem functions as a "static supply chain": repositories show a median inactivity of 782 days and a median technical lag of 4.2 years. While static quality scores appear high (mean = 99.4/100), operational benchmarking uncovers significant noise (false positives) and low recall. Furthermore, coverage is heavily biased toward legacy threats (Ransomware), leaving modern initial access vectors (Loaders, Stealers) severely underrepresented. These findings expose a systemic "double penalty": defenders incur high performance overhead for decayed intelligence. We argue that public repositories function as raw data dumps rather than curated feeds, necessitating a paradigm shift from ad-hoc collection to rigorous rule engineering. We release our dataset and pipeline to support future data-driven curation tools.
Understanding and addressing uncertainty is crucial for effective public transport design. This literature review examines key aspects of modelling and optimisation on network design, line planning and timetabling under uncertain conditions. We restrict the analysis to the case where some parameters in those mathematical problems have an uncertain value, that is, either characterised as a probability distribution, scenarios, or updated over multiple stages. The literature reveals the use of a wide range of concepts and models, the most common ones being robustness, multi-period planning, and stochastic programming. The research varies significantly in the selection of parameters to be unknown and/or uncertain and, in turn, those that are predetermined and deterministic. A critical analysis leads us to the following insights. The value of including uncertainty in the optimisation is often not quantified, and real-life applications that can estimate its benefits are scarce. The analysis reveals variations in terminology across different papers, with multiple overlapping and/or different concepts benefiting from similar mathematical approaches, highlighting the complexities researchers face. Our analysis indicates that only few articles use data to derive realistic or accurate scenarios and distributions to be used in stochastic optimisation approaches. Despite these complexities, ongoing advancements in modelling and optimisation techniques offer a promising path towards more effective and resilient public transport systems. These improvements ultimately enhance service quality and increase passenger satisfaction.
Florian Felten, Gabriel Apaza, Gerhard Bräunlich
et al.
Engineering design optimization seeks to automatically determine the shapes, topologies, or parameters of components that maximize performance under given conditions. This process often depends on physics-based simulations, which are difficult to install, computationally expensive, and require domain-specific expertise. To mitigate these challenges, we introduce EngiBench, the first open-source library and datasets spanning diverse domains for data-driven engineering design. EngiBench provides a unified API and a curated set of benchmarks -- covering aeronautics, heat conduction, photonics, and more -- that enable fair, reproducible comparisons of optimization and machine learning algorithms, such as generative or surrogate models. We also release EngiOpt, a companion library offering a collection of such algorithms compatible with the EngiBench interface. Both libraries are modular, letting users plug in novel algorithms or problems, automate end-to-end experiment workflows, and leverage built-in utilities for visualization, dataset generation, feasibility checks, and performance analysis. We demonstrate their versatility through experiments comparing state-of-the-art techniques across multiple engineering design problems, an undertaking that was previously prohibitively time-consuming to perform. Finally, we show that these problems pose significant challenges for standard machine learning methods due to highly sensitive and constrained design manifolds.
Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. However, engineering design poses a fundamentally different challenge for large language models (LLMs) compared with traditional textbook-style problem solving or factual question answering. Although existing benchmarks have driven progress in areas such as language understanding, code synthesis, and scientific problem solving, real-world engineering design demands the synthesis of domain knowledge, navigation of complex trade-offs, and management of the tedious processes that consume much of practicing engineers' time. Despite these shared challenges across engineering disciplines, no benchmark currently captures the unique demands of engineering design work. In this work, we introduce EngDesign, an Engineering Design benchmark that evaluates LLMs' abilities to perform practical design tasks across nine engineering domains. Unlike existing benchmarks that focus on factual recall or question answering, EngDesign uniquely emphasizes LLMs' ability to synthesize domain knowledge, reason under constraints, and generate functional, objective-oriented engineering designs. Each task in EngDesign represents a real-world engineering design problem, accompanied by a detailed task description specifying design goals, constraints, and performance requirements. EngDesign pioneers a simulation-based evaluation paradigm that moves beyond textbook knowledge to assess genuine engineering design capabilities and shifts evaluation from static answer checking to dynamic, simulation-driven functional verification, marking a crucial step toward realizing the vision of engineering Artificial General Intelligence (AGI).
The future of software engineering--SE 3.0--is unfolding with the rise of AI teammates: autonomous, goal-driven systems collaborating with human developers. Among these, autonomous coding agents are especially transformative, now actively initiating, reviewing, and evolving code at scale. This paper introduces AIDev, the first large-scale dataset capturing how such agents operate in the wild. Spanning over 456,000 pull requests by five leading agents--OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code--across 61,000 repositories and 47,000 developers, AIDev provides an unprecedented empirical foundation for studying autonomous teammates in software development. Unlike prior work that has largely theorized the rise of AI-native software engineering, AIDev offers structured, open data to support research in benchmarking, agent readiness, optimization, collaboration modeling, and AI governance. The dataset includes rich metadata on PRs, authorship, review timelines, code changes, and integration outcomes--enabling exploration beyond synthetic benchmarks like SWE-bench. For instance, although agents often outperform humans in speed, their PRs are accepted less frequently, revealing a trust and utility gap. Furthermore, while agents accelerate code submission--one developer submitted as many PRs in three days as they had in three years--these are structurally simpler (via code complexity metrics). We envision AIDev as a living resource: extensible, analyzable, and ready for the SE and AI communities. Grounding SE 3.0 in real-world evidence, AIDev enables a new generation of research into AI-native workflows and supports building the next wave of symbiotic human-AI collaboration. The dataset is publicly available at https://github.com/SAILResearch/AI_Teammates_in_SE3. > AI Agent, Agentic AI, Coding Agent, Agentic Coding, Software Engineering Agent
Armin Ariamajd, Raquel López-Ríos de Castro, Andrea Volkamer
The increasing importance of Computational Science and Engineering has highlighted the need for high-quality scientific software. However, research software development is often hindered by limited funding, time, staffing, and technical resources. To address these challenges, we introduce PyPackIT, a cloud-based automation tool designed to streamline research software engineering in accordance with FAIR (Findable, Accessible, Interoperable, and Reusable) and Open Science principles. PyPackIT is a user-friendly, ready-to-use software that enables scientists to focus on the scientific aspects of their projects while automating repetitive tasks and enforcing best practices throughout the software development life cycle. Using modern Continuous software engineering and DevOps methodologies, PyPackIT offers a robust project infrastructure including a build-ready Python package skeleton, a fully operational documentation and test suite, and a control center for dynamic project management and customization. PyPackIT integrates seamlessly with GitHub's version control system, issue tracker, and pull-based model to establish a fully-automated software development workflow. Exploiting GitHub Actions, PyPackIT provides a cloud-native Agile development environment using containerization, Configuration-as-Code, and Continuous Integration, Deployment, Testing, Refactoring, and Maintenance pipelines. PyPackIT is an open-source software suite that seamlessly integrates with both new and existing projects via a public GitHub repository template at https://github.com/repodynamics/pypackit.
Leonhard Applis, Yuntong Zhang, Shanchao Liang
et al.
The growth of Large Language Model (LLM) technology has raised expectations for automated coding. However, software engineering is more than coding and is concerned with activities including maintenance and evolution of a project. In this context, the concept of LLM agents has gained traction, which utilize LLMs as reasoning engines to invoke external tools autonomously. But is an LLM agent the same as an AI software engineer? In this paper, we seek to understand this question by developing a Unified Software Engineering agent or USEagent. Unlike existing work which builds specialized agents for specific software tasks such as testing, debugging, and repair, our goal is to build a unified agent which can orchestrate and handle multiple capabilities. This gives the agent the promise of handling complex scenarios in software development such as fixing an incomplete patch, adding new features, or taking over code written by others. We envision USEagent as the first draft of a future AI Software Engineer which can be a team member in future software development teams involving both AI and humans. To evaluate the efficacy of USEagent, we build a Unified Software Engineering bench (USEbench) comprising of myriad tasks such as coding, testing, and patching. USEbench is a judicious mixture of tasks from existing benchmarks such as SWE-bench, SWT-bench, and REPOCOD. In an evaluation on USEbench consisting of 1,271 repository-level software engineering tasks, USEagent shows improved efficacy compared to existing general agents such as OpenHands CodeActAgent. There exist gaps in the capabilities of USEagent for certain coding tasks, which provides hints on further developing the AI Software Engineer of the future.
The concept of personal rapid transit (PRT) has been proposed for over 70 years since 1953, and the practice of PRT has been advancing slowly worldwide. This paper analyzes advantages of PRT and applicable scenarios of PRT in the future city. The future city has a new functional structure, spatial form and public transportation system, and the era of autonomous driving is coming. This paper analyzes the reasons why PRT is suitable for future urban planning and design. With the advancement of various technologies and the decline of costs, through good multi-discipline planning and design, this paper focuses on how PRT can be perfectly integrated into the future urban space, architecture, landscape environment, and the road network. The PRT guideway could highlight the characteristics of future urban space landscape, avoiding existing visual invasion and other issues. As an important part of the public transport system in the future city, PRT can help achieve the goal of sustainable mobility once there are more practices. The next step for PRT is most likely to be the connection of urban medium- or large-carrying-capacity public transport. The paper takes Suzhou Taihu national tourism resort, China as an example for the recent expansion of PRT's practice.
Currently, the detection technology for road surface potholes, primarily focuses on the identification and segmentation, lacking the ability to quantitatively analyze the damage inflicted by road potholes. Therefore, this pa per proposes a method based on three-dimensional point clouds for the identification, segmentation, and reconstruction of road potholes, ultimately leading to the quantification of the damage volume. An RGB-D depth sensor is employed to collect point cloud data of road potholes. Voxel filtering and voxelization downsampling are used for denoising, filtering, and enhancing data processing efficiency. Surface segmentation is achieved through RANSAC (Random Sample Consensus) and Euclidean clustering, while the Alpha Shapes algorithm is utilized for three-dimensional volume reconstruction, facilitating the volumetric quantification of potholes. For evaluation, comparative experiments were conducted under different lighting conditions and shooting distances. The experimental results demonstrate that the proposed algorithm achieves an accuracy of 96.4% in volumetric damage measurement of road potholes, accurately determining the damage volume of pothole.
Vasileios Kouvakis, Stylianos E. Trevlakis, Alexandros-Apostolos A. Boulogeorgos
et al.
Demands for secure, ubiquitous, and always-available connectivity have been identified as the pillar design parameters of the next generation radio access networks (RANs). Motivated by this, the current contribution introduces a network architecture that leverages blockchain technologies to augment security in RANs, while enabling dynamic coverage expansion through the use of intermediate commercial or private wireless nodes. To assess the efficiency and limitations of the architecture, we employ Markov chain theory in order to extract a theoretical model with increased engineering insights. Building upon this model, we quantify the latency as well as the security capabilities in terms of probability of successful attack, for three scenarios, namely fixed topology fronthaul network, advanced coverage expansion and advanced mobile node connectivity, which reveal the scalability of the blockchain-RAN architecture.
Telecommunication, Transportation and communications
ZHOU Jie 1, 2, GUO Zhongqiu 1, SHI Zhenming 1, 2, LIU Chengjun 1, ZHOU Huade 1, BAN Chao 1
The centrifugal model test is an effective method to solve the problem of frozen soil engineering in a long time span. But the refrigeration end of the existing centrifugal model test devices can only control the temperature boundary, and can not form a continuous freezing wall in the soil body. In order to simulate the freezing process of tubular cold source in soil during artificial formation freezing, a set of frozen soil centrifugal test equipment using semiconductor refrigeration and controlled liquid nitrogen freezing is independently designed based on the TJ-150 geotechnical centrifuge of Tongji University. Ouring the test process, the semiconductor refrigeration equipment can realize the control of the boundary temperature of the freezing wall, and the liquid nitrogen freezing devices can realize the stable storage and fixed point transportation of liquid nitrogen. Based on this set of test equipment, a centrifuge micro-pore pressure static penetration test is carried out to explore the change rules of permeability coefficient of soft soil before and after freeze-thaw. The performance of the devices is tested under 15g centrifugal super-gravity, and the feasibility of measuring the permeability coefficient of soft clay before and after freeze-thaw by using the centrifuge micro-pore pressure static penetration test devices is preliminarily explored.
Engineering geology. Rock mechanics. Soil mechanics. Underground construction
In order to reveal the evolution law of adsorbed bound water and the deformation characteristics of high liquid limit soil under static and dynamic loading, Hainan high liquid limit soil and Hunan clayey sand were selected as comparative samples. Through consolidation, consolidation-creep, and dynamic triaxial tests, the deformation patterns of high liquid limit soil under different loading conditions were obtained. By combining nuclear magnetic resonance tests, the variation pattern of adsorbed bound water content in specimens before and after the consolidation and dynamic triaxial tests was studied. The results show that under static loading, the compression coefficient of high liquid limit soil meets the requirements of the Specifications for Design of Highway Subgrades (JTG D30—2019) for subgrade fill materials. The adsorbed bound water content is basically stable, and the consolidation-creep deformation is small. Under dynamic loading, the elastic strain and permanent axial strain of the specimen increase with increasing loading amplitude. Dynamic loading significantly reduces the adsorbed bound water content in the high liquid limit soil, leading to the large plastic cumulative deformation of the soil under cyclic loading. The study can provide a reference for the engineering application of high liquid limit soils as lower embankment fill.
This paper analyzes and compares patterns of U.S. domestic rail freight volumes during, and after the disruptions caused by the 2007-2009 Great Recession and the COVID-19 pandemic in 2020. Trends in rail and intermodal shipment data are examined in conjunction with economic indicators, focusing on the extent of drop and recovery of freight volumes of various commodities and intermodal shipments, and the lead/lag time with respect to economic drivers. While impacts from and the rebound from the Great Recessions were slow to develop, COVID-19 produced both profound disruptions in the freight market and rapid rebound, with important variations across commodity types. Energy-related commodities (i.e., coal, petroleum, and fracking sand), dropped during the pandemic while demand for other commodities (i.e., grain products and lumber, and intermodal freight). rebounded rapidly and in some cases grew. Overall rail freight experienced a rapid rebound following the precipitous drop in traffic in March and April 2020, achieving a near-full recovery in five months. As the recovery proceeded through 2020, intermodal flow, containers moving by rail for their longest overland trips, rebounded strongly, some exceeding 2019 levels. In contrast, rail flows during the Great Recession changed slowly with the onset and recovery, extending over multiple years. Pandemic response reflected the impacts of quick shutdowns and a rapid shift in consumer purchasing patterns. Results for the pandemic illustrate the resilience of U.S. rail freight industry and the multifaceted role it plays in the overall logistics system. Amid a challenging logistical environment, freight rail kept goods moving when other methods of transport were constrained.