Abstract: The review of space applications of MEMS sensors as well as the fi rst presentation
of plasma fluid optical gas microspectrometer and ion mass microspectrometer, developed in
Poland for future Venusian, Martian and Lunar missions have been presented. Additionally,
Polish subminiature biomedical lab-on-chip payload and its space tests at LOE has been
showed. Finally CSAC atomic microclock and its applications has been discussed.
Keywords: MEMS; Space; Miniaturization; Spectrometer; Lab-chip; CSAC
Highway engineering. Roads and pavements, Bridge engineering
Abstract Critical for metering and protection in electric railway traction power supply systems (TPSSs), the measurement performance of voltage transformers (VTs) must be timely and reliably monitored. This paper outlines a three-step, RMS data only method for evaluating VTs in TPSSs. First, a kernel principal component analysis approach is used to diagnose the VT exhibiting significant measurement deviations over time, mitigating the influence of stochastic fluctuations in traction loads. Second, a back propagation neural network is employed to continuously estimate the measurement deviations of the targeted VT. Third, a trend analysis method is developed to assess the evolution of the measurement performance of VTs. Case studies conducted on field data from an operational TPSS demonstrate the effectiveness of the proposed method in detecting VTs with measurement deviations exceeding 1% relative to their original accuracy levels. Additionally, the method accurately tracks deviation trends, enabling the identification of potential early-stage faults in VTs and helping prevent significant economic losses in TPSS operations.
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).
Optimal operation of chemical processes is vital for energy, resource, and cost savings in chemical engineering. The problem of optimal operation can be tackled with reinforcement learning, but traditional reinforcement learning methods face challenges due to hard constraints related to quality and safety that must be strictly satisfied, and the large amount of required training data. Chemical processes often cannot provide sufficient experimental data, and while detailed dynamic models can be an alternative, their complexity makes it computationally intractable to generate the needed data. Optimal control methods, such as model predictive control, also struggle with the complexity of the underlying dynamic models. Consequently, many chemical processes rely on manually defined operation recipes combined with simple linear controllers, leading to suboptimal performance and limited flexibility. In this work, we propose a novel approach that leverages expert knowledge embedded in operation recipes. By using reinforcement learning to optimize the parameters of these recipes and their underlying linear controllers, we achieve an optimized operation recipe. This method requires significantly less data, handles constraints more effectively, and is more interpretable than traditional reinforcement learning methods due to the structured nature of the recipes. We demonstrate the potential of our approach through simulation results of an industrial batch polymerization reactor, showing that it can approach the performance of optimal controllers while addressing the limitations of existing methods.
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.
Introduction. The problem of contact fatigue damage of rails has become especially urgent with the intensified railway freight load. An effective method of studying various damage factors is mathematical simulation of the dynamics of motion and accumulation of contact fatigue damage in the rail material. The damage accumulation simulation uses a curve of dependence of the number of wheel loading cycles or design rail section until contact fatigue failure on the value of the selected contact fatigue criterion. This curve could only be obtained in an experiment.Materials and methods. The authors performed bench-scale contact fatigue testing of roller-shape rail steel specimens. The test rig of original design created a load of up to 4000 N on the rollers. The experimental data were processed using the finite element method. The problem of rolling the specimen and the counterbody was solved in the elastoplastic formulation.Results. The authors studied the effects of the size and shape of the contact surfaces for the contact fatigue test specimens on the distribution of contact pressures. They designed and built a roller-on-roller contact fatigue test rig. The researchers tested rail steel specimens for contact fatigue. The results were processed using the finite element method with the plotting of contact fatigue curves of rail steel according to three criteria: combined, Dang Van criterion, amplitude value of maximum shear stress.Discussion and conclusion. The developed methodological approach yielded contact fatigue curves of rail steel. The curves may be used in simulations of the accumulation of contact fatigue damage in the rail material under different axle loads, dynamic loads arising when the rolling stock moves in curves and straight track sections with different rolling profiles of wheels and rails.
Purpose – The purpose of this study is to address the deficiency in safety monitoring technology for 27.5 kV high-voltage cables within the railway traction power supply by analyzing the grounding methods employed in high-speed railways and developing an effective monitoring solution. Design/methodology/approach – Through establishing a mathematical model of induced potential in the cable sheath and analyzing its influencing factors, the principle of grounding current monitoring is proposed. Furthermore, the accuracy of data collection and alarm function of the monitoring equipment were verified through laboratory simulation experiments. Finally, through practical application in the traction substation of the railway bureau on site, a large amount of data were collected to verify the stability and reliability of the monitoring system in actual environments. Findings – The experimental results show that the designed monitoring system can effectively monitor the grounding current of high-voltage cables and respond promptly to changes in cable insulation status. The system performs excellently in terms of data collection accuracy, real-time performance and reliability of alarm functions. In addition, the on-site trial results further confirm the accuracy and reliability of the monitoring system in practical applications, providing strong technical support for the safe operation of high-speed railway traction power supply systems. Originality/value – This study innovatively develops a 27.5kV high-voltage cable grounding current monitoring system, which provides a new technical means for evaluating the insulation status of cables by accurately measuring the grounding current. The design, experimental verification and application of this system in high-speed railway traction power supply systems have demonstrated significant academic value and practical significance, contributing innovative solutions to the field of railway power supply safety monitoring.
Transportation engineering, Railroad engineering and operation
Vittoriano Muttillo, Claudio Di Sipio, Riccardo Rubei
et al.
Producing accurate software models is crucial in model-driven software engineering (MDE). However, modeling complex systems is an error-prone task that requires deep application domain knowledge. In the past decade, several automated techniques have been proposed to support academic and industrial practitioners by providing relevant modeling operations. Nevertheless, those techniques require a huge amount of training data that cannot be available due to several factors, e.g., privacy issues. The advent of large language models (LLMs) can support the generation of synthetic data although state-of-the-art approaches are not yet supporting the generation of modeling operations. To fill the gap, we propose a conceptual framework that combines modeling event logs, intelligent modeling assistants, and the generation of modeling operations using LLMs. In particular, the architecture comprises modeling components that help the designer specify the system, record its operation within a graphical modeling environment, and automatically recommend relevant operations. In addition, we generate a completely new dataset of modeling events by telling on the most prominent LLMs currently available. As a proof of concept, we instantiate the proposed framework using a set of existing modeling tools employed in industrial use cases within different European projects. To assess the proposed methodology, we first evaluate the capability of the examined LLMs to generate realistic modeling operations by relying on well-founded distance metrics. Then, we evaluate the recommended operations by considering real-world industrial modeling artifacts. Our findings demonstrate that LLMs can generate modeling events even though the overall accuracy is higher when considering human-based operations.
Large Language Models (LLMs) have significantly advanced software engineering (SE) tasks, with prompt engineering techniques enhancing their performance in code-related areas. However, the rapid development of foundational LLMs such as the non-reasoning model GPT-4o and the reasoning model o1 raises questions about the continued effectiveness of these prompt engineering techniques. This paper presents an extensive empirical study that reevaluates various prompt engineering techniques within the context of these advanced LLMs. Focusing on three representative SE tasks, i.e., code generation, code translation, and code summarization, we assess whether prompt engineering techniques still yield improvements with advanced models, the actual effectiveness of reasoning models compared to non-reasoning models, and whether the benefits of using these advanced models justify their increased costs. Our findings reveal that prompt engineering techniques developed for earlier LLMs may provide diminished benefits or even hinder performance when applied to advanced models. In reasoning LLMs, the ability of sophisticated built-in reasoning reduces the impact of complex prompts, sometimes making simple zero-shot prompting more effective. Furthermore, while reasoning models outperform non-reasoning models in tasks requiring complex reasoning, they offer minimal advantages in tasks that do not need reasoning and may incur unnecessary costs. Based on our study, we provide practical guidance for practitioners on selecting appropriate prompt engineering techniques and foundational LLMs, considering factors such as task requirements, operational costs, and environmental impact. Our work contributes to a deeper understanding of effectively harnessing advanced LLMs in SE tasks, informing future research and application development.
Mariusz Wesołowski, Krzysztof Blacha, Adam Poświata
from the network of military and civil airports, are highway landing strips, which function,
importance and usefulness have taken on a special dimension in the current geopolitical
situation, especially during the ongoing armed conflict in Ukraine. Highway Landing Strips
(Polish. DOL) are specially prepared sections of public roads adapted to perform air
operations of take-off and landing of military aircraft (Polish. WSP) intended for operational
tasks during crisis and war, as well as tasks resulting from the implementation of the flight
training process. Detailed information on the requirements for DOL is presented in NO-17-
A207:2022 Airfield pavements – Airfield road strips – Requirements and tests [1]. The above
normative document outlines the minimum requirements for geometric dimensions, runway
obstacle free zones and DOL surface construction systems. Requirements for the basic
operating parameters of pavements on facilities used by road services have been presented. In
addition, these requirements should be primarily used in designing and constructing DOL,
modernizing and reconstructing the existing road sections of airfields, accepting the
performed works, and technical and operational assessment. The normative provisions are
appropriate for assessing the technical condition of DOLs throughout their entire technical
lifetime, especially during their use by military aircraft. The technical and operational
condition of the DOL surface has a direct impact on the safety of air operations. This enforces
the need for up-to-date, full knowledge about their technical condition, which will help make
the appropriate decisions to ensure their safe operation. The article presents the results of
testing the operational parameters of the newly built structures of the surface of the Highway
Landing Strip of Wielbark airport along provincial road No. 604 and the requirements of the
applicable defence standard NO-17-A207:2022. The possible operational hazards for
performing air operations by military aircraft and the ongoing works aimed at improving the
security of the DOL, which are part of the critical and defence infrastructure of the country,
are also discussed.
Keywords: Road section of the airport; Critical infrastructure; Defence; Security; Airport
pavement
Highway engineering. Roads and pavements, Bridge engineering
There is abundant observational data in the software engineering domain, whereas running large-scale controlled experiments is often practically impossible. Thus, most empirical studies can only report statistical correlations -- instead of potentially more insightful and robust causal relations. To support analyzing purely observational data for causal relations, and to assess any differences between purely predictive and causal models of the same data, this paper discusses some novel techniques based on structural causal models (such as directed acyclic graphs of causal Bayesian networks). Using these techniques, one can rigorously express, and partially validate, causal hypotheses; and then use the causal information to guide the construction of a statistical model that captures genuine causal relations -- such that correlation does imply causation. We apply these ideas to analyzing public data about programmer performance in Code Jam, a large world-wide coding contest organized by Google every year. Specifically, we look at the impact of different programming languages on a participant's performance in the contest. While the overall effect associated with programming languages is weak compared to other variables -- regardless of whether we consider correlational or causal links -- we found considerable differences between a purely associational and a causal analysis of the very same data. The takeaway message is that even an imperfect causal analysis of observational data can help answer the salient research questions more precisely and more robustly than with just purely predictive techniques -- where genuine causal effects may be confounded.
Alexander E. I. Brownlee, James Callan, Karine Even-Mendoza
et al.
Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. In this paper, we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI toolkit to call OpenAI's API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI.
Michael Dorner, Maximilian Capraro, Oliver Treidler
et al.
The engineering of complex software systems is often the result of a highly collaborative effort. However, collaboration within a multinational enterprise has an overlooked legal implication when developers collaborate across national borders: It is taxable. In this article, we discuss the unsolved problem of taxing collaborative software engineering across borders. We (1) introduce the reader to the basic principle of international taxation, (2) identify three main challenges for taxing collaborative software engineering making it a software engineering problem, and (3) estimate the industrial significance of cross-border collaboration in modern software engineering by measuring cross-border code reviews at a multinational software company.
V dopravě, spojích nebo jakékoli jiné oblasti lidé často činí rozhodnutí, která nejsou racionální. Jedním z vlivů, který to způsobuje, je nabídka něčeho zdarma. Tento článek se zaměřil na zkoumání tohoto vlivu mezi různými generacemi. Snahou bylo zjistit, jestli mladší generace reagují odlišným způsobem než generace starší. Významný rozdíl se neprokázal, při zkoumání tohoto problému se však ukázal (a potvrdil) jiný jev, a to vliv formulace nabídky se slovem „zdarma“ na rozhodování spotřebitelů.
Railroad engineering and operation, Industrial engineering. Management engineering
In order to make up for the lack of detection methods for catenary in China, a set of on-line monitoring system for catenary compensation devices was designed, realizing the functions of collecting, transmitting, storing, displaying and analyzing the <italic>b</italic>-value of the catenary compensation device. The data information was collected by the hardware detection terminal and wirelessly transmitted via GPRS. With website development, the data information was received and analyzed in real time to get the <italic>b</italic>-value and temperature fitting formula, and then predicted the change of <italic>b</italic>-value over the future time period. According to the <italic>b</italic>-value fluctuation,the contact suspension fault existing in the catenary was judged. With the testing and verification, the system has high measurement accuracy and strong stability.
In this paper, we present the current position of the research project ML-Quadrat, which aims to extend the methodology, modeling language and tool support of ThingML - an open source modeling tool for IoT/CPS - to address Machine Learning needs for the IoT applications. Currently, ThingML offers a modeling language and tool support for modeling the components of the system, their communication interfaces as well as their behaviors. The latter is done through state machines. However, we argue that in many cases IoT/CPS services involve system components and physical processes, whose behaviors are not well understood in order to be modeled using state machines. Hence, quite often a data-driven approach that enables inference based on the observed data, e.g., using Machine Learning is preferred. To this aim, ML-Quadrat integrates the necessary Machine Learning concepts into ThingML both on the modeling level (syntax and semantics of the modeling language) and on the code generators level. We plan to support two target platforms for code generation regarding Stream Processing and Complex Event Processing, namely Apache SAMOA and Apama.
Sahand Tabatabaei, Holger Haas, William Rose
et al.
Adiabatic operations are powerful tools for robust quantum control in numerous fields of physics, chemistry and quantum information science. The inherent robustness due to adiabaticity can, however, be impaired in applications requiring short evolution times. We present a single versatile gradient-based optimization protocol that combines adiabatic control with effective Hamiltonian engineering in order to design adiabatic operations tailored to the specific imperfections and resources of an experimental setup. The practicality of the protocol is demonstrated by engineering a fast, 2.3 Rabi cycle-long adiabatic inversion pulse for magnetic resonance with built-in robustness to Rabi field inhomogeneities and resonance offsets. The performance and robustness of the pulse is validated in a nanoscale force-detected magnetic resonance experiment on a solid-state sample, indicating an ensemble-averaged inversion accuracy of $\sim 99.997\%$. We further showcase the utility of our protocol by providing examples of adiabatic pulses robust to spin-spin interactions, parameter-selective operations and operations connecting arbitrary states, each motivated by experiments.
The exciting properties of micro- and nano-patterned surfaces found in natural species hide a virtually endless potential of technological ideas, opening new opportunities for innovation and exploitation in materials science and engineering. Due to the diversity of biomimetic surface functionalities, inspirations from natural surfaces are interesting for a broad range of applications in engineering, including phenomena of adhesion, friction, wear, lubrication, wetting phenomena, self-cleaning, antifouling, antibacterial phenomena, thermoregulation and optics. Lasers are increasingly proving to be promising tools for the precise and controlled structuring of materials at micro- and nano-scales. When ultrashort-pulsed lasers are used, the optimal interplay between laser and material parameters enables structuring down to the nanometer scale. Besides this, a unique aspect of laser processing technology is the possibility for material modifications at multiple (hierarchical) length scales, leading to the complex biomimetic micro- and nano-scale patterns, while adding a new dimension to structure optimization. This article reviews the current state of the art of laser processing methodologies, which are being used for the fabrication of bioinspired artificial surfaces to realize extraordinary wetting, optical, mechanical, and biological-active properties for numerous applications. The innovative aspect of laser functionalized biomimetic surfaces for a wide variety of current and future applications is particularly demonstrated and discussed. The article concludes with illustrating the wealth of arising possibilities and the number of new laser micro/nano fabrication approaches for obtaining complex high-resolution features, which prescribe a future where control of structures and subsequent functionalities are beyond our current imagination.