Hasil untuk "Railroad engineering and operation"

Muhammad Ovais Ahmad

Generative Artificial Intelligence (GenAI) tools (e.g., ChatGPT, Calude) have rapidly become integral to software development. These tools are especially attractive to students, as they can reduce cognitive load. However, their adoption also introduces a socio-cognitive risk: the accumulation of Comprehension Debt (CD). CD refers to the growing gap between what a development team knows about its codebase and what it actually needs to understand in order to maintain and modify it effectively. This qualitative study investigate how GenAI tools contribute to CD in the context of an undergraduate software engineering project. Our study is based on 621 reflective diaries from 207 students over eight weeks. We identify four CD accumulation patterns and one mitigating pattern in students' use of GenAI tools. The four accumulation patterns include: (1) AI-as-black-box code acceptance, (2) context-mismatch debt, (3) dependency-induced atrophy, and (4) verification-bypass. In contrast, the mitigating pattern involves students using GenAI as a comprehension scaffold, allowing them to build a deeper understanding of the code. We argue that CD is distinct from traditional technical debt because it resides in the collective cognition of development teams rather than in the codebase itself. Our findings highlight the need for explicit pedagogical strategies to mitigate CD in software engineering education, emphasizing verification practices, structured retrospectives, and active learning assessments.

D. S. Ershov

Introduction. In addition to ensuring reliability and safety, special attention should be paid to meeting the speed requirements of railway rolling stock in main lines in designing switch devices. Constructive solutions of switch facilities are determined by indicators of the dynamics of rolling stock when railway crews move along the track. According to the author, the design methods of switch curves do not meet modern speed requirements of rolling stock. The purpose of this article is to develop new approaches to the design of switch curves.Materials and methods. The author applied analysis method of dynamic and kinematic parameters of applied switch curves used in turnout switches constructions. Tensometric methods of the investigated parameters of turnout switches are obtained.Results. The analysis of turnout switch design methods is performed. Operational tests of the turnout switch with improved dynamic and kinematic characteristics were performed. The obtained results indicate the expediency of improving the design methodology of switch curves, the effectiveness of using geometric schemes of switch switches with tangential geometry.Discussion and conclusion. Comparative operational tests of turnout switches with the tangential and secant shapes of the switch curve proposed in the article show that the service life of the curved contact tongues increased. It is relevant to conduct similar tests with other types of turnout switches. Considering the results of tests and calculations, the next stage is planned to address the issue of adjusting the design methods of turnout switches products, especially for high-speed traffic.

Michał Grabia, Leszek Winsztal

Abstract: The 2024 SITA Baggage IT Insights report highlights advancements and persistent challenges in baggage handling within the aviation industry. Despite a drop in mishandled bags from 7.6 to 6.9 per 1,000 passengers in 2023, the increasing number of passengers translates to approximately 40 million mishandled bags in 2024. Delayed bags constitute 77% of these, with transfer bags being the primary type affected, especially with the rise in long- haul flights. Automation efforts, including self-service bag drop technologies and IATA Resolution 753 for luggage tracking, are helping reduce mishandling, supported by RFID technology improvements. Simultaneously, aviation faces pressure to reduce greenhouse gas emissions by 70% by 2050, as mandated by EU climate protection goals. Shifting short-haul traffic to rail is a strategic focus, with existing rail-airport integrations supporting this transition. Programs like “Check-in at the Train Station” in Switzerland showcase potential efficiencies. Poland's planned CPK airport may pioneer similar initiatives, marking baggage with RFID to ensure tracking amidst complex logistics. Successful tests by HADATAP and implementations at Polish airports demonstrate the viability of such systems. Introducing more logistics checkpoints could exacerbate delays unless effectively managed, emphasizing RFID's role in sustaining the system’s integrity and passenger experience. The integration of these technologies suggests a promising path forward, aligning with both operational efficiency and environmental sustainability. Keywords: RFID; IATA; Baggage; High speed rail; CPK; Mishandled bags

Justus Bogner, Roberto Verdecchia

From its early foundations in the 1970s, empirical software engineering (ESE) has evolved into a mature research discipline that embraces a plethora of different topics, methodologies, and industrial practices. Despite its remarkable progress, the ESE research field still needs to keep evolving, as new impediments, shortcoming, and technologies emerge. Research reproducibility, limited external validity, subjectivity of reviews, and porting research results to industrial practices are just some examples of the drivers for improvements to ESE research. Additionally, several facets of ESE research are not documented very explicitly, which makes it difficult for newcomers to pick them up. With this new regular ACM SIGSOFT SEN column (SEN-ESE), we introduce a venue for discussing meta-aspects of ESE research, ranging from general topics such as the nature and best practices for replication packages, to more nuanced themes such as statistical methods, interview transcription tools, and publishing interdisciplinary research. Our aim for the column is to be a place where we can regularly spark conversations on ESE topics that might not often be touched upon or are left implicit. Contributions to this column will be grounded in expert interviews, focus groups, surveys, and position pieces, with the goal of encouraging reflection and improvement in how we conduct, communicate, teach, and ultimately improve ESE research. Finally, we invite feedback from the ESE community on challenging, controversial, or underexplored topics, as well as suggestions for voices you would like to hear from. While we cannot promise to act on every idea, we aim to shape this column around the community interests and are grateful for all contributions.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy plays a critical role in software engineering (SE), influencing collaboration, communication, and user-centred design. Although SE research has increasingly recognised empathy as a key human aspect, there remains no validated instrument specifically designed to measure it within the unique socio-technical contexts of SE. Existing generic empathy scales, while well-established in psychology and healthcare, often rely on language, scenarios, and assumptions that are not meaningful or interpretable for software practitioners. These scales fail to account for the diverse, role-specific, and domain-bound expressions of empathy in SE, such as understanding a non-technical user's frustrations or another practitioner's technical constraints, which differ substantially from empathy in clinical or everyday contexts. To address this gap, we developed and validated two domain-specific empathy scales: EmpathiSEr-P, assessing empathy among practitioners, and EmpathiSEr-U, capturing practitioner empathy towards users. Grounded in a practitioner-informed conceptual framework, the scales encompass three dimensions of empathy: cognitive empathy, affective empathy, and empathic responses. We followed a rigorous, multi-phase methodology, including expert evaluation, cognitive interviews, and two practitioner surveys. The resulting instruments represent the first psychometrically validated empathy scales tailored to SE, offering researchers and practitioners a tool for assessing empathy and designing empathy-enhancing interventions in software teams and user interactions.

Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey et al.

Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.

Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri

Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such experiments and improving the system based on the experimental results still remain manual. These processes are labor-intensive and require multi-domain expertise. To address these challenges and enable anyone to build resilient systems at low cost, this paper proposes ChaosEater, a system that automates the entire CE cycle with Large Language Models (LLMs). It predefines an agentic workflow according to a systematic CE cycle and assigns subdivided processes within the workflow to LLMs. ChaosEater targets CE for software systems built on Kubernetes. Therefore, the LLMs in ChaosEater complete CE cycles through software engineering tasks, including requirement definition, code generation, testing, and debugging. We evaluate ChaosEater through case studies on small- and large-scale Kubernetes systems. The results demonstrate that it consistently completes reasonable CE cycles with significantly low time and monetary costs. Its cycles are also qualitatively validated by human engineers and LLMs.

Edson Florentino de Souza, Cássio Bragança, Diogo Ribeiro et al.

Abstract High-speed railway bridges are essential components of any railway transportation system that should keep adequate levels of serviceability and safety. In this context, drive-by methodologies have emerged as a feasible and cost-effective monitoring solution for detecting damage on railway bridges while minimizing train operation interruptions. Moreover, integrating advanced sensor technologies and machine learning algorithms has significantly enhanced structural health monitoring (SHM) for bridges. Despite being increasingly used in traditional SHM applications, studies using autoencoders within drive-by methodologies are rare, especially in the railway field. This study presents a novel approach for drive-by damage detection in HSR bridges. The methodology relies on acceleration records collected from multiple bridge crossings by an operational train equipped with onboard sensors. Log-Mel spectrogram features derived from the acceleration records are used together with sparse autoencoders for computing statistical distribution-based damage indexes. Numerical simulations were performed on a 3D vehicle–track–bridge interaction system model implemented in Matlab to evaluate the robustness and effectiveness of the proposed approach, considering several damage scenarios, vehicle speeds, and environmental and operational variations, such as multiple track irregularities and varying measurement noise. The results show that the proposed approach can successfully detect damages, as well as characterize their severity, especially for very early-stage damages. This demonstrates the high potential of applying Mel-frequency damage-sensitive features associated with machine learning algorithms in the drive-by condition assessment of high-speed railway bridges.

Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan et al.

Large Language Models (LLMs) are being used for a wide variety of tasks. While they are capable of generating human-like responses, they can also produce undesirable output including potentially harmful information, racist or sexist language, and hallucinations. Alignment methods are designed to reduce such undesirable outputs via techniques such as fine-tuning, prompt engineering, and representation engineering. However, existing methods face several challenges: some require costly fine-tuning for every alignment task; some do not adequately remove undesirable concepts, failing alignment; some remove benign concepts, lowering the linguistic capabilities of LLMs. To address these issues, we propose Parsimonious Concept Engineering (PaCE), a novel activation engineering framework for alignment. First, to sufficiently model the concepts, we construct a large-scale concept dictionary in the activation space, in which each atom corresponds to a semantic concept. Given any alignment task, we instruct a concept partitioner to efficiently annotate the concepts as benign or undesirable. Then, at inference time, we decompose the LLM activations along the concept dictionary via sparse coding, to accurately represent the activations as linear combinations of benign and undesirable components. By removing the latter ones from the activations, we reorient the behavior of the LLM towards the alignment goal. We conduct experiments on tasks such as response detoxification, faithfulness enhancement, and sentiment revising, and show that PaCE achieves state-of-the-art alignment performance while maintaining linguistic capabilities.

Johannes Schleiss, Aditya Johri, Sebastian Stober

Building up competencies in working with data and tools of Artificial Intelligence (AI) is becoming more relevant across disciplinary engineering fields. While the adoption of tools for teaching and learning, such as ChatGPT, is garnering significant attention, integration of AI knowledge, competencies, and skills within engineering education is lacking. Building upon existing curriculum change research, this practice paper introduces a systems perspective on integrating AI education within engineering through the lens of a change model. In particular, it identifies core aspects that shape AI adoption on a program level as well as internal and external influences using existing literature and a practical case study. Overall, the paper provides an analysis frame to enhance the understanding of change initiatives and builds the basis for generalizing insights from different initiatives in the adoption of AI in engineering education.

Horia Mărgărit, Amanda Bowman, Krishnageetha Karuppasamy et al.

In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges encountered in mapping the algorithm to physical hardware, and the software engineering considerations needed to achieve realistic results on today's non-fault-tolerant systems.

TIAN Heqiang

As one of consumable parts for railway electric locomotives, pantograph carbon contact strips are largely demanded, and more manufacturers engage in the trial production of pantograph carbon contact strips for electric locomotives. The current study is necessary in order to improve on-track trial operation of pantograph carbon contact strips for locomotives in the trial production stage. This paper summarized and analyzed the potential failure modes and corresponding causes of the carbon contact strips in service, expounded the necessity of the on-track adaptability trial for them, and summarized the preconditions of the on-track trial operation. In addition, an on-track adaptability trial scheme of pantograph carbon contact strips for locomotives was designed, covering five aspects of loading quantity and cycle of trial operation, tracking and monitoring during trial operation, disassembly and inspection after trial operation, evaluation of trial results and safety emergency plan. This scheme was applied to a new type of pantograph carbon contact strips for locomotives produced by a domestic manufacturer. The on-track trial process and test data comprehensively proved the feasibility of this trial scheme, not involving any effect to the normal operational service of the locomotive. The trial scheme can fully reflect and evaluate the adaptability and quality reliability in the on-track application of new locomotive pantograph carbon contact strips in trial production, and effectively guide the relevant parties in the actual operation of the pantograph carbon contact strips for locomotives on the trial site, thus reducing the safety risk of the on-track trial, and provide important technical support for the mass loading of pantograph carbon contact strips on locomotives.

ZHANG Jinping, ZHANG Kun, CHEN Wei et al.

Because of its excellent physical and chemical properties, silicon carbide (SiC) is suitable for manufacturing semiconductor devices working under high temperature and high power. Although SiC power diodes and metal-oxide-semiconductor field effect transistors (MOSFETs) have good device performance and are widely applied, the one-dimensional theoretical limit of them as unipolar devices still limits the further performance improvement of conventional SiC power devices. As a technology widely used on silicon-based devices, superjunction (SJ) structure can significantly improve the tradeoff between the breakdown voltage and specific on-resistance and thus enhance the performance of the devices. In recent years, SiC SJ devices have become a hot research topic and significant progress has been made in relevant researches. The latest research progress and development direction in the device design and simulation, modeling research, SJ manufacturing process technology of SiC SJ devices were reviewed and summarized in this paper.

Rudrajit Choudhuri, Dylan Liu, Igor Steinmacher et al.

Conversational Generative AI (convo-genAI) is revolutionizing Software Engineering (SE) as engineers and academics embrace this technology in their work. However, there is a gap in understanding the current potential and pitfalls of this technology, specifically in supporting students in SE tasks. In this work, we evaluate through a between-subjects study (N=22) the effectiveness of ChatGPT, a convo-genAI platform, in assisting students in SE tasks. Our study did not find statistical differences in participants' productivity or self-efficacy when using ChatGPT as compared to traditional resources, but we found significantly increased frustration levels. Our study also revealed 5 distinct faults arising from violations of Human-AI interaction guidelines, which led to 7 different (negative) consequences on participants.

XIE Jiankun, CHEN Xiaowei, WEI Haiju

Operating environment plays an important role to the noise inside the subway car. Tunnel environment leads to the increase of noise of the subway car and the rapid decrease of sound comfort, which seriously affects the passengers' physical and mental health and missing drop-off place. At present, the influence of tunnel environment on noise and acoustic comfort inside the subway car is lack of relevant investigation and theoretical research. Based on this, the noise inside and outside a subway on a certain line was measured. Only the sound transmission of wheel-rail source to the subway car through air was considered. Based on the sound ray tracking method, by establishing the acoustic response model of tunnel-subway in Odeon software, the effect of tunnel with sound absorption material on noise and acoustic comfort in subway was studied furtherly. The following conclusion is drawn: in the tunnel section, the total noise inside the subway car increases by 7 dB(A), normal communication distance is reduced to 0.1-0.2 m, and sound comfort is extremely poor. The effect of laying sound absorption material on track plate is better than that of tunnel wall. When the whole tunnel is covered with sound absorbing material, the noise inside the subway car can be reduced by about 6 dB(A), normal communication distance can be increased to 0.72 m, the sound environment in the subway car has been greatly improved .The simulation results can provide certain reference for practical engineering application.

Tao Chen, Miqing Li

In presence of multiple objectives to be optimized in Search-Based Software Engineering (SBSE), Pareto search has been commonly adopted. It searches for a good approximation of the problem's Pareto optimal solutions, from which the stakeholders choose the most preferred solution according to their preferences. However, when clear preferences of the stakeholders (e.g., a set of weights which reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice since it simplifies the search via converting the original multi-objective problem into a single-objective one and enable the search to focus on what only the stakeholders are interested in. This paper questions such a "weighted search first" belief. We show that the weights can, in fact, be harmful to the search process even in the presence of clear preferences. Specifically, we conduct a large scale empirical study which consists of 38 systems/projects from three representative SBSE problems, together with two types of search budget and nine sets of weights, leading to 604 cases of comparisons. Our key finding is that weighted search reaches a certain level of solution quality by consuming relatively less resources at the early stage of the search; however, Pareto search is at the majority of the time (up to 77% of the cases) significantly better than its weighted counterpart, as long as we allow a sufficient, but not unrealistic search budget. This, together with other findings and actionable suggestions in the paper, allows us to codify pragmatic and comprehensive guidance on choosing weighted and Pareto search for SBSE under the circumstance that clear preferences are available. All code and data can be accessed at: https://github.com/ideas-labo/pareto-vs-weight-for-sbse.

Lianying TAN, Zhi CHENG, Ruimeng YANG et al.

The nano-composites were prepared with ethylene-propylene diene rubber (EPDM) and organic montmorillonite(OMMT) nano-composite as basic material, and polyhedral oligomeric silsesquioxane(POSS), aluminium hydroxide(Al(OH)₃), expanded graphite(EG) as additives. The flame retardant properties and mechanical properties were tested by cone calorimeter and smoke density etc. The results showed that, compared with the pure EPDM/OMMT nano-composite, adding POSS,EG and Al(OH)₃ could effectively increase the charring and flame retardant properties. The mechanical properties of nano-composite prepared with EPDM/OMMT/EG or EPDM/OMMT/Al(OH)₃ were reduced. The properties of inner/outer windshield rubber material which was prepared with EPDM/OMMT/EG or EPDM/OMMT/Al(OH)₃ could meet the flame retardant and functional requirements. In addition, the tensile strength, low temperature and retardant performance were more excellent than the foreign samples. At present,the engineering application of multivariate nano-composite in a variety of high-speed train windshield has been realized.

Xuanzhe Liu, Diandian Gu, Zhenpeng Chen et al.

Deep learning (DL) has become a key component of modern software. In the "big model" era, the rich features of DL-based software substantially rely on powerful DL models, e.g., BERT, GPT-3, and the recently emerging GPT-4, which are trained on the powerful cloud with large datasets. Hence, training effective DL models has become a vital stage in the whole software lifecycle. When training deep learning models, especially those big models, developers need to parallelize and distribute the computation and memory resources amongst multiple devices in the training process, which is known as distributed deep learning training, or distributed training for short. However, the unique challenges that developers encounter in distributed training process have not been studied in the software engineering community. Given the increasingly heavy dependence of current DL-based software on distributed training, this paper aims to fill in the knowledge gap and presents the first comprehensive study on developers' issues in distributed training. To this end, we analyze 1,131 real-world developers' issues about using these frameworks reported on Stack Overflow and GitHub. We construct a fine-grained taxonomy consisting of 30 categories regarding the fault symptoms and summarize common fix patterns for different symptoms. Based on the results, we suggest actionable implications on research avenues that can potentially facilitate the distributed training to develop DL-based software, such as focusing on the frequent and common fix patterns when designing testing or debugging tools, developing efficient testing and debugging techniques for communication configuration along with the synthesis of network configuration analysis, designing new multi-device checkpoint-and-replay techniques to help reproduction, and designing serverless APIs for cloud platforms.

D. D. Awschalom, C. H. R. Du, R. He et al.

Quantum technology has made tremendous strides over the past two decades with remarkable advances in materials engineering, circuit design and dynamic operation. In particular, the integration of different quantum modules has benefited from hybrid quantum systems, which provide an important pathway for harnessing the different natural advantages of complementary quantum systems and for engineering new functionalities. This review focuses on the current frontiers with respect to utilizing magnetic excitatons or magnons for novel quantum functionality. Magnons are the fundamental excitations of magnetically ordered solid-state materials and provide great tunability and flexibility for interacting with various quantum modules for integration in diverse quantum systems. The concomitant rich variety of physics and material selections enable exploration of novel quantum phenomena in materials science and engineering. In addition, the relative ease of generating strong coupling and forming hybrid dynamic systems with other excitations makes hybrid magnonics a unique platform for quantum engineering. We start our discussion with circuit-based hybrid magnonic systems, which are coupled with microwave photons and acoustic phonons. Subsequently, we are focusing on the recent progress of magnon-magnon coupling within confined magnetic systems. Next we highlight new opportunities for understanding the interactions between magnons and nitrogen-vacancy centers for quantum sensing and implementing quantum interconnects. Lastly, we focus on the spin excitations and magnon spectra of novel quantum materials investigated with advanced optical characterization.

Emily C. Winfield, R. Rader, A. Zhivov et al.

Arctic climates provide unique challenges for designers of HVAC, plumbing, and thermal energy systems. The importance of considering the operation outside air temperatures, system reliability, and building resiliency cannot be understated. The paper describes best practice examples of robust and reliable systems with the emphasis on their redundancy, durability, and functionality. The paper also discusses the most common heating and ventilation system approaches used in arctic climate with the emphasis on the importance of a maintenance program that allows building operators to successfully troubleshoot and maintain buildings in the arctic. More detailed discussion of concepts presented in this paper can be found in the Guide [1] where these concepts are illustrated by best practice examples from U.S. military bases in Alaska and Søndre Strømfjord, the international airport of Greenland that previously was used as a U.S. military base. The paper results from experts’ discussions during the Consultation Forum “Thermal Energy Systems Resilience in Cold/Arctic Climates” [2] and research conducted under the IEA EBC Annex 73, the Environmental Security Technology Certification Program (ESTCP) Project “Technologies Integration to Achieve Resilient, Low-Energy Military Installations” and U.S. Army Program project 633734T1500 under Military Engineering Technology Demonstration. The paper is complementary to the ASHRAE Cold Climate Design Guide [3] with a focus on resilience of thermal energy systems.