PurposeAssociation of American railroads (AAR) standard automatic couplers are designed for much higher capacity than the normal operating loads. However, failure of knuckles and coupler bodies is still a common occurrence. Recent studies have shown that fatigue is the main reason behind such failures below the expected load. Moreover, knuckle failures occur more frequently than coupler body failures, which cause operational disruptions and also influence overall coupler life because of nonconforming contact between a new knuckle and an old coupler. In addition to new and old counterparts, undesired contact conditions are often the case with the new assembly due to casting-based manufacturing inaccuracies.Design/methodology/approachA study is thus carried out in this paper to understand the variation of load transfer paths and its consequences caused by dimensional variability. A finite element model of an E-type coupler's knuckle is developed and different possible contact conditions of the knuckle with the coupler head are simulated. Knuckles generally fail in pulling mode, during which the possible contacting elements of knuckle are pulling lugs, pin protector regions and pinholes. Due to dimensional variability, contact conditions may exist where an individual or a combination of these elements are in contact.FindingsSimulation results indicate that under regular operational conditions, having only the pulling lugs in contact reduces the risk of knuckle failure and maintains assembly integrity even if the knuckle fails. However, under extreme loading conditions, the safest scenario is when both pulling lugs and pin protector regions are in contact.Originality/valueThese findings are believed to assist in defining the dimensional variability limits to ensure the desired contacts between the mating surfaces of the knuckle and coupler body of railway couplers of AAR type. This work contributes to understanding implications of dimensional variability in the railway couplers. The insight presented are useful in design, manufacturing and maintenance of railway coupler's knuckle.
Transportation engineering, Railroad engineering and operation
José Peixoto, Alexis Gonzalez, Janki Bhimani
et al.
Programmable caching engines like CacheLib are widely used in production systems to support diverse workloads in multi-tenant environments. CacheLib's design focuses on performance, portability, and configurability, allowing applications to inherit caching improvements with minimal implementation effort. However, its behavior under dynamic and evolving workloads remains largely unexplored. This paper presents an empirical study of CacheLib with multi-tenant settings under dynamic and volatile environments. Our evaluation across multiple CacheLib configurations reveals several limitations that hinder its effectiveness under such environments, including rigid configurations, limited runtime adaptability, lack of quality-of-service support and coordination, which lead to suboptimal performance, inefficient memory usage, and tenant starvation. Based on these findings, we outline future research directions to improve the adaptability, fairness, and programmability of future caching engines.
Model-driven engineering (MDE) is believed to have a significant impact in software quality. However, researchers and practitioners may have a hard time locating consolidated evidence on this impact, as the available information is scattered in several different publications. Our goal is to aggregate consolidated findings on quality in MDE, facilitating the work of researchers and practitioners in learning about the coverage and main findings of existing work as well as identifying relatively unexplored niches of research that need further attention. We performed a tertiary study on quality in MDE, in order to gain a better understanding of its most prominent findings and existing challenges, as reported in the literature. We identified 22 systematic literature reviews and mapping studies and the most relevant quality attributes addressed by each of those studies, in the context of MDE. Maintainability is clearly the most often studied and reported quality attribute impacted by MDE. Eighty out of 83 research questions in the selected secondary studies have a structure that is more often associated with mapping existing research than with answering more concrete research questions (e.g., comparing two alternative MDE approaches with respect to their impact on a specific quality attribute). We briefly outline the main contributions of each of the selected literature reviews. In the collected studies, we observed a broad coverage of software product quality, although frequently accompanied by notes on how much more empirical research is needed to further validate existing claims. Relatively, little attention seems to be devoted to the impact of MDE on the quality in use of products developed using MDE.
Mikio Nakano, Hironori Takeuchi, Sadahiro Yoshikawa
et al.
This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With the advancement of large language models, the core technologies underlying dialogue systems have significantly progressed. As a result, dialogue system technology is now expected to be applied to solving various societal issues and in business contexts. To achieve this, it is important to build, operate, and continuously improve dialogue systems correctly and efficiently. Accordingly, in addition to applying existing software engineering knowledge, it is becoming increasingly important to evolve software engineering tailored specifically to dialogue systems. In this paper, we enumerate the knowledge areas of dialogue systems engineering based on those of software engineering, as defined in the Software Engineering Body of Knowledge (SWEBOK) Version 4.0, and survey each area. Based on this survey, we identify unexplored topics in each area and discuss the future direction of dialogue systems engineering.
Aiming at the shortcomings of the gazelle optimization algorithm, such as the imbalance between exploration and exploitation and the insufficient information exchange within the population, this paper proposes a multi-strategy improved gazelle optimization algorithm (MSIGOA). To address these issues, MSIGOA proposes an iteration-based updating framework that switches between exploitation and exploration according to the optimization process, which effectively enhances the balance between local exploitation and global exploration in the optimization process and improves the convergence speed. Two adaptive parameter tuning strategies improve the applicability of the algorithm and promote a smoother optimization process. The dominant population-based restart strategy enhances the algorithms ability to escape from local optima and avoid its premature convergence. These enhancements significantly improve the exploration and exploitation capabilities of MSIGOA, bringing superior convergence and efficiency in dealing with complex problems. In this paper, the parameter sensitivity, strategy effectiveness, convergence and stability of the proposed method are evaluated on two benchmark test sets including CEC2017 and CEC2022. Test results and statistical tests show that MSIGOA outperforms basic GOA and other advanced algorithms. On the CEC2017 and CEC2022 test sets, the proportion of functions where MSIGOA is not worse than GOA is 92.2% and 83.3%, respectively, and the proportion of functions where MSIGOA is not worse than other algorithms is 88.57% and 87.5%, respectively. Finally, the extensibility of MSIGAO is further verified by several engineering design optimization problems.
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.
We study the continual pretraining recipe for scaling language models' context lengths to 128K, with a focus on data engineering. We hypothesize that long context modeling, in particular \textit{the ability to utilize information at arbitrary input locations}, is a capability that is mostly already acquired through large-scale pretraining, and that this capability can be readily extended to contexts substantially longer than seen during training~(e.g., 4K to 128K) through lightweight continual pretraining on appropriate data mixture. We investigate the \textit{quantity} and \textit{quality} of the data for continual pretraining: (1) for quantity, we show that 500 million to 5 billion tokens are enough to enable the model to retrieve information anywhere within the 128K context; (2) for quality, our results equally emphasize \textit{domain balance} and \textit{length upsampling}. Concretely, we find that naively upsampling longer data on certain domains like books, a common practice of existing work, gives suboptimal performance, and that a balanced domain mixture is important. We demonstrate that continual pretraining of the full model on 1B-5B tokens of such data is an effective and affordable strategy for scaling the context length of language models to 128K. Our recipe outperforms strong open-source long-context models and closes the gap to frontier models like GPT-4 128K.
This paper focuses on cycling transport in the capital city of the Czech Republic – Prague. Main goal of the paper is to find out whether available measures for cycling transport support are taken there. The authors focus on the infrastructure dedicated for cyclists in Prague and they primarily focus on the safety of the users. Potentially dangerous and/or not comfortable examples are included as well.
Railroad engineering and operation, Industrial engineering. Management engineering
With the development of urban rail transit, metro has become the preferred mode of public transportation for citizens. In order to improve passengers' travel experience, realize the matching of transport capacity and transport demands, and reduce the congestion in waiting areas of urban rail transit platform, in this paper, the optimization of the passenger flow density on platform was researched based on the balance indexes of running chart. Firstly, based on the basic methods and theories of queuing theory, a computation model which takes the passenger flow density in waiting areas of urban rail transit platform as the research subject has been established, and the relationship between the balance of running chart and the passenger flow density in waiting areas on platform has been analyzed. Then, through the process of creating peak periods, creating transitional periods, connecting task lines, conflict resolution, and balance treatment, the automatic generation of the balanced running chart of urban rail transit has been realized. Finally, combined with passenger flow data of Changsha metro line 4, the effectiveness of the method has been verified by simulation. The simulation results show that the automatically compiled running chart based on this method has a lower maximum passenger flow density in waiting areas on platform in respect of the manually compiled running chart. Therefore, the algorithm has a better practical value.
Koopman operator theory has proven to be a promising approach to nonlinear system identification and global linearization. For nearly a century, there had been no efficient means of calculating the Koopman operator for applied engineering purposes. The introduction of a recent computationally efficient method in the context of fluid dynamics, which is based on the system dynamics decomposition to a set of normal modes in descending order, has overcome this long-lasting computational obstacle. The purely data-driven nature of Koopman operators holds the promise of capturing unknown and complex dynamics for reduced-order model generation and system identification, through which the rich machinery of linear control techniques can be utilized. Given the ongoing development of this research area and the many existing open problems in the fields of smart mobility and vehicle engineering, a survey of techniques and open challenges of applying Koopman operator theory to this vibrant area is warranted. This review focuses on the various solutions of the Koopman operator which have emerged in recent years, particularly those focusing on mobility applications, ranging from characterization and component-level control operations to vehicle performance and fleet management. Moreover, this comprehensive review of over 100 research papers highlights the breadth of ways Koopman operator theory has been applied to various vehicular applications with a detailed categorization of the applied Koopman operator-based algorithm type. Furthermore, this review paper discusses theoretical aspects of Koopman operator theory that have been largely neglected by the smart mobility and vehicle engineering community and yet have large potential for contributing to solving open problems in these areas.
In a quantum Stirling heat engine, the heat exchanged with two thermal baths is partly utilized for performing work by redistributing the energy levels of the working substance. We analyze the thermodynamics of a quantum Stirling engine operating in finite time. We develop a model in which a time-dependent potential barrier changes the energy-level structure of the working substance. The process takes place under a constant interaction with the thermal bath. We further show that in the limit of slow operation of the cycle and low temperature, the efficiency of such an engine approaches Carnot efficiency. We also show that the maximum output power , for the strokes that affect the energy levels, is obtained at an intermediate operating speed, demonstrating the importance of a finite-time analysis.
Hassan Hage, Emmanouil Seferis, Vahid Hashemi
et al.
Product Engineering Processes (PEPs) are used for describing complex product developments in big enterprises such as automotive and avionics industries. The Business Process Model Notation (BPMN) is a widely used language to encode interactions among several participants in such PEPs. In this paper, we present SMC4PEP as a tool to convert graphical representations of a business process using the BPMN standard to an equivalent discrete-time stochastic control process called Markov Decision Process (MDP). To this aim, we first follow the approach described in an earlier investigation to generate a semantically equivalent business process which is more capable of handling the PEP complexity. In particular, the interaction between different levels of abstraction is realized by events rather than direct message flows. Afterwards, SMC4PEP converts the generated process to an MDP model described by the syntax of the probabilistic model checking tool PRISM. As such, SMC4PEP provides a framework for automatic verification and validation of business processes in particular with respect to requirements from legal standards such as Automotive SPICE. Moreover, our experimental results confirm a faster verification routine due to smaller MDP models generated from the alternative event-based BPMN models.
This doctoral dissertation proposes a novel approach to enhance the development of smart services for the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS). The proposed approach offers abstraction and automation to the software engineering processes, as well as the Data Analytics (DA) and Machine Learning (ML) practices. This is realized in an integrated and seamless manner. We implement and validate the proposed approach by extending an open source modeling tool, called ThingML. ThingML is a domain-specific language and modeling tool with code generation for the IoT/CPS domain. Neither ThingML nor any other IoT/CPS modeling tool supports DA/ML at the modeling level. Therefore, as the primary contribution of the doctoral dissertation, we add the necessary syntax and semantics concerning DA/ML methods and techniques to the modeling language of ThingML. Moreover, we support the APIs of several ML libraries and frameworks for the automated generation of the source code of the target software in Python and Java. Our approach enables platform-independent, as well as platform-specific models. Further, we assist in carrying out semiautomated DA/ML tasks by offering Automated ML (AutoML), in the background (in expert mode), and through model-checking constraints and hints at design-time. Finally, we consider three use case scenarios from the domains of network security, smart energy systems and energy exchange markets.
We study the problem of estimating the total number of searches (volume) of queries in a specific domain, which were submitted to a search engine in a given time period. Our statistical model assumes that the distribution of searches follows a Zipf's law, and that the observed sample volumes are biased accordingly to three possible scenarios. These assumptions are consistent with empirical data, with keyword research practices, and with approximate algorithms used to take counts of query frequencies. A few estimators of the parameters of the distribution are devised and experimented, based on the nature of the empirical/simulated data. For continuous data, we recommend using nonlinear least square regression (NLS) on the top-volume queries, where the bound on the volume is obtained from the well-known Clauset, Shalizi and Newman (CSN) estimation of power-law parameters. For binned data, we propose using a Chi-square minimization approach restricted to the top-volume queries, where the bound is obtained by the binned version of the CSN method. Estimations are then derived for the total number of queries and for the total volume of the population, including statistical error bounds. We apply the methods on the domain of recipes and cooking queries searched in Italian in 2017. The observed volumes of sample queries are collected from Google Trends (continuous data) and SearchVolume (binned data). The estimated total number of queries and total volume are computed for the two cases, and the results are compared and discussed.
S. L. Samoshkin, S. D. Korshunov, O. S. Samoshkin
et al.
One of the activities of the JSC “FPK” is the creation of competitive advantages for passenger rail transport by formulating proposals for the provision of new services in long-distance trains. Currently, there is a need for intercity and even international transportation of automobiles for passengers traveling on long-distance trains.To resolve this issue, the JSC “FPK” developed a technical task, according to which the PKTB L JSC “RZD” designed a specialized passenger car for the transportation of automobiles in the long-distance trains. It was created on the basis of a 47D model car built in Germany, which was modernized during its overhaul.Prototype of the new car has passed a full range of tests in accordance with the requirements of the technical regulations of the Customs Union (TR CU) 001/2011 at the test center of the JSC NO “TIV”. Based on the positive results of the dynamic-strength, fireprevention, electrical and other tests, the Voronezh Car Repair Plant received from the Federal Agency for Railway Transport a certificate of compliance with the requirements of the TR CU and the right to manufacture an initial batch of cars.During impact tests, the low reliability of the standard thrustscrew fastening of the car wheels was established. In order to eliminate the noted drawback, a lock-cable mount was developed. Repeated impact tests have confirmed the effectiveness of the new wheel mounting design.Operation of the first batch of cars (8 and 5 units) showed a great demand for this type of service, especially on the directions Moscow—St. Petersburg—Moscow, Moscow—Helsinki—Moscow, Moscow—Adler—Moscow. In this regard, the issue of the development and manufacture of new cars with improved performance indicators (increasing the number of transported automobiles to 8–10 units instead of 4–5 units in the operated cars) is being worked out.
Yu. S. Romen, B. E. Glyuzberg, E. A. Timakova
et al.
Dimensions and tolerances in the “wheelset — railway track” system are interconnected, since the normative values of geometric parameters and shape of the elements of the wheelsets and the track's upper structure are directly de pendent. In 2018– 2019 in order to establish the minimum permissible thickness of wheel flanges for freight cars in operation and to determine the influence of the thickness and shape of wheel flanges on the safe passage of turnout elements, JSC “VNIIZhT” conducted comprehensive stu dies, including the development of a methodo logy and mathematical modeling of the interaction of wheelsets and turnout elements. Features of modeling the dynamic proces ses of the entry of the first bogie of a freight car into the railway turnout when running to the side track are presented. The process of moving a freight car along the turnout is described de pending on the geometric parameters of the wheelset and track elements of the turnout, the position in the gauge of its first bogie before running into the switch point. Results of calculations of the lateral forces of the interaction of the wheelset and the turnout show that when running on, the maximum forces occurred during wheel impacts in the turnout elements, which depend on the conditions of the bogie entering the turnout. The initial position of the bogie and the peculiarities of its motion in the gauge determine the position of the meeting point and the value of the angle of incidence, their combination determines the maximum value of the force when moving to the lateral track, which, depending on the entry conditions, can differ by more than twice. Calculations showed that a change in the angle of inclination of the flange from 60 to 70° leads to a decrease in the safety factor of rolling stock on the turnout by 1.5 times. Required minimum value of the inclination of the wheel flange, ensuring safety (qR parameter), is determined by the creation of conditions that do not allow the wheel to run onto the tip of the switch point. Under existing norms of wear of turnout elements and the relative position of their rail elements, as well as taking into account the results of the calculations, the permissible value of qR should be in the range of 6.0...6.5 mm.
Current day software development relies heavily on the use of service architectures and on agile iterative development methods to design, implement, and deploy systems. These practices result in systems made up of multiple services that introduce new data flows and evolving designs that escape the control of a single designer. Academic privacy engineering literature typically abstracts away such conditions of software production in order to achieve generalizable results. Yet, through a systematic study of the literature, we show that proposed solutions inevitably make assumptions about software architectures, development methods and scope of designer control that are misaligned with current practices. These misalignments are likely to pose an obstacle to operationalizing privacy engineering solutions in the wild. Specifically, we identify important limitations in the approaches that researchers take to design and evaluate privacy enhancing technologies which ripple to proposals for privacy engineering methodologies. Based on our analysis, we delineate research and actions needed to re-align research with practice, changes that serve a precondition for the operationalization of academic privacy results in common software engineering practices.
This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR). This approach is applied initially to a literature case, the simulation of the stokes problems, and in the following to a real-world industrial problem, inside a shape optimization pipeline for a naval engineering problem.