Research Software Engineers (RSEs) have become indispensable to computational research and scholarship. The fast rise of RSEs in higher education and the trend of universities to be slow creating or adopting models for new technology roles means a lack of structured career pathways that recognize technical mastery, scholarly impact, and leadership growth. In response to an immense demand for RSEs at Princeton University, and dedicated funding to grow the RSE group at least two-fold, Princeton was forced to strategize how to cohesively define job descriptions to match the rapid hiring of RSE positions but with enough flexibility to recognize the unique nature of each individual position. This case study describes our design and implementation of a comprehensive RSE career ladder spanning Associate through Principal levels, with parallel team-lead and managerial tracks. We outline the guiding principles, competency framework, Human Resources (HR) alignment, and implementation process, including engagement with external consultants and mapping to a standard job leveling framework utilizing market benchmarks. We share early lessons learned and outcomes including improved hiring efficiency, clearer promotion pathways, and positive reception among staff.
Sharon Guardado, Risha Parveen, Zheying Zhang
et al.
The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.
The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.
Davide Venturelli, Erik Gustafson, Doga Kurkcuoglu
et al.
We review the prospects to build quantum processors based on superconducting transmons and radiofrequency cavities for testing applications in the NISQ era. We identify engineering opportunities and challenges for implementation of algorithms in simulation, combinatorial optimization, and quantum machine learning in qudit-based quantum computers.
Introduction. The paper considers the operation of interregional express trains operated by electric motor-car rolling stock whose route partly runs along non-electrified sections. These sections have electric trains hauled by a passenger diesel locomotive that also supplies electricity for the electric train auxiliary needs and the cabin microclimate system. The research is intended to compare the energy indicators of different modes of motor-car rolling stock operation on mixed routes with electrified and non-electrified sections.Materials and methods. The paper analyses the actual operational consumption of fuel and energy resources for a number of electrified and non-electrified sections, including electricity supply for auxiliary needs of electric motor-car rolling stock and the cabin microclimate system. The paper compares the energy consumption of these sections for different series of tractive rolling stock.Results. The author obtained data on the fuel consumption of a diesel locomotive with an electric train and a diesel train depending on the train composition and time of year, as well as indicators of specific fuel consumption for a diesel locomotive driving an electric train and a diesel train in negative temperature zones, i. e. with operating heating.Discussion and conclusion. The study shows that a diesel locomotive for electric train traction is less feasible than a diesel train in terms of energy consumption. The author gives a preliminary assessment of comparative characteristics of energy consumption under different modes of motor-car rolling stock operation on non-electrified sections. If these research data remain relevant, the next stage should include data processing from on-board registration systems and comparative testing.
The safety and reliability of traction inverters are critical for the stable operation of high-speed EMU vehicles. To identify the operating status of the inverter, this paper proposed a fault diagnosis method based on the analysis of motor vibration signals combined with the vehicle system dynamics. In this method, the motor vibration signal is taken as the monitoring object, and the characteristic signal of abnormal motor vibration caused by current distortion during inverter failure is detected for judgement. Taking the traction transmission system of CRH2 EMU as an example, a simulation model of a three-level traction inverter circuit based on space vector pulse width modulation strategy was established to simulate the structural fault modes of the inverter. The simulation results indicate that inverter faults significantly affect the harmonic content of the output current on the AC side, especially the 5th, 7th, 11th, and 13th harmonic currents. These harmonics currents are converted into pulsating torques at frequencies of 6<italic>P</italic>(1-<italic>s</italic>) and 12<italic>P</italic>(1-<italic>s</italic>) after input to the traction motor, acting on the motor and causing corresponding harmonic components in its output vibration. Analysis of vibration signals of the traction motor during the actual operation of high-speed EMU reveals that under normal circumstances, the vibration signals do not contain obvious harmonic frequency components, while the presence of inverter faults leads to a very significant content of this frequency in vibration signals. Therefore, the vibration signal analysis method proposed in this paper can effectively monitor and diagnose the operating status of the traction inverter and has a certain engineering application value.
In recent years, China has achieved rapid development in the fields of EMUs and metros, with original equipmentmanufacturers (OEMs) making significant strides in exploring foreign markets. CRRC Changchun Railway Vehicle Co., Ltd. (CRRC Changchun) has received successive orders for metro vehicles from oversea cities, such as Boston and Los Angeles of USA. In response to the requirements of the Los Angeles metro rail project, CRRC Changchun has developed and trial-produced a metro bogie model with flexibly articulated frames and inboard axle boxes, based on American and European standards. The company has also completed various analyses and experimental verifications for this bogie model. This paper focuses on introducing its technical parameters, structural characteristics, and related calculations and verifications. The research results confirm the superior capacities of this bogie model in wheel load balancing and curve passing ability.
It is critical in the implementation of structural health monitoring for rolling stock to acquire load characteristics of vital interfaces such as air suspensions, auxiliary components, axle box springs, and rubber joints during the service life. This paper explored an estimation method for loads on bogie frames. Based on an initially established vehicle system dynamics model, lateral and vertical dynamics models were derived, incorporating localized refinements specific to rocker type axle boxes. Based on these models, Kalman filtering was applied for inversion estimation of system state quantities through the system state equation. The study results, which considered excitations from track irregularities, show a correlation surpassing 0.8 between the estimated and simulated values for vertical loads on the bogie frame, while a correlation exceeding 0.6 for lateral loads. These findings indicate consistent change trends in the time-frequency domain between the estimated and simulated values. Moreover, as the vehicle speed increases, the accuracy of the inversion results based on Kalman filtering progressively improve in correlation with the simulated values. Based on the above outcomes, conclusions can be drawn that the proposed load estimation method is sufficiently accurate and stable in the inversion calculations for loads on the critical interfaces of bogie frames. This method can serve as a data foundation for subsequent damage monitoring and evaluation, remaining life calculations, and optimizing the structural design for key bogie components.
Daniel R. Clarkson, Lawrence A. Bull, Chandula T. Wickramarachchi
et al.
Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used to learn such mappings are of limited availability which hinders the effectiveness of traditional supervised machine learning approaches. The current paper proposes a methodology for overcoming the issue of data scarcity by combining active learning with hierarchical Bayesian modelling. Active learning is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g.\ inspection and maintenance). Hierarchical Bayesian modelling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modelling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modelling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost -- maintaining predictive performance while reducing the number of inspections required.
Banking is a financial institution that collects all public funds in the form of deposits and manages these funds to maintain liquidity and security in processing funds aimed at maximizing profits. Banks must provide financial traffic services needed by all customers for both internal and external transactions. Some programs offered by banks in providing financial services include the provision of micro-business credit (KUR) aimed at improving the community's economy. However, the problem that arises in the potential provision of KUR assistance is that it often misses the target, resulting in many customers not optimally receiving financial services. C4.5 Algorithm is an accurate data mining method used for data prediction and processing for decision making. This research aims to predict banking customers in providing KUR using the C4.5 algorithm. The methodology used is the Cross-Industry Standard Process Model for Data Mining, employing the C4.5 algorithm. The prediction results of micro-business credit recipients using the C4.5 algorithm are excellent, as seen from the calculation of entropy value of 0.97 and gain value of 0.69, as well as the formation of decision trees with several determinant data sets such as data from the Ministry of Home Affairs, OJK's Slik, repayment capacity, types of businesses, and locations. The optimization of the C4.5 algorithm in data processing helps in determining customers more optimally, reducing mis-targeted micro-business credit assistance.Keywords: Customer, Algorithm C4.5, Data mining
The emerging modules without baseplate are widely used in the field of new energy. They have high requirements for the coating of thermal interface material, but there are few related studies. In this paper, simulation research was conducted for the changing rule of the junction temperature of chips on IGBT modules with and without baseplate when the void ratio of the thermal interface material (TIM) is changing. A precise numerical model was established to carry out steady-state thermal simulation experiment. According to the results, quantitative analysis was conducted on the degree to which the chip junction temperature is affected by TIM voids on modules with and without baseplate. The results show that the chip junction temperature of the module with baseplate is lower than that of the module without baseplate, and the chip junction temperature is about 8.578 K and 9.544 K lower, respectively. When the void ratio of TIM layer increases, the junction temperature rise rate of the module without baseplate is greater than that of the module with baseplate, and the temperature rise rate of the large chip and the small chip of the module without baseplate is about 32.190 times and 240.875 times respectively that of the module with baseplate. The junction temperature rise rate of large chip is lower than that of small chip, which is about 23 times and 3 times, on modules with baseplate and without baseplate respectively. Modules without baseplate are more sensitive to the state of the TIM layer and have higher requirements.
Purpose – Using the strong motion data of K-net in Japan, the continuous magnitude prediction method based on support vector machine (SVM) was studied. Design/methodology/approach – In the range of 0.5–10.0 s after the P-wave arrival, the prediction time window was established at an interval of 0.5 s. 12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning (EEW) magnitude prediction model (SVM-HRM) for high-speed railway based on SVM. Findings – The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm. Results show that at the 3.0 s time window, the magnitude prediction error of the SVM-HRM model is obviously smaller than that of the traditional τc method and Pd method. The overestimation of small earthquakes is obviously improved, and the construction of the model is not affected by epicenter distance, so it has generalization performance. For earthquake events with the magnitude range of 3–5, the single station realization rate of the SVM-HRM model reaches 95% at 0.5 s after the arrival of P-wave, which is better than the first alarm realization rate norm required by “The Test Method of EEW and Monitoring System for High-Speed Railway.” For earthquake events with magnitudes ranging from 3 to 5, 5 to 7 and 7 to 8, the single station realization rate of the SVM-HRM model is at 0.5 s, 1.5 s and 0.5 s after the P-wave arrival, respectively, which is better than the realization rate norm of multiple stations. Originality/value – At the latest, 1.5 s after the P-wave arrival, the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate, which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.
Transportation engineering, Railroad engineering and operation
There has been growing interest within the computational science and engineering (CSE) community in engaging with software engineering research -- the systematic study of software systems and their development, operation, and maintenance -- to solve challenges in scientific software development. Historically, there has been little interaction between scientific computing and the field, which has held back progress. With the ranks of scientific software teams expanding to include software engineering researchers and practitioners, we can work to build bridges to software science and reap the rewards of evidence-based practice in software development.
By seamlessly integrating everyday objects and by changing the way we interact with our surroundings, Internet of Things (IoT) is drastically improving the life quality of households and enhancing the productivity of businesses. Given the unique IoT characteristics, IoT applications have emerged distinctively from the mainstream application types. Inspired by the outlook of a programmable world, we further foresee an IoT-native trend in designing, developing, deploying, and maintaining software systems. However, although the challenges of IoT software projects are frequently discussed, addressing those challenges are still in the "crossing the chasm" period. By participating in a few various IoT projects, we gradually distilled three fundamental principles for engineering IoT-native software systems, such as just enough, just in time, and just for "me". These principles target the challenges that are associated with the most typical features of IoT environments, ranging from resource limits to technology heterogeneity of IoT devices. We expect this research to trigger dedicated efforts, techniques and theories for the topic IoT-native software engineering.
In view of the development trend of smart urban rail transit and the maintenance status of urban rail transit signal system, an integrated, intelligent and information-based intelligent operation and maintenance system of urban rail transit signal was developed based on the big data platform and micro service technology architecture system, which can realize the functions of intelligent monitoring, fault diagnosis, health management and operation and maintenance management of urban rail transit signal system, and make contributions to the production of signal equipment industry management provides decision support, reduce maintenance costs and improve operation efficiency.
Feras A. Batarseh, Rasika Mohod, Abhinav Kumar
et al.
The field of artificial intelligence (AI) is witnessing a recent upsurge in research, tools development, and deployment of applications. Multiple software companies are shifting their focus to developing intelligent systems; and many others are deploying AI paradigms to their existing processes. In parallel, the academic research community is injecting AI paradigms to provide solutions to traditional engineering problems. Similarly, AI has evidently been proved useful to software engineering (SE). When one observes the SE phases (requirements, design, development, testing, release, and maintenance), it becomes clear that multiple AI paradigms (such as neural networks, machine learning, knowledge-based systems, natural language processing) could be applied to improve the process and eliminate many of the major challenges that the SE field has been facing. This survey chapter is a review of the most commonplace methods of AI applied to SE. The review covers methods between years 1975-2017, for the requirements phase, 46 major AI-driven methods are found, 19 for design, 15 for development, 68 for testing, and 15 for release and maintenance. Furthermore, the purpose of this chapter is threefold; firstly, to answer the following questions: is there sufficient intelligence in the SE lifecycle? What does applying AI to SE entail? Secondly, to measure, formulize, and evaluate the overlap of SE phases and AI disciplines. Lastly, this chapter aims to provide serious questions to challenging the current conventional wisdom (i.e., status quo) of the state-of-the-art, craft a call for action, and to redefine the path forward.
Quantum technology is exploding. Computing, communication, and sensing are just a few areas likely to see breakthroughs in the next few years. Worldwide, national governments, industries, and universities are moving to create a new class of workforce - the Quantum Engineers. Demand for such engineers is predicted to be in the tens of thousands within a five-year timescale. However, how best to train this next generation of engineers is far from obvious. Quantum mechanics - long a pillar of traditional physics undergraduate degrees - must now be merged with traditional engineering offerings. This paper discusses the history, development, and first year of operation of the world's first undergraduate degree in quantum engineering. The main purpose of the paper is to inform the wider debate, now being held by many institutions worldwide, on how best to formally educate the Quantum Engineer.
Hans-Martin Heyn, Eric Knauss, Amna Pir Muhammad
et al.
Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting improvements on a societal level, yet they also bring with them new challenges for their development. In this paper we argue that significant challenges relate to defining and ensuring behaviour and quality attributes of such systems and applications. We specifically derive four challenge areas from relevant use cases of complex, AI-intense systems and applications related to industry, transportation, and home automation: understanding, determining, and specifying (i) contextual definitions and requirements, (ii) data attributes and requirements, (iii) performance definition and monitoring, and (iv) the impact of human factors on system acceptance and success. Solving these challenges will imply process support that integrates new requirements engineering methods into development approaches for complex, AI-intense systems and applications. We present these challenges in detail and propose a research roadmap.
Daniel Russo, Paul P. H. Hanel, Seraphina Altnickel
et al.
Following the onset of the COVID-19 pandemic and subsequent lockdowns, software engineers' daily life was disrupted and abruptly forced into remote working from home. This change deeply impacted typical working routines, affecting both well-being and productivity. Moreover, this pandemic will have long-lasting effects in the software industry, with several tech companies allowing their employees to work from home indefinitely if they wish to do so. Therefore, it is crucial to analyze and understand how a typical working day looks like when working from home and how individual activities affect software developers' well-being and productivity. We performed a two-wave longitudinal study involving almost 200 globally carefully selected software professionals, inferring daily activities with perceived well-being, productivity, and other relevant psychological and social variables. Results suggest that the time software engineers spent doing specific activities from home was similar when working in the office. However, we also found some significant mean differences. The amount of time developers spent on each activity was unrelated to their well-being, perceived productivity, and other variables. We conclude that working remotely is not per se a challenge for organizations or developers.