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.
In this work, Al–Mg alloys fabricated by combining continuous rheo-extrusion (CRE) and Sc modification were proposed for producing Al–Mg alloys with high efficiency and superior mechanical performance. The microstructural evolution and mechanical property response of the CREed Al–5Mg alloy with Sc modification were investigated. The grain refinement and strengthening mechanisms induced by nanoscale Al<sub>3</sub>Sc-phase particles in the alloy were discussed. The results showed that an obvious grain refinement effect was achieved in the CREed Al–5Mg alloy as the Sc content increased from 0 to 0.5 wt%, and the average grain size decreased from 52.6 μm to 2.4 μm, respectively. The primary Al<sub>3</sub>Sc-phase particles formed during solidification behaved as heterogeneous nucleation sites for the α-Al matrix, while the nanoscale Al<sub>3</sub>Sc-phase particles achieved during CRE enhanced the driving force of continuous dynamic recrystallization and the Zener drag force. As a result, a superior grain refinement effect was observed. The ultimate tensile strength, yield strength, and hardness of the alloy were enhanced as the Sc content increased from 0 to 0.5 wt%. Grain boundary strengthening, second-phase strengthening, and dislocation strengthening were the main strengthening mechanisms of the CREed Al–Mg–Sc alloys.
Retrofitting abandoned mine shafts and using them for compressed hydrogen energy storage is an effective way to reuse waste resources and to realize the “double carbon” strategy. Due to mining action, abandoned mine shafts are usually in a non-isobaric state, and the non-isobaric state reduces the reliability of energy storage in the chambers. Based on the shear energy release characteristics during the fracture process, a phase-field fracture model considering the evolution of gas pressure, temperature and shear energy release was developed and validated; secondly, based on the proposed phase-field model, the initiation and development of potential fracture surfaces of chambers with different widths of coal pillars and level differences were investigated; and lastly, the proposed phase-field fracture model was combined with actual projects to investigate the effect of different treatment methods on the potential fracture surfaces of chambers. Finally, the proposed phase-field fracture model was combined with the actual project to investigate the effect of the treatment and fracture triggers under different surrounding rock treatment methods. The results show that the proposed phase-field fracture model can better characterize the fracture initiation location, fracture triggers and development process of the potential fracture surface of the chamber; the width of the coal pillar directly affects the fracture initiation pressure of the chamber; the wider the coal pillar, the higher the fracture initiation pressure of the chamber, and the overall fracture surface is mainly shear; the fracture surface of the chamber is divided into primary fracture surface and secondary fracture surface, and the wider the width of the pillar, the deeper the secondary fracture surface develops; the level difference between the chamber and the mining site also affects the fracture initiation pressure of the chamber; the fracture initiation pressure is also influenced by the level difference between the chamber and the mining site. The difference between the level of the chamber and the quarry also affects the fracture initiation pressure. As the height difference between the chamber and the quarry increases, the fracture initiation pressure of the perimeter rock increases and then decreases, and the fracture surfaces appear to be bifurcated due to tensile stresses; the fracture stopping study of the potential fracture surfaces shows that the most effective way to improve the hydrogen storage performance of the chamber is to add more anchors at the bottom of the chamber, followed by grouting to strengthen the perimeter rock and the weak effect of filling in the void areas. The cause of fracture in the chamber with the anchor ropes was shear stress, and the cause of fracture development along the anchors was tensile stress.
Coal fly ash, a by-product of coal combustion in thermal power plants, offers valuable opportunities for reuse in construction and material engineering. This study explores the thermal behaviour of fly ash samples collected from different sections of the REK Bitola Power Plant. Thermal characteristics of the fly ashes obtained from hot stage microscopy revealed distinct transformation stages, including sintering, softening, and melting intervals. X-ray fluorescence analysis was employed for determination of the chemical composition, while sieve analysis was used for investigation of granulometry of the materials. The physical and chemical characteristics of the ashes, particularly their grain size and content of silica, alumina, calcium oxide, and iron oxide play a crucial role in determining their response to heat. These findings help guide more effective use of fly ash in environmentally friendly applications, supporting waste reduction and promoting sustainable practices.
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.
Hendrik Reiter, Patrick Rathje, Olaf Landsiedel
et al.
Process Mining is established in research and industry systems to analyze and optimize processes based on event data from information systems. Within this work, we accomodate process mining techniques to Cyber-Physical Systems. To capture the distributed and heterogeneous characteristics of data, computational resources, and network communication in CPS, the todays process mining algorithms and techniques must be augmented. Specifically, there is a need for new Distributed Process Mining algorithms that enable computations to be performed directly on edge resources, eliminating the need for moving all data to central cloud systems. This paper introduces the DPM-Bench benchmark for comparing such Distributed Process Mining algorithms. DPM-Bench is used to compare algorithms deployed in different computational topologies. The results enable information system engineers to assess whether the existing infrastructure is sufficient to perform distributed process mining, or to identify required improvements in algorithms and hardware. We present and discuss an experimental evaluation with DPM-Bench.
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.
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.
Large Language Models (LLMs) are revolutionizing software engineering (SE), with special emphasis on code generation and analysis. However, their applications to broader SE practices including conceptualization, design, and other non-code tasks, remain partially underexplored. This research aims to augment the generality and performance of LLMs for SE by (1) advancing the understanding of how LLMs with different characteristics perform on various non-code tasks, (2) evaluating them as sources of foundational knowledge in SE, and (3) effectively detecting hallucinations on SE statements. The expected contributions include a variety of LLMs trained and evaluated on domain-specific datasets, new benchmarks on foundational knowledge in SE, and methods for detecting hallucinations. Initial results in terms of performance improvements on various non-code tasks are promising.
Proto-personas are commonly used during early-stage Product Discovery, such as Lean Inception, to guide product definition and stakeholder alignment. However, the manual creation of proto-personas is often time-consuming, cognitively demanding, and prone to bias. In this paper, we propose and empirically investigate a prompt engineering-based approach to generate proto-personas with the support of Generative AI (GenAI). Our goal is to evaluate the approach in terms of efficiency, effectiveness, user acceptance, and the empathy elicited by the generated personas. We conducted a case study with 19 participants embedded in a real Lean Inception, employing a qualitative and quantitative methods design. The results reveal the approach's efficiency by reducing time and effort and improving the quality and reusability of personas in later discovery phases, such as Minimum Viable Product (MVP) scoping and feature refinement. While acceptance was generally high, especially regarding perceived usefulness and ease of use, participants noted limitations related to generalization and domain specificity. Furthermore, although cognitive empathy was strongly supported, affective and behavioral empathy varied significantly across participants. These results contribute novel empirical evidence on how GenAI can be effectively integrated into software Product Discovery practices, while also identifying key challenges to be addressed in future iterations of such hybrid design processes.
Alexandra Mazak-Huemer, Christian Huemer, Michael Vierhauser
et al.
With the increasing significance of Research, Technology, and Innovation (RTI) policies in recent years, the demand for detailed information about the performance of these sectors has surged. Many of the current tools are limited in their application purpose. To address these issues, we introduce a requirements engineering process to identify stakeholders and elicitate requirements to derive a system architecture, for a web-based interactive and open-access RTI system monitoring tool. Based on several core modules, we introduce a multi-tier software architecture of how such a tool is generally implemented from the perspective of software engineers. A cornerstone of this architecture is the user-facing dashboard module. We describe in detail the requirements for this module and additionally illustrate these requirements with the real example of the Austrian RTI Monitor.
Introduction. The purpose of the work is to compare different methods of determining the granulometric distribution of gypsum-containing waste generated during the neutralization of sulfuric acid with limestone. The object of the study is gypsum-containing product obtained in the process of utilization of sulfuric acid by limestone at the enterprises of mining, metallurgical, chemical and other industries. Materials and methods. Granulometric distribution was determined using sedimentation methods and laser diffraction method. In addition, microscopic description and characterization of the studied samples were performed. Results. In the course of the study the peculiarities of granulometric analysis of gypsum-containing wastes belonging to the category of saline soils were revealed. Discussion. In the article, special attention is paid to the performance of particle size analysis by laser diffraction. It is important to note that the results obtained by this method are presented in volume percentages and not in weight percentages. This makes it incorrect to directly compare the data obtained using classical methods with the results of laser analysis. Conclusion. It is established that the use of sedimentation methods is difficult due to pronounced coagulation of suspension, which affects the accuracy of the final results. It is shown that special attention should be paid to the stage of sample preparation before testing, including drying of gypsum-containing waste samples at temperatures not exceeding 60°C to avoid loss of crystallization water. Resume. The obtained results can be applied for engineering-geological assessment not only of gypsum-containing wastes of sulfuric acid neutralization, but also of other similar anthropogenic formations. These data are necessary to analyze the stability of waste storage facilities, to develop integrated approaches to their use and to reduce their negative impact on the environment.
Micro plasma arc welding (MPAW) is frequently used for joining thin sheets of ferrous and nonferrous materials. In this study, austenitic stainless steel 316L of 0.5 mm thin sheets are joined by using MPAW. The weld metallurgy is characterized by field electron scanning microscopy (FESEM), Transmission electron microscopy (TEM), and X-ray diffraction (XRD) techniques to evaluate different phases formation and their orientation in detail. Mechanical tests like tensile test, micro hardness test is also carried out to measure the joint quality. It is found that the weld joint is constituent of two major phases, δ-ferrite and austenite (γ), and few secondary phases like chromium carbides. The ferrite percentage in the fusion zone is higher than the as received base material. The fusion zone hardness is increased due to the presence of high amount of ferrite and carbides. The tensile fracture surface contains lots of dimples and voids, which indicates good ductility of the joint. A defect free and good joint efficiency is achieved by using MPAW.
Mining engineering. Metallurgy, Materials of engineering and construction. Mechanics of materials
The crystal structure of the new ternary ThNi6Si6 and UNi6Si6 intermetallic compounds has been investigated by means of both single crystal and powder X-ray diffraction followed by Rietveld refinement. Both compounds adopt the tetragonal CeNi6Si6-type structure, crystallizing in the space group P4/nbm (No. 125) (Pearson’s symbol tP52). This prototype is a ternary defect-derivative of the binary NaZn13-type. A group-theoretical analysis carried out in this work illustrates the structural relationship occurring among the aristotype NaZn13-type, the partially disordered CeNi6Si6 and the fully ordered YNi6Si6 hettotype crystal structures.Zero-field low-temperature electrical resistivity data show a metallic behaviour for both materials. DC magnetic measurements from room temperature down to 2 K reveal diamagnetic behaviour for ThNi6Si6, indicating that Ni atoms do not carry any magnetic moment. On the other hand, the effective magnetic moment per formula unit for UNi6Ni6 is found to be µeff = 3.07(1) µB, suggesting a trivalent (U3+) or tetravalent (U4+) state for U atoms with partial delocalization of 5 f electrons. No magnetic ordering down to 2 K is seen in UNi6Si6. No superconducting transition was detected, neither in UNi6Si6 nor in ThNi6Si6 in the available temperature interval (2 K ≤ T ≤ 300 K).
In order to further explore the hydrogeological conditions of Baotashan sandstone aquifer in Ningdong coalfield and provide basic data for the water disaster prevention and control of the lower coal floor sandstone, the hydrogeological parameters of the aquifer were calculated by dewatering test, and the feasibility of water drainage from aquifer was proved. The results show that: according to the data of dewatering borehole and one observation borehole, two observation boreholes from single borehole dewatering test of Baotashan sandstone, the average permeability coefficient calculated by analytical method is 0.301 m/d and 0.219 m/d, and the average influence radius is 691 m and 914 m, respectively. The average influence radius obtained by graphic method is 697 m, and the unit water inflow is 0.071-0.088 L/(s·m) according to three times of deep drawdown. The hydrogeological parameters obtained by dewater test are large than those obtained by pumping water test. The hydrogeological characteristics of Baotashan sandstone aquifer are weak permeability and water abundance, the water abundance is uneven, and the pressure and water temperature are high. Based on the results of dewater test and the analysis of boundary conditions in the study area, it is found that Baotashan sandstone aquifer has good drainage ability.