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.
Balkar Yildirim, Alan Albert Coley, Diego Fernando López
We investigate whether black holes can persist through the bounce with a minimal scale factor in a non-singular cosmology, whereby black holes from a previous contracting phase survive into the current expanding one. We do so by studying a generalized McVittie spacetime which embeds a spherically symmetric black hole in a positive spatial curvature bouncing FLRW cosmological background within the modified theory of teleparallel new general relativity. There are no further assumptions on the spacetime (e.g., on the form of the scale factor) initially, and the local evolution is derived from the field equations of the theory, utilizing a perturbative scheme which is valid ``near the bounce". To leading order we obtain a simple bounce solution similar to that in general relativity for a closed FLRW model with a positive cosmological constant, but in which the curvature term in the Friedmann equation is re-normalized within new general relativity. Qualitatively the minimum of the bounce at $t=0$ changes, but near the bounce the evolution remains symmetric. The central inhomogeneity evolves at higher perturbative orders, where the details depend on the arbitrary constants of the perturbative solution. Hence the evolution of the local horizon during the bounce changes qualitatively, where the effects depend on the signs of the perturbation, and the symmetry across the bounce is disrupted due to a linear term.
Optimal airfoil design remains a critical challenge in aerodynamic engineering, with traditional methods requiring extensive computational resources and iterative processes. This paper presents GEO-DSGA, a novel framework integrating hybrid geometric neural networks with deep symbiotic genetic algorithms for enhanced airfoil optimization. The methodology employs graph-based representations of airfoil geometries through a hybrid architecture combining graph convolutional networks with traditional deep learning, enabling precise capture of spatial geometric relationships. The parametric modeling stage utilizes CST, Bézier curves, and PARSEC methods to generate mathematically robust airfoil representations, subsequently transformed into graph structures preserving local and global shape characteristics. The optimization framework incorporates a deep symbiotic genetic algorithm enhanced with dominant feature phenotyping, applying biological symbiotic principles where design parameters achieve superior performance through mutual enhancement rather than independent optimization. This systematic exploration maintains geometric feasibility and aerodynamic validity throughout the design space. Experimental results demonstrate an 88.6% reduction in computational time while maintaining prediction accuracy within 1.5% error margin for aerodynamic coefficients across diverse operating conditions. The methodology successfully identifies airfoil geometries outperforming baseline NACA profiles by up to 12% in lift-to-drag ratio while satisfying manufacturing and structural constraints, establishing GEO-DSGA as a significant advancement in computational aerodynamic design optimization.
Carlo Brutti, Corrado Groth, Marco Evangelos Biancolini
Threaded connections are fundamental in engineering structures, yet their elastic–plastic behavior under load remains challenging to model analytically. The yield limit can be reached under relatively small external loads, and elastic–plastic behavior has predominantly been studied using finite element models. While these models are highly valuable, they are often restricted to specific cases. This paper presents a novel extension of Maduschka’s classical method, offering a fast and efficient analytical approach to evaluate the behavior of screw–nut–washer assemblies. The method tracks plastic strain progression from initial yielding to full yield conditions and is validated against high-fidelity axisymmetric and 3D finite element analyses (FEAs) across a range of thread dimensions (M16–M36). Results demonstrate strong agreement with FEA benchmarks while achieving significant computational speedups, making the method suitable for iterative and large-scale analyses. In addition, the comparison with results available in the literature further supports the reliability of the proposed method. Its robustness to variations in geometry, friction, and thread count positions it as a foundation for reduced-order models, ready for integration into complex finite element frameworks commonly used in structural health monitoring and digital twin technologies.
Armine Ulukhanyan, Luca Placidi, Anil Misra
et al.
In this contribution, the classical Cauchy first-gradient elastic theory is used to solve the equilibrium problem of a bidimensional (2D) reinforced elastic structure under small displacements and strains. Such a 2D first-gradient continuum is embedded with a reinforcement, which is modeled as a zero-thickness interface endowed with the elastic properties of an extensional Euler–Bernoulli 1D beam. Modeling the reinforcement as an interface eliminates the need for a full geometric representation of the reinforcing bar with finite thickness in the 2D model, and the associated mesh discretization for numerical analysis. Thus, the effects of the 1D beam-like reinforcements are described through proper and generalized boundary conditions prescribed to contiguous continuum regions, deduced from a standard variational approach. The novelty of this work lies in the formulation of an interface model coupling 1D and 2D continua, based on weak formulation and variational derivation, capable of accurately capturing stress distributions without requiring full geometric resolution of the reinforcement. The proposed framework is therefore illustrated by computing, with finite element simulations, the response of the reinforced structural element under uniform bending. Numerical results reveal the presence of jumps for some stress components in the vicinity of the reinforcement tips and demonstrate convergence under mesh refinement. Although the reinforcement beams possess only axial stiffness, they significantly influence the equilibrium configuration by causing a redistribution of stress and enhancing stress transfer throughout the structure. These findings offer a new perspective on the effective modeling of fiber-reinforced structures, which are of significant interest in engineering applications such as micropiles in foundations, fiber-reinforced concrete, and advanced composite materials. In these systems, stress localization and stability play a critical role.
Chemicals: Manufacture, use, etc., Textile bleaching, dyeing, printing, etc.
Mohammad Hematibahar, Ahmed Deifalla, Adham E. Ragab
et al.
ABSTRACT Experts have been interested in the behavior of concrete under impact loading because of its wide range of applications in construction projects. Due to their quasi‐brittle nature, failure modes related to concrete may occur without any prior warning signs of destruction, and they also expose the supporting element to the spread of damage. Finite element modeling and machine learning techniques are essential for conducting an adequate reliability investigation of the behavior of concrete samples as slabs and cubes under impact load. The research uses gradient boosting, random forest, lasso, linear regression, and support vector regression to create predictive models for the behavior of these two concrete models. The models were created by taking experimental concrete slab tests and 20 cubes into consideration. Design standards‐based statistical comparisons such as coefficient of determination and root mean square error are used to assess the efficacy of the generated models. These results show that with increasing impact load intensity, displacements and failures in the slab increase significantly. Using these models allows engineers to design more resistant and optimal structures against impact loads. This research shows that machine learning models, especially random forest and gradient boosting, can provide accurate predictions of failures and cracks in concrete under impact loads and are useful tools for analyzing concrete behavior under dynamic and complex conditions. The linear regression with a coefficient of determination (R2) of 0.995 and lasso regression with RMSE of 3.9 have the lowest accuracy, while random forest and gradient boosting models with R2 of 0.9991 and 0.2, 0.991 and 0.5. respectively, showed higher accuracy in predicting concrete cracks and failures.
Inadequate use of mobile phones from an early age, coupled with reduced hours dedicated to developing practical skills, has led to a decline in spatial skills at secondary level, resulting in a decline in the spatial skills of engineering graduates. There are students enrolling in engineering have had limited experience in graphics. The decrease in time allocated to the basic engineering courses has necessitated the condensing of material taught in these courses. Descriptive geometry is one such course and this paper aims to present a method to bridge this gap, by providing skills that enable increased working memory to maintain the mental construction after the visual stimulus has disappeared. A question appears: “Can exercises in visualizing the intersection of a polyhedron and a plan influence the development of spatial skills?” Visibility rules are the main knowledge used to solve them. The results’ comparison of two samples of spatial visualization and perception is analysed. Data analysis and average formula allow us to observe better results for male than female for the use of spatial visualization abilities and spatial perception one.
Architectural engineering. Structural engineering of buildings, Engineering design
Mohamed Ibrahim, Ayman Shamesldein, Hesham Sokairge
et al.
Abstract The development of geopolymer concrete is advancing rapidly worldwide. However, issues related to workability and setting time in geopolymer mixes, compared to traditional concrete mixtures, remain a challenge. These problems could be particularly concerning if geopolymer is used for masonry applications. The short setting time and rapid hardening may enhance the production rate of masonry units but require careful consideration to ensure practicality and quality control. This study examines the performance of lightweight geopolymer concrete (LWGPC) as an alternative to traditional lightweight concrete to be used in masonry unit’s production. The investigation focuses on the effects of free water, foaming agent content, and foam stabilizer on the mechanical and physical properties of LWGPC. Experimental results indicate that reducing the free water content increased the dry density from approximately 810 kg/m³ to 1030 kg/m³ and enhanced the compressive strength from 3.25 MPa to 5.61 MPa after 28 days. Conversely, increasing the foaming agent content decreased the dry density from 1024 kg/m³ to 680 kg/m³, accompanied by a reduction in compressive strength from 5.52 MPa to 2.28 MPa. The inclusion of foam stabilizer slightly reduced density (by up to 7%) and caused compressive-strength reductions ranging from a 4% to 42% within the tested mixes, increasing with foaming-agent content. These findings highlight the trade-offs between density, strength, and workability, offering valuable insights for optimizing LWGPC for masonry used in structural applications, and insulation purposes. Within the tested range, intermediate foaming-agent dosages (around 60–80 kg/m³) yielded the highest specific compressive strength, depending on stabilizer content. Additionally, a new set of equations was proposed to predict the compressive strength and dry density of lightweight foamed geopolymer concrete, with strong correlation to experimental results (R² = 0.93 for compressive strength and R² = 0.96 for dry density).
Human mitochondrial Complex I is one of the largest multi-subunit membrane protein megacomplexes, which plays a critical role in oxidative phosphorylation and ATP production. It is also involved in many neurodegenerative diseases. However, studying its structure and the mechanisms underlying proton translocation remains challenging due to the hydrophobic nature of its transmembrane parts. In this structural bioinformatic study, we used the QTY code to reduce the hydrophobicity of megacomplex I, while preserving its structure and function. We carried out the structural bioinformatics analysis of 20 key enzymes in the integral membrane parts. We compare their native structure, experimentally determined using Cryo-electron microscopy (CryoEM), with their water-soluble QTY analogs predicted using AlphaFold 3. Leveraging AlphaFold 3’s advanced capabilities in predicting protein–protein complex interactions, we further explore whether the QTY-code integral membrane proteins maintain their protein–protein interactions necessary to form the functional megacomplex. Our structural bioinformatics analysis not only demonstrates the feasibility of engineering water-soluble integral membrane proteins using the QTY code, but also highlights the potential to use the water-soluble membrane protein QTY analogs as soluble antigens for discovery of therapeutic monoclonal antibodies, thus offering promising implications for the treatment of various neurodegenerative diseases.
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.
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.
This lecture presents an overview of the basic concepts and fundamentals of Engineering Materials within the framework of accelerator applications. After a short introduction, main concepts relative to the structure of matter are reviewed, like crystalline structures, defects and dislocations, phase diagrams and transformations. The microscopic description is correlated with physical properties of materials, focusing in metallurgical aspects like deformation and strengthening. Main groups of materials are addressed and described, namely, metals and alloys, ceramics, polymers, composite materials, and advanced materials, where brush-strokes of tangible applications in particle accelerators and detectors are given. Deterioration aspects of materials are also presented, like corrosion in metals and degradation in plastics.
Context: Experiment replications play a central role in the scientific method. Although software engineering experimentation has matured a great deal, the number of experiment replications is still relatively small. Software engineering experiments are composed of complex concepts, procedures and artefacts. Laboratory packages are a means of transfer-ring knowledge among researchers to facilitate experiment replications. Objective: This paper investigates the experiment replication process to find out what information is needed to successfully replicate an experiment. Our objective is to propose the content and structure of laboratory packages for software engineering experiments. Method: We evaluated seven replications of three different families of experiments. Each replication had a different experimenter who was, at the time, unfamiliar with the experi-ment. During the first iterations of the study, we identified experimental incidents and then proposed a laboratory package structure that addressed these incidents, including docu-ment usability improvements. We used the later iterations to validate and generalize the laboratory package structure for use in all software engineering experiments. We aimed to solve a specific problem, while at the same time looking at how to contribute to the body of knowledge on laboratory packages. Results: We generated a laboratory package for three different experiments. These packages eased the replication of the respective experiments. The evaluation that we conducted shows that the laboratory package proposal is acceptable and reduces the effort currently required to replicate experiments in software engineering. Conclusion: We think that the content and structure that we propose for laboratory pack-ages can be useful for other software engineering experiments.
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.
Beata Skibska, Anna Gorąca, Magdalena Markowicz-Piasecka
Background. The effect of vitamin C on the skin. The skin is a protective barrier against harmful factors from the external environment, which is due to its unique structure. The skin contains vitamin C in various concentrations depending on the individual layers. Skin keratinocytes have the capacity to accumulate high concentrations of vitamin C, which may reduce inflammation caused by excessive exposure to UV irradiation.
Aim of the study. The aim of the study was to systematize knowledge about the topical use of vitamin C in the care or treatment of skin defects based on a literature review and to indicate potential benefits in damages related to the overproduction of reactive oxygen species (ROS) and the skin aging process.
Materials and methods. The literature review was performed by searching scientific databases: PubMed and Google Scholar. The search for relevant articles on the role of vitamin C in the skin was carried out using the following keywords: “vitamin C", "ascorbic acid", "magnesium ascorbyl phosphate", "ascorbyl-6-palmitate", "skin", "photoprotection", "photoaging". In order to increase the efficiency of work, the authors developed a concept of the publication that included division into subchapters that were assigned to individual authors in order to avoid duplicating information while editing the manuscript. The described methodology allowed for obtaining reliable information.
Results. Vitamin C protects the keratinocyte from apoptosis and increases cell survival. It acts as an antioxidant that plays an important role by stimulating collagen synthesis and assisting in antioxidant protection against UV-induced photodamage. Vitamin C also influences gene expression of antioxidant enzymes, the organisation and accumulation of phospholipids, and promotes the formation of the stratum corneum and the differentiation of the epithelial cells in general. The provision of vitamin C to the skin greatly assists wound healing and minimises raised scar formation.
Vitamin C supplementation for nutritional purposes is also important and can be combined with topical application. The effect of the comprehensive action of vitamin C is to protect tissues against oxidative damage by removing free radicals that damage the most important structures of the body: cell membranes, DNA and proteins.
Conclusions. The effectiveness of vitamin C affects the condition of the skin and the rate of its aging. This study contains examples of research methods for reconstructed human epidermis or keratinocytes, but the literature review shows that these models lack other skin components such as fibroblasts, Langerhans cells, melanocytes and hair follicles. The human skin model has been developed in laboratories and is currently limited by the lack of many critical biological and structural features of the skin. Engineering a human skin equipped with, among others, into immune cells and capable of generating all components, including appendages, is a major challenge. Therefore, further research is needed to elucidate the exact mechanisms of action of vitamin C using a human skin model that containing other skin components.
Urbanization has profound effects on administrative boundaries, resulting in the expansion of urban areas, particularly at the periphery. This rapid growth leads to significant changes in landcover and land use, as agricultural and natural open areas are progressively transformed into densely populated urban landscapes characterized by housing, commercial infrastructure, and transportation systems.
The capital city of Jordan, Amman, faces exceptional urban growth, with its population surpassing 4.5 million people. This unprecedented expansion has given rise to extensive urban landscapes, presenting challenges for planners who lack a holistic understanding of the wide-ranging impacts.
To address these complexities and make well-informed decisions, planners urgently require comprehensive, up-to-date information on the causes, chronology, and consequences of urbanization. Integrating high-precision satellite imagery, geoinformatics data, and topographic insights offers a promising avenue to develop comprehensive inventories of urban change and growth. Such knowledge acts as a vital resource, enabling accurate assessments of expanding built-up areas and their associated implications.
The use of high geometric resolution satellite imagery and geoinformatics data combined with topographic information and GIS could provide effective information to develop urban change and growth inventory which could be explored towards producing a very important signature for the built-up area changes.
A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.
In the context of performance‐based earthquake engineering (PBEE), response‐history analysis is currently considered an analytical tool for developing fragility curves. Typically, this involves subjecting a structural system to a large number of ground motion records (GMRs) representing seismic hazards at a site of interest and may be a time‐consuming task. To address this computational challenge, this study proposes a method for selecting a representative subset of GMRs that enables the reproduction of the fragility curve of the general GMR set. In this method, dimension reduction techniques are used to preferentially extract the principal features of earthquake intensity measures, which are applied to construct the feature space. Then, the divisive hierarchical clustering technique is applied to the feature space to obtain a subset of GMRs from the general set until the fragility curve converges. The performance of the proposed method is successfully demonstrated through various numerical examples that include a wide class of single‐degree‐of‐freedom systems and two steel‐frame buildings. The results confirm that the seismic hazard at a given site represented by a general GMR set can be covered in structural fragility estimation using a representative subset of GMRs selected based on the proposed method. The proposed method could contribute to significantly reducing the computational costs for structural fragility estimation without compromising the accuracy.
Janina Zaczek-Peplinska, Maria Elżbieta Kowalska, Edward Nowak
Periodic inventory and check surveys of the surfaces in engineering structures using terrestrial laser scanning require performing scans from many locations. The survey should be planned so as to obtain full coverage of the measured surface with a point cloud of appropriate density. Due to a variety of terrain obstacles in the close vicinity of the surveyed structure, structural and technical elements, as well as machinery and construction equipment (whose removal is impossible e.g. because of their role in the building and protection of the structure), it is often necessary to combine scans acquired from locations having different measurement geometry of the scene and performed in different lighting conditions. This makes it necessary to fill in blank spots with data of different spectral and geometric quality. This paper presents selected aspects of data harmonization in terrestrial laser scanning. The laser beam incidence angle and the scanning distance are assumed as parameters affecting the quality of the data. Based on the assumed minimum parameters for spectral data, an example of a harmonizing function for the concrete surface of a slurry wall was determined, and the methodology for determining its parameters was described. The presented solution for spectral data harmonization is based on the selection of reference fields representative of a given surface, and their classification with respect to selected geometric parameters of the registered point cloud. For geometric data, possible solutions to the harmonization problem have been analyzed, and criteria for point cloud reduction have been defined in order to obtain qualitatively consistent data. The presented results show that harmonization of point clouds obtained from different stations is necessary before their registration, in order to increase the reliability of analyses performed on the basis of check survey results in the assessment of the technical condition of a surface, its deformation, cracks and scratches.