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.
Michele Grimaldi, Patryk Cieslak, Eduardo Ochoa
et al.
Simulations are highly valuable in marine robotics, offering a cost-effective and controlled environment for testing in the challenging conditions of underwater and surface operations. Given the high costs and logistical difficulties of real-world trials, simulators capable of capturing the operational conditions of subsea environments have become key in developing and refining algorithms for remotely-operated and autonomous underwater vehicles. This paper highlights recent enhancements to the Stonefish simulator, an advanced open-source platform supporting development and testing of marine robotics solutions. Key updates include a suite of additional sensors, such as an event-based camera, a thermal camera, and an optical flow camera, as well as, visual light communication, support for tethered operations, improved thruster modelling, more flexible hydrodynamics, and enhanced sonar accuracy. These developments and an automated annotation tool significantly bolster Stonefish's role in marine robotics research, especially in the field of machine learning, where training data with a known ground truth is hard or impossible to collect.
Large Language Model (LLM)-based agents demonstrate advanced reasoning capabilities, yet practical constraints frequently limit outputs to single responses, leaving significant performance potential unrealized. This paper introduces MARINE (Multi-Agent Recursive IN-context Enhancement), a theoretically grounded framework that reconceptualizes test-time reasoning as iterative refinement of a persistent reference trajectory, fundamentally departing from conventional one-shot or multi-sample paradigms. The MARINE refinement operator systematically converts a base model's pass@N capabilities into near-optimal pass@1 performance. Rigorous theoretical analysis establishes that minimal feasible batches maximize expected performance gains under fixed invocation budgets, while logarithmically growing batch schedules ensure continuous improvement without computational constraints. Comprehensive evaluation on the BrowserComp-ZH benchmark demonstrates state-of-the-art results, with a 685B-parameter implementation achieving 46.0% pass@1 accuracy. Meanwhile, MARINE establishes a new paradigm for parameter-efficient reasoning: an 80B-parameter model augmented with MARINE matches the performance of standalone 1000B-parameter agents, reducing parameter requirements by over an order of magnitude. Notably, within a fixed computational budget, the proposed MARINE delivers higher-quality samples to alignment and optimization processes than traditional sampling-and-ranking strategies. Consequently, it has great potential to boost post-training efficiency.
Cloud architecture design presents significant challenges due to the necessity of clarifying ambiguous requirements and systematically addressing complex trade-offs, especially for novice engineers with limited cloud experience. While recent advances in the use of AI tools have broadened available options, system-driven approaches that offer explicit guidance and step-by-step information management may be especially effective in supporting novices during the design process. This study qualitatively examines the experiences of 60 novice engineers using such a system-driven cloud design support tool. The findings indicate that structured and proactive system guidance helps novices engage more effectively in architectural design, especially when addressing tasks where knowledge and experience gaps are most critical. For example, participants found it easier to create initial architectures and did not need to craft prompts themselves. In addition, participants reported that the ability to simulate and compare multiple architecture options enabled them to deepen their understanding of cloud design principles and trade-offs, demonstrating the educational value of system-driven support. The study also identifies areas for improvement, including more adaptive information delivery tailored to user expertise, mechanisms for validating system outputs, and better integration with implementation workflows such as infrastructure-as-code generation and deployment guidance. Addressing these aspects can further enhance the educational and practical value of system-driven support tools for cloud architecture design.
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.
This paper proposes an uncertainty-aware marine pollution source tracking framework for unmanned surface vehicles (USVs). By integrating high-fidelity marine pollution dispersion simulation with informative path planning techniques, we demonstrate effective identification of pollution sources in marine environments. The proposed approach is implemented based on Robot Operating System (ROS), processing real-time sensor data to update probabilistic source location estimates. The system progressively refines the estimation of source location while quantifying uncertainty levels in its predictions. Experiments conducted in simulated environments with varying source locations, wave conditions, and starting positions demonstrate the framework's ability to localise pollution sources with high accuracy. Results show that the proposed approach achieves reliable source localisation efficiently and outperforms the existing baseline. This work contributes to the development of full autonomous environmental monitoring capabilities essential for rapid response to marine pollution incidents.
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.
Rafael Catoia Pulgrossi, Nathan L R Williams, Yubin Raut
et al.
Marine provinces rarely include fine-resolution biological data, and are often defined spatially across only latitude and longitude. Therefore, we aimed to determine how phytoplankton distributions define marine provinces across 3-dimensions (i.e., latitude, longitude, and depth). To do this, we developed a new algorithm called \texttt{bioprovince} which can be applied to compositional biological data. The algorithm first clusters compositional samples to identify spatially coherent groups of samples, then makes flexible province predictions in the broader 3d spatial grid based on environmental similarity. We applied \texttt{bioprovince} to phytoplankton Amplicon Sequencing Variants (ASVs) from five, depth-resolved ocean transects spanning north-south in the Pacific Ocean. In the surface layer of the ocean, our method agreed well with traditional Longhurst provinces. In some cases, the method revealed that with more granular taxonomic resolution afforded by ASVs, traditional Longhurst provinces were divided into smaller zones. Also, one of the major advances of this method is its ability to incorporate a third dimension, depth. Indeed, our analysis found significant depth-wise partitions throughout the Pacific with remarkable agreement in the equatorial region with the base of the euphotic zone. Our algorithm's ability to delineate 3-dimensional bioprovinces will enable scientists to discover new ecological interpretations of marine phytoplankton ecology and biogeography. Furthermore, as compositional biological data inherently exists in three spatial dimensions in nature, bioprovince is broadly applicable beyond marine plankton, offering a more holistic perspective on biological provinces across diverse environments.
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.
Co Tan Anh Vu, Phan Van Hung, Dang Dinh Chien
et al.
Energy efficiency and CO2 emission reduction technologies play a vital role in the “Net-zero” strategy of the United Nations. Enhancing the power and fuel efficiency of waterway vehicles, particularly those with powerful propulsion systems, is becoming a major worldwide concern in the fight against emissions. Propeller boss cap fin systems are only one of the many ways that propulsion systems’ efficiency can be increased. Unfortunately, because they are either expensive or too difficult to install and operate, they are not appropriate for Vietnam’s offshore fishing fleets. In this study, a two-pitch propeller model was proposed, considering the operating characteristics of offshore fishing vessels with two stages: the free-running and the trawling stage. The hydrodynamic features of the fixed propeller and the propeller’s two pitches were analyzed using the numerical approach with a diameter of 1 m; a speed of 629 rpm; expanded area ratio of 0.5. The outcomes demonstrated that the two-pitch propeller’s overall efficiency with a pitch-blade of 0.6, consisting of the efficiency in working freely and the stage of trawling, is higher than the fixed propeller’s efficiency of the same size. Two-pitch propellers are seen as a significant step in bettering the planning of national fishing vessels’ emission reduction strategies.
Methanol, a renewable and sustainable fuel, provides an effective strategy for reducing greenhouse gas emissions when synthesized through carbon dioxide hydrogenation integrated with carbon capture technology. The incorporation of hydrogen into methanol-fueled engines enhances combustion efficiency, mitigating challenges such as pronounced cycle-to-cycle variations and cold-start difficulties. A simulation framework was developed using Python 3.13 and the Cantera 3.1.0 library to model the combustion system of a four-stroke spark-ignited (SI) methanol–hydrogen engine. This framework integrates a fractal turbulent combustion model with chemical reaction kinetics, complemented by early flame development and near-wall combustion models to address limitations during the initial and terminal combustion phases. The model was validated by using experimental data measured from a spark-ignited methanol engine. The effects of varying Hydrogen Energy Rates (HER) on engine power performance, combustion characteristics, and emissions (like formaldehyde and carbon monoxide) were subsequently analyzed under different operating loads, whilst the knock limit boundaries were established for different operational conditions. Findings demonstrate that increasing HER improves the engine power output and thermal efficiency, shortens the combustion duration, and reduces the formaldehyde and carbon monoxide emissions. Nevertheless, under high-load conditions, higher HER increases the knocking tendency, which constrains the maximum permissible HER decreasing from approximately 40% at 15% load to 20% at 100% load. The model has been developed into a Python library and will be open-sourced on Github.
As a zero-carbon energy carrier, hydrogen is playing an increasingly vital role in the decarbonization of maritime transportation. The hydrogen pressure reducing valve (PRV) is a core component of ship-borne hydrogen storage systems, directly influencing the safety, efficiency, and reliability of hydrogen-powered vessels. However, the marine environment—characterized by persistent vibrations, salt spray corrosion, and temperature fluctuations—poses significant challenges to PRV performance, including material degradation, flow instability, and reduced operational lifespan. This review comprehensively summarizes and analyzes recent advances in the study of high-pressure hydrogen PRVs for marine applications, with a focus on transient flow dynamics, turbulence and compressible flow characteristics, multi-stage throttling strategies, and valve core geometric optimization. Through a systematic review of theoretical modeling, numerical simulations, and experimental studies, we identify key bottlenecks such as multi-physics coupling effects under extreme conditions and the lack of marine-adapted validation frameworks. Finally, we conducted a preliminary discussion on future research directions, covering aspects such as the construction of coupled multi-physics field models, the development of marine environment simulation experimental platforms, the research on new materials resistant to vibration and corrosion, and the establishment of a standardized testing system. This review aims to provide fundamental references and technical development ideas for the research and development of high-performance marine hydrogen pressure reducing valves, with the expectation of facilitating the safe and efficient application and promotion of hydrogen-powered shipping technology worldwide.
ObjectiveTo alleviate the contradiction between bandwidth extension and manufacturability of dual-band FSS, the bandwidth extension technology based on stub loading is studied. Method First, the main factors causing the above contradiction by conventional bandwidth extension methods are analyzed. Second, the influence of stub loading on the electromagnetic characteristics of FSS is studied, such as the reactance characteristics, shape, and quantity of stubs. Finally, the first/second transmission passbands of FSS are extended by stub loading on the grid/slots, respectively, and the mitigation effect of stub loading technology on the above contradiction is analyzed in detail. Results Simulation results show that compared with the traditional bandwidth extension method, the stub loading technology can improve the dual-band bandwidth by 48.8% and 23.2% respectively under the same metal line width (0.1 mm), and the test results are in good agreement with the simulation results. Conclusion Stub loading can effectively alleviate the contradiction between multi-band bandwidth extension and manufacturability, providing theoretical guidance for engineering practice.
Changki Park, Christos Kontovas, Zaili Yang
et al.
Cybersecurity risks are becoming a major concern in the maritime industry due to the increasing reliance on information technology and operational technology systems. This paper aims to develop a new methodology to evaluate the effectiveness of risk control measures (RCMs). Six criteria influencing the choice of cybersecurity RCMs are identified through literature review. Expert opinions are used to assess major cybersecurity RCMs using the fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. The methodology prioritises the most viable RCMs using primary data collected from 100 experts. The findings indicate that the most effective cybersecurity control measures based on stakeholders’ opinions are “Effective Antivirus software management,” “Management of network devices,” and “Developing a cybersecurity strategy.” This paper contributes to maritime cybersecurity policy guidance by providing experimental evidence and offers a new decision tool to aid stakeholders in selecting the most suitable measures to address the relevant risks.
Three-dimensional (3-D) wing moving steadily over free water surface with the effects of finite depth has been investigated numerically by an iterative boundary element method (IBEM) developed originally before for cavitating 3-D hydrofoils advancing under free surface. The IBEM has been modified and extended to do this. The fluid is assumed inviscid, incompressible and the flow irrotational. All variables and equations are made non-dimensional. In this way the convergence of numerical scheme is achieved very quickly and consistently. The IBEM is based on the Green’s theorem. The wing problem (including its wake), the free surface problem and the bottom surface problem are solved separately with the effects of each other via their potential values. The 3-D wing surface, the free surface and bottom surface are modeled with constant strength source and constant strength doublet panels. The kinematic boundary condition is applied both on the wing surface and on the bottom surface. On the other hand, the linearized kinematic and dynamic combined condition is applied on the free water surface. The method is first applied to a rectangular wing with a high aspect ratio to compare the pressure distribution on mid-section strip with that of two-dimensional method. Later, the IBEM is applied to a tapered swept-back wing and the effects of finite depth on wing performance have been investigated. It is found that the shallower water depth caused an increase in Kelvin wedge angle, wave height and wave length as compared with in infinite depth case. It is also found that a decrease depth of bottom surface is caused an increase in loading on the wing. Journal of Naval Architecture and Marine Engineering, 21(1), 2024, P: 1- 14
Daniel San Martín, Guisella Angulo, Valter Vieira de Camargo
Self-adaptive systems (SASs) adjust their behavior at runtime in response to internal or external change. The MAPE-K model, which includes Monitors, Analyzers, Planners, Executors, and shared Knowledge, is a reference for structuring feedback loops. As SASs evolve, implementations can drift from the intended MAPE-K architecture, compromising planned quality attributes. Architectural Conformance Checking (ACC) addresses this risk by comparing the current implementation to a specification of the architecture. General purpose ACC techniques are flexible, but lack SAS specific semantics, leading to ambiguous specifications and missed violations. We present REMEDY, an ACC approach designed for MAPE-K based SASs. REMEDY provides three elements: a domain specific language for expressing planned architectures in MAPE-K terms, a tool that extracts the implemented architecture, and a conformance engine that reports violations. By encoding SAS domain rules and reusing MAPE-K abstractions, REMEDY reduces specification effort and lowers error rates relative to general ACC. We evaluate REMEDY through a robotic SAS case study and a controlled experiment with software engineering students. Results show higher modeling productivity and effective detection of architectural drift, supporting more reliable verification of conformance to the MAPE-K reference model.
As Artificial Intelligence (AI) has developed rapidly over the past few decades, the new generation of AI, Large Language Models (LLMs) trained on massive datasets, has achieved ground-breaking performance in many applications. Further progress has been made in multimodal LLMs, with many datasets created to evaluate LLMs with vision abilities. However, none of those datasets focuses solely on marine mammals, which are indispensable for ecological equilibrium. In this work, we build a benchmark dataset with 1,423 images of 65 kinds of marine mammals, where each animal is uniquely classified into different levels of class, ranging from species-level to medium-level to group-level. Moreover, we evaluate several approaches for classifying these marine mammals: (1) machine learning (ML) algorithms using embeddings provided by neural networks, (2) influential pre-trained neural networks, (3) zero-shot models: CLIP and LLMs, and (4) a novel LLM-based multi-agent system (MAS). The results demonstrate the strengths of traditional models and LLMs in different aspects, and the MAS can further improve the classification performance. The dataset is available on GitHub: https://github.com/yeyimilk/LLM-Vision-Marine-Animals.git.
Рассмотрены причины, вызвавшие интерес к науке о прочности с древнейших времен, и способы решения проблем обеспечения необходимой прочности архитектурных сооружений. Поиск эффективных форм высотных металлических сооружений привел к появлению ажурных стержневых ферменных конструкций, каковыми явились Эйфелева башня – памятники строительной механики и сетчатые оболочки гиперболоида вращения русского инженера В.Г. Шухова. Начиная с середины XIX века в судостроении начали применять железо и его сплавы. Для нового материала потребовались новые конструктивные формы, поиск которых формировался на основе анализа опыта эксплуатации и разрушений. На этом фоне формируются корабельные науки: теория корабля, корабельная архитектура и прочность корабля. Огромную роль в области отечественного судостроения сыграл А.Н. Крылов. Ему принадлежит заслуга создания классической теории килевой качки корабля на волнении, он создал предпосылки строительной механики корабля. Честь создания строительной механики корабля принадлежит И.Г. Бубнову -ученику А.Н. Крылова. Новые математические модели строительной механики корабля развивались П.Ф. Папковичем, Ю.А. Шиманским. Важным инструментом решения задач прочности инженерных сооружений становится метод конечных элементов, появившийся в пятидесятых годах прошлого века и завоевавший большую популярность за счет эффективного использования развившейся вычислительной техники. Современное состояние вычислительных технологий и возможности строительной механики корабля на сегодняшний день позволяют строить цифровые двойники силовых конструкций судов и разнообразных объектов морской техники, которые будут использоваться не только для проектирования, но и на всех этапах жизненного цикла. The reasons that have aroused interest in the science of strength since ancient times, and ways to solve the problems of ensuring the necessary strength of architectural structures are considered. The search for effective forms of high–rise metal structures led to the appearance of openwork rod trusses, which were the Eiffel Tower - monuments of structural mechanics and mesh shells of the hyperboloid of rotation by Russian engineer V.G. Shukhov. Starting from the middle of the XIX century, iron and its alloys began to be used in shipbuilding. For the new material, new constructive forms were required, the search for which was formed based on the analysis of the experience of operation and destruction. Against this background, ship sciences are being formed: ship theory, ship architecture and ship strength. A.N. Krylov played a huge role in the field of domestic shipbuilding. He is credited with the creation of the classical theory of the keel pitching of a ship on a wave, he created the prerequisites for the construction mechanics of a ship. The honor of creating the ship's structural mechanics belongs to I.G. Bubnov, a student of A.N. Krylov. New mathematical models of the ship's structural mechanics were developed by P.F. Papkovich and Yu.A. Shimansky. The finite element method, which appeared in the fifties of the last century and gained great popularity due to the effective use of advanced computing technology, becomes an important tool for solving problems of strength of engineering structures. The current state of computing technologies and the capabilities of the ship's structural mechanics today make it possible to build digital counterparts of power structures of ships and various objects of marine technology, which will be used not only for design, but also at all stages of the life cycle.