Hasil untuk "Ocean engineering"

Ian A. Cosden, Elizabeth Holtz, Joel U. Bretheim

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.

QingHao Zhou, Weisheng Li, Yidong Peng et al.

Pansharpening aims to generate high-resolution multispectral (HRMS) images from paired panchromatic (PAN) images and low-resolution multispectral (LRMS) images. Some deep learning models employ end-to-end skip connection to learn the differences between HRMS and LRMS images. Although these models achieve satisfactory pansharpening effects, their spectral information processing methods are inadequate, and the end-to-end residual connection may lead to inaccurate propagation of spectral information. Due to the differences in spectral range and resolution between PAN and multispectral (MS) images, direct injection of spatial information can introduce spectral distortion. To enhance spectral information fidelity and improve the injection of spatial-detail information, we propose a multiple spatial–spectral dual-injection balance network. Leveraging iterative refinement, the network performs a cascade of dual-injection stages. Each stage consists of a spatial injection subnetwork followed by its spectral counterpart. Within every stage, the spatial subnetwork first enriches spatial details; immediately afterward, the spectral subnetwork serially corrects spectral deviation. This “enhance-then-correct” synergy alternately refines sharpness and fidelity without mutual interference, ensuring balanced optimization and substantial performance gains. The spatial injection subnetwork comprises a global processing module and a local processing module, each designed to process global and local spatial information, respectively. The global processing module is effective for tasks involving small datasets. The spectral injection network emulates the structure of the spatial injection network to learn spectral details. To optimize the integration of these two distinct types of information, we developed an adaptive spatial attention module and adaptive channel attention module, and further designed spatial fusion module and channel fusion module based on them to enable different feature integration. Extensive experiments on three datasets demonstrate the superior performance and effectiveness of the proposed model.

Xing Hong, Mingfeng Yin, Qi Gao et al.

As an effective approach for obtaining accurate target location in complex scenarios, RGBT tracking methods have recently attracted significant attention. However, the fundamentally different physical imaging mechanisms of visible and infrared modalities induce substantial discrepancies in appearance characteristics and feature distributions, causing semantic misalignment in feature representations and consequently challenging Siamese-based trackers that depend on a shared embedding space and direct similarity matching. To this end, we propose a novel triple-stream channel-spatial collaborative aggregation network for efficient RGBT tracking, named SiamCCA, which contains two parallel feature extraction streams, and one feature fusion stream. First, a dynamic gating scale awareness (DGSA) module is designed to adaptively adjust unimodal feature representations through dynamic gating mechanism and multi-scale adaptive fusion without significantly increasing computational overhead. Second, a channel-spatial collaborative aggregation (CSCA) module is constructed to accurately capture long-range channel and spatial dependencies, facilitating the model to better extract cross-modal information. Third, a region proposal selection (RPS) module is established to obtain accurate tracking results according to confidence scores of the fusion response map. Finally, comprehensive experiments have been demonstrated on three RGBT benchmark datasets. The results illustrate that SiamCCA can sufficiently handle different challenging scenarios while maintaining real-time processing at 56 FPS, outperforming other state-of-the-art trackers.The code is available at https://github.com/Mrxing-abc/SiamCCA/tree/master.

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.

Liyuan Chen, Shuoling Liu, Jiangpeng Yan et al.

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.

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.

Fabian C. Peña

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.

Fernando Ayach, Vitor Lameirão, Raul Leão et al.

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.

Yibo Fu, Tianle Chu, Xinlong An et al.

Labyrinthulomycetes protists play important roles in organic matter decomposition and nutrient cycling in marine ecosystems. To better understand their distribution and potential ecological functions in Caofeidian seagrass beds of the Bohai Sea, we conducted high-throughput sequencing of samples collected from multiple habitats, including leaves (L), rhizosphere (R), sediments (S), and seawater (W). Our results revealed distinct habitat-specific patterns of community composition. <i>Oblongichytrium</i> and <i>Stellarchytrium</i> were dominant in certain samples, exhibiting clear differences across stations. <i>Oblongichytrium</i> showed particularly high abundance in leaf and seawater samples, likely reflecting the availability of particulate and dissolved substrates enriched by seagrass beds. In the rhizosphere, <i>Sicyoidochytrium</i>, <i>Stellarchytrium</i> and <i>Labyrinthula</i> were enriched, whereas unclassified Labyrinthulomycetes and Thraustochytriaceae lineages prevailed in seawater and specific leaf samples. Notably, a substantial proportion of sequences corresponded to unclassified lineages, potentially representing uncultured “seagrass-associated” taxa. Compared with previous reports, our study revealed both a significantly higher abundance of <i>Stellarchytrium</i> and a remarkably greater proportion of unclassified lineages, suggesting unique features of Labyrinthulomycetes communities in the Caofeidian seagrass ecosystem. These findings provide new insights into the ecological roles of Labyrinthulomycetes in seagrass beds and offer an important reference for future taxonomic and functional studies of this group.

Xiaojie Hou, Hongmei Yin, Xiaodie Qin et al.

ABSTRACT Pengqi sauce is a traditional Chinese naturally fermented aquatic product, and its unique flavor formation mechanism is closely related to microbial function. In this study, we combined metabolomics and microbiomics techniques to analyze the flavor evolution and the rules and mechanisms of bacterial flora action during the natural fermentation process of Wedelia sauce. On the basis of electronic nose, gas chromatography–mass spectrometry (GC–MS) and GC–ion mobility spectrometry (GC–IMS) techniques, nine core flavor components, including 2‐methylpyrazine, 1‐heptanol, tetrahydrothiophene, and isoamyl isobutyrate, which confer baking, fruity, and creamy aromas, were identified as key volatile components during the fermentation process. Meanwhile, the 16S rRNA sequencing technology was used for the first time to clarify the flavor profile of the Pengqi sauce fermentation system. The actual abundance changes of microorganisms in the Pengqi sauce fermentation system: Paracoccus, Nocardioides, and Marinilactibacillus, the absolute abundance of which reached 12,129, 9411, and 9113 copies/g, respectively, on Day 56, and still maintained a high level on Day 84. Orthogonal partial least squares discriminant analysis (OPLS‐DA) and Spearman correlation analyses indicated that the above genera showed very strong positive correlations (ρ > 0.9, p < 0.05) with key flavor substances such as 1‐hexanol and 2‐methylpyrazine, and their functional predictions indicated that they drove flavor formation through protein degradation, lipid oxidation, and glucose metabolism pathways. This study demonstrated that the absolute microbial quantification strategy can reveal the contribution of bacterial flora to fermentation flavor more accurately and provide a theoretical basis for process optimization and standardized production of traditional fermented foods.

Young Hoon Shin, Seung Jae Lee, Min Jun Lee

This paper proposes a novel frequency-domain methodology for analyzing floating offshore wind turbines (FOWTs) through stress transfer functions that consider wave and wind loads. The frequency-domain analysis model included linearizing methods of critical parameters (i.e., rotor thrusts by wind, restoring forces by the mooring system, and hydrodynamic pressures by waves). A reference wind turbine, the OC3-Hywind spar platform, was selected as the target model, and industry-standard software packages, OpenFAST and ABAQUS, were used for validation. The wave-induced motion response amplitude operators (RAOs) showed agreement with 2%, 4%, and 1% differences for the surge, heave, and pitch modes under the head–sea condition, respectively. Similarly, wind-induced motions exhibited 2% differences in the surge and pitch modes in the colinear condition. The obtained results were used to calculate the radiation pressures as an integrated form, and stress transfer functions were yielded by considering them with incident and diffraction pressures. The proposed approach showed good agreement with conventional time-domain methods in the stress spectrum, showing a 92% decrease in computational time with only a 4% difference in results. This computationally efficient methodology eliminates the need for time-domain coupled-load simulations and offers potential applications in fatigue analysis and the initial design stage of FOWTs.

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.

Adam Subel, Laure Zanna

The current explosion in machine learning for climate has led to skilled, computationally cheap emulators for the atmosphere. However, the research for ocean emulators remains nascent despite the large potential for accelerating coupled climate simulations and improving ocean forecasts on all timescales. There are several fundamental questions to address that can facilitate the creation of ocean emulators. Here we focus on two questions: 1) the role of the atmosphere in improving the extended skill of the emulator and 2) the representation of variables with distinct timescales (e.g., velocity and temperature) in the design of any emulator. In tackling these questions, we show stable prediction of surface fields for over 8 years, training and testing on data from a high-resolution coupled climate model, using results from four regions of the globe. Our work lays out a set of physically motivated guidelines for building ocean climate emulators.

Selma Aprilla Kardinan, Sedarnawati Yasni, Slamet Budijanto et al.

Bioactive peptides are promising functional ingredients. Due to its high protein content, snakehead fish (Channa striata) extract (SHFE) is one of the suitable parent proteins for bioactive peptides. This study aimed to investigate the production of SHFE-based antioxidative peptides in a conventional batch and continuous system facilitated by an enzymatic membrane reactor (EMR). The effects of different proteases (Alcalase, Neutrase), substrate concentrations, and enzyme-to-substrate ratios were investigated in the batch process. Continuous hydrolysis was then performed under the optimum conditions obtained from the batch process. The optimum conditions based on the antioxidant capacity measured by DPPH and FRAP assays were employing Alcalase with a substrate concentration [S] of 3% (w/v) and an enzyme-to-substrate ratio [E]/[S] of 10% (w/w). Continuous operation was shown to have been performed over a prolonged period, based on the calculated fouling rate. Furthermore, filtration of the resulting permeate with a smaller membrane pore size (2-kDa) increased the antioxidant capacity. This study is expected to increase the production of functional ingredients in snakehead fish.

Hongjian Ye, Weiming Chen, Diyong Wang et al.

Synthetic aperture radar (SAR), which can work normally under various meteorological conditions, has been widely researched and applied in marine vessel target monitoring and identification. Among the many research topics, due to the inconsistency of ship scale in SAR images, susceptibility to sea and land noise and clutter interference, resulting in a low detection rate of near-shore ship targets and inaccurate edge delineation of densely lined ships, a target detection algorithm based on deep convolutional neural network is proposed. The algorithm employs the channel-space grouping attention mechanism during feature extraction to enhance features by utilizing global positional and edge information associated with instances. The feature mobility fusion module is employed to merge features of various scales, bolster the interconnection among these features, and enhance multiscale ship target detection capabilities. The decoupled head is employed for ship target localization, while the angle-weighted intersection over union is used to mitigate regression errors. The experimental results show that the precision (P) achieved on HRSID and SSDD datasets reaches 94.81&#x0025; and 99.01&#x0025;, respectively, exceeding the control algorithm by more than 1.35&#x0025; and 0.94&#x0025;; the average precision (mAP) reaches 92.06&#x0025; and 99.50&#x0025;, respectively, exceeding the control algorithm by more than 2.32&#x0025; and 2.51&#x0025;; this indicates that the proposed algorithm has a good performance on SAR image ship detection and a strong generalization ability.

Liye Zhao, Yongxiang Gong, Zhiqian Li et al.

The precise wake model is crucial for accurately estimating wind farm loads and power, playing a key role in wake control within wind farms. This study proposes a segmented dual-Gaussian wake model, which is built upon existing dual-Gaussian wake models but places greater emphasis on the influence of initial wake generation and evolution processes on the wind speed profile in the near-wake region. The enhanced model optimizes the wake speed profile in the near-wake region and improves the accuracy of wake diffusion throughout the entire flow field. Furthermore, the optimized dual-Gaussian wake model is utilized to estimate the power output and blade root vibration loads in offshore wind farms. Through comparative analysis of high-fidelity simulation results and actual measurement data, the accuracy of the optimized dual-Gaussian wake model is validated. This approach offers high computational efficiency and provides valuable insights for load fluctuations and power estimation, thereby advancing the development of wake control strategies rapidly.

Shuzhan Li, Jin Yang, Guojing Zhu et al.

The vast reserves of natural gas hydrates in offshore areas present significant challenges to development. Surface well construction technology is crucial for the extraction of deepwater natural gas hydrates. To ensure the safety of the subsea wellhead during the drilling process for deepwater natural gas hydrates, a novel conductor suction pile device has been designed, comprising a combination of suction piles and surface conductors. And research has been conducted to investigate the lateral stability characteristics of the conductor suction pile. Drawing upon the pile foundation load-bearing theory and the equilibrium of the differential element, a theoretical analysis model and corresponding governing equations of the conductor suction pile system are established. A solution for a multi-point boundary value problem by simplifying the conductor suction pile system into a two-end free beam is proposed. The governing equations are then converted into a first-order differential equation system, and the four-stage Lobatto IIIa collocation method program for the multi-point boundary value problem is developed and resolved using MATLAB 2023a. Furthermore, a case study of a well in the South China Sea elucidates the effects of wellhead load and seabed soil properties on the lateral load-bearing capacity of the conductor suction pile system, verifying the collocation method’s validity against the results from the finite difference method. After conducting a comparative analysis of the lateral load-bearing performance between conductor suction piles and traditional surface conductors, it is observed that conductor suction piles exhibit lower horizontal displacement and bending moments compared to surface conductors. Therefore, conductor suction piles demonstrate a substantial safety margin. The research findings provide a theoretical basis for the lateral stability of conductor suction piles during deepwater natural gas hydrate drilling. This offers a safe and efficient method for surface well construction in the extraction of natural gas hydrates.

Daphné G. Lemasquerier, Carver J. Bierson, Krista M. Soderlund

The circulation in Europa's ocean determines the degree of thermal, mechanical and chemical coupling between the ice shell and the silicate mantle. Using global direct numerical simulations, we investigate the effect of heterogeneous tidal heating in the silicate mantle on rotating thermal convection in the ocean and its consequences on ice shell thickness. Under the assumption of no salinity or ocean-ice shell feedbacks, we show that convection largely transposes the latitudinal variations of tidal heating from the seafloor to the ice, leading to a higher oceanic heat flux in polar regions. Longitudinal variations are efficiently transferred when boundary-driven thermal winds develop, but are reduced in the presence of strong zonal flows and may vanish in planetary regimes. If spatially homogeneous radiogenic heating is dominant in the silicate mantle, the ocean's contribution to ice shell thickness variations is negligible compared to tidal heating within the ice. If tidal heating is instead dominant in the mantle, the situation is reversed and the ocean controls the pole-to-equator thickness contrast, as well as possible longitudinal variations.

Matthias Killer, Marius Wiggert, Hanna Krasowski et al.

Seaweed biomass presents a substantial opportunity for climate mitigation, yet to realize its potential, farming must be expanded to the vast open oceans. However, in the open ocean neither anchored farming nor floating farms with powerful engines are economically viable. Thus, a potential solution are farms that operate by going with the flow, utilizing minimal propulsion to strategically leverage beneficial ocean currents. In this work, we focus on low-power autonomous seaweed farms and design controllers that maximize seaweed growth by taking advantage of ocean currents. We first introduce a Dynamic Programming (DP) formulation to solve for the growth-optimal value function when the true currents are known. However, in reality only short-term imperfect forecasts with increasing uncertainty are available. Hence, we present three additional extensions. Firstly, we use frequent replanning to mitigate forecast errors. Second, to optimize for long-term growth, we extend the value function beyond the forecast horizon by estimating the expected future growth based on seasonal average currents. Lastly, we introduce a discounted finite-time DP formulation to account for the increasing uncertainty in future ocean current estimates. We empirically evaluate our approach with 30-day simulations of farms in realistic ocean conditions. Our method achieves 95.8\% of the best possible growth using only 5-day forecasts.This demonstrates that low-power propulsion is a promising method to operate autonomous seaweed farms in real-world conditions.