Hasil untuk "Ocean engineering"

Xianqi Meng, Yuefeng Zhao, Kaifa Cao et al.

Remote sensing image change captioning (RSICC) aims to generate textual descriptions of changes between bitemporal images. However, accurately describing fine-grained changes while capturing interscale relationships as well as distinguishing real changes from spurious changes (e.g., illumination, seasonal variations) are still major challenges for current methods. To address these issues, we propose DualFocus-CapNet, a novel model tailored for RSICC. DualFocus-CapNet employs a dual-stream architecture, where each stream is dedicated to processing a distinct pair of bitemporal features. Crucially, we introduce a scale-wise progressive convolution (ScalePro Conv) that employs a progressive scale-specific approach to decompose remote sensing features into pixel-level variations, regional continuities, and linear structures. Unlike conventional parallel multiscale processing methods, ScalePro Conv adopts a serial progressive structure to establish interscale relationships, thereby avoiding the fragmentation of feature information. Then, the bi-directional difference guided transformer (BDiGTrans) is proposed to eliminate interference from spurious changes by dynamically masking invariant regions and extracting bidirectional differential features. Furthermore, the cross-temporal adaptive fusion module (CTAF) is introduced to dynamically balance bitemporal features using learnable gating to enhance change discrimination and robust caption generation. Comprehensive experiments on the benchmark datasets LEVIR-CC and WHU-CDC show that our DualFocus-CapNet surpasses state-of-the-art change captioning methods in various evaluation metrics.

Yiming Zhou, Li Ren, Xuan Liu et al.

To address the peak-fluctuating cooling load of ocean-going fishing vessels and the dependency of traditional refrigeration systems on fuel-driven power, this study proposes an exhaust waste-heat-driven ammonia water absorption–compression hybrid refrigeration system. The proposed system was thermodynamically analyzed and simulated based on the principles of heat and mass transfer. Considering the full-cycle cooling demand, an objective optimization model with the goal of minimizing the total operating cost was established and solved using the Northern Goshawk Optimization (NGO) algorithm. Using real data from a fishing company, a voyage cycle of Lu Huang Yuan Yu 105 was selected as a case study. Results showed that NGO outperformed the Genetic Algorithm and Particle Swarm Optimization, achieving the smallest cooling deficit and faster convergence. Compared with the independent compression refrigeration system, the hybrid system reduced the cooling deficit by 9.7%, improved cooling capacity by over 35% during voyage, 5% during fishing, and 2% during processing, while lowering fuel consumption by 10% and efficiently utilizing exhaust heat. Sensitivity analysis identified optimal ranges for ammonia concentration and circulation ratio and highlighted the significant influence of cooling water temperature on system performance. This study provides a valuable reference for the design and optimization of low-grade waste-heat-driven hybrid refrigeration systems in maritime applications.

Junwei Yu, Mufeng Yang, Yepeng Ding et al.

The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.

Simon Cabanes, Thomas Gastine, Alexandre Fournier

We investigate the magnetic signature of oceanic circulation in Ganymede's subsurface ocean using kinematic induction modeling. Our approach couples zonal jet flows from rotating thermal convection simulations with magnetic field models incorporating Ganymede's internal dynamo and external contributions from Jupiter. We solve the induction equation in spherical geometry for deep-ocean (493 km) and shallow-ocean (287 km) scenarios with varying magnetic Reynolds numbers. Ocean flows generate a predominantly toroidal magnetic field through the omega-effect, with a weaker poloidal component pervading beyond the conductive ocean layer. For some, but not all, induction configurations, analysis of the time-averaged Lowes-Mauersberger spectra reveals that ocean-induced signals dominate at spherical harmonic degrees $\ell \geq 4$. Deep ocean scenarios with magnetic Reynolds numbers above unity produce surface magnetic signals up to 9 nT. Our results demonstrate that Ganymede's intrinsic magnetic field creates favorable conditions for detecting subsurface ocean dynamics, thus emphasizing the need for low-altitude orbits for the Juice probe.

Zongbing Yu, Yuxi Wu, Li Zou et al.

Wind seas and swells typically co-occur around islands, resulting in a wave spectrum with a frequency characterized by double or multiple peaks. Separating the wave energies of wind seas and swells is crucial for accurately estimating the wave loads exerted on offshore structures around islands. This study presents a novel method for accurately separating wind sea and swell, considering the effects of wind fields and topographic features around the island on the wave spectrum. The fetch around the island was determined by the wave conditions and wind sea growth curve with respect to wind velocity, and the local wind field was then vanished and the wave action balance model executed again to obtain the swell component. The remaining energy in the wave spectrum was regarded as wind sea. The proposed method was validated using the observed wave conditions during Typhoon Sarika in the Atoll area of the South China Sea. The method successfully separated the wind sea and swell, and both exhibit wave spectra, which differ from the separation frequency obtained by traditional methods. Furthermore, both the one-dimensional and directional spectra demonstrated excellent performance.

Na Liu, Guodong Wu, Yonggui Huang et al.

In large-scale multimodal remote sensing data archives, the application of cross-modal technology to achieve fast retrieval between different modalities has attracted great attention. In this article, we focus on cross-modal retrieval technology between remote sensing images and text. At present, there is still a large heterogeneity problem in the semantic information extracted from different modal data in the remote sensing field, which leads to the inability to effectively utilize intraclass similarities and interclass differences in the hash learning process, ultimately resulting in low cross-modal retrieval accuracy. In addition, supervised learning-based methods require a large number of labeled training samples, which limits the large-scale application of hash-based cross-modal retrieval technology in the remote sensing field. To address this problem, this article proposes a new unsupervised cross-autoencoder contrast hashing method for RS retrieval. This method constructs an end-to-end deep hashing model, which mainly includes a feature extraction module and a hash representation module. The feature extraction module is mainly responsible for extracting deep semantic information from different modal data and sends the different modal semantic information to the hash representation module through the intermediate layer to learn and generate binary hash codes. In the hashing module, we introduce a new multiobjective loss function to increase the expression of intramodal and intermodal semantic consistency through multiscale semantic similarity constraints and contrastive learning and add a cross-autoencoding module to reconstruct and compare hash features to reduce the loss of semantic information during the learning process. This article conducts a large number of experiments on the UC Merced Land dataset and the RSICD dataset. The experimental results of these two popular benchmark datasets show that the proposed CACH method outperforms the most advanced unsupervised cross-modal hashing methods in RS.

Chiara Favaretto, Duccio Bertoni, Alessandro Pozzebon et al.

The use of coarse sediments for beach nourishment represents an alternative solution to structural interventions for the protection of coasts that are exposed to energetic waves. Predicting the mass loss of fill material due to abrasion and scouring is crucial for proper nourishment design, as it has implications for the littoral sediment budget and sediment stability under extreme wave conditions. The present study aims at characterising the typical abrasion and chipping of coarse sediments used for beach nourishments, providing insights into their correlation with the wave energy flux incident on the coast. Estimation of weight loss and roundness variability is obtained through RFID (Radio Frequency IDentification) sediment tracking technology, 3D scanning techniques, and Los Angeles tests. The 1 year and 7 months in-situ investigation was carried out at Marina di Pisa (Italy), an artificial beach where the size of supplied pebbles was significantly reduced after few months of typical wave climate, jeopardizing the sediment stability and the lifetime of the nourishment. The methodology could be applied to other beaches and lithologies to establish a database of weight loss useful for coastal managers and engineers.

Elsy Ibrahim, Anne Gobin

The detection and exclusion of clouds and their shadows have been the primary focus of pixel contamination in spaceborne agricultural soil monitoring. However, contaminants affecting specific parts of agricultural fields, such as stationary features (large pylons and artificial soil cover) and dynamic sources (pylon shadows and passing aircraft with contrails), have been overlooked despite their importance in precision agriculture. This study investigates these underexplored sources of pixel contamination and their implications for agricultural soil monitoring. Using bare soil data from 2017 to 2023 and focusing on the field preparation period from mid-April to late May, we analyzed the effects of artificial soil cover, transmission tower shadows, and aircraft overflights on bare soil reflectance. These pixel contaminants significantly altered surface reflectance compared to clear bare soil pixels, with P <inline-formula><tex-math notation="LaTeX">$\leq$</tex-math></inline-formula> 0.0001 for artificially covered, P <inline-formula><tex-math notation="LaTeX">$\leq$</tex-math></inline-formula> 0.01 for aircraft-impacted and P <inline-formula><tex-math notation="LaTeX">$\leq$</tex-math></inline-formula> 0.05 for tower shadowed pixels. Artificial cover increased the surface reflectance of bare soil by 10&#x0025; to 50&#x0025; in the visible and near-infrared bands, with a smaller increase of 5&#x0025; in the shortwave infrared bands; pylon shadows reduced the surface reflectance by up to 5&#x0025; within a 10 m buffer around the shadow. Aircraft footprints caused a sixfold increase in reflectance, with contrails affecting large areas and increasing reflectance by up to 30&#x0025;. Important spectral indices for bare soil analyses were significantly affected by artificial cover, but not always by shadows or aircraft impact. The analysis provides insights into the spectral anomalies caused by pixel contaminants and highlights the need to account for such influences to improve the accuracy of spaceborne agricultural soil monitoring, particularly in small parcels or field zones.

Jing Wang, Shouwen Zhang, Yuanlong Li et al.

Abstract Interactions among the El Niño‐Southern Oscillation, Indian Ocean Basin mode (IOB), and Indian Ocean Dipole (IOD) significantly impact global climate variability and seasonal predictions. Traditionally, positive IOD (pIOD) and IOB warming events are associated with El Niño, driven by its influence on the tropical Indian Ocean through Walker Circulation anomalies. Our findings enrich this framework, revealing that a pIOD without El Niño can independently trigger IOB warming, and both types of pIODs can induce La Niña events. While El Niño primarily forces IOB warming and subsequent La Niña development via the atmospheric bridge across the Maritime Continent, pIODs independent of El Niño influence IOB warming through oceanic dynamics, which further favors La Niña development in the following year. The NMEFC‐CESM model sensitivity experiments underscore the critical role of thermocline processes in this mechanism, dependent on the pIOD's temperature amplitude, offering vital insights for forecasting post‐IOD, IOB, and La Niña events.

Yuanyuan Guo, Weizhong Li, Qi Peng et al.

Lossy compression exhibits remarkable capabilities in handling large volumes of data. However, information loss can affect spectral characteristics and spatial information to various degrees during hyperspectral compression. Therefore, it is essential to restrict the range of spectral changes, as each spectral curve corresponds to a distinct semantic context. In this article, we propose a spectral-constrained generative adversarial network (SCGAN) for hyperspectral compression. Specifically, SCGAN integrates compression and classification tasks within a unified framework. During generative adversarial learning, SCGAN uses a classification map to guide the generation of global spectral information. To deal with different hyperspectral images (HSIs) in one model, a three-stage training strategy is leveraged. Experiments conducted on three public HSI datasets illustrate that the proposed SCGAN effectively narrows the semantic gap. For instance, the average overall classification accuracy of SCGAN on Pavia University is above 0.9, which is the closest to the classification accuracy achieved with the original HSIs, even at a low bitrate of 0.05 bits per pixel per band.

Mia Mohammad Imran, Tarannum Shaila Zaman

Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the \textbf{Operationalization for LLM-based Annotation Framework (OLAF)}, a conceptual framework that organizes key constructs: \textit{reliability, calibration, drift, consensus, aggregation}, and \textit{transparency}. The paper aims to motivate methodological discussion and future empirical work toward more transparent and reproducible LLM-based annotation in software engineering research.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy plays a crucial role in software engineering (SE), influencing collaboration, communication, and decision-making. While prior research has highlighted the importance of empathy in SE, there is limited understanding of how empathy manifests in SE practice, what motivates SE practitioners to demonstrate empathy, and the factors that influence empathy in SE work. Our study explores these aspects through 22 interviews and a large scale survey with 116 software practitioners. Our findings provide insights into the expression of empathy in SE, the drivers behind empathetic practices, SE activities where empathy is perceived as useful or not, and the other factors that influence empathy. In addition, we offer practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

Yunli Feng, Sunwei Li, Daoyi Chen et al.

In order to study the scale effect in wave-structure interactions and the role that structure-related parameters (tension T or bending stiffness EI) plays, riser model tests under regular waves were conducted using the model with multiple geometric scales (1:15, 1:12 and 1:9) in a wave basin. The riser model used is a novel structural design combing the outer polyvinyl chloride pipe with the core steel rod which could be simplified as a cantilever beam. Different initial tension T acting on the riser are tested by adjusting the slotted weight. The results show that the amplitude varies in a cubic fashion with the distance from the fixed end. In addition, the influence of the wave period and top tension T on the amplitude are investigated, which ultimately leads to a dimensionless number π1 = KCd·TL2/EI where KC is the classical Keulegan–Carpenter number (KC), EI shows the bending stiffness of the riser model and L gives the pipe length. With the KC number revised to take the distance from the fixed end into the calculation, this parameter provides a good measure in estimating the amplitudes of the riser vibrations induced by the waves.

Chunhan Jin, Bin Wang, Tat Fan Cheng et al.

Abstract Tianshan Mountains are the headwater regions for the central Asia rivers, providing water resources for ecological protection and economic development in semiarid regions. Due to scarce observations, the hydroclimatic characteristics of the Tianshan Mountains Precipitation (TMP) measured over highland (>1500 m) regions remain to be revealed. Here, we show the TMP belongs to a monsoon-like climate regime, with a distinct annual range and a high ratio of summer-to-yearly rainfall, and exhibits six abrupt changes, dividing the annual cycle into six precipitation sub-seasons. Over the past 60 years, the yearly TMP has significantly increased by 17.3%, with a dramatic increase in winter (135.7%). The TMP displays a significant 40-day climatological intra-seasonal oscillation (CISO) in summer. The TMP CISO’s wet phase results from the confrontation of the eastward propagating mid-tropospheric Balkhash Lake Low and the southward migrating Mongolian High. The sudden changes in the two climatological circulation systems trigger TMP’s changes, shaping the 40-day CISO. Emerging scientific issues are also discussed.

Tat-Dat Bui, Hien Minh Ha, Thi Phuong Thuy Tran et al.

This study is to build a causality model to implement energy security strategies (ESSs) in approaching a world-regions comparison. This study contributes to ESSs by indicating a set of valid attributes and those attributes are interrelationships in nature. There is major global interest in ESSs due to the pressure to ensure sustainable energy supply sources. An adequate energy source is decisive for ensuring stable economic growth, enhancing social development, and protecting the environment. Nonetheless, in reviewing the energy literature, generating strategic attributes is still lacking, which leads to difficulties for policymakers in building, executing, and assessing energy policies. This study utilizes a hybrid method: text mining, cluster analysis, fuzzy Delphi method, fuzzy decision-making trial and evaluation laboratory, and entropy weight method. As a result, five aspects and 22 criteria from the data pool are validated. The causal model shows that the energy control system, strategic collaboration and technological capability are the priority. In practice, the effect aspects are waste-to-energy and energy resilience. Although the research trends on ESSs in different regions are quite similar, each continent still has unique concerns such as European countries with distributed energy resources, Asia and Oceania with decarbonization, African countries with new technologies, and Americas with energy planning.

Claudio Di Sipio, Riccardo Rubei, Juri Di Rocco et al.

Software engineering (SE) activities have been revolutionized by the advent of pre-trained models (PTMs), defined as large machine learning (ML) models that can be fine-tuned to perform specific SE tasks. However, users with limited expertise may need help to select the appropriate model for their current task. To tackle the issue, the Hugging Face (HF) platform simplifies the use of PTMs by collecting, storing, and curating several models. Nevertheless, the platform currently lacks a comprehensive categorization of PTMs designed specifically for SE, i.e., the existing tags are more suited to generic ML categories. This paper introduces an approach to address this gap by enabling the automatic classification of PTMs for SE tasks. First, we utilize a public dump of HF to extract PTMs information, including model documentation and associated tags. Then, we employ a semi-automated method to identify SE tasks and their corresponding PTMs from existing literature. The approach involves creating an initial mapping between HF tags and specific SE tasks, using a similarity-based strategy to identify PTMs with relevant tags. The evaluation shows that model cards are informative enough to classify PTMs considering the pipeline tag. Moreover, we provide a mapping between SE tasks and stored PTMs by relying on model names.

Johannes Schleiss, Aditya Johri

In this practice paper, we propose a framework for integrating AI into disciplinary engineering courses and curricula. The use of AI within engineering is an emerging but growing area and the knowledge, skills, and abilities (KSAs) associated with it are novel and dynamic. This makes it challenging for faculty who are looking to incorporate AI within their courses to create a mental map of how to tackle this challenge. In this paper, we advance a role-based conception of competencies to assist disciplinary faculty with identifying and implementing AI competencies within engineering curricula. We draw on prior work related to AI literacy and competencies and on emerging research on the use of AI in engineering. To illustrate the use of the framework, we provide two exemplary cases. We discuss the challenges in implementing the framework and emphasize the need for an embedded approach where AI concerns are integrated across multiple courses throughout the degree program, especially for teaching responsible and ethical AI development and use.

Bohui Zhang, Valentina Anita Carriero, Katrin Schreiberhuber et al.

Ontology engineering (OE) in large projects poses a number of challenges arising from the heterogeneous backgrounds of the various stakeholders, domain experts, and their complex interactions with ontology designers. This multi-party interaction often creates systematic ambiguities and biases from the elicitation of ontology requirements, which directly affect the design, evaluation and may jeopardise the target reuse. Meanwhile, current OE methodologies strongly rely on manual activities (e.g., interviews, discussion pages). After collecting evidence on the most crucial OE activities, we introduce \textbf{OntoChat}, a framework for conversational ontology engineering that supports requirement elicitation, analysis, and testing. By interacting with a conversational agent, users can steer the creation of user stories and the extraction of competency questions, while receiving computational support to analyse the overall requirements and test early versions of the resulting ontologies. We evaluate OntoChat by replicating the engineering of the Music Meta Ontology, and collecting preliminary metrics on the effectiveness of each component from users. We release all code at https://github.com/King-s-Knowledge-Graph-Lab/OntoChat.

Isaiah Lahr, Saghir Alfasly, Peyman Nejat et al.

Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and diagnosis to prognosis and prediction. Whole slide images (WSIs) are highly detailed digital representations of tissue specimens mounted on glass slides. Matching WSI to WSI can serve as the critical method for patient matching. In this paper, we report extensive analysis and validation of four search methods bag of visual words (BoVW), Yottixel, SISH, RetCCL, and some of their potential variants. We analyze their algorithms and structures and assess their performance. For this evaluation, we utilized four internal datasets ($1269$ patients) and three public datasets ($1207$ patients), totaling more than $200,000$ patches from $38$ different classes/subtypes across five primary sites. Certain search engines, for example, BoVW, exhibit notable efficiency and speed but suffer from low accuracy. Conversely, search engines like Yottixel demonstrate efficiency and speed, providing moderately accurate results. Recent proposals, including SISH, display inefficiency and yield inconsistent outcomes, while alternatives like RetCCL prove inadequate in both accuracy and efficiency. Further research is imperative to address the dual aspects of accuracy and minimal storage requirements in histopathological image search.

Guifang Sun, Zhandong Wang, Yi Lu et al.

With the rapid developments of marine resource exploitation, mounts of marine engineering equipment are settled on the ocean. When it is not possible to move the damaged equipment into a dry dock, welding operations must be performed in underwater environments. The underwater laser welding/cladding technique is a promising and advanced technique which could be widely applied to the maintenance of the damaged equipment. The present review paper aims to present a critical analysis and engineering overview of the underwater laser welding/cladding technique. First, we elaborated recent advances and key issues of drainage nozzles all over the world. Next, we presented the underwater laser processing and microstructural-mechanical behavior of repaired marine materials. Then, the newly developed powder-feeding based and wire-feeding based underwater laser direct metal deposition techniques were reviewed. The differences between the convection, conduction, and the metallurgical kinetics in the melt pools during underwater laser direct metal deposition and in-air laser direct metal deposition were illustrated. After that, several challenges that need to be overcame to achieve the full potential of the underwater laser welding/cladding technique are proposed. Finally, suggestions for future directions to aid the development of underwater laser welding/cladding technology and underwater metallurgical theory are provided. The present review will not only enrich the knowledge in the underwater repair technology, but also provide important guidance for the potential applications of the technology on the marine engineering.