Hasil untuk "Ocean engineering"

Wanying Kang, Yixiao Zhang

The habitability of Enceladus' subsurface ocean and the detectability of potential biosignatures depend on efficient ocean circulation and suitable ocean conditions. Directly probing the ocean is challenging because it lies beneath a thick ice shell; however, the ice thickness distribution is relatively well constrained and provides indirect insight into the underlying ocean dynamics. This study investigates how ocean circulation and the associated heat transport depend on ocean salinity and tide-induced vertical mixing using scaling analysis, supported by numerical simulations. We find that ocean circulation and equatorward heat convergence are stronger under extremely high or low salinity conditions than under intermediate salinity, and both increase with tidal mixing rates. Because the poleward thinning of Enceladus' ice shell cannot be maintained in the presence of strong equatorward ocean heat transport, these results place constraints on the ocean salinity, diffusivity, circulation timescale, and ocean dissipation rate. Energetic analysis further shows that Enceladus' ocean behaves like an extremely efficient heat pump (inefficient heat engine), potentially transporting up to 1000 times more heat across latitudes than the energy dissipated within the ocean itself, thereby placing strong constraints on the ocean's energy dissipation rate.

Octaviano Noel, Zubaydi Achmad, Ismail Abdi et al.

The Fiber Reinforced Plastic (FRP) material used in ships has advantages such as lighter weight, resistance to corrosion, and lower maintenance costs. However, transitioning from materials like steel and aluminum, commonly used in production, requires knowledge of the lifetime of FRP as a ship structure, allowing for an estimation of how long the ship can operate. Additionally, predicting the ship’s lifetime is also necessary to meet ship safety factors. This study employs numerical testing using the Finite Element Method to predict lifetime of patrol boats in Bangka Belitung waters. Wave height variations (0.5-2 meters) and wave direction (following sea, head sea, and beam sea) are considered at a constant speed of 22 knots. Fatigue life calculation is done by determining the bending moment load at various wave heights. The bending moment is in an Ansys static structural analysis to calculate stress and deformation. The highest stress occurs in the stern frame with the value of 18.347 MPa while the lowet is in the bow frame with the value of 16.682 MPa. By calculating the stress on each variation, the life prediction can be calculated. The life prediction of a patrol vessel is 32 years and 8 months.

Maria-Eirini Pegia, Bjorn or Jonsson, Anastasia Moumtzidou et al.

Satellite image change detection, where two images of the same area from different times are compared, is crucial for earth sensing and monitoring applications. Many learning-based detection methods have been proposed for this task, with different performance characteristics. Since these detection methods have been tested under different settings, comparing their performance across a variety of situations is difficult. The goal of this article is therefore to comprehensively compare the state-of-the-art detection methods from the literature, across a variety of dataset parameters. To that end, we analyze the impact of image resolution, training set size, and noise on learning performance. A first set of experiments, using a large set of high-resolution images, reveals that training set resolution should match the resolution of the images the model will be applied to, that larger training sets are beneficial, and that adding Gaussian noise improves performance. A second set of experiments, using a smaller set of low-resolution images, confirms that the training set should also be of the same low resolution, but shows that adding noise does not improve performance in this case. The results also indicate that BiasUNet is the most effective method for detecting changes between image pairs.

Xingdong Wang, Yudong Wang, Suwei Li

Polar snowmelt detection is of great importance for the study of global climate change, and synthetic aperture radar (SAR) images have been widely used for polar snowmelt detection because of its ability to provide round-the-clock, all-weather snowmelt detection. However, conventional snowmelt detection algorithms based on the SAR images have images that are susceptible to interference from coherent speckle noise, which leads to the problems of false pixel and missed change detection. To solve the above-mentioned problems, this article proposed a coherent speckle noise suppression algorithm for the SAR images based on the measure of heterogeneity. That is, the SAR images are divided into homogeneous regions, edge regions, and isolated strong scattering regions by the measure of heterogeneity, and different construction algorithms are used for different regions, which was applied to the Larsen C ice shelf. The results showed that the construction algorithm in this article achieved better results in noise suppression, structure preservation and detail retention, and the comprehensive performance was better in the homogeneous regions and edge regions, which could reduce the false alarm rate and leakage rate, and provided algorithmic support for the study of polar snowmelt detection.

Sang-Il Lee, Song-Yi Yi, Min Jung

This paper explores the legal framework surrounding the classification of “other persons” aboard vessels under the Ship Safety Act, particularly in comparison to international maritime conventions such as SOLAS. The term “other persons” has been a source of ambiguity and safety concerns, especially following several maritime accidents, including the 2024 collision near Yeoseodo. In Korea, truck drivers and other non-crew individuals have been permitted to board vessels as “other persons,” often exceeding permissible limits, raising significant safety and regulatory issues. This research examines the inconsistencies between Korea’s Ship Safety Act and international standards, noting that other major maritime nations impose stricter limits and clearer definitions on non-passenger personnel. Recommendations include aligning domestic laws with international conventions by redefining “other persons” and enforcing a stricter cap on non-crew passengers to enhance safety. The paper also addresses the need for categorizing individuals boarding vessels into clearer groups – crew, passengers, industrial personnel, and specialized personnel – to ensure legal clarity and improve compliance with global maritime safety standards. Through a comparative legal analysis, the paper advocates for the adoption of international norms in Korea’s maritime regulations.

Justus Bogner, Roberto Verdecchia

From its early foundations in the 1970s, empirical software engineering (ESE) has evolved into a mature research discipline that embraces a plethora of different topics, methodologies, and industrial practices. Despite its remarkable progress, the ESE research field still needs to keep evolving, as new impediments, shortcoming, and technologies emerge. Research reproducibility, limited external validity, subjectivity of reviews, and porting research results to industrial practices are just some examples of the drivers for improvements to ESE research. Additionally, several facets of ESE research are not documented very explicitly, which makes it difficult for newcomers to pick them up. With this new regular ACM SIGSOFT SEN column (SEN-ESE), we introduce a venue for discussing meta-aspects of ESE research, ranging from general topics such as the nature and best practices for replication packages, to more nuanced themes such as statistical methods, interview transcription tools, and publishing interdisciplinary research. Our aim for the column is to be a place where we can regularly spark conversations on ESE topics that might not often be touched upon or are left implicit. Contributions to this column will be grounded in expert interviews, focus groups, surveys, and position pieces, with the goal of encouraging reflection and improvement in how we conduct, communicate, teach, and ultimately improve ESE research. Finally, we invite feedback from the ESE community on challenging, controversial, or underexplored topics, as well as suggestions for voices you would like to hear from. While we cannot promise to act on every idea, we aim to shape this column around the community interests and are grateful for all contributions.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy plays a critical role in software engineering (SE), influencing collaboration, communication, and user-centred design. Although SE research has increasingly recognised empathy as a key human aspect, there remains no validated instrument specifically designed to measure it within the unique socio-technical contexts of SE. Existing generic empathy scales, while well-established in psychology and healthcare, often rely on language, scenarios, and assumptions that are not meaningful or interpretable for software practitioners. These scales fail to account for the diverse, role-specific, and domain-bound expressions of empathy in SE, such as understanding a non-technical user's frustrations or another practitioner's technical constraints, which differ substantially from empathy in clinical or everyday contexts. To address this gap, we developed and validated two domain-specific empathy scales: EmpathiSEr-P, assessing empathy among practitioners, and EmpathiSEr-U, capturing practitioner empathy towards users. Grounded in a practitioner-informed conceptual framework, the scales encompass three dimensions of empathy: cognitive empathy, affective empathy, and empathic responses. We followed a rigorous, multi-phase methodology, including expert evaluation, cognitive interviews, and two practitioner surveys. The resulting instruments represent the first psychometrically validated empathy scales tailored to SE, offering researchers and practitioners a tool for assessing empathy and designing empathy-enhancing interventions in software teams and user interactions.

Dinesh Eswararaj, Ajay Babu Nellipudi, Vandana Kollati

The automotive industry generates vast amounts of data from sensors, telemetry, diagnostics, and real-time operations. Efficient data engineering is critical to handle challenges of latency, scalability, and consistency. Modern data lakehouse formats Delta Parquet, Apache Iceberg, and Apache Hudi offer features such as ACID transactions, schema enforcement, and real-time ingestion, combining the strengths of data lakes and warehouses to support complex use cases. This study presents a comparative analysis of Delta Parquet, Iceberg, and Hudi using real-world time-series automotive telemetry data with fields such as vehicle ID, timestamp, location, and event metrics. The evaluation considers modeling strategies, partitioning, CDC support, query performance, scalability, data consistency, and ecosystem maturity. Key findings show Delta Parquet provides strong ML readiness and governance, Iceberg delivers high performance for batch analytics and cloud-native workloads, while Hudi is optimized for real-time ingestion and incremental processing. Each format exhibits tradeoffs in query efficiency, time-travel, and update semantics. The study offers insights for selecting or combining formats to support fleet management, predictive maintenance, and route optimization. Using structured datasets and realistic queries, the results provide practical guidance for scaling data pipelines and integrating machine learning models in automotive applications.

Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri

Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such experiments and improving the system based on the experimental results still remain manual. These processes are labor-intensive and require multi-domain expertise. To address these challenges and enable anyone to build resilient systems at low cost, this paper proposes ChaosEater, a system that automates the entire CE cycle with Large Language Models (LLMs). It predefines an agentic workflow according to a systematic CE cycle and assigns subdivided processes within the workflow to LLMs. ChaosEater targets CE for software systems built on Kubernetes. Therefore, the LLMs in ChaosEater complete CE cycles through software engineering tasks, including requirement definition, code generation, testing, and debugging. We evaluate ChaosEater through case studies on small- and large-scale Kubernetes systems. The results demonstrate that it consistently completes reasonable CE cycles with significantly low time and monetary costs. Its cycles are also qualitatively validated by human engineers and LLMs.

Hao Yang, Xin Hou, Huifan Chen et al.

Geographically separated populations of Culter alburnus in China exhibit marked physiological and behavioral characteristics, particularly between populations inhabiting the northern and southern part of Huai River (HR), indicating their adaptation to local environments. In this study, the morphological characteristics of C. alburnus were measured, and restriction site associated DNA (RAD) sequencing was used for C. alburnus to investigate the genomic features of the Xingkai Lake (XKL) population from the northern part and HR, Yangtze River (YR), and Dianshan Lake (DSL) populations from the southern part of China. Analyses results revealed significant morphological differences and population genetic structure among these populations, with pronounced genetic differentiation between the northern and the southern part. Notably, the northern (XKL) population exhibited significantly lower genetic diversity than the other three southern populations. Furthermore, genes involved in synaptic vesicle transmission regulation, androgen receptor signaling, and behavior regulation pathways, including oligophrenin 1, ch25h13, androgen receptor, and Rho kinase genes, showed selection signals with high genetic differentiation indices (Fst), significant nucleotide diversity (Pi) differences, and significantly differential expression values in the northern (XKL) population compared with southern populations. These findings indicate that these genes may influence the different adaptive reflex behavioral responses of northern and southern C. alburnus populations. This study highlights the genetic structure and adaptations of C. alburnus, providing important resources for its management and aquaculture.

Hang Yu, Yixi Zhao, Chongben Ni et al.

The diverse nature of hull components in shipbuilding has created a demand for intelligent robots capable of performing various tasks without pre-teaching or template-based programming. Visual perception of a target’s outline is crucial for path planning in robotic edge grinding and other processes. Providing the target’s outline from point cloud or image data is essential for autonomous programming, requiring a high-performance algorithm to handle large amounts of data in real-time construction while preserving geometric details. The high computational cost of triangulation has hindered real-time industrial applications, prompting efforts to improve efficiency. To address this, a new improvement called Directive Searching has been proposed to enhance search efficiency by directing the search towards the target triangle cell and avoiding redundant searches. Another improvement, Heritable Initial, reduces the search amount by inheriting the start position from the last search. Combining Directive Searching and Heritable Initial into a new method called DSHI has led to a significant efficiency advancement, with a calculation efficiency improvement of nearly 300–3000 times compared to the ordinary Bowyer–Watson method. In terms of outlines extraction, DSHI has improved the extraction efficiency by 4–16 times compared to the ordinary Bowyer–Watson methods, while ensuring stable outlines results, and has also increased the extraction efficiency by 2–4 times compared to PCL. The DSHI method is also applied to actual ship component edge-grinding equipment, and its effect meets the shipbuilding process requirements. It could be inferred that the new method has potential applications in shipbuilding and other industries, offering satisfying efficiency and robustness for tasks such as automatic edge grinding.

Hadi Mahdipour, Alireza Sharifi, Mehdi Sookhak et al.

Handling and quantifying two types of uncertainties, spatial and inherent, in land-cover segmentation using multiple satellite images constitutes a primary concern within the domain of segmenting multiple remote sensing images. Despite the comprehensive examination of the spatial uncertainty, the lack of a mathematical model addressing the inherent uncertainty in satellite images is notable. This article endeavors to address this gap by considering and focuses on the latter. Leveraging multiple high-resolution panchromatic remote sensing images as input data, the study aims to model all procedures in the image formation and preprocessing stages (applied to each input image before segmentation) as the producing factors of inherent uncertainty in the segmentation process. The input images, conceptualized as events in random processes, undergo fusion using the mean estimator, referred to as the proposed “ultrafusion” method in this article. The study demonstrates that the results of ultrafusion can be effectively modeled by normal-type fuzzy numbers. The parameters of these fuzzy numbers are estimated using the maximum likelihood method. Subsequently, fuzzy C-means, as the most renowned clustering method, is employed for segmentation. Analytical comparisons reveal the notable performance of the proposed algorithm in terms of accurate segmentation and computational efficiency. Affirming the efficacy of the proposed approach, simulation results validate the advantages by improving the overall accuracy, Kappa, and F1-score indices by about 0.86%, 0.52%, and 1.03%, respectively, in comparison with the most recent and similar method.

Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan et al.

Large Language Models (LLMs) are being used for a wide variety of tasks. While they are capable of generating human-like responses, they can also produce undesirable output including potentially harmful information, racist or sexist language, and hallucinations. Alignment methods are designed to reduce such undesirable outputs via techniques such as fine-tuning, prompt engineering, and representation engineering. However, existing methods face several challenges: some require costly fine-tuning for every alignment task; some do not adequately remove undesirable concepts, failing alignment; some remove benign concepts, lowering the linguistic capabilities of LLMs. To address these issues, we propose Parsimonious Concept Engineering (PaCE), a novel activation engineering framework for alignment. First, to sufficiently model the concepts, we construct a large-scale concept dictionary in the activation space, in which each atom corresponds to a semantic concept. Given any alignment task, we instruct a concept partitioner to efficiently annotate the concepts as benign or undesirable. Then, at inference time, we decompose the LLM activations along the concept dictionary via sparse coding, to accurately represent the activations as linear combinations of benign and undesirable components. By removing the latter ones from the activations, we reorient the behavior of the LLM towards the alignment goal. We conduct experiments on tasks such as response detoxification, faithfulness enhancement, and sentiment revising, and show that PaCE achieves state-of-the-art alignment performance while maintaining linguistic capabilities.

Johannes Schleiss, Aditya Johri, Sebastian Stober

Building up competencies in working with data and tools of Artificial Intelligence (AI) is becoming more relevant across disciplinary engineering fields. While the adoption of tools for teaching and learning, such as ChatGPT, is garnering significant attention, integration of AI knowledge, competencies, and skills within engineering education is lacking. Building upon existing curriculum change research, this practice paper introduces a systems perspective on integrating AI education within engineering through the lens of a change model. In particular, it identifies core aspects that shape AI adoption on a program level as well as internal and external influences using existing literature and a practical case study. Overall, the paper provides an analysis frame to enhance the understanding of change initiatives and builds the basis for generalizing insights from different initiatives in the adoption of AI in engineering education.

Saima Rashid, Mohammed K.A. Kaabar, Ali Althobaiti et al.

The main idea of this article is the investigation of atmospheric internal waves, often known as gravity waves. This arises within the ocean rather than at the interface. A shallow fluid assumption is illustrated by a series of nonlinear partial differential equations in the framework. Because the waves are scattered over a wide geographical region, this system can precisely replicate atmospheric internal waves. In this research, the numerical solutions to the fuzzy fourth-order time-fractional Boussinesq equation (BSe) are determined for the case of the aquifer propagation of long waves having small amplitude on the surface of water from a channel. The novel scheme, namely the generalized integral transform (proposed by H. Jafari [35]) coupled with the Adomian decomposition method (GIADM), is used to extract the fuzzy fractional BSe in R,Rn and (2nth)-order including gH-differentiability. To have a clear understanding of the physical phenomena of the projected solutions, several algebraic aspects of the generalized integral transform in the fuzzy Caputo and Atangana-Baleanu fractional derivative operators are discussed. The confrontation between the findings by Caputo and ABC fractional derivatives under generalized Hukuhara differentiability are presented with appropriate values for the fractional order and uncertainty parameters ℘∈[0,1] were depicted in diagrams. According to proposed findings, hydraulic engineers, being analysts in drainage or in water management, might access adequate storage volume quantity with an uncertainty level.

Yuzheng Yang, Qiang Gui, Yang Zhang et al.

In this study, the T-matrix method combined with the addition theorems of spherical basis functions is applied to semi-analytically compute the underwater far-field acoustic scattering of a pair of rigid spheroids with arbitrary incident angles. The involvement of the addition theorems renders the multiple scattering fields of each spheroid to be translated into an identical origin. The accuracy and convergence property of the proposed method are verified and validated. The interference of specular reflection wave and Franz wave can be spotted from the oscillations of the form function. Furthermore, the propagation paths of specular reflection and Franz waves are quantitatively analyzed in the time domain with conclusions that the Franz waves reach the observation point subsequent to specular reflection waves and the time interval between these two wave series is equal to the time cost of the Franz waves traveling along the sphere surfaces. Finally, the effects of separation distances, aspect ratios (the ratio of the polar radius to equatorial radius), non-dimensional frequencies, and incidence angles of the plane wave on the far-field acoustic scattering of a pair of rigid spheroids are studied by the T-matrix method.

Rudrajit Choudhuri, Dylan Liu, Igor Steinmacher et al.

Conversational Generative AI (convo-genAI) is revolutionizing Software Engineering (SE) as engineers and academics embrace this technology in their work. However, there is a gap in understanding the current potential and pitfalls of this technology, specifically in supporting students in SE tasks. In this work, we evaluate through a between-subjects study (N=22) the effectiveness of ChatGPT, a convo-genAI platform, in assisting students in SE tasks. Our study did not find statistical differences in participants' productivity or self-efficacy when using ChatGPT as compared to traditional resources, but we found significantly increased frustration levels. Our study also revealed 5 distinct faults arising from violations of Human-AI interaction guidelines, which led to 7 different (negative) consequences on participants.

Benjamin Idini, Francis Nimmo

Titan's ice shell floats on top of a global ocean revealed by the large tidal Love number $k_2 = 0.616\pm0.067$ registered by Cassini. The Cassini observation exceeds the predicted $k_2$ by one order of magnitude in the absence of an ocean, and is 3-$σ$ away from the predicted $k_2$ if the ocean is pure water resting on top of a rigid ocean floor. Previous studies demonstrate that an ocean heavily enriched in salts (salinity $S\gtrsim200$ g/kg) can explain the 3-$σ$ signal in $k_2$. Here we revisit previous interpretations of Titan's large $k_2$ using simple physical arguments and propose a new interpretation based on the dynamic tidal response of a stably stratified ocean in resonance with eccentricity tides raised by Saturn. Our models include inertial effects from a full consideration of the Coriolis force and the radial stratification of the ocean, typically neglected or approximated elsewhere. The stratification of the ocean emerges from a salinity profile where salt concentration linearly increases with depth. We find multiple salinity profiles that lead to the $k_2$ required by Cassini. In contrast with previous interpretations that neglect stratification, resonant stratification reduces the bulk salinity required by observations by an order of magnitude, reaching a salinity for Titan's ocean that is compatible with that of Earth's oceans and close to Enceladus' plumes. Consequently, no special process is required to enrich Titan's ocean to a high salinity as previously suggested.

Cheng Chen, A. Zhang, Jianqiang Chen et al.

Yue Kai, Shuangqing Chen, Kai Zhang et al.

In this paper, analytic solutions and dynamic properties of a variety of Boussinesq-type equations are established via the complete discrimination system for polynomial method. All the existing single traveling wave solutions to these equations as well as some new solutions are shown, and the Hamiltonian and topological properties to these equations are also presented. Considering the significance of the Boussinesq-type equations, our results would have wide applications in ocean engineering and fluid mechanics, like describing and predicting the solitary and periodic waves in various shallow water models.