Hasil untuk "Ocean engineering"

Takashi Obase, Takanori Kodama, Takao Kawasaki et al.

It has been hypothesized that the Earth may have experienced snowball events in the past, during which its surface became completely covered with ice. Previous studies used general circulation models to investigate the onset and climate of such snowball events. Using the MIROC4m coupled atmosphere--ocean climate model, this study examined the changes in the oceanic circulation during the onset of a modern snowball Earth and elucidated their evolution to steady states under the snowball climate. Abruptly changing the solar constant to 94% of its present-day value caused the modern Earth climate to turn into a snowball state after ~1300 years and initiated rapid increase in sea ice thickness. During onset of the snowball, extensive sea ice formation and melting of sea ice in the mid-latitudes caused substantial freshening of surface waters and salinity stratification. By contrast, such salinity stratification was absent if the duration between the change in the solar flux and the snowball onset was short. After snowball onset, the global sea ice cover and the buildup of salinity stratification caused drastic weakening in the deep ocean circulation. However, the meridional overturning circulation resumed within several hundred years after the snowball onset because the density flux by sea ice production weakens the salinity stratification. While the evolution of the oceanic circulation would depend on the continental distribution and the evolution of continental ice sheets, our results highlight the gradual growth of sea ice and associated brine rejection are essential factors for the transient evolution of the oceanic circulation in the snowball events.

Gaochao Wang, Kai Ma, Tianqing Huang et al.

Abstract Graylings (Thymallus) represent one of the earliest diverged salmonid subfamilies and provide crucial insights into the evolutionary consequences of the salmonid-specific whole genome duplication event (Ss4R). Here, we present the first chromosome-level genome assembly of the Amur grayling (Thymallus grubii), an economically and ecologically important species endemic to East Asian freshwater systems. The final assembly spans 1,754.51 Mb with 46 pseudo-chromosomes, achieving a contig N50 of 3.03 Mb and anchoring 99.97% of sequences to chromosomes. The genome contains 31,867 protein-coding genes with 97.58% functionally annotated. Repetitive sequences comprise 52.64% of the genome, dominated by DNA transposons (22.06%). BUSCO analysis indicates 97.9% completeness for conserved Actinopterygii genes. This high-quality genome assembly provides an essential resource for salmonid comparative genomics, aquaculture breeding programs, and conservation genetics of this economically important species.

Dongxue Li, Dingfeng Yu, Yidong Xu et al.

Since 2007, recurring green tides in the Yellow Sea have caused substantial ecological and socioeconomic impacts. Accurate and efficient automated extraction from remote sensing images is vital for monitoring these events. However, current extraction methods rely on a one-way strategy, where mixed pixels are first extracted and then decomposed. This often leads to under or overextraction due to the complex distribution of green tides and varying background water environments, and these errors are difficult to fully eliminate even after decomposition. This article proposes a novel approach that integrates mixed pixel decomposition with a feedback adjustment module to refine the extraction threshold. Utilizing the Google Earth Engine platform, we employ a convolutional neural network to establish relationships between algal endmembers and water endmembers in remote sensing images. Based on this, we perform mixed pixel decomposition on moderate resolution imaging spectroradiometer images. The feedback adjustment mechanism then refines the threshold in the adaptive OTSU method for extracting green tides, enabling more accurate and efficient automatic extraction in varying environments. Comparative analysis with high-resolution sensors [e.g., panchromatic (PAN), wide-field of view (WFV), and charge-coupled device (CCD)] shows an average relative difference of 8.19% in the extracted coverage area. This approach offers critical support for large-scale, long-term monitoring and early warning of green tides, and serves as a methodological reference for future research on marine algal bloom disasters.

Yongze Li, Xia Chai, Fengcheng Guo et al.

IntroductionThe coastal waters of the Leizhou Peninsula, as an important ecological transition zone in the northern South China Sea, face water quality issues that urgently require effective monitoring methods.MethodsBased on data from 188 stations across four seasons from 2020-2024, this study innovatively proposes a quantile statistical classification method based on mean and standard deviation, and compares results with typical bays at similar latitudes globally.ResultsThe research classifies regional water spectral types into four categories: Type I (17.22%) with high reflection narrow peaks at 570 nm; Type II (35.10%) featuring broad peaks at 540-560 nm; Type III (27.81%) showing gentle distribution across 500-570 nm; and Type IV (19.87%) with decreasing reflectance as wavelength increases, peaking around 500 nm.DiscussionResults indicate this region is dominated by mixed turbid waters (Types II and III accounting for 62.91%), providing scientific basis for water quality monitoring, aquaculture planning, algal bloom identification, and marine functional zoning, thus promoting regional marine ecological protection and sustainable resource utilization.

Youxiang Huang, Donglai Jiao, Xingru Huang et al.

Remote sensing images (RSIs) object detection is important in natural disaster management, urban planning and resource exploration. However, due to the large differences between RSIs and natural images (NIs), most of the existing object detectors for NIs cannot be directly used to process RSIs. Most existing models based on convolutional neural networks (CNNs) require additional design of specific attentional modules to relate small targets in RSIs to global positional relationships. In contrast, transformer-based models had to add modules to obtain more detailed information. This imposes additional computational overheads for deployment on edge devices. To solve the above-mentioned problem, we propose a hybrid CNN and transformer model (DConvTrans-LKA) to enhance the model's ability to acquire features and design a fusion of local and global attention mechanisms to fuse local features and global location information. To better fuse the feature and location information extracted by the model, we introduce a feature residual pyramid network to enhance the model's ability to fuse multiscale feature maps. Finally, we conduct experiments in three representative optical RSI datasets (NWPU VHR-10, HRRSD, and DIOR) to verify the effectiveness of our proposed DConvTrans-LKA method. The experimental results show that our proposed method reaches 61.7%, 82.1%, and 61.3% at mAP at 0.5, respectively, further demonstrating the potential of our proposed method in RSI object detection tasks.

Shenghui Li, Charles I. Addey, Raphaël Roman et al.

This paper highlights the urgent need to accelerate research and action on ocean carbon sinks through human intervention, known as the Global Ocean Negative Carbon Emissions (Global-ONCE) Programme, as a vital strategy in global efforts to mitigate climate change. Achieving “net zero” by 2050 cannot rely on emission reductions alone, emphasizing the necessity of complementary approaches. Global-ONCE’s mission extends beyond scientific exploration. It embodies a profound commitment to protecting and restoring blue carbon ecosystems, as well as implementing ocean-based solutions that are sustainable, equitable, and inclusive. Early career ocean professionals (ECOPs) are at the heart of these efforts, and their innovative approaches, technical expertise, and passion make them indispensable leaders in advancing ONCE initiatives. ECOPs bridge the gap between science and society, playing a relevant role in integrating cutting-edge research, technological advancements, and community-driven action to address climate threats. By bringing together diverse perspectives and leveraging their interdisciplinary expertise, ECOPs ensure that ONCE strategies are grounded in scientific rigor and practical feasibility. Through advocacy, education, and collaboration, ECOPs not only spearhead research and innovation but also inspire collective action to safeguard our oceans. This paper amplifies the critical role of ECOPs as agents of change and calls for a unified global commitment to harness the ocean’s potential for a climate-resilient future.

Mahdi Jaberzadeh Ansari, Ann Barcomb

Although it has been more than four decades that the first components-based software development (CBSD) studies were conducted, there is still no standard method or tool for component selection which is widely accepted by the industry. The gulf between industry and academia contributes to the lack of an accepted tool. We conducted a mixed methods survey of nearly 100 people engaged in component-based software engineering practice or research to better understand the problems facing industry, how these needs could be addressed, and current best practices employed in component selection. We also sought to identify and prioritize quality criteria for component selection from an industry perspective. In response to the call for CBSD component selection tools to incorporate recent technical advances, we also explored the perceptions of professionals about AI-driven tools, present and envisioned.

Thi Thuy Nga Nguyen, Clément Dorffer, Frédéric Jourdin et al.

Neural mapping schemes have become appealing approaches to deliver gap-free satellite-derived products for sea surface tracers. The generalization performance of these learning-based approaches naturally arises as a key challenge. This is particularly true for satellite-derived ocean colour products given the variety of bio-optical variables of interest, as well as the diversity of processes and scales involved. Considering region-specific and parameter-specific neural mapping schemes will result in substantial training costs. This study addresses generalization performance of neural mapping schemes to deliver gap-free satellite-derived ocean colour products. We develop a comprehensive experimental framework using real multi-sensor ocean colour datasets for two regions (the Mediterranean Sea and the North Sea) and a representative set of bio-optical parameters (Chlorophyll-a concentration, suspended particulate matter concentration, particulate backscattering coefficient). We consider several neural mapping schemes, and we report excellent generalization performance across regions and bio-optical parameters without any fine-tuning using appropriate dataset-specific normalization procedures. We discuss further how these results provide new insights towards the large-scale deployment of neural schemes for the processing of satellite-derived ocean colour datasets beyond case-study-specific demonstrations.

Pavel Perezhogin, Arun Balakrishna, Rahul Agrawal

Mesoscale eddies produce lateral (2D) fluxes that need to be parameterized in eddy-permitting (1/4-degree) global ocean models due to insufficient horizontal resolution. Here, we systematically apply methods from the 3D LES community to parameterize lateral vorticity fluxes produced by mesoscale eddies leveraging an explicit filtering approach together with a dynamic procedure. The developed subfilter closure is implemented into the GFDL MOM6 ocean model and is evaluated in an idealized double-gyre configuration, both a-priori and a-posteriori. For sufficiently resolved grids, the LES simulations converge to the filtered high-resolution data. However, limitations in the proposed closure are observed when the filter scale approaches the energy-containing scales: the a-priori performance drops and a-posteriori experiments fail to converge to the filtered high-resolution data. Nevertheless, the proposed closure is accurate in predicting the mean flow in a-posteriori simulations at all resolutions considered (1/2-1/8 degrees). Finally, we propose parameterizing the thickness fluxes using a Bardina model which further improves simulations at the coarsest resolutions (1/2-1/3 degrees).

W. Erick Rogers, Jim Thomson

Observational data from buoys are of primary importance during the development, calibration, and evaluation of ocean wave models, and these data are also used to make real-time corrections to operational models via data assimilation. By association, systematic inaccuracies in any buoy data are equally important, and thus when two buoy types provide systematically inconsistent information, this is a concern for anyone using an ocean wave model. This report is concerned with the accuracy of the high frequency portion of the ocean wave spectrum commonly observable by buoys, roughly 0.2 to 0.6 Hz. We evaluate four buoy types (two moored, two drifting) using two quantitative measures. The first involves comparing each type with a co-located ocean wave model. The second method involves evaluation of high frequency energy level as a function of wind speed. Both evaluation methods suggest that the Datawell Waverider (DWR) buoys have a strong tendency to report higher energy levels than the other three buoy types. We evaluate high frequency energy level using three different metrics (mean square slope, energy in a band of high frequencies, and spectral density at a single, specific band, 0.4 Hz), and the conclusions are found to be insensitive to the parameter used.

Jun Takeshita, Yuichiro Cho, Haruhisa Tabata et al.

Saturn's ice-covered moon Enceladus may host a subsurface ocean with biologically relevant chemistry. Plumes released from this ocean preserve information on its chemical state, and previous analyses suggest weakly to strongly alkaline pH (approximately pH 8--12). Constraining the pH requires identification of pH-sensitive minerals in plume deposits. Several analytical techniques could provide such mineralogical information, but few are practical for deployment on planetary missions. Raman spectrometers, which have recently advanced for \textit{in situ} exploration and have been incorporated into flight instruments, offer a feasible approach for mineral identification on icy moons. However, their applicability to pH estimation from plume-derived minerals has not been investigated. In this study, we evaluate whether Raman measurements of plume particles deposited on the surface of Enceladus can be used to distinguish between weakly and strongly alkaline subsurface ocean models. Fluids with pH values of 9 and 11 were frozen under vacuum conditions analogous to those on Enceladus. The resulting salt deposits were then analyzed using a flight-like Raman spectrometer. The Raman spectra show pH-dependent carbonate precipitation: NaHCO$_3$ and Na$_2$CO$_3$ peaks were detected at pH 9, whereas only Na$_2$CO$_3$ peaks were detected at pH 11. These findings demonstrate that Raman spectroscopy can distinguish pH-dependent carbonate phases. This capability allows us to constrain whether the pH of the subsurface ocean is weakly alkaline or strongly alkaline, which is a key parameter for assessing its chemical evolution and potential habitability.

Xu Xiang

Xingzhuang Zhao

Jinsong Liao, Fuhai Li, Jing Gong et al.

The utilization of recycled aggregate can significantly mitigate the extraction of natural sand and gravel. However, the practical application of recycled aggregate in engineering is impeded by its inherent characteristics, encompassing high water absorption, high crushing, and low apparent density. This study employed a soaking and air-drying method to enhance the strength of three types of aggregates with varying initial strengths by utilizing permeated crystalline materials. The durability of recycled aggregate concrete (RAC) was studied with three different aggregate replacement rates (0%, 50%, and 100%). The test results demonstrate that the slump, compressive strength, freeze resistance, and carbonation resistance of RAC exhibit a decreasing trend as the aggregate replacement rate increases. The freeze resistance and carbonation resistance of RAC are notably enhanced after incorporating permeated crystalline material. This study contributes to a sustainable and efficient solution for the treatment of construction waste, thereby enhancing the utilization rate of recycled concrete.

Zhiyuan Ji, Xueqian Wang, Zhihao Wang et al.

In this article, we consider the issue of change detection (CD) for heterogeneous remote sensing images. Existing deep learning-based methods for CD usually utilize square convolution receptive fields, which do not sufficiently exploit the contextual and boundary information in heterogeneous images. To address the aforementioned issue, we propose an enhanced and unsupervised Siamese superpixel-based network for CD in heterogeneous remote sensing images. Our newly proposed method innovatively combines superpixels with the square receptive fields to generate the boundary adherence receptive fields and better capture the contextual information than existing methods only with the regular square receptive fields. Furthermore, we utilize an adaptive superpixel merging module to prevent the oversegmentation of superpixels and strengthen the robustness of our method in terms of superpixel sizes. Experiments based on four real datasets demonstrate that the proposed method achieves higher accuracy than other commonly used CD methods in heterogeneous remote sensing images.

Jie Chen, Guijun Bi, Dake Zhao et al.

The steel/copper bimetallic structures inherit both the excellent thermal/electrical conductivity of copper alloys and the mechanical strength of steel, making it valuable for applications in heat exchangers, injection molds and the power industry. Laser powder bed fusion (LPBF) is a promising manufacturing method for the fabrication of steel/copper bimetallic structures, but the inhomogeneous heterogeneous interfaces still need to be subsequently processed to further enhance their mechanical properties. In this investigation, an attempt was made to optimize the interfacial microstructure of steel/copper bimetallic structures fabricated by the LPBF process through annealing treatment to improve their interfacial strength. As a result, the annealing treatment increased the interfacial bonding strength of the steel/copper bimetallic structure by 28% and without reducing the elongation. This is attributed to the elemental homogenization and internal stress release during heat treatment, as well as the mechanically interlocked structure of fine crystals embedded in coarse crystals formed by Cu/Fe interdiffusion at the boundary of the steel/fusion zone. However, no significant phase transitions were identified in the heterogeneous interface. A suitable heat treatment process can diffusely distribute the Cu/Fe immiscible phase and maintain fine grains. This work discusses in detail the microstructure evolution under different annealing treatments as well as the underlying mechanisms for the improvement of mechanical properties, providing a simple and effective method for post-treatment reinforcing the heterogeneous interface of 316L/CuSn10 bimetallic structure fabricated by LPBF.

Bin Lu, Ze Zhao, Luyu Han et al.

Accurately reconstructing the global ocean deoxygenation over a century is crucial for assessing and protecting marine ecosystem. Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. Besides, due to the high-cost data collection, the historical observations are severely sparse, leading to big challenge for precise reconstruction. In this work, we propose OxyGenerator, the first deep learning based model, to reconstruct the global ocean deoxygenation from 1920 to 2023. Specifically, to address the heterogeneity across large temporal and spatial scales, we propose zoning-varying graph message-passing to capture the complex oceanographic correlations between missing values and sparse observations. Additionally, to further calibrate the uncertainty, we incorporate inductive bias from dissolved oxygen (DO) variations and chemical effects. Compared with in-situ DO observations, OxyGenerator significantly outperforms CMIP6 numerical simulations, reducing MAPE by 38.77%, demonstrating a promising potential to understand the "breathless ocean" in data-driven manner.

Renan Lima Baima, Tiago Miguel Barao Caetano, Ana Carolina Oliveira Lima et al.

The primary objective is to emphasize the merits of active methodologies and cross-disciplinary curricula in Requirement Engineering. This direction promises a holistic and applied trajectory for Computer Engineering education, supported by the outcomes of our case study, where artifact-centric learning proved effective, with 73% of students achieving the highest grade. Self-assessments further corroborated academic excellence, emphasizing students' engagement in skill enhancement and knowledge acquisition.

Guosong Wang, Min Hou, Mingyue Qin et al.

Ocean forecasting is critical for various applications and is essential for understanding air-sea interactions, which contribute to mitigating the impacts of extreme events. State-of-the-art ocean numerical forecasting systems can offer lead times of up to 10 days with a spatial resolution of 10 kilometers, although they are computationally expensive. While data-driven forecasting models have demonstrated considerable potential and speed, they often primarily focus on spatial variations while neglecting temporal dynamics. This paper presents TSformer, a novel non-autoregressive spatiotemporal transformer designed for medium-range ocean eddy-resolving forecasting, enabling forecasts of up to 30 days in advance. We introduce an innovative hierarchical U-Net encoder-decoder architecture based on 3D Swin Transformer blocks, which extends the scope of local attention computation from spatial to spatiotemporal contexts to reduce accumulation errors. TSformer is trained on 28 years of homogeneous, high-dimensional 3D ocean reanalysis datasets, supplemented by three 2D remote sensing datasets for surface forcing. Based on the near-real-time operational forecast results from 2023, comparative performance assessments against in situ profiles and satellite observation data indicate that, TSformer exhibits forecast performance comparable to leading numerical ocean forecasting models while being orders of magnitude faster. Unlike autoregressive models, TSformer maintains 3D consistency in physical motion, ensuring long-term coherence and stability in extended forecasts. Furthermore, the TSformer model, which incorporates surface auxiliary observational data, effectively simulates the vertical cooling and mixing effects induced by Super Typhoon Saola.

Changhong Mou, Samuel N. Stechmann, Nan Chen

Sea ice plays a crucial role in the climate system, particularly in the Marginal Ice Zone (MIZ), a transitional area consisting of fragmented ice between the open ocean and consolidated pack ice. As the MIZ expands, understanding its dynamics becomes essential for predicting climate change impacts. However, the role of clouds in these processes has been largely overlooked. This paper addresses that gap by developing an idealized coupled atmosphere-ocean-ice model incorporating cloud and precipitation effects, tackling both forward (simulation) and inverse (data assimilation) problems. Sea ice dynamics are modeled using the discrete element method, which simulates floes driven by atmospheric and oceanic forces. The ocean is represented by a two-layer quasi-geostrophic (QG) model, capturing mesoscale eddies and ice-ocean drag. The atmosphere is modeled using a two-layer saturated precipitating QG system, accounting for variable evaporation over sea surfaces and ice. Cloud cover affects radiation, influencing ice melting. The idealized coupled modeling framework allows us to study the interactions between atmosphere, ocean, and sea ice floes. Specifically, it focuses on how clouds and precipitation affect energy balance, melting, and freezing processes. It also serves as a testbed for data assimilation, which allows the recovery of unobserved floe trajectories and ocean fields in cloud-induced uncertainties. Numerical results show that appropriate reduced-order models help improve data assimilation efficiency with partial observations, allowing the skillful inference of missing floe trajectories and lower atmospheric winds. These results imply the potential of integrating idealized models with data assimilation to improve our understanding of Arctic dynamics and predictions.